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Volume-2 Issue-3, July 2012, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

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Arif Ullah Khan, L.P.Bhaiya Sumit Kumar Banchhor

Paper Title:

Hindi Speaking Person Identification Using Zero Crossing Rate

Abstract: Information from speech recognition can be used in various ways in state of the art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text dependent speaker modeling techniques. In this paper text dependent speaker identification method is used. This system contains training phase, the testing phase and recognition phase. In the training phase, the feature word is extracted. During the testing phase, feature matching takes place. The feature that extracted is stored in the data base. During the recognition phase, the features are extracted by same techniques and are compared with the template in the database. Differences of physiological properties of the glottis and vocal tracts are partly due to age, gender and/or person differences. Since these differences are related in the speech signal, acoustic measures related to those properties can be helpful for speaker identification. Acoustic measure of voice sources were extracted from 3 utterances spoken by 10 peoples including 5 male and 5 female talkers (aged 19 to 25 years old). In this paper, the eature of the extraction takes place by Zero Crossing Rate (ZCR).

Speech recognition, feature extraction, zero-crossing rate.


1. Yiu - Kei Lau and Chok- Ki Chan,"Speech recognition based on zero-crossing rate", IEEE Transactions on acoustics, speech and signal processing, Vol.ASSP-33, No.1.
2. Costas panagiotakis and George tziritas, "A speech/music discriminator based on RMS and zero-crossings", IEEE transactions on multimedia.vol.7, no.1, February 2005.

3. Sumit Kumar Banchhor, Om Prakash Sahu, Prabhakar, "A Speech/Music Discriminator based on Frequency energy, Spectrogram and Autocorrelation", IJSCE, Volume-1, Issue-6, January 2012

4. Sumit kumar Banchhor and Arif Khan, "Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy", Volume 1- No.3, February 2012.

5. Bachu R.G, Kopparthi S, Adapa B, Barkana B.D, "Separation of voiced and unvoiced using zero crossing rate and energy of the signal".

6. Sumit kumar Banchhor and S. K. Dekate, "Text-dependent Method for Gender Identification through synthesis of voiced segments",IJEST, Volume- 3, No. 6, June 2011.





Rajiv Ranjan, V. K. Giri

Paper Title:

A Unified Approach of ECG Signal Analysis

Abstract: The bio-potentials generated by the muscles of the heart result in an electrical signal called electrocardiogram (ECG). It is one of the most important physiological parameter, which is being extensively used for knowing the state of the cardiac patients. Feature extraction of ECG is most essential task in the manual and automated ECG analysis for use in instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analysers. Recently, artificial intelligent tools such as neural networks, genetic algorithms, fuzzy systems, and expert systems have frequently been reported for detection and diagnostic tasks. This paper, therefore, is an attempt to review the work done by the different researchers in the area of ECG signal processing, analysis and interpretation during last five decades.

Arrhythmia, ECG analysis, ECG interpretation, Noise removal, Expert system, Artificial intelligence, Feature extraction.


1. Willems J L, Abreu-Lima, Arnaud P, Brohet C R, Denis B, Gehing J, Graham I, Herpen van G, Machado H, Michaelis J & Moulopoulous, Evaluation of ECG interpretation results obtained by computer and cardiologists, Methods of Information in Medicine, vol. 29,pp 308-316,1990.
2. Willems J L, Recommendations for measurement standards in quantitative electrocardiography, European Heart Journal, vol. 6, pp 815-825, 1985.

3. Pipberger H V, McManus C D & Pipberger H A, Methods of ECG interpretation in the AVA program, Methods of Information in Medicine, vol. 29, pp 337-341, 1990.

4. Bemmel van J H, Kors J A & Herpen van G, Methods of the Modular ECG Analysis System MEANS, Methods of Information in Medicine, vol. 29, pp 346-353, 1990.

5. Macfarlane P W, Methods of ECG interpretation in the Glasgow program, Methods of Information in Medicine, vol. 29, pp 354-361, 1990.

6. Rautaharju, P M, MacInnis P J, Warren J W, Wolf H K, Rykers & Calhoun, Methodology of ECG interpretation in the Dalhousie program; NOVAVODE ECG classification procedure for clinical trials and population health surveys, Methods of Information in Medicine, vol. 29, pp 362-374, 1990.

7. Zywietz Chr, Borovsky D, Gotsch G, & Joseph G, Methods of ECG interpretation in the Nagoya program , Methods of Information in Medicine, vol. 29, pp 375-385, 1990.

8. Degani R & Bortolan G, Methods of ECG interpretation in the Pad ova program, Methods of Information in Medicine, vol. 29, pp 386-392, 1990.

9. Arnaud P, Rubel P, Morlet D E, Fayn & Forlini M C, Methods of ECG interpretation in the Lyon program. Methods of Information in Medicine, vol. 29 pp 393-402, 1990.

10. Abreu-Lima & Marques de Sa, Interpretation of Short ECGs with a PC: The Porto Program, Methods of information in Medicine, vol. 29, pp 410-412, 1990.

11. Okada M, A digital filter for the QRS complex detection, IEEE Trans on BME, vol. 26, no 12, pp 700-703, December 1979.

12. Thakor N V, Webster J G & Tompkins W J, Estimation of QRS complex power spectra for design of a QRS filter, IEEE Trans on BME, vol.31, no 11, pp 702-706, November 1984.

13. Pan J & Tompkins W J, A real- time QRS detection algorithm, IEEE Trans on BME, vol.32, no 3, pp 230-236 March 1985.

14. Hamilton P S & Tompkins W J, Quantitative investigation of QRS detection rules using the MIT/BIH Arrhythmia database, IEEE Trans on BME, vol. 33, no 12,
pp 1157-1165, December 1986.

15. Laguna P, Vigo D, Jane R & Caminal P, Automatic wave onset and offset determination in ECG signals: validation with the CSE database, Computers in cardiology, Los Alamitos, CA, USA, IEEE Computer Society press, pp 167-170, 1992.

16. Escalona O J, Mitchell R H, Balderson P E & Harron D W G, Fast and reliable QRS alignment technique for high frequency analysis of Signal-averaged ECG, Medical & Biological Engineering & Computing, pp137-146, July 1993.

17. Naima F M A & Saxena S C, Analysis of ECG signal using mixed mathematical basis functions, IEEE MELECON, vol. 2, pp 139-142, 1985.

18. Naima F M A & Saxena S C, Computer-aided techniques for the extraction of ECG parameters, International Journal of System Science, vol. 20, no. 5, pp 747-757,1989.

19. Trahanias P E, An approach to QRS complex detection using mathematical morphology, IEEE Trans. on BME, vol.40, no. 2, pp 201-205, February 1993.

20. Laguna P, Jane R, Olmos S, Thakor N V, Rix H & Caminal P, Adaptive estimation of QRS complex wave features of ECG signal by the Hermite Model, Medical & Biological Engineering & Computing, pp 58-68, January 1996.

21. Murthy I S N & Prasad G S D, Analysis of ECG from Pole-zero models, IEEE Trans on BME, vol. 39, no. 7, pp 741-751, 1992
22. Saxena S C, Kumar Vinod & Hamde S T, Feature extraction from ECG signals using wavelet transforms for disease diagnostics, International Journal of
Systems Science,vol. 33,no. 13, pp 1073-1085,2002.

23. Saxena S C, Kumar Vinod & Hamde S T, QRS detection using new wavelets, Journal of Medical Engineering & Technology, vol. 26, no. 1, pp 7-15, January/February 2002

24. Paul S. Addison, J. N. Watson, G R Clegg, P A Steon & Colin E. Robertson, Finding Coordinated Atrial Activity During Ventricular fibrillation using wavelet decomposition, IEEE Engineering in Medicine and Biology, pp 58-61, January/ February 2002.

25. Eric lacier, R. Jane and Dana H Brooks, Improved alignment method for noisy high-resolution ECG and Holter records using multiscale cross-correlation, IEEE Trans. on BME, vol. 50, no.3, pp 344-353, March 2003.

26. J. P. Martinez, R. Almeida, S. Olmos, A.P. Rocha and P Laguna, A wavelet-based ECG delineator: Evaluation on standard databases, IEEE Trans. on BME, vol. 51, no.4, pp 570-581, April 2004.

27. Adam Josko and R. J. Rak, Effective simulation of signals for testing ECG analyser, IEEE Trans. on instrumentation and measurement, vol. 54, no.3, pp 1019-1024, June 2005.

28. Po-Ching Chen, Steven Lee and Cheng-Deng Kuo, Delineation of T-wave in ECG by wavelet transform using multiscale differential operator, IEEE Trans. on BME, vol. 53, no.7, pp 1429-1433, July 2006.

29. Rodrigo V. Andreao, B. Dorizzi and J. Boudy, ECG signal analysis through hidden Markov models, IEEE Trans. on BME, vol. 53, no.8, pp 1541-1549, August 2006.

30. P. Ravier, F. Leclerc, C. Dumez-Viou and G. Lamarque, Redefining Performance Evaluation tools for real-time QRS complex classification systems, IEEE Trans. on BME, vol. 54, no.9, pp 1706-1710, September 2007.

31. Turker Ince, S. Kiranyaz and M. Gabbouj, A generic and robust system for automated patient-specific classification of ECG signals, IEEE Trans. on BME, vol. 56, no.5, pp 1415-1426, May 2009.

32. R. Almeida, J. P. Martinez, A. P. Rocha and P. Laguna, Multilead ECG delineation using spatially projected leads from wavelet transform loops, IEEE Trans. on BME, vol. 56, no.8, pp 1996-2005, August 2009.

33. Dafang Wang, Robert M. Kirby and Chris R. Johnson, Resolution Strategies for the Finite-Element-Based solution of the ECG Inverse Problem, IEEE Trans. on BME, vol. 57, no.2, pp 220-237, February 2010.

34. Rik Vullings, Bert de Vries, and Jan W. M. Bergmans, An Adaptive Kalman Filter for ECG Signal Enhancement, IEEE Trans. on BME, vol. 58, no.4, pp 1094-1103, April 2011.

35. Vincent Jacquemet, Bruno Dub´e, R´eginald Nadeau, A. Robert LeBlanc, Marcio Sturmer, Giuliano Becker, Teresa Kus, and Alain Vinet Jacquemet et al , Extraction and Analysis of T waves in Electrocardiograms During atrial Flutter, IEEE Trans. on BME, vol. 58, no.4, pp 1104-1112, April 2011.





P.Deepa, S.N. Geethalakshmi

Paper Title:

A Comparative Analysis of Watershed and Color based segmentation for Fruit Grading

Abstract: In this paper, we presented two segmentation methods. Multi-scale edge detection with watershed segmentation and color based segmentation using K-means. Color based segmentation is based on fruit color and its difference. Mostly the damage part of the fruit will be of different color and that will be segmented by our algorithm very correctly. Second the watershed segmentation also segments the fruit based on color, shape and size of the damage. We compared the results of both segmentation results and the watershed segmentation outperforms the color based segmentation in all aspects.MATLAB image processing toolbox is used for the computation and Comparison results are shown with the segmented images.

Fruit grading, Multiscale edge detection, watershed segmentation, Region merging, Kmeans segmentation.


1. YQ Zhao, JX Liu, J Liu. Medical image segmentation based on morphological reconstruction operation [J], Computer Engineering and Applications, 2007,43 (10) :238-240.
2. J Tian. Medical Image Processing and Analysis [M], Beijing: Electronic Industry Press, 2003, 9: 35-43.

3. X Liu, ZS You. Multi-scale morphological edge detection method [J], Optical Engineering, 2003, 3 (30): 56-58.

4. Y Wei, XH Xu, T Jia, DZ Zhao. CT images of suspected pulmonary nodules extraction based on multiscale morphology filtering[J],

5. Northeastern University Journal, 2008, 3 (29): 91-93.

6. Patino. Fuzzy relations applied to minimize over segmentation in watershed algorithms [J], Pattern Recognition Letters, 2005, 26 (6): 819-828.

7. Vincent, L., Soille,P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 13,6(1991), 583-598

8. HB Liu, XQ He, XD Liu. Image segmentation method based on watershed and region merging[J], Computer Applications Research, 2007, 9(24): 307-30

9. Hongshe Dang, Jinguo Song, Qin Guo. A Fruit Size Detecting and Grading System Based on Image Processing. 2010 Second International Conference.

10. John B. Njoroge. Kazunori Ninomiya. NaoshiKondo and IlidekiToita. Automated Fruit Grading System using Image Processing. 2002 Auk 5-7.2MI2, osa*s.






Dhruba Ningombam, Chitra, Nitashi Kalita, Vinita Pat Pingua

Paper Title:

An Intelligent Voice Enabled Distance to Empty and Navigation System

Abstract: Recent years have witnessed a fast growth in automobile sector, leading to increased urge for an intelligent man machine interaction system for navigation. This paper describes the development and implementation of an intelligent speech agent based navigation system and distance to empty (DTE) calculation for autonomous land vehicle applications. This system, initially determines the current location using Global Positioning System (GPS). The GPS outputs NMEA (National Marine Electronics Association) sentence that contains information about current location including longitude and latitude. The input to the system i.e. the desired destination is through voice command and outputs the following-(i) the road distance and the amount of fuel required, through speech, (ii)the altitude difference between the current location and the destination, which is further used to calculate the mileage variation with altitude and (iii) displays the route from the current location to the destination on a map along with the prediction whether the user will be able to reach the desired destination with fuel left in the automobile, how much distance it can travel with the remaining fuel and how much additional fuel is required to be filled up to reach the destination.

Altitude difference, DTE (Distance to Empty), GPS (Global Positioning System), intelligent speech agent, man machine interaction, map, mileage variation, Navigation, NMEA (National Marine Electronics Association).


1. “Ultrasonic Sensor” http://en.wikipedia.org/wiki/Utrasonic-Sensor [Available Online].
2. Buxton, Suchowerskyj, Tempelhof “The Tavelpilot:A Second Generation Automotive Navigation System”. Vehicular Technology, IEEE,Feb 1991,volume 40,issue 1,PP.41-44

3. Ericc Abott and David Powell “Land Vehicle Navigation using GPS” in the proceedings of the IEEE, vol. 87, no. 1, January 1999.

4. Stanley K. honey, Newark, Walter B. Zavori, Palo Alto, kenneth A. milnes, fremont, Alan c. Philips, Los Altos, Marvin S. White, Jr., Palo Alto, George E. Loughmiller, Jr., Cupertino, All of Calif “Vehicle Navigation and Method”.

5. Jong Koo Kim, Hwaseong-city(KR) “Apparatus for indicating distance to empty of a vehicle and a method thereof”, Patent No-US2005/0060087A1,Mar 17,2005.

6. Micheal Negnevitsky, “Introduction to Knowlegde Based Intelligent Systems”, Artificial Intelligence-A Guide to Intelligent System, PP.1-17.

7. Chingtam Tejbanta Singh,Saurav Sen,Dhruba Ningombam “An Architecture for an Intelligent Home Automation System”, In the proceedings of Third Innovative Conference On Embedded Systems,Mobile Communication And Computing.PP.147-154.

8. “Microsoft’s Speech Programming Interface”, http://msdn.microsoft.com/en-us/library/ms717320%28v= VS.85%29.aspx [Available Online].

9. Peter A. Bromberg, “SAPI 5.1:Voice-Enabled Applications with VB”, http://en.wikipedia.org/wiki/3G [Available Online].

10. Immanuel A.R.Ashokaraj, Peter M.G.Silson, Antonios Tsourdos, and Brian A.White, “ Robust Sensor-Based Navigation for Mobile Robot”, IEEE Transaction on Instrumentation and Measurement, Vol.58,NO.3, March 2009.






V.Nagireddy, P.Karthik, D.Venkata Ratnam, P.S.Brahmanadam, B.Sada Siva Rao, K.Sarat Kumar, M.Ravi Kumar

Paper Title:

Autocovariance Ionospheric Prediction Model for GAGAN Applications

Abstract: Airports Authority of India (AAI) and Indian Space Research Organization (ISRO) are being jointly developing a satellite based augmentation system which is popularly known as GPS Aided Geo Augmented Navigation System (GAGAN) to cater civil aviation requirements in India. Forecasting of the ionospheric behaviour in advance can be used to set up early warnings of ionospheric threats for GAGAN system. In this paper, an ionospheric forecasting model is implemented on the basis of autocovariance method. The dual frequency GPS receiver’s data of Hyderabad (Geographic 17.410N,78.550 E) station located at the National Geophysical Research Institute (NGRI),Government of India is considered for the analysis. Time series of vertical Total Electron Content (TEC) for all visible satellites are calculated for quiet days and disturbed days. In this method, the first prediction point outside the data time interval in the future and in the past is computed and added at the beginning or at the end of data, respectively. Using this first prediction point, the next prediction point is computed consequently. Forecasting of ionospheric delay variations would be immensely useful for the protection of valuable communication satellites from space weather conditions.

autocovariance, forecasting, gps, gagan, and tec.


1. Kosek, W., The autocovariance prediction of the Earth rotation parameters, in Proceedings of the 7th International Symposium IAG: Geodesy and Physics of the Earth, Potsdam, Oct. 5–10, 1992, Montag, H. and Reigber, Ch., Eds., New York: Springer Verlag, 1993,pp. 443–446.
2. I. Stanis awska and Z. Zbyszyn´ski.,Forecasting of the Total Electron Content at a Single Location,Cosmic Research, Vol. 41, No. 4, 2003, pp. 353–356.

3. Ahmed El-Rabbany, “Introduction to GPS: The Global Positioning System”, Artech House Publishers, Boston, USA, 2001.

4. http://sopac.ucsd.edu/dataArchive..

5. Pristley, M.B., Spectral analysis and time series, Academic, San Diego, Calif., 1981.

6. Abdullah, M.; Strangeways, H.J. & Walsh, D.M.A. (2009). Improving ambiguity resolutionrate with an accurate ionospheric differential correction. Journal of Navigation,Vol.62, No. 1, pp. 151-166, ISSN: 0373-4633.

7. Kosek, W., Autocovariance prediction method of short period Earth rotation parameters, Artif. Satell., 1997, vol. 32, no. 2, pp. 75–85.

8. Pratap Misra and Per Enge, “Global Positioning System”,Ganga-Jamuna Press, NY, 2001.






Sumit Kumar Banchhor

Paper Title:

Discrimination between Speech and Music Signal

Abstract: Over the last few years major efforts have been made to develop methods for extracting information from audio-visual media, in order that they may be stored and retrieved in databases automatically. In this work we deal with the characterization of an audio signal, which is a part of a larger audio-visual system. Our goal was first to develop a system for segmentation of the audio signal, and then classify into one of two main categories: speech or music. The basic characteristics are computed in 2sec intervals. The result shows that the estimation of short time energy reflects more effectively the difference in human voice and musical instrument than zero crossing rate and spectrum flux.

Speech/music classification, audio segmentation, zero crossing rate, short time energy, and spectrum flux.


1. J. Foote. An overview of audio information retrieval. Multimedia Systems, pages 2-10, 1999.
2. E. Scheier and M. Slaney. Construction and evaluation of a robust multifeature speech/music discriminator. In Proc. IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing, 1997.

3. G. Tzanetakis and P. Cook. A framework for audio analysis based on classification and temporal segmentation. In Proc.25th Euromicro Conference. Workshop on Music Technology and Audio Processing, 1999.

4. E. Wold, T. Blum, D. Keislar, and J. Wheaton. Content-based classification, search, and retrieval of audio. IEEE Multimedia Magazine, pages 27-36, 1996.

5. J. Foote. An overview of audio information retrieval. Multimedia Systems, pages 2-10, 1999.

6. P. Moreno and R. Rifkin. Using the fisher kernel method for web audio classification. In Proc. IEEE Conf. on Acoustics, Speech and Signal Processing, pages 1921{1924, 2000.

7. M. Seck, F. Bimbot, D. Zugah, and B. Delyon. Two-class signal segmentation for speech/music detection in audio tracks. In Proc. Eurospeech, pages 2801-2804, Sept. 1999.






K.P.Aarthy, U.S.Ragupathy

Paper Title:

Detection of Lung Nodule Using Multiscale Wavelets and Support Vector Machine

Abstract: Lung cancer is the most common and leading cause of death in both men and women. Lung nodule, an abnormality which leads to lung cancer is detected by various medical imaging techniques like X-ray, Computerized Tomography (CT), etc. Detection of lung nodules is a challenging task, since the nodules are commonly attached to the blood vessels. Many studies have shown that early diagnosis is the most efficient way to cure this disease. This paper aims to develop an efficient lung nodule detection scheme by performing nodule segmentation through multiscale wavelet based edge detection and morphological operations; classification by using a machine learning technique called Support Vector Machine (SVM). This methodology uses three different types of kernels like linear, Radial Basis Function (RBF) and polynomial, among which the RBF kernel gives better class performance with a sensitivity of 92.86% and error rate of 0.0714.

Lung Nodule, Multiscale Wavelets, Support Vector Machine, Wavelet Transform.


1. S. Anthony P. Reeves, Antoni B. Chan, David F. Yankelevitz, Claudia I. Henschke, Bryan Kressler and William J. Kostis (2006), “On Measuring the Change in size of Pulmonary Nodules”, IEEE Transactions on Medical Imaging, Vol.25, No.4.
2. Rezaul.K.Begg, Marimuthu Palaniswami and Brendan Owen (2005), “Support Vector Machines for Automated Gait Classification”, IEEE Transactions on Biomedical Engineering, Vol.52, No.5.

3. Hyeokho Choi and Richard G. Baraniuk (2001), “Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models”, IEEE Transactions on Image Processing, Vol.10, No.9.

4. J. Canny (1986), “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.8, pp.679-698.

5. Jan Hendrik Moltz, Lars Bornemann, Jan-Martin Kuhnigk and Heinz-Otto Peitgen (2009), “Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans”, IEEE Journal of Selected Topics in Signal Processing, Vol.3, No.1.

6. John G. Webster (2007), “Medical Instrumentation Application and

7. Leslie Cromwell, Fred J. Weibell and Erich A. Pfeiffer (2007), “Biomedical Instrumentation and Measurements”, Prentice-Hall Inc., pp.213-242.

8. Manuel G. Penedo, Maria J. Carreira, Antonio Mosquera, and Diego Cabello (1998), “Computer-Aided Diagnosis: A Neural-Network-Based Approach to Lung Nodule Detection”, IEEE Transactions on Medical Imaging, Vol.17, No.6.

9. P. Korfiatis, S. Skiadopoulos, P. Sakellaropoulous, C. Kalogeropoulou and L. Costaridou (2007), “Combining 2D Wavelet Edge Highlighting and 3D Thresholding for Lung Segmentation in Thin-slice CT”, The British Journal of Radiology, pp.996-1005.

10. Qing-Hua Lu, Xian-Min Zhang (2005), “Multiresolution Edge Detection in Noisy Images Using Wavelet Transform”, Proceeding of the Fourth International Conference on Machine Learning and Cybernetics, Guanqzhou.

11. Stephane Mallat (1991), “Zero-Crossing of a Wavelet Transform”, IEEE Transactions on Information Theory, Vol.37, No.4.

12. Stephane Mallat and Sifen Zhong (1992), “Characterization of Signal from Multiscale Edges”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.14, No.7.

13. Xujiong Ye, Xinyu Lin, Jamshid Dehmeshki and Gareth Beddoe (2009), “Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT images”, IEEE Transactions on BioMedical Engineering, Vol.56, No.7.

14. Keng-Pei Lin and Ming-Syan Chen (2011), “On the Design and Analysis of the Privacy-Preserving SVM Classifier”, IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.11.

15. Durgesh K.Srivastava and Lekha Bhambhu (2009), “Data Classification using Support Vector Machine”, Journal of Theoretical and Applied Information Technology, Vol.12, No.1.






Bhagya. R, A. G. Ananth

Paper Title:

Study Of Transmission Characteristics Of 2x2 Mimo System for OFDM Multiplexing and Bpsk Modulation With ZF Equalizer And MMSE Receivers

Abstract: A detailed analysis of 2×2 MIMO (Multiple Input Multiple Output) combined with OFDM (Orthogonal Frequency Division Multiplexing) transmission system and BPSK modulation has been carried out. The BER performance of the system has been determined for Additive white Gaussian Noise (AWGN) presuming Flat fading Rayleigh channel. On the receiver side linear equalization techniques such as Zero Force Equalizer (ZF) and Minimum Mean Square Error (MMSE) detectors were employed for studying the BER performance. The simulation results show that for BER of ~10-4, the SNR required are ~34 dB for ZF equalizer and ~31dB for MMSE equalizer. The simulation results indicate that the MMSE equalizer shows better performance ~3 dB over the ZF equalizer. Further comparison of the 2X2 MIMO performance of OFDM with STBC multiplexing indicates comparable BER performances. The simulation results are presented and discussed in the paper. Key Words: Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), Space Time Block Coding (STBC), Binary Phase Shift Keying (BPSK), Bit Error Rate (BER), Signal to Noise Ratio (SNR), Zero Force Equalizer (ZFE) and Minimum Mean Square Error (MMSE).

(MIMO), (BER), (ZFE)


1. Chiani, M.Win, M.Z. Hyundong Shin “MIMO Networks: The Effects ofInterference”, IEEE Transactions on, Volume 56 Issue 1, On page(s): 336 -349, Jan 2010.
2. Dr.Jacob Sharony, “Introduction to Wireless MIMO–Theory and Applications”, IEEE LI, November 15, 2006.

3. Wrulich, M.; Mehlfuhrer, C.; Rupp, M, “Interference aware MMSE equalization for MIMO TxAA”, Communications, Control and Signal Processing, 2008.

4. Orlandos Grigoriadis, H. Srikanth Kamath, Iaeng, “BER Calculation Using Matlab Simulation For OFDM Transmission”, Proceedings of the International Multi-Conference of Engineers and Computer Scientists 2008 Vol II IMECS, Mar 2008, Pages 19-21.

5. Ryszard Struzak, “Channel & Modulation: Basics”, The Abdus Salam International Centre for Theoretical Physics ICTP, Trieste (Italy), 5 to 24 February 2007.

6. Allert van Zelst, and Tim C. W. Schenk, “Implementation of a MIMO OFDM-Based Wireless LAN System”,IEEE Transaction on Signal Processing, VOL.52, No. 2, Feb 2004, Pages 483-494.

7. Pramodini D V and A G Ananth, “Performance of 2x2 MIMO systems with narrowband detectors using linear and non-linear receive techniques”, International journal of electronics communication and Instrumentation engineering research.

8. Digital Communication: Third Edition, by John R. Barry, Edward A. Lee, David G. Messerschmitt .

9. Fundamentals of Wireless Communication, David Tse, Pramod Viswanath.






Mayukh Bose, Anshuman Bhattacharjee, Sudha R.

Paper Title:

Calculation of Induction Motor Model Parameters Using Finite Element Method

Abstract: The paper attempts to model a three phase squirrel cage induction motor and using Finite Element Method to obtain the finite element field solutions. The linear time harmonic vector field potential solution is used for the inductance determination. The Finite Element Analysis software used is FEMM.

FEM, FEMM, Finite Element Method, Induction Motor, Motor Modelling, Motor Parameters.


1. T W Nehl, F A Fouad and N A Demerdash, “Determination of saturated values of rotating machinery incremental and apparent by an energy perturbation model,” IEEE Transactions on Power Apparatus and Systems, 1992
2. M V Chari and P Silverster, “Analysis of turboalternator magnetic fields by finite elements,” IEEE Transactions on Power Apparatus and Systems, 1971

3. E Vassent, G Meunier, A Foggia and G Reyne, “Simulation of induction machine operation using a step by step finite element method coupled with circuits and mechanical equations,” IEEE Transactions on Magnetics, vol 27, no 6, 1991

4. A. Arkkio, “Finite element analysis of cage induction motors fed by static frequency converters,” IEEE Transactions on Magnetics, 1990

5. D. Dolinar et al., “Calculation of two-axis induction motor model parameters using finite elements,” IEEE Transactions on Energy Conversion, 12(2):133-142, June 1997.

6. M V Chari and P Silvester, “Analysis of turboalternator magnetic fields by finite elements”, IEEE Transactions on Power Apparatus and Systems

7. FEMM Documentation - http://www.femm.info/wiki/Documentation.






Kinjal A Joshi, Darshak G. Thakore

Paper Title:

A Survey on Moving Object Detection and Tracking in Video Surveillance System

Abstract: This paper presents a survey of various techniques related to video surveillance system improving the security. The goal of this paper is to review of various moving object detection and object tracking methods. This paper focuses on detection of moving objects in video surveillance system then tracking the detected objects in the scene. Moving Object detection is first low level important task for any video surveillance application. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey, I described Background subtraction with alpha, statistical method, Eigen background Subtraction and Temporal frame differencing to detect moving object. I also described tracking method based on point tracking, kernel tracking and silhouette tracking.

Object detection, background subtraction, Temporal frame diiferencing, object tracking, video surveillance, statistical methods.


1. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: active contour models. Int. J. Comput. Vision 1, 321–332, 1988.
2. V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. In IEEE International Conference on Computer Vision . 694–699, 1995.

3. N. Paragios, and R. Deriche.. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Patt. Analy. Mach. Intell. 22, 3, 266–280, 2000.

4. Comaniciu, D. And Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Analy. Mach. Intell. 24, 5, 603–619.

5. S. Zhu, and A. Yuille. Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Patt. Analy. Mach. Intell. 18, 9, 884–900, 1996.

6. Elgammal, A. Duraiswami, R.,Hairwood, D., Anddavis, L. 2002. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE 90, 7, 1151–1163.

7. Isard, M. And Maccormick, J. 2001. Bramble: A bayesian multiple-blob tracker. In IEEE International Conference on Computer Vision (ICCV). 34–41.

8. S. Y. Elhabian, K. M. El-Sayed, “Moving object detection in spatial domain using background removal techniques- state of the art”, Recent patents on computer science, Vol 1, pp 32-54, Apr, 2008.

9. Yilmaz, A., Javed, O., and Shah, M. 2006. Object tracking: A survey. ACM Comput. Surv. 38, 4, Article 13,December 2006

10. In Su Kim, Hong Seok Choi, Kwang Moo Yi, Jin Young Choi, and Seong G. Kong. Intelligent Visual Surveillance - A Survey. International Journal of Control, Automation, and Systems (2010) 8(5):926-939

11. A. M. McIvor. Background subtraction techniques. Proc. of Image and Vision Computing, 2000.

12. C. Stauffer and E. Grimson, “Learning patterns of activity using real time tracking,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, August 2000.

13. I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: real-time surveillance of people and their activities,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, August 2000.

14. Maniciu, D. And Meer, p. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Analy. Mach. Intell. 24, 5, 603–619.

15. ISARD, M. AND MACCORMICK, J. 2001. Bramble: A bayesian multiple-blob tracker. In IEEE International Conference on Computer Vision (ICCV). 34–41.

16. Elgammal, A.,Duraiswami, R.,Harwood, D., Anddavis, L. 2002. Background and foreground modeling using nonparametric kernel density estimation for visual
surveillance. Proceedings of IEEE 90, 7, 1151–1163.

17. Dockstader, S. And Tekalp, A. M. 2001a. Multiple camera tracking of interacting and occluded human motion. Proceedings of the IEEE 89, 1441–1455.

18. Christopher R. Wren, Ali J. Azarbayejani, Trevor Darrell, and Alex P.Pentland, ”Pfinder: Real-Time Tracking of the Human Body” in IEEETransactions on Pattern Analysis and Machine Intelligence, July 1997,19(7), pp. 780-785.

19. ] Chris Stauffer and Eric Grimson, ”Learning Patterns of Activity UsingReal-Time Tracking” in IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI),

20. WuU Z, and Leahy R. “An optimal graph theoretic approach to data clustering: Theory and its applications to image segmentation”. IEEE Trans. Patt. Analy. Mach. Intell. 1993.






Kevadia Kaushal, Prashant Swadas, Nilesh Prajapati

Paper Title:

Metamorphic Malware Detection Using Statistical Analysis

Abstract: Typically, computer viruses and other malware are detected by searching for a string of bits found in the virus or malware. Such a string can be viewed as a “fingerprint” of the virus identified as the signature of the virus. The technique of detecting viruses using signatures is known as signature based detection. Today, virus writers often camouflage their viruses by using code obfuscation techniques in an effort to defeat signature-based detection schemes. So-called metamorphic viruses transform their code as they propagate, thus evading detection by static signature-based virus scanners, while keeping their functionality but differing in internal structure. Many dynamic analysis based detection have been proposed to detect metamorphic viruses but dynamic analysis technique have limitations like difficult to learn normal behavior, high run time overhead and high false positive rate compare to static detection technique. A similarity measure method has been successfully applied in the field of document classification problem. We want to apply similarity measures methods on static feature, API calls of executable to classify it as malware or benign. In this paper we present limitations of signature based detection for detecting metamorphic viruses. We focus on statically analyzing an executable to extract API calls and count the frequency this API calls to generate the feature set. These feature set is used to classify unknown executable as malware or benign by applying various similarity function.

Metamorphic Virus, Malware Detection, API calls, Similarity measures.


1. P. Szor, The Art of Computer Virus Research and Defense Professional, 1st ed., Addison-Wesley, 2005.
2. Sung A H, Xu J, Chavez P, Mukkamala S, "Static Analyzer For Vicious Executables (SAVE)," Proceedings of 20th Annual Computer Security Applications Conference (ACSAC), pp. 326–334, IEEE Computer Society Press, ISBN 0-7695-2252-1 , 2004.

3. Karnik Abhishek, Goswami Suchandra, Guha Ratan, "Detecting Obfuscated Viruses Using Cosine Similarity Analysis," First Asia International Conference on Modelling & Simulation, IEEE, pp. 165 -170,2007.

4. Madhu K Shankarapani, Subbu Ramamoorthy, Ram S Movva, Srinivas Mukkamala, "Malware detection using assembly and API call sequences", journal in Computer Virology ,Springer, 2010.

5. S. Momina Tabish, M. Zubair Shafiq and Muddassar Farooq, "Malware Detection using Statistical Analysis of Byte-Level File Content", ACM, 978-1-60558-669-4, 2009.

6. V. Sai Sathyanarayan, Pankaj Kohli, and Bezawada Bruhadeshwar, "Signature Generation and Detection of Malware Families", Springer-Verlag Berlin Heidelberg 2008, pp . 336–349, 2008.

7. Wen Fu, Jianmin Pang, Rongcai Zhao, Yichi Zhang, Bo Wei, "Static Detection of API-calling Behavior from Malicious Binary Executables", International Conference on Computer and Electrical Engineering, IEEE, 2008.

8. Karnik Abhishek, Goswami Suchandra, Guha Ratan, "Detecting Obfuscated Viruses Using Cosine Similarity Analysis," First Asia International Conference on Modelling & Simulation, IEEE, pp. 165-170,2007.

9. S. Momina Tabish, M. Zubair Shafiq and Muddassar Farooq, "Malware Detection using Statistical Analysis of Byte-Level File Content", ACM, 978-1-60558-669-4, 2009.

10. Babak Bashari Rad, Maslin Masrom, "Metamorphic Virus Detection in Portable Executables Using Opcodes Statistic", Proceeding of the International Conference on Advance Science, Engineering and Information Technology 2011, ISBN 978-983-42366-4-9, 2011.

11. J. Bergeron, M. Debbabi, J. Desharnais, M. M. Erhioui, Y. Lavoie and N. Tawb, "Static Detection of Malicious Code in Executable Programs".

12. Mamoun Alazab, Sitalakshmi Venkataraman, Paul Watters, "Towards Understanding Malware Behavi our by the Extraction of API calls", Second Cybercrime and Trustworthy Computing Workshop, IEEE, 2010.

13. P Vinod, R Laxmi & Gaur M, “Survey on Malware Detection Methods”, Hack vol. 74, 2009.

14. IDA Pro, Online, www.hex-rays.com/

15. MySQL, Online, www.Wikipedia.org/

16. Python Programming, Online, www.python.org/






Mayur Rank, N.M. Patel

Paper Title:

Search Window Based Exemplar Approach for Image Inpainting

Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image. It removes an undesirable object from an image in visually plausible way. For filling the part of image, it uses information from the neighboring area. In this dissertation work, we present an Exemplar based method for filling in the missing information in an image, which takes structure synthesis and texture synthesis together. Image inpainting is done in such a manner, that it fills the damaged region or holes in an image, with surrounding color and texture. The algorithm is based on patch based filling procedure. First find target region using mask image and than find boundary of target region. For all the boundary points it defined patch and find the priority of these patches. It starts filling the target region from the highest priority patch by finding the best match patch from the search window. This procedure is repeated until entire target region is inpainted. We have perform experiment on many images and found that our algorithm successfully inpaint the target region.

Texture Synthesis, Structure Synthesis, Patch Propagation.


1. M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester. (2000) “Image inpainting.” in Proc. SIGGRAPH, pp. 417–424.
2. C. Ballester, V. Caselles, J. Verdera, M. Bertalmio, and G. Sapiro, (June 2001)“A variational model for filling-in gray level and color images,” in Proc. Int. Conf. Computer Vision, Vancouver, BC, Canada, pp. 10–16.

3. M. Bertalmio, A. L. Bertozzi, and G. Sapiro. (2001) “Navier–Strokes, fluid dynamics, and image and video inpainting.” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, pp. 417–424

4. T. Chan and J. Shen. (2002.) “Mathematical models for local non-texture inpaintings.” SIAM J. on Appl. Math, vol. 62, pp. 1019–1043.

5. M. Bertalmio, L. Vese, G. Sapiro, and S. Osher. (2003) “Simultaneous structure and texture image inpainting,” in Proc. Conf. Comp. Vision Pat-tern Rec., Madison, WI.

6. A. Criminisi, P. Perez, and K. Toyama. (2004) “Region filling and object removal by exemplar-based image inpainting.” IEEE Trans. Image Process, vol. 13, pp. 1200–1212.

7. A. Wong and J. Orchard, (2008) “A nonlocal-means approach to exemplar-based inpainting.” in Proc. the IEEE Int. Conf. Image Processing.





Kapildev Naina, Tanmay Pawar

Paper Title:

Removal of Occlusion for Abstraction of Class-Room Videos

Abstract: In this paper, we examine the case of occlusion removal from a class-room video in a remote class application. Written text on writing-board in sequence of video frames is occluded by a human subject. Our goal is to remove the occlusion, in our case it is human subject. As the human subject moves across the writing-board, he or she will occlude different parts or regions of the writing-board. The region of occlusion can be replaced by the contents of the frame in which the same region not occluded by the human subject. Thus, it requires to segment each of the frames into two types of region namely occluded and unoccluded. The frames are segmented based on a supervised statistical analysis. For decreasing the computation efficiency of proposed segmentation method, a multi-resolution analysis based on haar wavelet is implemented. In order to fill gaps in the output of the segmentation method, certain morphological operation are implemented which gives the final segmented regions in each frame. Finally, the human occlusion is removed based on the segmentation results by appropriate replacement of the content from the other frames.

Occlusion, Wavelet transformation, Statistical method.


1. Cormac Herley, “Automatic Occlusion Removal from Minimum Number of Images”, 0-7803-9134-9/05/$20.00 ©2005 IEEE
2. Amandeep kaur and Rakesh singh, “Wavelets for Edge Detection In Noisy Images”, in proceeding of NCCI 2010 (19-20 March) -National Conference on Computational Instrumentation CSIO Chandigarh, INDIA.

3. Pervez Akhtar, T. J. Ali & M. I. Bhatti “Edge Detection and Linking Using Wavelet Representation And Image Fusion”, Ubiquitous Computing and Communication Journal, Volume 3 Number 3.

4. Priyanka Mekala, Asad Davari, Jichang Tan, “Occlusion Detection Using Motion-Position Analysis”, in proceeding of 42nd South Eastern Symposium on System Theory, 2010 IEEE.

5. Maria Nadia Hilario, Jeremy R. Cooperstock, “Occlusion Detection for Front-Projected Interactive Displays”, in Proceeding of Pervasive Computing and Advances in Pervasive Computing, Austrian Computer Society, 2004.

6. ScottMcCloskey, KaleemSiddiqi, “Removal of Partial Occlusion from Single Images”, IEEE transaction on pattern analysis and machine intelligence, vol. 33, no.3.@2011 IEEE.

7. JeongHee Cha, HyunCheul Shin, “Occlusion Processing Method using Improved Object Contour Extraction Algorithm by Neighboring edge Search and MER in Simulation”, in proceeding of International Conference on Convergence and Hybrid Information Technology, 2008.

8. R. Gonzalez and R. Woods: Digital Image Processing, Pearson Education, Inc., 2nd edition, 2002.






Sivanaadhbaazi Karampudi, Sunil Kumar Dasari, Yedukondalu Ravuri, Nazeer Shaik, Veerababu Reddy

Paper Title:

M- Muster Using GPS

Abstract: This paper deals how to prevent the duplication or fraudulent of works in this society. This type of works may happen either in government organizations or in private organizations. This paper considers fraudulent of works happened in the government organizations. To prevent this type of works in the government organization, let’s introduce mobile muster technology using Global Positioning System (GPS). Now consider a contract work which was given by the government organizations to the contractor. The main aim of this technology is to provide some special software based mobile to the contractor or who involved in supervising the work. The duty of his /her is to show the work progress, labourer attendance, distributing daily wages to the daily laborers regularly. In order to avoid fraud in the work, first the concerned man who involved in this work should login into the application form from the field where the work is going on. Later capture the photographs of the finished work and the remaining work to do and also the labourer involved on that day. This total information is automatically sent to the web server by the mobile. The person location who sends all this data can identify by using the mobile IMEI number using GPS and that information is stored in the database. Every user can verify this information as up to the date by using web application. By this process we can avoid fraud in the work to some extent.

Mobile, GPS, IMEI, Muster, Ajax, Google API, J2ME.


1. Web page :<http:// en.wikipedia.org/wiki/Global_Positioning_System>, Last Accessed: 22th June 2009.
2. Dietel & Dietel, how to program,4th edition

3. James Keogh, J2ME:the complete reference, p.85-110.

4. Webpage:<http://bytes.com/topic/mobile development/answers/734707-html-parsingj2me>,.

5. A. J. Hoffman and P. Wolfe (1985), "History" in The Traveling Salesman Problem, Lawler, Lenstra, Rinooy Kan and Shmoys, eds., Wiley, 1-16.

6. Yuan, G., Zhang, Z. and Wei Shang Guan, (2008), “Research and Design of GIS in Vehi c l e Moni tor ing System, ” IEEE International on Internet Computing in Science and Engineering

7. Aloquili, O., Elbanna, A. and Al-Azizi, A., Aloquili, O., Elbanna, A. and Al-Azizi, A., Based on GIS Environment,” IET Software, 2009, 3.4, pp. 255-263.

8. Pati, N., “Occlusion Tolerant Object Recognition Methods for VideoSurveillance and Tracking of Moving Civilian Vehicles”, MS Thesis(Computer Engineering), University of North Texas, Denton, USA,December 2007

9. Insurence Issue Institue. “III Presentations”, Available:http://www.iii.org/media/hottopics/ insurance/ test4/

10. US Air Force Fact Sheet: Global Positioning Systems Wing, LosAngles Air Force Base, Available: http://www.losangeles.af.mil/library/ factsheets/factsheet.asp?id=5311

11. Telit Wirless Solutions, GSM/GPRS. available:http://www.telit.com/module/infopool/ download. php? id=165

12. Association, National Marine Electronics. NMEA 0183 Standard,Available: http://www.nmea.org /pub/0183/index.html

13. Network System Architects, Inc.. What is an IMEI? Available: http://www.nmea.org/pub/ 0183/ index.html

14. MySQL AB. MySQL Documentation, Available: http://dev.mysql.com/doc/

15. MySql AB Casestudies. Available: http://www.mysql.com/whymysql/casestudies/ mysql_cs_utel_en.pdf

16. Movable Type Scripts. Calculate distance, bearing and more between two Latitude/Longitude points, Available: http://www.movable-type.co.uk/ scripts/ latlong.html

17. The World Wide Web Consortium. The XMLHttpRequest Object. Available: W3C Working Draft: http://www.w3.org/tr/xmlhttprequest/

18. GrameenPhone. Internet services, Available: www.grameenphone.com/index.php?id=134






Maninder Jeet Kaur, Moin Uddin, Harsh K Verma

Paper Title:

Performance Evaluation of CSMA/TDMA Cognitive Radio Using Genetic Algorithm

Abstract: Channel Assignment is a very important issue in the field of Wireless Networks. In this paper, we have evaluated the performance of a Multiple Channel TDMA/CSMA spectrum sharing scenario. We have combined the TDMA and the non-persistent CSMA system with multiple channels and analyzed the throughput and the throughput performance of the individual systems as a function of the actual offered traffic level. In this paper we have analyzed TDMA and CSMA in Cognitive Radio, where the primary users have higher priority than secondary users and secondary users need to monitor the channel in order to avoid the interference to the primary users. TDMA users are considered as primary users who can access the channel at any time and CSMA users are considered as secondary users who can share the channel when it is free.

TDMA, CSMA, Cognitive Radio, Genetic Algorithm.


1. J Mitola III, “Cognitive radio: an integrated agent architecture for software defined radio,” Ph.D Thesis, KTH Royal Inst. Technology. Stockholm, Sweden, 2000.
2. C. Cormio and K.R. Chowdhury, “A survey on MAC protocols for cognitive radio networks,” Elsevier Ad Hoc Networks, vol. 7, 2009, pp. 1315-1329

3. P. Papadimitratos, S. Sankaranarayanan, and A. Mishra, “A bandwidth sharing approach to improve licensed spectrum utilization,” IEEE Communications Magazine, vol. 43, December 2005, pp. 10-14.

4. Q. Zhao, L. Tong, A. Swami, and Y. Chen, ”Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: a POMDP framework”, IEEE Journal on Selected Areas in Communications (JSAC),vol.25,no.3,April,2007,pp.589-600.

5. Y. Chen, Q. Zhao, and A. Swami, “Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors,” IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 2053- 2071, May, 2008.

6. IEEE P802.22/D0.1, “Draft Std for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specification: Policies and Procedures for Operation in the TV bands.”

7. I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, S. Mohanty, NeXt generation dynamic spectrum access cognitive radio wireless networks: a survey, Computer Networks Journal (Elsevier), Issue 13, 50, September 2006, pp. 2127–2159.

8. Z Yang, Y Yao , D Zheng, “TDMA for Primaty Users and CSMA for Secondary Users in a Cognitive Radio Network” 2010.

9. A. Ghasemi, E.S. Sousa, Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks, in: Proceedings of IEEE Consumer Communications and Networking Conference, January 2007, pp. 1022–1026.

10. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989

11. Rondeu, T W, C J Rieser, B Le, and C W Bostain. “Cognitive Radios with Genetic Algorithms: Intelligent Control of Software Defined Radios.” SDR Forum Technical Conference. Phoneix, FL: CWT, 2004. C-3 - C-8.

12. S.-Y. Lien, C.-C. Tseng, K.-C. Chen, Carrier sensing based multiple access protocols for cognitive radio networks, in: Proceedings of IEEE International Conference on Communications (ICC), May 2008, pp. 3208–3214.

13. L.G.Roberts, “ALOHA packet system with and without slots and capture,” Computer Commun.Rev,no.5,pp.28-42,1975.

14. R. Rom and M. Sidi, Multiple Access Protocols: Performance and Analysis, New York: Springer Verlag, 1990.

15. Zbigniew Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed., Verlag,1996.M

16. Harilaos G. Sandalidis, Peter P. Stavroulakis, J. Rodriguez-Tellez, Application of the genetic algorithm approach to a cellular dynamic channel allocation model, IMACS Symposium on Soft Computing in Engineering Applications, Athens, Greece,June 1998

17. J. H. Holland, Adaption in Natural and Artificial Systems,Ann. Arbor, MI: Univ. Michigan Press, 1975.

18. L.G.Roberts, “ALOHA packet system with and without slots and capture,” Computer Commun.Rev,no.5,pp.28-42,1975.

19. J. Arnbak and W. Blitterswijk, “Capacity of slotted ALOHA in Rayleigh fading channels,” IEEE Journal Selected Areas Communications,vol 5, pp 685-692, Feb 1987.






M. Karnan, N. Krishnaraj

Paper Title:

A Model to Secure Mobile Devices Using Keystroke Dynamics through Soft Computing Techniques

Abstract: In this mobile world, there are more mobile phones in than computers. Everyday more of these phones becomes smart phones. Nowadays, mobile devices functions like a mini computer, it becomes more attractive target for thieves. A reliable security application is needed to safeguard data and protect against theft. As mobile devices become more pervasive in our lives, there is a greater need to protect the data on such devices. The current PIN (Personal Identification Number) authentication in mobile device is weak and there is a demand of strong authentication. Biometrics adds an additional authentication and it provides most significant improvement in mobile security. In this research work, we proposed a hybrid authentication mechanism (keystroke, finger print and palm print) where biometric data’s are captured and user template can be generated. The template is used to check whether the user is authenticated person or an imposter.

PIN, Template, Keystroke dynamics, Finger print, palm print.


1. Chandran GC, Rajesh RS (2009). Performance Analysis of Multimodal Biometric System Authentication, Int. J. Comput. Sci. Network Security, 9: 3.
2. Chin YJ, Ong TS, Goh M K O, Hiew B Y (2009). Integrating Palmprint and Fingerprint for Identity Verification, Third International Conference on Network and System Security.

3. Das, S., Biswas, A., Dasgupta, S. & Abraham, A.2009b.Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. In Foundations of Computational Intelligence (3), 23–55.

4. De-Song Wang, Jian-Ping Li, A New Fingerprint -Based Remote User Authentication Scheme Using Mobile Devices , Apperceiving Computing And Intelligence Analysis, 2009. ICACIA 2009. Page(S): 65 – 68 , Chengdu, China

5. Duane Blackburn, Chris Miles, Brad Wing, Kim Shepard, Biometrics Overview,National Science and Technology Council (NSTC) Committee on Technology Committee on Homeland and National Security, 2007.

6. Jea.T.Y and Govindaraju V, “A minutia-based partial fingerprint recognition system,” Pattern

7. Karnan.M, ,Akila,M and Krishnaraj.N Biometric personal authentication using keystroke dynamics: A review. Applied Soft Computing, 1(2):1565–1573, March 2011.

8. Karnan.M, , Krishnaraj.N ,Bio password—keystroke dynamic approach to secure mobile devices. In IEEE International Conference on Computational Intelligence and Computing Research, pages 1–4, December 28–29, 2010, Tamilnadu, India, 2010.

9. Kenneth Revett , Behavioral Biometrics: A Remote Access Approach , John Wiley & Sons, Ltd. ISBN: 978-0-470-51883-0 , 2008

10. Kim, D. H., A. Abraham, and J. H. Cho, "A hybrid genetic algorithm and bacterial foraging approach for global optimization," Information Sciences, Vol. 177, 3918-3937, 2007.

11. Kumar, A., Zhang, David, (2006). Combining Fingerprint, Palmprint and Hand-shape for User Authentication, 18th Internation Conference on Pattern Recognition, ICPR 2006, 4, pp. 549 - 552.

12. Kumar.A, Wong D.C.M, Shen H.C, and Jain A.K, “Person verification using palmprint and hand geometry biometric” Proc. Audio- and Video-Based Biometric Person Authentication (AVBPA), pp.668-675, 2003.

13. Matyas S.M, Stapleton J, A biometric standard for information management and security, Computers & Security 19 (n. 2) (2000) 428–441.

14. Ross.A, Nandakumar.K, and Jain.A.K, Handbook of Multibiometrics, Springer Verlag, 2006.

15. Saevanee. H and Bhatarakosol P. User authentication using combination of behavioral biometrics over

16. Samir Nanavati, Michael Thieme, Raj Nanavati, Biometric’s Identity Verification in a Networked World, John Wiley and Sons Inc./Wiley Computer Publication,2003.

17. Yao YF, Jingb XY, Wong HS (2007). Face and palmprint feature level fusion for single sample biometrics recognition, Neurocomputing, 70: 1582-1586.

18. Zhou J, Su G, Jiang C, Deng Y, Li C (2007). A face and fingerprint identity authentication system based on multi-route detection, Neurocomputing, 70: 922-931.

19. www.findbiometrics.com/hand-and-finger/

20. www://subhb.org/2012/01/26/palm-recognition- technology -to-enable-mobile-biometrics/

21. www.mobilemag.com/2004/11/30/pantech-gi100- mobile-phone-with-biometric-fingerprint-recognition/






Bhavana Deshpande, H.K. Verma, Prachi Deshpande

Paper Title:

Fuzzy Based Median Filtering for Removal of Salt-and-Pepper Noise

Abstract: This paper presents a filter for restoration of images that are highly corrupted by salt and pepper noise. By incorporating fuzzy logic after detecting and correcting the noisy pixel, the proposed filter is able to suppress noise and preserve details across a wide range of salt and pepper noise corruption, ranging from 1% to 60%. The proposed filter is tested on different images and is found to produce better results than the Traditional Median Filter.

Salt-and-pepper noise, Median Filter, Fuzzy reasoning.


1. Kenny Kal Vin Toh, Nor Ashidi Mat Isa, “Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction”, IEEE SIGNAL PROCESSING
LETTERS, VOL. 17, NO. 3, MARCH 2010.

2. Kh. Manglem Singh, “Fuzzy Rule based Median Filter for Gray-scale Images”, Journal of Information Hiding and Multimedia Signal Processing, Volume 2, Number 2, April 2011.

3. W. K. Pratt, Digital Image Processing, John Wiley and Sons, 1978

4. J.W. Tukey, Exploratory Data Analysis, Addison-Wesley Mento Park, 1977

5. S. Zhang and M.A. Karim, “A new impulse detector for switching median filters”, IEEE Signal Processing Letters, vol 9, no 11, pp 360-363, Nov 2002.

6. K. S. Shrinivasan and D. Ebenezer, “ A New Fast and Efficient Decision based Algorithm for Removal of High Density Impulse Noises” , IEEE Signal Processing Letters, vol 14, No 3, March 2007.

7. F. Russo, G. Ramponi, “ A fuzzy filter for images corrupted by impulse noise”, IEEE Signal Processing Letters, vol 3, no 6, pp 168-170, June 1996.

8. Haixiang Xu, Guangi Zhu, Haoyu Peeng, Deshag Wang, “Adaptive fuzzy Switching Filter for images corrupted by Impulsive noise”, Elsevier, Pattern Recognition Letters, 25 (2004), 1657-1663.

9. Kenny Kal Vin Toh, Haidi Ibrahim, Muhammad Nasiruddin Mahyuddin, “Salt-and-Pepper Noise Detection and Reduction using Fuzzy Switching Median Filter”, IEEE Transactions on Consumer Electronics, Vol 54, No 4, November 2008.

10. R.C. Gonzalez, R.E. Woods, Digital Image Processing, Englewood Cliffs, Prentice Hall, 2002.

11. W. Luo, “Efficient removal of Impulse Noise from Digital Images”, IEEE Transactions on Consumer Electronics, Vol 52, no. 2, pp 523-527, May 2006.





Ch. Siva Rama Krishna, Vidhyacharan Bhaskar

Paper Title:

Spectrum Efficiency for Spatially Correlated MIMO OSFBC-OFDM Systems over Various Adaptation Policies

Abstract: In this paper, closed-form expressions for capacities per unit bandwidth for spatially correlated multiuser MIMO-OFDM systems employing Orthogonal Space-Frequency Block Coding (OSFBC) over multipath frequency-selective fading channels are derived for optimal power and rate adaptation, optimal rate adaptation with constant transmit power, channel inversion with fixed rate, and truncated channel inversion adaptation polices. A Signal to Noise Ratio (SNR) based user selection scheme is considered. Closed-form expressions are derived for spatially correlated OSFBC-OFDM system. Optimal power adaptation policy provides the highest capacity over the other adaptation policies. Capacity gains are more prominent for optimal rate adaptation with constant transmit power policy as compared to other adaptation policies.

Orthogonal space-frequency block coding; optimal power adaptation; optimal rate adaptation with constant transmit power; channel inversion with fixed rate; truncated channel inversion; outage probability.


1. Duman, T. M., and Ghrayeb, A., Coding for MIMO Communication Systems, John Wiley & Sons Ltd, West Sussex, England, 2007.
2. Yang, H., “A road to future broadband wireless access: MIMO-OFDM Based air interface,” IEEE Communication Magazine, vol. 43, no. 1, pp. 53–60, Jan. 2005.

3. Liew, T., and Hanzo, L., “Space–time trellis and space–time block coding versus adaptive modulation and coding aided OFDM for wideband channels,” IEEE Transactions on Vehicular Technology, vol. 55, no. 1, pp. 173–187, Jan. 2006.

4. Jiang, M., and Hango, L., “Multiuser MIMO-OFDM for next generation wireless systems,” Proceedings of the IEEE, vol. 95, no. 7, pp. 1430-1469, March 2007.

5. 5. Niyato, D., Hossain, E., and Bhargava, V., “Scheduling and admission control in power- constrained OFDM wireless mesh routers: Analysis and optimization,” IEEE Transactions on Wireless Communications, vol. 6, no. 10, pp. 3738– 3748, Oct. 2007.

6. Chieochan, S., and Hossain, E., “Adaptive radio resource allocation in OFDMA systems: A survey of the state-of-the-art approaches,” Wireless Communications and Mobile Computing, vol. 9, no. 4, pp. 513–527, April 2009.

7. Niyato, and D., Hossain, E., “Adaptive fair subcarrier/rate allocation in multirate OFDMA networks: Radio link level queuing performance analysis,” IEEE Transactions on Vehicular Technology, vol. 55, no. 6, pp. 1897–1907, Nov. 2006.

8. Zhang, Y. J., and Letaief, K. B., “Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems,” IEEE Transactions on Wireless Communications, vol. 3, no. 5, pp. 1566–1575, Sep. 2004.

9. Zhang, Y. J., and Letaief, K. B., “An efficient resource-allocation scheme for spatial multiuser access in MIMO/OFDM systems,” IEEE Transactions on Communications, vol. 53, no. 1, pp. 107–116, Jan. 2005.

10. Torabi, M., Ajib, W., and Haccoun, D., “Performance Analysis of scheduling schemes for Rate-adaptive MIMO OSFBC-OFDM Systems,” IEEE Transactions on Vehicular Technology, vol. 54, no. 5, pp. 2363-2379, June 2010.

11. Lee, W., “Estimate of channel capacity in Rayleigh fading environment,” IEEE Transactions on Vehicular Technology, vol. 39, no. 3, pp. 187–189, Aug. 1990.

12. Alouini, M., Abdi, A., and Kaveh, M., “Sum of gamma variables and performance of wireless communication systems over Nakagami fading channels,” IEEE Transactions on Vehicular Technology, vol. 50, no. 6, pp. 1471–1480, Nov. 2001.

13. Bhaskar, V., “Spectrum Efficiency Evaluation for MRC Diversity schemes Under Different Adaptation Policies Over Generalized Rayleigh Fading channels,” International Journal of Wireless Information Networks, vol. 14, no. 3, pp. 191-203, Sep. 2007.

14. Bhaskar, V., “Capacity evaluation for equal gain diversity scheme over Rayleigh fading channels,” International Journal of Electronics and communications, vol. 63, no. 3, pp. 235-240, Sep. 2008.

15. Tarokh, V., Jafarkhani, H., and Caldrebank, A. R., “Space –time block codes from orthogonal designs,” IEEE Transactions on Information Theory, vol. 45, no. 4, pp. 1456–1467, July 1999
16. Kim, I., “Exact BER analysis of OSTBCs in spatially correlated MIMO channels,” IEEE Transactions on Communications, vol. 54, no. 8, pp. 1365–1373, Aug. 2006.

17. Alouini, M. S., and Goldsmith, A. J., “Capacity of Rayleigh Fading Channels Under Different Adaptive Transmission and Diversity-Combining Techniques,” IEEE Transactions on Vehicular Technology, vol. 48, no. 4, pp. 11653–1181, July 1999.

18. Gradshteyn, I and Ryzhik, I., Table of Integrals, Series and Products, 6th edition, Academic press, London, 2000.





CH.RenuMadhavi, A.G.Ananth

Paper Title:

A Review of Heart Rate Variability and It’s Association with Diseases

Abstract: Heart Rate variability (HRV) is a powerful noninvasive tool which can be used to detect the status of cardio-autonomic function. Its analysis gives information about the cardiac health status .HRV measures specifically nonlinear measures can be sued as predictors of mortality, hence can be used to prevent the mortality, for both cardiac and no cardiac diseased subjects. Because of its significance in this paper a review has been presented on HRV analysis methods and HRV association with various diseases.

Heart rate variability, Diseases, Depression, Thyroid, Nonlinear measures, Analysis.


1. Sokolov M Mcliroy ,MB Chiten ,MD Vilange “Medical book Clinical Cardiology” 1990
2. Chua Kuang chua,Vinod Chandran,Rajendra U.Acharya and Lim Choo Min”Cardiac health diagnosis using Higher order spectra and support vector machine “ open Med Information J 2009,3:1-8februavary 2009

3. Kleiger RE,Stein PK, Bosner MS,Rottman JN,Time domain measurement of heart rate variability,cardiol Clin,1992,.10:487-497

4. Acharya UR,Kumar A,Bhat PS classification of cardiac abnormalities using heart rate signals.Med.Bio.Eng Comp 2004: 42(3);288-9[4a]

5. Goldberger AL,West BJ” Applications of nonlinear dynamics to clinical cardiology”Ann.NY.Acad Sci 504;pp195-213,1987

6. U.Rajendra Acharya ,paul joseph .k,kannathal n,lim CM,SuriJS,l”Heart rate Variability :a review” Med.Bio.Eng.Comput,2006

7. Hon EH, Lee ST.” Electronic evaluations of the fetal heart rate patterns preceding fetal death , further observations’, Am. J. Obstet Gynec, 87:pp 814-26, 1965.

8. Wolf MM, Varigos GA, Hunt D, Sloman JG.” Sinus arrhythmia in acute yocardial infarction”. Med J Aust.; 2:pp52-53, 1978

9. Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Multicenter Post-Infarction Research Group. Am J Cardiol. 1987; 59:256-62.

10. Keesam Jeong,Jeongwhan Lee,Kunsoo Shin,Juhn Ahn,Joongson Chon,Myoungho Lee “Astudy of relationship between heart rate variabilities and autonomic balance during head –up tilt”Proceedings of 19thInternationalConference-IEEE/EMBS,Nov1997,Chicago Usa,pp282-285

11. David LD, Billon N, Costagliola D, Jaillon P, Funck-Brentano C. reproducibility of noninvasive measurement and of short-term variability of blood pressure and heart rate in healthy volunteers. Br J Clin Pharmac 38: 109-115.1994

12. Huikuri HV. Heart rate variability in coronary artery disease. J Int Med 237: 349-357.1995

13. Task Force of the European Society of Cardiology and North American Society of Pacing and electrophysiology. Heart Rate Variability: Standards of measurement, physiological interpretation and clinical use. Eur Heart J. 1996;17:354–81Taskforce1996

14. B.W Hyndman, Zeelenberg C, “spectral analysis of heart rate variability revisited: comparison of methods”, proceedings of computers in cardiology, pp,719-722, 1993

15. Akselrod S, Gordon D, Ubel F.A, Shanon .D.C, Barger .A.C ,Cohen R.J, ”Power spectrum analysis of heart rate fluctuation a quantitative probe of beat to beat “cardiovascular control science,1981,213:pp220-22

16. WeiseF,HeydenreichF,KropfS,KrellD(1990)Intercorrelation heart rate variability and respiration in humanvolun21:17–24

17. Pomeranz B, Macaulay RJB, Caudill MA, Kutz I,Adam D,Kilborn KM,Barger AC,Shanon DC, Cohen RJ, Benson H “Assesment of autonomic function in humans by heart rate spectral analsis’,Ann J.Physiol 248:ppH151-153, 1995

18. M.F.Hilton ,JM Beattie,MJ Chappell,RA Bates Heart rate variability measurement error or chaos’computersincardiology, vol24,pp125-128, 1997

19. Yi Gang, and Marek Malik”Heart Rate Variability Analysis in General Medicine” Indian Pacing and Electrophysiology Journal (ISSN 0972-6292), 3(1): 34-40 ,2003

20. Hirohisa Mizato,Isao Takenchi,Osamu Tsuda,Kazuo Yana And Takeshi Goto’ Changesbin the heart rate variability and electrogastrogram due to the motion sickness’ proceedings of 18th International Conference of the IEEE Engineering in Medicine and biology society, Amsterdam, ,pp1636-1637, 1996

21. Khadra AS,AI Fahoum and H.AI- Nashash,”Detection of life threatening cardiac arrhythmias using wavelet transformation’ Medical and Biological Engineering and computing ,vol 35,pp626-632,1997.

22. G. Krstacic ”Non linear Analysis of Heart Rate Variability in Patients with Coronary Heart Disease” Computers in Cardiology 2002,29:673-675

23. Gley Kheder, Abdennaceur Kachouri, Mouhamed Ben Messouad, Mounir Samet “Application of a nonlinear dynamic method in the analysis of the HRV (Heart Rate Variability) towards clinical application : Tiresome diagnosis” IEEE proceeding, pp 177-182, 2006

24. A E Aubert, F Beckers, B Seps “Non-linear Dynamics of Heart Rate Variability in Athletes: Effect of Training” Computers in Cardiology, IEEEproceeding, pp 441-444,2002

25. Klein J.Ojama K”Thyroid hormone and bloodpressure regulation in hypertension ;pathology ,diagnosis and management(2nded)editedbyLaraghJHBrenneBM,Newyork,1995Raven,p2247-2262

26. Xing H.Shen Y.Chen H Wang Y Shen W ‘Heart rate variability andits response to thyroxine replacement therapy in patients with hypothyroidism”chin med J (Engl),114(9)pp:906-8,2001

27. Sujata Gautam, O. P. Tandon, R. Awashi,T. Sekhri and s. S. Sircar” Correlation of autonomic indices with thyroid status”, Indian J Physiol Pharmacol 47(2) pp164-170, 2003;

28. Bhat.A.N.,Kalsotra.L,Yograj.S”Autonomicreactivitywithalteredthyroidstatus”J.K.Sci,8,pp70-74,2007

29. Jinlong Chen,Yin Jiun Tseng,Hung Wen Chiu,Tzu Chien Hsiao And Woei Chyn Chu ‘Nonlinear analysis of heart ratedynamicsinhyperthyroidisism’2007,PhysiolMeas.vol28,no. 4’427-33

30. VijayaLakshmi,N.Vaney and S.V.Madhu”Effect of Thyroxine Therapy on Autonomic Status in Hypothyroid patients”IndianJ.PhysiolPharmacol ,53(3):219-226,2009

31. RasselKabir,Noorzahan Begum,Sultana Ferdous,Shelina Begum,Taskina Ali’Relation ship of Thyroid Hormones with Heart Rate Variability’Journal of Bangladesh Society of Physiologist,vol5,no.1,pp20-26,2010

32. Albert C Yang,Chen Jee Hong,Shih Jen Tsai,’Heart RateVariabilityinPsychriaticDisorders’TaiwaneseJournalofPsychiatry (Tapei)vol 24,no.2,2010 pp99-109

33. RooseSP”Depression,anxiety,andthecardiovascularsystem;thepsychiatrist’sperspective”J.Clin.Psychiatry,6298);pp19-22,2001

34. Gehi A,ManganoD,PipkinS,BrownerWS,Whooley MA: Depression and heart rate variabilityin patients with stable coronary heart disease: fi ndings from the Heart and Soul Study. Arch GenPsychiatry 2005;62:661-6.

35. Yeragani VK ”Major depression and long term heart period variability’Depress Anxiety 12:pp51-52,2000

36. KernelSayer,HuseyinGulec,MustafaGokce,IsmailAK“HeartratevariabilityinDepressedPatients”BulletinClinical Psychophamacology2002,vol12,N:3,pp 130-133

37. Cheng LI, Da-Kan Tang, Da-An. Zheng, Guang-Hong Ding, Chi-Sang poon, Guo-Qiang WU “Comparison of Nonlinear Indices in Analyses of Heart Rate Variability” 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008, pp 2145-2148.

38. Alan Jovic, Nikola Bogunovic “Feature Set Extension for Heart Rate Variability Analysis by Using Non-linear, Statistical and Geometric Measures” Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, June 22-25, pp 35-40, 2009

39. M.G. Signorini, M. Ferrario, M. Marchetti, A. Marseglia “Nonlinear analysis of Heart Rate Variability signal for the characterization of Cardiac Heart Failure patients” Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 2006, pp 3431-3434

40. P.Grassberger and Procaccia “Measuring the strangeness of strange attractors”, Physica D,1983, 9: pp189-208

41. D.Hoyer, K.Schmidt, R.Bauer, U.Zwiener, M.Kohler, B.L uthke, M.Eiselt “Nonlinear analysis of heart rate and respiratory dynamics’IEEE Eng.Med.Bio.Mag,1997,vol 16(1):pp31-39,1997

42. Xiaobo Miao.et.al” Heart rate variability characterization usingCorrelationDimension”2002,IEEE proceedings

43. Z.iafuzhu.WeiHe and Hao Yang “Comparitive analysisof heart rate variability between morbid group based on Correlation Dimension” IEEE,2008, 978-4244-,pp2252-2255

44. T.Penzaletal is heart rate variability the simple solution to diagnose sleep apnoea? Eur Respir J 2003; 22: pp870–871,

45. Luiz Carlos Marques Vanderlei, Carlo Marcelo Pastre, Rosangela Akemi Hoshi,Tatiana Dias de Carvalho,Moacir Fernandes de Godoy” Basic notions of heart rate variability and its clinical applicability” Rev.Bra Cir Cardiovasc,2009, vol 24(2),pp1-31

46. G Krstacic, D Gamberger, A Krstacic, T Smuc, D Milicic” The Chaos Theory and Non-linear Dynamics in Heart Rate Variability in Patientswith Heart Failure’ Computers in cardiology 2008,35:pp957-959

47. D.Hoyer, K.Schmidt, R.Bauer, U.Zwiener, M.Kohler, B.L uthke, M.Eiselt “Nonlinear analysis of heart rate and respiratory dynamics’IEEE Eng.Med.Bio.Mag,1997,vol 16(1):pp31-39,1997

48. Sun .Y, Chan, K.L, Krishnan.SM “Arrhythmia detection and recognition in ECG signals using nonlinear techniques”, Ann. Biomed.Eng,2000,28:pp5-3728

49. Rosenstein M,Colins JJ, De Luca CJ ” A practical method for calculating largest lyapunov exponent from small data sets”, Physica D,1993,65 :pp117-134

50. Dingfei GE, Narayana Srinivasan, Shankar M. Krishnan, ”Cardiac arrhythmia classification using autoregressive modeling”, Biomedical Engineering online, 2002,pp1:5

51. Alan Jovic, Nikola Bogunovic “Feature Set Extension for Heart Rate Variability Analysis by Using Non-linear, Statistical and Geometric Measures” Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, June 2009, pp 35-40.

52. Pincus SM: Approximate Entropy as a Measure of System Complexity. Proc Natl Acad Sci USA,1991, 88:pp2297-2301

53. Pincus SM,Vidcarello RR “Approximate entropy a regularity measure for heart rate analysis”Obstet.Gynecol’1992,79:pp249-55

54. Pincus SM, Goldberger AL: Physiological Time-Series Analysis: What Does Regularity Quantify? Am J Physiol ,1994, 266:pp1643-1656

55. J. S. Richman and J. R. Moorman “Physiological time-series analysis using approximate entropy and sample entropy,” Am. J. Physiol. Heart Circ. Physiol.2000, vol. 278, pp.2039–2049

56. M. Costa, Ary .I ,Goldberger ,K.Peng “Multiscale entropy analysisof biological signals,” Phys. Rev. E,2005, 71: 021 906, pp:1-18.

57. Voss et.al(2008 A Voss, R Schroeder, M Vallverdu, I Cygankiewicz, R Vazquez, A Bayes de Luna, P Caminal “Linear and Nonlinear Heart Rate Variability Risk Stratification in Heart Failure Patients” Computers in Cardiology 2008;35, pp 557−560

58. G.Krstacic ,D.Gamberger,A.Krstacic,T.Smuc,D.Millicic “The chaos and nonlinear dynamics in Heart Rate Variability in patients with Heart Failure “Computers in cardiology ,2008,35:pp957-959.

59. PhyllisK.SteinandAnandReddy”NonLinearHeartRateVariability and Risk Stratificationn Cardiovascular Disease” Indian Pacing and Electrophysiology Journal ,2005, 5(3): pp210-220

60. Saif Ahmed,Anjali Tejuja,Kimberely D.Newman,Ryan Zharichanski,Andrew JE seely“A review and analysis of heart rate variability and the diagnosis and prognosis of infection”Crit.care2009,13(6):232.






Shruti Jain, Pradeep K. Naik, Sunil V. Bhooshan

Paper Title:

Compendium Model of AkT for Cell Survival/Death and its Equivalent Bio-Circuit

Abstract: This paper demonstrates the compendium model of AkT (protein kinase B) for cell survival/ death. AkT is a central signaling molecule in the Tumor Necrosis factor-α (TNF), Epidermal Necrosis Factor (EGF) and Insulin pathway. Model demonstrates how AkT promotes cell survival by inactivating several targets, including forkhead transcription factors, p53, GSK-3B and caspase-9 and activating Bad, NF-κB and mTOR. On the basis of model for AkT we have made the truth tables, Boolean equations and then implement the equations using logic circuits and Bio-circuits showing cell survival and death. Heat map of 13 time points at 0, 5, 15, 30, 60, and 90 min and 2, 4, 8, 12, 16, 20, and 24 hr for ten cytokine treatments of TNF, EGF and Insulin has been taken for AkT signal. We have plotted their corresponding graph of time-dependent signals. The results obtain will give information on how the input signals inducing cell survival/ death should be modulated to achieve desire outputs.

AkT, Pro-Apoptotic, Anti-Apoptotic.


1. P. Cohen, Protein kinases - the major drug targets of the twenty-first century ? Nat. Rev. Drug Disc. 1, 2002, 309-315.
2. S.P. Staal, Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: amplification of AKT1 in a primary human gastric adenocarcinoma. Proc. Natl. Acad. Sci. U. S. A., 84 (1987), 5034–5037.

3. S.P Staal, J.W Hartley., and W.P. Rowe, Isolation of transforming murine leukemia viruses from mice with a high incidence of spontaneous lymphoma. Proc. Natl. Acad. Sci. U. S. A., 74 (1977), 3065–3067.

4. B. Vanhaesebroeck, and D. R. Alessi, The PI3K-PDK1 connection: more than just a road to PKB. Biochem. J. 346 (2000), 561–576.

5. Altomare, et al. 1995. Cloning, chromosomal localization and expression analysis of the mouse Akt2 oncogene. Oncogene. 11:1055–1060.

6. D. A. Altomare, G.E. Lyons, Y. Mitsuuchi, J. Q. Cheng, and J. R. Testa, Akt2 mRNA is highly expressed in embryonic brown fat and the AKT2 kinase is activated by insulin. Oncogene. 16 (1998), 2407–2411.

7. D. Brodbeck, P. Cron, and B. A. Hemmings, A human protein kinase Bγ with regulatory phosphorylation sites in the activation loop and in the C-terminal hydrophobic domain. J. Biol. Chem. 274 (1999), 9133–9136.

8. K. Nakatani, H. Sakaue, D. A. Thompson, R. J. Weigel, and R. A. Roth, Identification of a human Akt3 (protein kinase Bγ) which contains the regulatory serine phosphorylation site. Biochem. Biophys. Res. Comm. 257(1999), 906–910.

9. P.J. Coffer, J. Jin, and J. R. Woodgett, Protein kinase B (c-Akt): a multifunctional mediator of phosphatidylinositol 3-kinase activation. Biochem. J. 335 (1998):1-13.

10. D.P.Brazil, and B.A. Hemmings, Ten years of protein kinase B signalling: a hard Akt to follow. Trends Biochem. Sci. 26 (2001): 657–664.

11. B.A. Hemmings, Akt signaling: linking membrane events to life and death decisions. Science. 275(1997), 628–630.

12. T.F. Franke,. The protein kinase encoded by the Akt protooncogene is a target of the PDGF-activated phosphatidylinositol 3-kinase. Cell. 81 (1995), 727–736.

13. B.M.T. Burgering, and P.J. Coffer, Protein kinase B (c-Akt) in phosphatidylinositol-3-OH kinase signal transduction, Nature, 376 (1995), 599–602.

14. P. Cohen, D.R. Alessi, and D.A.E. Cross, PDK1, one of the missing links in insulin signal transduction? FEBS Lett. 410 (1997), 3–10.

15. K. Walker, Activation of protein kinase B β and γ isoforms by insulin in vivo and by 3-phosphoinositide-dependent protein kinase-1 in vitro: comparison with protein kinase B α. Biochem. J. 331(1998), 299–308.

16. Holgado-Madruga M, DK Moscatello, D. R. Emlet, R. Dieterich, A. J. Wong, Grb2-associated binder-1 mediates phosphatidylinositol 3-kinase activation and the promotion of cell survival by nerve growth factor. Proc Natl Acad Sci USA, 94 (1997) 12419–12424.

17. [17] A. Kauffmann-Zeh , P. Rodriguez-Viciana , E. Ulrich, C. Gilbert, P. Coffer, J. Downward, Suppression of c-Myc-induced apoptosis by Ras signalling through PI(3)K and PKB. Nature, 385 (1997), 544–548.

18. R. Dhand, K. Hara, I. Hiles, B.Bax , I. Gout, G. Panayotou, PI 3-kinase: structural and functional analysis of intersubunit interactions, EMBOJ, 13 (1994), 511–521.

19. Q Hu , A. Klippel , A. J. Muslin, W. J. Fantl, L. T. Williams, Ras-dependent induction of cellular responses by constitutively active phosphatidylinositol-3 kinase. Science. 268 (1995), 100–102.

20. H. Dudek, S. R. Datta, T. F. Franke, M. J. Birnbaum, R. Yao, G. M. Cooper, Regulation of neuronal survival by the serine-threonine protein kinase Akt. Science, 275 (1997), 661–665.

21. A. Brunet, A. Bonni, M. J. Zigmond, M. Z. Lin, P. Juo, L. S. Hu, Akt promotes cell survival by phosphorylating and inhibiting a Forkhead transcription factor. Cell, 96 (1999), 857–868.

22. A. Bonni , A. Brunet, A. E. West , S. R. Datta, M. A. Takasu, M. E. Greenberg, Cell survival promoted by the Ras-MAPK signaling pathway by transcription-dependent and independent mechanisms. Science. 286(1999), 1358–1362.

23. S. B. Maggirwar, P. D. Sarmiere, S. Dewhurst, R. S. Freeman, Nerve growth factor-dependent activation of NF-kappaB contributes to survival of sympathetic neurons. J Neurosci, 18 (1998), 10356–10365.

24. S. R. Datta, H. Dudek, X Tao, S. Masters, H. Fu, Y. Gotoh, Akt phosphorylation of BAD couples survival signals to the cell- intrinsic death machinery. Cell, 91(1997), 231–241.

25. M. H. Cardone, N. Roy, H. R. Stennicke, G. S. Salvesen, T. F. Franke, E. Stanbridge, Regulation of cell death protease caspase-9 by phosphorylation. Science, 282 (1998), 1318–1321.

26. Gaudet Suzanne, Janes A. Kevin, Albeck G. John, Pace A. Emily, Lauffenburger A. Douglas, and Peter K. Sorger A compendium of signals and responses trigerred by prodeath and prosurvival cytokines, Manuscript M500158-MCP200, July 18, 2005 .

27. R Weiss, S Basu, Device Physics of Cellular Logic Gates, First workshop on non-silicon computing, Boston, MA, 2002.

28. R Weiss, S Basu, S Hooshangi, A Kalmbach, D Karig, R Mehreja and I Netravali, Genetic circuit building blocks for cellular computation, communications, and signal processing, Natural Computing, pp.47– 84, 2003.

29. J. Van der Kaay, I. H. Batty, D. A. E. Cross, P. W. Watt, and C. P. Downes, A novel, rapid, and highly sensitive mass assay for phosphatidylinositol 3,4,5-trisphosphate (PtdIns(3,4,5)P3) and its application to measure insulin-stimulated PtdIns(3,4,5)P3 production in rat skeletal muscle in vivo J. Biol. Chem. 272 (1997), 5477–5481

30. B. A. Hemmings, Akt signaling: linking membrane events to life and death decisions Science, 275 (1997), 628–630.

31. D. Schmoll, K. S. Walker, D. R. Alessi, R. Grempler, A. Burchell , S. Guo, R. Walther, T. G. Unterman Regulation of glucose-6-phosphatase gene expression by protein kinase B alpha and the forkhead transcription factor FKHR. Evidence for insulin response unit-dependent effects of insulin on promotor activity. J. Biol. Chem, 275(2000), 36324-36333.





Sachin Kumar, Indu Bala Pauria, Anoop Singhal

Paper Title:

Optical Fiber Communication System Performance Using MZI Switching

Abstract: First a simple all-optical logic device, called Mach Zhender Inferometer is composed by using a Semiconductor Optical Amplifier (SOA) and an optical coupler. This device is used for generating the logical functions (AND, XOR) and a multiplexer and an Encoder is obtained using this device in Optical Tree Architecture. The simulation of Encoder and Multiplexer is done at a rate of 10 Gbit/s and both are simulated for different input logical combinations. Simulations indicate that the device is suitable to operate at much higher bit rate and also for different logical entities. Many lower-speed data streams can be multiplexed into one high-speed stream by means of Optical time division multiplexing (OTDM), such that each input channel transmits its data in an assigned time slot. The assignment is performed by a fast multiplexer switch (mux).The routing of different data streams at the end of the TDM link is performed by a demultiplexer switch (demux) and this demultiplexer is employed using MZI switch as it consists a semiconductor optical amplifier (SOA) and a optical coupler. In this chapter four channel OTDM is simulated at 40 Gbit/s and further it is investigated the impact of the signal power, pulse width and control signal power on BER.

All optical switch, Mach-Zehnder interferometer (MZI), Semiconductor optical amplifiers (SOA), Switching schemes, Spectrum analysis.


1. Jitendra Nath Roy, “Mach-Zehnder interferometer based tree architecture for all-optical logic and arithmetic operations”, Optik Int Light Electron Opt. (2009).
2. Koji Igarashi and Kazuro Kikuchi,“Optical Signal Processing by Phase Modulation and Subsequent Spectral Filtering Aiming at Applications to Ultrafast Optical Communication Systems”, IEEE journal of selected topics in quantum electronics, Vol. 14, No. 3, May/June.

3. K. Uchiyama, H. Takara, K. Mori, T. Morioka, “160 Gbit/s all-optical time-division demultiplexing utilizing modified multiple-output OTDM demultiplexer (MOXIC)”, Electron. Lett. 38 (2002) 1190–1191.

4. I. Shake, H. Takara, I. Ogawa, T. Kitoh, M. Okamoto, K. Magari, T. Ohara, S. Kawanishi,”160-Gbit/s full channel optical time-division de-multiplexer based on SOA- array integrated PLC and its application to OTDM transmission experiment”, IEICE Trans. Commun. 53 (1) (2005) 20–2096.

5. H. Le-Minh, Z. Ghassemlooy, W.P. Ng, “Crosstalk suppression in an all-optical symmetric Mach–Zehnder (SMZ) switch by using control pulses with unequal powers, Proceedings of the International Symposium on Telecommunication 2005 (IST 2005)”, Vol. 1, Shiraz, Iran, 2005, pp. 265–268.

6. M. Heid, S. Spalter, G. Mohs, A. Farbert, W. Vogt, H. Melchior, “160-Gbit/s demultiplexing based on a monolithically integrated Mach–Zehnder interferometer, Proceedings of the European Conference on Optical Communication (ECOC 2001)",Amsterdam,The Netherlands, September 30–October 4, 2001.

7. Haijiang Zhang, Pengyue Wen, and Sadik Esener,”Cascadable all-optical inverter based on a nonlinear vertical-cavity semiconductor optical amplifier”, Opt. Lett. 32, 1884-1886 (2007).

8. Yanming Feng, Xiaofan Zhao, Li Wang, and Caiyun Lou, “High-performance all- optical OR/NOR logic gate in a single semiconductor optical amplifier with delay interference filtering, Appl.”, Opt. 48, 2638-2641 (2009).

9. Jitendra Nath Roy and Dilip Kumar Gayen,“Integrated all-optical logic and arithmeticoperations with the help of a TOAD-based interferometer device--alternative approach M. F. Lane, D. Z. Chen, and D. Kokkinos, Managing Fiber Connections in NGN and Applications, in National Fiber Optic Conference”, OSA Technical Digest Series (CD) (Optical Society of America, 2007), paper NThA1.

10. Petrantonakis, P. Zakynthinos, D.Apostolopoulos, A.Poustie, G. Maxwell, and H. Avramopoulos,” All-Optical Four-Wavelength Burst Mode Regeneration Using Integrated Quad SOA-MZI Arrays”, IEEE PHOTONICS TECHNOLOGY LETTERS, VOL. 20, NO. 23, DECEMBER 1, 2008.

11. Colja Schubert, Jörn Berger, Stefan Diez, Hans Jürgen Ehrke, Reinhold Ludwig, Uwe Feiste, Carsten Schmidt, Hans G. Weber, Gueorgui Toptchiyski, Sebastian Randel, and Klaus Petermann, “omparison of Interferometric All-Optical Switches for Demultiplexing Applications in High-Speed OTDM Systems”, JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 20, NO. 4, APRIL 2002.

12. K. Kitayama, T. Kuri, J. J. Vegas Olmos, and H. Toda, “Fiber-Wireless Networks and Radio-over-Fiber Techniques, in Conference on Lasers and Electro-Optics/Quantum Electronics and Laser Science Conference and Photonic Applications Systems Technologies”, OSA Technical Digest (CD) (Optical Society of America, 2008), paper CThR4.

13. R. Llorente, T. Alves, M. Morant, M. Beltran, J. Perez, A. Cartaxo, and J. Marti,”Optical Distribution of OFDM and Impulse-Radio UWB in FTTH Networks”, in National 74 Fiber Optic Engineers Conference, OSA Technical Digest (CD) (Optical Society of America, 2008), paper JWA109.

14. Kuniharu Himeno, Shoichiro Matsuo, Ning Guan, and Akira Wada, Low-Bending-Loss Single-Mode “Fibers for Fiber-to-the-Home”, J. Light wave Technol. 23, 3494- (2005)

15. D. Iazikov, C. Greiner, and T. W. Mossberg, “Apodizable Integrated Filters for Coarse WDM and FTTH-Type Applications”, J. Light wave Technol. 22, 1402- (2004)

16. M. F. Lane, D. Z. Chen, and D. Kokkinos,”Managing Fiber Connections in NGN and Applications”, in National Fiber Optic Engineers Conference, OSA Technical Digest Series (CD) (Optical Society of America, 2007), paper NThA1.






B. R. Jadhavar, T. R. Sontakke

Paper Title:

2.4 GHz Propagation Prediction Models for Indoor Wireless Communications Within Building

Abstract: Different propagation models are presented for multi-storied Quantitative models aural area, that predicts effect of wall partitions, number of floors and building layout at 2.4 GHz using IEEE802.11b wireless network. Propagation models have been developed for these two buildings based on number of floors between transmitter and receiver. These models are indoor location detection system designer which relate signal strength log of distance. The measurement shows that the standard deviation between measured and predicted path loss is 12.4124 dB for all locations in one building and as small as 8.0948 dB on same floor. And in other building it is 10.2854 dB and minimum 8.5454 dB for same floor measurements. Floor attenuation factor for two buildings are 18.0304 dB and 28.5687 dB when transmitter and receivers have one floor in-between. The concrete wall attenuation factor was found to be 4.86 dB and hard board partition attenuation factor was 2.45 dB. Also contour plots for equal signal strength for measured data are presented. The results are quite logical as per building structure/layout.

WLAN, propagation model, free space, path loss model, floor attenuation factor, signal strength, contour plot.


1. T. S. Rappaport, “Charactrization of UHF multipath radio channels in factory buildings’’ IEEE Trans. Antennas Propagation, vol.37, pp.1058-1069, Aug1989
2. J-F. Lafortune and M. Lecours. Measurement and modelling of propagation losses in a building at 900 MHz. IEEE Tmnsactions on Vehicular Technology, 39(2):101-108, May 1990

3. Ding Xu, Jianhua Zhang, Xinying Gao, Ping Zhang, and Yufei Wu “Indoor office measurements and path loss models at 5.25 GHz” Beijing University of Posta and Telecommunications Beijing, China.

4. S. Y. Seidel and T. S. Rappapat, “914 MHz path loss pradiction models for indoor wimless communications in multi-floored buildings,” IEEE Trans. Antennas Propag., vol. 40, pp. 207–217, Feb. 1992

5. A. Goldsmith, Wireless Communications. Cambridge, UK: Cambridge University Press, 2005

6. J. Seybold, Introduction to RF Propagation. Hoboken, USA: Wiley Interscience, 2005.

7. D..A. Hawbaker and T. S Rappaport, “Indoor wideband radiowave propagation measurementat 1.3 GHz and 4GHz,” Electro letter, vol.26,pp.1800-1802, 1990.

8. C. Smith, “Propagation models” in cellular business Oct 1995, pp-72-76.

9. C L Holloway, P L Perini, R L DeL yser, K C Allen, “Analysis of composite walls and their effects on short path propagation modeling” in IEEE trans. On vehic, tech. vol.46, no. 3, Aug. 1997, pp-730-738

10. P L Perini, A U Bhonde “ Signal to noise ratio of Directional vs Omnidirectional antennas at 1990 MHz” in proc. Of 2000 IEEE Aerospace conf. March 2000.

11. Paul Marinier and G. Y. Delisle “Temporal variations of the Indoor Wir eless Millimeter Wave Channel”IEEE Trans. On Antennas and Propa.vol.46, No. 6, pp.928-934, June 1998.

12. H. Hashemi, “The indoor radio propagation channel,” Proc. IEEE,vol. 81, no. 7, pp. 943–968, Jul. 1993.

13. Rappaport, T. S., Wireless Communications, Prentice Hall PTR, New York1996

14. Paul Marinier and G.Y. Delisle. Temporal Variations of the Indoor Wireless Millimeter-Wave Channel.' IEEE Trans. on AP. Vo1.46, "0.6, pp.928-934, June 1998.

15. Devasirvatham, M. J. Krain and D. A. Rappaport, “Radio propagation measurement at 850 MHz, 1.7 GHz and 4 GHz inside two dissimilar office buildings” Electron letters, vol.26, no. 7. pp. 445-447, 1990.






A. Parvaresh, A. Hasanzade, S. M. A. Mohammadi, A. Gharaveisi

Paper Title:

Fault Detection and Diagnosis in HVAC System Based on Soft Computing Approach

Abstract: The fault detection and diagnosis (FDD) play an important role in the monitoring, repairing and maintaining of technical systems. In this paper, we presented a new method based on soft computing approach for FDD in a special type of HVAC system namely unitary system. In the proposed method, the feature vectors are extracted by applying wavelet transform to output signals of model. Then, a Takagi-Sugeno (T-S) fuzzy classifier detects and diagnoses the faults by use of extracted feature vectors, if the faults exist. The T-S fuzzy classifier needs to be trained. With inspiration from training formulation of support vector machine (SVM), the training process has been stated as an optimization problem. For solving the mentioned optimization problem, a reliable evolutionary algorithm namely differential evolution (DE) is used. One of the important types of faults in the unitary HVAC system is refrigerant leakage. FDD of refrigerant leakage is highlighted in the presented paper. The simulation has been done in MATLAB-Simulink and the efficacy of the proposed method is demonstrated based on the experimental results.

Differential Evolution Algorithm, Fault Detection and Diagnosis, Takagi-Sugeno Fuzzy Classifier, Unitary HVAC System, Wavelet Transform.


1. Robert M., "Fundamentals of HVAC Systems". Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. 2007
2. Steven X. Ding, "Model-based Fault Diagnosis Techniques Design Schemes, Algorithms, and Tools", Springer-Verlag Berlin Heidelberg, 2008

3. J. Navarro-Esbrı, V. Berbegall, G. Verdu, R. Cabello, R. Llopis, "A low data requirement model of a variable-speed vapor compression refrigeration system based on neural networks", International Journal of Refrigeration. vol. 30. pp. 1452-1459. sep 2007

4. Bo Fan, Zhimin Du, Xinqiao Jin, Xuebin Yang, Yibo Guo, "A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis", Journal of Building and Environment vol. 45. pp. 2698-2708. 2010

5. Yonghua Zhu, Xinqiao Jin, Zhimin Du, "Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal", Journal of Energy and Buildings vol. 44 pp. 7–16.2012

6. Zhimin Du, Xinqiao Jin, Yunyu Yang, "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network", Journal of Applied Energy. vol. 86. pp. 1624–1631. 2009

7. Shengwei Wang, Youming Chen, "Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network", Journal of Building and Environment. vol. 37 pp. 691 – 704. 2009

8. J. Liang, R. Du, "Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method", International Journal of Refrigeration. vol. 30 pp. 1104-1114. 2009

9. C.H. Lo, P.T. Chan, Y.K. Wong, A.B. Rad, K.L. Cheung, "Fuzzy-genetic algorithm for automatic fault detection in HVAC systems", Applied Soft Computing. vol. 7. pp. 554–560. 2009
10. Rasmussen, B. P., “Dynamic Modeling and Advanced Control of Air Conditioning and Refrigeration Systems,” Dept. of Mechanical Engineering, University of Illinois, 2005.

11. Abdul Wahab, Chai Quek, Chin Keong Tan, and Kazuya Takeda, "Driving Profile Modeling and Recognition Based on Soft Computing Approach", IEEE Transactions on Neural Networks, vol. 20, no. 4, April 2009.

12. Zacharias E. Gketsis, Michalis E. Zervakis, George Stavrakakis, "Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN", Electric Power Systems Research vol.79 pp. 1483–1494. 2009

13. S. Pittner and S. V. Kamarthi, “Feature extraction from wavelet coefficients for pattern recognition tasks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 1, pp. 83–88, Jan. 1999.

14. I. Daubechies and B. J. Bates, “Ten lectures on wavelets,” Journal of Acoustic. Soc. Amer., vol. 93, no. 3, p. 1671, Mar. 1993.

15. Albert boggess, francis j. narcowich, "A first course in wavelets with Fourier analysis", Prentice Hall, New Jersey, 2001.

16. T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Trans. Systems Man Cybernet. Vol. 15, pp. 116–132, 1985.

17. A.H. Zade, S.M.A. Mohammadi, A.A. Gharaveisi, “Fuzzy Logic Controlled Differential Evolution to Identification of Takagi-Sugeno Models”, International Journal of Engineering Research and Industrial Applications, vol. 5, no .I, 2012.

18. J.R. Jang, C. Sun, E. Mizutani," Neuro-Fuzzy and soft computing, Prentice Hall, 1997.

19. V. Vapnik, "The Nature of Statistical Learning Theory", Springer, New York, 1995.

20. C. Cortes, V. Vapnik, “Support vector networks”, Machine Learning Journal, Vol. 20, pp. 1–25, 1995.

21. B.E. Boser, I.M. Guyon, V. Vapnik, “A Training Algorithm for Optimal Margin Classifiers”, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, ACM Press, pp. 11-152, 1992.

22. R. Storn, K.V. Price, “Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces”, Technical Report TR-95-012, ICSI, 1995.

23. R. Storn, K.V. Price, “Minimizing the real functions of the ICEC 1996 contest by differential evolution”, Proceedings of the 1996 IEEE international conference on evolutionary computation, Nagoya, Japan, pp. 842–844. IEEE Press, New York, 1996.

24. R. Storn, Smith, M.H., Lee, M.A., Keller, J., J. Yen “on the usage of differential evolution for function optimization”, Proceedings of the 1996 biennial conference of the North American fuzzy information processing society – NAFIPS, pp. 519–523. IEEE Press, New York, 1996.

25. K.V. Price, R. Storn, Differential evolution: a simple evolution strategy for fast optimization”, Dr. Dobb’s Journal, Vol. 22, pp. 18–24, 1997.

26. R. Storn, K.V. Price, “Differential Evolution – a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization Vol. 11, pp. 341–359, 1997.

27. K. V. Price, R. Storn, J. Lampinen, Differential Evolution – A Practical Approach to Global Optimization, Springer, Heidelberg, 2005.






V. K. Titariya, A. C. Tiwari

Paper Title:

Parametric Investigation of the Air Curtain for Open Refrigerated Display Cabinets

Abstract: Air curtains are widely used in open refrigerated display cabinets (ORDC) as well as doorways of Conditioned Space and retail premises. The main purpose of the air curtain is to control the heat and moisture transfer between the conditioned space and the surrounding ambient also to reduce the air exchange between the two environments. This article presents comprehensive results which can be used to optimise and predict the performance of air curtains in cabinets. The parameter investigation in this article has investigated how the density, velocity and temperature of the air inside and outside the curtain vary as a function of height for various ambient climatic conditions. These results are obtained from the models, developed to enable quick calculations and parametric analyses for the designing and sizing purposes of refrigeration equipments.

Air curtain; extract grille; open refrigerated display cabinet; transition zone.


1. D. Stribling, Investigation into the design and optimisation of multideck refrigerated display cases, Ph.D. Thesis, Brunel University, July 1997, 188 pp.
2. F. Alamdari, Air curtains: commercial applications, Application Guide, 2/97, BSRIA (The Building Research and Information Association), 1997, 31 pp.

3. Schlichting, H., 1955. McGraw Hill, Graw H.

4. Hayes, F. C., and Stoecker, W. F.,1969. Heat Transfer Characteristics of the Air Curtain. ASHRAE Trans., no. 2120, 153-167, 1969.

5. Nguyen Quang Van, Influence of initial turbulence intensity on heat and moisture transfer through a recirculated air curtain, Ph.D. Thesis, University of Missouri-Rolla, Rolla, MO, 1975.

6. R.H. Howell, P. Adams, Effects of indoor space conditions on refrigerated display case performance, Final Report for ASHRAE. Project No. 596RP, 1991.

7. Adams, P. J., 1992. Merchandising vs. energy consumption in the supermarket. Heat. Pip. Air Cond., vol.64, no. 4, 53-58, 1992.04, USA.

8. Cortella, G., Manzan, M., et al., 1998. Computation of air velocity and temperature distributions in open display cabinets. International Conference "Advances in the refrigeration systems, food technologies and cold chain", IIF-IIR B2, C2, D1 and D2/3, 1998, p. 617-625, Sofia, Bulgaria.

9. Morillon, C., 1997. Meuble verticauxá rideaux d'áir: Etat des développements et perspectives. Journées Européennes du Froid-Interclima, 27-48,

10. Schlichting, H., 1955. McGraw Hill, Graw H.

11. Hayes, F. C. and Stoecker, W. F., 1969. Design data for air curtains. ASHRAE Trans., vol.75, no. II, 168-180,

12. Howell, R. H. and Shiabata, M., 1980. Optimum Heat Transfer through Turbulent Recirculated Plane Air Curtains. ASHRAE Transactions, vol. 86, Part 1, no. 2567, 188-200, 1980, US.






Gaurav Kakhani, S Gnanendra Reddy

Paper Title:

Evaluation of Routing Protocols for Mobile Ad hoc Networks

Abstract: Mobile Ad hoc network is a self-configuring infrastructure less network of mobile devices connected by wireless links. Each device in a MANET is free to move independently in any direction and links will be changed frequently with other devices. Each host acts like a router and forward to its neighbors. To forward packets between routers we have various MANET routing protocol. An Ad hoc routing protocol is a convention that controls how nodes decide which way to route packet between computing devices in a mobile ad hoc network.In this paper our main focus is to analyze, simulate and evaluate the performance of Routing Protocols (DSR, AODV and TORA).



1. T. Lin, “Mobile Ad-hoc Network Routing Protocols: Methodologies and Applications” (phd thesis).
2. S. Das, C. Perkins, and E. Royer. Performance comparison of two on-demand routing protocols for ad hoc networks. In INFOCOM’2000 (1), pages 3–12, 2012.

3. D.Johnson. Validation of wireless and mobile network models and simulation. In DARPA/NIST Network Simulation Validation Workshop, Fairfax, Virginia, USA, May 1999.

4. D.Johnson et al., Dynamic Source Routing for mobile Ad-hoc Networks, IETF MANET Draft, April2011.






R. Ramachandran, J. Thomas Joseph Prakash

Paper Title:

FPGA Based SOC for Railway Level crossing Management System

Abstract: It is to develop the FPGA based, System on Chip (SOC) to implement the safety system in Railways for level crossing. For the communication RF module[1] with the coverage of large distance, that is Outdoor line-of-sight: up to 15 miles (24 km) with high gain antenna is used. RF Transceiver has many salient features[2], such as, Advanced Networking & Security, True peer-to-peer (no "master" required) communications, Point-to-point & point-to-multipoint topologies supported, Continuous RF data stream up to 9600 bps, No configuration required for out-of-the-box and Support for multiple data formats (parity, start and stop bits, etc.). An article authored by Mr. R.K. Verma, Chief Engineer, Indian Railways exposes the Corporate Safety Plan [3] of reduction of accidents on Indian Railway (IR) by the year 2012-13. In which, he stated that Collisions of the Trains can be completely eliminated. Derailments can be reduced by 60% and fire accidents by 80%. But, he has not given assurance on possible improvement in level crossing (LC) accidents, as there is no control over the circumstances that lead to such accidents. Train Actuated Warning Device (TAWD) – for sensing an approaching train two kms ahead and to sound an audio-visual warning device at level crossing gates (mainly unmanned ones), helps to reduce accidents at level crossings by giving adequate warning to road users. LC accidents not only dominate in terms of frequency, but can be more severe in their consequences than other types of railway accidents, simply because they can involve injuries and fatalities to railway passengers, as well as, to road vehicle occupants and other users of LCs. Accidents at LCs accounting for 22% of the total accidents on IR were responsible for 49% of total fatalities during the last decade. Increasing road construction and road vehicle population create greater opportunity for LC accidents to happen. Therefore to avoid this, a well designed sophisticated security system is needed. Hence we developed a prototype system using FPGA based SOC to ensure safety, particularly at unmanned level crossing.

FPGA, SOC, Level crossing, cyclone II device, EP2C35F672C6.


1. Radio Frequency applications By Morris Hamington and Working with Radio Frequency by Cruis Leanardo.
2. http://www.digi.com/products/wireless-wired-embedded-solutions/zigbee-rf-modules/point-multipoint-rfmodules/xbee-pro-xsc#overview.

3. R.K. Verma, Chief Engineer, Indian Railways “ Level Crossing Management Information System

4. J.A.kalomiros, J.Lygouras, Design and Evolution of a hardware/software FPGA Based system for fast image processing, Microprocessor and Microsystems 32 (2008) 95-106.

5. www.BDTI.com Evaluating FPGAs for communicating infrastructure Applications, communications design conference, September 2003.

6. Mengmeng Zhang, Hao Zang, Design and Implementation wireless Transceiver System on FPGA, International conference on Innovative computing and communication, IEEE computer society, 2010: 355-357.

7. Hong luo, Cheng chang, Yan Sun, Advanced Sensor based on FPGA for wireless Multimedia Sensor networks, IEEE 2011.

8. Altera Cyclone II Device Handbook, CII5V1-3.3, Altera Corporation, February 2007.

9. Cyclone II device hand book; www.altera.com/literature/hb www.altera.com/literature/hb/cyc3/cyclone2_handbook.pdf

10. J.Manikandan, B.Venkataramani,Design of a real time Automatic speech recognition system using modified one against all SVM classifier, Microprocessor and Microsystems 35 (2011) 568-578.

11. Wiśniewski, Remigiusz (2009). Synthesis of compositional microprogram control units for programmable devices. Zielona Góra: University of Zielona Góra. pp. 153. ISBN 978-83-7481-293-1.

12. Cheung, Ken, FPGA Blog. "Xilinx Extensible Processing Platform for Embedded Systems.”April 27, 2010. Retrieved February 14, 2011.

13. Leibson, Steve, Design-Reuse. Xilinx redefines the high-end microcontroller with its ARM-based Extensible Processing Platform - Part 1." May. 03, 2010. easier to use. January 31, 2011. Retrieved February 15, 2011. Retrieved February 15, 2011.

14. Wilson, Richard, Electronics Weekly. Xilinx acquires ESL firm to make FPGAs.

15. www.NR-DC-ECO DC geared motor





Veerraju Gampala, Srilakshmi Inuganti, Satish Muppidi

Paper Title:

Data Security in Cloud Computing with Elliptic Curve Cryptography

Abstract: Cloud computing is one of today’s hottest research areas due to its ability to reduce costs associated with computing while increasing scalability and flexibility for computing services. Cloud computing is Internet based computing due to shared resources, software and information are provided to consumers on demand dynamically. Cloud computing is one of the fastest growing technology of the IT trade for business. Since cloud computing share disseminated resources via the network in the open environment, hence it makes security problems vital for us to develop the cloud computing applications. Cloud computing security has become the leading cause of hampering its development. Cloud computing security has become a hot topic in industry and academic research. This paper will explore data security of cloud in cloud computing by implementing digital signature and encryption with elliptic curve cryptography.

cloud computing, cloud security, data security, digital signature, encryption, elliptic curve cryptography.


1. Liu Peng, the definition and characteristics of cloud computing, http://blog.sina.com.cn/s/blog_5f0da5590100cmxw.html http://www.chinacloud.cn, March 9, 2009
2. Ya-Qin Zhang, the future of computing in the "cloud - Client", The Economic Observer reported, http://www.sina.com.cn, 2008 Nian 07 Yue 12 Ri 14:30

3. Jianfeng Yang and Zhibin Chen “Cloud Computing Research and Security Issues”

4. D. L. Ponemon, "Security of Cloud Computing Users," 2010.

5. C.Almond, "A Practical Guide to Cloud Computing Security," 27 August 2009 2009.

6. IBM, “Google and IBM Announced University Initiative to Address Internet-Scale Computing Challenges,” http://www-03.ibm.com/press/us/en/pressrelease/22414.wss.

7. http://en.wikipedia.org/wiki/Cloud_computing

8. http://www.cloudcomputing china.cn/Article/luilan/200909/306.html

9. http://searchcloudcomputing.techtarget.com/sDefinition/0,,sid201_gci1287881,00.html

10. http://www.boingboing.net/2009/09/02/cloud-computing-skep.html

11. Google, “Google app Engine,” http://code.google.com/appengine/.

12. http://cloudsecurity.trendmicro.com/





Ashwini Madane

Paper Title:

Identifying Keywords and Key Phrases

Abstract: Keywords and key phrases are widely used in large document collections. They describe the content of single documents and provide a kind of semantic metadata that is useful for a variety of purposes. Text mining is powerful tool to find useful and needed information from huge data set. For context based text mining, key phrases are used. Key phrases provide brief summary about the contents of documents. In document clustering, number of total cluster is not known in advance. In K-means, if prespecified number of clusters modified, the precision of each result is also modified. Therefore Kea, is algorithm for automatically extracting key phrases from text is used. In this kea algorithm, number of clusters is automatically determined by using extracted key phrases. Keameans clustering algorithm provide easy and efficient way to extract test document from large quantity of resources. Key phrase play important role in text indexing, summarization and categorization. Key phrases are selected manually. Assigning key phrases manually is tedious process that requires knowledge of subject. Therefore automatic extraction techniques are most useful.

Text mining, Key phrase extraction, key phrase.


1. TEXT MINING USING KEYPHRASE EXTRACTION, Shobha S. Raskar, BharatiVidyapeeth University, College of Engineering BVUCOE ,Dhankawadi,Pune, D. M. ThakoreBharatiVidyapeeth University, College of Engineering BVUCOE, Dhankawadi ,Pune.
2. National Library of Medicine, Unified Medical Language System, sixth experimental edition. Bethesda, MD, 1995.

3. B., Krulwich, & C., Burkey, “The Infofinder Agent-Learning User Interests through Heuristic Phrase Extraction,” IEEE Intelligent Systems & Their Applications, 12(5), 1997, pp. 22 - 27.

4. P.D. Turney,“Learning Algorithms for Keyphrase Extraction,” Information Retrieval, 2, 303-336, 2000.

5. I. Witten, E. Frank, G. W. Paynter, “Doman-Specific Keyphrase Extraction,” Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999, pp. 668-673.

6. A.Bookstein, S.T. Klein, T. Raita, “Clumping Properties of Content-Bearing Words,” Journal of the American Society for Information Science, 49(2), 1998, pp. 102-114.

7. J. Picard, “Finding content-bearing terms using term similarity,” Proceedings of Ninth Conference of the European Chapter of the Association for Computational Linguistics, Bergen, Norway, 1999.

8. C. Nevill-Manning, I. Witten and G. Paynter, “Browsing in Digital Libraries: a Phrase-Based Approach,” Proceedings of 1997 International Conference on Digital Libraries, 1997, pp. 230-236.

9. S. Sekine and R. Grishman, “A Corpus-based Probabilistic Grammar with only Two Non-terminals,” Fourth International Workshop on Parsing Technologies, Prague, 1995.

10. K. Deb, S. Agrawal, A. Pratap and T. Meyarivan, “A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multiobjective Optimization: NSGA-II,” Proceedings of the Parallel Problem Solving from Nature VI (PPSN-VI), 2000, pp. 849-858.

11. A. Messac and W. Chen, “The Engineering Design Descipline: Is its Confounding Lexicon Hindering its Evolution?” Proceedings of DETC’ 98, DTM-5658, 1998.






Ashwini Madane, Devendra Thakore

Paper Title:

An Approach for Extracting the Keyword Using Frequency and Distance of the Word Calculations

Abstract: A significant word used in indexing or cataloguing is regarded as a Keyword. Keywords provide a concise and precise high-level summarization of a document. They therefore constitute an important feature for document retrieval, classification, topic search and other tasks even if full text search is available. Keywords are useful tools as they give the shortest summary of the document. A keyword is identified by finding the relevance of the word with or without prior vocabulary of the document or the web page. Extracting keywords manually is an extremely difficult and time consuming process, therefore it is almost impossible to extract keywords manually even for the articles published in a single conference. Therefore there is a need for automated process that extracts keywords from documents. This paper concentrates on the extracting the keywords by understanding the linguistic, non-linguistic and various other approaches but applying the simple statistics approach.

Extraction methods, Keyword Frequency Count, Stemming, Tokenization.


1. P. D. Turney, Learning Algorithms for Keyphrase Extraction, Information Retrieval, 1999
2. E. Frank, G. W. Paynter, I. H. Witten, C. Gutwin, and C. G. Nevill-Manning. Domain-specific keyphrase extraction. In IJCAI, pages 668--673, 1999.

3. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, page 20, Wiley-Interscience, 2000.

4. G. Salton and M. J McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.

5. Thomas Bayes. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society (London). 53:370-418, 1763

6. P. Domingos and M. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2/3):103-130, 1997.

7. http://www.uni-weimar.de/medien/webis/research/events/tir-10/proceedings/wartena10-keyword-extraction-using-word-co-occurrence.pdf.







Paper Title:

Some Studies Based On Red, Load Modeling, Line Outage Using Voltage Stability Analysis

Abstract: There are many methods for overload relieving which have been reported for determining a secure operating point. Most of these methods use conventional optimization techniques, which are generally time–consuming from a computation point of view, especially for large systems. Further the conventional optimization technique updates all the controllers for most of the operating conditions. Under emergency conditions the operator, has to make quick decisions, with little concern for the theoretical optimality of the operating point and also the operator cannot move all the controllers to different settings within less time. In this context a simplified approach has been proposed in this paper for security oriented power system operation. The contribution of each generator for a particular overloaded line is first identified, then based on Relative Electrical Distance (RED) concept the desired proportions of generation for the desired overload relieving is obtained. Then based on the Generation Shift Sensitivity Factor (GSSF) concept the desired proportions of generation for the desired overload relieving is obtained. An attempt is also made to curtail the number of generators to be rescheduled based on GSSF for overload relieving Results obtained for network overload alleviation of 5 bus meshed system, IEEE 30-bus New England system are presented for illustration purposes. The change in load sharing and generation scheduling was examined when the slack bus was changed, generator node changed and placed in the midpoint of the system, increase in generator number, load modeling was calculated. For all the above cases the voltage stability indices (Ve, Lindex and MSV) were calculated, recorded and then compared for best performance to the previous cases. The generation scheduling was determined with line outage and subsequent load shedding.

Relative Electrical Distance, Generation Shift Sensitivity Factor, Congestion, Load modeling, slack bus, load sharing, power flow analysis, optimal power flow.


1. “Congestion management in open access” by G.Yesuratnam and D.Thukaram, International Journal Of Elcetrical Power System Research Volume 77, October 2007, Pages 1608-1618.
2. Voltage Stability Indices for stressed power system by P.A.Lof, G.Anderson andD.J.Hill, IEEE transactions on Power System Vol 8, No 1, Febrauray 1993.

3. Estimating Voltage Stability of a Power system by P.Kessel and H.Glavitsch, IEEE transactions on Power Delievery, Volume.PWRD-1 No.3,july 1986.

4. Load Modelling for power flow solution by DR. P.S.R.Murthy, Journal of the Institution of Engineers (India), Volume 53, December 1977

5. Power System Operation and Control by P.S.R.Murthy, Tata McGraw Hill, 1984.

6. Power System Analysis by Hadi Sadit, Tata McGraw Hill edition 2002.

7. Modern Power System Analysis by I.J Nagrath and D.P.Kothari Tata McGraw Hill Third Edition.

8. Power System Analysis, McGraw-Hill by J. Grainger and W. Stevenson New York, 1994.

9. Lecture Notes on Power Quality, 28th and 29th September 2007, department of Electrical Engineering, Osmania University.






Mary Jansi Rani. Y, Pon. L.T. Thai, John Peter. K

Paper Title:

Visually Lossless Compression for Color Images with Low Memory Requirement using Lossless Quantization

Abstract: In this paper a novel method is proposed to compress color images with no loss in quality. For the compression of the color image non uniform quantizers are used. These non uniform quantizers are implemented for different areas. The blocks are classified, predicted, encoded and decoded to get the resulted output. The blocks are classified based on principle component analysis. The output provides a compressed image with high quality. Inorder to improve the compression ratio vector quantization for color images is proposed. This provides good quality images with high PSNR values. The algorithm uses low memory requirement.

PCA, non uniform quantizers.compression ratio,vector quantization, PSNR values.


1. M. Weinberger, G. Seroussi and G. Sapiro, "The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS", IEEE Trans. Image Process., Vol. 9, August 2000, pp.1309-1324.
2. N. Moayeri, “A low-complexity, fixed-rate compression scheme for color images and documents”, Hewlett-Packard Journal, Nov, 1998, article 7.

3. T. Wiegand, G. J. Sullivan, G. Bjøntegaard and A. Luthra, “Overview of the H.264/AVC video coding standard”, IEEE Trans. Circuits Syst. Video Tech., Vol. 13, No. 7, July 2003, pp. 560-576.

4. Pen-Shu Yeh, Jack Venbrux, Prakash Bhatia and Warner Miller, “A visually lossless data compression technique for real-time frame/pushbroom space science imagers”, Proceedings of SPIE, Vol. 4115, 2000, pp. 320-331.

5. J. Kim and C.-M. Kyung, “A lossless embedded compression using significant bit truncation for HD video coding”, IEEE Trans. Circuits Syst. Video Tech., Issue 6, Vol. 20, June 2010, pp. 848-860.

6. R. M. Gray, “Vector quantization,” IEEE Acoustics, speech and Signal Processing Magazine, pp. 4-29, 1984.

7. M. Goldberg, P. R. Boucher and S. Shlien, “Image Compression using adaptive vector quantization,” IEEE Transactions on Communication, Vol. 34, No. 2, pp. 180-187, 1986.

8. T. Takahashi, “Apparatus and method for image coding”, Patent EP0871333 B1, Mitsubishi Electronics, March 2007.

9. M. Mishourovsky and M. Rychagov, “Visually lossless color compression technology”, SRC Signal Processing Group, Moscow, October, 2008.

10. T. Wiegand, G. J. Sullivan, G. Bjøntegaard and A. Luthra, “Overview of the H.264/AVC video coding standard”, IEEE Trans. Circuits Syst. Video Tech., Vol. 13,
No. 7, July 2003, pp. 560-576.

11. Pen-Shu Yeh, Jack Venbrux, Prakash Bhatia and Warner Miller, “A visually lossless data compression technique for real-time frame/pushbroom space science
imagers”, Proceedings of SPIE, Vol. 4115, 2000, pp. 320-331.

12. J. Kim and C.-M. Kyung, “A lossless embedded compression using significant bit truncation for HD video coding”, IEEE Trans. Circuits Syst. Video Tech., Issue 6, Vol. 20, June 2010, pp. 848-860.






V.M. SwornaKala, Kirpa Ganesh

Paper Title:

Avoiding Impact of Jamming Using Multipath Routing Based On Network Content

Abstract: Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this paper, I consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. I formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. I show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). I simulate the achievable throughput using our proposed traffic allocation method in several scenarios.By this paper I can only develop provide multiple paths in centralized manner. This increases server overhead. To avoid this I propose source specie routing in network.

I formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics.


1. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: Asurvey,” Comput. Netw., vol. 47, no. 4, pp. 445–487, Mar. 2005.
2. E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE J. Ocean. Eng., vol. 25, no. 1, pp. 72–83, Jan. 2000.

3. R. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems. New York: Wiley, 2001.

4. J. Bellardo and S. Savage, “802.11 denial-of-service attacks: Real vulnerabilities and practical solutions,” in Proc. USENIX Security Symp.,Washington, DC, Aug. 2003, pp. 15–28.

5. D. J. Thuente and M. Acharya, “Intelligent jamming in wireless networks with applications to 802.11 b and other networks,” in Proc. 25th IEEE MILCOM, Washington, DC, Oct. 2006, pp. 1–7

6. A. D.Wood and J. A. Stankovic, “Denial of service in sensor networks,” Computer, vol. 35, no. 10, pp. 54–62, Oct. 2002






Bussa V.R.R.Nagarjuna, Akula Ratna babu, Miriyala Markandeyulu, A.S.K.Ratnam

Paper Title:

Web Mining: Methodologies, Algorithms and Applications

Abstract: The World Wide Web is a popular and interactive medium to disseminate information today. It is a system of interlinked hypertext documents accessed via the Internet. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. With the recent explosive growth of the amount of content on the Internet, it has become increasingly difficult for users to find and utilize information and for content providers to classify and catalog documents on the World Wide Web. Traditional web search engines often return hundreds or thousands of results for a search, which is time consuming for users to browse. On-line libraries, search engines, and other large document repositories (e.g. customer support databases, product specification databases, press release archives, news story archives, etc.) are growing so rapidly that it is difficult and costly to categorize every document manually. To deal with these problems web mining is used. Web mining is the use of data mining techniques to automatically discover and extract information from the web documents and services. This paper presents an overview of web mining, its methodologies, algorithms and applications.

Data mining, Methodologies, Web mining, World Wide Web.


1. Chang , G. , M. J. Haeley , J. A. M. McHugh , J. T. L. Wang , Mining the World Wide Web: An Information Search Approach , Kluwer Academic Publishers , Boston, MA , 2001 .
2. S. Chakrabarti. mining the Web. Morgan Kaufmann, San Francisco, CA, 2003.

3. R.W. Cooley .Web usage mining: Discovery and application of Interesting patterns from Web data. PhD thesis , dept of computer science, university of Minnesota, May 2000.

4. R. Kosala and H. Blockeel. Web mining research: A survey. SIGKDD Explorations, 2(1), 2000.

5. Osmar R. Za¨ane. From resource discovery to knowledge discovery on the internet. Technical Report TR 1998-13, Simon Fraser University, 1998.

6. http://www.math.cornell.edu/~mec/Winter2009/RalucaRemus/ Lecture4/lecture4.html

7. Brin, S.; Motwani, R.; Page, L.; Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, 1998.






K.Ganapathi Babu, A.Komali, V.Mythry, A.S.K.Ratnam

Paper Title:

Web Mining using Semantic Data Mining Techniques

Abstract: The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining, Internet technology and World Wide Web, and for the more recent Semantic Web. Semantic Web Mining is the outcome of two new and fast developing domains: Semantic Web and Data Mining. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Data Mining is the nontrivial process of identifying valid, previously unknown, potentially useful patterns in data. Semantic Web Mining refers to the application of data mining techniques to extract knowledge from World Wide Web or the area of data mining that refers to the use of algorithms for extracting patterns from resources distributed over in the web. The aim of Semantic Web Mining is to discover and retrieve useful and interesting patterns from a huge set of web data. This web data consists of different kind of information, including web structure data, web log data and user profiles data. Semantic Web Mining is a relatively new area, broadly interdisciplinary, attracting researchers from: computer science, information retrieval specialists and experts from business studies fields. Web data mining includes web content mining, web structure mining and web usage mining. All of these approaches attempt to extract knowledge from the web, produce some useful results from the knowledge extracted and apply these results to the real world problems. This paper gives an overview of how the semantic web is used for mining the World Wide Web.

Data mining, Semantic web, Web mining, World Wide Web.


1. Stumme, G., Hotho, A., Berendt, B.: Semantic Web Mining: State of the art and future directions. Web Semantics: Science, Services and Agents on the World Wide Web 4(2) (2006) 124 – 143 Semantic Grid –The Convergence of Technologies.
2. Berendt, B., Hotho, A., Mladenic, D., van Someren, M., Spiliopoulou, M., Stumme, G.: A Roadmap for Web Mining: From Web to Semantic Web. Web Mining: From Web to Semantic Web Volume 3209/2004 (2004) 1–22.

3. Agrawal R. and Srikant R. (2000). Privacy-preserving data mining, In Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, Texas, 439-450.

4. Cooley, R., Mobasher, B. and Srivastava, J., (1997). Web Mining: Information and Pattern Discovery on the World Wide Web, 9th International Conference on Tools with Artificial Intelligence(ICTAI ’97), New Port Beach, CA, USA, IEEE Computer Society, 558-567.

5. T.R. Gruber, Towards Principles for the Design of Ontologies Used for Knowledge Sharing, in: N. Guarino, R. Poli (Eds.), Formal Ontology in Conceptual Analysis
and Knowledge Representation, Kluwer, Deventer, Netherlands, 1993.

6. Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The Semantic Web. Scientific American, 284(5):34-43.

7. Spiliopoulou, M., and Pohle, C. (2001). Data Mining for Measuring and Improving the Success of Web Sites. Data Mining and Knowledge Discover, 5(1-2):85-114.

8. Kosala, R., and Blockeel, H., (2000). Web Mining Research: A Survey, ACM 2(1):1-15.

9. Brin, S., and Page, L. (1998). The Anatomy of a Large-Scale Hyper textual Web Search Engine, Proceedings of the 7th International World Wide Web Conference, Elsevier Science, New York, 107-117.





Maruthi B H, M. Venkatarama Reddy, K. Channakeshavalu

Paper Title:

Finite Element Formulation for Prediction of Over-speed and burst-margin limits in Aero-engine disc

Abstract: Turbo-machinery disks are heavy, highly stressed components used in gas turbine mainly due to over speed and operating temperatures. The experimental burst test is always time consuming and very expensive. Hence the objective this work was to develop finite element (FE) prediction to find over-speed and burst-margin limits. The FE method for the thermal and mechanical analysis is used for the determination of the over-speed and burst margin limit for high-speed aero-engine turbine disc for the speed between 10,000 rpm to 22,000 rpm at variable temperatures. The result shows that the magnitude of the tangential stress components is higher than that of the radial stress components for all the discs under variable temperature distribution. The tangential stress components are higher at inner surface and decreases toward outer surface. The burst margin speed was predicted as per formula and obtained for 18,500, 19,000 and more than 22,000 rpm for thermal + blade load + centrifugal load, thermal + centrifugal load and only centrifugal load respectively.

Gas Turbine Disc, Over-speed, Burst Margin.


1. G Genta, M Gola and A Gugliotta “Axisymmetrical computation of the stress distribution in orthotropic rotating discs”. International Journal of Mechanical Science, vol.24(1), (1980), pp. 21-26
2. Naki Tutuncu, Effect of anisotropy on stresses in rotating discs. International Journal of Mechanical Sciences, vol. 37 (8), (1995), pp. 873-881

3. Ahmet N. Eraslan, Yusuf Orcan and Ugur Guven Elastoplastic analysis of nonlinearly hardening variable thickness annular disks under external pressure. Mechanics Research Communications, vol. 32 (3), (2005), pp. 306-315

4. Guowei Ma, Hong Hao and Yutaka Miyamoto, Limit angular velocity of rotating disc with unified yield criterion. International Journal of Mechanical Sciences, vol. 43 (5),(2001), pp.1137-1153

5. Ahmet N. Eraslan , Von mises yield criterion and nonlinearly hardening variable thickness rotating annular disks with rigid inclusion, Mechanics Research Communications, vol. 29, (5), (2002), pp. 339-350

6. Naki Tutuncu, Effect of anisotropy on inertio-elastic instability of rotating disks. International Journal of Solids and Structures vol37 (51), (2000), pp. 7609-7616

7. Naki Tutuncu Effect of anisotropy on inertio-elastic instability of rotating disks. International Journal of Solids and Structures, vol. 37, (51) , (2000), pp.7609-7616

8. Ahmet N. Eraslan, Elastic–plastic deformations of rotating variable thickness annular disks with free, pressurized and radially constrained boundary conditions, International Journal of Mechanical Sciences , vol. 45(4), (2003), pp. 643-667

9. A C J Luo and C. D. Mote, Asymmetric responses of rotating, thin disks experiencing large deflections. Computers & Mathematics with Applications, vol.45, (1-3), (2003), pp.217-228

10. K Kumar, Dr. Ajit Prasad and Dr. K Ramchandra, Critical issues in assessment of over speed and burst margin in aero engine discs, International Journal of Computer Applications in Engineering Technology and Sciences, vol.2 (1) (2010), pp. 85-90

11. R.S.J. Corran, S.J. Williams, Lifing methods and safety criteria in aero gas turbines, Engineering Failure Analysis, Volume 14, Issue 3, April 2007, Pages 518-528

12. Ravi C. Penmetsa, Ramana V. Grandhi, Adaptation of fast Fourier transformations to estimate structural failure probability, Finite Elements in Analysis and Design, Volume 39, Issues 5–6, March 2003, Pages 473-485.






K.Ganapathi Babu, A.Komali, V.Mythry, A.S.K.Ratnam

Paper Title:

Text Detection and Recognition in Images using Edge Based Detection and Bilinear Interpolation

Abstract: Text can be anything which conveys meaning through the use of signs. This revelation means that the realm of text can be extended from the form of written discourse to that of any object, regardless of whether it is aesthetic, written or spoken. ‘Text’ refers to anything which is capable of being read or interpreted. For example ‘Text’ may represent natural language like English or it may be useful for describing the contents of an image. Identifying text areas and recognizing the text is a complex task in image processing. Because the image may be grayscale or color or it represents a natural image, world map or space images. And the text appears in the image may also too small to recognize. This paper presents the combination of edge based detection and bilinear interpolation methods to identify and recognizing text in different images.

Bilinear interpolation, Edge based detection, Image, Text.


1. M. Swain, H. Ballard, Color indexing, Int. J. Comput. Vision 7 (1991) 11–32.
2. B. Manjunath, W. Ma, Texture features for browsing and retrieval of image data, IEEE Trans. Pattern Anal. Mach. Intell. 18 (8) (1996) 837–842.

3. F. Mokhtarian, S. Abbasi, J. Kittler, Robust and eAcient shape indexing through curvature scale space, in: British Machine Vision Conference, 1996, pp. 9–12.

4. D. Chen, H. Bourlard, J.-P. Thiran, Text identification in complex background using SVM, in: International Conference on Computer Vision and Pattern Recognition, 2001, pp. 621–626.

5. R.K. Srihari, Z. Zhang, A. Rao, Intelligent indexing and semantic retrieval of multimodal documents, Inf. Retri. 2 (2/3) (2000) 245–275.

6. M.B. Ahmad and T.S. Choi , Local Threshold and Boolean Function Based Edge Detection, IEEE Transactions on Consumer Electronics, Vol. 45, No 3. August 1999.

7. An approach of Canny edge detection with virtual hexagonal image structure, by Xiangjian He, pages 879-882, Control, Automation, robotics and vision, ICARCV 2008.

8. Historical review of OCR Research and Development, by S. Moric, Y. Suen and k. Yamamoto, P. 1029- 1057, IEEE, 1992.

9. A study of local and global thresholding techniques in Text categorization, Nayer M. Wanas, Dina A. Said, Naia H. Hegazy and Nevin M. Darwish, Fifth Australasian Data Mining conference 2006.






Manjula B.B, Venkatesh S .Sanganal, Hemalatha.K.N, Ravichandra V

Paper Title:

FPGA Implementation of Optimized 4 Bit BCD and Carry Skip Adders using Reversible Gates

Abstract: The project proposes design of BCD adder and implementation of Carry Skip adder using the new concept of Reversible logic gate to improve the design in terms of garbage’s and area on chip. Furthermore, in the recent years, reversible logic has emerged as a promising technology having its applications in low power CMOS, quantum computing, nanotechnology and optical computing because of it’s zero power dissipation under ideal conditions. It is not possible to realize quantum computing without reversible logic gates. Thus, the project will provide the reversible logic implementation of the conventional BCD adder using NG and NTG gate and Carry skip adder using TSG. The proposed reversible logic implementation of the 4- bit BCD adder is optimized to obtain minimum number of reversible logic gates and minimum number of garbage outputs. This project work on the reversible BCD circuits designed and proposed here form the basis of the decimal ALU of a primitive quantum CPU. The designed and optimized 4-bit reversible BCD adder and existing Carry skip adder are implemented in VHDL Using Xilinx ISE 10.1 tool and simulated using ModelSim SE 6.3f. Implemented on FPGA Spartan-II.

Reversible logic, Feyman gate, NOT Gate, Fredkin Gate, TSG Gate, New Toffoli Gate, New Toffoli Gate,TS-3 Gate, NTG, BCD etc.


1. “Novel BCD Adders and Their Reversible Logic Implementation for IEEE 754r Format” Himanshu Thapliyal, Saurabh Kotiyal and M.B Srinivas Center for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad-500019, India*Department of Computer Engineering, SIT, Kukas, Jaipur, India IEEE 2006.
2. “A Novel Reversible BCD Adder For Nanotechnology Based Systems” Majid Haghparast, Islamic Azad University, Science and Research Branch, Tehran, Iran, Tel American Journal of Applied Sciences ,2008 Science Publications.

3. ”Reduced Delay BCD Adder “Alp Arslan Bayrakc¸i and Ahmet Akkas¸Computer Engineering Department Koc¸ University 34450 Sarıyer, ˙Istanbul, Turkey, IEEE 2007.

4. ”A Beginning in the Reversible Logic Synthesis of Sequential Circuits” Himanshu Thapliyal and M.B Srinivas Centre for VLSI and Embedded System Technologies International Institute of Information Technology, Hyderabad.

5. “Design of a Reversible Binary Coded Decimal Adder by Using Reversible 4-bit Parallel Adder” Hafiz Md. Hasan Babu and Ahsan Raja Chowdhury Department of Computer Science and Engineering, IEEE 2005.

6. Reversible Logic Circuit Synthesis _Vivek V. Shende, Aditya K. Prasad, Igor L. Markov, and John P. Hayes Advanced Computer Architecture Laboratory, University of Michigan, Ann Arbor, MI 481092122

7. http://users.ece.gatech.edu/~leehs/ECE3055/homework/modelsim_tut.pdf

8. HDL CHIP DESIGN by Douglas J Smith, 1997 Edition.

9. J.Bhaskar “VHDL primer” 3rd edition.

10. Charles H Roth, “Digital System Design using VHDL”, Thomson Learning, 2004.

11. Douglas Perry, “VHDL Programming”, Tata McGraw-Hill publishing company Limited, 1998.

12. Bennett, C., “Logical Reversibility of Computation,” IBM Journal of Research and Development, 17, 1973, 525-532.

13. Hafiz Md. Hasan Babu, Md. Rafiqul Islam, Ahsan Raja Chowdhury and Syed Mostahed Ali Chowdhury,“Reversible Logic Synthesis for Minimization of Full adder Circuit”, IEEE Conference on Digital System Design 2003, Euro-Micro’03, Belek, Antalya, Turkey, 2003, pp. 50-54.

14. T. Toffoli., “Reversible Computing”, Tech memo MIT/LCS/TM-151, MIT Lab for Computer Science (1980).

15. R. Landauer, “Irreversibility and Heat Generation in the Computational Process”, IBM Journal of Research Development, 5, 1961, 183-191.

16. Md. M. H Azad Khan, “Design of Full-adder With Reversible Gates”, International Conference on Computer and Information Technology, Dhaka, Bangladesh, 2002, pp. 515-519.

17. E. Fredkin, T. Toffoli, “Conservative Logic”, International Journal of Theory. Physics, 21, 1982, pp. 219-253
18. Milburn, Gerard J., The Feynman Processor, Perseus Books, 1998.

19. R. Feynman, “Quantum Mechanical Computers”, Optical News, 1985, pp. 11-20.

20. T. Toffoli., “Reversible Computing”, Tech memo MIT/LCS/TM-151, MIT Lab for Computer Science (1980).

21. Hafiz Md. Hasan Babu, Md. Rafiqul Islam, Ahsan Raja Chowdhury and Syed Mostahed Ali Chowdhury, “On the Realization of Reversible Full-Adder Circuit”, International Conference on Computer and Information Technology, Dhaka, Bangladesh, 2003, Vol. 2, pp. 880-883.

22. Hafiz Md. Hasan Babu, Md. Rafiqul Islam, Ahsan Raja Chowdhury and Syed Mostahed Ali Chowdhury, “Reversible Logic Synthesis for Minimization of Full adder Circuit”, IEEE Conference on Digital System Design 2003, Euro-Micro’03, Belek, Antalya, Turkey,2003, pp. 50-54.

23. Hafiz Md. Hasan Babu, Md. Rafiqul Islam, Ahsan Raja Chowdhury and Syed Mostahed Ali Chowdhury, “Synthesis of Full-adder Circuit Using Reversible Logic”,17th International Conference on VLSI design 2004,Mumbai, India, 2004, pp. 757-760.

24. M. Perkowski, L. Jozwiak, P. Kerntopf, A Mishchenko, A. Al-Rabadi, A. Coppola, A. Buller, X Song, M. M. H. A. Khan, S Yanushkevich, V.S Shmerko, and M. Chzazowska-Jeske, “A general decomposition for reversible logic”, in: Proc. RM ‘01.






Parvathi R, Sankar M

Paper Title:

Fingerprint Authentication System using Hybrid Classifiers

Abstract: Fingerprints are considered as the most widely accepted biometric feature for uniquely identify a person in the field of biometrics. The existing system only contains bayes classifier to improve the retrieval speed and to perform one to many fingerprint matching. When compared to proposed system, the previous work degrades with performance features like accuracy, consistency and retrieval speed. This fingerprint authentication system uses the combination of Henry classification system at enrollment process and Bayes classification system at authentication process. This paper mainly focuses on fingerprint classification and presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups using Henry classification system. By the speed of Bayes classifier, this system does not depend on the huge amount of fingerprint images in database, one can capture large number of training samples per finger. It can improve the performance features like retrieval speed, consistency and accuracy by using the combination of classifiers.

Biometrics, fingerprint authentication, Henry classification, Bayes classifier, probabilistic recognition.


1. K. C. Leung and C. H. Leung, “Improvement of Fingerprint Retrieval by a Statistical Classifier”, IEEE Transactions on Information Forensics And Security, Vol. 6, No. 1, March 2011 ,Pp 59 -69.
2. Chander Kant & Rajender Nath , “Reducing Process-Time for Fingerprint Identification System”, International Journals of Biometric and Bioinformatics, Vol.3, Issue (1).

3. Jinwei Gu, Jie Zhou, and Chunyu Yang, “Fingerprint Recognition by Combining Global Structure and Local Cues”, IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1952 – 1964, 2006.

4. Ravi. J, K. B. Raj, Venugopal K. R,” Fingerprint Recognition Using Minutia Score Matching’, International Journal of Engineering Science and Technology, Vol.1 (2), 2009, 35-42.

5. Mary Lourde R and Dushyant Khosla, “Fingerprint Identification in Biometric Security Systems”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 5, October, 2010 ,1793-8163.

6. Monowar Hussain Bhuyan, Sarat Saharia, and Dhruba Kr Bhattacharyya, “An Effective Method for Fingerprint Classification’, International Arab Journal of e
Technology, Vol. 1, No. 3, January 2010.

7. Heeseung Choi, Kyoungtaek Choi, and Jaihie Kim, ‘Fingerprint Matching Incorporating Ridge Features with Minutiae”, IEEE Transactions on Information Forensics And Security, Vol. 6, No. 2, June 2011.

8. M. R. Girgisa, A. A. Sewisyb and R. F. Mansourc, “Employing Generic Algorithms for Precise Fingerprint Matching Based on Line Extraction”, Graphics, Vision and Image Procession Journal, vol. 7, pp. 51-59, 2007.

9. Luping Ji, Zhang Yi, “Fingerprint Orientation field Estimation using Ridge Protection’, The Journal of the Pattern Recognition, vol. 41, pp. 1491-1503, 2008.

10. Alessandra Lumini, and Loris Nann, “Advanced Methods for Two-Class Pattern Recognition Problem Formulation for Minutiae-Based Fingerprint Verification”, Journal of the Pattern Recognition Letters, vol. 29, pp. 142-148, 2008.

11. Sheng Li and Alex C. Kot, “Privacy Protection of Fingerprint Database”, IEEE Signal Processing Letters, Vol. 18, No. 2, February 2011

12. Keith Worden, Statistical Pattern Recognition, (lecture notes), September 2008.

13. D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar. Handbook of Fingerprint Recognition. (Springer- Verlag,2003).

14. Lawrence O ’ Gorman, Veridicom Inc., Chat ha m, NJ, Overview of fingerprint verification technologies, (Elsevier Information Security Technical Report, Vol. 3, No. 1, 1998).

15. Salil Prabhakar, Fingerprint Classification and Matching Using a Filterbank, Computer Science & Engineering, doctoral diss., Michigan State University,2001.

16. Ludmila I. Kuncheva, Combining Pattern Classifiers Methods and Algorithms, Bangor, Gwynedd, United Kingdom, September 2003.

17. F.A. Afsar, M. Arif and M. Hussain, Fingerprint Identification and Verification System using Minutiae Matching, National Conference on Emerging Technologies 2004.

18. Mohamed. S. M and Nyongesa.H, Automatic Fingerprint Classification System using Fuzzy Neural techniques, IEEE International Conference on Artificial Neural Networks, vol. 1, pp. 358-362, (2002).

19. The Henry Fingerprint Classification System, Available: www.namus.gov.






Y. Ramamohan, K. Vasantharao, C. Kalyana Chakravarti, A.S.K.Ratnam

Paper Title:

A Study of Data Mining Tools in Knowledge Discovery Process

Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. It uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools are available today. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. This paper presents an overview of the data mining tools like Weka, Tanagra, Rapid Miner, Orange.

Data mining, Rapid Miner, Tool, WEKA.


1. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
2. G. Piatetsky-Shapiro, U. M. Fayyad, and P. Smyth. From data mining to knowledge discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996.

3. The WEKA data mining sodtware: An update, Mark Hall, Eibe Frank, G. Holmes, B. Pfahringer, P. Reutemann, IH Witten, ACM SIGKDD Explorations, Newsletter, Pages 10-18, volume 11 issue 1, june 2009.

4. http://rapid-i.com/

5. http://eric.univ-lyon2.fr/~ricco/tanagra/

6. DBMiner: A System for Data Mining in Relational Databases and Data Warehouses, Data Mining Research Group, Intelligent Database Systems Research Laboratory School of Computing Science, Simon Fraser University, British Columbia, Canada, http: db.cs.sfu.ca DBMiner.

7. www.uea.ac.uk/polopoly_fs/1.3589!introductionkdd.pdf

8. http://orange.biolab.si/






Anand Pandey

Paper Title:

Performance Evaluation of DSR with Reference to Varying Number of Nodes

Abstract: Mobile ad hoc network is a dynamic network. In this network the mobile nodes dynamically form a temporary network without any centralized administration or the use of any existing network infrastructure. A number of routing protocols like Ad Hoc On-Demand Distance Vector Routing (AODV), Dynamic Source Routing (DSR) and Destination-Sequenced Distance-Vector (DSDV) have been proposed. The Dynamic Source Routing protocol (DSR) is an efficient routing protocol designed specifically for use in wireless ad hoc networks of mobile nodes. The DSR protocol is composed of the two mechanisms of "Route Discovery" and "Route Maintenance", which allow nodes to discover and maintain routes to arbitrary destinations. In this work an attempt has been made to compare the performance of DSR routing protocols for mobile ad hoc networks on the basis of varying number of nodes. The simulations are carried out using the ns-2 network simulator, which is used to run wired and wireless ad hoc simulations. Analyses of the results are done in Tracegraph with Matlab.

DSR, MANET, Performance Evaluation, Protocol.


1. Sarangapani, Jagannathan, “Wireless ad hoc and sensor networks: Protocols, Performance and Control”, CRC Press.
2. Xiaoyan Hong, Kaixin Xu, and Mario Gerla. “Scalable routing protocols for mobile ad hoc networks”, 2002.

3. Mehran Abolhasan, Tadeusz Wysocki, and Eryk Dutkiewicz, “A review of routing protocols for mobile ad hoc networks”, Technical report, Telecommunication and Information Research Institute, University of Wollongong, Wollongong, NSW 2522; Motorola Australia Research Centre, 12 Lord St., Botany, NSW 2525, Australia, 2003.

4. Laura Marie Feeney, “A taxonomy for routing protocols in mobile ad hoc networks”, Technical report, Swedish Institute of Computer Science, Sweden, 1999.

5. Dr. Uwe Roth, “Highly dynamic destination-sequenced distance-vector routing”,http://wiki.uni.lu/secan-lab/Highly+Dynamic+Destination-Sequenced+Distance-Vector+Routing.html.

6. Al-Sakib Khan Pathan and Choong Seon Hong, "Routing in Mobile Ad Hoc Networks", Guide to Wireless Ad Hoc Networks, Springer (London), (Edited by Sudip Misra, Isaac Woungang, and Subhas Chandra Misra), ISBN 978-1-84800-327-9, 2009, pp. 59-96

7. P. Jacquet, P. Muhlethaler, A. Qayyum, A. Laouiti, L. Viennot and T. Clausen, “Optimized Link State Routing Protocol”, Internet Draft, IETF MANET Working Group, draft-ietf-manet-olsr-04.txt, Mar. 2002.

8. R. G. Ogier, F. L. Templin, B. Bellur, M. G. Lewis, “Topology Broadcast Based on Reverse-Path Forwarding (TBRPF)”, Internet Draft, IETF MANET Working Group, draft-ietf-manet-tbrpf-05.txt, Mar. 2002.

9. G. Pei, M. Gerla, and T.-W. Chen, “Fisheye State Routing in Mobile Ad Hoc Networks”, Proceedings of Workshop on Wireless Networks and Mobile Computing, Taipei, Taiwan, Apr. 2000.

10. Perkins, C.E., and P. Bhagwat, “Highly dynamic destination sequenced distancevector routing (DSDV) for mobile computers:, Computer Communications Review,1994, pp. 234–244.

11. P. F. Tsuchiya, “The Landmark Hierarchy: a new hierarchy for routing in very large networks”, Computer Communication Review, vol.18, no.4, Aug. 1988, pp. 35-42.

12. Ching -Chuan Chiang, Hsiao-Kunag Wu, Winston Liu and Mario Gerla, “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel,” IEEE

13. Perkins CE, Royer EM, Chakeres ID (2003) Ad hoc On-Demand Distance Vector (AODV) Routing. IETF Draft, October, 2003, available at http://tools.ietf.org/html/draft-perkins-manet-aodvbis-00. Accessed 21 February 2008

14. Broch J, Johnson DB, Maltz DA (1999) The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks. IETF Draft, October, 1999, available at

15. http://tools.ietf.org/id/draft-ietf-manet-dsr-03.txt.

16. Tracegraph http://www.tracegraph.com/download.html

17. S.McCanne and S.Floyd,”Network Simulator”, http://www.isi.edu/nsnam/ns/






Chidolue C.A., Aginam C.H.

Paper Title:

Effect of Shear on Stress Distribution in Redundant Frames

Abstract: In this paper, shear-modified expressions for fixed end moments and reactions were obtained for various beam loading conditions using the shear modified stiffness coefficients of elastic beams derived by the authors. By taking the effect of shear on the behavior of beam elements into consideration, a set of modified homogeneous solution of the beam elastic curve equation was obtained and used to derive expressions for fixed end moments and shears for beams with various end conditions and loading. The shear-modified fixed end moment expressions were used to analyze redundant frames. The results of the analysis were then compared with those obtained using the traditional expressions for fixed end moments and shears.

Elastic curve, shear- modified stiffness coefficients, fixed end moments, redundant frames, stress distribution.


1. J.C. Bruch, and T.P. Mitchel, Mass-loaded clamped free Timoshenko beam. Journal of Sound and Vibration, l3 (2), 1987, pp 341-345.
2. A.K. Chugh, Stiffness matrix for a beam element including transverse shear and axial force effect.. International Journal for Numerical Methods in Engineering, 2, 1977, pp 1681-1697.

3. A.Dakov, and V. Kuznetsov, Structural Mechanics, MIR Publishers, Mosco, Translated from the Russian by B. Lachinov, 1985

4. T. Karamanski, T. Bobev, N. Kapitanov, T. Ganev, A. Popov, and I. Baicher, ‘Structural Mechanics’, First Edition, Technika press, Sofia, 1988.

5. N.N. Osadebe and C.A. Chidolue, Effect of Shear Deflections on Stiffness Coefficients and Matrix, Inter. Jour. of Engineering, 5 (4) , 2011, pp 169-180

6. N.N. Osadebe and C.A. Chidolue, 2012, An Alternative First Principle Approach for Determination of Elements of Beam Stiffness Matrix, Nigerian Journal of Technology, accepted for publication.

7. N.N. Osadebe and B.O. Mama, 1998, Combined effect of axial load and shear deformation on bending moment in framed structures. Proceedings Fourth Structural Engineering Analysis and Modeling, SEAM 4, Accra Ghana’ 1998.

8. C.S. Reddy, Basic structural analysis. Tata McGraw-Hill Publishing Company, New Delhi., Second edition., 1981






Tirumala Rao Pechetty, Raviteja Udumudi

Paper Title:

FPGA Implementation of Interference Avoidance and Hard To Intercept Frequency Agile Radar Processing

Abstract: Jamming is one of the main problems for the functioning of the radar. Surveillance radar or searching radar has to overcome the jamming environment during the operation of the work. Jamming is of Diversified which includes narrow band, wide band jamming or its combination. Hence the solution for the Diversified jamming is obtained by using fast self switching frequency agility technique and its function is very effective. The detailed steps to achieve this function are described and the function is realized with FPGA using Hardware Description Language.The practical application on a surveillance radar shows that the module has good real time and anti jamming capacity. Modelsim will be used for functional simulation and results verification. Xilinx ISE will be used for synthesis; place & route and bit file generation. Xilinx FPGA board will be used for results verification.

Anti-jamming; Diversified jamming; Frequency agility; FPGA; Fast self switching frequencies; Interference; Self adaptive.


1. Merrill I. Skolnik “Introduction to Radar systems” third edition.
2. Fred Nathanson, J. Patrick Rally “Radar Design Principles” second edition.

3. Merrill I. Skolnik, “RADAR HANDBOOK” 3rd edition.

4. U. Meryer-Base and L. Liu, Digital Signal Processing with Field Programmable Gate Arrays, 2nd ed., Beijing, China: Tsinghua University Press, 2006.

5. C. S. Li, J. Li, and C. G. Sun, “Anti-jamming Scheme Design of Ground-wave Over-the-horizon Radar to Radio Station,” Shipboard Electronic Countermeasure,
Vol.31, No.4, pp.45-46, Aug. 2008.

6. Y. Chen, “Analysis of Anti-jamming Technique of Search Radar,” Radio Engineering, vol. 37, No.7, pp. 44–46, 2007.

7. Y. Jiang and S. H. Huang, “Evaluation of Searching Radar ECCM Capability,” Ship Electronic Engineering, No.3, pp. 113–116, 2005 .

8. X. Y. Ma, J. B. Xiang, Y. S. Zhu and J. M. Qing, Radar Signal Processing, 1st ed., Changsha, Hunan, China: Hunan Science and Technology Press, 1999.

9. H. X. Huang and H. Xu, “Analysis and Evaluation on Radar Frequency Agility Capability,” Aerospace Electronic Warfare, No.1, pp: 21–24,2001.

10. Buccella.C “Digital control of power converters-A survey”, Industrial informatics, IEEE transactions, volume pp, issue 99, 2012.

11. Giorgio’s Dimitra Kopoulos, Christoforos Kachris, “Scalable arbiters and multiplexers for FPGA interconnection networks”. IEEE computer society, International conference on field programmable logic and application, 2011.

12. Agarwal.A “Design of FPGA based controller for trapezoidal modulated cyclo-inverter,” Information and Automation for Sustainability (ICIAFS) , International conference, 2010.

13. Ahmad,R. “Implementation of IEEE 802.15.4-based OQPSK- pulse shaping block on FPGA”.IEEE international conference on computer applications and industrial electronics (ICCAIE),2011.

14. Yuqing Huang, Zaiying Yao, Fanrong Shi, “Research of Jamming signal’s feature extraction of LFM pulse compression Radar,” Information engineering and applications lecture notes in electrical engineering volume 154, pp 839-846,2012.

15. Peng Gang, Hong Tao, “Design of a point target simulator for Guidance Seeker”. International conference on signal processing systems 2009.

16. http://en.wikipedia.org/wiki/Additive_white_Gaussian_noise.

17. Kim, Young-Han, “The Gaussain channel with noisy feedback”. Information theory, IEEE international symposium, ISIT 2007.

18. S Sukhsawas, K Benkrid. “A High level implementation of a high performance pipeline FFT on VIRTEX-E FPGA”. IEEE computer society annual symposium on VLSI emerging trends in VLSI systems Design,2004.






T.Sujatha, Ramesh Naidu G, P.Suresh B

Paper Title:

Measuring Semantic Similarity between Words Using Web Pages

Abstract: Semantic similarity measures play an important role in the extraction of semantic relations. Semantic similarity measures are widely used in Natural Language Processing (NLP) and Information Retrieval (IR). The work proposed here uses web based metrics to compute the semantic similarity between words or terms and also compares with the state-of-the-art. For a computer to decide the semantic similarity, it should understand the semantics of the words. Computer being a syntactic machine, it cannot understand the semantics. So always an attempt is made to represent the semantics as syntax. There are various methods proposed to find the semantic similarity between words. Some of these methods have used the precompiled databases like WordNet, and Brown Corpus. Some are based on Web Search Engine. The approach presented here is altogether different from these methods. It makes use of snippets returned by the Wikipedia or any encyclopedia such as Britannica Encyclopedia. The snippets are preprocessed for stop word removal and stemming. For suffix removal an algorithm by M. F. Porter is referred. Luhn’s Idea is used for extraction of significant words from the preprocessed snippets. Similarity measures proposed here are based on the five different association measures in Information retrieval, namely simple matching, Dice, Jaccard, Overlap, Cosine coefficient. Performance of these methods is evaluated using Miller and Charle’s benchmark dataset. It gives higher correlation value of 0.80 than some of the existing methods.

Semantic Similarity, Wikipedia, Web Search Engine, Natural Language Processing, Information Retrieval, Web Mining


1. Kilgarriff, “Googleology Is Bad Science,” Computational Linguistics, vol. 33, pp. 147-151, 2007.
2. M. Sahami and T. Heilman, “A Web-Based Kernel Function for Measuring the Similarity of Short Text Snippets,” Proc. 15th Int’l World Wide Web Conf., 2006.

3. D. Bollegala, Y. Matsuo, and M. Ishizuka, “Disambiguating Personal Names on the Web Using Automatically Extracted Key Phrases,” Proc. 17th European Conf. Artificial Intelligence, pp. 553- 557, 2006.

4. H. Chen, M. Lin, and Y. Wei, “Novel Association Measures Using Web Search with Double Checking,” Proc. 21st Int’l Conf. Computational Linguistics and 44th Ann. Meeting of the Assoc. for Computational Linguistics (COLING/ACL ’06), pp. 1009-1016, 2006.

5. M. Hearst, “Automatic Acquisition of Hyponyms from Large Text Corpora,” Proc. 14th Conf. Computational Linguistics (COLING), pp. 539-545, 1992.

6. E. Agirre, E. Alfonseca, K. Hall, J. Kravalova, M. Pasca, and A. Soroa, “A Study on Similarity and Relatedness Using Distributional and Wordnet-Based Approaches,” Proc. Human Language Technologies: The 2009 Ann. Conf. North Am. Chapter of the Assoc. for Computational Linguistics (NAACL-HLT ’09), 2009.

7. G. Hirst and D. St-Onge, “Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms,” WordNet: An Electronic Lexical Database, pp. 305-332, MIT Press, 1998.

8. T. Hughes and D. Ramage, “Lexical Semantic Relatedness with Random Graph Walks,” Proc. Joint Conf. Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL ’07), pp. 581-589, 2007.

9. E. Gabrilovich and S. Markovitch, “Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis,” Proc. Int’l Joint Conf. Artificial Intelligence (IJCAI ’07), pp. 1606-1611, 2007.

10. Y. Matsuo, J. Mori, M. Hamasaki, K. Ishida, T. Nishimura, H. Takeda, K. Hasida, and M. Ishizuka, “Polyphonet: An Advanced Social Network Extraction System,” Proc. 15th Int’l World Wide Web Conf., 2006.






Nitin Jain, Sachin Meshram, Shikha Dubey

Paper Title:

Image Steganography Using LSB and Edge – Detection Technique

Abstract: Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using “Edge detection Filter”. In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques.

Steganography, Steganalysis, edge detection, digital image, gray image, RGB image, Binary image, 8 pixel connectivity. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way


1. N. F. Johnson, S. Jajodia, “Exploring Steganography: Seeing the Unseen”, Computer Journal, IEEE, February 1998.
2. M. M. Amin, M. Salleh, S. Ibrahim, M.R.Katmin, M.Z.I. Shamsuddin, “Information Hiding using Steganography” Proceedings of 4th National Conference on Telecommunication Technology, Shah Alam, Malaysia, 2003.

3. N. Provos and P. honeyman, “Hide and seek : an introduction to steganography” IEEE Computer Society, 2003.

4. K. Curran, X.Li, R. Clarke, “An investigation in to the use of the least significant bit substitution technique in digital watermarking”, Journal of Applied Science 2 (3), pp. 648-654,2005.

5. R. J. Anderson, F. A. P. Petitcolas, “On the limits of steganography”, Journal of Selected areas in communications, 16(4), pp. 474-481, 1998.

6. G. J. Simmons, “The prisoners problem and the subliminal channel”, Proc. of CRYPTO, 1983.

7. R. Chandramouli, M.Kharrazi, N. Memon, “Image Steganography: Concepts and Practice”, proc. Of IWDW 2003, LNCS 2939, pp. 35-49, 2004.

8. R. Radhakrishnan, K. Shanmugasundaram, N. Memon, “Data Masking: A Secure-Covert Channel Paradigm”, 2002.

9. D. Neeta, K. Sehal, “Implementation of LSB Steganography and its Evaluation or Various Bits”, 2004.

10. C. Cachin, “An Information Theoretic model for steganography”, 2004.

11. J. Fridrich and M. Goljan, “Digital image steganography using stochastic modulation”, SPIE Symposium on Electronic Imaging, San Jose, CA, 2003.

12. A.D. Ker, “Steganalysis of LSB Matching in Grayscale Images”, IEEE signal processing letters, vol. 12, No 6, 2005.

13. K. B. Raja, N. Shankar, K. R. Venugopal, L. M. Patnaik, “Steganalysis of LSB Embedded Images using Variable threshold color pair analysis”, IEEE 2006.

14. C. Ming, Z. Ru, N. Xinxin, Y. Yixian, “Analysis of Curent Steganography Tools: Classification & Features”, Proc. of International Conference o Intelligent Information Hiding and Multimedia Signal Processing, 2006.

15. M. Niimi, H. Noda, E. Kawaguchi, “High Capacity and Secure Digital Steganography to Palette-Based Images”, 2002.

16. N. Wu, M. Hwang, “Data Hiding Current Status and key issues”, International Journal of Network Security, Jan 2007.

17. W. Luo, “Object-related illustration watermarks on cartoon images”, Feb 2004.

18. R. Alwan, F. Kadhim, A. Al-Taani, “Data Embedding based on better use of bits in image pixels” International Journal of signal processing, 2005.

19. K. M. Singh, L. S. Singh, A. B. Singh, K. S. Devi “Hiding Secret Message in Edge of the Image” International conference of Information and Communication Technology, ICICT 2007.

20. J. Silman, “Steganography and staganalysis: An overview”, SANS Institute 2001.

21. T. Jamil, “Steganography: The art of hiding information is plain sight”, IEEE potentials, 1999.

22. H. Wang, S. Wang, “Cyber warfare: steganography vs. staganalysis”, communication of the ACM, Oct 2004.

23. L.M. Marvel, Jr. C.G. Boncelet, C. Retter, “Spread spectrum steganography” IEEE Transactions on Image Processing, 1999.

24. I.Avcibas,N.Memon,B. Sankur,”steanalysis using image quality metrics” IEEE Trasactions on Image Processing, Feb 2003.

25. J.R. Kreen,”Steganography And Steganalysis”,jan 2004.

26. E. Sodipo, “Steganography in principle”.

27. http://www.mirrors.wiretapped.net/security/steganography/blindside

28. http://www.wbailer.com/wbstego

29. D. Artz, “Digital Steganography: Hiding Data wthin Data”, 2001.

30. Y. Wang, P. Moalin, “Steganalysis of Block DCT image steganography”.

31. J. Fridrich, M. Golan, D. Hogea, “Stegaalysis of JPEG Images: Breaking the F5 Algorithm”.

32. T. Morkel, J. H. P. Elloff, M.S. Olivier, “An Overview of Image Steganography”.

33. S. C. Chapra, “Applied Numerical Methods with Matlab”.

34. W. J. Palm, “Introduction to Matlab 7 for engineers”, 2005.

35. H. Moore, “Matlab for Engineers”, 2007.

36. Mehdi Hussain, M. Hussain,”Information hiding using edge boundries of objects”, International journal of security and application, 2011

37. Shriram K Vasudevan ,Dharmendra T. Shivaram R, ”Automotive Image ProcessingTechnology using Canny’s edge detector”,international journal of engineering and technology 2010.

38. Manish kaushal,Arjan Singh,Baljit singh,”Adaptive thresholding for edge detection in gray scale image”,2010.

39. P.D.Khandait,S.P,Khandait, “LSB technique for secure data communication”.

40. Rishi R. Rakesh, Prabal Chaudhari, E.A. Murthy,”Thresholding in edge detection A Statistical Approach”, IEEE Transaction on image processing,2004

41. R. Chandramauli, Nasir Menon,”Analysis of LSB based image steganography techniques,”0-7803-6725, IEEE 2001.

42. E. Nadernejad, S. sharifzadeh, H. Hassaupour, “Edge detection techniques evalution and comparision”,2008.

43. Mamta Juneja, Parvinder Singh Sadhu “Performance evalution of edge detection technique for image and spatial domain”, international journal of computer theory and Engineering, 2009.

44. Chang-Chou-Lin, Wen-Hsiang Tsai, “Secret image sharing with steganography andauthentication”,The journal of system and software 2004.

45. Ching-Nung Yang, Tse-Shih Chan, Kun Hsuan, ‘Improvements of image sharing with steganography and authentication’,The journal of system and software 2004.

46. Rupinder Kaur,Mandeep Kaur, Rahul Malhotra,”a new approach towards steganography ”, IJCSIT ,2011.

47. Sujay Narayana, Gaurav Prasad,”Two new approaches for second image steganographyusing cryptography techniques and type commisions ”, SIJIT,2011.

48. Kathryn Hempstalk, “Hiding behind corner-using edge in images for better Steganography”.

49. Amanpreet kaur,Renu Dhir, Geeta Sikka, “A new image steganography based on firscomponent alteration technique”,IJCSIS,2009.

50. Raman Maini,Dr. Himanshu Agrawal “study and comparision of various image edgeDetection techniques”, International journal of image processing.

51. N. Senthilkumaran, R.Rajesh, “Edge detection techniques for image segmentation-A Survey of soft computing approaches”, International journal of recent trends in engineering, 2009.






Geetali Banerji, Kanak Saxena

Paper Title:

Analysis of Data Mining Techniques on Real Estate

Abstract: Data mining techniques are broadly classified into two classes (i) Statistical Techniques and (ii) Knowledge Discovery. The continuing rapid growth of on-line data and the widespread use of databases necessitate the development of techniques for extracting useful knowledge and for facilitating database access. This paper analyzes the results of multilayer perceptron with pace regression and suggests a very efficient pattern which can be proved beneficial for knowledge discovery. The analysis is done using real estate data set which contains 5821 tuples and 43 attributes and determines that in India’s scenario the demographic details of a person plays a very prominent role in identifying the investment behavior of a customer. In multilayer perceptron model, input layer is followed by two hidden layers. The first hidden layer contains 21 nodes as per various attribute weight age followed by second hidden layer which assigns re-processed weights to each of the 21 nodes. If we are discarding the demographic details then the model which is available consists of 13 Sigmoid nodes and there is a major change in error rate and correlation. We have used WEKA for analysis and found that in general multilayer perceptron(selected) is more efficient then pace regression(complete) in terms of statistical methods, but in Indian perception pace regression(complete) is more efficient than multilayer(selected).

Multilayer Perceptron, Neural Network, Pace Regression.


1. Carbune P. L., ”Expanding the meaning of an application for Data Mining”, IEEE International Conference on Systems, man and Cybernetics, 1872-1873,2000
2. Han Jiawei and Kamber Micheline, ”Data Mining: Concepts and Techniques, Second Edition”, Morgan Kaufmann Publishers.

3. Hald A.,”Statistical Theory with Engineering Applications”, A Wiley Publications in Applied Statistics

4. R. Bouckaert Remco, Eibe Frank et. al, “WEKA Manual for Version 3-6-2”, January 11, 2010

5. Wang Yong and Witten H., “Pace Regression” Working Paper Series, ISSN 1170-487X, Working Paper 99/12, September 1999

6. Makhtar, M.; Neagu, D.C.; Ridley, M. “Predictive model representation and Comparison: Towards data and predictive mode governance”, Computational Intelligence (UKCI), 2010 UK Workshop on, 2010

7. Somesha, Sowmya; Lee, David N.; Loddick, Sean J, ”Gatecard reliability prediction analysis”, Power Electronics and Applications (EPE 2011), Proceedings of the 2011-14th European Conference on, 2011

8. Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth,” From Data Mining to Knowledge Discovery in Databases”, AI Magazine Volume 17 Number 3 (1996) (© AAAI)

9. Alex Berson, Stephen Smith, and Kurt Thearling, “An Overview of Data Mining Techniques”, Excerpted from the book Building Data Mining Applications for CRM.

10. Geetali Banerji, Kanak Saxena, ”Predictive Model- A Boon for real estate” , International Journal for Wisdom Based Computing Volume(1) 2, April 2012

11. sydney.edu.au/engineering/it/~irena/ai01/nn/8.html

12. Haykin, S. Neural Networks: A Comprehensive Foundation, Maxmillan, IEEE Press, 1994

13. Negnevitsky, M. Artificial Intelligence: A Guide to Intelligent Systems, Addison Wiesley, England, 2002.

14. Joseph P. Bigus, Data Mining with Neural Networks: Solving Business Problems From application development to Decision support, McGraw-Hill, NY, 1996






Sonia Goyat

Paper Title:

Genetic Key Generation for Public Key Cryptography

Abstract: The importance of cryptography can be judged by the fact that it is used almost everywhere. It is essential in e transactions, LAN data transfer, in Databases and even while storing data in our own computer. There are many methods of cryptography. Some of that have large complexity and are pretty cumbersome, while other are based on the theory of natural selection. The fact that selecting key for the public key cryptography is a selection process in which various keys can be categorized on the basis of their fitness, makes Genetic Algorithms a good contender for the process to be followed for generating key. Moreover, using Genetic Algorithm we can keep the strength of the key as good as any other key, still make the whole algorithm good enough to have a complexity as low as O(n2). The work is based on one such approach [1] and modifies the approach to generate keys that have more strength as compared to previous work [1].The work has been implemented and analyzed. The results obtained are good in terms of coefficient of autocorrelation. The samples satisfy the tests including gap test, frequency test etc.

Public Key Cryptography, One Time Pad, Genetic Algorithms, Vernam Cipher.


1. Harsh Bhasin, Nakul Arora, Reliability Infocom Technology and Optimization 2010, Conference Proceedings pages 226- 230.
2. Bethany Delman, Genetic Algorithms in Cryptography, MS Thesis 2004.

3. Norman D. Jorstad, CRYPTOGRAPHIC ALGORITHM METRICS, January 1997

4. ABDELSALAM ALMARIMI et al, A NEW APPROACH FOR DATA ENCRYPTION USING GENETIC ALGORITHMS, Published in: Proceeding CERMA '10 Proceedings of the 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference

5. Menezes, A., van Oorschot, P., & Vanstone, S. (1997). Handbook of Applied Cryptography Boca Raton: CRC Press

6. Harsh Bhasin, Supreet Singh, GA-Correlation Based Rule Generation for Expert Systems, IJCSIT, Volume 3, Issue 2, Pages 3733-3736

7. Harsh Bhasin, Surbhi Bhatia, Application of Genetic Algorithms in Machine learning, IJCSIT, Volume 2, Issue 5, Pages 2412-2415

8. Harsh Bhasin, Surbhi Bhatia, Use of Genetic Algorithms for Finding Roots of Algebraic Equations, IJCSIT, Volume 2, Issue 4, Pages 1693-1696

9. Harsh Bhasin, Gitanjali, Harnessing Genetic Algorithm for Vertex Cover Problem, International Journal on Computer Science and Engineering (IJCSE), Volume 4, Issue 2, 218 - 223.







Reena Sharma, R.S.Chhillar

Paper Title:

Novel Approach to Software Metrics

Abstract: Software metrics are becoming important day by day. Many metrics have been defined and have been related to class coupling cohesion etc. First of all it is difficult to choose the correct metrics for particular software and secondly most of the metrics only cater to requirements phase. It is to be understood that the importance of software metrics cannot be undermined in the design and implementation phase also. The work discusses the various techniques, their merits and demerits and intends to propose a new system for measuring the goodness of implementation phase. The concept of Object Oriented Software Metrics has also been explored. The proposed metrics uses the concept of Genetic Algorithms, which are based on the theory of natural selection. Thus, the work intends to introduce natural selection techniques for measuring the quality of software.

Software Metric, Measure, Object Oriented Metrics, Genetic Algorithms


1. An Introduction to Object oriented Programming and Smalltalk. Pinson Lewis and Richard S. Wiener Addison- Wesley pp 49-60, 1988.
2. S. R. Chidamber and C. F. Kemerer, “A Metrics Suite for Object Oriented Design,” IEEE Transactions on Software Engineering, Vol. 20, No. 6, pp. 476–493, 1994

3. A Comprehensive Assessment of Object-Oriented, Software Systems Using Metrics Approach, Sanjay Kumar Dubey et al., International Journal on Computer Science and Engineering, Vol. 02, No. 08, 2010, 2726-2730

4. Weyuker's Properties, Language Independency and Object Oriented Metrics, Published in: • Proceeding ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II Pages 70 - 81

5. A metrics suite for object oriented design, Software Engineering, IEEE Transactions on, March 1995, Churcher, N.I. ,Shepperd, M.J.; Chidamber, S. ; Kemerer, C.F., Volume 21 , Issue: 3, Pages: 263- 265

6. L. Prechelt, B. Unger, M. Philippsen and W. Tichy, “A controlled experiment on inheritance depth as a cost factor for code maintenance”, The Journal of Systems and Software, Vol. 65, 2003, pp. 115-126.

7. M. Alshayeb, and M. Li, “An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes”, IEEE Transactions on Software Engineering archive, Vol. 29, 2003, pp.1043 – 1049.

8. M. Cartwright, An Empirical view of inheritance, Information and Software Technology, Vol. 40, No. 4, 1998, pp. 795-799.

9. M. Tang, M. Kao and M. Chen, An Empirical Study on Object-Oriented Metrics, 6th IEEE International Symposium on Software Metrics, 1998.

10. Impact of Software Metrics on Object-Oriented Software Development Life Cycle, International Journal of Engineering Science and Technology, Vol.2 (2), 2010, 67-76

11. Factor analysis of source code metrics, D Coupal, Journal of Systems and Software, 1990 – Elsevier

12. Regression Testing Using Coupling and Genetic Algorithms, IJCSIT 3(1) ,Pages : 3255 – 3259, Harsh Bhasin, Manoj






Hak. J. Kim and Jonathan Modell

Paper Title:

Mobile App Design Tool for Smartphones: A Tutorial

Abstract: The paper presents the basics of mobile application creation for smartphones using the visual programming tool, ‘App Inventor for Android (AIA)’. The AIA was developed by Google to enable non-programmers to easily build mobile applications by dragging and dropping the block-based interfaces, like blocks of Lego, instead of writing lines and lines of code. It allows users to immediately build applications (e.g., location services and games) that interface with mobile technologies.

App Inventor, Mobile App, Smartphones, Designer, Block Editor.


1. Cook, D., and Das, S. (2012) Pervasive Computing at Scale: Transforming the State of the Art, Pervasive and Mobile Computing, vol. 8, issue 1, pp. 22-35.
2. MIT, “App Inventor for Android”, http://appinventoredu.mit.edu/ (accessed February 20, 2012).

3. Tyler, J. (2012) App Inventor for Android: Build Your Own Apps-No Experience Required !, Wiley Inc.

4. Wolber, D., Abelson, H., Spertus, E., and Looney, L. (2011) App Inventor: Create Your Own Android Apps, O’Reilly.

5. Kloss, J. (2012) Android Apps with App Inventor, Pearson Education, Inc.

6. Castro, L. & Fosso Wamba, S. (2007). An inside look at RFID technology. Journal of Technology Management & Innovation, 2(1), pp. 128-140.

7. Roussos G. and Kostakos V. (2009) RFID in Pervasive Computing: State-of-the-art and Outlook, Pervasive and Mobile Computing, Vol. 5(1), pp.110-131.






Mythry Vuyyuru, Pulipati Annapurna, K.Ganapathi Babu, A.S.K Ratnam

Paper Title:

An Overview of Cloud Computing Technology

Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (eg networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. It has become a significant technology trend, and many experts expect that cloud computing will reshape information technology (IT) processes and the IT marketplace. With the cloud computing technology, users use a variety of devices, including PCs, laptops, smart phones, and PDAs to access programs, storage, and application-development platforms over the Internet, via services offered by cloud computing providers. This paper presents an overview of cloud computing technology- deployment models, classes and characteristics.

Cloud computing, Infrastructure as a service, Platform as a service, Software as a service.


1. Grossman, R. L. (March/April 2009). The case for cloud computing. IEEE ITPro, 23–27.
2. Jens, F. (September 2008). Defining cloud services and cloud computing. http://blogs.idc.com/ie/?p=190

3. L. Wang, G. Laszewski, M. Kunze and J. Tao, “Cloud computing: a perspective study”, J New Generation Computing, 2010, pp 1-11.

4. Sun Microsystems (June 2009). Introduction to cloud computing architecture. White Paper, Sun Microsystems.

5. Keith Pijanowski’s blog,“Understanding public clouds : IaaS, PaaS, SaaS” on KeithPij.com , 5/11/2009 “http://www.keithpij.com/Home/tabid/36/EntryID

6. P. Mell and T. Grance, “Cloud Computing Definition”, National Institute of Standards and Technology, Version 15, 10-7-09






Panchakshari H.V, Girish D.P, Krishna M

Paper Title:

Investigation on Effect of Cryogenic Parameters on Wear Behavior of Aluminium-AL2O3 MMCSS Using Taguchi Method

Abstract: The objective of this research work was to investigate the effect of cryogenic parameters such as cryogenic temperature (-100, -150 and -196C), duration of cryogenic treatment (0, 25 and 50 h) and wt. % of reinforcement (0, 10 and 20 wt. %) on wear behavior of Al/Al2O3 metal matrix composites (MMCs). The Al2O3 particulate reinforced MMCs were fabricated by liquid metallurgy technique. Specimens so prepared were treated for different cryogenic temperatures for different treatment duration using liquid nitrogen. Both un treated and cryogenic treated specimens were tested on pin-on-disc for constant load 20 N, sliding speed of 1.5 m/s and sliding distance of 3 km. Taguchi method is applied for predicting the optimum cryogenic treatment parameter that gives the lowest wear rate to the castings. The experimental and analytical results showed that the Taguchi method was successful in predicting cryogenic parameters that give the lowest wear rate and the wt. %.

MMCs, Cryogenic treatment, microstructure, Wear rate, Taguchi.


1. Jun Wang, Ji Xiong, Hongyuan Fan, Hong-Shan Yang, Hao-Huai Liu, Bao-Luo Shen “Effects of high temperature and cryogenic treatment on the microstructure and abrasion resistance of a high chromium cast iron”, Journal of Materials Processing Technology, vol. 209(7) (2009), pp. 3236-3240
2. Hong-xiao CHI, Dang-shen MA, Qi-long YONG, Li-zhi WU, Zhan-pu ZHANG, Yong-wei WANG, “Effect of Cryogenic Treatment on Properties of Cr8-Type Cold Work Die Steel”, Journal of Iron and Steel Research, International, vol. 17(6), (2010), pp. 43-46,59

3. P. Poza, J. Llorca, “Fracture toughness and fracture mechanisms of Al-Al2O3 composites at cryogenic and elevated temperatures”, Materials Science and Engineering: A, vol. 206 (2), (1996), pp.183-193.

4. Joel Hemanth, “Tribological behavior of cryogenically treated B4Cp/Al–12% Si composites”, Wear, vol 258(11-12), (2005), pp. 1732-1744

5. K.H.W. Seah, J. Hemanth, “Cryogenic effects during casting on the wear behavior of aluminum-alloy/glass MMCs”, Composites Part A: Applied Science and Manufacturing, vol.38(5), (2007),pp. 1395-1402

6. Basavarajappa S., Chandramohan G., Paulo Davim J., "Application of Taguchi techniques to study dry sliding wear behaviour of metal matrix composites", Materials and Design, Vol. 28, 2007, pp. 1393 – 1398

7. A.R Kennedy, S.M Wyatt, “The effect of processing on the mechanical properties and interfacial strength of aluminium / TiC MMCs”, Composites Science and Technology, vol 60(2), (2000), pp.307-314

8. Taguchi G., Konishi S., "Taguchi methods, orthogonal arrays and linear graphs, tools for quality engineering", Dearborn, MI: American Supplier Institute, 1987, pp. 35 – 38.

9. S.C.Sharma, M.Krishna, P.S.Vizhian and A.Shashishankar: “Thermal effects on mild wear transition in dry sliding of aluminium 7075-short glass fibre composites” IMECHE J. Auto Eng. Part D: 2002, 216, 975.

10. S.Basavarajappa and G.Chandramohan “Wear Studies on Metal Matrix Composites: a Taguchi Approach”, J. Mater. Sci. Technol., Vol.21 No.6, 2005

11. A.P. Sannino, H.J. Rack, Review article, “Dry sliding wear of discontinuously reinforced aluminum composites: review and discussion”, Wear, Volume 189,
Issues 1–2, October 1995, Pages 1–19.






Maneela Tuteja, Gaurav Dubey

Paper Title:

A Research Study on importance of Testing and Quality Assurance in Software Development Life Cycle (SDLC) Models

Abstract: In recent years, software testing is becoming more popular and important in the software development industry. Indeed, software testing is a broad term encircling a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of most relevant challenges is proposed here. In it, the starting point is constituted by some important past achievements, while the destination consists of two major identified goals to which research ultimately leads, but which remains as reachable as goals. The routes from the achievements to the goals are paved by outstanding research challenges, which are discussed in the paper along with the ongoing work. Software testing is as old as the hills in the history of digital computers. The testing of software is an important means of assessing the software to determine its quality. Since testing typically consumes 40~50% of development efforts, and consumes more effort for systems that require higher levels of reliability, it is a significant part of the software engineering. Software testing is a very broad area, which involves many other technical and non-technical areas, such as specification, design and implementation, maintenance, process and management issues in software engineering. Our study focuses on the state of the art in testing techniques, as well as the latest techniques which representing the future direction of this area. Today, testing is the most challenging and dominating activity used by industry, therefore, improvement in its effectiveness, both with respect to the time and resources, is taken as a major factor by many researchers. The purpose of testing can be quality assurance, verification, and validation or reliability estimation. It is a tradeoff between budget, time and quality. Software Quality is the central concern of software engineering. Testing is the single most widely used approach to ensuring software quality.

SDLC, Software quality, Testing techniq Technique.


1. Accessibility Summit. (2006). Public Sector NeedsBetter Guidance On Web Accessibility, E-GovernmentBulletin (Issue 226, 13 November 2006)http://www.ukoln.ac.uk/webfocus/events/meetings/accessibility-summit-2006-11/egovernment-2006-11-13.php (Accessed August 30th2007)
2. Alexander, Dey. (2003). How accessible areAustralianusWeb03.http://ausweb.scu.edu.au/aw03/papers/alexander3/(Accessed August 30th 2007)BSI. (2005). PAS 78: Guide to good practice incommissioning accessible websites. British Standards Institute. http://www.bsi- global.com/en/Standards-and-Publications/Industry-Sectors/ICT/PAS-78/ (Accessed August 30th 2007) Carey, Kevin. (2005). Accessibility: The Current Situation and New Directions. Ariadne 44, June 2005. http://www.ariadne.ac.uk/issue44/carey/ (Accessed August 30th 2007)Chisholm, Wendy and Henry, Shawn. (2005). Interdependent components of Web accessibility.Proceedings of W4A at WWW2005: InternationalCross- Disciplinary Workshop on Web Accessibility.New York:ACM.Press .http://doi.acm.org/10.1145/1061811.1061818(Accessed Aug 30, 07)

3. Clark, Joe.(2006).To Hell with WCAG 2. A List Apart No. 217. http://alistapart.com/articles/tohellwithwcag2(Accessed August 30th 2007)Cooper, Martyn. 2006. Making online learning accessible to disabled students: an institutional case study. ALT-J-Research in Learning Technology, Vol. 14, No. 1, pp 103-115.DDA (2005) Disability Discrimination Act 2005.

4. Web Accessibility 3.0: Learning From The Past,Planning For The Future, Nevile, L. and Kelly, B.ADDW08. University of York, 22-24 September 2008. Retrieved February 4th 2009:http://www.ukoln.ac.uk/web-focus/papers/addw08/paper-2/

5. Contextual Web Accessibility - Maximizing the Benefit of Accessibility Guidelines, Sloan, D, Kelly, B., Heath, A., Petrie, H., Hamilton, F and Phipps, L.WWW 2006 Edinburgh, Scotland 22-26 May 2006.Conference Proceedings, Special Interest Workshops (CD ROM). RetrievedFebruary 4th 2009: http://www.ukoln.ac.uk/webfocus/papers/w4a-2006/

6. IMS. (2004). IMS Guidelines for Developing Accessible Learning Applications. Version 1.0 White Paper. IMS Global Learning Consortium. http://www.imsglobal.org/accessibility/#accguide (Accessed August 30th 2007) IWMW 20072007). Contextual Accessibility in Institutional Web Accessibility Policies. http://www.ukoln.ac.uk/webfocus/events/workshops/webmaster-2007/sessions/sloan/(Accessed August 30th 2007) Kelly, Brian, Phipps, Lawrie and Swift, Elaine. (2004).Developing A Holistic Approach for E-Learning Accessibility. Canadian Journal of Learning and Technology, 2004, Vol. 30, Issue 3.http://www.ukoln.ac.uk/web-focus/papers/cjtl-2004/(Accessed August 30th 2007)

7. Kelly, Brian, Sloan David, Phipps Lawrie, Petrie Helen and Hamilton, Fraser. (2005). Forcing standardization or accommodating diversity? A framework for applying the WCAG in the real world. Proceedings of the 2005 International Cross-Disciplinary Workshop on Web Accessibility (W4A) (Chiba, Japan, 10 May 2005). New York:ACM Press, 46-54.





Tina. S, R. Medona Selin, K. John Peter

Paper Title:

Personal Authentication Using Multimodel Fusion

Abstract: It is a Biometric used to authenticate a person, Fingerprint and Palmprint which is unique and permanent throughout a person’s life. A minutia matching is mostly used for fingerprint recognition, and can be classified as ridge ending and ridge bifurcation. Palmprint matching is a challenging problem because latent prints large number of minutiae in full prints, and the presence of many creases in latents and full prints. A match score estimate using the local ridge direction and frequency in palmprints developed a highly robust algorithm. This facilitates the extraction of ridge and minutiae features even in poor quality palmprints. . In this paper Fingerprint Recognition using Minutia Score Matching method. Distinctive information around each minutia is captured using a fixed-length minutia descriptor, Minutia Code andalignment-based minutiae matching algorithm is used to match two palmprints. In this paper person Verification based on fusion of Minutia Score Matching method for fingerprint and alignment-based minutiae matching algorithm for palmprints is presented.

Fingerprint Recognition, Binarization, Matching score and Minutia.


1. Ravi. J, K. B. Raja, Venugopal. K. R, “Fingerprint Recognition using Minutia Score matching”, International Journal of Engineering Science and Technology Vol.1 (2), 2009, 35-42.
2. Anil. K. Jain, “Latent Palmprint Matching”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 31, NO. X, XX 2009.

3. Arun Ross and RohinGovindarajan, “Feature Level Fusion in Biometric Systems”, IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1952 – 1964, (2006).

4. A. Rattani, D. R. Kisku, M. Bicego, “Feature Level Fusion of Face and Fingerprint Biometrics”, Academic Open Internet Journal, vol. 23, pp. 1-7, (2008).

5. Karthik Nandakumar and Anil K. Jain,” Local Correlation based Fingerprint Matching”, in Proc. of ICVGIP, Kolkata, ICVGIP'2004. pp. 503-508, December 2004.

6. S.K. Dewan, “Elementary, Watson: Scan a Palm, Find a Clue,” The New York Times, http://www.nytimes.com/, Nov. 2003,

7. The FBI’s Next Generation Identification (NGI), http://fingerprint.nist.gov/standard/presentations/archives/NGI_ Overview_Feb_2005.pdf, June 2008.

8. D. Zhang, W.K. Kong, J. You, and M. Wong, “Online Palmprint Identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sept. 2003.

9. Z. Sun, T. Tan, Y. Wang, and S.Z. Li, “Ordinal Palmprint Represention for Personal Identification,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. I: 279-284, 2005.

10. R.K. Rowe, U. Uludag, M. Demirkus, S. Parthasaradhi, and A.K. Jain, “A Multispectral Whole-Hand Biometric Authentication System,” Proc. Biometric Symp. (BSYM), Biometric Consortium Conf., pp. 1-6, Sept. 2007.

11. D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. Springer-Verlag, 2003.

12. Neurotechnology Inc., VeriFinger, http://www.neurotechnology. com, 2009.

13. FVC2006: the Fourth International Fingerprint Verification Competition, http://bias.csr.unibo.it/fvc2006/, 2009.

14. C. Wilson et al., “Fingerprint Vendor Technology Evaluation 2003: Summary of Results and Analysis Report,” NISTIR 7123,
http://fpvte.nist.gov/report/ir_7123_analysis.pdf, June 2004.

15. Ching-Tang Hsieh and Chia-Shing –u, “Humanoid Fingerprint Recognition Based on Fuzzy Neural Network”, International Conference on Circuit, Systems,
Signal and Telecommunications, pp. 85-90,(2007).

16. Liu Wei, “Fingerprint Classification using Singularities Detection”, International Journal of Mathematics and Computers in Simulation, issue 2, vol. 2, pp. 158-162, (2008).

17. Hartwing Fronthaler, Klaus kollreider, and Josef Bigun, “Local Features for Enhancement and Minutiae Extraction in Fingerprints”, IEEE Transactions on Image Processing, vol. 17, no, 3, pp. 354- 363, (2008).

18. Mana Tarjoman, and Shaghayegh Zarei, “Automatic Fingerprint Classification using Graph Theory”, Proceedings of World Academy of Science, Engineering and
Technology, vol. 30, pp. 831-835, (2008).

19. Bhupesh Gour, T. K. Bandopadhyaya and Sudhir Sharma, “Fingerprint Feature Extraction using Midpoint Ridge Contour Method and Neural Network”, International Journal of Computer Science and Network Security, vol. 8, no, 7, pp. 99-109, (2008).

20. Unsang Parh, Sharath Pankanti,and A. K. Jain, “Fingerprint Verification using SIFT Features”, SPIE Defense and Security Symposium, (2008).

21. Manvjeet Kaur, Mukhwinder Singh, Akshay Girdhar, and Parvinder S. Sandhu, “Fingerprint Verification System using Minutiae Extraction Technique”, Proceedings of World Academy of Science, Engineering and Technology vol. 36, pp. 497-502, (2008).

22. Haiping Lu, Xudong Jiang and Wei-Yun Yau, “Effective and Efficient Fingerprint Image Post processing”, International Conference on Control, Automation, Robotics and Vision, vol. 2, pp. 985- 989, (2002)

23. Prabhakar, S, Jain, A.K, Jianguo Wang, Pankanti S, Bolle, “Minutia Verification and Classification for Fingerprint Matching”, International Conference on Pattern Recognition vol. 1, pp. 25-29, (2002).

24. Ballan.M, “Directional Fingerprint Processing”, International Conference on Signal Processing, vol.2, pp. 1064-1067, (1998).ISSN: 0975-






R.HariKumar, N.S.Vasanthi, M.Balasubramani

Paper Title:

Performance Analysis of Artificial Neural Networks and Statistical Methods in Classification of Oral and Breast Cancer Stages

Abstract: Cancer staging can be divided into clinical and pathologic stage. In TNM (Tumor, Node, Metasis), prognostic tool have been identified and new methods for prognostic factors have been developed. This paper compares the classification accuracy of the TNM staging system along with that of Chi-Square Test, and Neural Networks. In this investigation, one hundred patients with breast cancer and One hundred twenty five oral cancer patients were studied. The data set using TNM variables (tumor size, number of positive regional nodes, distance metastasis, history of breast feeding, menstrual cycle, hereditary etc) of patients were used as input variables for both the classifications. When TNM classification and Chi-Square methods were compared, it was observed that Chi-Square classification closely followed that of clinical investigation. Artificial neural networks (MLP and RBF) are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to ANN to increase prognostic accuracy further.

Oral Cancer stages, Breast Cancer, TNM stages, Chi-square test, neural networks.


1. Perkin DM, Lara E “Estimates of the World wide frequency of sixteen major cancers” vol 41, pp 184-197, 1980.
2. American Cancer Society, Cancer Facts and figures, Atlanta (GA), the society, 1996.

3. Hermanek P, Sobin “International union Against Cancer TNM classification of malignant tumors, 4th Ed, 2nd revision” LH Editors, Berlin, Springer- Verlag; 1992.

4. Cancer Research Capign, Oral cancer, Fact sheet vol 14, no 1, 1990.

5. Hwang et al., “Recognition of Unconstrained Handwritten Numerals by A Radial Basis Function Network Classifier,” Pattern Recognition Letters, vol 18, pp-657-664, 1997.

6. Drazen.S.etal.,“Estimation of difficult –to- Measure process variables using neural networks, “ Proceedings of IEEE MELECON 2004,pp-387-390,May 12-15, Dubrovnik, Croatia .

7. Moreno.L. etal, “Brain maturation estimation using neural classifier” IEEE Transaction of Bio Medical Engineering, vol 42, no 2, pp-428-432, April 1995.

8. Tarassenko.L, Y.U.Khan, M.R.G.Holt, “Identification of inter-ictal spikes in the EEG using neural network analysis,” IEE Proceedings –Science Measurement Technology, vol 145, no 6, pp-270-278, November 1998.

9. H.Demuth and M.Beale, “Neural network tool box: User’s guide, Version 3.0,” Natick, MA, 1998.

10. G.Fung etal, “Fault Detection In Inkjet Printers Using Neural Networks,” Proceedings of IEEE SMC, vol 7, pp 22-26, Ottawa Canada, 6-9 0ctober 2002.

11. C B Gupta and Vijay Gupta, “An Introduction to Statistical Methods”, 22nd Ed., Vikas Publishing House Lt., 2001.

12. Guoqiang Peter Zhang , “ Neural Networks for Classification: A Survey ,”IEEE Transactions on Systems Man Cybernetics- Part C: Applications and Reviews, 30(4), pp 451-462, November 2000.

13. Meng Joo Er,Shiqian Wu,Juwei Lu, “ Face Recognition with Radial Basis Function (RBF) Neural Networks ,” IEEE Transactions on Neural Networks, 13 (3), pp 697-710, May 2002.

14. Jonathan lee etal., “ A Neural Network Approach to Cloud Classification ,” IEEE Transactions on Geosciences and Remote Sensing, 28 (5), pp 846-855, September 1990.

15. K .Srirama murty and B.Yegnannarayana, “Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition,” IEEE Signal Processing Letters, , 13 (1),pp 52-55, January 2006.

16. S. Haykin, “Neural networks a Comprehensive Foundation”, Prentice- Hall Inc. 2nd Ed. 1999.

17. www.breastcancer.org; Devilee, 2003.






Samiran Chatterjee, Partha Pratim Sarkar, Debasree Chanda (Sarkar), Santosh Kumar Chowdhury

Paper Title:

Compact Microstrip Antenna for WLAN and H-LAN Communication

Abstract: A single layer, single feed compact rectangular antenna is proposed. Resonant frequency has been reduced drastically by cutting unequal rectangular slots at the edge of the patch & a circle in the middle. The width of the rectangular slots is different to improve the gain bandwidth performance of the antenna. The antenna size has been reduced by 41.8% when compared to a conventional rectangular microstrip patch antenna with a maximum of 24.9 MHz bandwidth at –16.3 dB return loss at 3.41 GHz. The characteristics of the designed structure are investigated by using MoM based electromagnetic solver, IE3D. There is reasonable agreement these simulated data and measured value. An extensive analysis of the return loss, radiation pattern, gain of the proposed antenna is shown in this paper. The simple configuration and low profile nature of the proposed antenna leads to easy fabrication and make it suitable for the applications in Wireless communication system.

Compact, Frequency ratio, Patch, Resonant frequency, Slot.


1. I. Sarkar, P. P. Sarkar, S. K. Chowdhury A new compact printed antenna for mobile communication. 2009 Loughborough Antennas & Propagation Conference 16-17 Nov. 2009, Loughborough, UK.
2. J.-W. Wu, H.-M. Hsiao, J.-H. Lu and S.-H. Chang, “Dual broadband design of rectangular slot antenna for 2.4 and 5 GHz wireless communication”, IEE Electron. Lett. Vol. 40 No. 23,11th November 2004.

3. Samiran Chatterjee, Joydeep Paul, Kalyanbrata Ghosh, P. P. Sarkar, D.Chanda (Sarkar) and S. K. Chowdhury “A Compact Microstrip Antenna for WLAN
Communication”, National Conference of Electronics, Communication and Signal Processing, 2011, Paper ID: 116

4. Rohit K. Raj, Monoj Joseph, C.K. Anandan, K. Vasudevan, P. Mohanan, “ A New Compact Microstrip-Fed Dual-Band Coplaner Antenna for WLAN Applications”, IEEE Trans. Antennas Propag., Vol. 54, No. 12, December 2006, pp 3755-3762.

5. U. Chakraborty, S. Chatterjee, S. K. Chowdhury, and P. P. Sarkar, “Triangular Slot Microstrip Patch Antenna for Mobile Communication”, India Conference (INDICON), 2010 Annual IEEE, pp 4-7, Paper ID: 511

6. J. -Y. Jan and L. -C. Tseng, “ Small planar monopole Antenna with a shorted parasitic inverted-L wire for Wireless communications in the 2.4, 5.2 and 5.8 GHz bands” , IEEE Trans. Antennas and Propag., VOL. 52, NO. 7, July 2004, pp -1903-1905.

7. S. Chatterjee, U. Chakraborty, I.Sarkar, S. K. Chowdhury, and P. P. Sarkar, “A Compact Microstrip Antenna for Mobile Communication”, India Conference (INDICON), 2010 Annual IEEE, pp 1-3, Paper ID: 510

8. Danideh, A., R. S. Fakhr, and H. R. Hassani, “Wideband coplanar microstrip patch antenna,” Progress In Electromagnetics Research Letters, PIER 4, 81–89,

9. Samiran Chatterjee, Joydeep Paul, Kalyanbrata Ghosh, P. P. Sarkar and S. K. Chowdhury “A Printed Patch Antenna for Mobile Communication”, Convergence of Optics and Electronics conference, 2011, Paper ID: 15, pp 102-107

10. J. Bahl and P. Bhartia, “ Microstrip Antennas”, Artech House, Dedham, MA, 1980.

11. U. Chakraborty, S. Chatterjee, S. K. Chowdhury, and P. P. Sarkar, "A compact microstrip patch antenna for wireless communication," Progress In Electromagnetics Research C, Vol. 18, 211-220, 2011

12. R.Fallahi, A.-A.Kalteh, M. Golparvar Roozbahani, "A novel UWB elliptical slot antenna with band-notched characteristics," Progress In Electromagnetics Research C, Vol. 18, 211-220, 2011

13. E. O. Hammerstad, “ Equations for Microstrip Circuit Design”, Proc. Fifth European Microwave Conf. Pp 268-272, September 1975.

14. C. A. Balanis, “Advanced Engineering Electromagnetic”, John Wiley & Sons., New York, 1989.

15. Zeland Software Inc. IE3D: MOM-Based EM Simulator. Web: http://www.zeland.com






Indu Bala Pauria, Sachin Kumar, Sandhya Sharma

Paper Title:

Design and Simulation of E-Shape Microstrip Patch Antenna for Wideband Applications

Abstract: This paper presents the design and simulation of E-shape microstrip patch antenna with wideband operating frequency for wireless application. The shape will provide the broad bandwidth which is required in various application like remote sensing, biomedical application, mobile radio, satellite communication etc. The antenna design is an improvement from. Previous research and it is simulated using HFSS (High Frequency Structure Simulator) version 11 software. Coaxial feed or probe feed technique is used in the experiment. Parametric study was included to determine affect of design towards the antenna performance. The performance of the designed antenna was analyzed in term of bandwidth, gain, return loss, VSWR, and radiation pattern. The design was optimized to meet the best possible result. Substrate used was air which has a dielectric constant of 1.0006. The results show the wideband antenna is able to operate from 8.80 GHz to 13.49 GHz frequency band with optimum frequency at 8.73 GHz.

E-shape microstrip patch antenna, HFSS (High Frequency Structure Simulator) version 11 software, wideband.


1. Ge, Y.; Esselle, K.P.; Bird, T.S.; , "E-shaped patch antennas for highspeed wireless networks," Antennas and Propagation, IEEE Transactions on , vol.52, no.12, pp. 3213- 3219, Dec. 2004
2. B.-K. Ang and B.-K. Chung, "A wideband e-shaped microstrip patch antenna for 5 - 6 GHz wireless communications," Progress In Electromagnetics Research, Vol. 75, 397-407, 2007.

3. Yang, F.; Xue-Xia Zhang; Xiaoning Ye; Rahmat-Samii, Y.; "Wide-band E-shaped patch antennas for wireless communications," Antennas and Propagation, IEEE Transactions on , vol.49, no.7, pp.1094-1100, Jul 2001

4. Hadian, A.M.; Hassani, H.R.; , "Wideband Rectangular Microstrip Patch Antenna with U-Slot," Antennas and Propagation, 2007. EuCAP 2007. The Second European Conference on , vol., no., pp.1-5, 11-16 Nov. 2007

5. Vedaprabhu, B.; Vinoy, K.J.; , "A double U-slot patch antenna with dual Wideband characteristics," Communications (NCC), 2010 National Conference on , vol., no., pp.1-4, 29-31 Jan. 2010

6. Weigand, S.; Huff, G.H.; Pan, K.H.; Bernhard, J.T.; , "Analysis and design of broad-band single-layer rectangular U-slot microstrip patch antennas," Antennas and Propagation, IEEE Transactions on , vol.51, no.3, pp. 457- 468, March 2003

7. Verma, M.K.; Verma, S.; Dhubkarya, D.C.; , "Analysis and designing of E-shape microstrip patch antenna for the wireless communication systems," Emerging
Trends in Electronic and Photonic Devices & Systems, 2009. ELECTRO '09. International Conference on , vol., no., pp.324-327, 22-24 Dec. 2009

8. Wang, B.-Z.; Xiao, S.; Wang, J.; , "Reconfigurable patch-antenna design for wideband wireless communication systems," Microwaves, Antennas & Propagation, IET , vol.1, no.2, pp.414-419, April 2007

9. Cuming Microwave, "Flexible, Low Loss Foam," C-Foam PF-2 and PF-4 datasheet, 2011.

10. Kumar, G., and K. P. Ray. Broadband Microstrip Antennas. Boston: Artech House, 2003.

11. Micro Lambda, "E+ SMA connectors & Hermetic Seals," SMA connectors datasheet, 2011.






Vivek Kumar, Vrinda Gupta, Rohit Maurya

Paper Title:

A Study and Analysis of High Speed Adders in Power-Constrained Environment

Abstract: An overview of the performance of 1-bit full adder in different CMOS logic styles and in depth examination of the advantages and limitations of each of them with respect of speed and power dissipation are presented. Ten 1-bit full adder circuit based on these logic styles are chosen for the extensive evaluation. These circuits were redesigned at the transistor-level in tsmc 0.18 µm technology and comparison reported here uses Mentor Graphics ELDO simulations to assess their performance. The hybrid full adder exhibits not only the full swing logic and balanced outputs but also strong output drivability. The work presented in this paper gives a quantitative comparison of the adder cell performance.

Full Adder, logic devices, High-speed, Very large-scale integrated (VLSI) circuit.


1. Shams, T. Darwish, and M. Bayoumi, “Performance analysis of low power 1-bit CMOS full adder cells,” IEEE Trans. Very Large Scale Integr.(VLSI) Syst., vol. 10, no. 1, pp. 20–29, Feb. 2005.
2. Shams and M. Bayoumi, “Performance evaluation of 1-bit CMOS adder cells,” in Proc. IEEE Int. Symp. Circuit and Systems, Jul. 1999, pp. 27– 30.

3. F. Frutaci, M. Lanuzza, P. Zicari S. Perri, P. Corsonello “ Low Power Split Path Data Driven Dynamic Logic” published in Iet Circuit Devices & Systems 20th April 2009.

4. Rahul J. Gera and David H. K. Hoe “An Evaluation of CMOS Adders in Deep Submicron Processes” IEEE Symposium on System Theory, March 11-13, 2012.

5. J.-M. Wang, S.-C.Fang, and W.-S. Feng, “New efficient designs for XOR and XNOR functions on the transistor level,” IEEE J. Solid-State Circuits, vol. 29, pp. 780–786, July 1994.

6. R. Zimmermann and W. Fichtner, “Low-power logic styles: CMOS versus pass- transistor logic,” IEEE J. Solid-State Circuits, vol. 32, pp.1079–1090, July 1997.

7. N.Weste and K. Eshraghian, Principles of CMOSVLSI Design, A System Perspective. Reading, MA: Addison-Wesley, 2011.

8. Abdoul M. Rjoub, Al-Mamoon Al-Othman “The influence of the Nanometer Technology on performance of CPL Adders” Journal of computers, Vol. 5, NO. 3, March 2010

9. M. Alioto and G. Palumbo, “Analysis and comparison on full adder block in submicron technology,” IEEE Trans. Very Large Scale (VLSI)Syst., vol. 10, no. 6, pp. 806–823, Dec. 2002.

10. H.T. Bui, Y.Wang, Y. Jiang, “Design and analysis of low-power 10-transistor full adders using novel XOR-XNOR gates”, IEEE Trans. Circuits Syst. II. Analog Digit. Signal Process. 49 (1) (January 2002) 25–30.

11. Jin-Fa Lin, Yin-Tsung Hwang, Ming-HwaSheu, “A noval high- speed and energy efficient 10-transistor full adder design” IEEE Transactions on circuits and systems Vol. 54 No. 5, May 2007

12. Amir Ali Khatibzadeh, KaamranRaahemifar, “A study and comparison of full adder cells based on the standard static CMOS logic” Proceedings of the International Symposium on Low Power Design, 2004.

13. R. Rafati, S. M. fakhraie, K. C. Smith, “Low-power data-driven dynamic logic” IEEE International Symposium on circuits and systems, May 28-31, 2000.

14. S. Goel, S. Gollamudi, A. Kumar, M. Bayoumi, “On the design of low energy hybrid CMOS 1-bit full adder cells,” in Proc. of the 2004 47thMidwest Symposium on Circuits and Systems, vol.2, pp.209 - 212, July 2004.

15. SohanPurohit, Martin Margala, Marco Lanuzza, ’’New Performance/Power/Area Efficient, Reliable Full Adder Design”, Vol 53, Issue 17,18 May 2009.

16. F. Frutaci, M. Lanuzza, P. Zicari S. Perri, P. Corsonello “ Low Power Split Path Data Driven Dynamic Logic”published in Iet Circuit Devices & Systems 20th April 2009.

17. Chiou-Kou Tung, Yu-Cherng Hung, Shao-HuiShieh, and Guo-Shing Huang “A Low-Power High-Speed Hybrid CMOS Full Adder for Embedded System” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2007.






B. Sasikumar, R. Deepa

Paper Title:

Discovering Application Level Semantics for Data Compression using Hybrid Compression Technique

Abstract: Based on Natural phenomena many creatures form large social groups and move in regular patterns. Traditional works focus on finding the movement patterns of each single object or all objects. This paper propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in Wireless Sensor Networks (WSN). The algorithm consists of a local mining phase and a cluster ensembling phase. The local mining phase adopts the Variable Length Markov (VMM) model together with Probabilistic Suffix Tree (PST) to find the moving patterns, as well as Highly Connected Component (HCC) to partition the moving objects. The cluster ensembling phase utilizes Jaccard Similarity Coefficient and Normalized Mutual Information to combine and improve the local grouping results. The distributed mining algorithm achieves good grouping quality and robustness. Moreover this paper extends a technique called Hybrid Compression Technique (HCT) based on the location information of nodes in the WSN. HCT is formulated to reduce the amount of energy consumption and increases the lifetime of the WSN. The experimental result shows that the technique have good ability of approximation to manage the WSN, and have high data compression efficiency and leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

clustering, compression, hybrid, patterns, semantics, wireless sensor networks.


1. S. S. Pradhan, J. Kusuma, and K. Ramchandran, “Distributed Compression in a Dense Micro sensor Network,” IEEE Signal Processing Magazine, Vol. 19, No. 2, pp. 51-60, Mar. 2002.
2. A. Scaglione and S. D. Servetto, “On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks,” Proceedings of 8th Annual International Conference on Mobile Computing and Networking, pp. 140-147, 2002.

3. N. Meratnia and R. A. De By, “A New Perspective on Trajectory Compression Techniques,” Proceedings of ISPRS Commission II and IV, WG II/5, II/6, IV/1 and IV/2 Joint Workshop Spatial, Temporal and Multi- Dimensional Data Modelling and Analysis, Oct. 2003.

4. S. Baek, G. De Veciana, and X. Su, “Minimizing Energy Consumption in Large-Scale Sensor Networks through Distributed Data Compression and Hierarchical Aggregation,” IEEE Journal on Selected Areas in Communications, Vol. 22, No. 6, pp. 1130-1140, Aug. 2004.

5. C. M. Sadler and M. Martonosi, “Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks,” Proceedings of ACM Conference on Embedded Networked Sensor Systems, Nov. 2006.

6. I. F. Akyldiz, W. Su, Y. Sankarasubermanian, and E. Cayirici, “A Survey on Sensor Networks,” IEEE Communications Magazine, Vol. 40, No. 8, pp. 102–114, August 2002.

7. A. J. Goldsmith and S. B Wicker, “Design Challenges for Energy Constrained Ad hoc Wireless Networks,” IEEE Wireless Communications, Vol. 9, No. 4, 2002.

8. S. S. Pradhan, J. Kusuma, and K. Ramachandran, “Distributed Compression in a Dense Mircosensor Network,” IEEE Signal Processing Magazine, pp. 51–60, March 2002.

9. A. Scaglione and S. D. Servetto, “On the Interdependence of Routing and Data Compression in Multi-hop Sensor Networks,” Proceedings of ACM Mobicom, 2002.

10. J. Chou, D. Petrovic, and K. Ramchandran, “A Distributed and Adaptive Signal Processing Approach to Reducing Energy Consumption in Sensor Networks,”
Proceedings of IEEE Infocom, 2003.

11. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy Efficient Communication Protocol for Wireless Micro Sensor Networks,” in Hawaii International Conference on System Sciences, 2000.

12. S. S. Pradhan and K. Ramchandran, “Distributed Source Coding: Symmetric Rates and Applications to Sensor Networks,” Proceedings of IEEE Data Compression Conference, Snowbird, UT, pp. 363-372, Mar. 2000.

13. T. M. Cover and J. Thomas, Elements of Information Theory,. John Wiley and Sons, Inc., 1991.

14. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, 1992.

15. Q. Zhao and M. Effros, Optimal Code Design for Lossless and Near Lossless Source Coding in Multiple Access Networks, “Proceedings of Data Compression Conference, Snowbird, UT, 2001.






Tajinder Kaur, A. K. Verma

Paper Title:

Simulation and Analysis of AODV routing protocol in VANETs

Abstract: Vehicular Ad Hoc Network (VANET) is a form of Mobile Ad Hoc Networks (MANET). The field of VANETs started gaining attention in 1980s and has now been an active field of research and development. VANETs provide us with the infrastructure for developing new systems to enhance drivers’ and passengers’ safety and comfort. There are many routing protocols that have been proposed and assessed to improve the efficiency of VANET. Simulator tool has been preferred over outdoor experiment because it is simple, easy and cheap. In this paper, simulation of one of the routing protocols i.e. AODV is done on simulators which allow users to generate real world mobility models for VANET simulations. The tools used for this purpose are SUMO, MOVE and NS2. MOVE tool is built on top of SUMO which is an open source micro-traffic simulator. Output of MOVE is a real world mobility model and can be used by network simulator NS-2. Then graphs were plotted using Tracegraph for evaluation. Based on the simulation results obtained, the performance of AODV is analyzed and compared in three different node density i.e. 4, 10 and 25 nodes with respect to various parameters like Throughput, Packet size, Packet drops, End to End delay etc.



1. Vanet Simulator, Report for the Computer Security exam at the Politecnico di Torino Walter Dal Mut, Armand Sofack.
2. Kevin C. Lee, Uichin Lee, Mario Gerla,“Survey of Routing Protocols in Vehicular Ad Hoc Networks”, RoutingBookChapterKLULMario.pdf.

3. C. Perkins, E. Royer, and S. Das,”Ad hoc On-Demand Distance Vector (AODV) Routing," IETF, RFC 3561, 2003

4. Lee, K. C., Lee, U., & Gerla, M. (2010), “Survey of Routing Protocols in Vehicular Ad Hoc Networks”, Advances in Vehicular Ad-Hoc Networks: Developments and Challenge, Watfa, M. (Ed.), (pp. 149-170), 2010.

5. MOVE http://www.cs.unsw.edu.au/klan/move/.

6. Rapid Generation of Realistic Simulation for VANET Manual Updated 11 November 2009. Available at: http://www.lens1.csie.ncku.edu.tw/MOVE/Example%20for% 20step-by-step.pdf.

7. The Network Simulator ns-2. http://www.isi.edu/nsnam/ns/index.html.

8. NAM Network Animator. http://www.isi.edu/nsnam/nam/

9. SUMO http://sumo.sourceforge.net/

10. U.S. Census Bureau - Topologically Integrated Geographic Encoding and Referencing (TIGER) system, http://www.census.gov/geo/www/tiger.






P. Nishmi Irin, K. John Peter, I. Nancy Jeba Jingle

Paper Title:

Decentralized Data Offloading in High Performance Computing Centers Using Scratch Space

Abstract: This project addresses the issues associated with providing Decentralized Data Offloading service to HPC Centers. HPC centers are High Performance Computing centers that use Parallel Processing for running advanced applications more reliably and efficiently. The main concept of this project is the offloading of data from a HPC Center to the destination site in decentralized mode. This project uses the decentralization concept where it is possible for the end user to retrieve the data even when the center logs out. This is possible by moving the data from the center to the Scratch Space. From Scratch space the data is moved to the intermediate storage nodes 1..n and from the nth node the data is transferred to the destination site within a deadline. These techniques are implemented within a Production Job Scheduler which schedules the jobs and Bit Torrent tool is used for data transfer in a decentralized environment. Thus the total offloading times are minimized; data loss and offload delays are also prevented.

High Performance Data Management, HPC Center Serviceability.


1. F. Schmuck and R. Haskin, “GPFS: A Shared-Disk File System for Large Computing Clusters,” in Proc., 2002.
2. J. Bester, I. Foster, C. Kesselman, J. Tedesco, and S. Tuecke, “GASS: A Data Movement and Access Service for Wide Area Computing Systems,” in Proc., 1999.

3. M. Gleicher, “HSI: Hierarchical Storage Interface for HPSS,” in Proc., 2010.

4. J.W. Cobb, A. Geist, J.A. Kohl, S.D. Miller, P.F. Peterson, G.G. Pike, M.A. Reuter, T. Swain, S.S. Vazhkudai, and N.N. Vijayakumar, “The Neutron Science Teragrid Gateway: A Teragrid Science Gateway to Support the Spallation Neutron Source: Research Articles,” in Proc., 2007.

5. M. Christie and S. Marru, “The Lead Portal: A Teragrid Gateway and Application Service Architecture: Research Articles,” in Proc., 2007.

6. R. Wolski, N. Spring, and J. Hayes, “The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing,” in Proc, 1999.

7. H. Monti, A.R. Butt, and S.S. Vazhkudai, “Timely Offloading of Result-Data in Hpc Centers,” in Proc., 2008.

8. T. Kosar and M. Livny, “Stork: Making Data Placement a First Class Citizen in the Grid,” in Proc., 2008.

9. DMOVER: Scheduled Data Transfer for Distributed Computational Workflows, 2008.

10. Sherwood, R. Braud, and B. Bhattacharjee et al, “Slurpie: A Cooperative Bulk Data Transfer Protocol,” in Proc.,2004.





Vipin Kumar Yadav, Arun Kumar, Abhilasha Singh

Paper Title:

Design of IEEE1588 Based Precision Time Protocol Using PSOS

Abstract: The widespread clock synchronization standard, IEEE 1588, is purely master-slave based. The inherent disadvantage is that a failure of the master requires the election of a new one, during which the network nodes cannot be synchronized. The present paper proposes a compatible extension to the standard introducing architecture for fault-tolerant and seamless distribution of time information. In this paper the protocol is implemented using RTOS in a LAN with processor as one node. The processor having real time clock and is programmed for the synchronization between systems. The processor is the grand master clock which is going to generate clock pulses for all other nodes in the LAN. All the other systems in LAN will have a software stack that incorporates the full specification and functionality of the Precision Clock Synchronization Protocol along with a Timing Table and File Table. After the introduction of the architecture the common application of Ethernet is treated to outline the problems arising from this network type. The proposed solution significantly increases the accuracy of the synchronized network in case of failures and additionally provides backup strategies in case of network problems. Network Area Storage (NAS) is implemented as an application between the systems where communication is carried out using PTP.

Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems (PTP)[1], Proprietary silicon operating system(PSOS), Network Area Storage (NAS)


1. The Case for the Precision Timed (PRET) Machine Stephen A. Edwards_Edward A. Lee November 17, 2006
2. IEEE Std 1588™-2002: “IEEE Standard for a precision Clock Synchronization Protocol for Networked Measurement and Control Systems” http://ieee1588.nist.gov/

3. Systems J. Jasperneite, K. Shehab, and K. Weber. Enhancements to the time synchronization standard ieee-1588 for a system of cascaded bridges. In Proceedings of the 2004 IEEE International Workshop on Factory Communication Systems, pages 239–244

4. An introduction to enterprise data storage, by George Ou|Apr16,2007 http://articles.techrepublic.com.com/5100-10880-6176456.html

5. A Solution for Fault-Tolerant IEEE 1588,Jeff Allan and Dongik Lee, Dependable Real Time Systems Limited The Innovation Centre, 217 Portobello Road, Sheffield, S1 4DP, United Kingdom Phone/Fax: +44-(0)114 223 2301, E-mail: jeff.allan@drts.co.uk

6. IEEE 1588, Standard for a Precision Clock Synchronization Protocol, Prof. Hans Weibel, Zurich University of Applied Sciences, hans.weibel@zhwin.ch, 2005

7. pSOS Product Family, Copyright ã 1997 Integrated Systems, Inc. All rights reserved. Printed in U.S.A.

8. Document Title: pSOSystem System Calls Part Number: 000-5070-004 Revision Date: August 1997

9. IEEE 1588 Real-Time Networks with Hybrid Master Group Enhancements, Georg Gaderer, Institute of Computer Technology Vienna University of Technology, Gusshausstrasse 27-29, A-1040 Vienna, Austria, Georg.Gaderer@TUWien.ac.at, Patrick Loschmidt, Thilo Sauter , Research Unit for Integrated Sensor Systems Austrian Academy of Sciences, Viktor Kaplan Strasse 2 A-2700 Wiener Neustadt, Austria

10. {Patrick.Loschmidt, Thilo.Sauter}@OEAW.ac.at






Vijay Kumar, Geetu

Paper Title:

Channel Estimation on the bases of Multiuser Space time coding

Abstract: In wireless transmission the randomness of the communication channel leads to random fluctuation in the received signal. This fluctuation known as fading. The fading is a fundamental problem in wireless communications because of this fading effect we cannot get exact signal at the receiver side. So to improve the signal performance at the receiver side diversity technique is the best method. Diversity defined as the method of conveying information through multiple independent random fades. Diversity gain results multiple paths between base station and user terminal and coding gain results from how symbols are correlated across transmit antennas. Antenna selection reduces the system cost and complexity by reducing the number of radio frequency (RF) chains while still retaining full diversity. Space time coding arises from a technique known as diversity. This technique uses multiple-input and multiple output (MIMO) concepts to overcome the problem of fading and interference in a wireless channel. Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels that are exploited by using space-time coding. The purpose of paper is to development of fundamental space-time (ST) coding and modulation methods to achieve the gains provided by multiple antennas, in terms of both improved robustness of the link and a higher spectral efficiency. Space time codes (STC) provides the details of the encoding and decoding procedures. The codes are designed on an information theoretic criterion. This paper presents the progress made towards determining the capacity and diversity benefits of multiple antennas under different assumptions about the underlying channel. Simulations suggest that the resulting codes allow for effective high-rate data transmissions in multiple-antenna communication systems. Rayleigh channel model and evaluate its performance in term of BER. Finally, the simulation results will be used to analyze and compare their performance. The research will be conducted in MATLAB environment.

Diversity, space-time code, fading channels, wireless communications.


1. “The application of orthogonal designs to wireless communication,” in Proc. IEEE Information Theory Workshop, Killarney, Ireland, June.
2. M. Jankiraman: “Space-Time Codes and MIMO Systems” Artech House, 2000.

3. Y. Xie and C. N. Georghiades, “An EM-based channel estimation algorithm for OFDM with transmitter diversitv.” in Proc. of Globecom 2001.

4. Ye(Geoffrey) Li, “Simplified channel estimation for OFDM systems with multiple transmit antennas,” IEEE Trans. on Wireless Comm., vol. 1, no. 1 Ian. 2002.

5. J. Winters, J. Salz, and R. D. Gitlin, “The impact of antenna diversity on the capacity of wireless communication systems,” IEEE Trans. Commun., vol. 42. no. 2/3/4, Feb./Mar./Apr. 1994.

6. T.S. Rappaport, Wireless Communications: Principles and Practices, Upper Saddle River, N.J.: Prentice Hall PTR, 1996

7. R. F. Ormondroyd and J. S. Dhanoa, “Comparison of space-time block code and layered space-time MIMO systems for an underwater acoustic channel,” in Proc. of IEEE/MTS Oceans Conf., Europe, June 20-23 2005.

8. E. G. Larsson, G. Ganasan, P. Stoica and W. Wong, “On the performance of orthogonal space-time block coding with quantized feedback,” IEEE Commun. Letters, vol. 6, no. 11, pp. 487-489, Nov. 2002.

9. MATLAB Help Documents, Communications Toolbox. 2010.






B Sri Ramya, Shirin Bhanu Koduri, M.Seetha

Paper Title:

A Stateful Database Synchronization Approach for Mobile Devices

Abstract: This paper proposes a Stateful Synchronization of data between a server-side database and a mobile database. The Stateful Synchronization stage is an amalgamation of the Stateful and Synchronization modes. This means that invalidation reports are sent only to the affected clients (Stateful) and they are sent periodically (Synchronous).The existing Synchronization Algorithms based on comparison of message digest values of the selected rows of both server-side database and mobile database needed for Synchronization. But in the proposed approach the server keeps the information of the present state of data records at the mobile databases. The Server also transmits the invalidation information at periodic intervals to the clients, whenever the data record relevant to the client is invalidated or modified at the server. Here there is no need to calculate the Message Digest values for Synchronization of databases as the server maintains the state of clients.

Mobile Device, Server Side Database, Mobile Database, Stateful Synchronization, Invalidation Report.


1. Mi-Young Choi, Eun-Ae Cho, Dae-Ha Park, Chang-Joo Moon, Doo-Kwon Baik “A Database Synchronization Algorithm for Mobile Device”sIEEE Transactions on Consumer lectronics, Vol. 56, No. 2, May 2010
2. Tomasz Imielinski and B. R. Badrinath, "Mobile wireless computing: challenges in data management", Communications of the ACM, Volume 37 Issue 10, pp. 18-28, 1994.

3. Barbara, D., "Mobile Computing and Databases - A Survey", IEEE Transactions on Knowledge and Data Engineering, Vol. 11 No. 1, pp 108- 117, 1999.

4. EPFL, U. Grenoble, INRIA-Nancy, INT-Evry, U. Montpellier, "Mobile Database: a Selection of Open Issues and Research Direction", SIGMOD Record, Vol.33, No.2, pp.78-83, June, 2004.

5. Joshua Savill, “MobiLink Synchronization Profiles", A Whitepaper from Sybase iAnywhere., October 17th, 2008

6. Thomas Fanghänel, Jonas S Karlsson, Cliff Leung, " DB2 Everyplace Database Release 8.1: Architecture and Key Features", Datenbank- Spektrum, pp. 1~15, 5/2003

7. Gye-Jeong Kim, Seung-Cheon Baek, Hyun-Sook Lee, Han-Deok Lee, Moon Jeung Joe, " LGeDBMS: a small DBMS for embedded system with flash memory", 32nd international conference on very large data bases, pp. 1255~1258, 2006

8. john E. Canavan, "Fundamentals of Network Security", ARTECH HOUSE, INC., 2001, 61~62

9. Jonathan Knudsen, “WIRELESS JAVA : Developing with Java 2, Micro Edition”, A press, 2001, pp. 155.
11”MobiLink Synchronization User’s Guide”, Sybase, Inc., 2004, pp. 37 ~68, pp. 392~402






J.Baskaran, S.Thamizharasan, R.Rajtilak

Paper Title:

GA Based Optimization and Critical Evaluation SHE Methods for Three-level Inverter

Abstract: The Selective Harmonic Elimination Pulse-Width Modulation (SHE-PWM) has been an inclusive research area in the field of Power Converters. This technique offers a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. It involves the solution of non-linear transcendental equation sets representing the relation between the amplitude of the fundamental wave, harmonic components and the switching angles. This paper reports solutions to the Selective Harmonic Elimination (SHE) method based on novel usage of Artificial Intelligence (AI) technique such as Genetic algorithm (GA) for single-phase three-level inverter. This paper uses Matlab GA Toolbox to generate the gate pulse pattern to eliminate the required order of harmonics present in the demanded fundamental output voltage.

SHE-PWM, SPWM, GA Toolbox.


1. N. Mohan, T. M. Undeland, and W. P. Robbins, Power Electronics :Converters, Applications, and Design, 2nd ed. New York: Wiley,1995.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
2. P. N. Enjeti, P. D. Ziogas, and J. F. Lindsay, “Programmed PWM techniques to eliminate harmonics: a critical evaluation,” IEEE Trans. Ind. Applicat., vol. 26, pp.302–316, Mar./Apr. 1990. B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.

3. H. S. Patel and R. G. Hoft, “Generalized harmonic elimination and voltage control in thryristor inverters: part I—harmonic elimination,” IEEE Trans. Ind. Applicat., vol. 9, pp. 310–317, May/June 1973.J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.

4. H. S. Patel and R. G. Hoft, “Generalized harmonic elimination and voltage control in thryristor inverters: part II—voltage control technique,” IEEE Trans Ind Applicat., vol. 10, pp. 666–673, Sept./Oct. 1974.Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),” IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301].

5. J. Sun and I. Grotstollen, “Pulsewidth modulation based on real-time solution of algebraic harmonic elimination equations,” in Proc. 20th Int.Conf. Ind. Electron., Contr. Instrum. IECON, vol. 1, 1994, pp.79–84.

6. T.J. Liang, R.M. O’Connell, and R.G. Hoft, “Inverter harmonic reduction using walsh function harmonic elimination method”, IEEE Transactions on Industrial Electronics Vol. 12, No.6, pp. 971-982, November 1997.

7. V.G. Agelidis, A. Balouktsis and I. Balouktsis, "On applying a minimization technique to the harmonic elimination PWM control: the bipolar waveform", IEEE Power Electronics Letters, Vol. 2, June 2004, pp. 14.

8. V.G. Agelidis, A. Balouktsis, I. Balouktsis, and C. Cossar, "Multiple sets of solutions for harmonic elimination PWM bipolar waveforms analysis and experimental results", to appear in IEEE Transactions on Power Electronics 2005.

9. S. Siriroj, J. S. Lai and T. H. Liu "Optimum harmonic reduction with a wide range of modulation indexes for multilevel inverters", in Conf Rec. IEEE-IAS Annu. Meeting, Rome, Italy, pp. 2094-2099, Oct. 2000.

10. M. J. Schutten, D. A. Torrey, “Genetic Algorithms for control of Power Converters”, Conference Proceedings o IEEE PESC, 1955, pp. 1321-1326.

11. B. Ozpineci, J. O. P. Pinto, L. M. Tolbert, “Pulse-Width Optimization in a pulse density modulated high frequency AC-AC converter using Genetic Algorithms”, Conference Proceedings of IEEE International Conference on Systems, Man. and Cybernetics, 2001, pp. 1924-1929.

12. A. I. Maswood, Shen Wei and M. A. Rahman, “A Flexible Way to Generate PWM-SHE Switching Patterns Using Genetic Algorithm”, Conference Proceedings of IEEE (APEC), 2001, pp. 1130-1134.

13. B. Ozpineci, L. M. Tolbert and J. N. Chiasson, “Harmonic Optimization of Multilevel Converters Using Genetic Algorithm”, 35 Annual IEEE Power Electronics Specialists Conference, Germany 2004.






D. Archana, Kotyada. Kalyani, B. Shankar Prasad

Paper Title:

Efficiency Optimization Control of Induction Motor Using Fuzzy Logic

Abstract: Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy logic controller. Induction motor is modeled in synchronous reference frame in terms of dq form. The speed control of induction motor is the main issue achieves maximum torque and efficiency. Two speed control techniques, Scalar Control and Indirect Field Oriented Control are used to compare the performance of the control system with fuzzy logic controller. Indirect field oriented control technique with fuzzy logic controller provides better speed control of induction motor especially with high dynamic disturbances. The model is carried out using Matlab/Simulink computer package. The simulation results show the superiority of the fuzzy logic controller in controlling three-phase induction motor with indirect field oriented control technique.

Vector control, Fuzzy logic, Induction motor drive.


1. G. J Han, S. S. Shapiro, “Statiscal models in engineering, ” Jhon wile and sons, 1967.
2. O. W. Anderson, “Optimum design of electrical machines, ” IEEE Trans. Vol. PAS-86, 1967, pp. 707-711.

3. C. Li, A. Rahman, “Three-phase induction motor design optimization using the modified Hooke-Jeeves method, ” Int. J. Electrical Machines and Power Systems, Vol. 18, 1990, pp. 1-12.

4. R. Fei, E. F. Fuchs, H. Haung, “Comparison of two optimization techniques as applied to three-phase induction motor design, ” IEEE/PES winter meeting, new York, 1989.

5. K. Schittkowski, “NLPQL: a Fortran subprogram solving constraind nonlinear programming problems, ” Annals of Operation Research, Vol. 5, 1985, pp. 485-500.

6. J. Faiz, M.B.B. Sharifian, “Optimal design of three-phase InductionMotors and their comparison with a typical industrial motor, ” Computers and Electrical Engineering, vol. 27, 2001, pp. 133-144.

7. O. Muravlev, et al, “Energetic parameters of induction Motors as the basis of energy saving in a variable speed drive, ” Electrical Power Quality and Utilization, Vol. IX, No. 2, 2005.

8. Christian Koechli, et al, “Design optimization of induction motors for aerospace applications, ” IEE Conf. Proc. IAS, 2004, pp. 2501-2505.

9. W. Jazdzynski, “Multicriterial optimization of squirrel-cage induction motor design, ” IEE Proceedings, vol. 136, Part B, no.6, 1989.

10. K. Idir, et al, “A new global optimization approach for induction motor design, ” IEEE Canadian Conf. Proc. Electrical and Computer Engineering, 1997, pp. 870-873.

11. Bhim Singh, B. N. Singh, “Experience in the design optimization of a voltage source inverter fed squirrel cage induction motor”, Electric Power Systems Research, Vol. 26, 1993, pp. 155-161.

12. R. Ramarathnam, B. G. Desai, “Optimization of polyphase induction motor design: a nonlinear programming aproach”, IEEE Trans. Power Apparatus and Systems, Vol. PAS-90, No. 2, Mar. / Apr. 1971, pp. 570-578.

13. D. G. Bharadwaj, k. Venkatesan, R.B. Saxena, Induction motor design optimization using Constrained Rosenbrock Method (Hill Algoritm), Computer and Electrical Engineering, 6, 1979, 41-46.

14. C. J. Eriction, “Motor Design Features for Adjustable-Frequency Drives, ” IEEE Trans. Ind. Appl. Vol. 24, No. 2, 1988.

15. C. Singh, D.Sarkar, “Practical considerations in the optimization of induction motor design, ” IEE Proc-B, Vol. 139, No.4, 1992.






R.Nishanthi, Pon L.T.Thai, K.John Peter

Paper Title:

Structure-Preserving Image Retargeting With Compression Assessment and Adaptive Registration

Abstract: A number of algorithms have been proposed for image retargeting with image content retained as much as possible. But, they usually suffer from some artifacts in the results, such as ridge or structure twists. In this paper, a structure and content preserving image retargeting technique is used that preserves the content and image structure as best as possible. The image content saliency is estimated from the structure of the content using saliency map. A block structure energy is used for structure preservation along x and y directions. Block structure energy uses top-down strategy to constrain the image structure uniformly. However, the flexibilities of retargeting are different for different images. To overcome this problem, compression assessment scheme is used by combining the entropies of image gradient magnitude and orientation distributions. Finally, adaptive registration algorithm is applied. Adaptive registration is used to increase the PSNR ratio. Thus, the resized image is produced to preserve the structure and image content as best as possible. The global image structure is preserved and structure distortions are avoided.

Compressibility estimation, image retargeting, structure-preserving.


1. V. Setlur, S. Takagi, R. Raskar,M. Gleicher, and B. Gooch, “Automatic image retargeting,” ACM Trans. Graph., vol. 154, p. 4, 2004.
2. S. Avidan and A. Shamir, “Seam carving for content-aware image resizing, ACM Trans. Graph., vol. 26, no. 3, pp. 10.1–10.9, 2007.

3. L. Q. Chen, X. Xie, X. Fan, W. Y. Ma, H. J. Zhang, and H. Q. Zhou, “A visual attention model for adapting images on small displays,” ACM Multimedia Syst. J., vol. 9, no. 4, pp. 353–364,2003.

4. M. Rubinstein, A. Shamir, and S. Avidan, “Multi-operator media retargeting, ACM Trans. Graph., vol. 28, no. 3, pp. 1–11, 2009.

5. S.-F. Wang and S.-H. Lai, “Fast structure-preserving image retargeting,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., 2009, pp. 1049 – 1052

6. H. Liu, X. Xie, W. Y. Ma, and H. J. Zhang, “Automatic browsing of large pictures on mobile devices,” ACM Multimedia, pp. 148–155,2003.

7. F. Liu and M. Gleicher, “Automatic image retargeting with fisheye-view warping,” in Proc. ACM Symp. UIST, 2005, pp. 153–162.

8. R. Gal, O. Sorkine, and D. Cohen-Or, “Feature- aware texturing,” in Proc. Eurograph. Symp. Rendering, 2006, pp. 297–303.

9. Y.-S. Wang, C.-L. Tai, O. Sorkine, and T.-Y. Lee, “Optimized scale- and-stretch for image resizing,” ACM Trans. Graph., pp. 1–8, 2008.

10. Y. Pritch, E. Kav-Venaki, and S. Peleg, “Shift-map image editing,” in Proc. Int. Conf. Computing.






Sijoy Johnson, N. Kumaresan, Manu Poulose

Paper Title:

A Special Method for Analysing and Correction of Set Effects in PIC Microcontroller

Abstract: In this paper the fault tolerance behaviour of a PIC micro-controller has been checked. This experiment is based on injection of different transient faults in various points.. The experimental results have been compared in different aspects. Program counter is found to be as the most infected components after simulated results. The failure rate of this program counter is more than 50%. An SET at a node of combinational part may cause a transient pulse at the input of a flip-flop and consequently is latched in the flip-flop and generates a soft-error. When an SET conjoined with a transition at a node along a critical path of the combinational part of a design, a transient delay fault may occur at the input of flip-flop. Thus, studying the behaviour of the SET in these kinds of circuits needs special attention. This paper studies the dynamic behaviour of SET Effects in PIC microcontroller with massive critical paths in the presence of an SET. We also propose novel flip-flop architecture to mitigate the effects of such SETs in combinational circuits. Furthermore, the proposed architecture can tolerant a Single Event Upset (SEU) caused by particle strike on the internal nodes of a flip-flop.

ADC, Single Event Transients (SET),Single event upsets (SEU) ,UART.


1. Y. Nakamura, and K. Hiraki. , “Highly Fault-Tolerant FPGA Processor by Degrading Strategy,” Pacific Rim International Symposium on Dependable Computing, pp. 75-78, 2002.
2. J. Aidemark, J. Vinter, P. Folkesson, and J. Karlsson, “GOOFI: Generic Object-Oriented Fault Injection Tool,” International Conference on Dependable Systems and Networks, pp. 83-88 , 2001.

3. D. Gil, J. Garcia, J. C. Baraza, and P. J. Gil, “A Study of the Effects of Transient Fault Injection Into the VHDL Model of a Fault-Tolerant Microcomputer System,” 6th IEEE International On-Line Testing Sysmposium, pp. 73-79, 2000.

4. M. Rebaudengo, M. Sonza Reorda, and M. Violante, “Analysis of SEU Effects in a Pipelined Processor,” Proceedings of the IEEE International On-Line Testing Sysmposium, pp. 206-210, 2002.

5. E. Chaboot, J. McCluskey, J. Wu, and Y. Sun, “Microcontroller-Based Artificial Synapse,” Proceedings of the IEEE 31st Annual Northeast in Bioengineering Conference, pp. 30-31, 2005.

6. K. Tunlasakun, K. Kirtikara, S. Thepa, V. Monyakul, “A Microcontroller-Based Islanding Detection For Grid Connected Inverter,” The 47th Midwest Symposium on Circuits and Systems, pp.267-269, 2004.

7. S. Krishnamohan, and N. R. Mahapatra, “A high efficiency technique for reducing soft errors in static CMOS circuits,” Proc. IEEE Inter. Conf. Computer Design (ICCD’04), Oct. 2004, pp.126-131.

8. D. G. Mavis, and P. H. Eaton, “Soft error rate mitigation techniques for modern microcircuits,” IEEE Proc. Annual Reliability Physics Sym., 2004, pp. 216-225.

9. S. Sharifi, M. Hosseinabady, and Zainalabedin Navabi, “Reducing Power, Time and Data Volume in SoC Testing Using Selective Trigger Scan Architecture”, International Symposium on Defect and Fault Tolerance in VLSI Systems (DFT 2003), 2003, , pp. 352-360.

10. R. Kuppuswamy, P. DesRosier, D. Feltham, R. Sheikh, and P. Thadikaran, “Full hold-scan system in microprocessors: Cost/Benefit analysis,” Intel Technology Journal, Vol. 18, No. 1, Feb. 2004; http://developer.intel.com/technology/itj/2004/volume08isssu e01/.

11. J. M. Rabaey, A. Chandrakasan, and B. Nikolic, “Digital Integrated Circuits,” Pearson Education, Inc. Upper Saddle River, New Jersey 07458, 2003.






Naveen Mukkapati, Ch.V. Bhargavi

Paper Title:

Detecting Policy Anomalies in Firewalls by Relational Algebra and Raining 2D-Box Model

Abstract: Firewalls are crucial elements in the computer networks. Due to lack of tools for analyzing firewall policies, most firewalls on the internet have been plagued with policy anomalies. In this paper, we propose a method; which analyzes the firewall by using Relational Algebra and Raining 2D-Box Model. It can find out all the anomalies in the firewall rule-set in the format that is usually used by many firewall products such as Cisco Access Control List, IPTABLES, IPCHAINS and Check Point Firewall-1. While the existing analyzing methods consider the anomalies between any two rules in the firewall rule-set, we consider more than two rules together at the same time to find out the anomaly. Therefore we can find all the hidden anomalies in the firewall rule-set. Results from analyzing can be used with the proposed rules-combination method presented in this paper, to minimize the firewall rule without changing the policy. This method could help administrator to analyze and modify a complex firewall policy.

Firewall, policy, relational algebra, correlation anomaly, raining 2D-Box Model.


1. Ehab Al-Shaer and Hazem Hamed. "Firewall Policy Advisor for anomaly Detection and Rule Editing". IEEE/IFIP Integrated Management IM'2003, March 2003.
2. P. Eronen and J. Zitting. "An Expert System for Analyzing Firewall Rules". Proceedings of 6th Nordic Workshop on Secure IT-Systems (NordSec 2001), November 2001.

3. S. Hazelhusrt. "Algorithms for Analyzing Firewall and Router Access Lists". Technical Report TR-WitsCS-1999 Department of Computer Science, University of the Witwatersrand, July1999.

4. Abraham Silberschatz. Henry F. Korth, Sudharsan S. "Database System Concepts, 3rd Edition". Tata McGraw-Hill,1997.

5. Chotipat Pornavalai and Thawatchai Chomsiri. "Firewall Policy Analyzing by Relational Algebra". The 2004 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2004), JULY 2004.

6. Chotipat Pornavalai and Thawatchai Chomsiri. "Firewall Policy Analyzing by Relational Algebra". Draft Technical Report, Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Thailand, January 2004.






B. F. Momin, P. M. Yelmar

Paper Title:

Modifications in K-Means Clustering Algorithm

Abstract: In our study, we introduce modifications in hard K-means algorithm such that algorithm can be used for clustering data with categorical attributes. To use the algorithm for categorical data, modifications in distance and prototype calculation are proposed. To use the algorithm on numerical attribute values, mean is calculated to represent centre, and euclidean distance is used to calculate distance. Whereas, to use it on categorical attribute values, proportional representation of all the categorical values (probability) is used to represent center, and proportional weight difference is used as distance measure. For mixed data, we used discretization on numerical attributes to convert these attribute in categorical attribute. And algorithm used for categorical attributes is used. Other modifications use the combined fundamentals from rough set theory, fuzzy sets and possibilistic membership incorporated in k-means algorithm for numeric value only data. Same modifications are applied on the algorithm developed for categorical, and mixed attribute data. Approximation concept from rough set theory deals with uncertainty, vagueness, and incompleteness. Fuzzy membership allows dealing with efficient handling of overlapping clusters. Possibi1istic approach simply uses the membership value of data point in a cluster that represents the typicality of the point in the cluster, or the possibility of the point belonging to the cluster. Noise points or outliers are less typical; hence typicality-based (possibilistic) memberships reduce the effect of noise points and outliers. To verify the performance of algorithms DB index and objective function values are used.

Categorical data, clustering, fuzzy membership, k-means, possibilistic membership, rough set.


1. P. Maji and S. K. Pal, “Rough–fuzzy C-medoids algorithm and selection of bio-basis for amino acid sequence analysis,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 6, pp. 859–872, Jun. 2007.
2. Nikhil R. Pal, Kuhu Pal, James M. Keller, and James C. Bezdek,” A Possibilistic Fuzzy c-Means Clustering Algorithm,” IEEE Trans. Fuzzy Syst, Vol. 13, no. 4, Aug 2005.

3. S. Mitra, H. Banka, and W. Pedrycz, “Rough–fuzzy collaborative clustering,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 4, pp. 795–805, Aug. 2006.

4. Zhexue Huang, Michael K. Ng.,” A fuzzy k-modes algorithm for clustering categorical data,” IEEE Trans. on fuzzy systems., Vol 7 No 4. August 1999.

5. Chen Ning, Chen An, Zhou Long-xiang ,” Fuzzy k-prototypes algorithm for clustering mixed Numeric and categorical valued data,” Journal of software Vol.12 No. 8,2001.

6. R. Krishnapuram and J. M. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98–110, May 1993.

7. Pawan Lingras, Min Chen, and Duoqian Miao,” Rough Cluster Quality Index Based on Decision Theory,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 7, July 2009.

8. Tapas Kanungo,David M. Mount, Nathan S. Netanyahu,Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, "An Efficient k-Means Clustering Algorithm: Analysis and Implementation", IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 24, NO. 7, PP. 881-892, 2002.

9. Sankar K. Pal , Pabitra Mitra ,” Multispectral Image Segmentation Using the Rough-Set-Initialized EM Algorithm”, IEEE Transactions on Geoscience and Remote Sensing”, VOL. 40, NO. 11, PP. 2495-2501, 2002.

10. Jacek M. Leski ,” Generalized Weighted Conditional Fuzzy Clustering”, IEEE Trans. on Fuzzy Systems , VOL. 11, NO. 6, PP. 709-715, 2003.

11. Joshua Zhexue Huang, Michael K. Ng, Hongqiang Rong, and Zichen Li,” Automated Variable Weighting in k-Means Type Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 27, NO. 5, PP. 657-668, 2005.

12. Jian Yu,” General C-Means Clustering Model”, IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 27, NO. 8, PP.1197-2111, 2005.

13. Carlos Ordonez ,” Integrating K-Means Clustering with a Relational DBMS Using SQL”, IEEE Trans. Knowl. Data Eng.,, VOL. 18, NO. 2, PP. 188-201, 2006.

14. Francesco Masulli, Stefano Rovetta,” Soft Transition From Probabilistic to Possibilistic Fuzzy Clustering”, IEEE Trans. on Fuzzy Systems, VOL. 14, NO. 4, PP.516-527, 2006.

15. Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, and Zengyou He,” On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 29, NO. 3, PP. 503-507, 2007.

16. Hung-Leng Chen, Kun-Ta Chuang, and Ming-Syan Chen, “On Data Labeling for Clustering Categorical Data”, IEEE Trans. Knowl. Data Eng.,VOL. 20, NO. 11, PP.1458-1471, 2008.

17. Eduardo Raul Hruschka, Ricardo J. G. B. Campello, Alex A. Freitas, and Andre C. Ponce Leon F. de Carvalho ,” A Survey of Evolutionary Algorithms for Clustering”, IEEE Trans. Syst., Man, Cybern.—Part C: Appl. And Review, Vol. 39, No. 2,PP.133-155,2009.

18. Lin Zhu, Fu-Lai Chung, and Shitong Wang,” Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions”, IEEE Trans. Syst., Man, Cybern. B, Cybern, VOL. 39, NO. 3, PP.578-591, 2009.

19. Pradipta Maji and Sankar K. Pal,” Rough Set Based Generalized Fuzzy C-Means Algorithm and Quantitative Indices,” IEEE Trans. Syst., Man, Cybern. B, Cybern , vol. 37, no. 6, Dec 2007.

20. J iawei Han, Micheline Kamber,”Data Mining: Concepts and Techniques”,Second Edition,Elesvier Publications,2006.






Brijesh Shah, Satish Shah, Y P Kosta

Paper Title:

Novel Improved Fuzzy C-Mean Algorithm for MR-Image Segmentation

Abstract: Image segmentation is a very important part of image processing. This paper presents an image segmentation approach using improved fuzzy c-mean (FCM) algorithm. The improved fuzzy c-mean algorithm is formulated by modifying the distance measurement of the original fuzzy c-mean algorithm. The Euclidean distance in the fuzzy c-mean algorithm is replaced by the jaccard index and correlation distance, and thus the corresponding algorithm is derived and called as the improved fuzzy c-mean algorithm which is never earlier reported and that is shown to be more robust than original fuzzy c-mean algorithm. Experimental results are conducted on MR-images show that the proposed algorithms have better performance when noise and other artifacts are present than the original algorithms.

Improved fuzzy c-mean algorithm, jaccard index, Medical image processing, image segmentation.

1. D. L. Pham, C. Y. Xu, and J. L. Prince, “A survey of current methods in medical image segmentation,” Annual Review on Biomedical Engineering, vol. 2, pp. 315–37, 2000 [Technical report version, JHU/ECE 99-01, Johns Hopkins University].
2. Liew AW-C, and H. Yan, “Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images,” Current Medical Imaging Reviews, vol. 2, no. 1, pp.91–103, 2006.

3. S. C. Chen, D. Q. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure”, IEEE Transactions Systems Man Cybernet, vol. 34, no. 4, pp. 1907-1916, 2004

4. Ruspini, E., Numerical methods for fuzzy clustering. Information Science 2, 319-350, 1969

5. Amiya halder, Soumajit pramanik, Arindam kar, "Dynamic image segmentation using fuzzy c-mean based genetic algorithm", International journal of
computer applications(0975-8887),volume 28-No.6,Augast 2011

6. Bezdek, J.C., Pattern Recognition with Fu77y Objective Function Algorithrns. Plenum, New York, 1981

7. J.C. Dunn, "A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact, Well Sepa-rated Clusters", Journal of Cybernetics, Vol. 3, No. 3, pp. 32-57, 1974

8. J.-S. Jang, C.-T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, USA, 1997.






Rakesh Dwivedi, Anil Kumar, S. K. Ghosh

Paper Title:

Study of Fuzzy Based Classifier Parameter Using Fuzzy Matrix

Abstract: In the area of remote sensing, the decision making are not generally deterministic due to the involvement of fuzziness in the classification of remotely sensed imagery. A considerable number of identification errors are due to pixels that show an affinity with several information classes. The fuzzy concept is a valuable tool for dealing with classification problems. In remote sensing classification, fuzzy based classifiers are becoming increasingly popular. Due to the wide acceptance of fuzzy c-mean (FCM) and possibilistic c-means (PCM) classifiers, this has been used as a benchmark to evaluate the performance of other classifiers with optimized value of weighting exponent ‘m’ in this research. Evaluation of soft classification through FERM, SCM and Fuzzy kappa coefficient, using Euclidean norm based measures led to an improvement wherein FCM-Overall accuracy (MIN-LEAST) operator reflects higher classification accuracy, i.e., 97% and the value of Fuzzy Kappa coefficient is 0.97 with minimum uncertainty in it, for the optimized value of weighting exponent ‘m’ i.e. 4.0. In this experimentation two supervised classifiers namely FCM and PCM have been selected to demonstrate the improvement in the classification accuracy by FERM, SCM, MIN-MIN, MIN-LEAST, Fuzzy Kappa coefficient and uncertainty in SCM and Fuzzy Kappa coefficients.

Fuzzy c-Mean (FCM), Fuzzy Error Matrix (FERM), Possiblistic c-Mean (PCM), Sub-pixel confusion-uncertainty matrix(SCM),


1. Foody, G. M., Lucas, R. M., Curran, P. J. and Honzak, M., “Non-linear mixture modelling without end-members using an artificial neural network,” International Journal of Remote Sensing, vol. 18, no. 4, 1997. pp. 937 – 953.
2. Yannis, S. A., and Stefanos, D. K., Fuzzy Image Classification Using Multi-resolution Neural with Applications to Remote Sensing. Electrical Engineering Department, National Technical University of Athens, Zographou 15773, Greece, 1999.

3. Foody, G. M., “Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data,” International Journal of Remote Sensing, vol. 17, no. 7, 1996, pp. 1317 – 1340.

4. Dunn J. C., "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters," Journal of Cybernetics, 1973, pp. 3: 32 – 57.

5. Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, USA, 1981.

6. Binaghi, E., Brivio, P. A., Chessi, P. and Rampini, A. A fuzzy Set based Accuracy Assessment of Soft Classification, Pattern Recognition letters, 20, 1999, pp. 935 – 948.

7. Okeke, F. and Karnieli, A., “Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analysis. Part I: Algorithm development,” International Journal of Remote Sensing, 27 (1 – 2), 2006, pp. 153 – 176.

8. Kanellopoulos, I., Varfis, A., Wilkinson, G. G. and Megier, J., “Land cover discrimination in SPOT HRV imagery using an artificial neural network: a 20-class experiment,” International Journal of Remote Sensing, vol. 13, no.5, 1992, pp. 917 – 924.

9. Kerdiles, H. and Grondona, M. O., “NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa,” International Journal of Remote Sensing, vol. 16, no. 7, 1996, pp. 1303 – 1325.

10. Verhoeye J., and Robert D. W., Sub-pixel Mapping of Sahelian Wetlands using Multi-temporal SPOT Vegetation Images. Laboratory of Forest Management and Spatial Information Techniques, Faculty of Agricultural and Applied Biological Sciences, University of Gent, 2000.

11. Zhang, J., Foody, G. M., “Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network Approaches,” International Journal of Remote Sensing, vol. 22, No. 4, 1998, pp. 615–628
12. Shi, W., Z., Ehlrs, M., and Temphli, K., “Analysis modeling of positional and thematic uncertainty in integration of remote sensing and GIS,” Transaction in GIS,
vol. 3, 1999, pp. 119 – 136.

13. Kumar, A., Ghosh, S. K. and Dadhwal, V. K. “Advanced Supervised Soft Multi-Spectral Image Classifier”, Map Asia 2005 conference, Jakarta. Indonesia, 2005.

14. Krishnapuram ,R., and Keller J., M., “A possibilistic approach to clustering,” IEEE Transactions on Fuzzy Systems, vol. 1, 1993, pp. 98 – 108.

15. Foody, G.M. and Arora, M. K., Incorporating mixed pixels in the training, allocation and testing stages of supervised classification, Pattern Recognition Letters, 17, 1996, pp. 1389 – 1398.

16. Foody G. M., “Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data,” ISPRS Journal of Photogrammetry and remote sensing, vol. 50, 1995, pp. 2 – 12.

17. Shabanov, N. V., Lo, K., Gopal, S., and Myneni, R. B., “Subpixel burn detection inModerate Resolution Imaging Spectroradiometer 500-m data with ARTMAP neural networks,” Journal of Geophysical Research, vol. 110, 2005, pp. 1 – 17.

18. Pontius, R. G., Jr., and Cheuk, M. L., “A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions,” International Journal of Geographical Information Science, vol. 20(1), 2006, pp. 1 – 30.

19. Lewis, H. G., and Brown, M., “A generalized confusion matrix for assessing area estimates from remotely sensed data,” International Journal of Remote Sensing, vol. 22, 2001, pp. 3223 – 3235.

20. Pontius, R. G., Jr., and Connors, J., (2006). Expanding the conceptual, mathematical and practical methods for map comparison. Proc. of the Spatial Accuracy Meeting 2006. Lisbon, Portugal 16 pp., (available from www.clarku.edu/~rpontius).

21. Kuzera, K., and Pontius, R. G., Jr., (2004) Categorical coefficients for assessing soft-classified maps at multiple resolutions. Proc. of the joint meeting of The 15th Annual Conference of the International Environmental Society and the 6th Annual Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Portland ME, 28 June–1 July 2004, Portland Maine (St. Paul, Minnesota: Spatial Accuracy Symposium).

22. Silván-Cárdenas, J. L., and Wang L., (2007) Sub-pixel confusion–uncertainty matrix for assessing soft classifications, Remote Sensing of Environment (2007), doi: 10.1016/j.rse.2007.07.017.

23. Aziz,M. A. Evaluation of soft classifiers for remote sensing data, unpublished Ph.D thesis, IIT Roorkee, Roorkee, India, 2004.

24. Kumar, A., Ghosh, S. K., and Dadhwal V. K. (2006). A comparison of the performance of fuzzy algorithm versus statistical algorithm based sub-pixel classifier for remote sensing data. Proceedings of mid-term symposium ISPRS, 8–11th May 2006, ITC-The Netherlands.






Rohit Sharma, Pankaj Singh, Pankaj Agarwal, Himanshu Tiyagi

Paper Title:

Automatic Braking System for Trains using Radio Frequency

Abstract: In Indian railway one of the most problems is the accident. These problems regarding the trains and their corresponding tracks can be solved very easily. In this model, we have used two RF based sender and receiver kits to provide the necessary functions to the train locomotives. So now when the two trains are anywhere near, the paddle brakes provided stops either of the two locomotives without causing accidents. The project implemented by us is actually very much possible to be implemented on a larger and real scale.

So now when the two trains are anywhere near.


1. GM/RT2041 Braking System Requirements and Performance for Trailer Coaching Stock
2. GM/RT2042 Braking System Requirements and Performance for Traction Units

3. Federal Railroad Administration Electronically Controlled Pneumatic Braks(http://www.fra.dot.gov/downloads/PubAffairs/ECP%20Brakes%20FINAL.pdf)

4. Braking Principles for Rail Vehicles (http://www.rgsonline.co.uk/Railway_Group_Standards/Rolling%20Stock/Railway%20Group%20Standards/GMRT2045%20Iss%202.pdf)

5. Railway TechnologyToday 7(edited by kanji wako) Braking Sytem (http://www.jrtr.net/jrtr20/pdf/F52_Tech.pdf)






Dushyantha N.D, Mrityunjaya V. Latte, Glenston Hadlee Miranda, Sayanu Pamidighantam

Paper Title:

Experimental and Simulated case Studies of Lamb Wave Interactions with Structural flaws and Image Reconstruction for Structural Diagnostics

Abstract: The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure by reconstructing spatial image. The proposed algorithm is implemented on experimentally obtained data on Aluminum plate and simulated data on a structural steel plate. In both the cases, the data is post processed and analyzed by implementing the location identification algorithm to reconstruct the image. the proposed work uses two methods to analyze and compare baseline signals with recorded signals. Firstly before subtraction phase of recorded signal is phase corrected. In second method time windowed correlation is used to extract the signals only due to damage. Using the signal received from first method and second method the images are constructed separately and using image fusion technique new image is constructed. This algorithm improves the accuracy of positioning, estimating the shape and size of damage. The mean deviation from the actual location of the flaw to the predicted location is (1.136 mm, 0.979 mm).

Lamb waves, Image fusion, NDT, Time window correlation, Phase correction.


1. Wang Bo He-Wei, “A new method based on matrix perturbation and improved residual force vector for structural damage detection”, IEEE International Conference on Computer Science and Information Technology, 8-11 Aug 2009, pp. 61-64
2. Sarvajith M, BrijeshKumar Shah, Dushyanth. N .D, S. Jana “A Novel Approach for Bearing Fault Detection and Classification using Acoustic Emission Technique ”, Proceedings of International Conference on Advances in Computer Science and Electronics Engineering - ICACSEE 2012, Mumbai India 25-26th March, 2012.

3. Mix, P. Introduction to Non-Destructive Testing – A training guide, New York, John Wiley & Sons (1987).

4. L. Satyanarayan, C.V. Krishnamurthy, K.V. Mohan, K. Balasubramaniam, Simulation of ultrasonic phased array technique for imaging and sizing of defects using longitudinal waves, International Journal of Pressure Vessels and Piping, vol. 84, pp 716-729, 2007.

5. Satyarnarayan, L., Chandrasekaran, J, Maxfield, B., & Balasubramaniam, K. (2008). Circumferential higher order guided wave modes for the detection and sizing of cracks and pinholes in pipe support regions. NDT & E International, 41(1) 32–43 (2008).

6. Michaels, J. E. Detection, localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors. Smart Materials and Structures, 17(3), 035035. doi:10.1088/0964-1726/17/3/035035, (2008).

7. Su, Z., Ye, L., & Bu, X. (2006). A damage identification technique for CF / EP composite laminates using distributed piezoelectric transducers. Composite Structures, 57(2002), 465-471.

8. Victorov, Rayleigh and Lamb Waves: Physical Theory and Applications_Plenum, New York, 1967

9. N.D. Dushyantha, Mrityunjaya V. Latte and Sayanu Pamidighantam “Two Dimensional Spatial Fusion Technique for Imaging of Damages in Plates using Lamb
Waves” Proceedings of Third International Conference on Sensors and Related Networks (SENNET-12), VIT University, Vellore, India. Jan. 19-21, 2012. pp.206-209.

10. Bikash Ghose, Krishnan Balasubramaniam, C V Krishnamurthy and A Subhananda Rao “ Two Dimensional FEM Simulation of Ultrasonic Wave Propagation in Isotropic Solid Media using COMSOL”, COMSOL Conference, 2010, India.

11. Soumya P.S, Dushyantha.N.D, Surabhi K, Vinay Krishnappa, Glenston Hadlee Miranda “Investigation of acoustic structure interaction for flawed structure” Proceedings of International Symposium on Physics and Technology of Sensors University of Pune, India 8-10 March 2012,

12. So, H. C., Ching, P. C., Ho, H. C., & Chan, Y. T. A novel constrained algorithm for delay estimation in the presence of multipath transmissions. IEEE International Conference on Acoustics Speech and Signal Processing, 313-316 vol.1. Ieee. doi:10.1109/ICASSP.1993.319118, (1993).

13. Tania Stathaki, Image Fusion: Algorithms and Applications, Academic Press (2008)

14. W.K. Pratt, `Digital Image Processing', Wiley, Third Edition (2001).






Manveen Singh Chadha, Rambir Joon, Sandeep

Paper Title:

Simulation and Comparison of AODV, DSR and AOMDV Routing Protocols in MANETs

Abstract: A Mobile Ad-hoc Network (MANET) is a dynamic wireless network that can be formed without the need for any pre-existing infrastructure in which each node can act as a router. Mobile ad hoc network (MANET) is an autonomous system of mobile nodes connected by wireless links. Each node operates not only as an end system, but also as a router to forward packets. The nodes are free to move about and organize themselves into a network. These nodes change position frequently. The main classes of routing protocols are Proactive, Reactive and Hybrid.A Reactive (on-demand) routing strategy is a popular routing category for wireless ad hoc routing The design follows the idea that each node tries to reduce routing overhead by sending routing packets whenever a communication is requested.In this work an attempt has been made to compare the performance of three prominent on demand reactive routing protocols for MANETs:- Ad hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR) protocols and Ad-hoc On-demand Multipath Distance Vector Routing (AOMDV) . DSR and AODV are reactive gateway discovery algorithms where a mobile device of MANET connects by gateway only when it is needed. AOMDV was designed primarily for highly dynamic ad hoc networks where link failures and route breaks occur frequently. It maintains routes for destinations in active communication and uses sequence numbers to determine the freshness of routing information to prevent routing loops. It is a timer-based protocol and provides a way for mobile nodes to respond to link breaks and topology changes. The performance differentials are analyzed using varying simulation time. These simulations are carried out using the ns-2 network simulator. The results presented in this work illustrate the importance in carefully evaluating and implementing routing protocols in an ad hoc environment.



1. Performance Comparison of Mobile Ad-hoc Network Routing Protocol, International Journal of Computer Science and Network Security (IJCSNS), VOL.7 No.11, pp. 77-84 November 2007.
2. Mobile Ad hoc network (MANET), www.ietf.org/html.charter/manetcharter

3. D. Bertsekas and R. Gallager, Data Networks, 2nd ed. (Prentice Hall, Upper Saddle River, NJ, 1987).

4. G. Pei, M. Gerla, and T.-W. Chen, “Fisheye State Routing: A Routing Scheme for Ad Hoc Wireless Networks,” Proceedings of the IEEE ICC 1 (June 2000): 70

5. C. E. Perkins and P. Bhagwat, “Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers,” Proceedings of the ACM SIGCOMM 1994 Conference (August 1994): 234–44.

6. H. Ehsan and Z. A. Uzmi (2004), “Performance Comparison of Ad HocWireless Network Routing Protocols”, IEEE 8th International Multitopic Conference, Proceedingsof INMIC, pp.457 – 465, December 2004.

7. V. Park and S. Corson, “Temporally Ordered Routing Algorithm (TORA) Version 1, Functional specification”, IETF Internet draft, http://www.ietf.org/internet-drafts/draft-ietf-manet-tora-spec-01.txt, 1998.

8. D. B. Johnson and D. A. Maltz, ―Dynamic Source Routing in Ad Hoc Wireless Networks, Mobile Computing, Chapter 5, pp. 153-181, Kluwer Academic Publishers, 1996

9. J. Broch, D.A. Maltz, D. B. Johnson, Y-C. Hu, J. Jetcheva, “A performance comparison of Multi-hop wireless ad-hoc networking routing protocols”, in the proceedings of the 4th International Conference on Mobile Computing and Networking (ACM MOBICOM ’98), pp. 85-97, October 1998.

10. Md. Golam Kaosar, Hafiz M. Asif, Tarek R. Sheltami, Ashraf S. Hasan Mahmoud, “Simulation-Based Comparative Study of On Demand Routing Protocols for MANET”, available at http://www.lancs.ac.uk, Internaional Conference on Wireless Networking and Mobile Computing, Vol. 1, pp. 201 – 206, December 2005.

11. C. Hedrick, Routing Information Protocol, Request for Comments 1058, June 1988.

12. C.E. Perkins, E.M. Royer, Ad-hoc on-demand distance vector routing, in: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Application.Evolution and future directions of the ad hoc on-demand distance-vector routing protocol by Elizabeth M. Belding-Royer and Charles E. Perkins

13. C.E. Perkins, E.M. Belding-Royer, S.R. Das, Ad hoc On-Demand Distance Vector (AODV) Routing, IETF Internet Draft, draft-ietf-manet-aodv-13.txt, Work in Progress.

14. C.E. Perkins, E.M. Royer, S.R. Das, M.K. Marina, Performance comparison of two on-demand routing protocols for ad hoc networks, IEEE Personal Communications Magazine, special issue on Ad hoc Networking 8 (1) (2001)

15. S. Gwalani, E.M. Belding-Royer, C.E. Perkins, AODVPA: AODV with path accumulation, in: Proceedings of the IEEE Symposium on Next Generation Internet (NGI), Anchorage, AK, May 2003

16. M. Marina, S. Das, On-demand multipath distance vector routing in ad hoc networks, in: Proceedings of the International Conference on Network Protocols (ICNP), Riverside, CA, November 2001

17. H.D.Trung, W.Benjapolakul, P.M.Duc, “Performance evaluation and comparison of different ad hoc routing protocols”, Department of Electrical Engineering, Chulalongkorn University, Bangkok, Thailand, May 2007

18. Z. J. Haas and M. R. Pearlman, “The Performance of Query Control Schemes for the Zone Routing Protocol,” ACM/IEEE Trans. Net. 9 (August 2001)

19. The VINT Project, The network simulator - ns-2. http://www.isi.edu/nsnam/ns/

20. ad hoc networks technologies and protocol by prasant mohapatra university of california‚ davis srikanth v. krishnamurthy university of california‚ riverside

21. Wireless Networking by Praphul Chandra, Daniel M. Dobkin, Alan Bensky, Ron Olexa, David A. Lide, and Farid Dowla ISBN: 978-0-7506-8582-5

22. Network Routing Algorithms, Protocols, and Architectures by Deepankar Medhi and Karthikeyan Ramasamy

23. Luke Klein-Bernd, ―A Quick Guide to AODV Routing, National Institute of Standards and Technology, US






Ashish Kumari, Noor Mohammad, Chetna

Paper Title:

Specification Representation and Automatic Test Case Generation using System Model

Abstract: Finite State Machine is used to model the requirement specification of the system by formal description languages. In this paper, I have presented a approach which is used to represent the requirement specification and automatically generate all possible test cases which should be executed to test that particular system [5].Requirement specification are represented using extended finte state machine which uses the state transition diagram that shows how system changes states and action and variable used during each transition. Based on information given in the state transition diagram, all possible test cases are generating by traversing the graph using Depth First Search.

Regression testing, extended finite state machine, Specification-based testing, State Transitions, path, Data dependency, Control dependency, SDG.


1. Ynaping Chen, Robert L. Probert and Hasan Ural, “Regression Test Reduction Using Extended Dependence Analysis” in SOQUA’07, September 3-4 2007, ACM Transaction, Dubrovnik, Croatia, 2007.
2. Korel, B., Tahat, L.H., and Vaysburg, B., “Model-based regression test reduction using dependence analysis”, In Proc. of ICSM’02 (Montréal, Canada, October 3-6, 2002). IEEE Computer Society Press, Washington, DC, 2002, 214-223.

3. Chen, Y., Rosenblum, D., VO, K., "Testtube: A System for Selective Regression Testing," Proceedings of the 161h International Conference on Software Engineering.

4. Tahat, L., Vaysburg, B., Korel, B., Bader, A., "Requirement-Based Automated Black-Box Test Generation," Proceedings of the 25th Annual IEEE International Computer Software and Applications Conference (COMPSAC), Chicago, IL, pp. 489-495

5. Vaysburg, B., Tahat, L., Korel, B., Bader, A., " Automating Test Case Generation from SDL Specifications," Proceedings of the 18th International Conference on Testing Computer Software (TCS), Bethesda, MD, pp. 130-139.

6. Dick, J., Faivre, A., "Automating the Generation and Sequencing of Test Case from Model-Based Specification," Proceedings of the Industrial Strength Formal
Methods, 51h International Symposium on Formal Methods, pp. 268-284, Springer-Verlag, Apri11992.

7. Dssouli, R., Saleh, K., Aboulhamid, E., En-Nouaary, A., Bourhfir, C., "Test Development For Communication Protocols: Towards Automation," Computer Networks, 31, pp. 1835-1872, 1999

8. Ferrante K., Ottenstein K., Warren J., "The Program Dependence Graph and its Use in Optimization," ACM Transactions on Programming Languages and Systems, 9(5),pp. 319-349, 1987.

9. Ryan Voigt, Kareem Fazal, Hassan Reza,“Specification-based Testing Method Using Testing Flow Graphs” ICSEA '07 Proceedings of the International Conference on Software Engineering Advances, ISBN:0-7695-2937-2

10. Vaysburg, B., Tahat, L., Korel, B., "Dependence Analysis in Reduction of Requirement Based Test Suites," to appear in Proceedings of IEEE International Symposium on Software Testing and Analysis (ISSTA), Rome, Italy, 2002.

11. Ashish Kumari, Dr. Rahul Rishi, “Specification Representation and Test Case Reduction by Analyzing the Interaction Patterns in System Model”, Proceedings of IJCSMS, Vol. 12, Issue 01, January 2012, ISSN (Online): 2231-5268.






Poonam Yadav, Rakesh Kumar Gill, Naveen Kumar

Paper Title:

A Fuzzy Based Approach to Detect Black hole Attack

Abstract: A Wireless network is a dynamic network with large no. of nodes. As the traffic increases over the network such type of network suffers from the problems like congestion and packet loss. But in case of Mobile network there is one more problem regarding the life of the network. A network can be affected from some Black hole attack over the network As a result some loss of information occur over the communication. The packet loss is acceptable up to some threshold value but as there is more packet loss we need some solution for this. The same solution is presented in this paper. Here we are presenting a fuzzy based decision to check a node is infected by Black hole attack or node. The proposed system will identify the attack over the node as well as provide the solution to reduce the data loss over the network.

Wireless, Mobile, Elimination, Black Hole, Fuzzy Rule.


1. Proceedings of IEEE GLOBECOM '01, 2001-11.K Whitehose, D Culler. Calibration as Parameter Estimation in Sensor Networks [C]. In: First ACM International Workshop on Wireless Sensor Networks and Application, Atlanta GA, 2002-09.
2. An Overview of Wireless Sensor Network and Applications V. Rajaravivarma, Yi Yang, and Tang Yang Computer Electronics, School of Technology 0-7803-7697-8/03/$17.000 2 008 IEEE

3. Ye W, Heidemann J, Estrin D, applications of wireless sensor networks. In: Proc 21St Int'l Annual Joint Conf IEEE Computer and Communications Societies (INFCOM 2002), New York, NY, June 2002.

4. Low Power Locating Algorithms For Wireless Sensors Network , Xiang-zhong Meng, Bing Wu, Hui Zhu and Yao-bin Yue Xiang-zhong Meng Proceedings of the 2006 IEEE

5. Node Sensing & Dynamic Discovering Routes for Wireless Networks Arabinda Nanda, Amiya Kumar Rath and Saroj Kumar (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 3, March 2010

6. Topological Hole Detection in Wireless Sensor Networks and its Application Stefan Funke Computer Science Department Gates Bldg. 375Stanford University, CA 94305, U.S.A.

7. Energy Aware Routing for Low Energy Ad Hoc Sensor Networks Rahul C. Shah and Jan M. Rabaey

8. Route Aware Predictive Congestion Control Protocol for Wireless Sensor Networks Carl Larsen, Maciej Zawodniok, Member, IEEE, and Sarangapani Jagannathan, Senior Member, IEEE Singapore, 1-3 October 2007






Sandeep, Rambir Joon, Manveen Singh Chadha

Paper Title:

Simulation & Comparison of CSFQ, RED & FRED Queuing Techniques

Abstract: Today’s Internet only provides Best Effort Service. Traffic is processed as quickly as possible, but there is no guarantee of timelines or actual delivery. With the rapid transformation of the Internet into a commercial infrastructure, demands for service quality have rapidly developed. People of the modern world are very much dependent on various network services like VOIP, Videoconferencing and File Transfer. Different types of Traffic Management systems are used in those services. Queuing is one of the very vital mechanisms in traffic management system. Each router in the network must implement some queuing discipline that governs how packets are buffered while waiting to be transmitted. This paper gives a comparative analysis of three queuing systems CSFQ, RED and FRED. The study has been carried out on some issues like: Throughput, packet end to end delay and packet delay fraction rate the simulation results shows that CSFQ technique has a superior quality than the oth techniques.

RED (Random Early Drop), FRED (Flow random Early Drop) and CSFQ (Core Stateless fair Queuing)


1. Masayoshi Nabeshima (2002) “Adaptive CSFQ: determining the packet dropping probability adaptively for achieving fair bandwidth allocations in SCORE networks” conference on computer communications, Yokosuka-shi, Kanagawa, Japan , pp. 1-2
2. Ion Stoica , Scott Shenker , Hui Zhang (2003) “CSFQ : Achieving Approximately Fair Bandwidth Allocations in High Speed Networks” IEEE/ACM transactions on Networking, Vancouver, pp 33-46.

3. H.Gundersen and F.T. Grimstad (2001), “QoS for Real Time Traffic” IEEE computer and Communication proceedings, New York, pp. 50-55

4. K.Ahlin (2003) “Quality of Service in IP Networks”, Linkoping University, pp. 10-18

5. C.Semeria (2001) “Supporting differentiated service classes, queue scheduling disciplines” white paper, pp 5-25. www.juniper.net.

6. N.Alborz (2002) “Implementation and Performance Simulation of Virtual Clock Scheduling Algorithm in IP Networks” , Washington, pp.

7. Foursticks CTO Alan Noble (2003) “Network Performance Technology” Whitepaper, pp. 8

8. T. P. Lee and J. Siliquini (2007) “Deficit Round Robin with hop-by-hop credit based flow control” IEEE published, pp 1-4.

9. E.C. Popovici and T. Radulescu (2007) “Coding scheme impact on the IP QoS network utilization and voice quality” pp 2-9

10. T.Subash and S.Indira Gandhi (2006) “Performance Analysis of Scheduling Disciplines in Optical Networks” , IEEE Published, pp 1-5.

11. N. Sulaiman, R.Carrasco and G. Chester (2006) “Analyzing the Performance of Voice over Internet Protocol in a 3G Network”, IEEE Published, pp 1-5

12. L.Le, K. Jeffay and F.D Smith (2006) “A Loss and Queuing-Delay Controller for Router Buffer Management”, IEEE, pp 1-10

13. O.Salami and H.A Chan (2006) “Multistage Queuing and Scheduling of IP traffic for QoS Provisioning”, IEEE, pp 1-6.

14. M. Gospodinov (2004) “The affects of different queuing disciplines over FTP, Video and VoIP Performance”, International conference, pp 1-5.

15. I. Miloucheva, A. Nassri and A. Anzaloni (2004) “Automated analysis of network QoS parameters for Voice over IP applications”, pp 1-10.

16. I. Bouazizi (2003) “Size-Distortion Optimization for Application-Specific Packet Dropping, the Case of Video Traffic”, IEEE Published, pp 1-6.

17. H.M. Chaskar and U.Madhow (2003) “Fair Scheduling With Tunable Latency:A Round-Robin Approach” , IEEE , pp 1-5 ,9.

18. M. F. de Castro, A. M’hamed, Dominique gaiti, Mauro Oliveira (2003). “Simulated Internet Traffic Behavior under Different QoS Management Scenarios”, IEEE, pp 1-7.

19. A. Bitorika, M.Robin and M. Huggard (2003) “An Evaluation Framework for Active Queue Management Schemes” , IEEE, pp 1-7.

20. F.Palmieri (2003) “Improving the performance in multimedia streaming networks: A differentiated service approach” , IEEE, pp 1-9

21. Salil S. Kanhere and H.sethu (2001) “Low latency gurantee rate scheduling using extended round robin” pp 1, 3,8.

22. G. Zhang, H.T. Mouftah (2001) “End-to-End QoS Guarantees over Diffserv Networks”, IEEE, pp 1-9.

23. Salil S. Kanhere, Alpa B. Parekh and H.Sethu (2000) “Fair and Efficient Packet Scheduling in Wormhole Networks”, IEEE, pp 1-9.

24. D.A. H, M.Rumsewicz and L.L.H. Andrew (1999) “Impact of Flow Control on Quality of Service Driven Packet Scheduling Disciplines”, IEEE, pp 1-6.

25. P.Goyal, H.M.Vin, and H.Cheng (1997) “Start-Time Fair Queueing: A Scheduling Algorithm for Integrated Services Packet Switching Networks”, IEEE, pp 1, 2, 11-14.

26. M.Shreedar and G.Varghese (1995) “Efficient Fair Queuing using Deficit Round Robin”, IEEE, pp 1-5, 7-12.

27. R.D. Fellman, R. Grygiel, and I.Chu (1995) “The Effect of Preemptive Queuing in a Priority-Based Real-Time Network”, pp 1-4.

28. A. Parekh and G.Gallager (1994) “A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Multiple Node Case”, IEEE, pp 1, 12-13.

29. A. Parekh and R.G. Gallager (1993) “A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single-Node Case”, IEEE, pp 1-2.

30. A.Dhemers, S.Keshav and S. Shenkar (1990) “Analysis and Simulation of a Fair Queuing algorithm”, pp 1-12

31. S. Jamaloddin Golestani (1990) “Congestion-Free Transmission of Real-Time Traffic in Packet Networks” pp 1-8.

32. Ningning Hu, Liu Ren, Jichuan Chang (2001) “ Evaluation of Queue Management Algorithms” Course Project report for Computer Networks, pp. 1-14






Ramu Korukonda, K J Sreeja

Paper Title:

Development of Data Simulation and Processing GUI for Underwater Applications Using PCI Add-On Card

Abstract: In application areas like data decoding, analog signal processing, telemetry, underwater vehicles etc, monitoring of analog and digital signals of various subsystems and mission parameters is very essential. Especially in the case of underwater vehicles, the performance evaluation is done offline because it is not possible to transmit data from vehicle either by cable or wireless. This calls for the onboard recording system to record different parameters and download those parameters for offline analysis. Parameters like navigation, control and target parameters should be transferred between vehicle and mothership for successful run, this is accomplished via surface, acoustic and fibre optic modem’s depending on the conditions. This paper provides an overview of the development of an interactive simulation system and Graphical User Interface (GUI) for an underwater robotic vehicle. Computer simulations and graphical interfaces are effective ways for studying the behaviour of a system well before it is developed and deployed. It allows lot of flexibility in the design and greater scope for improvement without major financial commitments. The underwater vehicle and its sub-system properties, control inputs (parameter adjustments, maneuvers, etc), and the underwater environment are graphically represented on an user interface that can be easily accessed by the user for offline analysis and for further modifications.

Underwater vehicle, Onboard recording system, simulation system, GUI.


1. “Online Object Monitoring With Go4 V4.4” , J. Adamczewski-Musch, H. G. Essel, and S. Linev, IEEE Trans. On nuclear science vol.58 no. 4 Aug.2011
2. “TCP/IP Tutorial and Technical Overview”, Lydia Parziale David, T. Britt Chuck Davis and Wei Liu, IBM Corp. , 8th edition in 2006

3. Richard Grier, Serial Communication Handbook.

4. “A New On-Board Data Handling System for Spacecraft Flight Control Simulator, Wu Jinjie, Hu Min, Gao Yudong, 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS)

5. “University of Phoenix - Computer Programming Concepts and Visual Basic” David I. Schneider, Pearson Custom Publishing special edition.

6. “Integrated simulation system for Rapid Development of Autonomous Underwater vehicles”,D.P. Brutzman, Y. Kanayama & M.J. Zyda, Proc. of the IEEE Oceanic Engineering Society AUV92 Conference, Jun. 1992.

7. “Control of an underwater vehicle using H-infinity synthesis”, Issac kaminor,Antonio pascoal, 30th IEEE conference on decision and control, Brighton, England, Dec.1991

8. “http://www.mathworks.com/products/simulink/, website of The Math Works, Inc., developer and distributor of Simulink (access link for Simulink product information).

9. “Development of a Matlab-Based Graphical User Interface Environment for PIC Microcontroller Projects”Sang-Hoon Lee, Yan-Fang Li, and Vikram Kapila, Proc. Of ASEE, 2004

10. “A Hardware-in-the-loop Simulation System of Heave Compensation of Deepsea Mining”, Qiong Hu, Shaojun Liu, Proc. Of ISOPE Ocean Mining Symposium, sept. 2009

11. “VIRTUAL MIL-STD-1553” Nicholas Downing, United Space Alliance, LLC, Houston, 25th Digital Avionics Systems Conference Oct. 15, 2006.

12. “Multiplex Applications Handbook”, DOD, Washington, USA, Nov.1st 1988

13. “Network Primer and Programming Tutorial for the Model 2701 Ethernet- Based DMM/Data Acquisition System” Keithley Instruments, Inc., 2002.

14. “A Multiprocessor System-on-a-Chip Design Methodology for Networking Applications” Valentina Salapura, Christos J. Georgiou, Indira Nair IBM Research Division Thomas J. Watson Research Center, may 7th, 2004.






Tathagat Chakraborty, Akik Biswas, Sudha R.

Paper Title:

Analysis of Power Transformer Insulation Design Using FEM

Abstract: A transformer is a device that transfers electrical energy from one circuit to another through inductively coupled conductors—the transformer's coils. A varying current in the first or primary winding creates a varying magnetic flux in the transformer's core and thus a varying magnetic field through the secondary winding. This varying magnetic field induces a varying electromotive force (EMF), or "voltage", in the secondary winding. This effect is called inductive coupling. If a load is connected to the secondary, current will flow in the secondary winding, and electrical energy will be transferred from the primary circuit through the transformer to the load. In an ideal transformer, the induced voltage in the secondary winding (Vs) is in proportion to the primary voltage (Vp) and is given by the ratio of the number of turns in the secondary (Ns) to the number of turns in the primary (Np) as follows: By appropriate selection of the ratio of turns, a transformer thus enables an alternating current (AC) voltage to be "stepped up" by making Ns greater than Np, or "stepped down" by making Ns less than Np. In the vast majority of transformers, the windings are coils wound around a ferromagnetic core, air-core transformers being a notable exception. Transformers range in size from a thumbnail-sized coupling transformer hidden inside a stage microphone to huge units weighing hundreds of tons used to interconnect portions of power grids. All operate on the same basic principles, although the range of designs is wide. While new technologies have eliminated the need for transformers in some electronic circuits, transformers are still found in nearly all electronic devices designed for household ("mains") voltage. Transformers are essential for high-voltage electric power transmission, which makes long-distance transmission economically practical. Finite element modeling (FEM) is a useful and commonly used tool in the solution of electromagnetic problems that arise in the design of power transformers. With approximately 30% of all transformer failures being due to insulation breakdown (due to excessive electrostatic stress), electrostatic FEM techniques are providing engineers with a valuable means of more accurately quantifying the electric stress in their designs. The validity of FEM, in general, always depends on having sound modeling assumptions and techniques. In addition, this problem introduced further complications that required carefully considered assumptions and treatments.



1. Elecnet Package Manual
2. Volna package Manual.

3. Karsai K, Kerenyi D. and Kiss L, “Large power transformer”, Elsevier, Amsterdam, 1987.

4. Kulkarni S.V, Khaparde S,A, “Transformer Engineering Desing and Practices” 2004

5. Moser, H.P Transformerboard, Scientia Electrica, 1979

6. Wirgau.K.A, “Inductance calculation of an air-core disk winding”, IEEE, Trans., 1976.

7. Alfred I. Jones, Bynum E.Smith, Daniel J.Ward, “Considerations for higher voltage distribution”, IEEE Transactions of Power Delivery, Vol.7, No.2, April 1992.

8. Frederick W.Grover, “Inductance Calculations”, Nostrand Company, 1947.

9. Yoshikazu Shibuya, and Shigeto Fujita, “High Frequency Model and Transient Response of Transformer Windings”, IEEE, 2002.

10. O.Honorati, E.Santini, “New approach to the analysis of impulse voltage distribution in transformer windings”, IEE Proceedings,Vol.137,No.4, July 1990.






Suchita Kamble, N. N. Mhala

Paper Title:

Controller for Network Interface Card on FPGA

Abstract: The continuing advances in the performance of network servers make it essential for network interface cards (NICs) to provide more sophisticated services and data processing. Modern network interfaces provide fixed functionality and are optimized for sending and receiving large packets. Network interface cards allow the operating system to send and receive packets through the main memory to the network. The operating system stores and retrieves data from the main memory and communicates with the NIC over the local interconnect, usually a peripheral component interconnect bus (PCI). Most NICs have a PCI hardware interface to the host server, use a device driver to communicate with the operating system and use local receive and transmit storage buffers. NICs typically have a direct memory access (DMA) engine to transfer data between host memory and the network interface memory. In addition, NICs include a medium access control (MAC) unit to implement the link level protocol for the underlying network such as Ethernet, and use a signal processing hardware to implement the physical (PHY) layer defined in the network. To execute and synchronize the above operations NICs also contents controller whose architecture is customized for network data transfer. In this paper we present the architecture of application specific controller that can be used in NICs.

ALU, Fast adder, Network interface card, RAM, ROM, Universal shit register, Instruction decoder.


1. Toshio Fujisawa, et al, “A Single-Chip 802.11a MAC/PHY With a 32-b RISC Processor”, in IEEE Journal Of Solid-State Circuits, Vol. 38, No. 11, November 2003.
2. J. R. Allen, et al, “IBM PowerNP network processor: Hardware, software, and applications,” in IBM Journal of Research & Development, Vol. 47, No. 2/3 March/May 2003.

3. Xiaoning Nie, et al, “A New Network Processor Architecture for High-speed Communications,” in IEEE Workshop on Signal Processing Systems, 1999.

4. H. Peter Hofstee, “Power Efficient Processor Architecture and The Cell Processor,” in Proceedings of the 11th International Symposium on High-Performance Computer Architecture, 2005.

5. D. L. Perry, “ VHDL”, Tata Mcgraw Hill Edition, 4th Edition, 2002.

6. C. Maxfiled, “The Design Warriors Guide to FPGAs”, Elsevier, 2004.

7. J. Bhaskar, “ VHDL Primer”, Pearson Education, 3rd Edition, 2000.

8. J. Bhaskar, “ VHDL Synthesis Primer”, Pearson Education, 1st Edition, 2002.






Nishant Pathak, Sudhanshu Prakash Tiwari

Paper Title:

Travelling Salesman Problem Using Bee Colony With SPV

Abstract: Challenge of finding the shortest route visiting each member of a collection of locations and returning to starting point is an NP-hard problem. It is also known as Traveling salesman problem, TSP is specific problem of combinatorial optimization studied in computer science and mathematical applications. In our work we present a solution for TSP problem using ABC with SPV rule. In this method we extend Artificial Bee Colony algorithm using SPV rule. Artificial bee colony algorithm solves real coded optimization problems but travelling salesman problem is a discrete optimization problem for converting the ABC algorithm to solve TSP problem SPV rule is used. Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent behavior of honey bee swarm. In the proposed method we extend ABC with SPV rule for local search strategy. The experimental results show that our proposed ABC with SPV performs better than GA (Genetic Algorithm), our ABC with SPV model can reach broader domains in the search space and show improvements in both precision and computational time.

ABC, Artificial Bee Colony, GA, Genetic Algorithm, TSP, SPV.


1. Applegate, D. L.; Bixby, R. M.; Chvátal, V.; Cook, W. J. (2006), “The Traveling Salesman Problem”, ISBN 0691129932.
2. C.A. Silvaa, lM.C. Sousaa, T.A. Runkler, "Rescheduling and optimization of logistic processes using GA and ACO," Engineering Applications of Artificial Intelligence., vol. 21, pp. 343-352, 2008.

3. M. Griltschel, M. Padberg, "Ulysses 2000: In Search of Optimal Solutions to Hard Combinatorial Problems," Technical Report, NewYork University Stern School of Business, 1993.

4. G. Zhao, W. Luo, H. Nie, C. Li, "A Genetic Algorithm Balancing Exploration and Exploitation for the Travelling Salesman Problem,"in Proceedings of the 2008 Fourth International Conference onNatural Computation, 2008, pp. 505-509.

5. Dervis Karaboga • Bahriye Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm” J Glob Optim (2007), pp 459-471

6. W.-L. Zhong, l Zhang, W.-N. Chen, "A Novel Discrete Particle Swarm Optimization to Solve Traveling Salesman Problem," in Proc. IEEE Int. Conf. Evol. Comput. (CEC), 2007, pp. 3283-3287.

7. L.-P. Wong, M.Y. Hean Low, C.S. Chong, "A Bee Colony Optimization Algorithm for Traveling Salesman Problem," Second Asia International Conference on Modelling & Simulation, 2008,pp. 818-823.

8. S. Nonsiri, S. Supratid, "ModifYing Ant Colony Optimization," IEEE Conference on Soft Computing in Industrial Applications, 2008,pp. 95-100.

9. Marco Dorigo. “Ant Colonies for the Traveling Salesman Problem”. IRIDIA, UniversitéLibre de Bruxelles. IEEE Transactions on Evolutionary Computation, 1(1):53–66. 1997.

10. D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey, Technical Report-TR06, 2005.

11. D. Karaboga, B. Akay, "A Survey: Algorithms Simulating Bee Swarm Intelligence," Artificial Intelligence Review., vol. 31, pp. 68-85, 2009.

12. D. E. Goldberg, “Genetic Algorithm in Search. Optimization and Machine Lerning”, Addison Wesley, 1989.

13. G-. G. Jin, S-. R. Joo, “A Study on a Real-Coded Genetic Algorithm,”

14. Journal of Control, Automation, and Systems Engineering, vol. 6, no. 4, pp. 268-274, April 2000.

15. H. Sengoku and I. Yoshihara, "A Fast TSP Solver using GA on JAVA," in Proc. of 3rd International Symposium on Artificial Life and Robotics, vol. I, 1998, pp. 283-288.

16. G.A. Croes, "A method for solving traveling salesman problems,"

17. Operations Research.vol. 6, pp. 791-812, 1958.

18. G. Reineit, "TSPLIB-A traveling salesman problem library," ORSA Journal on Computing., vol. 3, no. 4, pp. 376-384, 1991.

19. XH. Shi, Y.e. Liang, H.P. Lee, C. Lu, Q.x. Wang, "Particle swarm optimization-based algorithms for TSP and generalized TSP," Information Processing Letters., vol 103, pp. 169-176,2007.

20. L. Davis, “Handbook of Genetic Algorithms”, Von Nostrand Reinhold, N.Y., 1991






Nidhi Singh, Devendra Singh, Minakshi Sanadhya

Paper Title:

Command Mode Decoding Logic for Bus Management Unit

Abstract: Command Processing Logic is command execution in spacecraft. The commands and the clock extracted from incoming signal are sent to the command processing logic. The commands are transmitted with redundancy and with encoding. Each command is transmitted four times. To detect error in commands encoding is used. The command processing logic should take care of redundancy and also should extract the correct command bit from the incoming bit stream. Ones correct command is detected the command processing logic produce an authentic pulse. With the rising edge of the authentic pulse, the commands are stored in an auxiliary register. The command processing logic also contains the logic to produce pulse commands. Command Decoding Logic the command decoder extracts the command information, the message bits and stores it in auxiliary resister which can be read using CPU. The command decoder then checks for the command bits and produce the pulse commands.

VHDL, auxiliary register, command decoder, pulse commands, authentic pulse, BMU, command processing logic, Command Decoding Logic.


1. KC Chang,”Digital Design and modelling with VHDL and Synthesis” IEEE computer society press, 1997.
2. James and Armistrong “VHDL Design representation and synthesis”, Prentice Hall PTR, Second Edition, 2001.

3. John F. Wakerly, “Digital design principles and practices”, Prentice Hall 2001

4. Ismail, Roones, “Digital Concepts and Applications”, Harcout Brace College publishers, second Edition, 1994.

5. Tri T. Ha “Digital Satellite communication” C Graw Publications, second Edition 1990.

6. F.Anceau, “A Synchronous approach for clocking VLSI systems”, proceedings of IEEE journal of solid state circuits, Feb-1982.

7. D.G. Messeschmit,” Synchronization in digital system design”, proceedings of IEEE journal on communication, oct-1990.

8. “A simple scheme for PSK demodulation” by P.Balasubramaniam, P.M.Aravindakshan, K.parameswaran and V.K Agrawal.

9. “Optimal Binary communication”, Israll Korn, John P. Fonseka and Shaoui Xing, Sept.2003






Mukesh Arora, Shubhi Jain, Abha Sharma

Paper Title:

Multi Band Circularly Polarized Microstrip Patch Antennas for Mobile Communication

Abstract: This paper represents a circularly polarized microstrip antenna for many kind of wireless communication applications. Circular polarization can be achieved with asymmetries. The emphasis is on to increase the bandwidth of the antenna. In this paper various kinds of techniques has been used to provide circular polarization like triangular slits inserted at the corners, corners of the patch are truncated etc. The proposed antennas have been discussed with Aperture Coupled feeding. Measured results show that radiation patterns with good CP Characteristics are obtained for multi bands. HFSS software used to simulate the antennas.

Microstrip, Bandwidth, Aperture Coupled Feed, HFSS


1. Antenna Theory, C.Balanis, Wiley, 2nd edition (1997), Chapter 14. ISBN 0-471-59268-4.
2. D. M. Pozar, “Microstrip antenna aperture-coupled to a microstripline,”Electron. Lett., vol. 21, no. 2, pp. 49-50, Jan. 1985.

3. Michael Paul Civerolo,” Aperture Coupled Microstrip Antenna Design and Analysis”Thesis, California Polytechnic State University; June2010

4. Y. Sung, “Dual-Band Circularly Polarized Pentagonal Slot Antenna,” IEEE Antennas And Wireless Propagation Letters, vol. 10, Nov.2011.

5. J. R. James, P. S. Hall, and C. Wood, Microstrip Antenna Theory and Design. London, U.K.: Peregrinus, 1981.

6. J. C. Batchelor and R. J. Langley, “Microstrip annular ring slot antennas for mobile applications,” Electron. Lett., vol. 32, no. 18, pp.
7. 1635–1636, Aug. 1996.

8. A. Hakim, C. Laurent, G. Marjorie, L. Jean-Marc, and P. Odile, “Reconfigurable circularly polarized antenna for short-range communication systems,” IEEE
Trans. Antennas Propag., vol. 54, no. 6, pp. 2856–2863, Jun. 2006.

9. J. S. Row, “Design of square-ring microstrip antenna for circular polarisation,” Electron. Lett., vol. 40, no. 2, pp. 93–95, Jan. 2004.

10. W.-S. Chen, C.-K. Wu, and K.-L. Wong, “Square-ring microstrip antenna with a cross strip for compact circular polarization operation,” IEEE Trans. Antennas Propag., vol. 47, no. 10, pp. 1566–1568, Oct. 1999.

11. M. K. Fries, M. Grani, and R.Vahldieck, “A reconfigurable slot antenna with switchable polarization,” IEEE Microw. Wireless Compon. Lett., vol. 13, no. 11, pp. 490–492, Nov. 2003.

12. Ansoft”HFSS’11.

13. S. Maci, G. Biffi Gentili, P. Piazzesi, and C. Salvador, “Dual-band slot-loaded patch antenna,” IEE Proc. Microw. Antennas Propagat. 142, 225–232, June 1995.

14. M. Muraguchi, T. Yukitake, and Y. Naito, “Optimum design of 3-dB branch-line couplers using microstrip lines,” IEEE Trans. Microwave Theory Tech. 31, 674–679, 1983.

15. J. S. Kuo, Novel broadband designs of microstrip antennas, Ph.D. dissertation, Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, 2001.






B.Rajani, P.Sangameswara Raju

Paper Title:

Enhancement of Power Quality by Optimal Placement of Dstatcom For Voltage Sag Mitigation Using Ann Based Approach

Abstract: DSTATCOM is one of the equipments for voltage sag mitigation in power systems. Voltage sag has been considered as one of the most harmful power quality problem as it may significantly affect industrial production. This paper presents an Artificial Neural Network (ANN) based approach for optimal placement of Distribution Static Compensator (DSTATCOM) to mitigate voltage sag under faults. Voltage sag under different type of short circuits has been estimated using MATLAB/SIMULINK software. Optimal location of DSTATCOM has been obtained using a feed forward neural network trained by post-fault voltage magnitude of three phases at different buses. Case studies have been performed on IEEE 30-bus system and effectiveness of proposed approach of DSTATCOM placement has been established.

Power quality, Voltage sag mitigation, DSTATCOM,ANN.


1. IEEE StandardsBoard (1995), “IEEE Std. 1159-1995” IEEE recommended practice for monitoring electric power quality, IEEE Inc.NewYork.
2. Bollen. M , “Understanding power quality problems: Voltage sags and interruptions,”Piscataway, NJ, IEEE Press, 2nd edition 2000.

3. Pirjo Heine and Matti Lehtonon , “Voltage sag distributions caused by power system faults”, IEEE Transactions on Power Systems,Vol. 18, No.4, pp. 1367-1373, November 2003.

4. Ghosh and G. Ledwich, “Power quality enhancement using custom power devices,” Kluwer Academic Publisher, London 2002.

5. L. Gyugyi , “Reactive power generation and control by thyristor circuits,” IEEE Transactions on Industry applications, Vol IA-15,No.-5, pp. 521-532, Sept/Oct 1979.

6. Y H. Song and A.T.Johns, “ Flexible AC Transmission Systems ( FACTs) ,” IEE Power and Energy Series, London, UK, 1999.

7. R. Arnold, “Solutions to the Power quality problems,” Power Engineering Journal, Vol. 15, No.-2, pp. 65-73, April 2001.

8. Olimpo Anaya-Lara and E. Acha, “Modeling and analysis of custom power systems by PSCAD/EMTDC”, IEEE Transactions on Power Delivery, Vol. 17, No. 1, pp. 266-272, January 2002.

9. Arindam Ghosh and Gerard Ledwich, “Compensation of distribution system voltage using DVR”, IEEE Transactions on Power Delivery, Vol. 17, No. 4, pp. 1030-1036, October 2002.

10. D. Mahinda Vilathgamuwa, A.A.D. Ranjith Perera and S. S. Choi, “Voltage sag compensation with energy optimized Dynamic Voltage Restorer”, IEEE Transactions on Power Delivery, Vol. 18, No. 3, pp. 928-936, July 2003.

11. H. Masdi, N. Marium, S. Mahmud, A. Mohamed, S. Yusuf , “Design of a prototype D-STATCOM for voltage sag mitigation”, IEEE National Power and Energy Conference 2004, PECon 2004, pp. 61-66, Kuala Lumpur (Malaysia), November 29-30, 2004.

12. Elandy and Magady M.A. Salama, “Mitigation of voltage disturbances using adaptive perceptron-based control algorithm”, IEEE Transactions on Power Delivery, Vol. 20, No. 1, pp. 309-318, January 2005.

13. Elnady and Magdy M.A. Salama ,” Unified approach for mitigating voltage sag and voltage flicker using the D-STATCOM ,” IEEE Transactions on Power Delivery, Vol. 20, No.2, pp. 992-1000, April 2005.

14. S. S. Choi, J. D. Li and D. Mahinda Vilathgamuwa, “A generalized voltage compensation strategy for mitigating the impacts of voltage sags/swells”, IEEE Transactions on Power Delivery, Vol. 20, No.3, pp. 2289-2297, July 2005.

15. Han, B. Bae, H. Kim and S. Baek, “Combined operation of Unified Power-Quality Conditioner with Distributed Generation”, IEEE Transactions on Power Delivery, Vol. 21, No. 1, pp. 330-338, January 2006.

16. Ravi Kr. S. V. and S. Siva Nagaraju, “Power quality improvement using D-STATCOM and DVR,” International Journal Medwell of Electrical Power Engg.1: pp. 368-376, 2007.

17. H. K. Al-Hadidi, A. M. Gole and David A. Jacobson, “Minimum power operation of cascade inverter-based Dynamic Voltage Restorer”, IEEE Transactions on Power Delivery, Vol. 23, No. 2, pp. 889-898, April 2008.

18. H. K. Al-Hadidi, A. M. Gole and David A. Jacobson, “ A novel configuration for a cascaded inverter-based Dynamic Voltage Restorer with reduced energy storage requirements”, IEEE Transactions on Power Delivery, Vol. 23, No. 2, pp. 881-888, April 2008.

19. S. A. Saleh, C. R. Moloney and M. Azizur Rahman, “Implementation of a Dynamic Voltage Restorer system based on discrete wavelet transform”, IEEE Transactions on Power Delivery, Vol. 23, No. 4, pp. 2366-2375, October 2008.

20. Pedro Roncero-Sanchez and Enrique Acha, “Dynamic Voltage Resorer based on flying capacitor multilevel converters operated by repetitive control”, IEEE Transactions on Power Delivery, Vol. 24, No. 2, pp. 951-960, April 2009.

21. Benachaiba and B. Ferdi, “Power quality Improvement using DVR, American Journal of Applied Sciences, Vol. 6, No. 3, pp. 396-400, 2009.

22. B. Bae, J. Jeony, J. Lee and B. Han, “Novel sag detection method for line-interactive Dynamic Voltage Resorer”, IEEE Transactions on Power Delivery, Vol. 25, No. 2, pp. 1210-1211, April 2010.

23. Woo Cheol Lee, Dong Myung Lee and Taeck Kie Lee, “New control scheme for a Unified Power Quality Compensator-Q with minimum active power injection”, IEEE Transactions on Power Delivery, Vol. 25, No.2, pp. 1068-1076, April 2010.

24. Liu Yingying, Xu Yonghai, Xiao Xianging, Zhu Yongqiang, Guo Chunlin, “A stages comensation and control strategy for series Power-Quality regulator”, IEEE Transactions on Power Delivery, Vol. 25, No. 4, pp. 2807-2813, October 2010.

25. Ebrahim Babaei, Mohammad Farhadi Kangarlu and Mehran Sabahi, “Mitigation of voltage disturbances using Dynamic Voltage Restorer based on direct converters”, IEEE Transactions on Power Delivery, Vol. 25, No. 4, pp. 2676-2683, October 2010.

26. J. V. Milanovic and Y. Zhang , “ Modeling of FACTS Devices for voltage sag mitigation studies in large power systems ,” IEEE Transactions on Power Delivery , Vol. 25, No. 4, pp. 3044-3052, October 2010.

27. Jovica V. Milanovic and Yan Zhang, “Global minimization of financial losses due to voltage sags with FACTS based devices”, IEEE Transactions on Power Delivery, Vol. 25, No. 1, pp. 298-306, January 2010.

28. Yan Zhang and Jovica V. Milanovic, “Global voltage sag mitigation with FACTS based devices”, IEEE Transactions on Power Delivery , Vol. 25, No. 4, pp. 2842-2850, October 2010.

29. E.A.Mohamed , N.D. Rao, “Artificial Neural Network based fault diagnostic system for electric power distribution feeders,” Electric Power System Research, Vol. 35, No. 1, pp. 1-10, 35 October 1995.

30. Ernesto Vazquez, Hector J. Altuve, Oscar L. Chacon, “Neural network approach to fault detection in electric power systems”, IEEE International conference on Neural Networks, Vol. 4, pp. 2090-2095, June 3-6, 1996, Washington, DC, USA.

31. F.J. Alcantare, J. R. Vazquez, P. Salmeron, S.P. Litran, M.I. Arteaga Orozco, “On line detection of voltage transient disturbances using ANNs,” International
Conference on Renewable Energies and Power Quality (ICREPQ 09) 15th to 17th April 2009, Valencia, Spain.

32. S. M. Halpin and L. L. Grigsby, “A comparison of fault calculation procedures for industrial power distribution systems: The past, the present, and the future”, IEEE International Conference Industrial Technology, Guangzhon, China, 1994.

33. MATLAB 7.0 User’s Guides for SIMPOWER SYSTEMS And Neural Network Toolbox.

34. “Power systems test case archieve” available at h`ttp://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm






M.K.Sharma, Vintesh Sharma, Rajesh Dangwal

Paper Title:

Reliability Analysis of A System Using Intuitionstic Fuzzy Sets

Abstract: In General fuzzy sets are used to analyze the system Reliability. Present paper attempts to review the fuzzy/possibility tools when dealing with reliability of series-parallel network systems. Various issues of reasoning-based approaches in this framework are reviewed, discussed and compared with the standard approaches of reliability. To analyze the fuzzy system reliability, the reliability of each component of the system is considered as a trapezoidal intuitionstic fuzzy number. Trapezoidal intuitionstic fuzzy number arithmetic operations are also performed to evaluate the fuzzy reliability of the system. A numerical example is also given to illustrate the method.

Fuzzy sets, Intuitionstic Fuzzy sets, Intuitionstic Fuzzy Numbers, Reliability, (α, β)- Cuts, Network System AMS Subject Classification: - 60K10


1. Kaufmann and M.M.Gupta, “Fuzzy mathematical models in Engineering and Management sciences (North-Holland Amsterdam, (1988).
2. M.K.Sharma and D. Pandey, “Profust and Posfust Reliability a Network System” Journal of Mountain Research, Vol. 2, 2007, 97-112.

3. K.Y. Cai, “System failure and fuzzy methodology: an introductory overview” Fuzzy sets and systems, 83(1996), 113-133.

4. M.K.Sharma and D. Pandey, “Reliability analysis of multistate fault tree model” Mathematics Today, Vol. 25(2009) 7-21.

5. M.K.Sharma and D.Pandey, “Vague Set Theoretic Approach to Fault Tree Analysis” Journal of International Academy of Physical Sciences, Vol. 14 No. 1(2010), 1-14.

6. Zadeh L. A. “Fuzzy sets”, Information and Control, Vol 8, No. 3, pp. 338-353, 1965.

7. Atanassov, K. “Intuitionistic fuzzy sets”. Fuzzy Sets and Systems, Vol 20, No. 1, pp.87-96, 1986.

8. Gau W. L, Buehrer D. J. “Vague sets”, IEEE Transactions on Systems, Man, and Cybernetics Vol 23, pp. 610-614, 1993.

9. P. Burillo, H. Bustince and V. Mohedano, Some definition of intuitionistic fuzzy number, Fuzzy based expert systems, fuzzy Bulgarian enthusiasts, September 28-30, 1994, Sofia, Bulgaria.

10. Szmidt, E, Kacprzyk, J. “Intuitionistic fuzzy sets in decision making”, Notes IFS, Vol 2, No. 1, pp. 15-32, 1996.

11. Atanassov, K. Intuitionistic Fuzzy Sets: Theory and Applications, Physica-Verlag, Heidelberg, New York, 1999.

12. K.T. Atanassov, G. Gargov, Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 31 (3) (1989) 343–349.

13. K.T. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Sets and Systems 33 (1) (1989) 37-46.

14. K.T. Atanassov and G. Gargov, Elements of intuitionistic fuzzy logic, Part I, Fuzzy Sets and Systems, 95 (1) (1998) 39–52.

15. A.I. Ban, Nearest Interval Approximation of an Intuitionistic Fuzzy Number, Computational Intelligence, Theory and Applications (Bernd Reusch (Ed.)), Springer-Verlag, Berlin, Heidelberg 2006 (p-229-240).

16. Szmidt, E, Kacprzyk, J. “Distances between intuitiionistic fuzzy sets”, Fuzzy Sets and Systems, Vol 114, pp. 505-518, 2000.

17. H.B. Mitchell, Ranking-Intuitionistic Fuzzy Numbers, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12 (3) (2004), 377-386.

18. M.H. Shu, C.H. Cheng and J.R. Chang, Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit Board assembly, Microelectronics and Reliability, 46 (12) (2006), 2139-2148.

19. G.S. Mahapatra, and T.K. Roy, Reliability Evaluation using Triangular Intuitionistic Fuzzy Numbers Arithmetic Operations, Proceedings of World Academy of Science, Engineering and Technology, Malaysia, 38 (2009), 587-585.

20. L.S.Srinath, “Reliability Engineering.” East-West Press Private Limited, New Delhi, Fourth Edition, 1985.






Neelam Tyagi, Simple Sharma

Paper Title:

Weighted Page Rank Algorithm Based on Number of Visits of Links of Web Page

Abstract: The World Wide Web consists billions of web pages and hugs amount of information available within web pages. To retrieve required information from World Wide Web, search engines perform number of tasks based on their respective architecture. When a user refers a query to the search engine, it generally returns a large number of pages in response to user’s query. To support the users to navigate in the result list, various ranking methods are applied on the search results. Most of the ranking algorithms which are given in the literature are either link or content oriented. Which do not consider user usage trends. In this paper, a page ranking mechanism called Weighted PageRank Algorithm based on Visits of Links (VOL) is being devised for search engines, which works on the basis of weighted pagerank algorithm and takes number of visits of inbound links of web pages into account. The original Weighted PageRank algorithm (WPR) is an extension to the standard PageRank algorithm. WPR takes into account the importance of both the inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. The proposed algorithm is used to find more relevant information according to user’s query. So, this concept is very useful to display most valuable pages on the top of the result list on the basis of user browsing behavior, which reduce the search space to a large scale. The paper also presents the comparison between original and VOL method.

World Wide Web, Search Engine, PageRank, Inbound Link, Outbound Link.


1. N. Duhan, A. K. Sharma and Bhatia K. K., “Page Ranking Algorithms: A Survey, Proceedings of the IEEE International Conference on Advance Computing, 2009, 978-1-4244-1888-6.
2. S. Brin, and Page L., “The Anatomy of a Large Scale Hypertextual Web Search Engine”, Computer Network and ISDN Systems, Vol. 30, Issue 1-7, pp. 107-117, 1998.

3. Larry Page, and Sergey Brin, Rajeev Motwani, Terry Winograd, “The PageRank Citation Ranking: Bring Order to the Web”, Technical report in Stanford U, 1998.

4. R. Cooley, B. Mobasher, and Srivastava, J., "Web Mining: Information and pattern discovery on the World Wide Web”. In proceedings of the 9th IEEE International Conference on tools with Artificial Intelligence (ICTAI’ 97).Newposrt Beach,CA 1997.

5. J. Kleinberg, “Hubs, Authorities and Communities”, ACM Computing Surveys, 31(4), 1999.

6. Gyanendra Kumar, Neelam Duahn, and Sharma A. K., “Page Ranking Based on Number of Visits of Web Pages”, International Conference on Computer & Communication Technology (ICCCT)-2011, 978-1-4577-1385-9.

7. R. Kosala, and H. Blockeel, “Web Mining Research: A Survey”, SIGKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining Vol. 2, No. 1 pp 1-15, 2000.

8. Wenpu Xing and Ghorbani Ali, “Weighted PageRank Algorithm”, Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR ’04), IEEE, 2004.






Naveen Kumar, Parmender Balyan, Poonam Yadav

Paper Title:

An Optimized Parametric Approach for Improving Handover in WiMax

Abstract: WiMAX network is one of the high speed networks that provide the high level security and reliability over the network. One of the common problems in WiMax network is the handover mechanism. Most of the chances of data loss and intrusion are during the handover process. To get the better efficiency and throughput an efficient handover mechanism is required. This paper proposes an optimized handover scheme for mobile WiMAX networks that tries to minimize packet loss during handover. With the help of simulation we will show that this scheme is more efficient then traditional handover. The proposed approach is the parametric approach where some parameters are used as the decision factor to elect the nearby base station.

WiMAX, VoIP, Handover, Efficient, Parametric


1. Dragorad Milovanovic, Zoran Bojkovic, “On performance of TCP and VoIP traffic in mobile WiMAX networks”, Issue 5, Volume 9, May 2010
2. Maneesh Bakshi, VoIP / Multimedia over WiMAX (802.16), 9, Sep. 2006.

3. IEEE Xplore – “Security Challenge and Defense in VoIP Infrastructures by D Butcher” – 2007.

4. S.C. Wang, H.H. Liang and K.Q. Yan, “Capability Based Clustering Mechanism in WiMAX”, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2009 Vol I IMECS 2009, March 18 - 20, 2009, Hong Kong.

5. Harshal A. Arolkar, GLS Institute of Computer,Technology, Ellisbridge, Ahmedabad, Gujarat, India “ Ant Colony based Approach for Intrusion Detection on Cluster Heads in WSN”

6. IEEE Std 802.16-TM 2004 (Revision of IEEE Std 802.16-2001), “IEEE standard for local and metropolitan area networks – Part 16: Air interface for fixed and mobile broadband wireless access systems,” Oct. 2004.

7. Sik Choi, Gyung-Ho Hwang, Taesoo Kwon, ”Fast Handover Scheme for Real-Time Downlink Services in IEEE 802.16e BWA System”, IEEE VTC 2005 spring, Sweden, May 2005, Vol. 3 (2005), pp. 2028-2032.

8. Doo Hwan Lee, etc, “Fast Handover Algorithm for IEEE802.16e Broadband Wireless Access System”, Proceedings of 1st International Symposium on Wireless Pervasive Computing, Jan 2006, pp 142-145.

9. IEEE 802.16e 2005, “IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems - Amendment 2: Physical and medium access control layers for combined fixed and mobile operation in licensed bands and corrigendum 1,” February 2006

10. P. Mach and R. Bestak, “WiMAX Performance Evaluation” IEEE conference on Networking, ICN ’07, Apr. 2007, pp.17.

11. Paul Boone, Michel Barbeau and Evangelos Kranakis, “Strategies for Fast Scanning and Handovers in WiMax/802.16” Proceedings of 2nd International Conference on Access Networks, Aug 2007,pp 1-7.

12. Jenhui Chen, Chih-Chieh Wang and Jiann-Der Lee, “Pre-Coordination Mechanism for Fast Handover in WiMAX Networks” The 2nd IEEE International
Conference on Wireless Broadband and Ultra Wideband Communications, Aug 2007,pp 15

13. A. Belghith and L. Nuaymi, “WiMAX Capacity Estimation and Simulation Results”, Proceedings of IEEE International Conference on Vehicular Technology Conference, VTC Spring, Oct 2008, pp 1741

14. Shen Gu Jiajing Wang , “An enhanced handover target cell selection algorithm for WiMAX network” Proceedings of IEEE APCC 2009 15th Asia-Pacific Conference ,Oct 2009, pp 774-777.

15. Qi Lu, Maode Ma,“ A Location-Aware Fast Handover Scheme for Mobile WiMax Networks” Proceedings of IEEE 7th International Conference on Information, Communications and Signal Processing, Macau, Dec 2009,pp 1.

16. Pareit, Daan Petrov, Viktor Lannoo, Bart Tanghe, “A Throughput Analysis at the MAC Layer of Mobile WiMAX” , ” Proceedings of IEEE International Conference on Wireless Communications and Networking Conference (WCNC) , April 2010,pp1






Y. Sukanya, Sridivya Pathapati

Paper Title:

FSK Modem Using PSoC

Abstract: The trend to low-cost distributed data communications over the switched telephone network has developed the need to integrate even more functions into a single device. Until recently, the baseband to pass band (and vice versa) transformation on the serial data produced by a typical data terminal relied on expensive and bulky external MODEMS to perform that function. This paper will describe the implementation of a single-chip FSK Modem circuit based on a Programmable System on Chip that integrates a modulator, a demodulator, filters, timers and a baud-rate generator to accomplish that transformation. The goal is to demonstrate the possibilities offered by system-on-chip programmable devices in specific processing systems, where the costs make the use of specific integrated circuits unaffordable. A common way of reducing system costs when low baud rates are acceptable is to use an FSK modem. The modem is responsible for both the transmission and reception of the data encoded signal. The device is capable of transmitting and receiving FSK signals in simplex, half-duplex and asymmetrical full-duplex modes over two-wire lines and symmetrical full-duplex signals over four-wire telephone lines. The programming environment of PSoC i.e., PSoC Designer Integrated Development Environment will assist in configuring, source code compiling, building, and debugging the system that runs from internal memory of the PSoC device.

FSK, Modem, PSoC, PWM, Programmable.


1. PSoC Microcontroller Datasheet [Online]. Available: http://www.cypressmicro.com
2. A. Doboli, and E. Curry,Introduction to Mixed Signal Embedded Design, San Jose, CA USA :Cypress University Alliance 2008.

3. David R. Smith, "Digital Transmission Systems", Kluwer International Publishers, 2003, ISBN 1-4020-7587-1

4. Simon Haykin, "Digital Communications", John Wiley & Sons, 1988. ISBN 978-0-471-62947-4.

5. John Proakis, "Digital Communications", 4th edition, McGraw-Hill, 2000. ISBN 0-07-232111-3.

6. Lorraine J. Plaga ,”A General analysis of two FSK modem demodulators”, IEEE Transactions on Consumer Electronics, Vol. CE-28, No. 4, November 1982, Consumer Strategic Marketing Phoenix, Arizona 85062.

7. Monte Mar, Member, IEEE, Bert Sullam, Member, IEEE, and Eric Blom,”An Architecture for a Configurable Mixed-Signal Device”, IEEE Journal of Solid-State Circuits, VOL. 38, NO. 3, March 2003.

8. PSoCDeviceDatasheets_CY8C29x66_E, http://www.cypress.com//

9. J. Faura, C. Horton, P. V. Duong, J. Madrenas, M. A. Aguirre, and J.M. Insenser, “A novel mixed-signal programmable device with on-chip microprocessor,” in Proc. Custom Integrated Circuits Conf. (CICC’97), Santa Clara, CA, 1997, pp. 103–106.

10. David Tomanek, DAT Consulting Cons, Pasadena, “What is PSoC”, Applied Electronics (AE), 2010 International Conference on California, USA

11. Dennis Seguine, “Simplified FSK Detection”, Application Note, Cypress Microsystems

12. Channel(communications) http://en.wikipedia.org/wiki/Communication_channel.






Rezwan Ahmad, Md. Imdadul Islam, M. R. Amin

Paper Title:

Determination of Medium Access Probability of Cognitive Radio under Different Fading Channels

Abstract: The correct decision in detecting the presence of the primary users is a vital requirement in cognitive radio network. Incorporation of spatial false alarm makes the derivation of probability of correct decision a difficult task. The previous literature performs the task only for the case of the received signal under Normal distribution of the fading channel. In this paper, we enhance the work for three small scale fading channels: Rayleigh, Rician and Nakagami-m Fading Channels to get the real scenario of a cognitive radio network in an urban area. The impact of fading parameters and sensing range on the profile of probability of correct decision is also investigated to optimize the performance of the network.

Cognitive radio, spectrum sensing, spectrum hole, medium access probability, spatial false alarm.


1. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, pp. 201-220, Feb. 2005.
2. D.Cabric, S.Mishra, and R.W.Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Proc. 38th Asilomar Conf. Signals,Systems and Computers, Pacific Grove,CA, 2004, pp. 772-776.

3. A. Ghasemi and E. S. Sousa, “Spectrum Sensing in Cognitive Radio Networks: The Cooperation-Processing Trade-Off,” Wiley Wireless Commun. and Mobile Comp. Special Issue on Cognitive Radio, Software-Defined Radio, and Adaptive Wireless Systems, vol. 7, no. 9, pp. 1049–60, Nov. 2007

4. Jun Ma, Geoffery Ye Li, and Biing Hwang (Fred) Juang, “Signal Processing in Cognitive Radio,” in Priceeding of the IEEE, May 2009, vol. 97, no. 5, pp.805-823.

5. A Ghasemi, ES Sousa, “Interference aggregation in spectrum-sensing cognitive wireless networks,” IEEE J. Select Topics Signal Process., vol. 2, no. 1, pp. 41–55, 2008.

6. Weijia Han, Jiandong Li, Qin Liu, and Linjing Zhao, “Spatial False Alarms in Cognitive Radio,” IEEE Commun. Lett.., vol. 15, no. 5, pp. 518 – 520, May. 2011.

7. Masha Derakhshani, Tho Le-Ngoc, “Aggregate Interference and capacity-Outage Analysis in a Cogitive Radio Network,” IEEE Trans. Veh. Technol. , vol. 61, no, 1, pp. 196-207, January 2012.

8. Masha Derakhshani, Tho Le-Ngoc, “Beacon transmitter placement on aggregate interference and capacity-outage in a cognitive radio network,” in 72nd IEEE Vehicular Technology Conference, Ottawa, Canada, Sep. 2010, pp. 1-5.

9. Marco Cardenus-Jaurez, Mounir Ghogho, “Spectrum Sensing and Throughput Trade-off in Cognitive radio under Outage constraint over Nakagami Fading,” IEEE Commun. Lett., vol. 15, no. 10, pp. 1110-1113, October 2011.

10. A. Ghasemi and E. S. Sousa, “Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments,” in Proc. IEEE 1st Symp. Dynamic Spectrum Access Networks, Baltimore, MD, Nov. 2005, pp. 131–36.

11. F F. Digham, M.S. Alouini, M. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Trans Commun., vol. 55, no. 1, pp. 3575–3579, Jan. 2007.

12. D. Cabric, “Experimental study of spectrum sensing based on energy detection and network cooperation,” IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 4502-4507, Nov. 2008.





Chetna, Harpal Tanwar, Navdeep Bohra

Paper Title:

An Approach to Reduce Web Crawler Traffic Using Asp.Net

Abstract: Now days search engine transfers the web data from one place to another. They work on client server architecture where the central server manages all the information. A web crawler is a program that extracts the information over the web and sends it to the search engine for further processing. It is found that maximum traffic (approximately 40.1%) is due to the web crawler. The proposed scheme shows how web crawler can reduce the traffic using Dynamic web page and HTTP GET request using asp.net.

Crawler, Search Engine, WWW.


1. http://en.wikipedia.org/wiki/Web_search_engine
2. Toshiyuki Takahashi, Hong Soon sang, Kenjiro Taura “World Wide Web Crawler”, Takahashi wwwc2002.

3. Shekhar Misra, Anurag Jain, Dr. A.K. Sachan “A Query based Approach to Reduce the Web Crawler Traffic using HTTP Get Request and Dynamic Web Page” ,International Journal of Computer Applications (0975 – 8887), Volume 14– No.3, January 2011.

4. Articles about Web Crawlers available at - <<http://en.wikipedia.org/~/~/# Examples_ of_ Web_ Crawlers.

5. Sharma A.K, Dixit. A and Singhal N. “Design of a Priority Based Frequency Regulated Incremental Crawler”, 2010 International Journal of Computer Applications (ISSN: 0975 –8887,) Volume 1 – No. 1, (pp: 42-47)

6. Yuan, X.M. and J. Harms, “An efficient scheme to remove crawler traffic from the internet”, Proceedings of the 11th International Conference on Computer Communications and Networks, Oct 2002. 14-16, IEEE CS Press, (pp: 90-95).

7. Ikada, Satoshi, “Bandwidth Control System and method capable of reducing traffic congestion on content servers” Dec 2008.

8. Bal. S and Nath. R, “Filtering the Web pages that are the not modified at remote site, without downloading using mobile crawler”. Information Technology journal 9(2)2010, ISSN 1812-5638, Asian Network for sciencetific information, (pp: 376-380).

9. Sushil Kumar, Deepak jhangu, Bharti mittal “Design a Web Crawler using VB.NET Technology”, BMU, 2010.

10. Shekhar Misra et al, “Smart Approach to Reduce the Web Crawling Traffic of Existing System using HTML based Update File at Web Server”, International Journal of Computer Applications (0975 – 8887), Volume 11– No.7, December 2010.






O. M. Elzeki, M. Z. Rashad, M. A. Elsoud

Paper Title:

Overview of Scheduling Tasks in Distributed Computing Systems

Abstract: Distributed System is large scale computing environment that includes many subscribed resources to perform tasks more rapidly, stability, accuracy and availability. Nowadays, grid computing and cloud computing are widely common available distributed environment. In these computing there is many tasks requires to be executed by the available resources to achieve best performance, minimal total time for completion, shortest response time, utilization of resource usage and etc. Because of these different intentions and high performance of computing environment, we need to design, develop, propose a scheduling algorithm to outperform appropriate allocation map of tasks due to different factors. In this paper we present a package of reviews based on different factors which affect scheduling process such as communication cost and execution time. Here we have studied many different homogenous algorithms and state of art algorithms that can be applied in grid, cloud or both. These algorithms have different perspectives, working principles, domains and others.

Cloud, Distributed, Grid, Scheduling.


1. Journal of Theoretical and Applied Information Technology. (2011, April 9). [Online]. Available: http://www.jatit.org/distributed-computing/grid-vs-distributed.htm.
2. Dr.D.I.Georage Amalarethinam, P.Muthulakshmi, "An Overview of the Scheduling Policies and Algorithms in Grid Computing", " International Journal of Research and Review in Computer Science (IJRRCS)", vol. 2,no. 2, April 2011, pp. 280-294.

3. Prof. Robert van Engelen, "Concepts and Architecture of Grid Computing", "Advanced Topics Spring 2008, HPC II", spring 2008.

4. T. Casavant and J. Kuhl, "A Taxonomy of Scheduling in General Pupose Distributed Computing Systems", "IEEE Trans. on Software Engineering", vol. 14, no. 2, February 1988, pp. 141-154.

5. M. Arora, S. K. Das, R. Biswas, "A Decentralized Scheduling and Load Balancing Algorithm for Heterogeneous Grid Environments", "Proc. Of International Conference on Parallel Processing Workshops (ICPPW’02)", Vancouver, British Columbia Canada, August 2002, pp. 499-505.

6. Fatos Xhafa, Ajith Abraham, "Computational models and heuristic methods for Grid scheduling problems", “Future Generation Computer Systems 26", 2010, pp. 608-621.

7. El-Rewini, H., Ali, H.H., Lewis, T. Task scheduling in multiprocessing systems, IEEE Journal, December 1995, vol. 28, pp. 27-37.

8. U. Schwiegelshohn, R. Yahyapour, "Analysis of First-Come-First-Serve parallel job scheduling", " Proceedings of the 9th SIAM Symposium on Discrete Algorithms", 1998, pp. 629-638.

9. M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen and R. F. Freund, "Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems", "J. of Parallel and Distributed Computing", vol. 59, no. 2, November 1999, pp.107-131.

10. R. Armstrong, D. Hensgen, and T. Kidd, “The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions”, “7th IEEE Heterogeneous Computing Workshop (HCW '98)”, 1998, pp. 79-87.

11. R. F. Freund, M. Gherrity, S. Ambrosius, M. Campbell, M. Halderman, D. Hensgen, E. Keith,T.Kidd, M. Kussow, J. D. Lima, F. Mirabile, L. Moore, B. Rust, and H. J. Siegel, “Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet”, “7th IEEE Heterogeneous Computing Workshop (HCW '98)”, 1998, pp. 184-199.

12. R. F. Freund and H. J. Siegel, “Heterogeneous processing”, IEEE Comput. 26, June 1993.

13. Rasmus V. Rasmussen, Michael A. Trick . Round Robin scheduling – a survey, European Journal of Operational Research, vol. 188, Issue 3, August 2008, pp. 617–636.

14. Ruay-Shiung Chang, Jih-Sheng Chang, Po-Sheng Lin, “An ant algorithm for balanced job scheduling in grids”, “Future Generation Computer Systems 25”, 2009, pp. 20–27.

15. M. Maheswaran, Sh. Ali, H. Jay Siegel, D. Hensgen, R. F. Freund, “Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems”, “Journal of Parallel and Distributed Computing”, vol. 59, 1999, pp. 107-131.

16. T. D. Braun, H. Jay Siegel, N. Beck, L. L. Boloni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, and B. Yao, “A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems”, in “Journal of Parallel and Distributed Computing”, vol. 61, 2001, pp. 810-837.

17. Saeed Parsa, Reza Entezari-Maleki, “RASA: A New Grid Task Scheduling Algorithm”, “International Journal of Digital Content Technology and its Applications”, vol. 3, no. 4, December 2009, pp. 91-99.

18. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic. “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility”, “Future Generation Computer Systems, 25”, June 2009, pp. 599-616.

19. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose, and Rajkumar Buyya, “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms”, unpublished.

20. R. Braun, H. Siegel, N. Beck, L. Boloni, M. Maheswaran, A. Reuther, J. Robertson, M. Theys, B. Yao, D. Hensgen and R. Freund, "A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing System", Journal of Parallel and Distributed Computing Systems, vol. 61, no. 6, 2001, pp.810-837.

21. He. X, X-He Sun, and Laszewski. G.V, "QoS Guided Min-min Heuristic for Grid Task Scheduling," Journal of Computer Science and Technology, vol. 18, 2003, pp. 442-451.

22. Kamalam.G.K and Muralibhaskaran.V, "A New Heuristic Approach:Min-Mean Algorithm For Scheduling Meta-Tasks On Heterogenous Computing Systems", "International Journal of Computer Science and Network Security", vol.10, no.1, January 2010.

23. Sameer Singh Chauhan,R. Joshi. C, "QoS Guided Heuristic Algorithms for Grid Task Scheduling", "International Journal of Computer Applications", vol. 2, no.9, June 2010, pp 24-31.

24. Singh. M and Suri. P.K, "QPS A QoS Based Predictive Max-Min, Min-Min Switcher Algorithm for Job Scheduling in a Grid", "Information Technology Journal", vol. 7, Issue. 8, 2008, pp. 1176-1181.

25. T. Kokilavani, Dr. D.I. George Amalarethinam, "Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing", "International Journal of Computer Applications", vol. 20, no. 2, April 2012, pp. 43-49.

26. Hai Zhong, Kun Tao, Xuejie Zhang, "An Approach to Optimized Resource Scheduling Algorithm for Open-source Cloud Systems “, 5th Annu. Conf. China Grid Conference, China, 2010.

27. Y. Yang, K. Liu, J. Chen, X. Liu, D. Yuan and H. Jin, "An Algorithm in SwinDeW-C for Scheduling Transaction-Intensive Cost-Constrained Cloud Workflows", 4th IEEE International Conference on e-Science, 374-375, Indianapolis, USA, December 2008.

28. Suraj Pandey, LinlinWu, Siddeswara Mayura Guru, Rajkumar Buyya, "A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments", unpublished

29. Cui Lin, Shiyong Lu,” Scheduling ScientificWorkflows Elastically for Cloud Computing”, 4th International Conf. IEEE on Cloud Computing, 2011.

30. W. Chen, J. Zhang, “An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements”, "IEEE Transactions on
Systems, Man, and Cybernetics - Part C: Applications and Reviews", vol. 39, no. 1, January 2009.

31. R.Madhubala, "An Illustrative study on Cloud Computing", "International Journal of Soft Computing and Engineering", vol. 1, issue. 6, January 2012, pp. 286-290.






Rambir Joon, Sandeep, Manveen Singh Chadha

Paper Title:

Analysis of WIMAX Handover

Abstract: WIMAX is Wireless Interoperability for Microwave Access. It is a telecommunication technology that provides wireless data over long distances in several ways, from point-to-point links to full mobile cellular type access. The main consideration of Mobile Wimax is to achieve seamless handover such that there is no loss of data. In Wimax both mobile station (MS) and base station (BS) scans the neighbouring base stations for selecting the best base station for a potential handover. Two types of handovers in wimax are: Hard handover (break before make) and Soft handover (make before break). To avoid data loss during handover we have considered soft handovers in this paper. We have proposed a technique to select a base station for potential soft handover in Wimax. We have developed a base station selection procedure that will optimize the soft handover such that there is no data loss; handover decision is taken quickly and thus improving overall handover performance. We will compare the quality of service with hard handover and soft handover. We have analysed the proposed technique with an existing scheme for soft handover in Wimax with simulation results.

IEEE 802.16e, Hard Handover, Soft Handover.


1. Zdenek Becvar, Jan Zelenka, Implementation of Handover Delay Timer into WiMAX, (http://fireworks.intranet.gr).
2. IEEE P802.16e/D12, “Air Interface for Fixed and Mobile Broadband Wireless Access Systems: Amendment for Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands”, October 2005.

3. Arpan Mandal, Mobile wimax: pre-handover optimization using hybrid Base Station selection procedure, University of Canterbury, 2008.

4. Andrews, J. G., A. Ghosh, Fundamentals of WiMAX : Understanding broadband wireless networking, Prentice Hall ,et al. (2007).

5. T. Bchini, N. Tabbane, E. Chaput, S. Tabbane and A-L. Beylot, Performance of Soft Handover – FBSS Compared to Hard Handover in case of High Speed in IEEE 802.16e for Multimedia Traffic, 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 22-26, TUNISIA 2009.

6. IEEE Std: “Air Interface for Fixed and Mobile Broadband Wireless Access Systems,” IEEE 802.16e, Part 16, February 2006.

7. Hyang Duck Cho, JaeKyun Park, Keumsang Lim, Jongha Kim, Wooshic Kim,The Mobile Controlled handover method for Fixed Mobile Convergence between WLAN, CDMA and LAN.

8. Liu, Zhou,Challenges and Solutions for Handover Issues in 4G Wireless Systems, An Overview.

9. Yue Chen Soft Handover Issues in Radio Resource Management for 3G WCDMA Networks,2003.

10. WiMAX Forum. “WiMAX End-to-End Network Systems Architecture,” Draft Stage 2: Architecture Tenets, Reference Model and Reference Points, June 2007.

11. Youngbin Im, Hakyung Jung, Ji Hoon Lee, Vertical Handovers in Multiple Heterogeneous Wireless Networks: A Measurement Study for the Future Internet.

12. Zdenek Becvar, Jan Zelenka ,Handovers in the Mobile WiMAX.

13. Michael Carlberg Lax and Annelie Dammander ,WiMAX - A Study of Mobility and a MAC-layer Implementation in GloMoSim.

14. N. P. Singh , Brahmjit Singh, Performance Enhancement of Cellular Network Using Adaptive Soft Handover Algorithm.






Chitrakant Singh, Nitin Jain

Paper Title:

Signal Acquisition for Software GPS Receiver

Abstract: This paper presents the signal acquisition scheme. Compared to conventional GPS receivers, this design provides flexibility and low cost for algorithm redesign and different IF frequency selection capability. Complete Digital Down Conversion from IF to baseband I/Q and acquisition scheme are implemented using various software IPs of System Generator (SysGen) tool in a single Field Programmable Gate Array (FPGA) virtex-5. With simulation results, it is proved that this approach performs reliable digital down conversion and acquisition for software GPS receiver.

GPS Receiver, DDC, FFT, Acquisition


1. James Bao-Yen Tsui, Fundamentals of Global Positioning System Receivers- A Software Approach, Jhon Willey & Sons, Inc., Hoboken, NJ, 2005.
2. L.Wu, “Signal Acquisition and Tracking for Software GPS Receiver”, 2009, IEEE.

3. W. Lijun, “Data Acquisition and Processing for Software GPS Receivers”,2008, ICMMT Proceedings, IEEE.

4. Sophia Y. Zheng, “Signal Acquisition and Tracking for Software GPS Receiver”. Master’s Thesis, Virginia Polytechnic Institute and State University, Blacksburg, 2005.

5. K. krumvieda, Premal M, Dr. W. Kober, “A Complete IF Software GPS Receiver: A Tutorial about the details”.

6. Peter Rinder, Nicolaj Bertelsen. Design of a single frequency, GPS software receiver. Master’s thesis. Aalborg University 2004






M. E. Akbarpour, M. R. Karami Mollaei

Paper Title:

Image Enhancement by Thresholding on Wavelet Coefficient

Abstract: The different wavelet transform-based methods of the image De-noising by thresholding on wavelet coefficients are discussed in this paper. These methods include different ways of adaptive calculating of threshold value and also kinds of thresholdig function. After examining the existing methods, a simple and efficient method based on local features of each pixel has been proposed. At last the proposed method has been compared the other existing methods and it is obtained that the proposed method, despite of the simplicity, has the same efficiency as some of the common complex methods. In addition some times it has better response than the complex methods.

Discreet wavelet transform (DWT), image enhancement, wavelet thresholding.


1. Elyasi I., Zarmehi S., Elimination Noise by Adaptive Wavelet Threshold, Word Academy of Science, Engineering and technology 56, 2009.
2. Sudha S., Suresh G.R., Sukanesh R., Wavelet Based Image Denoising Using Adaptive Subband Thresholding, International Journal of Soft Computing 2(5): 628-632, 2007.

3. Chang S.G., Yu B., Vetterli M., Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 9, SEPTEMBER 2000

4. Gnanadurai D., Sadasivam V., An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising, International Journal of Information and Communication Engineering 2:2 2006.
5. Nasri M., Nezamabadi-pour H., Image denoising in the wavelet domain using a new adaptive thresholding function, Neurocomputing 72 (2009) 1012–1025.

6. Mohideen S.K., Perumal S.A., Sathik M.M., Image De-noising using Discrete Wavelet transform, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.1, January 2008.

7. Hou Z., Adaptive singular value decomposition in wavelet domain for image denoising, Pattern Recognition 36 (2003) 1747 – 1763.

8. Yap K.H., Guan L., Perry S.W., Wong H.S., Adaptive Image Processing, Second Edition, CRC Press, 2010.

9. Wang Z., Simoncelli E.S., Bovik A.C., MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ASSESSMENT, Proceedings of the 37Th IEEE Asilmor conference an signals, systems and computer, pacific Grove, CA, NOV. 9-12, 2003.





Ashish Tiwari, Mukesh Kumar, A.K.Jaiswal, Rohini Saxena

Paper Title:

Superposition Coding Scheme for Subcarrier & Bit Allocation in OFDM System

Abstract: Orthogonal Frequency Division Multiple Access (OFDMA) refers to aradio transmission technique based on dividing the frequency channelinto narrowband sub-channels. In this dissertation studied that margin adaptive - superposition coding (MA-SC) algorithm and rate adaptive - superposition coding (RA-SC) algorithm, where at most two users can share each subcarrier as compared to MA and RA algorithm, where each subcarrier is shared by only single user without SC scheme. Then apply SC scheme over MA and RA to achieve maximum system throughput with separate power constraint for real and non-real time users, ensuring that QoS requirement for real time and proportional fairness among non-real time users is satisfied. The overall computational complexity of RA-SC algorithm is same as RA algorithm. In MA-SC, complexity increases in some of the steps due to addition of SC scheme over MA algorithm, but overall complexity remains the same. Matlab simulations are being carried out to show that the performance of algorithm in terms of power required and throughput.

MA-SC, RA-SC, OFDM Superposition coding.


1. G. J. G. Andrews and R. Muhamed. Fundamentals of WiMAX. Prentice Hall, 2007.
2. H. Besbes and S. Najeh. A simple superposition coding scheme for optimizing resource allocation in downlink OFDMA systems. Wireless Pers Communications, 2010.

3. N. S. J. A. Besbes, H. and S. Smirani. Resource allocation algorithm in downlink of OFDMA systems based on users classification and superposition coding. In Proc. SCS 2009, Djerba, Tunisia.

4. Q. Du and X. Zhang. Effective capacity of superposition coding based mobile multicastin wireless networks. In Proc. of IEEE ICC 2009, Dresden, Germany.

5. L. G. Kivanc, D. and H. Liu. Computationally efficient bandwidth allocation and power control for OFDMA. In IEEE Transactions on Wireless Communications,
volume2, pages 1150–1158, November 2003.

6. H. Najeh, S. and A. Bouallegue. Greedy algorithm for dynamic resource allocationin downlink of OFDMA system. In Proc. 2nd ISWCS 2005 Siena, Italy.

7. H. Najeh, S. and A. Bouallegue. A simple watermarking system. In Proc. ISCCSP2004, Hammamet, Tunisia, Mars.

8. W. Rhee and J. M. Cioffi. Increasing in capacity of multi user OFDM system usingdynamic sub channel allocation. In Proc. IEEE Int. Vehicular Tech. Conf., volume 2,pages 1085–1089, 2000.

9. A. J. G. Shen, Z. and B. L. Evans. Optimal power allocation in multi user OFDMsystems. In Proc. IEEE GLOBECOM ’03, San Francisco, CA.

10. H. K. C. L. K. B. Wang, W. and S. Bahk. Resourceallocation for heterogeneousservices in multiuser OFDM systems. In Proc. IEEE Globecom’ 2004, Dallas, Texas,volume 6.

11. S. Z. A. J. G. Wong, I. C. and B. L. Evans. A low complexity algorithm for proportionalresource allocation in OFDMA systems. In Proc. IEEE SIPS 2004, Austin,Texas, October 13-15.

12. T. C. Y. C. Wong, C. Y. and R. S. A real time sub-carrier allocation schemefor multiple access downlink OFDM transmission. In Proc. IEEE VTC 1999-Fall,September.

13. Z. Z. Yu, G. and Y. e. a. Chen. Sub carrier and bit allocation for OFDMA systems withproportional fairness. In IEEE WCNC 2006, Las Vegas, USA, volume 3, April 3-6.

14. Z. Z. Yu, G. and Y. e. a. Chen. A novel resource allocation algorithm for real timeservices in multiuser OFDM systems. In IEEE 63 vehicular technology conference,Melbourne, Australia,, volume 3, pages 1156–1160, May 7-10 2006.

15. Z. Z. Yu, G. and P. Qiu. Adaptive subcarrier and bit allocation in OFDM systemssupporting hetrogeneous services. Wireless Pers Communications, pages 1057–1070,2007.

16. G. Zhang. Sub carrier and bit allocation for real-time services in multi user OFDMsystems. In Proc. IEEE ICC’ 04, Paris, France.






Sijitha Issac , K.vanamathi

Paper Title:

Modelling of single Sided Linear Induction Motor by MATLAB/SIMULINK

Abstract: This paper describes a generalized model of the single sided linear induction motor and its computer simulation using MATLAB/SIMULINK. Constructional details of various sub-models for the single sided linear induction motor are given and their implementation in SIMULINK is outlined. SLIM is studied using the simulation model developed.

MATLAB modelling; simulation; SIMULINK; SLIM modelling.


1. R.M Pai, Ion Boldea “ A complete equivalent circuit of a linear induction motor with sheet secondary”IEEE Trantransactions on magnetics, 24,No.1 ,pp 639-654 ,1988.
2. George H Abdou and Sherif A sheriff, “Theoretical and experimental design of LIM in automated manufacturing systems”IEEE Transactions on industry applications,vol 27,N0.2,1991.

3. N Sadow Ski, Y.Lefevre, M.Lajoie-Mazone J;Cros, “Finite element torque calculation in electrical machines while considering the movement” IEEE Trantransactions on magnetics, 28,No.2 ,pp 1410-1413,1992
4. V.V Vadher and I.R Smith,“Performance of segmented rotor tubular linear induction motor” IEEE Trantransactions on magnetics, 29,No.6 ,pp 2941

5. G.E Adams “Tubular linear induction motor for hydraulic capsule pipeline-finite element analysis” IEEE Transactions on Energy conversion, Vol.8,No.2, pp. 251-256,1993.

6. Jorge Freitas,Jorge Dias,Avelar Freitas,Carlos Cabrita “Optimized design of the tubular induction actuator aided by computer”,IEEE,1994,PP 770-772

7. Atencia J,Rico,A Garcia, Florez.J(2001) “A low cost linear induction motor for laboratary experiments”International journalof EEE pp 118-134






Parikha Chawla, Parmender Singh, Taruna Sikka

Paper Title:

Enhance Throughput in Wireless Sensor Network Using Topology Control Approach

Abstract: This paper is associated with implementation of topology control approach to enhance throughput in wireless sensor network. A wireless sensor network is characterized by limited energy supply and large nodes. To maximise the network lifetime of wireless sensor network the topology control is the considered to be the important process. Every attempt is being made to reduce the energy consumption and to enhance throughput of the wireless sensor node. Topology Control aims at network-wide goals, for example, extending network-lifetime minimizing average delay. Network topology control is about the management of network topology to support network-wide requirements. Topology Control Algorithms can be divided into transmission-power-based algorithms and duty-cycle-based algorithms according to their energy saving approaches. Two energy efficient topology control algorithms will be used by utilizing both clustering and adjusting transmission power.

algorithms, clustering throughput, topology control, sensor network, wireless.


1. S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Tag Hong, “A Tiny Aggregation Service for Ad-Hoc Sensor Networks” SIGOPS Opemting System Review 96, SI (December 2002), pp. 131-146.
2. G.J. Pottle, and W.J. Kaiser, “Embedding the Internet: Wireless Integrated Network Sensors” Communications of the ACM 43,5 [May 2000], pp. 51-58
3. A. Depedri, A. Zanella, R. Verdone, An energy efficient protocol for wireless sensor networks, in: Autonomous Intelligent Networks and Systems (AINS 2003), Menlo Park, CA, June 30–July 1, 2003.

4. Xinhua Liu, Fangmin Li and Hailan Kuang, An Optimal Power-controlled Topology Control for Wireless Sensor Networks in International Conference on Computer Science and Software Engineering, pp 550-555, 2008.

5. Mostafa I. Abd-El-Barr, Mohamed A. M. Youssef, Mariam M. Al-Otaibi, Wireless sensor networks – part I: Topology and Design issues, pages-1165-1168, 2005.

6. Azadeh Forghani, Amir Masoud Rahmani, Ahmad Khademzadeh, QCTC: QoS-Based Clustering Topology Control Algorithm for Wireless Sensor Networks, in International Conference on Advanced Computer Theory and Engineering, pp 966-970, 2008.

7. A. Depedri, A. Zanella, R. Verdone, An energy efficient protocol for wireless sensor networks, in: Autonomous Intelligent Networks and Systems (AINS 2003), Menlo Park, CA, June 30–July 1, 2003.

8. Huan Yan, Zun-wen He, Jian-gu, ang Jia,A Self-adaptive Clustering Algorithm for Wireless Sensor Network, pages 1-4, 2009.

9. M. Bani Yassein, A. Al-zou'bi, Y. Khamayseh, W. Mardini: Improvement on LEACH Protocol of Wireless Sensor Network, vol. 3, 2009.

10. F. Shebli, I. Dayoub, A. Okassa M'foubat, A. Rivenq and J. M. Rouvaen, Minimizing energy consumption within wireless sensors networks using optimal transmission range between nodes, IEEE International Conference on Signal Processing and Communications (ICSPC 2007), pp 105-109, 2007.

11. Seapahn Megerian and Miodrag Potkonjak, "Wireless sensor networks," Book Chapter in Wiley Encyclopedia of Telecommunications, Editor: John G. Proakis, 2002.

12. Q. Li, Z. Qingxin, and W. Mingwen, "Design of a distributed energy efficient clustering algorithm for heterogeneous wireless sensor networks," Computer Communications, vol. 29, pp. 2230-7, 2006.






Manoj Kr. Dahiya, Ritu Goel, Sandeep Kumar, Vikas Kaushik

Paper Title:

Comparison of Bow shape Microstrip Antenna and Rectangular patch Microstrip Antenna

Abstract: In recent era, the use of wireless communication system increasing rapidly. Now a day’s, there is demand of small size wireless system which requires development of small size microstrip antenna. Microstrip antenna has wide range of application in wireless communication system due to their low profile, small size, low weight and low cost. Microstrip antenna is also is used in low power transmitting and receiving application as it has capability of low power handling. Microstrip antenna can be operated over a wide range of frequency but mostly, it is operated at resonant frequency of 3GHz. At 3GHz resonant frequency, losses (return loss & tangent loss) are very low and VSWR <2. In this paper, bow shape & rectangular shape microstrip antenna of same size (Length, Height, Width) with dielectric Bismuth niobato substrate (BiNbO4) of Ɛr=47.8 is designed and resonant frequency is calculated

Microstrip antenna can be operated over a wide range of frequency but mostly.


1. Jagtar Singh, A.P Singh, T.S. Kamal, “On the Design of Triangular Microstrip Antenna for Wireless Communication”, International Journal of Computer Applications-IJCA , Feb. 2012.
2. T.Durga Prasad, K. V. Satya Kumar, MD KhwajaMuinuddin, Chisti B. Kanthamma, V.Santoshkumar, “Comparisons of Circular and Rectangular Microstrip Patch Antennas”, International journal of Communication Engineering Applications-IJCEA,Vol 02, Issue 04, July 2011.

3. D.Rakesh, P.RakeshKumar,Prof. Habibullakhan,KCh Sri Kavya, B.T.P.Madhav, K,Prabhu Kumar, S BalaDurga Prasad, “Performance Evaluation of Microstrip Square Patch Antenna on Different Substrate Materials”, Journal of Theoretical and Applied Information Technology, 30th April 2011.

4. P.N. Misra, “Planar Rectangular Microstrip Antenna for Dualband Operation”, IJCST, Vol. 2, Issue 3, September 2011

5. .Kumar Ashok, Singh Pushpendra, D.Sunita, “Analysis and Applications of Microstrip Antennas Having Magnetic Materials”, 1st International Conference on Innovations and Advancements in Information and Communication Technology-ICIAICT 2012.

6. M. Paulson, S.O. Kundukulam, C.K. Aanandan ,P. Mohanan, “Resonance Frequencies of Compact Microstrip Antenna”, Electronic Letter, 13th september 2001, Vol. 37, No. 19.

7. Ramesh Garg, PrakashBhartia, InderBahl, ApisakIttipiboon, “Microstrip Antenna Design Handbook”,2001.

8. C. A. Balanis, Antenna Theory, John Wiley & Sons, Inc., 1997.

9. K.D.Parsad Antenna and Wave Propagation,Satya Parkashan,2005.

10. I.J. Bahal and P. Bhartia, Microstripantennas,Artecch House,Dedham,MA,1980.

11. R.Malmathanraj, S. ThamraniSlevi, “Artificial neural networks in parameter optimization of rectangular microstrip antenna,”

12. Rajeev Wakodkar et al, “Neurocomputational analysis of square microstrip antenna characteristics,”IJECT Vol.1, Dec.2010.

13. Kin-Lu Wong, Compact and Broadband Microstrip Antennas, Jon Wiley & Sons, Inc.,2002

14. J. R. James, P. S. Hall, and C. Wood, Microstrip Antenna Theory and Design, Peter Perigrinus,London, 1981.

15. Sanad, M. "A Small Size Microstrip Antenna Having a Partial Short Circuit" IEEE International Conference on Antenna and Propagation, vol. 1, pp. 465-471, April1995

16. J. R. James and P. S. Hall, Handbook of Microstrip Antennas, Peter PerigrinusLtd.,London, 1989.

17. GirirajPrajapati* and K.C Mahajan “ investigations of a bow tie microstrip antenna in wireless application” international journal of engineering sciences & management issn: 2277-5528 2(1): jan-mar., 2012






Vinay Pratap Singh, Arun Agrawal, Shyam Babu Singh

Paper Title:

Analytical Discussion of Single Electron Transistor (SET)

Abstract: Single-electron transistor (SET) is a key element of current research area of nanotechnology which can offer low power consumption and high operating speed. Single electron transistor [SET] is a new nanoscaled switching device because single-electron transistor retains its scalability even on an atomic scale and besides this; it can control the motion of a single electron. Here, scalability means that the performance of electronic devices increases with a decrease of the device dimensions. Since, Power consumption is roughly proportional to the electron number transferred from voltage source to the ground in various logic operations. Therefore, the single- electron transistor [SET] is generally utilized as an ULSI element to reduce the power consumption of ULSIs. Thus, the Single electron transistor [SET] can offer low power consumption and the controlled tunneling of a single electron makes its high operating speed. The goal of this paper is to discuss about the basic physics of nano electronic device ‘Single electron transistor [SET]’ which is capable of controlling the transport of only one electron. In this paper, we also focus on some basic device characteristics like ‘Coulomb blockade’, single electron tunneling effect & ‘Coulomb staircase’ on which this Single electron transistor [SET] works and the basic comparison of SET & FET characteristics and also its [SET] advantages as well as disadvantages to make a clear picture about the reason behind its popularity in the field of nanoelectronics.

Coulomb blockade, Classical theory, Quantum dot, single electron tunneling.


1. Quantum information technology based on single electron dynamics, NIT basic research laboratories Atsugi-shi, 243-0198 Japan, Vol. 1 No.3 June 2003.
2. Konstantin k. Likharev, “Single- electron device and threir applications”, Proc. IEEE Vol. 87, PP. 606-632, April 1999.

3. Qiaoyan Yu, student member IEEE, “Single-electron devices”, Manuscript received Dec. 12, 2006.

4. Andreas Scholze, “Simulation of single-electron devices,” Ph.D. dissertation, Univ. of Jena, Germany, 2000.

5. Om kumar and Manjit kaur, “Single electron transistor: Applications & problems”, International journal of VLSI design and communication system (VLSICS) Vol. 1, No. 4, Dec. 2010.

6. Gregory S. Dubejsky, “Fabrication and DC characterization of Single electron transfers at low temperature”, M.S. Thesis, Queen’s Univ. Kingston, Ontario, Canada, Aug. 2007.

7. Souvik Sarkar, A. K. Biswas, Ankush Ghosh, Subir K. Sarkar, “Single electron based binary multipliers with overflow detection”, International Journal of engineering, Science and Tech. Vol. 1, No. 1, 2009, PP. 61-73.

8. L .J. Yen, Ahmad Radzi Mat Isa, Karsono Ahmad Dasuki, “Modeling and Simulation of single-electron transistor”, et al. / Journal of Fundamental Science 1 (2005) 1-6.

9. Amiza Rasmi & Uda Hashim “Single-electron transistor (SET): Literature Review” journal 2005, koieg University, Malaysia.

10. M. N. Leuenberger and E. R. Mucciolo, Phys. Rev. Lett. 97, 126601 (2009).

11. C. Romeike, M.R. Wegewijs, and H. Schoeller, Phys. Rev. Lett. 96, 196805 (2008).

12. Karan deep, “Simulation and E-beam patterning of single electron transistor” M.S. thesis in electrical engineering, Univ. of Texas at Arlington, Dec. 2006

13. Single Charge Tunneling: Coulomb Blockade Phenomena in Nanostructures, eds. H. Grabert and M. H. Devoret (Plenum Press, New York, 1992)

14. D.V. Averin and K.K Likharev, in Mesoscopic Phenomena in Solids, eds. B.L. Altshuler, P.A. Lee, and R.A. Webb (Elsevier, Amsterdam, 1991)

15. X. Jehl, M. Sanquer; J. Gautier, M.Vinet, “Nanoelectronics with CMOS transistors:electrostatic and quantum effects” International journal of nanotechnology Vol. x, No. x, xxxx





Ramachandra A C, Raja K B, Venugopal K R, L M Patnaik

Paper Title:

Face Recognition based on Logarithmic Fusion of SVD and KT

Abstract: The identification of a person based on biometric is accurate and robust compared to traditional methods of identifying a person using PIN, ID cards etc., In this paper Face Recognition based on Logarithmic Fusion of SVD and KT (FRLSK) is proposed. The Singular Value Decomposition (SVD) is applied on face images to derive Co-efficients. The Co-efficient Matrix of SVD are resized to 64x64 to form features. The test image SVD features are compared with SVD feature of database images using Euclidian distance, Equal Error Rate (EER) and Total Success Rate are computed (TSR). The Kekre Transform (KT) is applied on Resized (64x64) face images to form features. The test image KT Features are compared with KT features of Database images using Euclidian distance to compute EER and TSR. The EER and TSR values obtained by SVD techniques are fused with the value of EER and TSR obtained from KT using logarithmic transforms to get better value of EER and TSR. It is observed that the value of EER and TSR are better in the case of proposed algorithm compared to existing algorithm.

Biometrics, SVD, KT, Total Success Rate.


1. S. Zeenathunisa, A. Jaya and M. A. Rabbani, “A Biometric Approach Towards Recognizing Face In Various Dark Illuminations,” International Conference on Electronics, Communication and Computing Technologies, pp 1-7, 2011.
2. Jinxia Ni and Zhongxi Sun, “Feature Extraction Using Gabor Feature-based IFDA,” Second International Conference on Intelligent Control and Information Processing, pp 174-176, 2011.

3. Seyed Omid Shahdi and S. A. R. Abu-Bakar, “Frequency Domain Feature-Based Face Recognition Technique for Different Poses and Low-Resolution
Conditions,” International Conference on Imaging Systems and Techniques, pp 322-326, 2011.

4. Ngoc-Son Vu and Alice Caplier, “Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching,” IEEE Transactions on Image Processing, vol. 21, no. 3, pp 1352-1365, 2012.

5. Shih-Ming Huang and Jar-Ferr Yang, “Improved Principal Component Regression for Face Recognition Under Illumination Variations,” IEEE Signal Processing Letters, vol. 19, no. 4, pp 179-182, April 2012.

6. Reza Ebrahimpour, Masoom Nazari, Mehdi Azizi and Ali Amiri “Single Training Sample Face Recognition Using Fusion of Classifiers,” International Journal of Hybrid Information Technology, Vol. 4, No. 1, pp January, 2011.

7. Ferdinando Samaria and Andy Harter, “Parameterization of a Stochastic Model for Human Face Identification,” Proceedings of Second IEEE Workshop on Applications of Computer Vision, December 1994.

8. P. N. Bellhumer, J. Hespanha, and D. Kriegman, “Eigen faces vs. fisher faces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, Vol.17 No.7 pp711--720, 1997.

9. Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi & Jiro Gyoba, “Coding Facial Expressions with Gabor Wavelets,” Proceedings on Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp. 200-205.

10. J.J.J. Lien, T. Kanade, J.F. Cohn, C.C. Li,. “Automated facial expression recognition” Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 390-395, 1998.

11. Neil Muller Lourenc¸o Magaia and B. M. Herbs, “Singular Value Decomposition, Eigenfaces, and 3D Reconstructions,” Society for Industrial and Applied Mathematics, Vol. 46, No. 3, pp. 518–545, 2004.

12. H. B. Kekre, Tanuja K, Sarode and Meena S. Ugale“Performance Comparison of Image Classifier Using DCT, Walsh, Haar and Kekre’s Transform,” International Journal of Computer Science and Information Security, Vol. 9, no. 7, 2011.

13. H. B. Kekre, Archana Athawale and Dipali Sadavarti, “Algorithm to enerate Kekre’s Wavelet Transform from Kekre’s Transform,” International Journal of Engineering Science and Technology, Vol. 2, no. 5, pp. 756-767, 2010.

14. H. B. Kekre and Kavita Sonawane, “Query based Image Retrieval using Kekre’s, DCT and Hybrid wavelet Transform over First and Second Moment,”
International Journal of Computer Applications, Volume 32, No.4, pp 13- 18, 2011.

15. Muhammad Sharif, Saad Anis, Mudassar Raza, Sajjad Mohsin, “Enhanced SVD Based Face Recognition, ” Journal of Applied Computer Science and Mathematics, vol. 12, no. 6, pp. 49-53, 2012.





Nimain Charan Nayak, C. Christober Asir Rajan

Paper Title:

Hydro-Thermal Scheduling by an Evolutionary Programming Method with Cooling-Banking Constraints

Abstract: This Paper proposes a new algorithm for solving the Unit Commitment problem in Hydrothermal Power System using an Evolutionary Programming method with Cooling-Banking constraints. The main objective of this paper is to find the generation scheduling by committing the generating units such that the total operating cost can be minimized by satisfying both the forecasted load demand and various operating constraints of the generating units. An initial population of parent solutions is generated at random. Here the parents are obtained from a pre-defined set of solutions i.e. each and every solution is adjusted to meet the requirements. Then, random recommitment is carried out with respect to the unit’s minimum down times. The best population is selected by Evolutionary Strategy. Numerical results are shown comparing the cost solutions and computation time obtained by using the Evolutionary Programming method with cooling and banking constraints than conventional methods like Dynamic Programming.

Hydro-Thermal Scheduling, Evolutionary Programming, Unit Commitment.


1. WL.Snyder, HD.Powell, and C.Rayburn, “Dynamic Programming approach to Unit Commitment”, IEEE Trans. Power Systems, Vol.3, No.2, pp. 339-350, 1987.
2. J.M.Ngundam, F.Kenfack, T.T.Tatietse, “Optimal Scheduling of Large – Scale Hydrothermal Power Systems Using the Lagrangian Relaxation Technique”, International Journal of Electrical Power & Energy Systems, Vol. 22, No.1, pp.237-245, 2002.

3. Chuan-ping Chang, Chih-wen Liu, Chun-chang Liu, “Unit Commitment by Legrangian Relaxation and Genetic Algorithms”, IEEE Trans. Power Systems, Vol.15, No.2, pp.707-714, 2000.

4. N.P.Padhy, “Unit Commitment using Hybrid Models: A Comparative Study for Dynamic Programming, Expert Systems, Fuzzy System and Genetic Algorithms”, International Journal of Electrical Power & Energy Systems, Vol.23, No.1, pp.827-836, 2000.

5. A.H.Mantawy, Youssef L. Abdel-Magid, Shokri Z.Selim, “A Unit Commitment By Tabu Search”, IEE Proc. Generation, Transmission and Distribution, Vol. 145, No. 1, pp.56-64, 1998.

6. Kit Po Wong, Suzannah, Yin Wa Wong, “Combined Genetic Algorithm / Simulated Annealing / Fuzzy Set Approach to Short – Term Generation Scheduling with Take-or-pay Fuel Contract”, IEEE Trans. Power Systems, Vol. 11, No.1, pp. 128-135, 1996.

7. H.T.Yang, P.C.Yang and C.L.Huang, “Evolutionary Programming Based Economic Dispatch for Units with Non-smooth Fuel Cost Functions”, IEEE Trans. Power Systems, Vol.11, No.1, pp.112-117, 1996.

8. A.J.Wood And B.F.Woolenberg, Power Generation and control 2nd Edn., New York: John Wiley and Sons., 1996.

9. D.B.Fogel, Evolutionary Computation, Toward a New Philosophy of Machine Intelligence, New York: IEEE Press, 1995.






V. Sitha Ramulu, Ch. N. Santhosh Kumar, K. Sudheer Reddy

Paper Title:

A Study of Semantic Web Mining: Integrating Domain Knowledge into Web Mining

Abstract: The Semantic Web and Web Mining are two fast-developing research areas, which have many points in contact. Web mining applies data mining techniques on Web content, usage, and structure. Methods of Web content mining can, on the one hand, be used to create semantic annotations from Web page content; on the other hand, content mining can profit from content that is already structured in XML, RDF, or ontological format. Methods of Web usage mining can profit from semantically enriched descriptions of the Web pages visited; this will provide for the identification of more meaningful patterns within site visits, and better site improvements, recommendations, and personalization options based on these patterns. Usage patterns can in turn serve to improve the semantic annotations of pages. The third form of Web Mining, Web structure mining, utilizes the hyperlink structure. Crawlers that take into account structure as well as semantic content can significantly improve search engine results. The rapid development of the field means that Semantic Web Mining now plays a wide range of dierent roles. Semantic web mining is the combination of the semantic web and web mining. This paper presents an overview of semantic web mining.

Domain knowledge, Ontologies, Semantic web, Web mining.


1. R. Cooley, B. Mobasher, J. Srivastava, Web Mining: Information and Pattern Discovery on the World Wide Web, in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'97).
2. R. Kosala, H. Blockeel, Web Mining Research: A Survey, SIGKDD Explorations, 2(1):1-15, 2000.

3. B. Berendt, A. Hotho, G. Stumme, Towards Semantic Web Mining, in Proceedings of 1st International Semantic Web Conference (ISWC 2002).

4. Loh, S., Wives, L.K., & de Oliveira, J.P. (2000). Concept-based knowledge discovery in texts extracted from the Web. SIGKDD Explorations, 2(1), 29-39.

5. Ghani, R., & Fano, A. (2002). Building recommender systems using a knowledge base of product semantics. Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, Malaga, Spain.

6. Horrocks, I. (2002). DAML+OIL: A description logic for the semantic Web. IEEE Data Engineering Bulletin, 25(1), 4-9.

7. Marian Nodine, William Bohrer, Anne Hiong, “semantic Brokering over dynamic heterogeneous data sources in Infosleuth”, 15th international conference on Data Engineering, pages 358- 365, 1999.





Pallavi Sahu, Sunil R.Gupta

Paper Title:

Deployment Techniques in Wireless Sensor Networks

Abstract: In this paper, we study coverage with connectivity properties in large wireless sensor networks (WSN). Coverage is one of the main research interest in wireless sensor network, it is use to determine the quality of service of the networks. Therefore this paper aims to review the common strategies use in solving coverage problem in WSN. The strategies reviewed are categorised in to three groups based on the approaches used; force based, grid based or computational geometry based approach.

Connectivity, coverage, network lifetime, sensors, Voronoi diagram.


1. Koustubh Kulkarni, Sudip Sanyal, “Dynamic Reconfiguration of Wireless Sensor Networks”, International Journal of Computer Science and Applications Vol. 6, No. 4, pp 16–42, 2009.
2. Giuseppe Anastasi, Marco Conti, “Energy conservation in wireless sensor networks: A survey”, Ad Hoc Networks 7 (2009) 537–568.

3. Ali Chamam, Student Member Samuel Pierre, Senior Member, IEEE, “On the Planning of Wireless Sensor Networks: Energy-Efficient Clustering under the Joint Routing and Coverage Constraint”, IEEE Transaction On Mobile Computing, Vol. 8, No. 8, August 2009.

4. Nor Azlina ab. Aziz, Kamarulzaman Ab. Aziz, and Wan Zakiah Wan Ismail “Coverage Strategies for Wireless Sensor Networks”, World Academy of Science, Engineering and Technology 50 2009.

5. Raymond Mulligan, Wireless Sensor and Mobile Ad-hoc Networks (WiSeMAN) Research Lab Department of Computer Science, Hofstra University Hempstead, NY 11549, “Coverage in Wireless Sensor Network: A Survey”, Network Protocol and Algorithms, Vol.2, No.2, April 2010.

6. Marcos Augusto M. Vieira, Luiz Filipe M. Vieira, Linnyer B. Ruiz, Antonio A.F. Loureiro, Antonio O. Fernandes, Jos´e Marcos S. Nogueira “Scheduling Nodes in Wireless Sensor Networks: A Voronoi Approach”, Local Computer Networks,2003.28th annual IEEE International conference , pages 423-429.






Pramod Bhat, Mandeep Singh

Paper Title:

Classification of Stroke Using Texture Analysis on CT images

Abstract: Correct diagnosis of the stroke type is very important for proper medication as any delay or wrong diagnosis may become fatal to the patient. Many methods have been developed to diagnose stroke using MRI images. In this work we have used unenhanced CT images for diagnosis stroke using texture features and classifiers. Five different classifiers have been used and they are combined to get better diagnosis accuracy. The accuracy of classifier ensemble output was 85.39% and the area under ROC (AUC) was found to be about 93 % for every classes. The method proves very effective for diagnosis of stroke with good accuracy and able to differentiate acute, chronic and hemorrhage successfully.

Texture features, Classifier ensemble, CT scan


1. Taylor, F. C. (2009). Stroke In India. Health San Francisco, (Dalal 2007), 28-30.
2. Dalal PM, Malik S, Bhattacharjee M, Trivedi ND, Vairale J, Bhat P, Deshmukh S, Khandelwal K, Mathur VD: Population-Based Stroke Survey in Mumbai, India: Incidence and 28-Day Case Fatality. Neuroepidemiology 2008; 31:254-261.

3. Chawla, M.; Sharma, S.; Sivaswamy, J.; Kishore, L.T.;, "A method for automatic detection and classification of stroke from brain CT images," Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE , vol., no., pp.3581-3584, 3-6 Sept. 2009

4. Matesin, M.; Loncaric, S.; Petravic, D.; "A rule-based approach to stroke lesion analysis from CT brain images," Image and Signal Processing and Analysis, 2001. ISPA 2001. Proceedings of the 2nd International Symposium on , vol., no., pp.219-223, 2001

5. Hudyma, E.; Terlikowski, G.; , "Computer-aided detecting of early strokes and its evaluation on the base of CT images," Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on, vol., no., pp.251-254, 20-22 Oct. 2008

6. Fuk-hay Tang; Douglas K. S; Daniel H.K ;, “An image feature approach for computer-aided detection of ischemic stroke,” Computers in Biology and Medicine,2011. vol., no. 41, Jul.2008

7. Barnathan, M.; Jingjing Zhang; Miranda, E.; Megalooikonomou, V.; Faro, S.; Hensley, H.; Del Voile, L.; Khalili, K.; Gordon, J.; Mohamed, F.B.; , "A texture-based methodology for identifying tissue type in magnetic resonance images," Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on , vol., no., pp.464-467, 14-17 May 2008

8. Haralick, Robert M.; Shanmugam, K.; Dinstein, Its'Hak;, "Textural Features for Image Classification," Systems, Man and Cybernetics, IEEE Transactions on , vol.3, no.6, pp.610-621, Nov. 1973.

9. Chung-Ming Wu; Yung-Chang Chen; Kai-Sheng Hsieh; , "Texture features for classification of ultrasonic liver images," Medical Imaging, IEEE Transactions on , vol.11, no.2, pp.141-152, Jun 1992.

10. Christodoulou, C.I.; Pattichis, C.S.; Pantziaris, M.; Nicolaides, A.;, "Texture-based classification of atherosclerotic carotid plaques," Medical Imaging, IEEE Transactions on , vol.22, no.7, pp.902-912, July 2003.

11. Keyvanrad, M.A.; Homayounpour, M.M.;, "Feature selection and dimension reduction for automatic gender identification," Computer Conference, 2009. CSICC 2009. 14th International CSI , vol., no., pp.613-618, 20-21 Oct. 2009.






Deepak Mishra, Manish Shrivastava

Paper Title:

Optimal Service Pricing for Cloud Based Services

Abstract: Cloud computing is a profound revolution in the way it offers the computation capability. The main objective now is to reduce the cost of deploying a service in the cloud and having proper coordinative in between models. Public, private, and hybrid cloud environments all face the performance limitations inherent in today’s applications and networks. In order for enterprises to maximize the flexibility and cost savings of the Public, private, and hybrid cloud they must overcome the same latency and bandwidth constraints that challenge distributed IT infrastructure environments. By overcoming application and network performance problems, Cloud Steelhead accelerates the process of migrating data and applications to the cloud, and accelerates access to that data from anywhere Cloud Computing applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in a time-efficient manner and also applied some prediction technique in between correlation models with the use of cooperative cache from self as well as different hybrid cloud.

cloud data management, data services, cloud service pricing, Cooperative Cache, Prediction Technique.


1. G.R. Bitran and R. Caldentey, “An Overview of Pricing Models for Revenue Management,” Manufacturing and Service Operations Management, vol. 5, no. 3, pp. 203-209, 2003.
2. N. Bruno and S. Chaudhuri, “An Online Approach to Physical Design Tuning,” Proc. Int’l Conf. Data Eng. (ICDE ’07), 2007.

3. X.-R. Cao, H.-X. Shen, R. Milito, and P. Wirth, “Internet Pricing with a Game Theoretical Approach: Concepts and Examples,” IEEE/ACM Trans. Networking, vol. 10, no. 2, pp. 208-216, Apr. 2002.

4. C. Chen, M. Maheswaran, and M. Toulouse, “Supporting Co-Allocation In an Auctioning-Based Resource Allocator for Grid Systems,” Proc. 16th Int’l Parallel and Distributed Processing Symp. (IPDPS ’02), 2002.

5. S. Choenni, H.M. Blanken, and T. Chang, “On the Selection of Secondary Indices In Relational Databases,” Data and Knowledge Eng., vol. 11, no. 3, pp. 207-233, 1993.

6. D. Dash, Y. Alagiannis, C. Maier, and A. Ailamaki, “Caching All Plans with One Call to the Optimizer,” Proc. Self-Managing Database Systems (SMDB), 2010.

7. D. Dash, V. Kantere, and A. Ailamaki, “An Economic Model for Self-Tuned Cloud Caching,” Proc. IEEE Int’l Conf. Data Eng. (ICDE ’09), 2009.

8. C. Ernemann, V. Hamscher, and R. Yahyapour, “Economic Scheduling In Grid Computing,” Proc. Eighth Int’l Workshop Job Scheduling Strategies for Parallel Processing (JSSPP ’02), 2002.

9. G. Gallego and G. van Ryzin, “Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons,” Management Science, vol. 40, no. 8, pp. 999-1020, 1994.

10. A. Ghose, V. Choudhary, T. Mukhopadhyay, and U. Rajan,“Dynamic Pricing: A Strategic Advantage for Electronic Retailers,” Proc. Conf. Information Systems and Technology (CIST), 2003.

11. I.E. Grossmann and Z. Kravanja, Large-Scale Optimization with Applications: Optimal Design and Control. Springer, 1997.

12. M. Guay and T. Zhang, “Adaptive Extremum Seeking Control of Nonlinear Dynamic Systems with Parametric Uncertainty,” Automatica, vol. 39, pp. 1283-1294, 2003.

13. L. He and J. Walrand, “Pricing Differentiated Internet Services,” Proc. IEEE INFOCOM, pp. 195-204, 2005.

14. http://aws.amazon.com/, 2011.

15. http://code.google.com/appengine/, 2011.

16. http://tomopt.com/tomlab/, 2011.

17. http:/www.cern.ch/, 2011.

18. http://www.gogrid.com/, 2011.

19. http://www.microsoft.com/azure/, 2011.

20. http://www.sdss.org/, 2011.

21. M. Kradolfer and D. Tombros, “Market-Based Workflow Management,” Int’l J. Cooperative Information Systems, vol. 7, pp. 297-314, 1998.

22. J. Li and R. Yahyapour, “Negotiation Model Supporting Co-Allocation for Grid Scheduling,” Proc. IEEE/ACM Seventh Int’l Conf. Grid Computing, 2006.

23. Z. Lin, S. Ramanathan, and H. Zhao, “Usage-Based Dynamic Pricing of Web Services for Optimizing Resource Allocation,” Information Systems and E-Business Management, vol. 3, no. 3, pp. 221-242, 2005.

24. T. Malik, X. Wang, R. Burns, D. Dash, and A. Ailamaki, “Automated Physical Design In Database Caches,” Proc. Workshop Self-Managing Database Systems (SMDB), 2008.

25. T. Malik, R.C. Burns, and A. Chaudhary, “A Financial Option Based Grid Resources Pricing Model: Towards an Equilibrium between Service Quality for User and Profitability for Service Providers,” Proc. Advances in Grid and Pervasive Computing, pp. 13- 24, 2009.

26. V. Marbukh and K. Mills, “Demand Pricing and Resource Allocation In Market-Based Compute Grids: A Model and Initial Results,” Proc. Int’l Conf. Networking (ICN), pp. 752-757, 2008.

27. Y. Masuda and S. Whang, “Dynamic Pricing for Network Service: Equilibrium and Stability,” Management Science, vol. 45, no. 6, pp. 857-869, 1999.

28. M. Morari and J.H. Lee, “Model Predictive Control: Past, Present and Future,” Computers and Chemical Eng., vol. 23, no. 4/5, pp. 667- 682, 1999.

29. R.A. Moreno, “A.B.: Job Scheduling and Resource Management Techniques In Economic Grid Environments,” Proc. Across Grids 2003, pp. 25-32, 2004.

30. Y. Narahari, C.V.L. Raju, K. Ravikumar, and S. Shah, “Dynamic Pricing Models for Electronic Business,” Dynamic Pricing Models for Electronic Business, vol. 30, pp. 231-256, 2005.

31. Series of Meetings of the EPFL-IC-IIF-DIAS Lab with the Data Management Group of the European Organization for Nuclear Research (CERN) Started on the, Dec. 2008.

32. S. Papadomanolakis, D. Dash, and A. Ailamaki, “Efficient Use of the Query Optimizer for Automated Database Design,” Proc. 33rd Int’l Conf. Very Large Data
Bases (VLDB ’07), pp. 1093-1104, 2007.

33. K. Schnaitter, N. Polyzotis, and L. Getoor, “Modeling Index Interactions,” Proc. VLDB Endowment, vol. 2, no. 1, pp. 1234- 1245, 2009.

34. B. Srinivasan, D. Bonvin, E. Visser, and S. Palanki, “Dynamic Optimization of Batch Processes: Ii. Role of Measurements In Handling Uncertainty,” Computers and Chemical Eng., vol. 27, pp. 27-44, 2003.

35. M. Stonebraker, P.M. Aoki, W. Litwin, A. Pfeffer, A. Sah, J. Sidell, C. Staelin, and A. Yu, “Mariposa: A Wide-Area Distributed Database System,” Int’l J. Very Large Data Bases, vol. 5, no. 1, pp. 48-63, 1996.

36. A.Sulistio, K. Kyong Hoon, and R. Buyya, “Using Revenue Management to Determine Pricing of Reservations,” Proc. IEEE Int’l Conf. e-Science and Grid Computing, pp. 396-405, 2007.

37. X. Wang, T. Malik, R.C. Burns, S. Papadomanolakis, and A. Ailamaki, “A Workload-Driven Unit of Cache Replacement for Mid-Tier Database Caching,” Proc. 12th Int’l Conf. Database Systems for Advanced Applications (DASFAA ’07), pp. 374-385, 2007.

38. M.P. Wellman, W.E. Walsh, P.R. Wurman, and J.K. Mackie-mason, “Auction Protocols for Decentralized Scheduling,” Games and Economic Behavior, vol. 35, pp. 271-303, 2001.

39. K.-Y. Whang, G. Wiederhold, and D. Sagalowicz, “Separability: An Approach to Physical Database Design,” IEEE Trans. Computers, vol. C-33, no. 3, pp. 209-222, Mar. 1984.

40. P.-S. You and T.C. Chen, “Dynamic Pricing of Seasonal Goods with Spot and Forward Purchase Demands,” Computer and Math. Applications, vol. 54, no. 4, pp. 490-498, 2007.

41. www.vmware.com

42. http://en.wikipedia.org/wiki/Google%2B

43. http://www.mcafee.com/us/products/security-as-a- service/index.aspx

44. http://www.oracle.com/technetwork/oem/cloud-mgmt-496758.html

45. Verena Kantere, Debabrata Dash, Gre´ gory Franc¸ois, Sofia Kyriakopoulou, and Anastasia Ailamaki, “Optimal Service Pricing for a Cloud Cache”, IEEE Transaction On Knowledge & Data Engineering, VOL. 23, NO. 9, SEPTEMBER 2011

46. http://searchcloudcomputing.techtarget.com

47. http://storagedecisions.techtarget.com/seminars/cloud_storage.html

48. http://www.dba-oracle.com/art_builder_cpu_io.htm

49. http://windows.microsoft.com/en-IN/windows/explore/cloud

50. http://www.webopedia.com/TERM/C/cloud_database.html

51. S.N.Sivnandan, S.N.Deepa, ” Introduction To GeneticAlgorithm”, ISBN 978-3-540-73189-4 Springer Berlin Heidelberg New York.






Manisha Shekhar, L Shekhar

Paper Title:

Application of Information Technology & Computer Programming In Arsenic Mapping

Abstract: Arsenic in drinking waters, with a special focus on developing and third world countries where the problem is exacerbated by flooding and depressed economic conditions. The reason behind this review was to summarize the Technologies currently being investigated to remove for compiling this report is to provide background material and a description of competing technologies currently described in the literature for arsenic removal. Based on the sophistication and applicability of current technologies. This paper presents the use of GIS for integrated analysis of spatial and non spatial data to present the magnitude and distribution of arsenic concentration and TW user’s socio economic information un most attractive way, which is very helpful in identify and visualize the arsenic affected areas and proper planning to implemented area based arsenic mitigation options. Latest technologies have been used to (Information & Comm. Technologies (ICT)) is tools have been used for participatory decision making in the drinking water sectors including pilot projects. GIS, remote sensing, satellite image processing and other software tools are used to assess water quality & enable efficient management of water resources.

Toxic effect of arsenic, mitigation, arsenic contamination, treatment of arsenic poisoning


1. www.encyclopedia.com
2. www.wikipedia.com

3. Barcelona M., Gibb j. P. Helfrich j. A and Garske E.E. Practical guide for ground water sampling. Lllinois State Water Survey ISWS 1985,374.

4. Claasen H.C. Guidelines and techniques for obtaining water samples that accurately represent the water quality for an aquifer.U.S Geological Survey, open File Report1982, 49 pp.82-102.

5. Aronof, S: Geographic Information System: A Management Perspective, WDL Publication, Ottwa, Canada, 1989.

6. Burrough, P.A. and R.A. McDonnell: Principles of Geographical Information Systems, Oxford University Press, 1998, pp.98-161

7. SHOUMEN PAUL, Senior GIS Specialist, Geographical Solution Research Centre, Dhaka, Bangladesh.

8. Auden, J .B. Geological Discussion of the Satpura hypotheses and Garo-Rajmahal gap.Proceeding, National Institute of Science India, 15, 1949, 315-310

9. Astolfi E, Maccagno, A, Fernandez, J.C.G, Vaccara, R. and Stimola, R, Relation between arsenic in drinking water and skin cancer.Biological Trace Element Research 3, 1981,pp. 133-143.

10. WHO (1997). Environmental health criteria 194: Aluminium. International Programme on Chemical Safety. World Health Organization, Geneva. P.11. Retrieved
29-07-04 from http://www.inchem.org/documents/ehc/ehc/ehc 194.htm

11. WRRI Groundwater assessment report on the Accra Plains. Unpublished Technical Report, water Resources Research Institute (CSIR). Accra, 1996

12. WHO.Guidelines for drinking water quality: Addendum World Health Criteria, 3rd edition. World Health Organization, Geneva, 200
13. Gautheir Julien (2004). Arsenic Contamination in North 24- Parganas. Mappingn and capacity Building. (MSc Thesis) Technical university of Denmark






Kamala Devi.K, Agnes Anto, K.John Peter

Paper Title:

Curvelet Transform and Multi Structure Elements Morphology by Reconstruction based Retinal Image Analysis

Abstract: Curvelet transform is a multi scale transform that can represent the edges along curves much more efficiently.Retinal images play important roles in finding of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Automated image processing has the potential to support in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. Proposed paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic color photography. Highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the path physiology of diabetes. There is a deficiency of missing some thin vessels is because of utilizing a simple thresholding method. My contribution is to implement a technique that will also be applicable for small length blood vessels.

Keywords: Blood vessel segmentation,curvelet transform,multistructure elements morphology, morphological operators by reconstruction,retinal image.


1. E. J. Candès and D. L. Donoho, “Curvelets—A surprisingly effectivenonadaptive representation for objects with edges,” in Curve and Surface Fitting: Saint-Malo 1999,A. Cohen, C. Rabut, and L. L. Schumaker, Eds. Nashville, TN: Vanderbilt University Press, 1999.
2. S. Dua, N. Kandiraju, and H.W. Thompson, “Design and implementation of a unique blood-vessel detection algorithm towards early diagnosis of diabetic retinopathy,” in Proc. IEEE Int. Conf. in Inf. Technol., Coding Comput., 2005, pp. 26–31.

3. A.M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of center lines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.

4. K. Estabridis and R. Defigueiredo, “Blood vessel detection via a multiwindow parameter transform,” in Proc. 19th IEEE Symp. Comput.-Based Med. Syst. (CBMS’06), pp. 424–429.

5. E. Ardizzone, R. Pirrone, O Gambino, and S. Radosta, “Blood vessels and feature points detection on retinal images,” in Proc. 30th Annu. Int. IEEE EMBS Conf., Aug. 2008, pp. 2246–2249.

6. S. Supot, Ch. Thanapong, P. Chuchart, and S. Manas, “Automatic segmentation of blood vessels in retinal image based on fuzzy k-median clustering,” in Proc. IEEE Int. Conf. Integr. Technol.,Mar. 2007, pp. 584– 588.

7. Ch. Wu, G. Agam, and P. Stanchev, “A general framework for vessel segmentation in retinal images”, in Proc. IEEE Int. Symp. Comput. Intel. Rob. Autom., Jun. 2007, pp. 37–42.

8. E. J. Cand`es and D. L.Donoho, “Curvelets—A surprisingly effective nonadaptive representation for objects with edges”, in Curves and Surfaces. Nashville, TN: Vanderbilt Univ. Press, 1999, pp. 123–143.

9. E. Cand`es, L. Demanet, D. Donoho, and L. Ying, “Fast discrete curvelet transforms”, Multiscale Model. Simul., vol. 5, no. 3, pp. 861–899, 2006.

10. Y. Zhao, W. Gui, and Zh. Chen, “Edge detection based on multi-structure elements morphology”, Proc. 6th World Congr. Intell. Control Autom., pp. 9795–9798, 2006.

11. M. Larsen, J. Godt, N. Larsen, H. Lund-Andersen, A. K. Sjølie, E.

12. Agardh, H. Kalm, M. Grunkin, and D. R. Owens, “Automate detection of fundus photographic red lesions in diabetic retinopathy,”Investigat. Opht. Vis. Sci., vol. 44, no. 2, pp. 761–766, 2003

13. F. Zana and J. C. Klein, “A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform,” IEEETrans. Med. Imag., vol. 18, pp. 419–428, May 1999.

14. O. Chutatape, L. Zheng, and S. Krishman, “Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters,” in Proc.IEEE Int. Conf. Eng. Biol. Soc., vol. 20, 1998, pp. 3144–3149.

15. Y. A. Tolias and S. M. Panas, “A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering,” IEEE Trans. Med. Imag., vol.17, pp. 263–273, Apr. 1998.

16. L. Gagnon, M. Lalonde, M. Beaulieu, and M.-C. Boucher, “Procedure to detect anatomical structures in optical fundus images,” Proc. SPIE Med.Imaging: Image Processing, vol. 4322, pp. 1218–1225, 2001.

17. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum,“Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag., vol. 8, pp. 263–269, Sept.1989.






Sp Victor, S Suresh Kumar

Paper Title:

Planned Obsolescence – Roadway to Increasing E-Waste in Indian Government Sector

Abstract: Today, there is a pressing need for the Indian electronics industry to persistently track and unravel the complexities of the global supply chain, which is now being reshaped by a gamut of environmental compliance norms that have come into force. Without an actionable ‘India Strategy’ relying on a set of appropriately benchmarked environment management policies and implementation programmes, the competitiveness and growth of the electronics and information technology (IT) industry are bound to be hamstrung. It is, therefore, absolutely essential for companies to develop robust practices to avoid high non-compliance costs. Action in the global market place for cleaner technology processes and recycling programmes has already gathered significant momentum. This thesis makes an attempt to re-discover the path of planned obsolescence resulting in the generation of e-waste in the Indian Government sector and the proposed actions towards control of growth of e-waste.

E-Waste (Electronic Waste), BER (Beyond Economical Repairs), MSW (Municipal Solid Waste), DGS&D RC (Director General of Supply and Disposal Rate Contract, WEEE (Waste Electrical and Electronic Equipment), End Of Life, Survey.


1. G.I.E. Media, Inc and Gale Group 2003.
2. www.tendersindia.info.

3. Environmentally Sound management of e-waste, As approved vide MoEF letter No. 23-23/2007-HSMD dt. March 12, 2008

4. http://www.defence.pk/forums/india-defence/14271-indian-defense-procurement-policies.html

5. Electronics Waste Management in the United States: Approach One, ed. O.o.S. Waste. 2008: U.S. Environmental Protection Agency.

6. Guide Jr., V.D.R., R.H. Teunter, and L.N.V. Wassenhove, Matching Demand and Supply to Maximize Profits from Remanufacturing. Manufacturing & Service Operations Management, 2003. 5(4): p. 303-316.

7. Kondoh, S., et al., Total performance analysis of product life cycle considering the deterioration and obsolescence of product value. International Journal of Product Development, 2008. 6(3): p. 334-352.

8. Guide Jr., V.D.R. and L.N.v. Wassenhove, The Reverse Supply Chain, in Harvard Business Review. 2002.

9. Management of Electronic Waste in the United States: Approach Two. 2007: U.S. Environmental Protection Agency.

10. A. YOSHIDA, T. TasakiA. Terazono, ,waste management 29(2009):1602-1614.

11. Dahmus, J.B., et al., Modeling the Economic and Environmental Performance of Recycling Systems. IEEE International Symposium on Electronics and the Environment. 2008.

12. http://www.thaindian.com/newsportal/health/20 10-vancouver-olympic-medals-made-from-e-waste_100325904.html#ixzz0zHP6Y7ld
13. http://www.thaindian.com/newsportal/enviornm ent/government-to-set-up-e-waste-treatment- facilities-ramesh_ 100337317.html#ixzz0zHPXLA
14 Source : Author






A.Komali, V.Satish Kumar, K.Ganapathi Babu, A.S.K.Ratnam

Paper Title:

3D Color Feature Extraction in Content-Based Image Retrieval

Abstract: Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images. "Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colours, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results. This paper proposes 3D colour feature extraction for comparing the contents.

QBIC (Query by Image Content), CBVIR (Content Based Visual Information Retrieval), Color Space, Texture, Conventional Color Histogram , CMY, HSV


1. M. A. Stricker and M. Orengo, “Similarity of color images”, Proc. of the SPIE conference on the Storage and Retrieval for Image and Video Databases III
2. G. Pass and R. Zabih, “Histogram Refinement for Content Based Image Retrieval”, 3rd IEEE Workshop on Applications of Computer Vision

3. M. Swain and D. Ballard, “Color indexing”, International Journal of