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Volume-2 Issue-1: Published on March 05, 2012
Volume-2 Issue-1: Published on March 05, 2012
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S. No

Volume-2 Issue-1, March 2012, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Sumit Kumar Banchhor, Arif Khan

Paper Title:

Musical Instrument Recognition using Spectrogram and Autocorrelation

Abstract: Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (flute, guitar and harmonium), which yields a high recognition rate. A large solo database is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. The basic characteristics are computed in 1sec interval and result shows that the estimation of spectrogram and autocorrelation reflects more effectively the difference in musical instruments.

  Speech/music classification, audio segmentation, spectrogram, autocorrelation.


1.       K.D. Martin: Sound-Source Recognition: A Theory and Computational Model, Ph.D. thesis, MIT, 1999
2.       A. Livshin, X. Rodet: Musical Instrument Identification in Continuous Recordings, Proc. of the 7th Int. Conference on Digital Audio Effects (DAFX-04), Naples, Italy, October 5-8, 2004

3.       A. Eronen, A. Klapuri: Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features, Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2000, pp. 753-756

4.       T. Kitahara, M. Goto, H. Okuno: Musical Instrument Identification Based on F0-Dependent Multivariate Normal Distribution, Proc. of the 2003 IEEE Int'l Conf. on Acoustic, Speech and Signal Processing (ICASSP '03), Vol.V, pp.421-424, Apr. 2003

5.       A. Eronen: Musical instrument recognition using ICA-based transform of features and discriminatively trained HMMs, Proc. of the Seventh International Symposium on Signal Processing and its Applications, ISSPA 2003, Paris, France, 1-4 July 2003, pp. 133-136

6.       G. De Poli, P. Prandoni: Sonological Models for Timbre Characterization, Journal of New Music Research, Vol 26 (1997), pp. 170-197, 1997




A.R. Eskandari, M. Naser-Moghaddasi and M. Eskandari

Paper Title:

Reconstruction of Shape and Position for Scattering Objects by Linear Sampling Method

Abstract:   This paper presents an approach for shape and position reconstruction of a scattering object using microwaves where the scatterer is assumed to be a homogenous dielectric medium. The employed technique assumes no prior knowledge of the scatter’s material properties like electric permittivity and conductivity, and the far-field pattern is used as the only primary information in identification. The approach proposed consists of retrieving the shape and the position of the scattering object using a linear sampling method. The technique results in high computational speed and efficiency. In addition, the technique can be generalized for any scatterer structure. Numerical results are used to validate the feasibility of the proposed approach.

   Shape Reconstruction, Inverse Scattering, Microwave Imaging, Linear Sampling Method (LSM).


1.       Soldovieri, F., Brancaccio, A., Leone, G., and Pierri, R.: “Shape reconstruction of perfectly conducting objects by multiview experimental data”, IEEE Trans. Geosci. Remote Sens., 2005, 43, (1), pp.65-71.
2.       Eskandari, M., and Safian R.: “Inverse scattering method based on contour deformations using a fast marching method”, Inverse Problems (IOP), 2010, 26, (9), 19.

3.       Jin, T., and Zhou, Z.: “Refraction and dispersion effects compensation for UWB SAR subsurface object imaging”, IEEE Trans. Geosci. Remote Sens., 2007, 45, (12), pp.4059-4066.

4.       Fischer, C., Herschlein, A., Younis, M., and Wiesbeck, W.: “Detection of antipersonnel mines by using the factorization method on multistatic ground-penetrating radar measurements”, IEEE Trans. Geosci. Remote Sens., 2007, 45, (1), pp. 85-92.

5.       Benedetti, M., Donelli, M., Martini, A., Pastorino, M., Rosani, A., and Massa, A.: “An innovative microwave imaging technique for nondestructive evalution: Applications to civil structures monitoring and biological bodies inspection”, IEEE Trans. Instrum. Meas., 2006, 55, pp.1878-1884.

6.       Li, G.H., Zhao, X., and Huang, K.M.: “Frequency dependence of image reconstruction of linear sampling method in electromagnetic inverse scattering”, Progress In Electromagnetics Research Symposium Proceedings, Xi'an, China, March 2010, pp.611-614.

7.       Colton D., Haddar H., and Piana M.: “The linear sampling method in inverse electromagnetic scattering theory”, Inverse Problems (IOP), 2003, 19, (1), pp.105-137.

8.       Potthast R.: “Stability estimates and reconstruction in inverse scattering using singular sources”, J. Comp. Appl. Math., 2000, 114, pp.247-274.

9.       Fotouhi, M., and Hesaaraki, M.: “The singular sources method for an inverse problem with mixed boundary conditions”, Journal of Mathematical Analysis and Applications, 2005, 306, (1), pp.122-135.

10.     Kirsch A.: “The factorization method for Maxwell's equations”, Inverse Problems, 2004, 20, (6), pp.117-134.

11.     Colton, D., and Kirsch A.: “A simple method for solving inverse scattering problems in the resonance regions”, Inverse Problems, 1996, 13, pp.383-393.

12.     Cakoni, F., and Colton, D.: Qualitative Methods in Inverse Scattering Theory, Berlin, Germany: Springer Verlag, 2006.

13.     Cakoni, F., Colton, D., and Haddar, H.: “The linear sampling method for anisotropic media”, Journal of Computational and Applied Mathematics, 2002, 146, pp.285-299.

14.     Catapano, I., Crocco, L., and Isernia, T.: “Improved sampling methods for shape reconstruction of 3-D buried targets”, IEEE Transaction on Geoscience and Remote Sensing, 2008, 46, (10), pp.3265-3273.

15.     Cakoni, F., Colton, D., and Haddar, H.: “The linear sampling method for anisotropic media”, Journal of Computational and Applied Mathematics, 2002, 146, pp.285-299.

16.     Cakoni, F., Colton, D., and Monk, P.: “The inverse electromagnetic scattering problem for a partially coated dielectric”, Journal of Computational and Applied Mathematics, 2007, 204, pp.256-267.

17.     Bazan, F.S.V.: “Fixed-point iterations in determining a Tikhonov regularization parameter”, Inverse Problems, 2008, 24, pp.1-15.

18.     Colton, D., Piana, M., and Potthast, R.: “A simple method using Morozov's discrepancy principle for solving inverse scattering problems”, Inverse Problem, 1999, 13, pp.1477-1493.

19.     Pelekanos, G., and Sevroglou, V.: “Shape reconstruction of a 2D-elastic penetrable object via the L-curve method”, Journal of Inverse Ill-Posed Problems, 2006, 14, (4), pp.1-16.

20.     Harrington, R. F.:  Field Computations by Moment Methods, New York: MacMillan, 1968.

21.     Gibson, W. C.: The Method of Moments in Electromagnetics, Chapman & Hall/CRC, 2008.




Shiv Kumar, Aditya Shastri

Paper Title:

Design of Simulator for Automatic Voice Signal Detection and Compression (AVSDC)

Abstract:    A good amount of work has been done in the field of compression, voice signal detection, and spectrum analysis which has been generated a number of results in the past few decades. In this research, following three important problems have been identified:
1.      To distinguish between constitutional and unconstitutional Voice: It is an important task to identify authenticity of recorded voice of the specific person. Here it has been tried to develop a Simulator which identifies constitutional and unconstitutional voice.

2.      To identify words sequence:It is an important task to recognize words sequence in the recorded voice. Sometimes voice may be recorded fast, clear, or loud. Here it has been tried to develop a simulator to checkout whether recorded words are in proper sequence are not.

3.      To develop a simulator which does not change file extension and quality of voice signal after compression: Normally, after compression, file extension is changed and quality of the voice signal is deteriorated. Here it has been tried not to change extension of the file after compression with minor distortion in voice signal.
As per review of above three problems, it is being considered a simulator may be designed which may resolve above problems. With this view, the research title is chosen as “Design of Simulator for Automatic Voice Signal Detection and Compression (AVSDC)” which is suitable for pervasive computing, voice signal detection, and spectrum analysis. AVSDC is divided into following two parts:
1.      Automatic Voice Signal Detection (AVSD)

2.      Automatic Voice Signal Compression (AVSC)

Automatic Voice Signal Detection (AVSD) is used to identify constitutional and unconstitutional voice signal automatically which is performed on the basis of frequency, pitch value, formant value, and sequence of words in the voice signal for several samples of the same voice. An underline purpose of AVSD is to identify fake voice in the security system. Frequency is being mapped to the frequency domain by computing its DFT using the FFT algorithm. Sequence of words is computed by continuously computing difference between absolute averages of two adjacent significant windows and comparing it to a predefined threshold. Word Identification System is part of AVSD which is designed to checkout whether recorded words in proper sequence are not. Normally, sometimes spoken words of voice may be recorded very fast, smoothly, or loudly. The main idea behind the word identification system is to first train it with several versions of the same word, thus yielding a “reference fingerprint”. Then, subsequent words can be identified based on how close they are to this fingerprint. The whole idea is evaluated on the basis of Euclidean distance theory. Automatic Voice Signal Compression (AVSC) takes .wav stereo file as an input and compress 50 to 60 percent of the source file at about 45 kbps with high quality voice signal by taking the help of adaptive wavelet packet decomposition and psychoacoustic model. AVSC takes .wav stereo file as an input and creates .wav mono file after compression. After compression minor distortion is also possible. The main feature of AVSC is that file extension does not change after compression. In other words, compression is done from .wav to .wav extension. AVSC takes .wav stereo file as an input and after compression it creates .wav mono file as an output. AVSC also computes entropy and SNR (Signal to Noise Ratio) of the source file during the compression.

   MatLab7.0, Euclidean Distance Theory, Wavelet, Frequency Volue, Pitch Value, Average Significant Window


1.       Jalal Karam, "Various Speech Processing Techniques For Speech Compression And    Recognition", proceedings of world academy of science, engineering and technology volume 26 December 2007 ISSN 1307-6884, © 2007
2.       Sarantos Psycharis “The Didactic Use Of Digital Image Lossy Compression Methods For The Vocational Training Sector”, University of Agean, Proceeding of Current Developments in Technology-Assisted Education 2006(FORMATEX 2006)

3.       P R Deshmukh, "Multi-wavelet Decomposition for Audio Compression",  IE(I) Journal-ET, Vol 87 , July 2006

4.       Background on the Psychoacoustic Model “W.A.V.S. Compression”

5.       Stefan Wabnik, Gerald Schuller, Ulrich Kr ¨amer and Jens Hirschfeld, “Frequency warping In Low delay Audio Coding”, ICASSP 2005, 0-7803-8874-7/05/$20.00 ©2005 IEEE

6.       Shaleena Jeeawoody, “Voice Analysis and Recognition as a Car Theft Deterrent”, California State Science Fair 2008, Project No: J1307, Ap2/08.

7.       Khalid Saeed, “Sound and Voice Verification and Identification a Brief Review of Töeplitz Approach”, Znalosti 2008, pp. 22-27, ISBN 978-80-227-2827-0.FIIT STU Bratislava, .stav informatiky a softvÈrovÈho inæinierstva, 2008.

8.       Maria Markaki, Andre Holzapfel, Yannis Stylianou “Singing Voice Detection using Modulation Frequency Features”, Computer Science Department, University of Crete, Greece.

9.       Hongwu YANG, Dezhi HUANG, Lianhong CAI, “Perceptually Weighted Mel–Cepstrum Analysis Of Speech Based On Psychoacoustic Model”, IEICE TRANS. INF. & SYST., VOL.E89-D,
No.12 December 2006

10.     Samar Krimi, Kais Ouni, Noureddine Ellouze, “Realization of a Psychoacoustic Model for MPEG-1 using Gammachirp WaveLet”, Proceeding in EUSIPCO,2005

11.     Tsung-Han Tsai, Yi-Wen Wang, Shih-Way Hung “An MDCT-Based psychoacoustic model co-processor design for MPEG-2/4  AAC audio encoder” Proceeding in the 7th International  Conference on Digital Audio Effects (DAFx’04), Naples, Italy,  October 5-8, 2004

12.     Ganesh K Venayagamoorthy, Viresh Moonasar, Kumbes Sandrasegaran, “Voice Recognition Using Neural Networks”,Institute of Information Science and Technology (IIST), Massey University, New Zealand, Published in IEEE in 1998, Val. No. 0-7803-5054-5.0029

13.     Andress Holzinger with assistance from G. Searle, “Multimedia Basics”, Volume 1: Technology, ISBN: 81-7008-243-9, Firewall Media Publication

14.     Electronic, Electrical & Computer Engineering, University of Birhingham

15.     Audio Signal Processing and Coding “Andress Sparias Ted Painter Venkatraman

16.     Johan F. Koegel Buford, “Multimedia System”, ISBN: 81-7808-162-8, Tenth Indian Reprint, Pearson Publication

17.     J.N. Holmes, Speech Synthesis and Recognition, Chapman & Hall, London, 1988

18.     Math Work on MatLab

19.     Amara Graps, Institute of Electrical & Electronics Engineers, “An Introduction to Wavelet”, Published by the IEEE Computer Society, 10662 Los Vaqueros Circle, Los Alamitos, CA 90720, USA, Vol. 2, and Num. 2.

20.     Engin Avci, “An Automatic System for Turkish Word Recognition Using Discrete Wavelet Neural Network Based on Adaptive Entropy”, International Conference-Elazig, Turkey-Dec 06, 2007 , The Arabian Journal for Science and Engineering, Volume 32, Number 2B, Paper Received 5 September 2005; Revised 17 February 2006; Accepted 20 December 2006

21.     Trung Nghia Phung, Ngoc Phan Vu, "A Low Bit Rate Wide-Band Speech Coder In The Perceptual Wavelet Packet Domain", International Symposium on Electrical & Electronics Engineering 2007 - Oct 24, 25 2007 - HCM City, Vietnam

22.     Bal´azs Bank, Federico Avanzini, Gianpaolo Borin, Giovanni De Poli, Federico Fontana, Davide Rocchesso, “Physically Informed Signal Processing Methods for Piano Sound Synthesis: A Research Overview”, EURASIP Journal on Applied Signal Processing 2003:10, 941–952, ©2003 Hindawi Publishing Corporation

23.     N. R. Chong, I. Burnett†, J. F. Chicharo,"A new waveform interpolation coding scheme based on pitch synchronous wavelet transform" decomposition, IEEE Transactions On Speech And Audio Processing, Vol. 8, No. 3, May 2000

24.     Frank A Russo, Lola L Cuddy, Alexander Galembo, William Forde Thompson, "Sensitivity to tonality across the pitch range", Perception, 2007, volume 36, pages 781-790, DOI: 10.1068/p5435, ISSN 0301-0066 (print)

25.     Kazunori Mano, "Design of a Toll-Quality 4-KBIT/S Speech Coder Based on Phase-Adaptive PSI-CELP",IEEE Proceeding 1997

26.     Sorin Dusan, James Flanagan, Amod Karve, Mridul Balaraman, “Speech Coding Using Trajectory Compression and Multiple Sensors”, Center for Advanced Information Processing (CAIP)- Rutgers University, Piscataway, NJ, U.S.A.

27.     Murali Mohan Deshpande, K.R.Ramakrishnan, “A Novel BWE Scheme Based On Spectral Peaks In G.729 Compressed Domain”, Indian Institute of Science, Department of Electrical Engineering, Bangalore, India

28.     V. Vijaya Kumar1, U.S.N.Raju2, M. Radhika Mani3 , A.L.Narasimha Rao 4, “Wavelet based Texture Segmentation methods based on Combinatorial of Morphological and Statistical Operations”, Proceeding in IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008

29.     N. Ruiz Reyes, M. Rosa Zurera, F. López Ferreras, D. Martínez Muñoz, J.M. Villafranca, “ A New Cost Function To Select The Wavelet Decomposition For Audio Compression”, SPAIN

30.     Marcus Purat, Peter Noll, “Audio Coding with a dynamic wavelet packet decomposition based on frequency-Varying Modulated Lapped Transforms” Proceeding in IEEE 0-7803-3192-3/96 @1996

31.     Mohamed Cherif Amara Korba, Djemil Messadeg, Rafik Djemili, Hocine Bourouba, “Robust Speech Recognition Using Perceptual Wavelet Denoising and Mel-frequency Product Spectrum Cepstral Coefficient Features”, Proceeding in International Conference  Informatica-32 (2008) 283-288

32.     Jong-Tzy Wang, Ming-Shan Lai, Kai-Wen Liang, Pao-Chi Chang, “Adaptive wavelet quantization Index Modulation Technique for audio watermarking”, National Central University-Tiwan

33.     N. Ruiz Reyes, M. Rosa Zurera, F. Lopez Ferreras, D. Martínez Munoz, “A New Perceptual Entropy-based Methods to Achieve Signal Adpted Wavelet Tree in A low Bit Rate Perceptual Audio Coder”, SPAIN

34.     N. Ruiz Reyes, M. Rosa Zurera, F. Lopez Ferreras, P. Jarabo Amores, P. Vera Candeas, “On the Coding Gain of Dynamic Huffman Coding Applied to a Wavelet Based Perceptual Audio Coder”, Escuela Universitaria Politénica 23700 Linares - Jaén (SPAIN)

35.     Grigor Marchokov, Atanas Gotchev, Zdravko Nikolov, “A Wavelet-Packet Based Speech Coding Algorithm” Ministry of Education and Science, National Science Council, under the Grand No: MU-I-003/96, Proceeding in 2001. Sofia

36.     Frank Kurth, Michael Clausen, “Filter Bank Tree and M-Band Wavelet Packet Algorithms in Audio Signal Processing”, Universit¨at Bonn, Institut f¨ur Informatik V, R¨omerstr. 164, D-53117 Bonn, Germany.

37.     O. Farooq, S. Datta, J. Blackledge, “Blind Tamper Detection in Audio using Chirp based Robust Watermarking”, Proceeding in WSEAS TRANSACTIONS on SIGNAL PROCESSING, ISSN: 1790-5052, Issue 4, Volume 4, April 2008

38.     Latha Pillai, "Quantization", XAPP615 (v1.1) June 25, 2003, 1-800-255-7778

39.     George Tzanetakis, Georg Essl, Perry Cook, "Audio Analysis using the Discrete Wavelet Transform", Computer Science Department *also Music Department

40.     HE Dong-Mei and GAO Wen, "Wideband Speech And Audio Coding Based On Wavelet Transform And Psychoacoustic Model", Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001

41.     Ciprian Doru Giurc¢aneanu , Ioan T¢abus¸ and Jaakko Astola, "Integer wavelet Transform Based Lossless Audio Compression", Signal Processing Laboratory, Tampere University of Technology-Finland

42.     David Darlington, Laurent Daudet and Sandler, "Digital Audio Effects in the wavelet domain", Proc. of 5th Int. Conference on Digital Audio Effects (DAFX-02), Hamburg, Germany, September 26-28, 2002

43.     Claudia Schremmer, Thomas Haenselmann , Florian Bömers, "Wavelets In Real-Time Digital Audio Processing: A Software For Understanding Wavelets In Applied Computer Science", Department of Praktische Informatik IV, University of Mannheim, Germany

44.     F. Abramovich and B.W. Silverman, "Wavelet Decomposition Approaches to statistical Inverse Problem",  University of Bristol (U.K.)

45.     Gavriel Yarmish, "The Simplex Method Applied to Wavelet Decomposition", Proceedings of the 10th WSEAS International Confenrence on Applied Mathematics, Dallas, Texas, USA, November 1-3, 2006

46.     Deepa Kundur , Dimitrios Hatzinakos, "Digital Watermarking Using Multi-resolution Wavelet Decomposition", Natural Sciences and Engineering Research Council (NSERC) of Canada and by Communications and Information Technology Ontario (CITO).

47.     Caroline Chaux, Laurent Duval2 and Jean-Christophe Pesquet,"2D Dual-Tree M-Bandwavelet Decomposition", Universit de Marne-la-Vall¥ee, Champs-sur-Marne, France

48.     Chalermchon Satirapod, Clement Ogaja, Jinling Wang and Chris Rizos, "An Approach to GPS Analysis incorporating Wavelet Decomposition", School of Geomatic Engineering, The University of New South Wales, Sydney NSW 2052, AUSTRALIA

49.     V. A. Baturin  and I. V. Mironova, "Low-Degree Solar Oscillation Spectrum with Wavelet Decomposition",  PACS numbers : 96.60.Ly, 95.75.Wz, 95.75Pq, DOI: 10.1134/S1063773706020071

50.     Sina Jahanbin, Hyohoon Choi, Alan C. Bovik, Kenneth R. Castleman, "Three Dimensional Face Recognition Usingwavelet Decomposition Of Range Images", Laboratory for Image and Video Engineering, The University of Texas, Austin, Texas

51.     K. Kwak and W. Pedrycz, "Review on “Face Recognition Using Fuzzy Integral and Wavelet Decomposition Method” IEEE Trans. Syst. Man Cyb. 34, 1 (Aug. 2004)

52.     A.S. RYBAKOV, "Wavelet Decomposition And Fractal Analysis For Joint Measurements Of Laser Signal Delay And Amplitude", Automatic Control and Computer Sciences, Vol.35, No.3, pp.11-19, 2001, Avtomatika I Vychislitel’naya Tekhnika, Vol.35, No.3, pp.14-24, 2001

53.     W. Kosek And W. PopiNski, " Forecasting Pole Coordinates Data By Combination Of The Wavelet Decomposition And Auto-covariance Prediction", Space Research Centre, Polish Academy Of Sciences, Warsaw, Poland

54.     G. A. Blackburn , J. G. Ferwerda, "Improving The Quantification Of Leaf Biochemistry And Water Content Through Wavelet Decomposition Of Reflectance Spectra", Department of Geography, Lancaster University, Lancaster, LA1 4YB, UK -

55.     Rade Kutil and Andreas Uhl, "Optimization of 3-D Wavelet Decomposition on Multiprocessors", Journal of Computing and Information Technology - CIT 8, 2000, 1, 31–40

56.     R. Martinez-Noriega, H. Kang, B. Kurkoski, K. Yamaguchi and M. Nakano-Miyatake, "Audio Watermarking Based on Wavelet Transform and Quantization Index Modulation", National Polytechnic Institute of Mexico

57.     Needeljko Cvejic, Tapio Seppanen, "A Wavelet Domain LSB Insertion Algorithm for high capacity audio Stegnography", Media Team Oulu, Information Processing laboratory, Finland

58.     S.Manikandan, "Speech Enhancement Based On Wavelet De-noising", Academic Open Internet Journal, Valume17, 2006, ISSN-1311-4360

59.     Rachid Moussaoui, Jean Rouat, Roch Lefebvre, "Wavelet Based Independent Component Analysis For Multi-Channel Source Separation", Departement de genie electrique et de genie informatique, Universite de Sherbrooke, Quebec, Canada

60.     Pavel Rajmic and Jan Vlach, "Real-Time Audio Processing Via Segmented wavelet Transform" Proc. of the 10th Int. Conference on Digital Audio Effects (DAFx-07), Bordeaux, France, September 10-15, 2007

61.     Bastiaan Kleijn , “Speech Signal Processing”, Speech, Music and Hearing TMH/KTH Annual Report 2001

62.     Turkish Word Recognition Using Discrete Wavelet Neural Network Based on Adaptive Entropy”, International Conference-Elazig, Turkey-Dec 06, 2007, the Arabian Journal for Science and Engineering, Volume 32, Number 2B.

63.     Murtaza Bulut, “Mult-level emotional speech analysis resynthesis”, Proposed thesis for Ph.D. in Electrical  Engineering, University of Southern California (USC), Los Angeles, CA, (graduation expected in November 2007).

64.     Michael I Mandel, “Recognition and Organization of Speech and Audio (LabROSA)”, Ph.D. candidate in the department of Electrical Engineering at Columbia University, Proposed work for 2009 (Jan) exp.

65.     Graziano Bertini, Federico Fontana, Diego Gonzalez, Lorenzo Grassi, Massimo Magrini “Voice Transformation Algorithms With Real Time Dsp Rapid Prototyping Tools”, Proceeding In International Conference –Eusipco 2005

66.     Marcos Faundez-Zanuy, “Non Liner speech Processing”, Proceeding in ISCpad 66, 19-22 April-2005, Barcelona, Spain

67.     Keikichi HIROSE, “Speech Prosody 2004”, Proceeding in ISCApad 56,

68.     International Conference: Speech Prosody 2004 on March 23 -26, 2004-Japan,

69.     Kyogu Lee,"Pitch Perception: Place Theory, Temporal Theory, and Beyond", IEE 391 Special Report (Autumn 2004), Center for Computer Research in Music and Acoustics (CCRMA),
Music Department, Stanford University

70.     Douglas A. Reynolds, Larry P. Heck, “Automatic Speaker Recognition” Presented at the AAAS 2000 Meeting Humans, Computers and Speech Symposium 19 February 2000, Nuance Communications United States Air Force.

71.     Matti Karjalainen, Tero Tolonen,"Multi-Pitch and Periodicity Analysis Model for Sound Separation and Auditory Scene Analysis", Laboratory of Acoustics and Audio Signal Processing-Helsinki University of Technology          

72.     Brett A. St. George, Ellen C. Wooten, Louiza Sellami “Speech Coding and Phoneme classification Using MATLAB and NeuralWorks” Department of Ellectrical Engineering,U.S. Naval Academy-Annapolis, MD 21402

73.     John-Paul Hosom, Lawrence Shriberg, Jordan R. Green, “Diagnostic Assessment of Childhood Apraxia of Speech Using Automatic Speech Recognition (ASR) Methods”, Oregon Health & Science University –Beaverton, Accepted for Publications by NIH Public access.

74.     Dalibor Mitrovic, Matthias Zeppelzauer, Christian Breiteneder, “ Discrimination and Retrieval of Animal Sounds”,

75.     Viresh Moonsasar, Ganesh k. Venayagamoorty,"Artificial Neural Network Based Automatic Speaker Recognition using a hybrid technique for feature extraction", Proceedings, IEEE Trans on Acoustics, Speech and Signal,

76.     Fracarro Radioindustrie, CitecVoice, Alpikom, “Robust Speech Recognition”, Copyright © Partners of the DICIT consortium.

77.     Nachiappan, PM Abdul Manan Ahmad “Speech Coding Effects On Recognition Accuracy For Timit”, Postgraduate Annual Research Seminar 3-4 July 2007,( PARS-07)

78.     M. Herrera Martinez, "Evaluation of Audio Compression Artifacts",  Acta Polytechnica Vol. 47 No. 1/2007

79.     Nadine E. Miner, Thomas P. Caudell, "Using Wavelets to Synthesize Stochastic-based Sounds for Immersive Virtual Environments", Dept. of Electrical and Computer Engineering, University of New Mexico

80.     D Kumar, P Carvalho, M Antunes, J Henriques, M Maldonado, R Schmidt, J Habetha, "Wavelet Transform And Simplicity Based Heart Murmur Segmentation", ISSN 0276-6547  Computers in Cardiology 2006;33:173-176

81.     Dmitriy Genzel and Eugene Charniak, “Entropy Rate Constancy in Text”, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia,
July 2002, pp. 199-206.

82.     Rong Zhang1, Rongshan Yu, Qibin Sun, Wai-Choong Wong, “A New Bit-Plane Entropy Coder for Scalable Image Coding” 0-7803-9332-5/05/$20.00 ©2005 IEEE

83.     Hariharan Subramanian, “Audio Signal Classification” M.Tech. Credit Seminar Report, Electronic Systems Group, EE. Dept, IIT Bombay, Submitted November2004

84.     M. Bank, S. Podoxin, V. Tsingouz, “Estimation of non distortion audio signal compression”, Department of Communication Engineering, Center for Technological Education Holon.

85.     Guillaume Gravier, Scott Axelrod, Gerasimos Potamianos, Chalapathy Neti, “Maximum Entropy And Mce Based Hmm Stream Weight Estimation For Audio-Visual Asr” IBM T. J. Watson Research Center-USA

86.     Chi-Min Liu, Wen-Chieh Lee, and Yo-Hua Hsiao, “M/S Coding Based On Allocation Entropy”, Proc. of the 6th Int. Conference on Digital Audio Effects (DAFX-03), London, UK, September 8-11, 2003




T. D. Dongale, T .G. Kulkarni, P. A. Kadam, R. R. Mudholkar

Paper Title:

Simplified Method for Compiling Rule Base Matrix

Abstract:    The main paradigm shift of fuzzy control lies in the implementation of control strategies in the form of knowledge based algorithm described by fuzzy logic. The fuzzy logic system designer either explores his own knowledge or elicits from domain expert. The knowledge pertaining to control strategy is expressed in the form of IF-THEN fuzzy rules. In Fuzzy Logic Control (FLC), the rules are expressed in the form of matrix table. Filling up consequent premises in the rule table is a tedious job. We present here simple numeric method to compile consequent part of fuzzy rules. This greatly reduces an over burden on system designer. The method reported in this paper is quite handy for those were not expert in writing fuzzy rules for FLC of interest. The paper demonstrates the numerical approach to frame the rule base. It involves simple arithmetic addition and subtraction operation. In case of highly non-linear system the straight forward approach fails. In such cases, we suggest corrective terms to the rule base. The comparison of rule base designed by direct human logic with that of numerical approach practiced in case studies validates the success of the numeric approach for compiling rule base matrix presented in paper.

   Decision Matrix, Fuzzy Logic, Fuzzy logic control, Fuzzy Reasoning, IF-THEN Rules.


1.        Bart Kosko,“Neural network and fuzzy system- a dynamic approach to machine Intelligence”, University of south California, Pentice Hall of India, 2001.
2.        Chitra, Assistant Professor (senior), VIT University, Vellore, India, T.Meenakshi  Assistant Professor, Jansons Institute of Technology, India. J. Asha Professor, I.F.E.T.  College of engineering, India. “Fuzzy logic controller for cascaded H-bridge multi level Inverter”, ISSN: 0975-5462 Vol. 2 no. 2 Feb. 2011.

3.        Essam Natsheh and Khalid A. Buragga, “Comparison between conventional and fuzzy logic PID controllers for controlling DC motors”.

4.        Mohan Akole, Barjeev Tyagi, “Design of Fuzzy controller for non-linear model of inverted Pendulum-cart system”, XXXII National systems conference, NSC 2008, December 17-19, 2008.

5.        Navin Govind, Senior systems engineer, Intel Corporation, Chandler, “Fuzzy logic control With the Intel 8XC196 Embedded Microcontroller”.

6.        R.Aruimozhiyal, K.Bhaskaran, N.Devarajan, J.Kanagraj, “Real time matlab interface for speed Control of induction motor drive using dspic 30f4011”, International journal of computer application (0975-8887) Volume 1 No.5.

7.        R.R.Joshi, R.A.Gupta and A.K.Wadhawani, “Intelligent Controller for DTC Controlled  matrix Converter cage drive system”,  Iranian journal of electrical and computer engineering, Vol. 7, No. 1, Winter-Spring 2008.

8.        Rohin M. Hilloowala, Student member, Adel.M.Sharaf IEEE Senior member, IEEE, “A rule-based fuzzy logic controller for a PWM inverter in photo-voltaic energy conversion Scheme”.

9.        R.R.Yagar and D.P. Filev, “Essentials of fuzzy modeling and Control”, Essentials of fuzzy modeling and control, Institute of lona college, A Wiley interscience publication, 1994.  

10.     Salman Mohagheghi, member, IEEE, Ganesh K.Venayagamoorthy, Senior member, IEEE, Satish Rajagopalan, member, IEEE and G. Harley, Fellow, IEEE. “Hardware Implementation of a mamdani Fuzzy logic controller for a static compensation multimachine power system”.

11.     Stamations V. Kartalopoulos, “Understanding Neural network and fuzzy logic, basic concept and application”, AT & T Bell lab, IEEE Neural Network Counsil, sponsor, Prentice Hall of India, 2005.

12.     Sungchul Jee, Yoram Korean, “Adaptive fuzzy controller for feed drives of a CNC machine tool”, Mechatronics14 2004, 299-326. 2003 Elsevier Ltd




P.K.Dhal,  C.Christober Asir Rajan

Paper Title:

Transient Stability Improvement using Hybrid Controller Design for STATCOM

Abstract:    This paper proposes a transient stability improvement using hybrid controller design for STATCOM with static synchronous time critical error and better damping system oscillations after a short circuit fault. This article on a STATCOM Control for transient stability improvement has proposed a hybrid system with fuzzy and neural controller to meet with the addition of Lyapunov stability criterion to the ability and conditions as well. The performance is analyzed using digital simulation with (SMIB) with infinite bus.

   Fuzzy Logic, Neural Network, lyapunov energy function, STATCOM, transient stability.


1.       J. S. Lai and F. Z. Peng, Multilevel converters—A new breed of power converters, IEEE Trans. Ind. Appl., Vol. 32, no. 3, May/Jun. 1996, pp. 509–517.
2.       F. Z. Peng, J. -S. Lai, J. W. McKeever, and J. VanCoevering, A multilevel VSI with separate DC sources for static VAR generation, IEEE Trans. Ind. Appl., Vol. 32, no. 5, Sep./Oct. 1996, pp. 1130–1138.

3.       P. M. Bhagwat and V. R. Stefanovic, Generalized structure of a multilevel PWM inverter, IEEE Trans. Ind. App., Vol. 19, Nov./Dec. 1983, pp. 1057–1069.

4.       M. Marchesoni and M. Mazzucchelli, Multilevel converter for high power ac drives: A review, IEEE Symp. Indl. Electrs., 1993, pp.38–43.

5.       H. Akagi, The state-of-the-art of power electronics in Japan, IEEE Trans. Power Electron. Vol. 13, Mar. 1998,  pp. 345–356.

6.       G. Carrara, S. Gardella, M. Marchesoni, R. Salutari, and G. Sciutto, A new multilevel PWM method: A theoretical analysis, IEEE Trans. Power Electron., Vol. 7, July 1992,  pp. 497–505.

7.       B. Mwinyiwiwa, Z. Wolanski, and B. T. Ooi, Microprocessor-implemented SPWM for multi converters with phase-shifted triangle carriers, IEEE Trans. Ind. Appl. Vol. 34, May/June 1998, pp. 487–494.

8.       S. Ogasawara, J. Takagaki, H. Akagi, and A. Nabae, A novel control scheme of a parallel current-controlled PWM inverter, IEEE Trans. Ind. Applicat., Vol. 28, Sept. / Oct. 1992, pp. 1023–1030.

9.       F. Ueda, K. Matsui, M. Asao, and K. Tsuboi, Parallel-connections of PWM inverters using current sharing reactors, IEEE Trans. Power Electron. Vol. 10, Nov. 1995, pp. 673–679.

10.     D.Daniolos, M.K.Darwish and P.Mehta, “Optimised PWM inverter control using Artificial Neural Networks”, IEE 1995 Electronics Letters Online, No. 19951186, 14 August 1995, pp. 1739-1740.

11.     A.M.Trzynadlowski and S.Legowski, “Application of Neural Networks to the Optimal Control of Three-Phase Voltage-Controlled Inverters”, IEEE Transactions on Power Electronics, Vol.9, No.4, July 1994, pp.397-402.

12.     M.Mohaddes, A.M.Gole and P.G.McLaren, “A Neural Network controlled Optimal pulse-width modulated STATCOM”, IEEE Transactions on Power Delivery, Vol. 14, Issue:2, April 1999, pp.481-488.

13.     S. Mori, et al., Development of a Large Static Var Generator Using Self-Commutated Inverters for Improving Power Systems Stability, IEEE Trans. Power Delivery, Vol. 8, No.1, Feb.1993, pp. 371-377.

14.     N. Seki, H. Uchino, Converter Configurations and Switching Frequency for GTO Reactive Power Compensator,  IEEE Trans. on Industry Applications, Vol. 33, No. 4, July/August 1997.

15.     S.A. Al-Mawsawi, Fuzzy Control and Dynamic Performance of            STATCOM, IETECH J. of Elec. Analysis, 2007, Vol.1, No. 2, pp. 104-115.

16.     A. Ajami, S.H. Hosseini, Application of a Fuzzy Controller for Transient   Stability Enhancement of AC Transmission System by STATCOM, Intl. Joint Conf. ICASE, October 2002, pp. 6059 – 6063. 




Manisha Sharma, Harendra Kumar, Deepak Garg

Paper Title:

An Optimal Task Allocation Model through Clustering with Inter-Processor Distances in Heterogeneous Distributed Computing Systems

Abstract:    Distributed computing systems (DCS) are of current interest due to the advancement of microprocessor technology and computers networks.  It consists of multiple computing   nodes that communicate with each other by message passing mechanism. Reliability and communication over distances are the main reasons for building the DCS. In distributed computing systems, partitioning of applications software in to modules and proper allocation of modules among processors are important factors for efficient utilization of resources. We consider the problem of m-modules and n-processors (m >> n).  In this paper a mathematical model for finding optimal cost and optimal reliability to the problem is presented considering DCS with heterogeneous processors in such a way that the allocated load on each processor is balanced. The results obtained by the present model are compared with the recent models and comparison results show that the model is very effective.

  Distributed computing system, Module allocation, Inter module communication, Reliability, Data transfer rate, Inter processor distance.


1.        Ghafoor and J. Yang, “A Distributed Heterogeneous Supercomputing Management System”, IEEE Comput., 1993Vol.6, pp. 78-86 .
2.        D.F.Towsley, “Allocating Programs Containing Branches and Loops within a Multiple Processor System”,IEEE Trans. Software Eng. SE-12,10, 1986, pp.1018-1024.

3.        Chu W.W., “Optimal File Allocation in a Multiple Computing System”, IEEE Trans. on Computer, Vol.C-18, 1969, pp.885-889.

4.        Dessoukiu-EI  O.I.  and  Huna  W.H., “Distributed Enumeration  on Network Computers”, IEEE  Trans.  on Computer, Vol.. C-29, 1980,pp.818-825.

5.        J.B.Sinclayer, “Optimal Assignment in Broadcast Network”, IEEE Trans. on Computer, Vol.37 (5), 1988, pp.521-531.

6.        Richard, R.Y., Lee, E.Y.S. and  Tsuchiya  M., “A Task  Allocation  Model for  Distributed  Computer  System”, IEEE Trans. on  Computer,Vol.C-31, 1982,pp.41-47.

7.        Min-Sheng Lin, “A Linear-Time Algorithm for Computing K-Terminal Reliability on Proper Interval Graphs”,IEEE Trans.Reliability, Vol.51, 2002, pp.58-62.

8.        Baca, D.F., “Allocation Modules to Processors in a Distributed System”, IEEE Trans. on Software Engineering, Vol.15, 1989, pp.1427-1436

9.        D. Fernindez- Baca, “Allocating Modules to Processors in a Distributed System”, IEEE Trans. Software Eng. SE-15, 11, 1989, pp. 1427-1436.

10.     Kumar, A.. “An Algorithm for Optimal Index to Task Allocation Based on Reliability and Cost”, published in the proceedings of International Conference on Mathematical Modeling held at Roorkee, 2001, pp.150-155.

11.     Kumar, V. Singh, M.P. and Yadav, P.K., “An Efficient Algorithm for Allocating Tasks to Processors in a Distributed Systems”, Proc.  of the 19th National System Conference, SSI, Held at Combatore,  India, 1995, pp.82-87 .

12.     Kumar, V., Singh, M.P. and  Yadav, P.K., “A Fast  Algorithm  for Allocating  Tasks in  Distributed Processing System”, Proc. of the 30th Annual Convention  of CSI held at Hyderabad,  India, 1995, pp.347-358.

13.     Peng, D.T.,Shin, K.G.and Abdel, Z. T.F., “Assignment Scheduling  Communication Periodic  Tasks  in Distributed Real Time  System”, IEEE Trans. on Software Engg. Vol.SE-13, 1997, pp.745-757 .

14.     Sagar,G., Sarje, A.K., “Task Allocation Model for Distributed System”, Int. J. System Science,Vol.22, 1991,.pp.1671-1678 .

15.     Singh, M.P., Kumar, V., Kumar, A., “An Efficient Algorithm for Optimizing Reliability Index in Tasks-Allocation”, Acta Ciencia Indica, Vol.XXVM, 1999, pp. 437-444.

16.     Srinivasan, S., Jha.  K.N.,“Safety and Reliability Driven Task Allocation in Distributed Systems”, IEEE Trans. on Parallel and Distributed System, Vol.10, 1999, pp.  238-250.

17.     Yadav, P.K., Kumar, A.,“An  Efficient  Static  Approach  for Allocation  through  Reliability  Optimization in Distributed Systems”, Presented at the International Conference on Operations Research for Development (ICORD  2002) held at Chennai.

18.     Zahedi, E., Ashrafi, N., “Software Reliability Allocation based on structure, Utility, Price and Cost”, IEEE Trans. on Software Engineering, Vol.-17, 1991, pp.  345-356 .

19.     Yadav P.K., Singh M.P., Sharma K., “Tasks Allocation Model for Reliability and Cost Optimization in Distributed Computing System, International Journal Of Modeling, Simulation, and Scientific Computing, Vol.2, No.2, 2011, pp.131-149.

20.     Yadav P.K., Singh M.P., Kumar H., “Scheduling Algorithm: Tasks Scheduling Algorithm for Multiple Processors with Dynamic Reassignment”, Journal of Computer Systems, Networks and Communications, Article ID-578180, 2008, pp.1-9.

21.     Bokhari, S.H., “Dual Processor Scheduling with Dynamic Re-Assignment”, IEEE Transactions on Software Engineering, vol. 5, 1979, pp. 341-349.




Kapil Jain, Pradyumn Chaturvedi

Paper Title:

Matlab -based Simulation & Analysis of Three - level SPWM Inverter

Abstract:    The multilevel began with the three level converters. The elementary concept of a multilevel converter to achieve higher power to use a series of power semiconductor switches with several lower voltage  dc source to perform the power conversion by synthesizing a staircase voltage waveform. However, the output voltage is smoother with a three level converter, in which the output voltage has three possible values. This results in smaller harmonics, but on the other hand it has more components and is more complex to control. In this paper,  different three level inverter topologies and SPWM technique has been applied to formulate the switching pattern for three level inverter that minimize the harmonic distortion at the inverter output. Simulation result has discussed.



1.        J. S. Lai and F.Z. Peng “Multilevel Converters – A new breed of power converters” IEEE Trans. Ind Applicant , Vol. 32, May/June 1996.
2.    Jose Roderiguez, Jih-Sheng Lai and Fang Zheng Reng, “Multilevel Inverters” A survey of topologies ,control, and applications “,IEEE Trans. On Ind.Electronics, vol No.[4], August 2002.                                                

3. A. Nabae, I Takashashi, and H. Akagi, “A new neutral –point clamped PWM inverter,” IEEE Trans. Ind Application Vol. No. IA-17,PP 518-523,Sept/oc  1981.                                                                                         

4.  P.K.Chaturvedi, S. Jain, Pramod Agrawal “ Modeling , Simulation and Analysis of Three level Neutral Point CLAMPED inverter using matlab/Simulink/Power System Blockst”                                                      

5. Bor-Ren Lin & Hsin – Hung Lu “ A Novel Multilevel PWM Control Scheme of the AC/DC/AC converter for AC Drives”IEEE Trans on ISIE, 1999.                                                                                                              

6.        B. R. Lin & H- H Lu “ multilevel AC/DC/AC Converter for AC Drives” IEEE Proceding electronics Power application, Vol 146, No. 4, July 1999.

7.        DAI Bin “ A new control scheme for voltage Source Inverter Without DC Link Capacitor Under Abnormal Input Voltage Conditions” IEEE Tran.2009.
8.        K. Arab tehrani, H. Andriasioharana, I. Rasonarivo & F.M. Sargos “A Multilevel Inverter Model” IEEE Trans. 2008.                                            

9.        Siriroj Sirisukprasert, Jih- Sheng Lai & Tina – Hua Liu “Optimum harmonics Reduction With A wide Range Of  Modulation Indexes for Multilevel Converters” IEEE Trans Ind Application Electronics ,Vol 49 , No. 4, August 2002.

10.     G.Bhuvaneshwari and Nagaraju “Multilevel inverters – a comparative study” vol .51 No.2 march – April 2005.                                                  

11.     Siriroj Sirisukprasert “Optimum harmonics reduction”.                  

12.     A. M. Massoud, S.J. Finney and B.W. Williams “Control Techniques for Multilevel Voltage Source Inverters” IEEE proce. 2003.                  

13.     B.R. Lin and H.H. Lu “Multilevel AC/DC/AC converter for AC drives” IEE E Proc.—Electr. Power Application, Vol. 146, No. 4, July 1999.          

14.     M. A. EL- Barky, S.H. Arafah “Simulation and Implemetaion of Three Phase Three Level Inverter” SICE july 25- 27, 2001, nagoya..




Abhishek Arvind Gulhane, Abrar Shaukat Alvi

Paper Title:

Noise Reduction of an Image by using Function Approximation Techniques.

Abstract:    In this proposed work, an efficient simple, fast technique is given to remove noise of an image which is mostly introduced due to environmental changes. We focus on the noise issues that changes image pixels value either on or off. The pixels are easily identified as noisy pixels in grayscale image but it is difficult to recognize in RGB color image. Reason behind it is that, any color combination with white (pixel on) or black (pixel off) generate other color. This paper focus on such technique that reduces the noise in both grayscale and RGB image with recovery of originality of source image.

   Random Function Approximation, Salt Peeper Noise, Luminance, Noise Blur.


1.        M. Gabbouj, E. J. Coyle, and N. C. Gallager, “An overview of median  and stack filtering,” Circuit Syst. Signal Process., vol. 11, no. 1, pp.7- 45,1992.
2.        S. E. Umbaugh, Computer Vision and Image Processing. Upper Saddle River, NJ: Prentice-Hall, 1998.

3.        M. Nachtegael and E. E.Kerre, “Connections between binary, gray-scaleand fuzzy mathematical morphologies,” Fuzzy Sets Syst., to be published.

4.        “Decomposing and constructing fuzzy morphological operations over-cuts: Continuous and discrete case,” IEEE Trans. Fuzzy Syst., vol. 8, pp. 615–626, Oct. 2000.

5.        Shuqun Zhang and Mohammad A. Karim. A new impulse detector for switching median filters. IEEE SIGNAL PROCESSING LETTERS, VOL. 9, NO. 11, NOVEMBER 2002, 2002.

6.        Tao Chen and Hong Ren Wu. Adaptive impulse detection using center- weighted median filters. IEEE SIGNAL PROCESSING LETTERS, VOL. 8, NO. 1, JANUARY 2001, 2001.

7.        Constantine Butakoff Igor Aizenberg, Member and Dmitriy Paliy. Impulsive noise removal using threshold boolean filtering based on the impulse detecting functions. IEEE SIGNAL PROCESSING LETTERS, VOL. 12, NO. 1, JANUARY 2005, 2005.

8.        E. Davies Machine Vision: Theory, Algorithms and Practicalities, Academic Press, 1990, Chap. 3.

9.        A. C. Bovik, “Streaking in median filtered images,” IEEE Trans.

10.     J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixture of Gaussian in the wavelet domain,”   IEEE Trans. Image Processing, vol. 12, no. 11, 2003, pp. 1338-1351.

11.     P. Perona and J. Malik, “Scale-space and edge detection using anisotropic iffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, 1990, pp. 629-639.




Hala M. A. Mansour, Labib Francis Gergis, Mostafa A. R. Eltokhy, Hoda Z. Said

Paper Title:

Performance Analysis for Concatenated Coding schemes with Efficient Modulation Techniques

Abstract:    In digital communication systems, channel coding is the method of adding redundancy to the data in order to reduce the frequency of errors or to increase the capacity of a channel. Concatenated codes are the most superior class of codes making achievable channel capacity almost at par with the Shannon limits. Concatenated codes are error correcting codes constructed by combining two or more simple codes through an interleaver in order to obtain powerful coding schemes. In this paper a special construction of concatenated convolutional coding scheme called parallel-serial concatenated convolutional code (P-SCCC) is presented. The upper bound to the bit error probability of the proposed code is evaluated. Results showed that the error performance of this proposed code scheme is better than that of both classical serial and parallel concatenated convolutional codes. The performance of the proposed code has been studied with different types of digital modulation schemes.

   Code concatenation, convolutional code, frequency shift keying, phase shift keying, and quadrature amplitude modulation.


1.       G.Forney, Concatenated Codes, Cambridge, MA:MIT Press, 1966.
2.       A. Glavieux C. Berrou and P. Thitimajshima,”Near Shannon limits error-correcting coding and decoding:  Turbo codes,” IEEE int. Conf. Commun. (ICC), Geneva, Switzerland, May 1993, pp. 1064-1070.

3.       S. Benedetto, G. Montors, “Design of parallel concatenated convolutional codes” IEEE Transactions on Communications, vol. 44, No. 5, May 1996.

4.       S. Benedetto, G. Montorsi “Unveiling Turbo Codes: “Some results on parallel concatenated coding schemes” IEEE Transactions on Information Theory, vol. 42 No. 2, March 1996.

5.       S. Benedetto, D. Divsalar, G. Montorsi, F. Pollara, “Serial concatenation of interleaved codes: Performance analysis, design, and iterative decoding” IEEE Transactions on Information Theory, vol. 44, No. 3, May 1998.

6.       Sason, I., Shamai S. “Improved upper bounds on the performance of Parallel and Serial Concatenated Turbo codes via their ensemble distance spectrum” Information Theory, 1998 Proceedings. 1998 IEEE International Symposium, Issue date: 16-21 Aug. 1998, pp 30, Date of current version: Aug. 2002.

7.       Barg A., Zemor G. “Concatenated codes: Serial and Parallel” Information Theory, IEEE Transaction, vol. 51, Issue: 5, Apr. 2005.

8.       National Taipei University of Technology, Department of Electrical Engineering,, Chung-Hsiao E. Rd., Taipei, Taiwan “Bandwidth efficient concatenated coding schemes” IET Commun.,  5 January 2010, Vol. 4, Iss. 1, pp. 26–31.

9.       Dimakis, C.E.; Kouris, S.S.; Avramis, S.K, “Performance evaluation of concatenated coding schemes on multilevel QAM signaling in non-Gaussian products environment” Communications, Speech and vision, IEEE Proceedings I, vol. 140, pp. 265-276, Aug. 2002.                                                                          

10.     Dengsheng Lin, Shaoqian Li, “Joint design of concatenated SPC codes and QAM modulation” Information Communications & Signal Processing, 2007 6th International Conference, pp.1-4, Issue date 10-13-Dec. 2007.

11.     Le Goff S.Y., Khoo B.K., Sharif B.S.,    Tsimenidis C.C. “Design of power- and bandwidth-efficient turbo-coded modulation schemes using constellation shaping” Communications, IEEE Proceedings, vol. 152, Issue: 6, pp. 1125-1133, Dec. 2005.

12.     Graell I Amat, Rasmussen L.K, Brannstrom “Unifying analysis and design of rate-combatable concatenated codes” IEEE Transactions on Communications, Vol. 59, Issue: 2, pages 343-351. Feb. 2011.

13.     Vahid Asghari, Sonia Aıssa, “Parallel-Serial concatenated coding: design and bit error probability performance” Electrical and Computer Engineering, CCECE 2008 Canadian Conference on 4-7 May 2008, pp489 – 492.

14.     Fuqin Xiong, Digital Modulation Techniques. ARTECH HOUSE, INC. 2006.

15.     Krishna R. Narayanan, Gordon L. Stuber” Performance of Trellis-Coded CPM with Iterative Demodulation and Decoding” IEEE Transactions on communications, Vol. 49, No. 4, April 2001.

16.     J. Anderson, T. Aulin, and C.-E. Sundberg, Digital phase modulation. New York: Plenum Press, 1986.

17.     David K. Asano, Tastuji Hayashi, Ryuji Kohono “Modulation and processing gain tradeoffs in DS-CDMA spread spectrum systems” International Symposium on spread spectrum techniques & applications, 1998.




Sandeep Kumar, Gourav Sharma, Gurdeepinder Singh

Paper Title:

AGC & AVR of Interconnected Thermal Power System While Considering the Effect of GRCs

Abstract:    As the interconnected power system transmits the power from one area to another system frequency will inevitable deviate from scheduled frequency, resulting in a frequency error. A control system is essential to correct the deviation in the presence of external disturbances and structural uncertainties to ensure a safe and smooth operation of power system. Thus design of Automatic Generation Control (AGC) and Automatic Voltage Regulator (AVR) system play a vital role in the automation of power system. This paper deals with automation of three area interconnected reheat thermal power with consideration of Generation Rate Constraint (GRCs). The primary object of the AGC is to balance the total system generation against system load and losses, while considering the effect of Generation Rate Constraint (GRCs). So that the desired frequency and power interchange with neighboring systems are maintained in order to minimize the transient deviations and to provide zero steady state error in appropriate short time. Further the role of automatic voltage control is to maintain the terminal voltage of synchronous generator in order to maintain the bus bar voltage. Otherwise bus bar voltage goes beyond permitted limit.

   Area Control Error (ACE), Automatic Generation Control (AGC), Automatic Voltage Control (AVC), Automatic Voltage Regulator (AVR), Generation Rate Constraints (GRCs).


1.        I.J. Nagrath, and D.P. Kothari,, “Power system engineering,”  Tata McGraw Hill Co., New Delhi, Ch: 8, 2001, pages 339-378.
2.        Hadi Saadat, “power system analysis,” Tata Mcgraw hill, Ch: 12, 2002, pages 527-579.

3.        O.I. Elgard, “Electrical Energy System theory an Introduction”, McGraw-Hill, New Delhi, 2005, Ch: 9, pages 299-361.

4.        C. Concordia and L.K. Kirchmayer, “Tie-line Power and Frequency Control of Electric Power Systems-Part II”, AIEE Trans., Volume 73, part III A, 1954, pp. 133-146.

5.        V. Donde, M.A. Pai, and I.A. Hiskens, “Simulation and Optimization in an AGC System after deregulation,” IEEE transaction on Power System, vol.16, No. 3, 2001,  pp 481-488.

6.        D.N. Ewart, “Automatic Generation control- Performance under Normal Conditions,” System engineering for power: Status and Prospects, U.S Government Document, CONF-750867, 1975,  pp 1-14.

7.        G. V. Hicks and Jeyasurya, B, “An investigation of automatic generation control for an isolated transmission system,” IEEE Canadian Conference on Electrical and Computer Engineering, Vol. 2, 1997, pages: 31- 34.       

8.        Li Pingkang and Ma Yongzhen, “Some New Concept in Modern Automatic Generation Control Realization,” IEEE Trans. on Power System, 1998,  pp. 1232.

9.        Dong Yao and Zhiqiang Gao, “Load Frequency Control for Multiple-Area Power Systems,” American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA, 2009.   





Gurudatt Kulkarni, Niraj Patil, Pradip Patil

Paper Title:

Private Cloud Secure Computing

Abstract:   Cloud computing is an increasingly popular paradigm for accessing computing resources. In practice, cloud service providers tend to offer services that can be grouped into three categories: software as a service, platform as a service, and infrastructure as a service. This paper discuss the characteristics and benefits of private cloud computing. It proceeds to discuss the private cloud characteristics and formation as well as implementation. This paper aims to provide a means of understanding and investigating Private cloud... This paper also outlines the responsibilities of private cloud provider and the facilities to consumer

   Private, public Cloud, Pass, Azure.


2.       “Adopting Cloud Computing: Enterprise Private Clouds”, Shyam Kumar Doddavula and Amit Wasudeo Gawande, SETLabs Briefings, VOL 7 NO 7 2009


4.       Cloud Computing: A Study of Infrastructure As A Service (Iaas), Sushil Bhardwaj, Leena Jain, Sandeep Jain, International Journal Of Engineering And Information Technology.






Shrikrishan Yadav, Santosh Kumar Singh, Krishna Chandra Roy

Paper Title:

A Smart and Secure Wireless Communication System: Cognitive Radio

Abstract:    Trust is an important concept in human interactions which facilitates the formation and continued existence of functional human societies. The radio frequency spectrum is a limited natural resource and hence its efficient use is of the greatest importance. Cognitive radio is a smart wireless communication system that is conscious of its surrounding environment, learns from the environment and adapts its internal states by making corresponding changes in certain operating parameters in real time. In this paper, we search the adaptive characteristics of cognitive radio in secure and reliable communication. But how a communication system can be made reliable such that there occur no eavesdropping and information leakage. The possible solutions include integrating the merits of spread spectrum modulation, using encryption algorithms and it’s potential to switch over various frequency bands. In the development of future wireless communication systems, the spectrum utilization will play an important key role due to the shortage of unallocated spectrum. The main tasks of the cognitive radio are to provide highly reliable communications whenever and wherever needed and how to utilize the radio spectrum efficiently. Cognitive radio can be the best communication system in an emergency condition as Earthquake, flood and Tsunami etc when all communication systems are failed to provide information and to communicate each other.

Decryption, Encryption, Primary User, Radio Frequency Spectrum, Secondary User, Spectrum Analysis.


1.        Federal Communications Commission, “ Spectrum Policy Task Force ,” Rep. ET Docket no. 02-135, Nov. 2002.
2.        P. Kolodzy et al., “Next generation communications: Kickoff meeting,” in Proc. DARPA, Oct. 17, 2001.

3.        M. McHenry, “Frequency agile spectrum access technologies,” in FCC Workshop Cogn. Radio, May 19, 2003.

4.        G. Staple and K. Werbach, “The end of spectrum scarcity,” IEEE Spectrum, vol. 41, no. 3, pp. 48–52, Mar. 2004.

5.        J. Mitola et al., “Cognitive radio: Making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999.

6.        J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.

7.        A. Ralston and E. D. Reilly, Encyclopedia of Computer Science. New York: Van Nostrand, 1993, pp. 186–186.

8.        R. Pfeifer and C. Scheier, Understanding Intelligence. Cambridge, MA: MIT Press, 1999, pp. 5–6.

9.        M. A. Fischler and O. Firschein, Intelligence: The Brain, and the Computer, ser. MA. Reading: Addison-Wesley, 1987, p. 81.

10.     B. Fette, “Technical challenges and opportunities,” presented at the Conf. Cogn. Radio, Las Vegas, NV, Mar. 15–16, 2004.

11.     J. Mitola, Ed., “Special issue on software radio,” in IEEE Commun.  Mag., May 1995.

12.     Software Defined Radio: Origins, Drivers, and International Perspectives, W. Tuttlebee, Ed., Wiley, New York, 2002.

13.     Software Defined Radio: Architectures, Systems and Functions,M.Milliger et al., Eds., Wiley, New York, 2003.

14.     FCC, Cognitive Radio Workshop, May 19, 2003, [Online].Available:

15.     Proc. Conf. Cogn. Radios, Las Vegas, NV, Mar. 15–16, 2004. York: Springer-Verlag, 1999.

16.     T. R. Shields, "SDR Update," Global Standards Collaboration, Sophia Antipolis, France, Powerpoint Presentation GSC10_grsc3(05)20, 28 August - 2 September 2005.

17.     P. Kolodzy, "Definition of Cognitive Radios." Hoboken, NJ: Wireless Network Security Center (WiNSeC) of the Stevens Institute of Technology, 2005.

18.     K. Nolan and J. Grosspietsch, "Cognitive Radio WG," SDR Forum, Brussels, Belgium, Powerpoint Presentation 14 September 2005.

19.     Digham, F., M. Alouini, and M. Simon. 2003. On the energy detection of unknown signals over fading channels. Proc. IEEE Int. Conf. on Communications. 5: 3575-3579.

20.     Digham,F., M. Alouini, and M. Simon. 2007. On the Energy Detection of Unknown Signals Over Fading Channels IEEE Transactions on Communications 55: 21-24

21.     P. Mannion, "Smart radios stretch spectrum," in Electronic Engineering Times (EETimes), vol. 2006: A Global Sources and CMP joint venture, 2006.

22. Fette.




Amir Aliabadian, Esmaeil Akbarpour, Mohammad Yosefi

Paper Title:

Kernel Based Approach toward Automatic object Detection and Tracking in Surveillance Systems

Abstract:   A modified object-tracking algorithm that uses the flexible Metric Distance Transform kernel and multiple features for the Mean shift procedure is proposed and tested. The Faithful target separation based on RGB joint pdf of the target region and that of a neighborhood surrounding the object is obtained. The non-linear log-likelihood function maps the multimodal object/background distribution as positive values for colors associated with foreground, while negative values are marked for background. This replaces the more usual Epanechnikov kernel (E-kernel), improving target representation and localization without increasing the processing time, minimizing the similarity measure using the Bhattacharya coefficient. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.

   Modified Object tracking, Distance Transform kernel, Mean Shift, Bhattacharyya coefficient, log-likelihood function maps.


1.        COMANICIU, D. AND MEER, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEETrans. Patt. Analy. Mach. Intell. 24, 5, 603–619.
2.        COMANICIU, D., RAMESH, V., AND MEER, P. 2003. Kernel-based object tracking. IEEE Trans. Patt. Analy. Mach.Intell. 25, 564–575.

3.        JEPSON, A., FLEET, D., AND ELMARAGHI, T. 2003. Robust online appearance models for visual tracking. IEEETrans. Patt. Analy. Mach. Intell. 25, 10, 1296–1311.

4.        KANG, J., COHEN, I., ANDMEDIONI, G. 2003. Continuous tracking within and across camera streams. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 267–272.

5.        KANG, J., COHEN, I., AND MEDIONI, G. 2004. Object reacquisition using geometric invariant appearance model. In International Conference on Pattern Recognition (ICPR). 759–762.

6.        KHAN, S. AND SHAH, M. 2003. Consistent labeling of tracked objects in multiple cameras with over lapping fields of view. IEEE Trans. Patt. Analy. Mach. Intell. 25, 10, 1355–1360.

7.        LOWE, D. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 2, 91–110.

8.        COLLINS, R. AND LIU, Y. 2003. On-line selection of discriminative tracking features. In IEEE International Conference on Computer Vision (ICCV). 346–352.

9.        SATO, K. AND AGGARWAL, J. 2004. Temporal spatio-velocity transform and its application to tracking and interaction. Comput. Vision Image Understand. 96, 2, 100–128.

10.     SERBY, D., KOLLER-MEIER, S., AND GOOL, L. V. 2004. Probabilistic object tracking using multiple features. In IEEE International Conference of Pattern Recognition (ICPR). 184–187.

11.     Jeakar, J. And Venkatesh, R.,2008. Robust object tracking with background-weighted local kernels. Computer Vision and Image Understanding.296-307.

12.     Babaiian,A. And Bayesteh, R., 2008. Target Tracking Using Wavelet Features and RVM Classifier. Fourth International Conference on Natural Computation. 575-578.

13.     Venkatesh,R. And Suresh, S., 2010. Online adaptive radial basis function networks for robust object tracking. Computer Vision and Image Understanding.297-310.

14.     Yu, J. And Tan,J. 2009. Object density-based image segmentation and its applications in biomedical image analysis. Computer methods and programs in biomedicine.193-204.

15.     Babaiian,A. And Rastegar, S.2009. Modify Kernel Tracking Using an Efficient Color Model and Active Contour. 41st Southeastern Symposium on System Theory University of Tennessee Space Institute. 59-63.

16.     Rastegar,S. And Babaiian, A.2009. Airplane Detection and Tracking Using Wavelet Features and SVM Classifier. 41st Southeastern Symposium on System Theory University of
Tennessee Space Institute. 64-67.

17.     Li,Q. And Qu, W.2010: Real-time interactive multi-target tracking using kernel-based trackers. The International Conference on Image Processing (ICIP): 689-692.




Shailesh S. Dhok

Paper Title:

Credit Card Fraud Detection Using Hidden Markov Model

Abstract:    The most accepted payment mode is credit card for both online and offline in today’s world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.

   Internet, online shopping, credit card, e-commerce security, fraud detection, Hidden Markov Model.


1.        Ghosh, S., and Reilly, D.L., 1994. Credit Card Fraud Detection with a Neural-Network, 27th Hawaii International l Conference on Information Systems, vol. 3 (2003), pp. 621- 630.
2.        Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572-577 (2002).

3.        Stolfo, S. J., Fan, D. W., Lee, W., Prodromidis, A., and Chan, P. K., 2000. Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project, Proceedings of DARPA Information Survivability Conference and Exposition, vol. 2 (2000), pp. 130-144.

4.        Aleskerov, E., Freisleben, B., and Rao, B., 1997. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of IEEE/IAFE: Computational Intelligence for Financial Eng. (1997), pp. 220-226.

5.        M.J. Kim and T.S. Kim, “A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection,” Proc. Int’l Conf. Intelligent Data Eng. and Automated Learning, pp. 378-383, 2002.

6.        W. Fan, A.L. Prodromidis, and S.J. Stolfo, “Distributed Data Mining in Credit Card Fraud Detection,” IEEE Intelligent Systems, vol. 14, no. 6, pp. 67-74, 1999.

7.        R. Brause, T. Langsdorf, and M. Hepp, “Neural Data Mining for Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. Tools with Artificial Intelligence, pp. 103-106, 1999.

8.        C. Chiu and C. Tsai, “A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. e-Technology, e-Commerce and e Service, pp. 177-181, 2004.

9.        C. Phua, V. Lee, K. Smith, and R. Gayler, “A Comprehensive Survey of Data Mining-Based Fraud Detection Research,” http://,  Mar. 2007.

10.     S. Stolfo and A.L. Prodromidis, “Agent-Based Distributed Learning Applied to Fraud Detection,” Technical Report CUCS-014-99, Columbia Univ., 1999.

11.     C. Phua, D. Alahakoon, and V. Lee, “Minority Report in Fraud Detection: Classification of Skewed Data,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 50-59, 2004.

12.     V. Vatsa, S. Sural, and A.K. Majumdar, “A Game-theoretic Approach to Credit Card Fraud Detection,” Proc. First Int’l Conf. Information Systems Security, pp. 263-276, 2005

13.     S.S. Joshi and V.V. Phoha, “Investigating Hidden Markov Models Capabilities in Anomaly Detection,” Proc. 43rd ACM Ann. Southeast Regional Conf., vol. 1, pp. 98-103, 2005.

14.     S.B. Cho and H.J. Park, “Efficient Anomaly Detection by Modeling Privilege Flows Using Hidden Markov Model,” Computer and Security, vol. 22, no. 1, pp. 45-55, 2003.

15.     D. Ourston, S. Matzner, W. Stump, and B. Hopkins, “Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks,” Proc. 36th Ann. Hawaii Int’l Conf. System Sciences, vol. 9, pp. 334-344, 2003.

16.     X.D. Hoang, J. Hu, and P. Bertok, “A Multi-Layer Model for Anomaly Intrusion Detection Using Program Sequences of System Calls,” Proc. 11th IEEE Int’l Conf. Networks, pp. 531-536, 2003.

17.     T. Lane, “Hidden Markov Models for Human/Computer Interface Modeling,” Proc. Int’l Joint Conf. Artificial Intelligence, Workshop Learning about Users, pp. 35-44, 1999.




P. S. Anish, S. Ramarajan, T. Arun Srinivas, M. Sasikumar

Paper Title:

Voltage Balancing in SVM Controlled Diode Clamped Multilevel Inverter for Adjustable drives

Abstract:   The work describes a transformer less medium voltage adjustable-speed induction motor drive consisting of two back-to-back connected five-level diode-clamped converters. Due to the feedback from the load to the dc link nodes, there is a chance of voltage imbalance. In this paper the methods for voltage balancing are discussed and simulated. The usage of switching techniques to employ voltage balancing rather than the external circuitry is being discussed. Proper switching results in the control of average current through the nodes and hence the non symmetrical charging and discharging of the dc split capacitors can be avoided. The first phase of work explains the output using the multicarrier pulse width modulation technique and the second phase deals with the modification done using the Space vector Pulse Width Modulation (SVPWM) technique. Voltage balancing is achieved with lesser harmonic content while using the SVPWM technique.

   Medium-voltage drives, multilevel inverters, Space vector modulation, voltage balancing.


1.        Nabae, I. Takahashi, and H. Akagi, “A new neutral-point-clamped PWM inverter,” IEEE Trans. Ind. Appl., vol. IA-17, no. 5, pp. 518–523,Sep. 1981.
2.        J. S. Lai and F. Z. Peng, “Multilevel converters—A new breed of power converters,” IEEE Trans. Ind. Appl., vol. 32, no. 3, pp. 506–517,May/Jun. 1996.

3.        J.M. Erdman, R. J. Kerkman, D.W. Schlegel, and G. L. Skibinski, “Effect of PWM inverters on ac motor bearing current shaft voltage,” IEEE Trans Ind. Appl., vol. 32, no. 2, pp. 250–259, Mar./Apr. 1996.

4.        Wu and F. DeWinter, “Voltage stress on induction motors in medium voltage (2300-6900-V) PWM GTO CSI drives,” IEEE Trans.Power Electron., vol. 12, no. 2, pp. 213–220, Mar. 1997.

5.        P. W. Hammond, “A new approach to enhance power quality for mediumvoltage AC drives,” IEEE Trans. Ind. Appl., vol. 33, no. 1, pp. 202–208,Jan./Feb. 1997.

6.        E. Cengelci, S. U. Sulistijo, B. O. Woo, R. Teodrescu, and F. Blaabjerg,“A new medium-voltage PWM inverter topology for adjustable-speed drives,” IEEE Trans. Ind. Appl., vol. 35, no. 3, pp. 628–637,May/Jun. 1999.

7.        M. Rastogi, P. W. Hammond, and R. H. Osman, “High performance, high reliability medium voltage drives,” in Proc. IEEE Power Electron. DriveSyst. Conf., 2001, vol. 1, pp. 259–264.

8.        Z. Pan, F. Z. Peng, K. A. Corzine, V. R. Stefannovic, J. M. Leuthen, and S. Gataric, “Voltage balancing control of diode-clamped multilevel rectifier/inverter systems,” IEEE Trans. Ind. Appl., vol. 41, no. 6, pp. 1698–1706, Nov./Dec. 2005.

9.        H. Akagi, H. Fujita, S. Yonetani, and Y. Kondo, “A 6.6-kV transformerless STATCOM based on a five-level diode-clamped PWM converter: System design and experimentation of a 200-V, 10-kVA laboratory model,” in Conf. Rec. IEEE IAS Annu. Meeting, 2005, pp. 557–564.

10.     Newton, M. Sumner, and T. Alexander, “Multi-level converters: A real solution to high voltage drives?” IEE Colloq. Dig., no. 1997/091, pp. 3/1–3/5, 1997.

11.     L. M. Tolbert, F. Z. Peng, and T. G. Habetler, “Multilevel converters for large electric drives,” IEEE Trans. Ind. Appl., vol. 35, no. 1, pp. 36–44, Jan./Feb. 1999.

12.     J. C. Das and R. H. Osman, “Grounding of AC and DC low-voltage and medium-voltage drive system,” IEEE Trans. Ind. Appl., vol. 34, no. 1, pp. 205–216, Jan./Feb. 1998




Mukesh Kumar, Anand Chauhan, Rajat Kumar

Paper Title:

A Deterministic Inventory Model for Deteriorating Items with Price Dependent Demand and Time Varying Holding Cost under Trade Credit

Abstract:    In this proposed research, we developed a deterministic inventory model for price dependent demand with time varying holding cost and trade credit under deteriorating environment, supplier offers a credit limit to the customer during whom there is no interest charged, but upon the expiry of the prescribed time limit, the supplier will charge some interest. However, the customer has the reserve capital to make the payments at the beginning, but decides to take the benefit of the credit limit. This study has two main purposes, first the mathematical model of an inventory system are establish under the above conditions. Second this study demonstrate that the optimal solution not only exists but also feasible. Computational analysis illustrates the solution procedure and the impact of the related parameter on decision and profits.

   Deterioration, price dependent Demand, Trade credit, time varying holding cost.


1.       A. M. M. Jamal, B. R. Sarkar and S. Wang, (1997), an ordering policy for deteriorating items with allowable shortage and permissible delay in payment, Journal of Operational Research Society, 48 , 826-833.
2.       B. G. Kingsmand, (1983), The effect of payment rules on ordering and stockholding in purchasing, Journal of Operational Research Society, 34, 1085-1098.

3.       Burwell T. H., Dave D. S., Fitzpatrick K. E. and Roy M. R. (1997), Economic lot size model for price-dependent demand under quantity and freight discounts, International Journal of Production Economics, 48(2) 141-155.

4.       C. B. Chapman, S. C. Ward, D. F. Ward and M. G. Page (1985), Credit policy and inventory control, Journal of Operational Research Society, 35, 1055-1065.

5.       C. W. Haley and R. C. Higgin (1973), Inventory policy and trade credit financing,  Management Science, 20, 464-471.

6.       C. K. Jaggi and S. P. Aggarwal (1994), Credit financing in economic ordering policies of deteriorating items, International Journal of Production Economics, 34, 151-155.

7.       Chung K. and Ting P. (1993), An heuristic for replenishment of deteriorating items with a linear trend in demand, Journal of Operational Research Society, 44, 1235-1241.

8.       Covert R. P. and Philip G. C.(1973), An EOQ model for items with Weibull distribution deterioration, AIIE Transactions, 5, 323-326.

9.       Chang CT, Wu SJ, Chen LC. (2009a), Optimal payment time with deteriorating items under inflation and permissible delay in payments. Int. J. Syst. Sci., 40,985-993.

10.     Chang HC, Ho C.H, Ouyang L.Y and Su C.H(2009b),. The optimal pricing and ordering policy for an integrated inventory model when trade credit linked to order quantity. Appl. Math. Model, 33,2978-2991.

11.     Chen L.H, Kang F.S(2010) , Integrated inventory models considering permissible delay in payment and variant pricing strategy. Appl. Math. Model, 34,36-46.

12.     Chen M.L and Cheng M.C.(2011),  Optimal order quantity under advance sales and permissible delays in payments, African Journal of Business Management 5(17), 7325-7334.

13.     Deb M. and Chaudhuri K. S. (1986), An EOQ model for items with finite rate of production and variable rate of deterioration, Opsearch, 23 ,175-181.

14.     Giri B. C. and Chaudhuri K. S. (1997), Heuristic models for deteriorating items with shortages and time-varying demand and costs, International Journal of Systems Science, 28, 53-159.

15.     Goh M. (1994), EOQ models with general demand and holding cost functions, European Journal of Operational Research, 73, 50-54.

16.     Hariga M. A. and Benkherouf L. (1994), Optimal and heuristic inventory replenishment models for deteriorating items with exponential time-varying demand, European Journal of Operational Research, 79 ,123-137.

17.     J. T. Teng, C. T. Chang and S. K. Goyal (2005), Optimal pricing and ordering policy under permissible delay in payments, International Journal of Production Economics, 97 ,121-129.

18.     Jaggi C. K., Goel S. K and Mittal M (2011) , Pricing and Replenishment Policies for Imperfect Quality Deteriorating Items Under Inflation and Permissible Delay in Payments, International Journal of Strategic Decision Sciences (IJSDS) 2( 2), 20-35.

19.     Jalan A. K. and Chaudhuri K. S. (1999), Structural properties of an inventory system with deterioration and trended demand, International Journal of Systems Science, 30 , 627-633.

20.     Kumar M., Tripathi R. P. and Singh S. R. (2008), Optimal ordering policy and pricing with variable demand rate under trade credits, Journal of National Academy of Mathematics, 22 ,111-123.

21.     Kumar M, Singh S. R. and Pandey R. K. (2009), An inventory model with quadratic demand rate for decaying items with trade credits and inflation, Journal of Interdisciplinary Mathematics, 12(3), 331-343.

22.     Kumar M, Singh S. R. and Pandey R. K. (2009), An inventory model with power demand rate, incremental holding cost and permissible delay in payments, International Transactions in Applied Sciences, 1, 55-71.

23.     Mondal B., Bhunia A. K. and Maiti M. (2003), An inventory system of ameliorating items for price dependent demand rate, Computers and Industrial Engineering, 45(3), 443-456.

24.     Muhlemann A. P. and Valtis-Spanopoulos N. P. (1980), A variable holding cost rate EOQ model, European Journal of Operational Research, 4, 132-135.

25.     Ray, Ajanta (2008), an inventory model for deteriorating items with price dependent demand and time-varying holding cost, Journal of AMO, 10, 25-37.

26.     R. L. Bregman (1993), The effect of extended payment terms on purchasing, Computers in Industry, 22 ,311-318.

27.     S. P. Aggarwal and C. K. Jaggi (1995), Ordering policies of deteriorating items under permissible delay in payments, Journal of Operational Research Society 46 , 458-462.

28.     S. K. Goyal (1985), Economic order quantity under conditions of permissible delay in payments, Journal of Operational Research Society, 36, 335-338.

29.     Shah Y. K. and Jaiswal M. C. (1977), an order-level inventory model for a system with constant rate of deterioration, Opsearch, 14, 174-184.

30.     Weiss H. J. (1982), Economic order quantity models with non-linear holding cost, European Journal of Operational Research, 9 , 56-60.

31.     You S. P. (2005), Inventory policy for products with price and time-dependent demands, Journal of Operational Research Society, 56 , 870-873.




Seyed  Zeinolabedin Moussavi, Aliakbar Rahmani

Paper Title:

Comparison and Inspection of Harmonic Effects in PMSM and Induction Motors

Abstract:    Regarding to different kinds of load, domestic electrical appliances, increasing application of further electrical equipment’s which leads to consumption of electric energy, destructive electromagnetic sources EMI added. Recognizing this source and it's side effects on performance of electronic and electrical equipment that could be in form of conductive, inductive and radiated is outstanding. An ideal electric machine is a system that electric energy is applied in pure sinusoid waveform flow has no loss in the heat form. However  in practice, elements and equipment’s with nonlinear characteristic, specially power electronic equipment’s and storage elements of energy could arise higher frequency  harmonics  causing losses in the form of heat.  Numerous electrical motors used in industrial manufacturing companies cause notably heat losses especially then induction motors. The fact that complexity of interconnection between stator and rotor can consider as source of higher harmonics and energy losses, attention is paying from induction motors into Permanent Magnet Synchronous Motors (PMSM). The  paper, make a comparison between PMSM and widely used induction motors from the view point of  higher frequency harmonics and shows the advantage of PMSM in this regards.

  Torque Control, Induction Motors, Energy Consumption, Harmonic Sources, Permanent Magnetic Synchronous Motors (PMSM), Ripple.


1.        Theory of Synchronous Machines Generalized Method of Analysis –Part I" AIEE Trans., Vol. 48, July 1929, PP.716-727.

3.        R. Rabinovici, “Eddy current losses of permanent magnet motors,” Proc. IEE-Elect. Power Appl., vol. 141, no. 1, pp. 7–11, 1994.

4.        Mohammadreza Hassan Zadeh1,Arash Kiyoumarsi2 Electrical Engineering Department,Abhar Islamic Azad University,22,Iran startup and steady-state performance of interior- permanent magnet induction Motors

5.        B. K. Bose, Power Electronics and Variable Frequency Drives, Technology and Applications. Piscataway, NJ: IEEE Press, 1997

6.        C. Mi, G. R. Slemon, and R. Bonert, “Modeling of iron losses of  permanent- magnet synchronous motors,” IEEE Trans. Ind. Appl., vol. 39, no. 3, pp. 734–742, May/Jun. 2003

7.        C. Mi, G. R. Slemon, and R. Bonert, “Minimization of iron losses of permanent magnet synchronous machines,” IEEE Trans. Energy onvers.,vol. 20, no. 1, pp. 121–127, Mar. 2005.

8.        D.C.Hanselman. Brushless Permanent –Magnet Motor Design. McGraw-Hill,Inc.1994

9.        Peter Campbell. Permanent Magnet Materials and Their Application. Cambridge University Press, Cambridge, 1994  

10.     K. M. Rahman and H. A. Toliyat, “Sensorless operation of permanent magnet AC (PMAC) motors with modified stator windings,” in Conf. Rec. IEEE-IAS Annu. Meeting, vol. 1, 1996, pp. 326–333.




Sangeetha.M, Arumugam.C, Sapna P.G, Senthil Kumar .K.M

Paper Title:

Reliability Data Analysis Procedures for Comparing Failure Rates of the System Using Optimal Truncation of Short Tests

Abstract:    A test was described for two systems, long term and short term with an exponentially distributed time between failures. The test is intended for checking the ratio MTBFl /MTBFs exceeds or equals a prescribed value, versus one that it is less than the prescribed value, by means of long term tests with large average sample number in the earlier system. Our proposed system focus on improving test by using low average sample number in short term which is having the advantage of economy in time requirement and cost. It produces optimum truncated test called binomial Sequential Probability Ratio Test. Criteria are proposed for determining the characteristics of truncated test followed with the discretizing effect of truncation on error probabilities with a view to optimization of its parameters. The search algorithm for truncation apex used in this system achieves closeness to the optimum which depends on successful choice of the initial approximation, search boundaries and on the search step. The enhanced reliability of modern technological systems, combined with the reduced time quotas allotted for creating new system is capable of yielding a highly efficacious test which increases reliability and feasibility of decisions.

   MTBF, Short Truncate Test, Long Term, ADP


1.        Y. H. Michlin and G. Grabarnik, “Sequential testing for comparison of the mean time between failures for two systems,” IEEE Trans. Reliab.,vol. 56, no. 2, pp.   321–331, June 2007.
2.        A. Wald, Sequential Analysis. NY: John Wiley & Sons, 1947, pp. 22–180.

3.        Y. H. Michlin and R. Migdali, “Test duration in choice of  helicopter maintenance policy,” Rel. Eng. & Syst.  Safety, vol. 86, no. 3, pp. 317–321, Dec. 2004.

4.        L. A. Aroian, “Sequential analysis-direct method,” Technometrics, vol. 10, pp. 125–132, 1968.

5.        Y. H. Michlin and Y. Shai, “Sequential testing for MTBF ratio in comparative reliability evaluation,” in Proc. 15th Int. Conf. Israel Society for Quality, Jerusalem, 2004, pp. 264–268.

6.        R. A.Wijsman, “Stopping times: Termination,  moments, distribution,” in Handbook of Sequential Analysis, B. K.  Ghosh and P. K. Sen, Eds. New York: Marcel Dekker, 1991, pp. 67–119.

7.        A.Wald and J.Wolfowitz, “Optimum character of the sequential probability ratio test,” Ann. of Math. Stat., vol. 19, no. 3, pp. 326–339, 1948.

8.        B. Eisenberg and B. K Ghosh, “The sequential probability ratio test,” in Handbook of Sequential Analysis, B. K. Ghosh and P. K. Sen, Eds.New York: Marcel Dekker, 1991, pp. 47–66.

9.        A. E. Mace, Sample Size Determination. NY: Robert E. Krieger Pub.Co, 1974, pp. 110–114.

10.     “Reliability test methods, plans, and environments for engineering, development, qualification, and production,” pp. 32–42, MIL-HDBK-781A, US DOD, 1996.

11.     “Reliability data analysis techniques-procedures for comparison of two constant failure rates and two constant failure (event) intensities,” IEC 61650, 1997.




Sanjay Patel, O. P. Vyas, Hansa Mehra

Paper Title:

Interfacing of Sensor Network to Communication Network for Disaster Management

Abstract:   This paper deals with the sensor network and communication network for disaster management, in which the concerned authorities dealing in disaster management get the message on their mobile phones about disaster information. Now a days number of small disasters like fire, chemical leakage, pollution etc, happen frequently and need immediate relief action. In this paper the authors have developed a technique for immediate information release for quick action to such events. In this technique, we have used sensors which sense the disaster information and transfer this information to the mobile user using GSM RS 232 Modem and MDE 8051 development board.

   GSM, MDE0851 board, KEIL, AT command


1.        Harper, M., The use of thermal Desorption in Monitoring for the Chemical Weapons Demilitarization Program. Journal of Environmental Monitoring 2002, 4, 688-694.
2.        Siegmund M. Redl, Matthias K. Weber, Malcolm W. Oliphant,”an introduction to GSM”, Boston: artech House, 1995

3.        Vijay K. Garg, Joseph E.Wilkes,”principles and applications of GSM”, Prentice Hall, 1999.

4.        GSM Technical Specification, “digital cellular telecommunications system, Global System for Mobile communications (GSM)”, ETSI SMG,GSM 07.05,nov1997

5.        J.W. Grate, M.H. Abraham, Solubility interactions and the design of chemically selective sorbent coatings for chemical sensors and arrays, Sens. Actuators B: Chem. 3 (1991) 85–111.

6.        J.C. Chen et. al. Coherent Acoustic Array Processing and Localization on Wireless Sensor Networks. Proc. of the IEEE, 91(8), August 2003.

7.        McLoughlin, M. P., Allmon, W. R., Anderson, C. W., Carlson,M. A., DeCicco, D. J., and Evancich, N. H., “Development of a Field-Portable Time-of-Flight Mass Spectrometer System,” Johns HopkinsAPL Tech.

8.        National Institute for Occupational Safety and Health, Guidance for Protecting Building Environments from Airborne Chemical, Biological, or Radiological Attacks, DHHS (NIOSH) Pub. 2002-139 (May2002).

9.        Oppenheimer, A., Nuclear, Biological and Chemical detection: To detect and Protect. Jane's Defence Weekly 2004.







Chinmay Chandrakar, M.K. Kowar

Paper Title:

Denoising ECG Signals Using Adaptive Filter Algorithm

Abstract:   One of the main problem in biomedical data processing like electrocardiography is   the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields, random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper  type of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference. Results of simulations in MATLAB are presented. In this we have used Recursive Least Squares (RLS). RLS   algorithm is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. Least-squares algorithms aim at the minimization of the sum of the squares of the difference between the desired signal and the model filter output .When new samples of the incoming signals are received at every iteration, the solution for the least-squares problem can be computed in recursive form resulting in the recursive least-squares (RLS) algorithms. The RLS algorithms are known to pursue fast convergence even when the Eigen value spread of the input signal correlation matrix is large. These algorithms have excellent performance when working in time-varying   environments. All these advantages come with the cost of an increased computational complexity and some stability problems, which are not as critical in LMS-based algorithms.

   ECG Signal, Dirichlet’s Condition, Adaptive Filter.


1.        Paulo S.R. Denis, "Adaptive filtering Algorithms and Practical implementation”.
2.        O. Sayadi and M. B. Shamsollahi, “Model-based fiducial points  extraction for baseline wander electrocardiograms,” IEEE Trans. Biomed. Eng., vol.55, pp. 347-351, Jan.2008.

3.        Y. Der Lin and Y. Hen Hu, “Power-line interference detection and suppression in ECG signal  Processing,”. IEEE Trans. Biomed. Eng.,vol.55, pp. 354-357, Jan.2008.

4.        N. V. Thakor and Y.-S. Zhu,Applications of  adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection,”. IEEETransactionson Biomedical Engineering, vol. 38, no. 8,
pp. 785-794, 1991.

5.        Farhang-Boroujeny, B., Adaptive Filters-  Theory  and Applications,    John Wiley and Sons.Chichester,UK, 1998.

6.        P. E.McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, “A dynamical model for generating synthetic elctrocardiogram signals,”  IEEE Transactions on Biomedical Engineering, vol. 50, no.3, pp. 289-294, 2003.




Seyed  Zeinolabedin Moussavi, Aliakbar Rahmani

Paper Title:

Position and Speed Control of Permanent Magnet Motors, State Space Approach

Abstract:    Present paper is analyzing the permanent magnet  dc motor (PMDC) through state space variables so that command speed without consequence resulted from voltage and power and load fluctuations  can  be obtained. For this purpose, we should write equations of permanent magnetic motor and then by applying these equations and known methods of control, try for making desirable behavior of these motors, and by using MATLAB software in coding, analyzing real behavior of motor could be possible, and regarding to these results, planning for future of a system in front of short circuit and load fluctuation could be possible. We are trying to reduce dangers resulted from mistakes in experiments.

  Permanent magnetic motor, modern control, efficiency, permanent magnetic motors, control, permanent magnet motor, sensorless, torque fluctuation.


1.        po ieee transactions on industrial electronics, vol. 49, no. 1, february 2002sition-Sensorless Control of Surface-Mount Permanent-Magnet AC (PMAC) Motors at Low Speeds
2.        J.FGeras and M.Wing"permanent Magnet Motor Technology". New York: Marcel Dekker,(1998)

3.        E/ Ch.Andresenand R. Keller, 'Squirrel Cage Induction Motor or permanent Magnet Motor synchronous Motor". Symp. On Power Electronnics,Electr. Drives. Advanced Electr. Motor
SPEED AM,96, Capri, Italy (1996)

4.        G.R.Slemon,"High-efficiency drives using permanent Magnet motors" in proc. Int. conf.    Industrial Electronics, control and Instrumentation, Maui, HI, 1991, vol. 2. Pp. 725-730.

5.        R. F. Schiferl and C. M. Ong, “Six phase synchronous machine with AC and DC stator connections, Part I: Equivalent circuit representation and steady-state analysis,” IEEE Trans. Power App. Syst., vol. PAS-102, Aug. 1983.

6.        G. Bertotti, Hysteresis in Magnetism for Physicists, Material Scientists, and Engineers. New York: Academic, 1998.

7.        Investigation of Influences of Various Losses on Electromagnetic Torque for Surface-   Mounted     Permanent Magnet Synchronous Motor ieee transactions on power electronics, vol. 18, no. 1, january 2003

8.        Iron Loss Model for Permanent-Magnet Synchronous Motors ieee transactions on magnetics, vol. 43, no. 8, august 2007

9.        IBM Corporation and sspower Technology, Hilliard, OH 43026 USA. Iron Loss Model for Permanent-Magnet Synchronous Motors.IEEE  Transactions on Magnetics, VOl,43,no.8

10.     Mohammadreza Hassan Zadeh1,Arash Kiyoumarsi2 Electrical Engineering Department,Abhar Islamic Azad University,22,Iran startup and steady-state performance of interior- permanent magnet induction Motors.




R.Hari Kumar, C.Ganesh Babu, P.Shri Vignesh

Paper Title:

Earlier Detection of Oral Cancer from Fuzzy Based Photo Plethysmography

Abstract:   The main objective of this paper is to detect the occurrence of cancer in its early stages from Fuzzy based photoplethysmography. One of the key problems in the treatment of cancer is the early detection of the disease. Often, cancer is detected in its advanced stages, when it has compromised the function of one or more vital organ systems and is widespread throughout the body. Methods for the early detection of cancer are of utmost importance and are an active area of current research. The photo Plethysmography readings are taken for the patients in Madurai, Chennai, and Coimbatore regions and are converted to a quantized value and then classified using the fuzzy logic in accordance with clinical standards of TNM (Tumor Node Metastatic) codes. This method helps people to get rid of the glitches of cancer and also to cure the cancer in its early stage. It is a cost effective method and it needs no trained persons to operate. This paper can be further improved by a designing of VLSI fuzzy processor, which is capable of dealing with complex fuzzy inferences systems. It can also be made user friendly and it can be made available in all health care centers. The results can be made within short period without any delay for further processing.

   Early Detection of Cancer, TNM Codes, photo Plethysmography, Fuzzy logic.


1.       Jindal G.D.,Nerukkar S.N.,Pendukar S.A.,Babu.J.P.,Kelkar M.D.,Despande A.K., and Parulkar G.B(1990a):’diagnosis of peripheral arterial occlusive disease using impedance plethysmography’ J.Postgrad.Med.,36,pp.147-153.
2.       Nyober.J.(1960):’regional pulse volume and perfusion flow measurements: Electrical impednce plethysmography, Arch Int.Med.,105,pp.264-276.

3.       Pethig R.,’Dielectric properties of live tissues-clinical physics and physiological measurement’ Volume 8,pp 5-12,1987.

4.       F Martin Mc Neil and Ellen Thro,’ Fuzzy logic a practical approach ‘, forwarded by R.Vage Ap Professional, 1994.

5.       Berenji.H.R.,’A reinforcement learning based architecture for fuzzy logic control’, Int J. Approximate reasoning, Vol.6,pp.267-292,1992.

6.       Fuzzy Logic Toolbox User’s Guide, Revised for MATLAB R2007a, the Mathworks inc.,2007.

7.       G.Ascia, V.Ctania, and M. Russo-VLSI Hardware Architecture for Complex fuzzy systems, IEEE transactions on Fuzzy systems Vol.7, No.5 Oct 1999.pp 553-570




K.M. Pandey, A.P. Singh

Paper Title:

Numerical Simulation of Combustion Chamber without Cavity at Mach 3.12

Abstract:    In this Simulation, supersonic combustion of hydrogen at Mach 3.12 has been presented. The combustor has a single fuel injection perpendicular to the main flow from the base.  Finite rate chemistry model with K-ε model have been used for modeling of supersonic combustion. The pressure rise due to the combustion is not very high on account of global equivalence ratio being quite low. Within the inlet the shock-wave-boundary- layer interactions play a significant role. The combustor without cavity is found to enhance mixing and combustion while increasing the pressure loss, compared with the case without cavity to the experimental results. The OH mass fraction is less almost by an order to that of water mass fraction The OH mass fraction decreases as the gas expands around the injected jet and the local mixture temperature falls, However OH species are primarily produced in the hot separation region upstream of the jet exit and behind the bow shock and convected downstream with shear layer. The geometry results shows the better mixing in combustion chamber, caused by more extreme shear layers and stronger shocks are induced which leads loss in total pressure of the supersonic stream.

   Hydrogen, Shear layers, Stabilization, stagnation temperature, Supersonic combustion.


1.       V.A. Zabaykin and  A.A. Smogolev, “3-D Structure Of Hydrogen Flame In Supersonic high-Enthalpy Flow,” West-East High Speed Flow Field Conference 19-22, November 2007 Moscow, Russia.
2.       AntonellaIngenito and Claudio Bruno, “Physics and Regimes of Supersonic Combustion”, AIAA Journal, Vol. 48, No. 3, March 2010.

3.       J. H. Tien, and R. J. Stalker, “Release of Chemical Energy by Combustion in a Supersonic Mixing Layer of Hydrogen and Air”, COMBUSTION AND FLAME 130:329–348 (2002).

4.       Adela Ben-Yakar and Ronald K. Hanson, “Cavity Flame-Holders for Ignition and Flame Stabilization in Scramjets: An Overview”, Journal Of Propulsion And Power Vol. 17, No. 4, July–August 2001.

5.       Jeong-Yeol Choi, Fuhua Ma and Vigor Yang, “ Dynamics Combustion Characteristics in Scramjet Combustors with Transverse Fuel Injection”, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 10 - 13 July 2005, Tucson, Arizona, AIAA 2005-4428.

6.       T. K. G. Anavaradham, B. U. Chandra, V. Babu and S. R. Chakravarthy and S. Panneerselvam   Experimental and numerical investigation of confined unsteady supersonic flow over cavities”, The Aeronautical Journal March 2004  pp.135-144.

7.       A. Ben-Yakar and R. K. Hanson, “Experimental Investigation Of Flame-Holding Capability of Hydrogen Transverse Jet In Supersonic Cross-Flow”, Twenty-Seventh Symposium (International) on Combustion/The Combustion Institute, 1998/pp. 2173–2180.

8.       Tianwen Fang, Meng Ding, Jin Zhou, “Supersonic Flows Over Cavities”, Front. Energy Power Engineering. China 2008, 2(4): 528–533

9.       In-Seuck Jeung and Jeong-Yeol Choi, “Numerical Simulation of Supersonic Combustion for Hypersonic Propulsion”, 5th Asia-Pacific Conference on Combustion,The University of Adelaide, Adelaide, Australia 18-20 July 2005.

10.     Kyung Moo Kim, Seung Wook Baek and Cho Young Han Numerical study on supersonic combustion with cavity-based fuel injection”, International Journal of Heat and Mass Transfer 47 (2004) 271–286.

11.     Tarun Mathur, “Supersonic Combustion Experiments with a Cavity-Based Fuel Injector”, Journal of Propulsion and Power Vol. 17, No. 6, November–December 2001.

12.     J. Philip Drummond, Glenn S. Diskin, and Andrew D. Cutler, “Fuel-Air Mixing And Combustion In Scramjets”, American Institute of Astronautics and Aeronautics (AIAA-.2002-3878).

13.     Yves Burtschell, GhislainTchuenb and David E. Zeitoun, “H2 injection and combustion in a Mach 5 air inlet through a ViscousMach Interaction”, European Journal of Mechanics B/Fluids 29 (2010) pp.351-356.

14.     D. Haworth, B. Cuenot, T. Poinsot, and R. Blint, “Numerical simulation of turbulentpropane-air combustion with non-homogeneous reactants: initial results”, Center for Turbulence Research Proceedings of the Summer Program 1998, pp.5-24.

15.     YiguangJu and Takashi Niioka, “Ignition Simulation of Methane/Hydrogen Mixtures in a Supersonic Mixing Layer”, Combustionand Flame 102:462-470 (1995)

16.     Nitin K. Gupta, Basant K. Gupta, Narayan Ananthkrishnan_Gopal R. Shevare,IkSoo Park and Hyun Gull Yoon, “Integrated Modeling and Simulation of an Air-breathing Combustion System Dynamics”, American Institute of Aeronautics and Astronautics, pp.1-31.

17.     M. Akbarzadeh and M. J. Kermani, “Numerical Computation of Supersonic-Subsonic Ramjet Inlets; a Design Procedure”, 15th. Annual (International) Conference on Mechanical Engineering-ISME2007 May 15-17, 2007, Amirkabir University of Technology, Tehran, Iran ISME2007-3056.

18.     Stephen J. Mattick and Steven H. Frankel, “Numerical Modeling of Supersonic Combustion:Validation and Vitiation Studies Using FLUENT”, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 10 - 13 July 2005, Tucson, Arizona, AIAA 2005-4287

19.     A. Balabel, A.M. Hegab, S. Wilson, M. Nasr, S. El-Behery, “Numerical Simulation of Turbulent Gas Flow in a Solid Rocket Motor Nozzle”, 13th International Conference on Aerospace Sciences & Aviation Technology, ASAT- 13, May 26 – 28, 2009, Paper: ASAT-13-pp-13.

20.     A.T. Sriram and D. Chakroborty “Numerical Simulations Of Staged Transverse Injection Into Mach 2 Flow Behind Backward-Facing Step”, Proceedings of the International Conference on Aerospace Science and Technology,26 - 28 June 2008, Bangalore, India, INCAST 2008-119.




K.M. Pandey, S.K. Reddy K.K.

Paper Title:

Numerical Simulation of Wall Injection with Cavity in Supersonic Flows of Scramjet Combustion

Abstract:    A supersonic combustion ramjet engine (scramjet) is one of the most promising air-breathing propulsive systems for future hypersonic vehicles, and it has drawn the attention of an ever increasing number of researchers. This work involves an application of computational fluid dynamics to a problem associated with the flow in the combustor region of a scramjet. A cavity wall injector is an integrated fuel injection approach, and it is a new concept for flame holding and stabilization in supersonic combustors. The presence of a cavity on an aerodynamic surface could have a large impact on the air flow surrounding it, and this makes a large difference to the performance of the engine, namely it may improve the combustion efficiency and increase the drag force. The objective of the work was to design the four wall injector model with cavity using gambit, study the combustion processes of air- fuel (h2) mixture for the wall injector models with inlet air at Mach number 2 and inlet fuel at Mach number 2 and compare the performance of the different wall injector models. There are several key issues that must be considered in the design of an efficient fuel injector. Of particular importance are the total pressure losses created by the injector and the injection processes that must be minimized since the losses reduce the thrust of the engine. In this analysis, the two-dimensional coupled implicit Reynolds averaged Navier-Stokes (RANS) equations, the standard k-ε Turbulence model, sst-kω Turbulence and the eddy-dissipation reaction model have been employed to investigate the flow field in a hydrogen-fuelled scramjet combustor with a cavity design and to analyze the combustion processes. Numerical results are obtained with the fluent solving sst-kω Turbulence model to have the best results of all models. The grid independent test was also carried out. The profiles of static pressure, static temperature, and two components of velocity and mole fraction of hydrogen at various locations of the flow field are presented. Computed values using sst-kω turbulence model are found to have good overall agreement with results obtained from literature reviews and some discrepancies were observed for static pressure and static temperature in the vicinity of the jets due to unsteadiness in the shock system.

   Scramjet engine, Mach number 2, RANS Equations, Turbulence model.


1.        Wei Huang, Shi-bin Luo, Mohamed Pourkashanian, Lin Ma, Derek B.Ingham, Jun Liu and Zhen-guo Wang; “Numerical Simulations of a Typical Hydrogen Fueled Scramjet Combustor with a Cavity Flameholder”; WCE 2010, London, UK, July 2010.
2.        In-Seuck Jeung, Jeong-Yeol Choi; “Numerical Simulation of Supersonic Combustion for Hypersonic Propulsion”; 5th Asia-Pacific Conference on Combustion, 18-20 July 2005.

3.        Jeong-Yeol Choi, Fuhua Mab, Vigor Yang; “Combustion oscillations in a scramjet engine combustor with transverse fuel injection”; Proceedings of the Combustion Institute 30, 2005, pp:2851–2858.

4.        K.M. Pandey, A.P. Singh; “Numerical analysis of combustor flow fields in Supersonic flow regime with finite rate Chemistry model”; ISST Journal of Mechanical Engineering, Vol. 1 No.2, (July - December 2010), p.p. 81-90.

5.        K.M.Pandey, T.Sivasakthivel; “Recent Advances in Scramjet Fuel Injection - A Review”; International Journal of Chemical Engineering and Applications, ISSN: 2010-0221, Vol. 1, No. 4, December 2010.

6.        Weipeng Li, Taku Nonomura, Akira Oyama and Kozo Fujii; “LES Study of Feedback-loop Mechanism of Supersonic Open Cavity Flows”; 40th Fluid Dynamics Conference and Exhibit, AIAA 2010-5112, 28 June - 1 July 2010.

7.        Y. Moriyoshi, K. Suga, M. Kubota; “Modeling of Cavitation Phenomenon inside a Nozzle under High Fuel Pressure Condition”; 11th ICLASS July 2009.

8.        Michael K. Smart; “Scramjet Inlets”; Brisbane 4072 AUSTRALIA

9.        Md. Mahbubul Alam, Shigeru Matsuo, Toshiaki Setoguchi; “Passive Suppression of Cavity-Induced Pressure Oscillation in An Axisymmetric Supersonic Flow”; 29- 31 December 2007, Dhaka, Bangladesh, ICME 2007

10.     B.V.N. Charyulu1, R. Manoj, B. Rajinikant, D.K. Tripathi, A. Rolex, Vikrant Satya, V. Ramanujachari, S. Panneerselvam; “Experimental investigations of ramp-cavity based Supersonic combustor”; International Conference on Aerospace Science and Technology, Bangalore, India, 26-28 June, 2008.

11.     Sean M. Torrez, James F. Driscoll, Matthias Ihme, Matthew L. Fotia; “Reduced-Order Modeling of Turbulent Reacting Flows with Application to Ramjets and Scramjets”; Journal of propulsion and power; vol. 27, No. 2, March–April 2011.

12.     Kathleen Tran; “One Dimensional Analysis Program for Scramjet and Ramjet Flow paths”; Blacksburg, VA, December 8, 2010.




Anurag Porwal, Rohit Maheshwari, B.L.Pal, Gaurav Kakhani

Paper Title:

An Approach for Secure Data Transmission in Private Cloud

Abstract:    In the cloud, the data is transferred among the server and client. Cloud security is the current discussion in the IT world. This research paper helps in securing the data without affecting the network layers and protecting the data from unauthorized entries into the server, the data is secured in server based on users’ choice of security method so that data is given high secure priority.

   Cloud, Private Cloud, Security, Secure data Transmission.


1.       Lombardi F, Di Pietro R. Secure virtualization for cloud computing. Journal of Network Computer Applications (2010), doi:10.1016/j.jnca.2010.06.008.
2.       Subashini S, Kavitha V., “A survey on security issues in service delivery models of  cloud computing,” Journal of Network and Computer Applications (2011) vol. 34 Issue 1, January 2011 pp. 1-11.

3.       Sudha.M, Bandaru Rama Krishna rao, M.Monica, “A Comprehensive approach to ensure secure data communication in cloud environment” International Jornal Of computer Applications, vol. 12. Issue 8, pp. 19-23.

4.       Balachander R.K, Ramakrishna P, A. Rakshit, “Cloud Security Issues, IEEE International Conference on Services Computing (2010),” pp. 517-520.

5.       Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou, “Ensuring Data Storage Security in Cloud Computing” proceeding of International workshop on Quality of service 2009”, pp.1-9.

6.       Gary Anthes, “Security in the cloud,” In ACM Communications (2010), vol.53, Issue11, pp. 16-18.

7.       Kresimir Popovic, Željko Hocenski, “Cloud computing security issues and challenges,” MIPRO 2010, pp. 344-349.

8.       Kikuko Kamiasaka, Saneyasu Yamaguchi, Masato Oguchi, “Implementation and Evaluation of secure and optimized IP-SAN Mechanism,” Proceedings of the IEEE International Conference on Telecommunications, May 2007, pp. 272-277.

9.       Luis M. Vaquero, Luis Rodero-Merino, Juan   Caceres1, Maik Lindner, “A Break in Clouds: Towards a cloud Definition,” ACM SIGCOMM Computer Communication Review, vol. 39, Number 1, January 2009, pp. 50-55.

10.     Patrick McDaniel, Sean W. Smith, “Outlook:    Cloudy with a chance of security challenges and improvements,” IEEE Computer and reliability societies (2010), pp. 77-80.

11.     Sameera Abdulrahman Almulla, Chan Yeob Yeun, “Cloud Computing Security Management,” Engineering systems management and its applications (2010), pp. 1-7.

12.     Steve Mansfield-Devine, “Danger in Clouds”, Network Security (2008), 12, pp. 9-11.

13.     Anthony T. Velte, Toby J.Velte, Robert Elsenpeter, Cloud Computing: A Practical Approach, Tata Mc GrawHill 2010.




T.P.Mote, S.D.Lokhande

Paper Title:

Temperature Control System Using ANFIS

Abstract:    This paper describes three important aspects: design, simulation and Implementation of Adaptive Neuro fuzzy system  applied to the temperature variable of a thermal system with a range of 250C to 500C.An Adaptive Neuro Fuzzy Inference System (ANFIS) based controller is proposed for water temperature control. The generation of membership function is a challenging problem for fuzzy sytems and the response of fuzzy systems depends mainly on the membership functions. The ANFIS based input – output model is used to tune the membership functions in fuzzy system. Experimental results are compared with the conventional PID Controller and Neural Network Controller. All the controllers are tested in various operating conditions and varying set point changes and also for disturbance rejection. This shows that better performance can be achieved with ANFIS tuning.

   ANFIS, Artificial neural network, PID, Temperature control.


1.        Kaijun Xu, Xiaoping Qiu ,Xiaobing ,Li Yang Xu  “A dynamic neuro-fuzzy controller for gas-Fired water heater”, 3304-9/08 /2008 IEEE.
2.        Valdez D., Ortiz V., Cabrera A. and Chairez I “Extended Kalman FilterWeights Adjustment For Neonatal Incubator Neurofuzzy Identification”, 0-7803-9489-5/06/$20.00/©2006 IEEE.

3.        Otman M. Ahtiwash and Mohd Zaki Abdulmuin, V.N. Alexandrov “An Adaptive Neuro-Fuzzy Approach for Modeling and Control of Nonlinear Systems”, ICCS 2001, LNCS 2074, 198–207, 2001. Springer-Verlag Berlin Heidelberg.

4.        J. A. Vieira, F. Morgado Dias and A. M. Mota  “Hybrid Neuro-Fuzzy Network-Priori Knowledge Model in Temperature Control of a Gas Water Heater System”, 0-7695-2457-5/0 /2005, IEEE.

5.        S.Ravi P .A.Balakrishnan, “Modeling And Control of an ANFIS Temperature Controller For Plastic Extrusion Process”, 978-1-4244-7770- 8/10/2010 IEEE.

6.        Advanced Control Schemes for Temperature Regulation of Air Heat Plant 0-7803-5406- 0/99/1999, IEEE.

7.        Marzuki Khalid and Sigeru Omatu “A Neural Network Controller for Temperature Control System”, 0272- 1708/92/1992IEEE.

8.        Ajay B Patil “Adaptive Neuro Fuzzy Controller for Process Control System”, 978-1-4244-2806-9/08/2008, IEEE.




Prashana Balaji V., Anvita Gupta Malhotra, Khushhali Menaria

Paper Title:

Flux Balance Analysis of Melanogenesis Pathway

Abstract:    A computational model could serve as a conventional engineering approach to uncover the biochemistry of the metabolic pathways. These would dynamically mimic the pathways in-silico. Flux Balance Analysis (FBA) is one such method wherein characterization of growth yields, bio-energy production, environmental conditions and robustness under knock out & knock down can be studied. We have built a comprehensive dynamic platform of integrated network for melanogenesis pathway containing 6 major reactions. Wherein detailed stoichiometric matrix of the pathway reactions is constructed followed by defining constrains and objective function. Subsequently, these are optimized using linear programming to give us resultant fluxes. Using this model, vulnerability of the enzymes in these pathways are studied; essentiality of participating enzymes are established and varied computational gene knock-out experiments which can decipher effect of inhibition on metabolic circuit are performed. Results of the simulations were in corroboration with published results and predictions were validated. However, this platform can enables us to make elaborate prediction in the known modeled domain and later with amalgamation of more modelled pathways into this network; a comprehensive virtual cell can be constructed.

   Melanogenesis, Flux Balance Analysis (FBA), Pheomelanin, Eumelanin, Systems Biology.


1.       Donald G. Jackson., Matthew D. Healy., Daniel B. Davison., “Binformatics: not just for sequences anymore”, Biosilico, Vol. 1 (3), 2003, pp. 103-111.
2.       Hiroaki Kitano., “Systems Biology: A Brief Overview”, Science, Vol. 295, 2002, pp. 1662-1664

3.       Barabási AL, Oltvai ZN.,“Network biology: understanding the cell's functional  organization.”, Nature Reviews Genetics, Vol. 5, 2004, pp. 101-113

4.       Covert MW, Famili I, Palsson BO.,“Identifying constraints that govern cell behavior: a key to converting conceptual to computational models in biology?”, Biotechnology and Bioengineering, Vol. 84 (7), 2003, pp. 763-772

5.       Jeffrey D Orth, Ines Thiele,  Bernhard Ø Palsson, “What is flux balance analysis?”, Nature Biotechnology, Vol. 28, 2010, pp. 245-248

6.       Price ND, Reed JL, Palsson BO.” Genome-scale models of microbial cells: evaluating the consequences of constraints”, Nature Reviews Microbiology, Vol. 2,2004, pp. 886–897.

7.       Hendrik P. J. Bonarius, Georg Schmid and Johannes Tramper,” Flux analysis of underdetermined metabolic networks: the quest for the missing constraints”, Vol. 15, 1997, pp. 308-314

8.       Jose Neptuno Rodriguez-Lopez$,J ose Tudelap, Ramon VaronS, Francisco Garcia- Carmonap,  Francisco Garcia-Canovaspll, “Analysis of a Kinetic Model for Melanin Biosynthesis Pathway”, The Journal for Biological Chemistry, Vol. 267(6), 1992, pp. 3801-3810

9.       Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M., “KEGG for integration and interpretation of large-scale molecular datasets”, Nucleic Acids Research, 2011 (Nov 10), pp. 1-6

10.     Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P, Karthikeyan AS, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Paley S, Popescu L, Pujar A, Shearer AG, Zhang P, Karp PD., "The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases",Nucleic Acids Research, Vol. 38, 2009, pp. 473-479

11.     Karthik Raman, Preethi Rajagopalan and Nagasuma Chandra, “Principles and Practices of Pathway Modelling”, Current Bioinformatics, Vol. 1, 2006, pp. 147-160

12.     Jong Min Lee, Erwin P.Gianchandani and Jason A. Papin, “Flux balance analysis in the era of  metabolomics”, Briefings in Bioinformatics., Vol. 7(2), 2006, pp. 140-150

13.     Cornish-Bowden A, Hofmeyr JH., “The role of stoichiometric analysis in studies of metabolism: an example.”, Journal of Theoretical Biology , Vol. 216(2), 2002, pp. 179-191

14.     Amit Varma & Bernhard O. Palsson., “Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use”, Nature Biotechnology, Vol. 12, 1994, pp. 994 - 998

15.     Kenneth J Kauffman, Purusharth Prakash, Jeremy S Edwards, “Advances in flux balance analysis”, Current Opinion in Biotechnology, Vol. 14(3), 2003, pp. 491-496

16.     Karthik Raman and Nagasuma Chandra, “Flux balance analysis of biological systems: applications and challenges”, Briefings in Bioinformatics, Vol. 10(4), 2009, pp. 435- 449

17.     Klamt S, Schuster S., “Calculating as many fluxes as possible in underdetermined metabolic networks.”, Molecular Biology Reports, Vol. 29(1-2), 2002, pp. 243-248

18.     Erwin P. Gianchandani, Arvind K. Chavali and Jason A. Papin, “The application of flux balance analysis in systems biology”, Reviews: Systems Biology and Medicine, Vol. 2 (3), 2010, pp. 372-382.

19.     Förster J, Famili I, Fu P, Palsson BØ, Nielsen J. Forster J, “Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.” Genome Research, Vol. 13, 2003, pp. 244–53.

20.     Dantzig, G.B., A. Orden, and P. Wolfe, "Generalized Simplex Method for Minimizing a Linear Form Under Linear Inequality Restraints," Pacific Journal Math., Vol. 5,1955, pp. 183–195

21.     Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ. “Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox.” Nature Protocols, Vol. 2, 2007, pp.727–738.

22.     Zhang, Y., "Solving Large-Scale Linear Programs by Interior-Point Methods Under the MATLAB Environment," Technical Report TR96-01, Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, July 1995.

23.     The MathWorks, Inc., MATLAB 4.2, 24 Prime Park Way, Natick MA, 1994.

24.     Edwards, J. S. & Palsson, B. O., “How will bioinformatics influence metabolic engineering?” Biotechnology and Bioengineering, Vol. 58, 1998, pp. 162–169.

25.     Schallreuter K, Slominski A, Pawelek JM, Jimbow K, Gilchrest BA., “What controls melanogenesis?”, Experimental Dermatologist., Vol. 7(4), 1998, pp. 143-50.

26.     Raman K, Rajagopalan P, Chandra N., “Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs.”, PLoS Computational Biology., Vol. 1(15), 2005, pp. 349-358

27.     Zhenping Li, Rui-Sheng Wang, Xiang-Sun Zhang, “Drug Target Identification Based on Flux Balance Analysis of Metabolic Networks”, The Fourth International Conference on Computational Systems Biology, 2010 (September 9-11), pp. 331-338

28.     Schilling C. H., Palsson B. O., "The underlying pathway structure of biochemical reaction networks", Proceedings of the National Academy of Sciences, Vol. 270(3), 2003, pp. 415-421

29.     Papin JA, Price ND, Wiback SJ, Fell DA, Palsson BO., “Metabolic pathways in the post-genome era.”, Trends in Biochemical Sciences, Vol. 28(5), 2003, pp. 250-258

30.     Edwards J. S., Ibarra R. U., Palsson B. O., "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data", Nature Biotechnology, Vol. 19(2), 2001, pp. 125-130

31.     Covert MW, Palsson BO., “Constraints-based models: regulation of gene expression reduces the steady-state solution space”, Journal for Theoritical Biology, Vol. 221 (3), 2003, pp. 309-25.

32.     Wiechert W., "Modeling and simulation: tools for metabolic engineering", Journal of Biotechnology, Vol. 94(1), 2002, pp. 37-63




N.Manikandan, M.Sakthiganesh, P.J.Kumar, M.Senthil Kumar

Paper Title:

Web based Farmers Bulletin for agricultural development using PAP Approach

Abstract:    In the present era entire world is focusing on agricultural development because of increased population and decreased agricultural production. Reason for decrease in production of agricultural products differs from place to place.  The main aim here is to support the farmers in their decision making on which mechanism to choose best for a better productivity at their arms reach. The proposed system focused to increase the profit of the farmer by increasing the efficiency of agricultural input and reducing the cost and risk of production. This can be achieved by providing timely advice to the farmer like, dynamic weather forecasting and use of knowledge engineering to extract best suitable Agricultural information from various source. The  PAP (Preprocess Associate and Predict) architecture is used for performing knowledge extraction and prediction process. This technique can handle all type of information.

   Agricultural Input, PAP


1.        Semantic Web based Integrated Agriculture Information Framework by  Muhammad Shoaib, Amna Basharat, Second International Conference on Computer Research and Development-2010
2.        2008 SAARC AGRINET(  has been formed and that was the good initiative for making the Library of Agricultural Information.

3.        An ongoing research at MOTOROLA Corporation on the topic “Precision Agriculture- A smart farming technique “ which aims at Information based Agriculture development.

4.        O. Folorunso, et al.. An Agent-based model for Agricultural Ecommerce System. Informantion Technology Journal, 2006,(2):230.
Hebei Agricultural University” IEEE-2010
6.        A Building an e-Agriculture     Business Integration Platform with Web Services Composition by Jianqiang Hu, FengE Luo, Guiping Liao IEEE conference of information sphere-2008

7.        Network Computing for Agricultural information System by Seishi Ninomiya, Matthew Laurenson and Takuji Kiura.

8.        Developing agricultural models using MetBroker by  Laurenson, M. R., A. Otuka and S. Ninomiya.

9.        S.Chaudhuri, Umeshwar Dayal, V.Ganti, Database Technology for decision support system, IEEE Computer.

10.     Role of Information Technology in Agriculture and its scope in India, S.C. Mittal.

11.     DEMBroker -Consistent access for software applications to digital elevation models by Lurenson, M. R. and S. Ninomiya.

12.     A model of decision-making and information flows for information-intensive agriculture by Fountas, S.




M.A.P. Chamikara, Y.P.R. D. Yapa, S.R.Kodituwakku, J. Gunathilake

Paper Title:

SL-SecureNet: Intelligent Policing Using Data Mining Techniques

Abstract:    Many police departments all around the world lack of good and efficient crime recording and analysis systems. The vast geographical diversity and the complexity of crime patterns have made the analyzing and recording of crime data even difficult. According to the Sri Lankan police department, they face these problems for many years. This paper presents an intelligent crime analysis and recording system designed to overcome problems that appear mainly in the Sri Lankan police department. The proposed system is a GIS based system which comprises of data mining techniques such as Hotspot detection, Crime clock, Crime comparison, Crime pattern visualization, Outbreaks detection and the Nearest police station detection. Salient features of the proposed system include a rich environment for crime data analysis and a simplified environment for location based data analysis. It facilitates the identification of various types of crimes in detail and assists the police personals to  control and prevent such incident efficiently. The SL-SecureNet was tested for about 1000 crime records. The test results indicated that it functions in an efficient and reliable manner.

   Crime Analysis, Crime Investigation, Data Mining, Intelligent Policing


1.       Crime Mapping and Reporting System. (2011, August 31). [Online]. Available:
2.       Intelligent Mapping System. (2010, October 16). [Online]. Available:

3.       OpenLayers: Free Maps for the Web. (2010, September 15). [Online]. Available:

4.       GeoServer. (2010, September 15). [Online]. Available:

5.       GeoExt. (2010, September 15). [Online]. Available:

6.       PostGIS. (2010, September 15). [Online]. Available:

7.       Craig Walls & Ryan Breidenbach, Spring in Action, 2nd  Edition, Manning Publications, USA(2005).

8.       Time Series. (2010, September 21). [Online]. Available:

9.       Classification Methods. (2010, September 21). [Online]. Available:

10.     What is MySQL?. (2010, September 23). [Online]. Available:

11. Grave Crime Abstract for Full Year  2010 for Whole Island  From 01.01.2010 To 31.12.2010. (2010, September 26). [Online]. Available:  

12.     Chen, H.,W.Chung, et al.(2004). Crime data mining: a general framework and some examples. Computer 37 (4):50-56.




Pravin D. Pardhi, Prashant L. Paikrao, Devendra S. Chaudhari

Paper Title:

Introduction to Query Techniques for Large CBIR Systems

Abstract:    Content-based image retrieval (CBIR) has received much research interest since couple of decades. The query technique for CBIR using relevance feedback is being used by the researchers, to search desired image from huge collection of visual data. This paper reviews various processes of image search and few query techniques.

   Content-based image retrieval (CBIR), image search, query technique, relevance feedback (RF).


1.       D. Brahmi and D. Ziou, “Improving CBIR systems by integrating semantic features”, Proceedings of the First Canadian Conference on Computer and Robot Vision, 2004.
2.       D. Liu, K. A. Hua, K. Vu, and N. Yu, “Fast Query Point Movement Techniques for Large CBIR Systems”, IEEE Transactions on Knowledge and Data Engineering, vol. 21, No. 5, pp. 729─743, 2009.

3.       G. Rafiee, S.S. Dlay, and W.L. Woo, “A Review of Content-Based Image Retrieval”, CSNDSP, pp. 775─779, 2010.

4.       I.J. Cox, M.L. Miller, T.P. Minka, T.V. Papathomas, and P.N. Yianilos, “The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments”, IEEE Trans. Image Processing, vol. 9, No. 1, pp. 20─37, 2000.

5.       J. M. Traina, J. Marques, and C. Traina Jr., “Fighting the Semantic Gap on CBIR Systems through New Relevance Feedback Techniques”, Proceedings of the Nineteenth IEEE Symposium on Computer-Based Medical Systems, 2006.

6.       M. Borowski, L. Brocker, S. Heisterkamp, J. Loffler, “Structuring the Visual Content of Digital Libraries Using CBIR Systems”, IEEE, pp. 288─293, 2000.

7.       M. Flickner, H.S. Sawhney, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content : The QBIC System”, Computer, vol. 28, No. 9, pp. 23─32, Sept. 1995.

8.       M. Jankovic, G. Zajic, V. Radosavljevic, N. Kojic, N. Reljin, M. Rudinac, S. Rudinac, B. Reljin,  “Minor component analysis (MCA) Applied to Image Classification in CBIR Systems”, Eighth Seminar on Neural Network Applications in Electrical Engineering, IEEE, pp. 11─16, 2006.

9.       O. D. Robles, J. L. Bosque, L. Pastor and A. Rodriguez, “Performance analysis of a CBIR system on shared-memory systems and heterogeneous clusters”, Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception, 2005.

10.     O. Marques, L. M. Mayron, G. B. Borba and H. R. Gamba, “On The Potential of Incorporating Knowledge of Human Visual Attention into CBIR Systems”, IEEE, pp. 773─776, 2006.

11.     S. Rudinac, M. Uscumlic, M. Rudinac, G. Zajic, B. Reljin, “Global Image Search vs. Regional Search in CBIR Systems”, Eighth International Workshop on Image Analysis for Multimedia
Interactive Services, IEEE, 2007.

12.     Y. M. Wong, S. C. H. Hoi, and M. R. Lyu, “An Empirical Study on Large-Scale Content-Based Image Retrieval”, pp. 2206─2209, 2007.




Chetna Chand, Amit Thakkar, Amit Ganatra

Paper Title:

Sequential Pattern Mining:  Survey and Current Research Challenges

Abstract:    The concept of sequence Data Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. The problem was first introduced in the context of market analysis. It aimed to retrieve frequent patterns in the sequences of products purchased by customers through time ordered transactions. Later on its application was extended to complex applications like telecommunication, network detection, DNA research, etc. Several algorithms were proposed. The very first was Apriori algorithm, which was put forward by the founders themselves. Later more scalable algorithms for complex applications were developed. E.g. GSP, Spade, PrefixSpan etc. The area underwent considerable advancements since its introduction in a short span. In this paper, a systematic survey of the sequential pattern mining algorithms is performed. This paper investigates these algorithms by classifying study of sequential pattern-mining algorithms into two broad categories. First, on the basis of algorithms which are designed to increase efficiency of mining and second, on the basis of various extensions of sequential pattern mining designed for certain application.  At the end, comparative analysis is done on the basis of important key features supported by various algorithms and current research challenges are discussed in this field of data mining.

   Sequential Pattern, Sequence Database, Itemsets, Apriori.


1.       Rakesh Agrawal Ramakrishna Srikant, “Mining Sequential Patterns”, 11th Int. Conf. on Data Engineering, IEEE Computer Society Press, Taiwan, 1995 pp. 3-14.
2.       Srikant R. and Agrawal R., “Mining sequential patterns: Generalizations and performance improvements”, Proceedings of the 5th International Conference Extending Database Technology, 1996, 1057, 3-17.

3.       F. Masseglia, F. Cathala, and P. Poncelet, “The PSP Approach for Mining Sequential Pattern”, In Proc. 1998 European Symp. Principle of Data Mining and Knowledge Discovery (PKDD’98), Nantes, France, Sept. 1998, pp. 176–184.

4.       M. Garofalakis, R. Rastogi, and K. Shim, "SPIRIT: Sequential pattern mining with regular expression constraints", VLDB'99, 1999.

5.       Han J., Dong G., Mortazavi-Asl B., Chen Q., Dayal U., Hsu M.-C., ”Freespan: Frequent pattern-projected sequential pattern mining”, Proceedings 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD’00), 2000, pp. 355-359.

6.       Han, J., Pei, J., Mortazavi-Asl, B. and Zhu, H., “Mining access patterns efficiently from web logs”, In Proceedings of the Pacific- Asia Conference on Knowledge Discovery and Data Mining (PAKDD’00) Kyoto Japan, 2000.

7.       M. Zaki, "SPADE: An efficient algorithm for mining frequent sequences”, Machine Learning, 2001.

8.       J. Pei, J. Han, B. Mortazavi-Asi, H. Pino, "PrefixSpan: Mining Sequential Patterns Efficiently by Prefix- Projected Pattern Growth", ICDE'01, 2001.

9.       Helen Pinto Jiawei Han Jian Pei Ke Wang, “Multidimensional Sequential Pattern Mining”, In Proc. 2001 Int. Conf. Information and Knowledge Management (CIKM’01), Atlanta, GA, Nov. 2001 pp. 81–88.

10.     AYRES, J., FLANNICK, J., GEHRKE, J., AND YIU, T., “Sequential pattern mining using a bitmap representation”, In Proceedings of the 8th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining-2002.

11.     Chen, Y.L., Chiang, M.C. and Ko, M.T, “Discovering time interval sequential patterns in sequence databases”, Expert Syst. Appl., Vol. 25, No. 3, 2003, pp. 343–354.

12.     Yan, X., Han, J., and Afshar, R., “CloSpan: Mining closed sequential patterns in large datasets”, In Third SIAM International Conference on Data Mining (SDM), San Fransico, CA, 2003, pp. 166–177.

13.     Jian Pei, Jiawei Han, Wei Wang, “Constraint-based sequential pattern mining: the pattern growth methods”, J Intell Inf Syst , Vol. 28, No.2, ,2007, pp. 133 –160.

14.     NIZAR R. MABROUKEH and C. I. EZEIFE, ”A Taxonomy of Sequential Pattern Mining Algorithms”, ACM Computing Surveys, Vol. 43, No. 1, Article 3, Publication date: November 2010.

15.     J. Han, J. Pei, and X. Yan, StudFuzz,”Sequential Pattern Mining by Pattern-Growth: Principles and Extensions”, 180, 2005, pp. 183–220.

16.     J.Pei, J.Han, B.MortazaviAsl, J.Wang, H.Pinto, Q.Chen, U.Dayal and M.-C.Hsu, “Mining sequential patterns by pattern-growth: The PrefixSpan approach”, IEEE Transactions on Knowledge and Data Engineering, vol.16, no.11, 2004, pp. 1424-1440.

17.     Yen-Liang Chen, Mi-Hao Kuo, Shin-Yi Wu, Kwei Tang, ”Discovering Recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”, Electronic Commerce Research and Applications 8 (2009), 2009, pp. 241–251.

18.     Hao-En Chueh, “Mining Target-Oriented Sequential Patterns with Time-Interval”, International journal of computer science & information Technology (IJCSIT) Vol.2, No.4, August 2010.

19.     Yen-Liang Chen, Ya-Han Hu, “The consideration of recency and compactness in sequential pattern mining”, In Proceedings of the second workshop on Knowledge Economy and Electronic Commerce, Vol. 42, Iss. 2 ,pp. 1203-1215, 2006.

20.     Ya-Han Hu, Fan Wu, “Mining Multi-level Time-interval Sequential Patterns in Sequence Databases”, Chieh-I Yang, 2010.




Rakesh Kumar, Jyotishree

Paper Title:

Effect Of Polygamy With Selection In Genetic Algorithms

Abstract:   Genetic algorithms are based on evolutionary ideas of natural selection and genetics. Important operators used in GA are selection, crossover and mutation, where selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. A number of selection operators have been used in the past like roulette wheel selection, ranked selection, elitism etc. where elitism is used to enforce the preservation of best solution found so far unless a new best individual is discovered. Elitism is implemented by copying the best individual of a generation into the next generation without any change. In this paper a particular form of elitism, polygamy, is proposed and implemented in which in each generation the best individual is selected and that  participates in crossover with all other individuals  in the mating pool created by any other selection mechanism. Polygamy has also been observed in a number of animals like lion, elk, baboons etc. Results obtained show the improvement over traditional selection operators available in literature.

   genetic algorithm, polygamy, rank selection, roulette wheel, selection.


1.       J.Holland, Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, 1975.
2.       D.E. Goldberg, Genetic algorithms in search, optimisation, and machine learning, Addison Wesley Longman, Inc., ISBN 0-201-15767-5, 1989.

3.       K.A. De Jong,  An Analysis of the behavior of a class of genetic adaptive systems (Doctoral dissertation, University of Michigan) Dissertation Abstracts International 36(10), 5140B University Microfilms No. 76/9381, 1975. 

4.       Robert John Paxton, Male mating behaviour and mating systems of bees:an overview, Article published by EDP Sciences,

5.       C.W.Ahn and R.S.Ramakrishna, “Elitism-baseed compact genetic algorithms”, IEEE transactions on Evolutionary ComputatIon, Vol 7, no 4, August 2003, 2003, pp 367-385.

6.       M.Musnjak and M.Golub, “ Using a set of elite individuals in a genetic algorithm”, Proceedings of 26th International Conference on Information Technology Interfaces 2004, 2004, pp 531-535.

7.       J.Zhong,Xiaomin Hu, Min Gu and Jun Zhang, “Comparison of performance between different selection strategies on simple genetic algorithms”, Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2005

8.       Francisco B. Pereira and Jorge M. C. Marques, “A Hybrid Evolutionary Algorithm for Cluster Geometry Optimization: the importance of structural elitism”, Proceedings  of Eighth International Conference on Hybrid Intelligent Systems, 2008, pp 911-914.

9.       Gu min and Yang feng, “An improved genetic algorithm based on polygymy”, Proceedings of Third International Symposium on Intelligent Information Technology and Security Informatics, 2010 pp 371-373.

10.     Wei Cheng, Haoshan Shi, Xipeng Yin, And Dong Li, “An Elitism Strategy Based Genetic Algorithm For Streaming Pattern Discovery In Wireless Sensor Networks”, IEEE Communications Letters, Vol. 15, No. 4, 2011,pp 419-421.

11.     D.E. Golberg and K.Deb, “A comparative analysis of selection schemes used in genetic algorithms”, Foundations of Genetic Algorithms, San Mateo, CA, Morgan Kaufmann, 1991, pp 69-93.

12.     D. Fogel, Evolutionary Computation, IEEE Press, 1995.

13.     Melanie Mitchell, An Introduction to genetic algorithm,. Prentice Hall of India, New Delhi, ISBN-81-203-1358-5, 1996.

14.     Rakesh Kumar and Jyotishree, “Blending roulette wheel selection & rank selection in genetic algorithms”, proceedings of 3rd International conference on machine learning and computing, V4, IEEE catalog number CFP1127J-PRT, ISBN 978-1-4244-9252-7, 2011,  pp 197-202.

15.     J.E. Baker, “Adaptive selection methods for genetic algorithms”, Proceedings of an International Conference on Genetic Algorithms and their applications, 1985,  pp 101-111.

16.     D. Whitley,  “The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best”, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, 1989,  pp.116-121.

17.     T.Back and F.Hoffmeister, “Extended Selection Mechanisms in Genetic Algorithms”,ICGA4,  1991 pp 92-99.

18.     M.Perling and T.S.Gene, “A Relational-Functional Genetic Algorithm for the Travelling Salesman Problem”, Technical Report,    Universitat   Kaiserslautern, ISSN 09460071, 1997.




Ram Krishna Rathore, Amit Sarda, Rituraj Chandrakar

Paper Title:

An Approach to optimize ANN Meta model with Multi Objective Genetic Algorithm for multi-disciplinary shape optimization

Abstract:  In several design cases, designers need to optimize a number of responses concurrently. A general approach for the multiple response cases optimization start with using the regression models to calculate the correlations between response functions and control factors. Then, a system for collecting various response functions together into a one quantity, such as an objective function, is engaged and, at last, an optimization technique is used to calculate the best combinations for the control functions. A different method proposed in this paper is to use an artificial neural network (ANN) to calculate the parameter response functions. At the optimization stage, a multi objective genetic algorithm (MOGA) is used in combination with an objective functions to establish the optimum conditions for the control functions. A crane hook example has been taken to optimize multiple shape parameter responses to with stand a new loading condition. The results estimate the reduction in mass and sufficient factor of safety to show the proposed approach for the optimization of multi- disciplinary shape optimization problems.

   ANN, MOGA, Shape optimization, Meta modeling


1.       R. Noorossana, Sam Davanloo Tajbakhsh and A. Saghaei, “An artificial neural network approach to multiple-response optimization”, The International Journal of Advanced Manufacturing Technology, Volume 40, Numbers 11-12, 1227-1238, DOI: 10.1007/s00170-008-1423-7 (2008)
2.       M. Oudjenea, L. Ben-Ayed, A. Delam´ezi`erb, J.-L. Batoz, “Shape optimization of clinching tools using the response surface methodology with Moving Least-Square approximation”, journal of materials processing technology 209 ( 2009 ) pp. 289–296

3.       Muromaki, T.; Hanahara, K.; Nishimura, T.; Tada, Y.; Kuroda, S.; Fukui, T., “Multi-Objective Shape Design of Crane-Hook Taking Account of Practical Requirement”, Institute of Materials, London England ,2011, ISBN No- 1861250045

4.       Rashmi Uddanwadiker, Stress Analysis of Crane Hook and Validation by Photo-Elasticity, Scientific research, vol. 3, p.p.935-941, August 26, 2011

5.       Daryoush Safarzadeh, Daryoush Safarzadeh, Shamsuddin Sulaiman, Faieza Abdul Aziz, Desa Bin Ahmad and Gholam Hossein Majzoobi,“An investigation into the hook dynamics and effect of hook parameters on the sway angles in hydraulic cranes”, Scientific Research and Essays Vol. 6(6), pp. 1303-1316, 18 March, 2011

6.       Abbasi, B., & Mahlooji, H. Improving response surface methodology by using artificial neural network and simulated annealing. Expert Systems with Applications (2011), doi:10.1016/j.eswa.2011.09.036

7.       H. A. Rothbart, “Mechanical Design Handbook: Measurement, Analysis, and Control of Dynamic Systems,” McGraw-Hill, Columbus, 2006

8.       S. S. Bhavikatti, “Finite Element Analysis,” New Age International, New Delhi, 2007.

9.       P. Seshu, “Textbook of Finite Element Analysis,” PHI learning Pvt. Ltd., New Delhi, 2004

10.     Myers RH, Montgomery DC. Response surface methodology. New York: John Wiley & Sons Inc.; 1995.

11.     J. W. Dally and W. F. Riley, “Experimental Stress analysis,” Springer Publisher, New York, 1993.

12.     Chiao CH, Hamada MS (2001) Analyzing experiments with correlated multiple responses. J Qual Technol 33(4):451–465

13.     Khuri AI, Conlon M (1981) Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23:363–375

14.     Kim KJ, Byun JH, Min D, Jeong IJ (2001) Multiresponse surface optimization: concept, methods, and future directions. Tutorial, Korea Society for Quality Management

15.     Tong LI, Hsieh KL (2000) A novel means of applying neural networks to optimize the multiresponse problem. Qual Eng 13 (1):11–18

16.     Vining GG (1998) A compromise approach to multiresponse optimization. J Qual Technol 30(4):309–313

17.     Ortiz F, Simpson JR, Pignatiello JJ, Heredia-Langner A (2004) A genetic algorithm approach to multiple-response optimization. J Qual Technol 36:432–450

18.     Zhou, L., Zheng, W.X., 2006. Moving least square Ritz method for vibration analysis of plates. J. Sound Vib. 290, 968–990.

19.     Barlet, O., Batoz, J.L., Guo, Y.Q., Mercier, F., Naceur, H.,Knopf-Lenoir, C., 1996. The inverse approach and mathematical programming techniques for optimum design of sheet forming parts. ASME (3), 227–232.

20.     Batoz, J.L., Guo, Y.Q., Mercier, F., 1998. The inverse approach with simple triangular shell elements for large strain predictions of sheet metal forming parts. Eng. Comput. 6–7 (15), 864–892.

21.     Liew, K.M., Huang, Y.Q., Reddy, J.N., 2004. Analysis of general shaped thin plates by the moving least-squares differential quadrature method. Finite Elem. Anal. Des. 40, 1453–1474.

22.     Naceur, H., Ben-Elechi, S., Batoz, J-L., Knopf-Lenoir, C., 2008. Response surface methodology for the rapid design of aluminium sheet metal forming parameters. Mater. Des. 29, 781–790.

23.     Hussler-Combe, U., Korn, C., 1998. An adaptive approach with the element-free-Galerkin method. Compt. Methods Appl. Mech. Eng. 162, 203–222.

24.     B. Ross, B. McDonald and S. E. V. Saraf, “Big Blue Goes Down. The Miller Park Crane Accident,” Engineering Failure Analysis, Vol. 14, No. 6, 2007 pp. 942-961.

25.     Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–38.

26.     Christopher, M., Su, H., & Ismail, M. (1993). Yield optimization of analog MOS integrated circuits including transistor mismatch. In Proceedings of 1993 IEEE international symposium on circuits and systems (Vol. 3, pp. 1801–1804).

27.     Joshi, Sh., Sherali, H. D., & Tew, J. D. (1998). An enhanced response surface methodology (RSM) algorithm using gradient deflection and second order search strategies. Computers and Operations Research, 25(7/8), 531–541.

28.     Kemper, P., Müller, D., & Thümmler, A. (2006). Combining response surface methodology with numerical methods for optimization of Markovian models. IEEE Transactions on Dependable and Secure Computing, 3(3).

29.     Almeida, M., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76, 965–977.

30.     Barnwal, S., Bayoumi, A. E., & Hutton, D. V. (1993). Prediction of flank wear and engagement from force measurements in end milling operations. Wear, 170, 255–266.

31.     Khoo, L. P., & Chen, C. H. (2001). Integration of response surface methodology with genetic algorithms. International Journal of Advanced Manufacturing Technology, 18, 483–489.

32.     Kirkpatrick, S., Gerlatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

33.     Kleijnen, J. P. C., den Hertog, D., & Angun, E. (2004). Response surface methodology’s steepest ascent and step size revisited. European Journal of operational Research, 159, 121–131.




Ram Kumar Singh, Amit Ashtana

Paper Title:

Architecture Of Wireless Network

Abstract:  To allow for wireless communications among a specific geographic area, an base stations of communication network must be deployed to allow sufficient radio coverage to every mobile users. The base stations, successively, must be linked to a central hub called the MSC (mobile switching centre). The mobile switching centre allow connectivity among the PSTN (public switched telephone network) and the numerous wireless base stations, and finally among entirely of the wireless subscribers in a system. The global telecommunications control grid of PSTN which associate with conventional (landline) telephone switching centre (called central office) with MSCs all around the world.

   Network, MSC, PSTN, Cellular system.


1.        H. Zhang, Y. Zheng, M. A. Khojastepour, and S. Rangarajan, .Cross- Layer Optimization for Streaming Scalable Video over Fading Wireless Networks,. in IEEE JSAC, vol. 28, no. 3, April

2.        “Switching and signalling broadband ISDN-B-ISDN application protocols for access signalling,” ITU-T recommendation Q.2963.3, May. 1998.

3.        M.Ghanbari, “Two-Layer Coding of Video Signals for VBR Networks,” IEEE JSAC, vol. 7, no.5, pp.771-781, June 1989.

4.        G. Bhanage, I. Seskar, R. Mahindra, and D. Raychaudhuri, .Virtual basestation: Architecture for an open shared wimax framework,. in ACM SIGCOMM VISA Workshop, 2010.

5.        G.  Liebl,  T.  Schierl,  T.  Wiegand,  and T.  Stockhammer, .Advanced wireless multiuser video streaming using the scalable video coding extensions of h.264/mpeg4-avc,. in IEEE ICME, 2006.

6.        G.Karlsson and M. Vetterli, “Packet Video and Its Integration into the Network Architecture,” IEEE JSAC, vol. 7, no.5, pp.739-751, June 1989.

7.        M. Burza, J. Kang, and P. V. D. Stok, .Adaptive streaming of mpegbased audio/video content over wireless networks,. Multimedia, vol. 2, no. 2, April 2007.

8.        S. Ortega and M. Khansari,” Rate control for video coding over variable bit rate channels with applications to wireless transmission,” in Proc. IEEE Int. Conf. Image Processing, Oct. 1995.




Ashwini Gulhane, Prashant L. Paikrao, D. S. Chaudhari

Paper Title:

A Review of Image Data Clustering Techniques

Abstract:    In order to the find the close association between the density of data points, in the given data set of pixels of an image, clustering provides an easy analysis and proper validation. In this paper various clustering techniques along with some clustering algorithms are described. Further k-means algorithm, its limitations and a new approach of clustering called as M-step clustering that may overcomes these limitations of k-means is included. 

   M-step clustering, k-means clustering.


1.        S. Anitha Elavarasi, Dr. J. Akilandeswari, Dr. B. Sathiyabhama,” A survey on partition clustering algorithms”, International Journal of Enterprise Computing and Business SystemInternational Systems, vol. 1, pp. 1-13, 2011.
2.        Monika Jain, Dr. S.K.Singh,” A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets”, International Journal of Managing Information Technology (IJMIT) Vol.3, No.4, November 2011, pp. 23-39.

3.        Juntao Wang, Xiaolong Su,” An improved K-Means clustering algorithm”, IEEE proceeding, pp. 44-46, 2011.

4.        Harikrishna Narasimhan, Purushothaman Ramraj” Contribution-Based Clustering Algorithm for Content-Based Image Retrieval”, 2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, pp. 442-447.

5.        Shi Na, Liu Xumin, Guan yong,” Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm”, Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63-67, 2010.

6.        Wenbing Tao, Hai Jin and Yimin Zhang,” Color Image Segmentation Based on Mean Shift and Normalized Cuts”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 37, NO. 5, OCTOBER 2007, pp. 1382-1389.

7.        Periklis Andritsos,” Data Clustering Techniques”, March 11, 2002.

8.        A. Jain , M. Murty, and P. Flynn " Data clustering: a review.," ACM Computing Surveys, vol. 31,pp. 264-323,1999.

9.        Siddheswar Ray,  Rose H. Turi,” Determination of Number of Clusters in K-Means Clustering andApplication in Colour Image Segmentation”, IEEE proceeding.




Glory H. Shah, C. K. Bhensdadia, Amit P. Ganatra

Paper Title:

An Empirical Evaluation of Density-Based Clustering Techniques

Abstract:    Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods. It is the art of detecting groups of similar objects in large data sets without having specified groups by means of explicit features. The problem of detecting clusters of points is challenging when the clusters are of different size, density and shape. The development of clustering algorithms has received a lot of attention in the last few years and many new clustering algorithms have been proposed. This paper gives a survey of density based clustering algorithms. DBSCAN [15] is a base algorithm for density based clustering techniques. One of the advantages of using these techniques is that method does not require the number of clusters to be given a prior nor do they make any kind of assumption concerning the density or the variance within the clusters that may exist in the data set. It can detect the clusters of different shapes and sizes from large amount of data which contains noise and outliers. OPTICS [14] on the other hand does not produce a clustering of a data set explicitly, but instead creates an augmented ordering of the database representing its density based clustering structure. This paper shows the comparison of two density based clustering methods i.e. DBSCAN [15] & OPTICS [14] based on essential parameters such as distance type, noise ratio as well as run time of simulations performed as well as number of clusters formed needed for a good clustering algorithm. We analyze the algorithms in terms of the parameters essential for creating meaningful clusters.  Both the algorithms are tested using synthetic data sets for low as well as high dimensional data sets.

   DBSCAN, OPTICS, DENCLUE, Spatial Data, Intra Cluster, Inter Cluster.


1.        Ms K. Santhisree, Dr. A. Damodaram, SSM-DBSCAN and SSM-OPTICS : Incorporating new similarity measure for Density based clustering of Web usage data, in International Journal on Computer Sciences and Engineering, August 2011
2.        S. Chakraborty, Prof. N. K. Nagwani, Analysis and Study of Incremental DBSCAN Clustering Algorithm, International Journal of Enterprise Computing And Business Systems, Vol. 1, July 2011

3.        M. Parimala, D. Lopez, N. C. Senthilkumar, A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases, International Journal of Advanced Science and Technology, Vol. 31, June 2011.

4.        Dr. Chandra. E, Anuradha. V. P, A Survey on Clustering Algorithms for Data in Spatial Database Management System, International Journal of Computer Applications, Col. 24, June 2011

5.        J. H. Peter, A. Antonysamy, An optimized Density based Clustering Algorithm, International Journal of Computer Applications, Vol. 6, September 2010

6.        A. Ram, S. Jalal, A. S. Jalal, M. Kumar, A Density based Algorithm for Discovering Density varied clusters in Large Spatial Databases, International Journal of Computer Applications, Vol. 3, June 2010

7.        Tao Pei, Ajay Jasra, David J. Hand, A. X. Zhu, C. Zhou, DECODE: a new method for discovering clusters of different densities in spatial data, Data Min Knowl Disc, 2009

8.        Zhi-Wei SUN, A Cluster Algorithm Identifying the clustering Structure, International Conference on Computer Science and Software Engineering, 2008

9.        Marella Aditya,”DBSCAN And its Improvement”,june 2007

10.     Stefan Brecheisen, Hans-Peter Kriegel, and Martin Pfeifle, Multi-step Density Based Clustering, Knowledge and Information Systems, Vol. 9, 2006

11.     A. Moreira, M. Y. Santos and S. Carneiro, Density-based clustering algorithms-DBSCAN and SNN, July 2005

12.     M. Rehman and S. A. Mehdi, Comparision of Density-Based Clustering Algorithms, 2005

13.     Levent Ertoz, Michael Steinback, Vipin Kumar, Finding Clusters of Different Sizes, Shapes, and Density in Noisy, High Dimensional Data, Second SIAM International Conference on Data Mining, San Francisco, CA, USA, 2003

14.     Yong-Feng Zhou, Qing-Bao Liu, S. Deng, Q. Yang, An Incremental Outlier Factor Based Clustering Algorithm, Proceedings of First International Conference on Machine Learning and Cybernetics, Beijing, 4-5 Nov 2002

15.     M. Ankerst, M. M. Breunig, H. P. Kriegel and J. Sander, OPTICS: Ordering Points To Identify Clustering Structure, at International Conference on Management of Data, Philadelphia, ACM 1999

16.     Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu,  A Density- Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, The Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA, 1996

17.     X. Wang, H. J. Hamilton.  A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets.

18.     K. Mumtaz, Dr. K. Duraiswamy, An Analysis on Density Based Clustering of Multidimensional Spatial Data in Indian Journal of Computer Science and Engineering Vol 1 No 1 8-12

19.     D.T.Pham and A.A. Afify, Clustering techniques and their applications in engineering

20.     Pavel Berkhin, Survey of Clustering Data Mining Techniques

21.     Mariam Rehman, Syed Atif Mehdi, Comparision of Density-Based Clustering Algorithms

22.     S. Maji, R. S. Mondal, S. Banerjee, DBSCAN Algorithm with automated parameter selection

23.     X. Wang, H. J. Hamilton.  A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets.

24.     Alexander Hinneburg, Hans-Henning Gabriel, DENCLUE 2.0: Fast Clustering based on kernel Density Estimation”, Martin-Luther-University, Germany

25.     Tutorial for WEKA

26.     Weka manual for version3.6.3 by Eibe Frank and Mark Hall

27.     Data mining Concepts and Techniques by Jiawei Han and Kamber

28.     Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann Series in Data Management Systems.




Pushpaja V. Saudagare, D.S. Chaudhari

Paper Title:

Facial Expression Recognition using Neural Network –An Overview

Abstract:    In many face recognition systems the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position and orientation. So using various modeling techniques it is convenient to recognize various facial expressions. In the field of image processing it is very interesting to recognize the human gesture by observing the different movement of eyes, mouth, nose, etc. Classification of face detection and token matching can be carried out any neural network for recognizing the facial expression. This paper reviews various techniques of facial expression recognition systems using MATLAB (neural network) toolbox.

   face recognition, neural network, and facial expression recognition. 


1.        J. L. Raheja and U. Kumar “Human Facial Expression Recognition from Detected in Captured Image Using Back Propagation Neural Network” International Journal of Computer Science and Information Technology, February 2010.
2.        M. Agrawal, N. Jain, M. Kumar and H. Agrawal “Face Recognition using Eigen Faces and Artificial Neural Network” International Journal of Computer Theory and Engineering, August 2010

3.        A. R. Nagesh-Nilchi and M. Roshanzamir “An Efficient Algorithm for Motion Detection Based Facial Expression Recognition using Optical Flow” International Journal of Engineering and Applied Science 2006.

4.        C.C. Chibelushi and F. Bourel “ Facial Expression Recognition: A Brief Tutorial overview”, 2002.

5.        A. Sulistijono, Z. Darojah, A. Dwijotomo, D. Pramdihanto “ Facial Expression Recognition usin Backpropagation”,2002  

6.        J. Chang and J. Chen “Automated Facial Expression Recognition System using Neural Networks”, Journal of the Chinese Institute of Engineers, pp. 345-356 (2001).

7.        R. Q. Feitosa, M. M. B. Vellasco “Facial Expression Classification using RBF and Back Propagation Neural Network”,2001

8.        P. Brimblecombe “ Face Detection using Neural Network”, Meng Electronic Engineering School of Electronics and Physical Sciences, University of Surrey, 




Hadi Razmi, Atabak Mashhadi Kashtiban

Paper Title:

Nonlinear PID-Based Analog Neural Network Control for a Two Link Rigid Robot Manipulator And Determining the Maximum Load Carrying Capacity

Abstract:    An adaptive controller of nonlinear PID-based analog neural networks is developed for the point to point and orientation-tracking control of a two link rigid robot manipulator. In each case, the maximum load carrying capacity of robot manipulator subject to accuracy and actuators constraints is obtained. In comparison with conventional PID method, the use of neural network controller can increase maximum load carrying capacity of robot manipulators. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. Computer simulations were carried out in two axes manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and increasing the maximum load carrying capacity of this manipulator.

   Analog neural network, Adaptive control, Maximum load carrying capacity, Nonlinear PID control.


1.       K.J. Astrom, and T. Hagglund, “Automatic tuning of simple regulators with specification on phase and amplitude margins,” Automatica, vol. 20, 1984, pp. 645–651.
2.       K.J. Astrom, and T. Hagglund, PID Controller: Theory, Design, and Tuning. Research Triangle Park, NC, USA, 1995.

3.       C.C. Hang, K.J. Astrom, and W.K. Ho, “Refinements of the Ziegler-Nichols tuning formula,” IEE Proc. Control Theory Appl., vol. 138, 1991, pp. 111–118.

4.       A.J. Koivo, Fundamentals for Control of Robotic Manipulators. Wiley, New York, 1989.

5.       F.L. Lewis, C.T. Abdallah, and D.M. Dawson, Control of Robot Manipulators. Macmillan, New York, 1993.

6.       R.J. Schilling, Fundamentals of Robotics: Analysis and Control. Prentice-Hall, EnglewoodCli4s, NJ, 1998.

7.       I. Cha, and C. Han, “The auto-tuning PID controller using the parameter estimation,” IEEE/RSJ International Conference on Intelligent Robots and Systems South Korea, 1999, p. 46.

8.       T.Y. Kuc, and W.G. Han, “Adaptive PID learning of periodic robot motion,” 37th IEEE Conference on Decision and Control USA, 1998, p. 186.

9.       D. Sun, and J.K. Mills, “High-accuracy trajectory tracking of industrial robot manipulator using adaptive-learning schemes,” American Control Conference USA, 1999, p. 1935.

10.     Y. Li, Y.K. Ho, and C.S. Chua, “Model-based PID control of constrained robot in a dynamic environment with uncertainty,” IEEE International Conference on Control Applications USA, 2000, p. 74.

11.     P.C.Y. Chen, J.K. Mills, and G. Vukovich, “Neural network learning and generalization for performance improvement of industrial robots,” Canadian Conference on Electrical and Computer Engineering Canada, 1996, p. 566.

12.     C. Clifton, A. Homaifar, and M. Bikdash, “Design of generalized Sugeno controllers by approximating hybrid fuzzy-PID controllers,” IEEE International Conference on Fuzzy Systems USA, 1996, p. 1906.

13.     L.B. Gutierrez, F.L. Lewis, and J.A. Lowe, “Implementation of a neural network tracking controller for a single flexible link: comparison with PD and PID controller,” IEEE Trans. Ind. Electron., Vol. 45, 1998, pp. 307–318.

14.     S.J. Huang, and J.S. Lee, “A stable self-organizing fuzzy controller for robotic motion control,” IEEE Trans. Ind. Electron., vol. 47, 2000, pp. 421–428.

15.     Y.H. Kim, and F.L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulators,” IEEE Trans. Systems Man Cybernet., vol. 30, 2000, pp. 22–31.

16.     F.L. Lewis, A. Yesildirek, and K. Liu, “Multilayer neural-net robot controller with guaranteed tracking performance,” IEEE Trans. Neural Networks, vol. 7, 1996, pp. 388–399.

17.     D. Misir, H.A. Malki, and G. Chen, “Graphical stability analysis for a fuzzy PID controlledrobot arm model,” IEEE International Conference on Fuzzy Systems USA, 1998, p. 451.

18.     A.T. Vemuri, and M.M. Polycarpou, “Neural-network-based robust fault diagnosis in robotic systems,” IEEE Trans. Neural Networks, vol. 8, 1997, pp. 1410–1420.

19.     B.K. Yoo, and W.C. Ham, “Adaptive control of robot manipulator using fuzzy compensator,” IEEE Trans. Fuzzy Systems, vol. 8, 2000, pp. 186–199.

20.     O. Barambones, and V. Etxebarria, “Robust neural control for robotic manipulators,” Automatica, vol. 38, 2002, pp. 235–242.

21.     L. Behera, S. Chaudhury, and M. Gopal, “Neuro adaptive hybrid controller for robot manipulator tracking control,” IEE Proceedings D, Control Theory and its Applications, vol. 143, 1996, pp. 270–275.

22.     M. Ertugrul, and O. Kaynak, “Neuro sliding mode control of robotic manipulators,” Mechatronics, vol. 10, 2000, pp. 239–263.

23.     R. J. Wai, “Tracking control based on neural network strategy for robot manipulators,” Neurocomputing, vol. 51, 2003, pp. 425–445.

24.     M. Thomas, H.C. Yuan-Chou, and D. Tesar, “Optimal actuator sizing for robotic manipulator based on local dynamic criteria,” ASME Journal of Mechanisms, Transactions and Automation, vol. 107, 1985, pp. 163-169.

25.     L.T. Wang, and B. Ravani, “Dynamic load carrying capacity of mechanical manipulators-part I: problem formulation,” Transactions of ASME, Journal of dynamic system, Measurment and control, vol. 110, 1988, pp. 46-52.

26.     M.H. Korayem, Y. Yao, and A. Basu, “Load carrying capacity for a two-link planar flexible arm,” Proc. Thirteen Canadian Congress of Applied Mechanics, vol. 2, June 1991.

27.     M.H. Korayem, and A. Basu, “Dynamic load carrying capacity of robotic manipulators with joint elasticity imposing accuracy constraints,” Robotics and Autonomous Systems, vol. 13, 1994, pp. 219-229.

28.     J. J. Slotine, and W. Li, “On the adaptive control of robots manipulators,” International Journal of Robotics Research, vol. 6, 1987, pp. 49–59.

29.     T.D.C. Thanh, and K.K. Ahn, “Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network,” Mechatronics, vol. 16, 2006, pp. 577–587.




Ashish B. Ingale, D. S. Chaudhari

Paper Title:

Speech Emotion Recognition

Abstract:    In human machine interface application, emotion recognition from the speech signal has been research topic since many years. To identify the emotions from the speech signal, many systems have been developed. In this paper speech emotion recognition based on the previous technologies which uses different classifiers for the emotion recognition is reviewed. The classifiers are used to differentiate emotions such as anger, happiness, sadness, surprise, neutral state, etc. The database for the speech emotion recognition system is the emotional speech samples and the features extracted from these speech samples are the energy, pitch, linear prediction cepstrum coefficient (LPCC), Mel frequency cepstrum coefficient (MFCC). The classification performance is based on extracted features. Inference about the performance and limitation of speech emotion recognition system based on the different classifiers are also discussed.

   Classifier, Emotion recognition, Feature extraction, Feature Selection.


1.       M. E. Ayadi, M. S. Kamel, F. Karray, “Survey on Speech Emotion Recognition: Features, Classification Schemes, and Databases”, Pattern Recognition 44, PP.572-587, 2011.
2.       I. Chiriacescu, “Automatic Emotion Analysis Based On Speech”, M.Sc. THESIS Delft University of Technology, 2009.

3.       T. Vogt, E. Andre and J. Wagner, “Automatic Recognition of Emotions from Speech: A review of the literature and recommendations for practical realization”, LNCS 4868, PP.75-91, 2008.

4.       S. Emerich, E. Lupu, A. Apatean, “Emotions Recognitions by Speech and Facial Expressions Analysis”, 17th European Signal Processing Conference, 2009.

5.       A. Nogueiras, A. Moreno, A. Bonafonte, Jose B. Marino, “Speech Emotion Recognition Using Hidden Markov Model”, Eurospeech, 2001.

6.       P.Shen, Z. Changjun, X. Chen, “Automatic Speech Emotion Recognition Using Support Vector Machine”, International Conference On Electronic And Mechanical Engineering And Information Technology, 2011.

7.       D. Ververidis and C. Kotropoulos, "Emotional Speech Recognition: Resources, Features and Methods", Elsevier Speech communication, vol. 48, no. 9, pp. 1162-1181, September, 2006.

8.       Z. Ciota, “Feature Extraction of Spoken Dialogs for Emotion Detection”, ICSP, 2006.

9.       E. Bozkurt, E, Erzin, C. E. Erdem, A. Tanju Erdem, “Formant Position Based Weighted Spectral Features for Emotion Recognition”, Science Direct Speech Communication, 2011.

10.     C. M. Lee, S. S. Narayanan, “Towards detecting emotions in spoken  dialogs”, IEEE transactions on speech and audio processing, Vol. 13, No. 2, March 2005.




Nikita Bhatt, Amit Thakkar, Amit Ganatra

Paper Title:

A Survey & Current Research Challenges in Meta Learning Approaches based on Dataset Characteristics

Abstract:    Classification is a process that predicts class of objects whose class label is unknown. According to No Free Lunch (NFL) theorem, there is no single classifier that performs better on all datasets. Meta learning is one of the approaches that acquired knowledge based on the past experience. The knowledge in Meta-Learning is acquired from a set of meta-examples which stores the features of the problem and the performance obtained by executing a set of candidate algorithms on Meta Features. Based on the experience acquired by the system during training phase, ranking of the classifiers is provided based on considering various measures of classifiers.

   Classification, Meta Learning, Ranking


1.       Pavel B. brazdil and Carlos Soares ,”A Comparision of Ranking Methods for Classification Algorithm Selection,2000.
2.       R. Vilalta and Y. Drissi. A perspective view and survey of meta-learning. Journal of Artificial Intelligence Review, 18(2):77–95, 2002.

3.       Marcilio Souto,RicardoB.C.Prudencio,RodrigoG.F.Soares, “Ranking and Selecting Clustering Algorithms Using a Meta-Learning Approach”,2008.

4.       Christophe Giraud-Carrier, Chair,DanVentura,Yiu-Kai Dennis Ng Eric Mercer,SeanWarnick, “Relationships among Learning Algorithms and Tasks”, Proceedings of the International Conference on Machine Learning and Applications,2011.

5.       Ajay Kumar Tanwari,JamalAfridi,M.ZubairShafiq and MuddassarFarooq, “Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets”,nexginrc, Evolutionary Computation, Machine Learning Scheme for  Classification of Biomedical Datasets,Springer,2009

6.       MykolaPechenizkiy, “Data Mining Strategy Selection via Empirical and Constructive Induction”, Finland, 2003.

7.       Stuart Moran,YulanHe,Kecheng Liu, “An Empirical Framework for Automatically Selecting the Best Bayesian Classifier”, Proceedings of the World Congress Engineering 2009 Vol I WCE 2009, July 1-3,London,U.K,2009.

8.       SilviuCacoveanu,CameliaVidrighin,RodicaPotolea, “Evolution Meta-Learning Framework For automatic Classifier Selection”,2005.

9.       ShawkatAli,Kate A. Smith, “On learning algorithm selection for classification”,Applied Soft Computing Volume 6,Issue 2,119-138,January 2006.

10.     Ricardo B.C.Orudencio and Teresa B. Ludermir, “Selective Generation of training examples in active meta-learning”, International Journal of Hybrid Intelligent Systems,2008.

11.     C.M. van der walt and E.Barnard, “Data Characteristics that determines classifier performance”,2008.

12.     Myra Spiliopoulou, Alexis Kalousis, Lukas C. Faulstich and Theoharis, “NOEMON: An Intelligent Assistant for Classifier Selection”, Citeseer,2000.

13.     AlexandrosKalousis and Melanie Hilario, “Algorithm selection via meta learning”,2002.

14.     Ricardo Vilalta, Christophe Giraud-Carrier, PavelBrazdil, Carlos Soares, “Using Meta Learning to support Data Mining”, 32-45,2004.

15.     S.Appavu alias Balamurugan, Dr.R.Rajaram, G.Athiappan, M.Muthupandian, “Data Mining Techniques for suspicious Email Detection: A Comparative Study”, IADIS European Conference Data Ming, Madurai,2007.

16.     C. Giraud-Carrier, R.Vilalta and P. Brazdil, “Introduction to the special issue on meta-learning”, Machine Learning 54, 187–193, 2004.

17.     Carlos Soares and Pavel B. Brazdil, “Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information”,Principles of Data Mining and Knowledge Discovery,SpringerLink,2002.

18.     Ricardo B.C. Prudencio and Teresa B. Ludermin, “Combining Uncertainity Sampling Methods for Active Meta Learning”, Ninth International Conference on Intelligent Systems Design and Applications, 220-225,2009.

19.     MihaGrcar, BlazFortun, DunjaMladeni“kNN Versus SVM in the Collaborative Filtering Framework”, Data Science and Classification,2002.

20.     Ricardo B.C. Prudencio and Teresa B. Ludermin, “Active Meta-Learning with Uncertainty Sampling and Outlier Detection”, IEEE World Congress on Computational Intelligence, 2010.

21.     Ricardo B.C. Prudencio and Teresa B. Ludermin, “Uncertainty Sampling Methods for Selecting Datasets in Active Meta Learning”, Proceedings of International joint Conference on Neural Networks, San Jose, California, USA, July 31-August 5, 1082-1089, 2011.

22.     D. Michie, D. Spiegelhalter and D. Taylor, Machine Learning,

23.     Neural and Statistical Classification, Ellis Horwood, New York, 1994.

24.     D.H.Wolpert and W.G.Macready. No Free Lunch Theorems for search. Technical Report SFI-TR-95-02-010, The santa Re Institute,1996.

25.     P Brazdil ,C.Soares and J.dacosta, “Ranking Learning algorithms: Using IBL and meta-learning on accuracy and time results,” Machine Learning,vol.50,np-8,pp.911-921,2004.

26.     D.H.Wolpert. The supervised learning no-free-lunch theorems. In proceedings of the sixth on-line word conference on soft computing in Industrial applcations, pages 325-330,2001.

27.     B.Pfahringer, H.Bensusan and C.Girand carrier, Meta learning by landmarking various learning algorithms, in proceedings of the 17th international conference on Machine Learning (ICML-2000),2000,743-750.

28.     “The bias-variance tradeoff”,



31.     “Semantic Assisted, Multiresolution Image Retrieval in 3D Brain MR Volumes”, AzharQuddus, Waterloo, Ontario, Canada, 2010.

33.     “Classification via Decision Trees in WEKA”, DEPAUL UNIVERSITY,




Purvi Prajapati, Amit Thakkar, Amit Ganatra

Paper Title:

A Survey and Current Research Challenges in Multi-Label Classification Methods

Abstract:   Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also done comparative analysis of multi label classification methods on the basis of theoretical study and than on the basis of simulation done on various data sets.

   Classification, Single label problem, Multi label problem


1.        Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification: An Overview, International Journal of Data Warehousing and Mining, David Taniar (Ed.), Idea Group Publishing, 3(3), pp. 1-13, 2007.
2.        Outline:Multi Label Classification

3.        Read, J.; Pfahringer, B.; Holmes, G.; Dept. of Comput. Sci., Univ. of Waikato, Hamilton. A Pruned Problem Transformation Method for Multi-Label Classification.  Data Mining, 2008. ICDM '08. Eighth IEEE International Conference, pages: 995 – 1000, 15-19 Dec. 2008.

4.        G. Tsoumakas and I. Vlahavas. Random k-labelsets: An ensemble method for multilabel classification. In Proceedings of the 18th European Conference on Machine Learning (ECML 2007), 2007.

5.        Learning from Multi Label Data

6.        Eva Gibaja, Manuel Victoriano, Jose Luis Avila-Jimenez, Sebastian Ventura. A TDIDT Technique for Multi-label Classification, IEEE, 2010.

7.        G. Tsoumakas, I.Katakis, and I. Vlahavas. “Mining Multi-label Data”, Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach (Ed.) Springer, 2nd edition 2010.

8.        Araken M Santos, Anne M P Canuto and Antonino Feitosa Neto. A Comparative Analysis of Classification Methods to Multi-label Tasks in Different Application Domains. International journal of computer information systems and idustrail management applications. ISSN 2150-7988 volume 3, pp 218-227, 2011.

9.        Klaus Brinker and Johannes Furnkranz and Eyke Hullermeier. A Unified Model for Multilabel Classification and Ranking. Proceedings of the 2006 conference on ECAI.




K.M. Pandey, A. Surana and D. Deka

Paper Title:

Numerical Analysis of Helicopter Rotor at 400 RPM

Abstract:    In this paper  the main objective of this simulation is to analyze the flow around an isolated main helicopter rotor at a particular main rotor speed of 400 rpm, and angle of attack of 8 degrees and blades of the helicopter Eurocopter AS350B3 which uses the blade profile of standard ONERA OA209 airfoil during hovering flight conditions. For CFD analysis, the Motion Reference Frame (MRF) method with standard viscous k-ε turbulent flow model was used on modeling the rotating rotor operating in hovering flight. The Ansys fluent was used for the purpose of analysis.

   Aerodynamics, CFD, helicopter, hovering, MRF, rpm.


1.        Caradonna, F. X. and Isom, M. P. (1972). “Subsonic and Transonic Potential Flow over Helicopter Rotor Blades”, AIAA Journal, No. 12, pp. 1606-1612.
2.        Chang, I.C. (1984), “Transonic Flow Analysis for Rotors”, NASA TP 2375.

3.        Xu, M., Mamou, M. and Khalid, M. (2002). “Numerical Investigation of Turbulent Flow Past a Four- Bladed Helicopter Rotor Using k-ω SST Model”, The 10th Annual Conference of CFD Society of Canada, Windsor.

4.        Strawn, R. C. and Djomehri, M. J. (2001). “Computational Modeling of Hovering Rotor and Wake Aerodynamics,” American Helicopter Society 57th Annual Forum, Washington, DC.

5.        Sides, J., Pahlke, K. and Costes, M. (2001). “Numerical Simulation of Flow Around Helicopter at DLR and ONERA”, Editions Scientifiques et Medicales Elsevier.

6.        FLUENT News 2002 (11)2, pp: s9

7.        Dario Fusato, Roberto Celi (2001). “Design sensitivity analysis for helicopter flight dynamic and aeromechanic stability”, 57th Annual Forum of the American Helicopter Society, Washington D.C.

8.        A. González, R. Mahtani, M. Béjar, A. Ollero “control and stability analysis of an autonomous helicopter”

9.        Seawook Lee, Hyunmin Choi, Leesang Cho, Jinsoo Cho “Aerodynamic analysis of the helicopter rotor using the time-domain panel method”, ICAS 2010, 27th international congress of the aeronautical sciences.

10.     Nik Ahmad Ridhwan, Nik Mohdi and Abas Ab. Wahabii. “Numerical Analysis of an Isolated Main Helicopter Rotor in Hovering and Forward Flight.”

11.     Yihua Cao, Ziwen Yu. “Numerical simulation of turbulent flow around helicopter ducted tail rotor”, Aerospace Science and Technology 9 (2005) 300–306

12.     W.R.M. Van Hoydonck(2006). “Development and Validation of a Numerical Blade Element Helicopter Model in Support of Maritime Operations.”

13.     A document on the internet “Unity3D Helicopter Tutorial”. []

14.     An image on the internet []

15.     Froude RE. Trans Inst Naval Architects,1889;30:390.

16.     Rankine WJM. On the mechanical principles of the action of propellers. Trans Inst Naval Architects (British), 1865;6(13).

17.     K.M. Pandey, G.Kumar, D.Das, D. Deka, A. Surana and H.J.  Das, CFD analysis of an isolated main helicopter rotor for a hovering  Flight,IRACST-Engineering Science and Technology: An International journal(ESTIJ), Vol. 2, No.1 Feb,2012, PP.131-137




R.Gomathi, A.K.Gnanasekar, V.Nagarajan

Paper Title:

Performance Analysis using Adaptive Decision for Parallel Interference Cancellation Receiver in Asynchronous Multicarrier DS-CDMA Systems

Abstract:   In this paper, we present and analyze the performance of asynchronous multicarrier direct-sequence code division multiple-access (DS-CDMA) system using adaptive decision at the receiver. In addition to that parallel interference cancellation (PIC) scheme is presented at the receiver. The PIC scheme offers better interference suppression capability. At the last stage, the interference cancelled outputs from all the subcarriers are maximal ratio combined (MRC) and feeds viterbi decoder. Convolutionally coded multicarrier DS-CDMA system compares BER from the decision which helps in further improvement.

   Interference cancellation, Multiple access Interference.


1.        S. Kondo and L. B. Milstein, “Performance of multicarrier DS-CDMA systems,” IEEE Trans. Commun., vol. 44, no. 2, pp. 238-246, Feb.1996.
2.        D. N. Rowitch and L. B. Milstein, “Convolutionally coded multicarrier DS CDMA systems in a multipath fading channel - Part I: Performance analysis,” IEEE Trans. Commun., vol. 47, pp. 1570-1582, Oct. 1999.

3.        Hanzo, L., Yang, L.L.,kuan,E.L. and Yen,K. “Single and Multi-carrier DS-CDMA: Multi-user Detection, Space-Time Spreading , Synchronisation,Standards and Networkinng”, (2003), John Wiley & Sons.

4.        S.Hara and R.Prasad,”Overview of multicarrier CDMA”, IEEE Communications Magazine, vol.35,no.12,pp.126-133,1997.

5.        Kumar, Shankar K.R. and chockalingam,  “Parallel Interference  Cancellation  in  multi-carrier  DS-CDMA Systems,”  In:2004  IEEE  International  Conference  on Communications , 20-24 June, Paris,vol.5,2874-78.

6.        B.Smida,L.Hanzo,Sofiene Affes, “Exact BER performance of asynchronous MC-DS-CDMA over fading channels,” IEEE Transactions on Wireless Communications, Volume 9,Issue 4,April 2010.

7.        Gohary, R.H.; Mourad, H.M.; Al-Hussaini, E.K. , “An adaptive parallel interference cancellation system employing soft decisions for asynchronous DS/CDMA channels” , Global Telecommunications Conference, 3145 – 3147,vol.5 ,IEEE, 2001.

8.        Maged Ahmed , Ahmed El-Mahdy , Kairy El-Barbary, “Performance Analysis of Adaptive Hard Decision Parallel Interference Cancellation Receiver in Asynchronous Multicarrier DS-CDMA System”, National Radio Science Conference,PP-771-780,978-1-4244-5247-7/09/$26.00 ©2011 IEEE.




Reema Patel, Amit Thakkar, Amit Ganatra

Paper Title:

A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems

Abstract:    Despite of growing information technology widely, security has remained one challenging area for computers and networks. In information security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Currently many researchers have focused on intrusion detection system based on data mining techniques as an efficient artifice. Data mining is one of the technologies applied to intrusion detection to invent a new pattern from the massive network data as well as to reduce the strain of the manual compilations of the intrusion and normal behavior patterns. This article reviews the current state of art data mining techniques, compares various data mining techniques used to implement an intrusion detection system such as Decision Trees, Artificial Neural Network, Naïve Bayes, Support Vector Machine and K- Nearest Neighbour Algorithm by highlighting advantages and disadvantages of each of the techniques. Finally, a discussion of the future technologies and methodologies which promise to enhance the ability of computer systems to detect intrusion is provided and current research challenges are pointed out in the field of intrusion detection system.

   Classification, Data Mining, Intrusion Detection System


1.        Amoroso EG (1999) Intrusion detection: an introduction to internet surveillance, correlation, trace back, traps, and response. Intrusion.Net Books, NJ
2.        Lunt, T.F. (1989). Real -Time Intrusion Detection.  Proceedings from IEEE COMPCON.

3.        James Cannady, Jay Harrell (1996). A comparative Analysis of current Intrusio n Detection Technologies.

4.        (SANS: FAQ: Data Mining in Intrusion Detection)

5.        W. Lee. A Data Mining Framework for Constructing   Features and Models for Intrusion Detection Systems. PhD Thesis, Computer Science Department, Columbia University, June 1999.

6.        W. Lee and S. Stolfo. Data Mining Approaches for Intrusion Detection. In proceedings of the 7th USENIX Security Symposium, 1998. 

7.        Data Mining Machine Learning Techniques – A Study on Abnormal Anomaly Detection System. M. Sathya Narayana, B. V. V. S. Prasad,A. Srividhya,K. Pandu Ranga Reddy. Issue 6, September 2011, International Journal of Computer Science and Telecommunications, Vol. Volume 2, pp. 8-14. ISSN 2047-3338 .

8.        W. Lee, S.J. Stolfo, K.W. Mok, Algorithms for Mining System Audit Data, in Proc. KDD, 1999.

9.        J. Cannady. Artificial Neural Networks for Misuse Detection. National Information Systems Security Conference, 1998.

10.     S. Mukkamala, G. Janoski, A. Sung. Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of IEEE International Joint Conference n Neural Networks, pp.1702-1707, 2002 

11.     Valdimir V. N. The Nature of Statistical Learning Theory, Springer, 1995. 

12.     G.V.Nadiammai, S.Krishaveni, M.Hemalatha – “A comprehensive Analysis and study in intrusion detection system using data mining Techniques”. IJCA, Volume 35 –No.8, December 2011.

13.     KDD Cup 1999 Dataset:




Rajesh Shrivastava, Pooja Mehta (Gahoi)

Paper Title:

Analysis of Secure Mobile Agent System

Abstract:    As a recently emerging distributed computing paradigm, mobile-agent technology attracts great interests because of its salient merits. However, it also brings significant security concerns, among which the security problems between a mobile agent and its platforms are of primary importance. While protecting a platform (platform or host security) can benefit from the security measures in a traditional client-server system, protecting a mobile agent (mobile-agent or code security) has not been met in traditional client-server systems and is a new area emerging with mobile-agent technology. We analyzed the different types of security issues related to mobile agent. After analysis, we found that there are many kind of technology available to ensure mobile agent security. But not a single technology provides complete solution for the same. We proposed an algorithm in which     we use monitoring agent and dummy agent in place of original mobile agent. Monitoring agent checks the behavior of next node in the network. If monitoring agent finds the node suspicious, it sends the alert acknowledgment to original agent and original agent doesn’t travel to that suspicious node.

   Mobile agent, distributed systems, security.


1.        Shashank, Srivastava and G.C Nandi, "Protection of Mobile agent and its Itinerary from Malicious host", International Conference on Computer & Communication Technology (ICCCT)-2011, pp 405-411.
2.        Yi, Liu1 and Yong Ding, "An Optimistic Payment Protocol with Mobile Agents in Hostile Environments", 2011 International Conference on Network Computing and Information Security, pp 218-222.

3.        Rajwinder Singh and Mayank Dave, "Rescuing Data of Mobile Agents Blocked by Malicious Hosts in e-Service Applications", 2011 International Conference on Multimedia, Signal Processing and Communication Technologies, pp 24-27.

4.        Fan Linna and Liu Jun, "A Free-Roaming Mobile Agent Security Protocol against Colluded Truncation Attack without Trusted Third Party", Business Management and Electronic Information (BMEI), 2011 International Conference, Volume: 2, pp 14 - 18.

5.        Bennet Yee. A sanctuary for mobile agents. In J. Vitek and C. Jensen, editors, Secure Internet Programming, volume 1603 in LNCS, pages 261–274, New York, NY, USA, 1999. Springer-Verlag Inc.

6.        Tomas Sander and Christian Tschudin. Towards mobile cryptography. In Proceedings of the IEEE Symposium on Security and Privacy, pages 215–224, Oakland, CA, May 1998. IEEE Computer Society Press.

7.        Tomas Sander and Christian F. Tschudin. Protecting Mobile Agents Against Malicious Hosts.In Giovanni Vigna, editor, Mobile Agent Security, pages 44–60. Springer-Verlag: Heidelberg,Germany, 1998.

8.        Ichiro Satoh. Selection of Mobile Agents. In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’04). IEEE Computer Society Press, 2004.

9.        J. White, “Mobile Agents White Paper,” General Magic Inc., 1996.

10.     D. Milojici, ”Mobile agent applications”, IEEE concurrency, July-Sep 1999, pp 80- 90.

11.     Chandra Krintz, Security in agent-based computing environments using existing tools. Technical report, University of California, San Diego, 1998.

12.     Joshua D. Guttman and Vipin Swarup. Authentication for mobile agents. In LNCS, pages114–136. Springer, 1998.

13.     Neeran Karnik. Security in Mobile Agent Systems. PhDthesis, Department of Computer Science and Engineering. University of Minnesota,1998.

14.     Tomas Sander and Christian F. Tschudin. Protecting Mobile Agents Against Malicious Hosts.In Giovanni Vigna, editor, Mobile Agent Security, pages 44–60. Springer-Verlag: Heidelberg,Germany, 1998.

15.     Bennet Yee. Using Secure Coprocessors. PhD thesis, Carnegie Mellon University, 1994.

16.     Fritz Hohl. Time limited blackbox security: Protecting mobile agents from malicious hosts. In G. Vigna, editor, Mobile Agents and Security, volume 1419 in LNCS, pages 92–113. Springer-Verlag, Berlin, 1998.

17.     Neelesh Kumar Panthi, Ilyas Khan, Vijay k. Chaudhari, “Securing Mobile Agent Using Dummy and Monitoring Mobile Agents”, IJCSIT Vol. 1 (4) , 2010, 208-211.




D.Sasirekha,  E.Chandra

Paper Title:

Text To Speech: A Simple Tutorial

Abstract:  Research on Text to Speech (TTS) conversion is a large enterprise that shows an impressive improvement in the last couple of decades. This article has two main goals. The first goal is to summarize the published literatures on Text to Speech (TTS), with discussing about the efforts taken in each paper. The second goal is to describe specific tasks concentrated during Text to Speech (TTS) conversion namely, Preprocessing & text detection, Linearization, Text normalization, prosodic phrasing, OCR, Acoustic processing and Intonation. We illustrate these topics by describing the TTS synthesis. This system will be highly useful for an illiterate and vision impaired people to hear and understand the content, where they face many problems in their daily life due to the differences in their script system. This paper starts with the introduction to some basic concepts on TTS synthesis, which will be useful for the readers who are less familiar in this area of research.



1.       Frances Alias, Xavier Servillano, Joan Claudi socoro and  Xavier Gonzalvo “Towards High-Quality Next Generation Text-to-Speech Synthesis:A multi domain Approach by Automatic Domain Classification”,IEEE Transactions on AUDIO,SPEECH AND LANGUAG PROCESSING, VOL16,NO,7 september 2008.
2.       Qing Guo, Jie Zhang, Nobuyuki Katae, Hao Yu , “High –Quality Prosody Generation in Mandrain  Text-to-Speech system”, FujiTSu Sci.Tech,J., vol.46, No.1,pp.40-46 ,2010.

3.       Gopalakrishna anumanchipalli,Rahul Chitturi, Sachin Joshi, Rohit Kumar, Satinder Pal Singh,R.n.v Sitaram,D.P.Kishore, “Development of Indian Language Speech Databases for Large Vocabulary Speech Recognition System”,

4.       A.Black, H.Zen and K.Tokuda “Statistical parametric speech synthesis”, in proc.ICASSP, Honolulu, HI 2007, vol IV, PP 1229-1232.

5.       G.Bailly, N.Campbell and b.Mobius, “ISCA special session: Hot topics in speech synthesis”, in proc.Eurospeech,Genea, Switzerland, 2003, pp 37-40.

6.       M.Ostendorf and I.Bulyko, “The impact of speech recognition on speech synthesis”, in proc, IEEE Workshop Speech Synthesis, Santa Monica,2002,pp. 99-106.

7.       Text To Speech Synthesis - a knol by Jaibatrik Dutta  .

8.       Silvio Ferreia,Celina Thillou, Bernaud Gosselin, “From Picture to Speech: an Innovative Application for Embedded Environment”,

9.       M.Nageshwara Rao, Samuel Thomas, T.Nagarajan and Hema A.Muthy, “Text-to-Speech Syntheis using syllable line units”

10.     Jindrich Matousek, Josef Psutks, Jiri Krita, “Design of speech Corpus for Text-to-Speech Synthesis”




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

Paper Title:

A Study of Role Based Access Control policies and Constraints

Abstract:  Access control policies are constraints that protect computer-based information resources from unauthorized access. Role-Based Access Control (RBAC) is used by many organizations to protect their information resources from unauthorized access. RBAC policies are defined in terms of permissions that are associated with roles assigned to users. A permission determines what operations a user assigned to a role can perform on information resources. Role-based access control (RBAC) is also a powerful means for laying out higher-level organizational policies such as separation of duty, and for simplifying the security management process. One of the important aspects of RBAC is authorization constraints that express such organizational policies. This paper presents an overview of Role- based access control policies and constraints.

   Constraints, RBAC, Policies, UML.


1.        R. Sandhu, E. Coyne, H. Feinstein, C. Youman. Rolebased access control models, IEEE Computer, vol. 29, no. 2, pp. 38–47, Feb. 1996.
2.        American National Standards Institute Inc. Role Based Access Control, ANSI-INCITS 359-2004, 2004.

3.        G.J. Ahn and M. E. Shin. Role-based authorization constraints specification using object constraint language. In Proceedings of the 10th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE ’01), pages 157–162, Cambridge,  Massachusetts, June 2001.

4.        J. Warmer and A. Kleppe. The Object Constraint Language, Second Edition. Addison-Wesley, 2003.

5.        D.F. Ferraiolo, D.R. Kuhn, R. Chandramouli, Role-based access control, Artec House, Boston, 2003.

6.        J. Rumbaugh, I. Jacobson, G. Booch. The Unified Modeling Language Reference Manual, Second Edition. Reading, Mass., Addison Wesley Longman, 2004.




Hota H.S., Sahu Pushpanjali

Paper Title:

A Comparative Study of Different Statistical Techniques Applied to Predict Share Value of  State Bank of India (SBI)

Abstract:   Prediction of share value is one of the critical job and is necessary for the current financial scenario, due to the high uncertainty prediction system can not predict the share value with high accuracy. In this piece of research work an attempt is made to analyze the prediction based on statistical techniques with special reference to the share value of   State Bank of India (SBI). The data that is downloaded consists share value for open, close, volume, high, and low in equal interval of time from Jan-2003 to May-2011. Two different techniques ARIMA and Exponential Smoothing is used to compare the accuracy. Statistical measure are carried out and it is found  that expert modeler is working well for the prediction of share value of SBI. The future value for the next 5 months from May-2011 from both the models are also evaluated

   Expert modeler, Exponential Smoothing, Auto Regressive Integrated Moving Average (ARIMA).


1.       G.E.P. Box, G.M.Jenkins and G.C. Reinsel “Time series analysis  forecasting  and  control “  Third edition Englewood clifts NJ prentice hall 1994.
2.       Web Source last accessed on January 2012..

3.       Vatsal  H. Shah,”Machine Learning Techniques for Stock Prediction”.

4.       Binoy.B.Nair, V.P Mohandas, N. R. Sakthivel, ” A Decision Tree- Rough Set Hybrid  System for Stock Market Trend Prediction”.       

5.       Dr.B.N.Gupta(1995),”Statistics”, Sahitya Bhawan Publishers

6.       R.J.Frank, N.Davey, S.P.Hunt Department of Computer Science, University of Hertfordshire,

7.       Javier Contreras ,Francisco J. Nogales and Autonio J.Conejo “ ARIMA models to prtedict   next-day electricity prices “ IEEE transcation on power systems vol 18 ,No 3 august 2003.

8.       SPSS Clementine Release 12.0 help fille

9.       Box, G.E.P, Jenkins ,G.M. “Time series analysis forecasting and control “,San Francisco ,CA:Holden 




K.Poornima, R.Kanchana

Paper Title:

A Method to Align Images Using Image Segmentation

Abstract:   Most high level interpretation task rely on image alignment process. In this work, a method for automated image alignment through image segmentation is proposed. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. This new approach mainly consists in combining several segmentations of the pair of images to be registered. It can be applied to a pair of satellite images with simulated translation, and to real remote sensing examples comprising different viewing angles, different acquisition dates and different sensors. This process allows the alignment of pairs of images (multitemporal and multisensor) with differences in rotation and translation, with small differences in the spectral content, leading to the subpixel accuracy.

   Image alignment, Image segmentation, Wiener filtering.


1.       L.G. Brown, “A Survey of image registration techniques”,  comput. Surv., vol. 24, no. 4, pp. 325-376,1992.
2.       C.I. Chang, Y. Du, J. Wang, S.M. Guo, and P.D. Yhouin,  “Survey and comparative analysis of entropy and relative entropy thresholding techniques”,IEEEProc.-Vis. Image      signal Process., vol. 153, no. 6, pp. 837-850, 2006.

3.       H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color   image segmentation: Advances and prospects,” Pattern Recognit., vol. 34, pp.2259–2281, 2001.

4.       R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2002.

5.       J. S. Lim, Two-Dimensional Signal and Image Processing.  Upper Saddle River, NJ: Prentice-Hall, 1990.

6.       L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion   simulations,” IEEE Trans. Pattern Anal. Mach.Intell., vol.
13, no. 6, pp. 583–598, Jun. 1991.

7.       D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp.        91–110, Nov. 2004.

8.       J. Ma, J. C.-W. Chan, and F. Canters, “Fully automatic   subpixel image registration of multiangle CHRIS/Proba data,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 7, pp. 2829–2839, Jul. 2010.

9.       K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, “A comparison of affine region detectors,” Int. J. Comput. Vis., vol. 65, no. 1/2, pp. 43–72, Nov. 2004

10.     P. Dare and I. Dowman, “An improved model for automatic feature-basedregistration of SAR and SPOT images,” Proc. ISPRS  J. Photogramm. Remote Sens., vol. 56, no. 1, pp. 13–28, Jun. 2001.

11.     K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615–1630, Oct. 2005.

12.     K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, “A comparison of affine region detectors,” Int. J. Comput. Vis., vol. 65, no. 1/2, pp. 43–72, Nov. 2004.




S. J. Suji Prasad, Susan Varghese, P. A. Balakrishnan

Paper Title:

Particle Swarm Optimized I-PD Controller for Second Order Time Delayed System

Abstract:    In this paper, I-PD controller is optimized using particle swarm intelligence for a Second Order Time Delayed System. Optimization is done on the basis of performance indices like settling time, rise time, peak overshoot, ISE (integral square error) and IAE (integral absolute error). In industrial processes, PID controllers and its variants are most preferred though there are significant developments in the control systems. If the parameter of controller is not properly designed, then desired control output may fail. The simulation results with optimized I-PD controller proved to be giving better performances compared with Ziegler Nichols and Arvanitis tuning.

   Proportional integral and derivative (PID); Proportional kick; Derivative kick; Settling time; Rise time and Tuning.


1.        J. Astrom, and T. Hagglund. “The future of PID control”, Control Engineering Practice, vol. 9, November 2001, pp. 1163–1175.
2.        Nagaraj B, P. Vijayakumar (2011), ‘A Comparative Study Of PID Controller Tuning Using GA, EP, PSO AND ACO’, Journal of Automation, Mobile Robotics & Intelligent Systems,Volume 5, No 2 pp 42-48

3.        Kiam Heong Ang, Gregory Chong and Yun Li (2005), ‘PID Control System Analysis, Design, and Technology’, IEEE Transactions on control systems technology, vol. 13, no. 4

4.        Yun li, Kiam Heong Ang, and Gregory  c.y. Chong (2006), ‘PID Control System Analysis and Design - Problems, Remedies and Future Directions’, IEEE Control systems magazine,  pp32-41

5.        Aidan O'Dwyer (2006), ‘Handbook of  PI and PID Controller Tuning Rules’, (2nd Edition),Published by ICP

6.        Riccardo Poli ,James Kennedy and Tim Blackwell (2007), ‘Particle swarm optimization-An overview’,  Springer Science , Business Media, LLC

7.        Russell C Eberhart and Yuhui Shi (2001), ‘Particle Swarm Optimization:  Developments, Applications and Resources’,  IEEE conference

8.        Tushar Jain and M. J. Nigam, “Optimization of PD-PI Controller Using Swarm Intelligence”, International journal of computational cognition, vol. 6, no. 4, December 2008.

9.        Wen-wen Cai, Li-xin Jia, Yan-bin Zhang,Nan Ni (2010), ‘Design and Simulation of Intelligent PID Controller Based on Particle Swarm Optimization’, IEEE conferences

10.     Jacqueline Wilkie, Michael Johnson, Reeza  Katebi (2002), ‘Control Engineering an Introductory Course’,  pp 529-565

11.     Giriraj Kumar S.M, Deepak Jayaraj and Anoop R Kishan (2010), ‘PSO based Tuning of a PID Controller for a High Performance Drilling Machine’ International journal of computer applications, volume I-No.19

12.     Rania Hassan, Babak Cohanim and Olivier de Weck (2004), ‘A Copmarison of Particle Swarm Optimization and the Genetic Algorithm’, American Institute of Aeronautics and Astronautics.




K.M.Pandey, Jagannath Rajshekharan and Sukanta Roga

Paper Title:

Wall Static Pressure Variation In Sudden Expansion In Flow Through De Laval Nozzles At Mach 1.74 And 2.23 In Circular Ducts Without Cavities: A Fuzzy Logic Approach

Abstract:    In this paper the analysis of wall static pressure variation has been done with fuzzy logic approach to have smooth flow in the duct. Here there are three area ratio choosen for the enlarged duct, 2.89, 6.00 and 10.00. The primary pressure ratio is taken as 2.65 and cavity aspect ratio is taken as 1 and 2. The study is analysed for length to diameter ratio of 1,2,4 and 6. The nozzles used are De Laval type and with a Mach number of 1.74 and 2.23. The analysis based on fuzzy logic theory indicates that the length to diameter ratio of 1 is sufficient for smooth flow development if only  the basis of wall static pressure variations is considered. Although these results are not consistent with the earlier findings but this opens another method through which one can analyse this flow. This result can be attributed to the fact that the flow coming out from these nozzles are parallel one. 

   wall static pressure, area ratio, pressure ratio, De Laval nozzle, Mach number.


1.        Wick, R.S., The effect of boundary layer on sonic flow through an abrupt cross sectional areachange, Journal of the Aeronautical Sciences, Vol. 20, p. 675-682 (1953).
2.        Korst, H., ‘Comments on the effect of boundary layer on the sonic flow through an abrupt cross sectional area change’, Journal. of Aeronautical Sciences, Vol. 21, p. 568 (1954).

3.        Hall, W.B and Orme, E.M., ‘Flow of a compressible fluid through a sudden enlargement in a pipe, Proceedings of Institution of Mechanical Engineers’, Vol. 169, p. 007-1022 (1955).

4.        Benedict.R.P and Steltz, W.G., ‘ A generalized approach to one dimensional gas dynamics’, Trans. ASME (power), Vol. 84, p. 44 (1962).

5.        Zadeh, L.A., 1965, “Fuzzy Sets,” Information Control.,Vol. 8. pp. 338-353.

6.        Anderson, J. S and Williams, T. T., ‘Base pressure and noise produced by the abrupt expansion of air in a cylindrical duct’, Journal of Mechanical Engineering Science, Vol. 10, No. (3), p. 262-268 (1968).

7.        Zadeh, L.A., 1973, “Outline of a new approach to the analysis of complex systems and decision Processes”, Information Science, Vol. 9, pp.43-80.  

8.        Durst.F., Melling, A and Whitelaw, J.J.,  Low Reynolds number flow over a plane symmetric sudden expansion’, Journal of Fluid mechanics, Vol. 12, No.6, p.739 (1974).

9.        Cherdon, W., Durst, F. and Whitelaw, J.H., ‘Asymmetric flows and instabilities in symmetric ducts with sudden expansions’, Journal of Fluid Mechanics, Vol. 84, part  1, p.13 (1978).

10.     Brady, J.F. and Acrivos, A., ‘Closed cavity laminar flows at a moderate Reynolds numbers’, Journal of Fluid Mechanics, Vol. 115, p. 427 (1982).

11.     Yang, B.T and Yu, M. H., The flow field in a suddenly enlarged combustion chamber, AIAA  Journal, Vol. 21, No.1, p. 92-97 (1983).

12.     Raghunathan S and Mabey, D. G., Passive shockwave/boundary-layer control on a wall mounted model, AIAA Journal,  Vol. 25, No. 2, p. 275-278 (1987).

13.     Raghunathan, S., Pressure fluctuation measurements with passive shock/boundary layercontrol, AIAA journal, Vol. 25, No. 5,  p. 626-628 (1987).

14.     Raghunathan, S., Effect of porosity strength on passive shockwave/boundary layer control,  AIAA journal , Vol. 25, No. 5,  p. 757-758 (1987).

15.     Wilcox, J.F. Jr., Passive venting system for modifying cavity flow fields at supersonic speeds,AIAA Journal, Vol. 26, No. 3,  p. 374-376 (1988).

16.     Tanner, M., Base cavities at angles of incidence, AIAA Journal, Vol. 26, No. 3, p. 374-377 (1988).

17.     Rathakrishnan, E., Ramanaraju, O. V., and Padmanabhan, K., Influence of cavities on suddenly expanded flow field, Mechanics Research communications, Vol. 16 (3), p. 139-146 (1989).

18.     Vishwanath   P.R.  and Patil, S.R., ‘Effectiveness of passive devices for axi symmetric base drag reduction at Mach 2’, Journal of Spacecraft, p. 234, (May-June 1990).

19.     Kruiswyk, R.W and Dutton. J. C., ‘Effect of base cavity on subsonic near wake flow’, AIAA Journal, Vol. 28, No. 11, p.1885-1895 (1990).

20.     Pandey,K.M., Studies on flow through nozzles with sudden expansion, Ph.D. thesis, Department of Mechanical Engineering, IIT Kanpur(1994).
21.     Pandey K M, Studies on flow through nozzles in sudden expansion with passive control, International conference at IISc, Bangalore on recent advances in Mechanical Engineering, Dec 20-22, 1995, pp 1511-1518, Narosa publications, New Delhi, India, 1995, Volume 2
22.     Pandey K M, Base flow in flow though nozzles with sudden expansion:  a study in supersonic regime-2nd National Conference on Fluid Machinery, Dept of Mechanical Engg, PSG College of Technology, Coimbatore, India, June 28-29, 1996, pp 256-265, Allied publishers Ltd. New Delhi, India, 1996...

23.     Pandey K M, Pressure loss in flow through nozzles with sudden expansion:  a comparison between supersonic and subsonic flow regime, International Conference on Advances in Mechanical and Industrial Engg, Dept of Mechanical and Industrial Engg., Roorkee, India, Feb 6-8, 1997, pp 417-426, Ajay printers and publishers, Roorkee, India, Volume 2 .

24.     Pandey K M , S A Khan and Rathakrishnan E, Passive control of base flows, Proceedings of the National Seminar on Recent Advances in Experimental Mechanics, Dept of Aerospace Engg, IIT Kanpur, Mar 15-16, 2000, Allied Publications Ltd, New Delhi, India 2000 PP-57-71.

25.     Dixit, U.S., Dixit, P.M., ‘Application of fuzzy set theory in the scheduling of a Tandem cold-Rolling Mill’, ASME, Vol. 122, p.494- 500 (2000)..

26.     Rathakrishnan E., ‘Effect of ribs on suddenly expanded flows’, AIAA Journal, Vol. 39, No. 7, p.1402-1404 (2001).

27.     Dixit, U.S., Robi, P.S., Sarma, D.K., ‘A systematic procedure for the design of a cold rolling mill’, Material Processing Technology , Vol. 121,  p. 69- 76 (2002).

28.     Abburi, N.R., Dixit, U.S., ‘A knowledge-based system for the prediction of surface roughness in turning process’, Robotics and Computer-Integrated manufacturing, Vol. 22, p. 363- 372 (2006).

29.     Pandey, K.M., and Rathakrishnan E., ‘Influence of cavities on flow   development in sudden expansion’, International Journal of Turbo  and Jet Engines, Vol. 23, p. 97- 112 (2006).

30.     Pandey, K.M., and Rathakrishnan E., ‘Annular cavities for Base flow control’, International Journal of Turbo and Jet Engines, Vol. 23, p. 113- 127 (2006).




S.Joshuwa, E.Sathishkumar, S.Ramsankar

Paper Title:

Advanced Rotor Position Detection Technique for Sensorless BLDC Motor Control

Abstract:     Brushless DC Motor drives have made a successful entrance into various sectors of industry such as aerospace, automotive and home appliances due to its simple structure. The accurate knowledge of the rotor position is required for good performance of brushless DC motors the need for the rotor angle information in BLDC has been satisfied by use of some form of rotor position sensor. The position sensor used in BLDC drives have the disadvantages of additional cost, electrical connections, mechanical alignment problems, and disadvantage of being inherent source of unreliability. These bottlenecks results in several sensor less technique in recent years. A proposed sensor less scheme is used to overcome the disadvantages of sensored scheme. The rotor position detection can be estimated even at standstill and running conditions. The methods which is proposed in this project is  1. Back EMF ZCD 2. RF Injection method.

   Brushless DC Motor, Back EMF ZCD


1.        P. P. Carney and J. F Watson, “Review of position-sensor less operation of permanent-magnet machines,” IEEE Trans. Ind. Electron., vol. 53, no. 2, pp. 352–362, Apr. 2006.
2.        C.-H. Chen and  M.-Y. Cheng, “New cost effective sensor less commutation  method for brushless dc motors without phase shift circuit and neutral voltage,” IEEE Trans. Power Electron., vol. 22, no. 2, pp. 644–653, Mar.2007

3.        C.-G. Kim,  J.-H. Lee, H.-W. Kim, and M.-J. You, “Study on maximum torque generation for sensor less controlled brushless DC motor with trapezoidal back EMF,” IEE Proc.-Electro. Power Appl., vol. 152, no. 2, pp. 277–291, Mar. 2005

4.        J.X. She and S. Iwasaki, “Sensor less control of ultrahigh-speed PM  brushless motor using PLL and third harmonic back EMF,” IEEE Trans. Ind. Electron., vol. 53, no. 2, pp. 421–428, Apr. 2006.

5.        P. Damodharan, R. Sandeep, and K. Vasudevan, “Simple position sensor less starting method for brushless DC motor,” IEEE Electro. Power Appl., vol. 2, no. 1, pp. 49–55, Jan. 2008.

6.        D. K. Kim, K. W. Lee, and B. I. Kwon, “Commutation torque ripple reduction  in a position sensor less brushless dc motor drive,” IEEE Trans. Power Electron., vol. 21, no. 6, pp. 1762–1768, Nov. 2006

7.        C.-G. Kim, J.-H. Lee, H.-W. Kim, and M.-J. Youn, “Study on maximum torque generation for sensor less controlled brushless DC motor with trapezoidal back EMF,” IEE Proc.-Electro. Power Appl., vol. 152, no. 2, pp. 277–291, Mar. 2005.

8.        J. H. Song and I. Choy, “Commutation torque ripple reduction in brushless  dc motor drives using a single dc current sensor,” IEEE Trans. Power Electron., vol. 19, no. 2, pp. 312–319, Mar. 2004.

9.        S. Wu, Y. Li, X. Miao, “Comparison of Signal Injection Methods for sensor less control of PMSM at Very Low Speeds”, IEEE Power Electronics Specialists Conference, PESC 2007, June 2007 pp. 568 – 573.

10.     M. Eskola, H. Tuusa, “Sensor less Control of Salient Pole PMSM Using a Low –Frequency Signal Injection”, European Conference on Power Electronics and Applications, Sept. 2005, pp. 1- 10

11.     S. Ogasawara, H. Akagi, “An Approach to Real-Time Position Estimation at Zero and Low Speed for a PM Motor Based on Saliency”, IEEE Transactions on Industry Applications, Vol. 34, No. 1, Jan./Feb 1998, pp. 163-168

12.     Joohn Sheok Kim, Seung Ki Sul, “New Stand-Still Position Detection Strategy for PMSM Drive without Rotational Transducers”, Conference Proceedings of the Ninth Annual Applied Power Electronics Conference and Exposition, APEC '94., Vol. 1, 13-17 Feb. 1994, pp.363 – 369.




Diponkar Kundu, Dilip Kumar Sarker, Md. Galib Hasan, Pallab Kanti Podder, Md. Masudur Rahman

Paper Title:

Performance Analysis of an InGaAs Based p-i-n Photodetector 

Abstract:    an InGaAs based p-i-n photodetector model is chosen in order to find out quantum efficiency, photocurrent density, and normalized frequency response with and without RC effect. Normalized frequency response is the most important factor in order to analysis the performance of p-i-n photodetector. Quantum efficiency, photocurrent density, normalized frequency response curves are obtained by formulation which is done from structure and MATLAB simulation. A relation for the fiber-to-waveguide coupling efficiency has also been used to calculate the overall quantum-efficiency of waveguide photodetector [1]. Normalized frequency response is obtained by varying value of frequency dependent transfer function of equivalent circuit model of p-i-n photodetector with frequency. For enhancing bandwidth of photodetector, the parametric values of photodetector such as reverse bias junction capacitance and resistance, has been optimized. The effect of carrier trapping at a heterointerface has also been considered to study the frequency dependence of the photocurrent at low-bias voltages [1].

   p-i-n photodetector, quantum efficiency, photocurrent density, normalized frequency response.


1.       Nikhil Ranjan Das, Senior Member, IEEE and M. Jamal Deen, Fellow, IEEE “A Model for the Performance Analysis and Design of Waveguide p-i-n Photodetectors” IEEE Transactions on Electron Devices Vol. 53 No. 4 , April 2005.
2.       Nikhil Ranjan Das, Senior Member, IEEE and  M. Jamal Deen, Fellow, IEEE  “Calculating the Photocurrent and Transit-Time-Limited Bandwidth of a Hetero structure p-i-n Photodetector”IEEE Journal of Quantum Electronics Vol. 37 No.12 December 2001.

3.       Paul K. Yu UCSD, Jacobs School of Engineering “Equivalent Circuit Analysis of Harmonic Distortions in Photodiode” University of California Post prints 1998.

4.       Kazutoshi Kato, Member, ZEEE, Susumu Hata, Kenji Kawano, Junichi Yoshida, SeniorMember, IEEE, and Atsuo Kozen “ A High-Efficiency 50 GHz InGaAs Multimode Waveguide Photodetector” IEEE Journal of Quantum Electronics Vol. 28 No.12 December 1992.

5.       S. D. McDougall, M. J. Jubber, O. P. Kowalski, J. H. Marsh, and J.S. Aitchison, “GaAs/AlGaAs waveguide pin photodiodes with nonabsorbing input facets fabricated by quantum well intermixing,” Electron. Lett., vol. 36, pp. 749–750, 2000.

6.       C. L. Ho, M. C. Wu, W. J. Ho, J. W. Liaw, and H. L. Wang, “Effectiveness of the Pseudowindow for edge-coupled InP-InGaAs-InP PIN photodiodes,” IEEE J. Quantum Electron., vol. 36, no. 3, pp. 333–338, Mar. 2000.

7.       Jasprit Singh “ Optoelectronics, An Introduction To Materials And Devices” (Book)

8.       John M. Senior “ Optical Fiber Communication Principles and Practice” Second Edition Prentice Hall of India (Book)




Shah Kruti R., Bhavika Gambhava

Paper Title:

New Approach of Data Encryption Standard Algorithm

Abstract:   The principal goal guiding the design of any encryption algorithm must be security against unauthorized attacks. Within the last decade, there has been a vast increase in the accumulation and communication of digital computer data in both the private and public sectors. Much of this information has a significant value, either directly or indirectly, which requires protection. The algorithms uniquely define the mathematical steps required to transform data into a cryptographic cipher and also to transform the cipher back to the original form. Performance and security level is the main characteristics that differentiate one encryption algorithm from another. Here introduces a new method to enhance the performance of the Data Encryption Standard (DES) algorithm is introduced here. This is done by replacing the predefined XOR operation applied during the 16 round of the standard algorithm by a new operation depends on using two keys, each key consists of a combination of 4 states (0, 1, 2, 3) instead of the ordinary 2 state key (0, 1). This replacement adds a new level of protection strength and more robustness against breaking methods.

   DES, Encryption, Decryption.


1.       National Bureau of Standards – Data Encryption Standard, Fips Publication 46,1977.
2.       O.P. Verma, Ritu Agarwal, Dhiraj Dafouti,Shobha Tyagi “ Performance Analysis Of Data Encryption Algorithms “ , 2011

3.       Gurjeevan Singh, Ashwani Kumar Singla, K.S.Sandha “ Performance Evaluation of Symmetric Cryptography Algorithms, IJECT, 2011.

4.       Diaa Salama, Abdul Elminaam, Hatem Mohamed Abdul Kader and Mohie Mohamed Hadhound “ Performance Evaluation of Symmetric Encryption Algorithm “, IJCSNS, 2008

5.       Dr. Mohammed M. Alani “ Improved DES Security” ,International Multi-Conference On System, Signals and Devices, 2010

6.       Tingyuan Nie, Teng Zhang “ A Study of DES and Blowfish Encryption Algorithm”,TENCON, 2009

7.       Afaf M. Ali Al- Neaimi, Rehab F. Hassan “ New Approach for Modified Blowfish Algorithm Using 4 – States Keys” , The 5th International Conference On Information Technology,2011

8.       J.Orlin Grabbe “The DES Algorithm Illustrated”

9.       Dhanraj, C.Nandini, and Mohd Tajuddin “ An Enhanced Approch for Secret Key Algorithm based on Data Encryption Standard”, International Journal of Research And Review in Computer Science, August 2011

10.     Gurjeevan Singh, Ashwani Kumar, K.S. Sandha “A Study of New Trends in Blowfish Algorithm ”, International Journal of Engineering Research and Application,2011

11.     W. Stallings, Cryptography and Network Security: Principles and Practices, 5th ed., Prentice Hall, 1999.

12.     B.Scheier, Applied Cryptography : Protocols, Algorithms and Source Code in C,2nd ed.., John Wiley & Sons, 19995.




H. S. Behera, Ratikanta Pattanayak, Priyabrata Mallick

Paper Title:

An Improved Fuzzy-Based CPU Scheduling (IFCS) Algorithm for Real Time Systems

Abstract:   Till now various types of scheduling algorithms are used for determining which process should be executed  by the CPU when there are multiple no. of processes to be executed.There are many conventional approaches to schedule the tasks but no one is absolutely ideal. In this paper an improved  fuzzy technique has been proposed to overcome the drawbacks of other algorithms for better CPU utilization,throughput and to minimize waiting time and turn around time.

  Task, process, fuzzification, priority, cpu utilization,fuzzy scheduler, turnaround time,scheduling effeciency


1.        Shata J. Kadhim , Kasim M. Al-Aubidy :  ComputerEng. Dept, Al-     Blaqa’’Design and Evaluation of  a Fuzzy Based CPU schedulilnlg Algorithm’’ Applied University, Al-Salt, Jordan Computer Eng. Dept, Philadelphia University, Amman, Jordan,Springer-verlag Berlin Heidelberg 2010, CCIS 70,pp. 45-52,2010
2.        Stallings, Stallings, W.: Operating Systems Internals and Design Principles, 5th edn. Prentice-Hall,Englewood Cliffs (2004).

3.        Yaashuwanth  .C, Dr. R. Ramesh: ,”Design of Real Time Scheduler Simulator and Devlopment of Modified Round Robin Architecture “,IJCSNS ,VOL.10 No.3,March (2010)

4.        C. Lin and S. A. Brandt, "Efficient soft real-time processing in an integrated system," in Proc. 25th IEEE Real-Time Systems  Symp.,(2004).

5.        I. E. W. Giering and T. P. Baker, "A tool for the deterministic  scheduling of real-time programs implemented as periodic Ada tasks," Ada Lett., vol. XIV, pp. 54-73, (1994).

6.        Shahzad, B., Afzal, M.T.: ,”Optimized Solution to Shortest Job First by Eliminating the Starvation”. In: The 6th Jordanian Inr. Electrical an Jordan (2006)  

7.        Mr . Jeegar A Trivedi and  Dr.Priti Srinivas Sajja ,”Improving efficiency of round robin scheduling using neuro fuzzy approach ” ,IJRRCS vol.2,No. 2,April 2011

8.        Mahdi Hamzeh,Sied Mehdi Fakhraie and  Caro Lucas ,”Soft real time  fuzzy task scheduling for multiprocessor systems”,world academy of science,engineering and technology 28 (2007).




Sripathy Mallaiah, Krishna Vinayak Sharma, M Krishna

Paper Title:

Development and Comparative Studies of Bio-based and Synthetic Fiber Based Sandwich Structures

Abstract:   The present work was to focus on the investigation of the flexural and fatigue behaviour of flatwise, edgewise compression and water absorption of E-glass/ epoxy, jute/ epoxy, bamboo/epoxy, glass-jute/epoxy, glass-bamboo, Jute/bamboo /Polyurethane foam sandwich composites. Both natural and synthetic based sandwich composites were synthesized with different fabric and polyurethane foam. The fiber/ resin ration for glass/epoxy is 65:35 and all other natural fibers composites are 50:50 ratio of fibre to resin weight fraction. The sandwich specimens were prepared by hand adopting the lay-up method. This was followed by compression at room temperature.  Bamboo/glass hybrid structure yields higher value of core shear stress and facing bending stress. This is higher than both pure glass, bamboo. This shows how effectively hybridization can be used to tailor materials for our specific use.

   Natural fiber, polyurethane foam, sandwih structure, synthatic fiber.

1.       Williams GI, Wool RP. (2000), Composites from natural fibers and soy oil resins. Appl Compos Mater, vol. 7: pp. 421–32.
2.       Bledzki AK, Gassan J. (1999) Composites reinforced with cellulose based fibres. Prog Polym Sci, vol.24: pp. 221–74.

3.       Steeves C.A. and Fleck N. A.  (2004) Material selection in sandwich beam construction. Scripta materialia, Vol.50, pp.1335-1339.

4.       Gassan J. (2002), A study of fibre and interface parameters affecting the fatigue behaviour of natural fibre composites. Compos Part A: Appl Sci Manuf, vol. 33(3): pp. 369–74.

5.       Wool RP, Kusefoglu S, Zhao R, Palmese GI, Khot SN. High modulus polymers and composites from plant oils. Patent number: 6,121,398.

6.       Can E, Kusefoglu S, Wool RP (2002). Rigid thermosetting liquid molding resins from renewable resources: (2) copolymers of soyoil monoglycerides maleates with neopentyl glycol and bisphenol-A maleates. J Appl Polym Sci 2002;83:972.

7.       Anon. (2002) The competitiveness of natural fibers based composites in the automotive sector: the Sisal Agribusiness in Brazil. In:Materials Research Society Symposium––Proceedings, vol. 702, p. 113–39.

8.       Santulli C. (2001) Post-impact damage characterisation on natural fibre reinforced composites using acoustic emission. NDT and E International vol. 34(8): pp. 531–6.

9.       Van de Velde K, Kiekens P (2001). Thermoplastic pultrusion of natural fibre reinforced composites. Compos Struct, vol.54(2–3): pp.355–60.

10.     Mohanty AK, Misra M, Drzal LT (2001). Surface modifications of natural fibers and performance of the resulting biocomposites: an overview. Compos Interfaces, vol.8(5): pp. 313–43.

11.     Gassan J, Chate A, Bledzki AK. (2001) Calculation of elastic properties of natural fibers. J Mater Sci vol..36(15): pp.3715–20.

12.     Eichhorn SJ, Baillie CA, Zafeiropoulos N, Mwaikambo LY, Ansell MP, Dufresne A, (2001). Current international research into cellulosic fibres and composites. J Mater Sci vol.36(9): pp. 2107–31.

13.     Steeves C.A. and Fleck N. A.  (2004) Collapse mechanisms of sandwich beams with composite faces and a foam core, loaded in three-point bending. Part I; analytical models and minimum weight design. International Journal of Mechanical Sciences, Vol.46, pp. 561-583.




Shyama M, P.Swaminathan

Paper Title:

Digital Linear and Nonlinear Controllers for Buck Converter

Abstract:    Both linear PID controllers and fuzzy controllers are designed and implemented for a buck converter. Comparison between the two controllers is made in the aspect of design, implementation and experimental results. Design of fuzzy controllers is based on heuristic knowledge of the converter and tuned using trial and error, while the design of linear PID and PI controllers is based on the frequency response of the buck converter. Implementation of linear controllers is quite straightforward, while implementation of fuzzy controllers has its unique issues. A comparison of experimental results indicates that the performance of the fuzzy controller is superior to that of  the linear PID and PI controllers. The fuzzy controller is able to achieve faster transient response, has more stable steady-state response, and is more robust under different operating points.

   DC-DC Converter, Buck Converter,PID controller, Fuzzy logic controller


1.       A. Prodic and D. Maksimovic, “Design of a digital PID regulator based on look-up tables for control of high-frequency dc–dc converters,” in Proc. IEEE Workshop Comput. Power Electron. , Jun. 2002, pp. 18–22..
2.       Y. Duan and H. Jin, “Digital controller design for switchmode power con-verters,” in Proc. 14th Annu. Appl. Power Electron. Conf. Expo., Dallas,TX, Mar. 14–18, 1999, vol. 2, pp. 967–973.

3.       R.P.SevernsandG.E.Bloom, Modern DC-to-DC Switchmode Power Converter Circuits. New York: Van Nostrand Reinhold, 1985.

4.       ] K.M.PassinoandS.Yurkovich, Fuzzy Control. Reading, MA: Addison-Wesley, 1997.

5.       A. Gad and M. Farooq, “Application of fuzzy logic in engineering prob-lems,” in Proc. 27th Annu. Conf. IEEE Ind. Electron. Soc., Denver,CO,Nov. 29–Dec. 2, 2001, vol. 3, pp. 2044–2049.

6.       S. Sanchez-Solano, A. J. Cabrera, I. Baturone, F. J. Moreno-Velo, andM. Brox, “FPGA implementation of embedded fuzzy controllers for ro-botic applications,” IEEE Trans. Ind. Electron. , vol. 54, no. 4, pp. 1937–1945, Aug. 2007.

7.       S. Chakraborty, M. D. Weiss, and M. G. Simões, “Distributed intelligent energy management system for a single-phase high-frequency ac micro-grid,” IEEE Trans. Ind. Electron. , vol. 54, no. 1, pp. 97–109, Feb. 2007.

8.       G. O. Cimuca, C. Saudemont, B. Robyns, and M. M. Radulescu, “Control and performance evaluation of a flywheel energy-storage system asso-ciated to a variable-speed wind generator,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1074–1085, Aug. 2006.

9.       M. Cheng, Q. Sun, and E. Zhou, “New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive,” IEEE Trans. Ind.Electron. , vol. 53, no. 3, pp. 814–821, Jun. 2006.

10.     R.-J. Wai and K.-H. Su, “Adaptive enhanced fuzzy sliding-mode control for electrical servo drive,” IEEE Trans. Ind. Electron., vol. 53, no. 2,pp. 569–580, Apr. 2006.

11.     P. Mattavelli, L. Rossetto, G. Spiazzi, and P. Tenti, “General-purpose fuzzy controller for dc–dc converters,” IEEE Trans. Power Electron.,vol. 12, no. 1, pp. 79–86, Jan. 1997.

12.     W.-C. So, C. K. Tse, and Y.-S. Lee, “Development of a fuzzy logic controller for dc–dc converters: Design, computer simulation, and exper-imental evaluation,” IEEE Trans. Power Electron. , vol. 11, no. 1, pp. 24–32, Jan. 1996.

13.     C. Cecati, A. Dell’Aquila, A. Lecci, and M. Liserre, “Implementation issues of a fuzzy-logic-based three-phase active rectifier employing only voltage sensors,” IEEE Trans. Ind. Electron. , vol. 52, no. 2, pp. 378–385 Apr. 2005.

14.     A. G. Perry, G. Feng, Y.-F. Liu, and P. C. Sen, “Design method for PI-like fuzzy logic controllers for dc–dc converter,” IEEE Trans. Ind. Electron. ,vol. 54, no. 5, pp. 2688–2695, Oct. 2007.

15.     Y. Shi and P. C. Sen, “Application of variable structure fuzzy logic controller for dc–dc converters,” in Proc. 27th Annu. Conf. IEEE Ind.Electron. Soc., Denver, CO, Nov. 29–Dec. 2, 2001, vol. 3, pp. 2026–2031.

16.     L. Guo, J. Y. Hung, and R. M. Nelms, “Design and implementa-tion of sliding mode fuzzy controllers for buck converters,” in Proc. IEEE Int. Symp. Ind. Electron. , Montreal, QC, Canada, Jul. 10, 2006,pp. 1081–1087.

17.     K. Viswanathan, R. Oruganti, and D. Srinivasan, “Nonlinear function con-troller: A simple alternative to fuzzy logic controller for a power electronic converter,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1439–1448, Oct. 2005.

18.     R. W. Erickson and D. Maksimovic, Fundamentals of Power Electronics.Norwell, MA: Kluwer, 2001.

19.     L. Guo, R. M. Nelms, and J. Y. Hung, “Comparative evaluation of linear PID and fuzzy control for a boost converter,” in Proc. 31st Annu. Conf. IEEE Ind. Electron. Soc. , Raleigh, NC, Nov. 2005, pp. 555–560.




U.L.Sindhu, V.Sindhu, P.S.Balamurugan

Paper Title:

Privacy Aware Monitoring Framework For Moving Top-K Spatial Join Queries

Abstract:    In moving object environment, it’s unfeasible for database to track the random object movement and to store the locations of object exactly all the times. The basic issue in case of moving object monitoring is efficiency and privacy. We used a framework for moving object to hide their own identities by execution of probabilistic range monitoring queries. The Privacy-aware monitoring framework for spatial join queries which is flexible, it addresses two issues; such as “efficiency and privacy” in monitoring moving object. Because of blurring exact position of object and increase in unnecessary updates costs it’s not possible to provide accurate result. So, we propose an efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial query processing for the problem of privacy aware monitoring framework. This develop an efficient query processing, evaluation and reevaluation based on spatial queries which could be effective for computing safe zones that guarantee correct results until the user remains in safe zone, the reported results will be valid and no limiting of frequent updates from objects. The Voronoi Cell Optimization technique which accelerates depth sorting by clustering polygon has been implemented. Our solution is common for moving queries employ safe zones. In our performance study, we compare it with an existing approach using simulation. Our proposed approach outperforms than the conventional approaches without compromising much on the concept of safe zone to save computation and communication costs.

   Nearest-neighbor queries; probabilistic queries; range queries; spatial databases


1.        Beresford, A.; Stajano, F. (2003): Location  Privacy in Pervasive Computing, IEEE Pervasive Computing, vol. 2, no. 1, pp. 46-55.
2.        Cai, Y.; Hua K.A,; Cao, G. (2004):  Processing Range-Monitoring Queries on Heterogeneous Mobile Objects, Proc. IEEE Int’l Conf. Mobile  Data Management (MDM),.

3.        Chen, J.; Cheng, R. (2007): Efficient Evaluation  Of Imprecise Location- Dependent Queries, Proc. IEEE Int’l Conf. Data Eng. (ICDE), pp. 586-595.

4.        Cong, G.; Jensen, C. S; Wu, D. (2009): Efficient retrieval of the top-k most relevant spatial web objects, in PVLDB, pp. 337–348.

5.        Gedik B.; Liu, L. (2005): Location Privacy in Mobile Systems: A Personalized Anonymization Model, Proc. IEEE Int’l Conf. Distributed Computing Systems (ICDCS), pp. 620-629.                                    

6.        Gedik B.; Liu, L. (2008): Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms, IEEE Trans. Mobile Computing, vol. 7, no. 1, and

7.        Hu, H; X Xu, J.; Lee, D.L. (2005): A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects, Proc. ACM SIGMOD, pp. 479-490.




Swagatika Devi

Paper Title:

K-ANONYMITY: The History of an IDEA

Abstract:    Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. In this paper, we discuss the concept of k-anonymity, from its original proposal illustrating its enforcement via generalization and suppression. We also discuss different ways in which generalization and suppressions can be applied to satisfy k- anonymity. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery. We provide an overview of the different techniques and how they relate to one another. The individual topics will be covered in sufficient detail to provide the reader with a good reference point. The idea is to provide an overview of the field for a new reader from the perspective of the data mining community.

   K-Anonymity, Generalization, Suppression, Pattern discovery.


1.       W. E. Winkler. Advanced Methods for Record Linkage, Proceedings of the Section on Survey Research Methods, American Statistical Society, 467-472.
2.       R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proceedings of the 2000 ACM SIGMOD on Management of Data.

3.       P. Samarati. Protecting respondents’ identities in micro data release. IEEE Transactions on Knowledge and Data Engineering, 13(6):1010-1027. 2001.

4.       V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis. State-of-the-art in privacy preserving data mining. SIGMOD Rec., 33(1):50.57, 2004.

5.       T. M. Truta, A. Campan and P. Meyer. Generating Micro data with p-sensitive k-anonymity Property. SDM 2007: 124-141

6.       A.Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-Diversity: Privacy beyond k-anonymity. In ICDE, 2006.

7.       D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the twentieth ACM PODS, 2001.

8.       P. Samarati: Protecting Respondents’ Identities in Microdata Release. IEEE Trans. Knowl. Data Eng. 13(6): 1010-1027 (2001).

9.       G. Aggarwal, T. Feder ,K. Kenthapadi,R. Motwani, R. Panigrahy, D. Thomas , A. Zhu : Anonymizing Tables. ICDT Conference, 2005.

10.     C. Bettini , Wang XS, S. Jajodia  (2005). Protecting privacy against location- based personal identification. In Proc. of the Secure Data Management, Trondheim, Norway.

11.     V.S. Iyengar : Transforming Data to Satisfy Privacy Constraints. KDD Conference, 2002.

12.     D. Hand, H. Mannila, and P. Smyh. Principles of Data Mining. The MIT Press, 2001.

13.     M. Kantarcioglu, J. Jin, and C. Clifton. When do data mining results violate privacy? In Proceedings of the tenth ACM SIGKDD, 2004.

14.     G. Aggarwal, T.Feder ,K. Kenthapadi,R. Motwan, R. Panigrahy, D. Thomas ,A. Zhu: Approximation Algorithms for k-anonymity. Journal of Privacy Technology, paper 20051120001, 2005.

15.     K. Wang , B.C.M. Fung , G.Dong : Integarting Private Databases for Data Analysis. Lecture Notes in Computer Science, 3495, 2005.

16.     S. Zhong S., Z. Yang , R. Wright : Privacy-enhancing k-anonymization of customer data, In Proceedings of the ACM SIGMOD-SIGACT-SIGART Principles of Database Systems, Baltimore, MD. 2005.




V.Sindhu, U.L.Sindhu, P.S.Balamurugan

Paper Title:

Efficient and Dynamic Behaviour of Continuous Query in Unstructure Overlay Network

Abstract:  The main objective of the peer to peer content distribution systems are to register for a long term presence in a network and to publish its own data to that network. These requirements can be done by having some set of indexing and routing techniques. For this solution, a sequence of approaches has been already proposed by the existing researchers. But these approaches are not flexible for these systems and too complex. In the unstructured p2p system it uses to retrieve the data if it matches. Also, certain limitations are obtained. In order to solve this problem, we propose an approach of continuous query in unstructured overlay network with consistency maintenance. In peer-to-peer, consistency maintenance is widely used techniques for high system performance. This approach is to support the continuous queries in unstructured overlay networks. It achieves high efficiency and consistency maintenance at a significantly low cost. Simulation results demonstrate the effectiveness of our proposed approach in comparison with other existing approaches.

   consistency maintenance, continuous query, peer to peer


1.        J. Chen, L. Ramaswamy, and A. Meka, “Message Diffusion in Unstructured Overlay Networks,” Proc. Sixth IEEE Int’l Symp . Network Computing and Applications (NCA),2007.
2.        Gnutella Home Page,, 2008.

3.        G. Xie , Z. Li, and Z. Li, “Efficient and Scalable Consistency Maintenance for Heterogeneous Peer-to-Peer Systems,” IEEE Trans. Parallel and Distributed Systems, vol. 19, no. 12, pp. 1695-1708, Dec. 2008.

4.        A. Muthitacharoen, B. Chen, and D.M. Eres, “A Low-Bandwidth Network File System,” Proc. ACM Symp. Operating Systems Principles (SOSP), pp. 174-18, 2001.

5.        rsync,, 2009

6.        R. Baldoni, C. Marchetti, A. Virgillito, and R. Vitenberg, “Content-Based Publish-Subscribe over Structured Overlay Networks, ”Proc. 25th IEEE Int’l Conf. Distributed Computing Systems (ICDCS),2005.

7.        E. Cohen and S. Shenker, “Replication Strategies in Unstructured Peer-to-Peer Networks,” Proc. ACM SIGCOMM ’02, Aug. 2002.

8.        Y. Chawathe, S. Ratnasamy , L. Breslau, N. Lanham, and S. Shenker, “Making Gnutella-Like P2P Systems Scalable,” Proc.ACM SIGCOMM ’03, 2003.

9.        Stoica, R. Morris, D. Karger, M.F. Kaashoek, and H. Balakrishnan, “Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications,” Proc. ACM SIGCOMM ’01, Aug. 2001.

10.     X. Chen, S. Ren, H. Wang, and X. Zhang, “SCOPE: Scalable Consistency Maintenance in Structured P2P Systems,” Proc.IEEEINFOCOM,2005




K.Thirumalai kannan, B.Senthil Kumar

Paper Title:

Heat Transfer and Fluid Flow Analysis in Plate-Fin and Tube Heat Exchangers with Different Shaped Vortex Generators

Abstract:    Numerical analyses were carried out to study the heat transfer and flow in the plate-fin and tube heat exchangers with different shaped vortex generators mounted behind the tubes. The eects of dierent span angles a (α = 30°, 45° and 60°) are investigated in detail for the Reynolds number ranging from 500 to 2500. Numerical simulation was performed by computational fluid dynamics of the heat transfer and fluid flow. The results indicated that the triangle shaped winglet is able to generate     longitudinal vortices and improve the heat transfer performance in the wake regions. The case of α = 45° provides the best heat transfer augmentation than rectangle shape winglet generator in case of inline tubes. Common flow up configuration causes significant separation delay, reduces form drag, and removes the zone of poor heat transfer from the near wake of the tubes.

   Vortex generator; Common flow up; Heat transfer enhancement; Plate-fin and tube heat exchanger.


1.        Chunhua Min , Chengying Qi, Xiangfei Kong, Jiangfeng Dong (2010)“Experimental study of rectangular channel with modified rectangular longitudinal vortex generators” International Journal of Heat and Mass Transfer 53 ,pp .3023–3029
2.        F.J. Edwards, G.J.R. Alker, The improvement of forces convection surface heat transfer using surfaces protrusions in the form of (A) cubes and (B) vortex generators, in Proceedings of the 5th International Conference on Heat Transfer, Tokyo, vol. 2, 1974, pp.244–248.

3.        P.A. Eibeck, J.K. Eaton, Heat transfer eects of a longitudinal vortex embedded in a turbulent boundary layer, ASME J. Heat Transfer 109 (1987)         37–57.

4.        W.R. Pauley, J.K. Eaton, Experimental study of the development of longitudinal vortex pairs embedded in a turbulent boundary layer, AIAA J. 26 (1988) 816–823.

5.        S.T. Tiggelbeck, N.K. Mitra, M. Fiebig, Experimental investigations of heat transfer enhancement and flow losses in a channel with double rows of longitudinal vortex generators, Int. J. Heat Mass Transfer 36 (1993)         2327- 2337.

6.        M. Fiebig, H. Guntermann, N.K. Mitra, Numerical analysis of heat  transfer and flow loss in a parallel plate heat exchanger element with longitudinal vortex generators as fins, ASME J. Heat Transfer 117 (1995) 1064–1067.

7.        G. Biswas, K. Torii, D. Fujii, K. Nishino, Numerical and experimental determination of flow structure and heat transfer eects of longitudinal vortices in channel flow, Int. J. Heat Mass Transfer 39 (1996) 3441–3451.

8.        M.C. Gentry, A.M. Jacobi, Heat transfer enhancement by delta-wing-generated tip vortices in flat-plate and developing channel flows, ASME J. Heat Transfer 124 (2002) 1158–1168.

9.        A. Sohankar, L. Davidson, Eect of inclined vortex generators on heat transfer enhancement in a three dimensional channel, Number. Heat Transfer, Part A 39 (2001) 433–448.

10.     M. Fiebig, A. Valencia, N.K. Mitra, Wing-type vortex generators for fin-and-tube heat exchangers, Exp. Therm. Fluid Sci. 7 (1993) 287–295.

11.     A. Valencia, M. Fiebig, N.K. Mitra, Heat transfer enhancement by longitudinal vortices in a fin-and-tube heat exchangers element with flat tubes, ASME J. Heat Transfer 118 (1996) 209–211.

12.     K. Torii, K.M. Kwak, K. Nishino, Heat transfer enhancement accompanying pressure-loss reduction with winglet type vortex generators for fin-tube heat exchangers, Int. J. Heat Mass Transfer 45 (2002) 3795–3801.

13.     C.C. Wang, J. Lo, Y.T. Lin, C.S. Wei, Flow visualization of annular and delta winglet vortex generators in fin-and tube heat exchanger application, Int. J. Heat Mass Transfer 45 (2002) 3803–3815.

14.     C.N. Lin, J.Y. Jang, Conjugate heat transfer and fluid flow analysis in fin-tube heat exchangers with wave-type vortex generators, J. Enhanc. Heat Transfer 9 (2002) 123–136. experiments, ASME, Mech. Eng.         75 (1953) 3–8..

15.     Jin-Sheng Leu , Ying-Hao Wu , Jiin-Yuh Jang , (2004) ,“Heat transfer and fluid flow analysis in plate-fin and tube heat exchangers with a pair of block shape vortex generators” International Journal of Heat and Mass Transfer        47  ,pp.4327–4338

16.     K. Torii, K.M. Kwak, K. Nishino(2002)  “Heat transfer enhancement accompanying pressure-loss reduction with winglet-type vortex generators for fin-tube heat exchangers” International Journal of Heat and Mass Transfer 45, pp.3795–3801 

17.     Jainender Dewatwal “Design of Compact Plate Fin Heat Exchanger”

18.     Yunus A.Cengel “Heat and Mass transfer”

19.     Yunus A.Cengel “Fluid mechanics”




K.M.Pandey, S.Chakraborty, K.Deb

Paper Title:

CFD Analysis of Flow through Compressor Cascade

Abstract:   This work aims at analyzing the flow behavior through a compressor cascade with the help of Computational Fluid Dynamics using the FLUENT software. An attempt has been made to study the effect of angle of attack or flow incidence angle on various flow parameters viz. static pressure, dynamic pressure, turbulence and their distribution in the flow field and predict the optimum range of angle of attack based on the above observations. Particularly, two principle parameters viz. the static pressure rise for the compressor cascade and the turbulence kinetic energy are considered in this analysis. It is observed that maintaining a slightly positive angle of flow incidence of +2 to +6 degrees is advantageous.

   Cascade, CFD, Total Pressure, Temperature Magnitude, Viscosity, Thermal Conductivity


1.       Li Qiushi, Wu Hong, and Zhou Sheng, “Application Of Tandem Cascade To Design Of Fan With Supersonic Flow”, Chinese Journal of Aeronautics, Elsevier; 23(2010):9-14
2.       F.Bakhtar and K.S So, “A Study Of Nucleating Flow Of Steam In A Cascade Of Supersonic Blading By The Time-Marching Method”, International Journal Of Heat & Fluid Flow, Vol. 12, No.1,Butterworth-Heinemann,1991.

3.       J. Delery & G. Meauze, “A Detailed Experimental Analysis Of The Flow In A Highly Loaded Fixed Compressor Cascade: The Iso-Cascade Co-Operative Programme On Code Validation”, Aerospace Science & Technology 7,Elsevier;2009:1-9.

4.       A.X. Lio and C.X. Lin, “Three BEM Schemes For The Calculating Of Subsonic Compressible Plane Cascade Flow”, Engineering Analysis with Boundary Elements 11; 1993 : 25-32.

5.       H. Forsching, “Aero-elastic stability of Cascades in Turbo-Machinery”, Prog. Aerospace Science, Vol. 30 Pergamon;1994: 213-216.

6.       B.T. Lebele Alawa, H.I. Hart, S.O.T. Ogaji, S.D. Probert, “Rotor Blades’ Profile Influence On A Gas Turbine’s Compressor Effectiveness”, Applied Energy 85 , Elsevier; 2008, 494-505.

7.       U. K. Saha, B. Roy, “Experimental Investigations on Tandem Compressor Cascade Performance at Low Speeds”, Experimental Thermal and Fluid Science, Elsevier; 1997; 14:263-276.




K.M.Pandey, Sushil Kumar, Jyoti Prakash Kalita

Paper Title:

Wall Static Pressure variation in sudden expansion in cylindrical ducts with cavities for supersonic flow for Mach 1.58 and 2.06: A Fuzzy Logic Approach

Abstract:    In this paper the analysis of wall static pressure variation has been done with fuzzy logic approach to have smooth flow in the duct. Here there are three area ratio chosen for the enlarged duct, 2.89, 6.00 and 10.00. The primary pressure ratio is taken as 2.65 and cavity aspect ratio is taken as 1 and 2. The study is analyzed for length to diameter ratio of 1,2,4 and 6. The nozzles used are De Laval type and with a Mach number of 1.74 and 2.23 and conical nozzles having Mach numbers of 1.58 and 2.06. The analysis based on fuzzy logic theory indicates that the length to diameter ratio of 1 is sufficient for smooth flow development if only the basis of wall static pressure variations is considered.

   air ratio, De Laval nozzle, Mach number, pressure ratio, wall static pressure.


1.        Wick, R.S., 1953, The effect of boundary layer on sonic flow through an abrupt cross sectional area change, Journal of the Aeronautical Sciences, Vol. 20, p. 675-682.
2.        Korst, H., 1954, ‘Comments on the effect of boundary layer on the sonic flow through an abrupt cross sectional area change’, Journal. of Aeronautical Sciences, Vol. 21, p. 568.

3.        Hall, W.B and Orme, E.M., 1955, ‘Flow of a compressible fluid through a sudden enlargement in a pipe, Proceedings of Institution of Mechanical Engineers’, Vol. 169, p. 007-1022.

4.        Benedict.R.P and Steltz, W.G., 1962,  ‘ A generalized approach to one dimensional gas dynamics’, Trans. ASME (power), Vol. 84, p. 44.

5.        Zadeh, L.A., 1965, “Fuzzy Sets,” Information Control.,Vol. 8. pp. 338-353.

6.        Anderson, J. S and Williams, T. T., 1968, ‘Base pressure and noise produced by the abrupt expansion of air in a cylindrical duct’, Journal of Mechanical Engineering
Science, Vol. 10, No. (3), p. 262-268.

7.        Zadeh, L.A., 1973, “Outline of a new approach to the analysis of complex systems and decision Processes”, Information Science, Vol. 9, pp.43-80. 

8.        Durst.F., Melling, A and Whitelaw, J.J., 1974, ‘ Low Reynolds number flow over a plane symmetric sudden expansion’, Journal of Fluid mechanics, Vol. 12, No.6, p.739.

9.        Cherdon, W., Durst, F. and Whitelaw, J.H., 1978, ‘Asymmetric flows and instabilities in symmetric ducts with sudden expansions’, Journal of Fluid Mechanics, Vol. 84, part 1, p.13.

10.     Brady, J.F. and Acrivos, A., 1982, ‘Closed cavity laminar flows at a moderate Reynolds numbers’, Journal of Fluid Mechanics, Vol. 115, p. 427.

11.     Yang, B.T and Yu, M. H., 1983, The flow field in a suddenly enlarged combustion chamber, AIAA Journal, Vol. 21, No.1, p. 92-97.

12.     Raghunathan S and Mabey, D. G., 1987, Passive shockwave/boundary-layer control on a wall mounted model, AIAA Journal, Vol. 25, No. 2, p. 275-278.

13.     Raghunathan, S., 1987,b Pressure fluctuation measurements with passive shock/boundary layer control, AIAA journal, Vol. 25, No. 5, p. 626-628 .

14.     Raghunathan, S., 1987, Effect of porosity strength on passive shockwave/boundary layer control, AIAA journal, Vol. 25, No. 5, p. 757-758. Wilcox, J.F. Jr., 1988, Passive venting system for modifying cavity flow fields at supersonic speeds, AIAA Journal, Vol. 26, No. 3, p. 374-376.

15.     Tanner, M., Base cavities at angles of incidence, 1988, AIAA Journal, Vol. 26, No. 3, p. 374-377.

16.     Rathakrishnan, E., Ramanaraju, O. V., and Padmanabhan, K., 1989, Influence of cavities on

17.     Suddenly expanded flow field, Mechanics Research communications, Vol. 16 (3), p. 139-146.

18.     Vishwanath   P.R.  and Patil, S.R., 1990, ‘Effectiveness of passive devices for axi symmetric base drag reduction at Mach 2’, Journal of Spacecraft, p. 234, (May-June).

19.     Kruiswyk, R.W and Dutton. J. C., 1990, ‘Effect of base cavity on subsonic near wake flow’, AIAA Journal, Vol. 28, No. 11, p.1885-1895.

20.     Pandey,K.M., 1994, Studies on flow through nozzles with sudden expansion, Ph.D. thesis, Department of Mechanical Engineering, IIT Kanpur.

21.     Pandey K M, 1995, Studies on flow through nozzles in sudden expansion with passive control, International conference at IISc, Bangalore on recent advances in Mechanical Engineering, Dec 20-22, pp 1511-1518, Narosa publications, New Delhi, India, 1995, Volume 2 .

22.     Pandey K M, 1996, Base flow in flow though nozzles with sudden expansion:  a study in supersonic regime-2nd National Conference on Fluid Machinery, Dept of Mechanical Engg, PSG College of Technology, Coimbatore, India, June 28-29, pp 256-265, Allied publishers Ltd. New Delhi, India.

23.     Pandey K M, 1997,  Pressure loss in flow through nozzles with sudden expansion:  a comparison between supersonic and subsonic flow regime, International Conference on Advances in Mechanical and Industrial Engg, Dept of Mechanical and Industrial Engg., Roorkee, India, Feb 6-8, pp 417-426, Ajay printers and publishers, Roorkee, India, Volume 2 .

24.     Pandey K M , S A Khan and Rathakrishnan E, 2000, Passive control of base flows, Proceedings of the National Seminar on Recent Advances in Experimental Mechanics, Dept of Aerospace Engg, IIT Kanpur, Mar 15-16, , Allied Publications Ltd, New Delhi, India 2000 PP-57-71.

25.     Dixit, U.S., Dixit, P.M., 2000, ‘Application of fuzzy set theory in the scheduling of a Tandem cold-Rolling Mill’, ASME, Vol. 122, p. 494- 500.

26.     Rathakrishnan E., 2001, ‘Effect of ribs on suddenly expanded flows’, AIAA Journal, Vol. 39, No. 7, p.1402-1404 .

27.     Dixit, U.S., Robi, P.S., Sarma, D.K., 2002, ‘A systematic procedure for the design of a cold rolling mill’, Material Processing Technology , Vol. 121,  p. 69- 76 .

28.     Abburi, N.R., Dixit, U.S., 2006, ‘A knowledge-based system for the prediction of surface roughness in turning process’, Robotics and Computer-Integrated manufacturing, Vol. 22, p. 363- 372 .

29.     Pandey, K.M., and Rathakrishnan E., 2006, ‘Influence of cavities on flow development in sudden expansion’, International Journal of Turbo and Jet Engines, Vol. 23, p. 97- 112.

30.     Pandey, K.M., and Rathakrishnan E, 2006, ‘Annular cavities for Base flow control’, International Journal of Turbo and Jet Engines, Vol. 23, p. 113- 127.

31.     R. Jagannath, N.G.Naresh and K.M.Pandey, 2007 ‘Studies on Pressure loss in sudden expansion in flow through nozzles: A Fuzzy Logic Approach’, ARPN Journal of Engineering and Applied Sciences, Vol 2, No.2, p. 50 –61, April.




 Wiqas Ghai, Navdeep Singh

Paper Title:

Analysis of Automatic Speech Recognition Systems for Indo-Aryan Languages: Punjabi A Case Study

Abstract:   Punjabi, Hindi, Marathi, Gujarati, Sindhi, Bengali, Nepali, Sinhala, Oriya, Assamese, Urdu are prominent members of the family of Indo-Aryan languages. These languages are mainly spoken in India, Pakistan, Bangladesh, Nepal, Sri Lanka and Maldive Islands. All these languages contain huge diversity of phonetic content. In the last two decades, few researchers have worked for the development of Automatic Speech Recognition Systems for most of these languages in such a way that development of this technology can reach at par with the research work which has been done and is being done for the different languages in the rest of the world. Punjabi is the 10th most widely spoken language in the world for which no considerable work has been done in this area of automatic speech recognition. Being a member of Indo-Aryan languages family and a language rich in literature, Punjabi language deserves attention in this highly growing field of Automatic speech recognition. In this paper, the efforts made by various researchers to develop automatic speech recognition systems for most of the Indo-Aryan languages, have been analysed and then their applicability to Punjabi language has been discussed so that a concrete work can be initiated for Punjabi language.

   Maximum likelihood linear regression, Learning vector quantization, Multi layer perceptron, Cooperative heterogeneous artificial neural network.


1.       Sarma, M. P.; Sarma, K. K., “Assamese Numeral Speech Recognition using Multiple Features and Cooperative LVQ – Architectures”,  International Journal of Electrical and Electronics 5:1, 2011.
2.       Sarma, M.; Dutta, K.; Sarma, K. K., “Assamese Numeral Corpus for Speech Recognition using Cooperative ANN Architecture”, International Journal of Electrical and Electronics Engineering 3:8 2009.

3.       Chowdhury, S. A., “Implementation of Speech Recognition System for Bangla”, BRAC University, DHAKA, Bangladesh, August 2010.

4.       Hasnat, M. A., Molwa, J.,Khan, M., “Isolated and Continuous Bangla Speech Recognition: Implementation, Performance and application perspective”,2007.

5.       Samudravijaya, K., “Computer Recognition of Spoken Hindi”. Proceeding of International Conference of Speech, Music and Allied Signal Processing, Triruvananthapuram, pages 8-13, 2000.

6.       Kumar, K.; Aggarwal, R.K., “Hindi Speech Recognition System Using HTK”, International Journal of Computing and Business Research, ISSN (Online): 2229-6166, Volume 2 Issue 2, May 2011.

7.       Aggarwal, R.K. and Dave, M., “Using Gaussian Mixtures for Hindi Speech Recognition System”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No. 4, December 2011.

8.       Sivaraman. G.; Samudravijaya, K., “Hindi Speech Recognition and Online Speaker Adaptation”, International Conference on Technology Systems and
Management: ICTSM-2011, IJCA.

9.       Gawali, Bharti W.,  Gaikwad, S., Yannawar, P., Mehrotra Suresh C., “Marathi Isolated Word Recognition System using MFCC and DTW Features (2010)”, Int. Conf. on Advances in Computer Science 2010, DOI: 02.ACS.2010. 01.73.

10.     Gaikwad, S.; Gawali, B.; Mehrotra, S. C.; “POLLY CLINIC INQUIRY SYSTEM USING IVR IN MARATHI LANGUAGE”, International Journal of Machine Intelligence, ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 3, 2011, pp-142-145.

11.     Mohanty, S.; Swain, B. K., “Continuous Oriya Digit Recognition using Bakis Model of HMM”,  International Journal of Computer Information Systems, Vol. 2, No. 1, 2011.

12.     Mohanty, S.; Swain, B. K., “Markov Model Based Oriya Isolated Speech Recognizer-An Emerging Solution for Visually Impaired Students in School and Public Examination”, Special Issue of IJCCT Vol. 2 Issue 2, 3, 4; International Conference On Communication Technology-2010.

13.     Nadungodage, T.; Weerasinghe, R., “Continuous Sinhala Speech Recognizer”, Conference on Human Language Technology for Development, Alexandria, Egypt, May 2011.

14.     Raza, A., Hussain, S., Sarfraz, H., Ullah, I., and Sarfraz, Z., “An ASR System for Spontaneous Urdu Speech”, Proceedings of O-COCOSDA’09 and IEEE Xplore, 2009.

15.     Sarfraz, H.; Hussain, S.; Bokhari, R.; Raza, A. A.; Ullah, I.; Sarfraz, Z.; Pervez, S.; Mustafa, A.; Javed, I.; Parveen, R., “Large Vocabulary Continuous Speech Recognition for Urdu”, International Conference on Frontiers of Information Technology, Islamabad, 2010.

16.     Kumar, R., Singh, C., Kaushik, S., “Isolated and Connected Word Recognition for Punjabi Language using Acoustic Template Matching Technique”, 2004.

17.     Kumar, R., “Comparison of HMM and DTW for Isolated Word Recognition System for Punjabi Language”, International Journal of Soft Computing 5(3):88-92, 2010.




R. Kandiban, R. Arulmozhiyal

Paper Title:

Design of Adaptive Fuzzy PID Controller for Speed control of BLDC Motor

Abstract:    Brushless DC motors (BLDCM) are widely used for many industrial applications because of their high efficiency, high torque and low volume. This paper proposed an improved Adaptive Fuzzy PID controller to control speed of BLDCM. This paper provides an overview of performance conventional PID controller, Fuzzy PID controller and Adaptive Fuzzy PID controller. It is difficult to tune the parameters and get satisfied control characteristics by using normal conventional PID controller. As the Adaptive Fuzzy has the ability to satisfied control characteristics and it is easy for computing. The experimental results verify that a Adaptive Fuzzy PID controller has better control performance than the both Fuzzy PID controller and conventional PID controller. The modeling, control and simulation of the BLDC motor have been done using the software package MATLAB/SIMULINK.

   Brushless DC (BLDC) motors, proportional integral derivative (PID) controller, Fuzzy PID controller, Adaptive Fuzzy PID controller.


1.       J.E Miller, "Brushless permanent-magnet motor drives," Power Engineering Joumal,voI.2, no. 1 , Jan. 1988.
2.       Q.D.Guo,X.MZhao. BLDC motor principle and technology application [M]. Beijing: China electricity press,2008.

3.       Uzair Ansari, Saqib Alam, Syed Minhaj un Nabi Jafri, ”Modeling and Control of Three Phase BLDC Motor using PID with Genetic Algorithm”, 2011 UKSim 13th International Conference on Modelling and Simulation,pp.189-194.             

4.       K. Ang, G. Chong, and Y. Li, “PID control system analysis, design and technology,” IEEE Trans.Control System Technology, vol. 13, pp. 559-576, July 2005.

5.       Atef Saleh Othman Al-Mashakbeh,“ Proportional Integral and Derivative Control of Brushless DC Motor”, European Journal of Scientific Research 26-28 July 2009, vol. 35, pg 198-203.

6.       Chuen Chien Lee, “Fuzzy Logic in Control Systems:Fuzzy Logic  controller–Part 1” 1990 IEEE.

7.       Chuen Chien Lee, “Fuzzy Logic in Control Systems : Fuzzy Logic controller Part 2” 1990 IEEE.

8.       Zdenko Kovaccic and Stjepan Bogdan, “Fuzzy Controller design Theory and Applications”, ©  2006  by  Taylor  &  Francis  Group. international, 2002.

9.       R.Arulmozhiyal and K.Baskaran, “Implementation of Fuzzy PI Controller for Speed Control of  Induction Motor Using FPGA”, Journal of Power Electronics, Vol.10, No.1, Jan 2010, pp.65-71




Shreya Jain, Samta Gajbhiye

Paper Title:

Comparing and Selecting Appropriate Measuring Parameters for K-means Clustering Technique

Abstract:    Clustering is a powerful technique for large scale topic discovery from text. It involves two phases: first, feature extraction maps each document or record to a point in a high dimensional space, then clustering algorithms automatically group the points into a hierarchy of clusters. Hence to improve the efficiency & accuracy of mining task on high dimensional data the data must be pre-processed by an efficient dimensionality reduction method. Recently cluster analysis is popularly used data analysis method in number of areas. K-Means is one of the well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroids. In this paper, a certain k-means algorithm for clustering the data sets is used and the algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. Also in this paper, we deal with the analysis of different sets of k-values for better performance of the k-means clustering algorithm.

   Data Mining, Text Mining, Clustering, K-Means Clustering, Silhouette plot.


1.        Vishal Gupta & Gurpreet S.Lehal ,”A Survey of Text Mining Techniques & Application “ ,Journal of Emerging Technologies in Web Intelligence ,Aug 2009.
2.        Atika Mustafa, Ali Akbar, and Ahmer Sultan , ” Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization”,   International Journal of Multimedia and Ubiquitous Engineering , Vol. 4, No. 2, April, 2009 .

3.        Raymond J.Mooney & Razvan Bunescu ,  “Mining Knowledge from Text using Information Extraction “.

4.        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, JULY 2002.

5.        Bjornar Larsen and Chinatsu Aone ,” Fast and Effective Text Mining Using Linear-time Document Clustering”,SRA International, 2000.

6.        ZHEXUE HUANG , “Extensions to the k-Means Algorithm for Clustering  Large Data Sets with Categorical Values “ ,Kluwer Academic Publishers ,1998.

7.        Rajashree Dash, Debahuti Mishra, Amiya Kumar Rath, Milu Acharya ,” A hybridized K-means clustering approach for high dimensional dataset” , International Journal of Engineering, Science and Technology, Vol. 66 2, No. 2, 2010, pp. 59-66.

8.        Charles Elkan , “ Using the Triangle Inequality to accelerate k- Means” , Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003 .




Vimala.C, V.Radha

Paper Title:

A Family of Spectral Subtraction Algorithms for Tamil Speech Enhancement

Abstract:    Speech enhancement aims to improve the speech quality by using various techniques and algorithms. Over the past several years there has been considerable attention focused on the enhancement of speech degraded by several types of noise. The degradation of speech due to the presence of noise causes severe difficulties in various communication environments. Noise suppression has numerous applications like Human Computer Interaction, hands-free communications, Voice over IP (VoIP), hearing aids, teleconferencing system etc. For this issue there is always a unique need for the technique which offers expected outcome with limited complexity in implementation. Hence, in this paper a family of spectral subtraction techniques is employed for Tamil speech noise cancellation due to its simplicity. The algorithms adopted for this research work are namely basic spectral subtraction, Non linear Spectral Subtraction, MultiBand Spectral Subtraction, Minimum Mean Square Error (MMSE), and Log Spectral MMSE. All these algorithms are analyzed and implemented for two types of noises namely white and babble noise. The performances of these algorithms are estimated based on SNR and MSE measures. Based on the experimental results, the Non linear spectral subtraction algorithm provides better results than any other adopted algorithms.

   Speech enhancement, Tamil Speech, Spectral Subtraction, Non linear Spectral Subtraction, MMSE, Log Spectral MMSE, SNR and MSE.


1.        Anuradha R. Fukane, Shashikant L. Sahare, “Different Approaches of Spectral Subtraction method for Enhancing the Speech Signal in Noisy Environments”, International Journal of Scientific & Engineering Research, Volume 2, Issue 5, May-2011,ISSN 2229-5518.
2.        M. Berouti, R. Schwartz, and J. Makhoul, Enhancement of speech corrupted by acoustic noise,Proc. IEEE ICASSP , Washington DC, April 1979, 208-211.

3.        S.F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoust., Speech, Signal Process., vol.27, pp. 113-120, Apr. 1979

4.        Ekaterina Verteletskaya, Boris Simak, “Noise Reduction Based on Modified Spectral Subtraction Method”, IAENG International Journal of Computer Science, 38:1, IJCS_38_1_10.