Volume-4 Issue-1

Download Abstract Book

S. No

Volume-4 Issue-1, March 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Sulekha Gope, Sujit Das

Paper Title:

Fuzzy Dominance Matrix and its Application in Decision Making Problems

Abstract: This paper introduces the concept of fuzzy dominance matrix (FDM) for solving multiple attribute decision making (MADM) problems. During decision making process, dominance of one expert over others plays an important role to find out the optimal alternative(s). In uncertain decision making problems, often dominances are expressed using linguistic variables which can be represented by fuzzy dominance degree. We have proposed an algorithmic approach to solve multiple attribute decision making problems using FDM. Finally the proposed algorithm is illustrated using a numerical example.

Keywords: Fuzzy Set, Fuzzy Dominance Matrix, Fuzzy Decision Matrix, Multiple Attribute Decision Making.

References:

1. R. Harris, “Introduction to Decision Making,” VirtualSal, http:// www.virtualsalt.com/crebook5.htm, accessed December 30, 2013.
2. F. Herrera, S. Alonso, F. Chiclana, and E. Herrera-Viedma, “Computing with words in decision making: foundations, trends and prospects,” Fuzzy Optimization and Decision Making, vol. 8, no. 4, pp. 337–364, 2009.
3. W. Pedrycz, P. Ekel, and R. Parreiras, “Fuzzy Multicriteria Decision-Making: Models, Methods and Applications,” John Wiley and Sons, Chicester, 2011.
4. J. Liu, L. Martinez, H. Wang, R.M. Rodriquez, and V. Novozhilov, “Computing with words in risk assessment,” International Journal of Computational Intelligence Systems, vol. 3, no. 4, pp. 396–419, 2010.
5. R. R. Yager, “Aggregation of ordinal information,” Fuzzy Optimization and Decision Making, vol. 6, no. 3, pp. 199–219, 2007.
6. Y. H. Chen, T. C. Wang, and C.Y. Wu, “Multi-criteria decision making with fuzzy linguistic preference relations,” Applied Mathematical Modelling, vol. 35, no. 3, pp. 1322–1330, 2011.
7. M. Roubens, “Fuzzy sets and decision analysis,” Fuzzy Sets and Systems, vol. 90, no. 2, pp. 199–206, 1997.
8. F. Herrera and E. Herrera-Viedma, “Linguistic decision analysis: steps for solving decision problems under linguistic information,” Fuzzy Sets and Systems, vol. 115, no. 1, pp. 67–82, 2000.
9. F. Herrera, E. Herrera-Viedma, and F. Chiclana, “Multiperson decision-making based on multiplicative preference relations,” European Journal of Operational Research, vol. 129, no. 2, pp. 372–385, 2001.
10. S. Das, M. B. Kar, and S. Kar, “Group Multi Criteria Decision Making using Intuitionistic Multi Fuzzy Sets,” Journal of Uncertainty Analysis and Applications, 1:10, Springer, 2013, doi:10.1186/2195-5468-1-10.
11. S. Das, S. Karmakar, T. Pal, and S. Kar, “Decision Making with Geometric Aggregation Operators based on Intuitionistic Fuzzy Sets,” in Proc. of 2nd International Conference of Business and Information Management (ICBIM 2014), Durgapur, January 9-11, 2014, (in press).
12. S. Das and S. Kar, “The Hesitant Fuzzy Soft Set and its Application in Decision Making,” in Proc. of International Conference on Facets of Uncertainties and Applications (ICFUA 2013), Kolkata, December 7-9, 2013, (in press).
13. S. Das and S. Kar, “Intuitionistic Multi Fuzzy Soft Set and its Application in Decision Making,” Fifth International Conference on Pattern Recognition and Machine Intelligence (PReMI), Kolkata, December 10-14, 2013, P. Maji et al. (Eds.): Lecture Notes in Computer Science (LNCS), 8251, Springer, pp. 587–592.
14. Z. Xu, “Group decision making based on multiple types of linguistic preference relations,” Information Sciences, vol. 178, no. 2, pp. 452–467, 2008.
15. R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decision making,” IEEE Transactions on Systems, Man and Cybernetics, vol. 18, no. 1, pp. 183–190, 1988.
16. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
17. K. Atanassov, “Intuitionistic Fuzzy Sets,” Springer Physica-Verlag, Heidelberg, 1999.
18. K. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, pp. 87- 96, 1986

1-4

http://blueeyesintelligence.org/2checkout_download.html

2.

Authors:

Amandeep Kaur, Aayushi

Paper Title:

Image Segmentation Using Watershed Transform

Abstract: Image segmentation is one of the most important categories of image processing. The purpose of image segmentation is to divide an original image into homogeneous regions. It can be applied as a pre-processing stage for other image processing methods. There exist several approaches for image segmentation methods for image processing. The watersheds transformation is studied in this report as a particular method of a region-based approach to the segmentation of an image. First, the basic tool, the watershed transform is defined. It has been shown that it can be implemented by applying flooding process on grey tone image. This flooding process can be performed by using basic morphological operations. The complete transformation incorporates a pre-processing and post-processing stage that deals with embedded problems such as edge ambiguity and the output of a large number of regions. Watershed Transform can be applied to gray scale images, textural images and binary images. The watershed transform has been widely used in many fields of image processing, including medical image segmentation.

Keywords: Flooding, Gradiant, Segmentation, Watershed Transform,.

References:
1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Prentice-Hall, 2002, pp. 617-620.
2. Jackway PT. “Gradient watersheds in morphological scale space”,1999, IEEE Trans Image Proc 5:913-21
3. Jia Wang, Hanqing Lu, Gerard Eude, Qingshan Liu, “A Fast Region Merging Algorithm For Watershed Segmentation”,2004,pp.781-784.
4. Qing Chen, Xiaoli Yang, Emil M. Petri. “Watershed Segmentation for Binary Images with Different Distance Transforms”,2006, pp.111-116
5. Nagaraja Rao, Dr. V. Vijay Kumar, C. Nagaraju. “A New Segmentation Method Using Watersheds on grey level images”,2006, pp.275-278.
6. Marco A.G. de Carvalho a, Roberto de A. Lotufo a, Michel Couprie b. “Morphological segmentation of yeast by image analysis” ,Image and Vision Computing ,vol.25,2007,pp. 34–39.
7. N. Lu, X. Z. Ke “A Segmentation Method Based on Gray-Scale Morphological Filter and Watershed Algorithm for Touching Objects Image”, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).
8. H.P. Ng, S. Huang, S.H. Ong, K.W.C. Foong, P.S. Goh, W.L. Nowinski, “Medical Image Segmentation Using Watershed Segmentation with Texture-Based Region Merging”, 2008,pp. 4039-4042
9. Yurong Ge,Wen jhang,Guangrog ji.2009 “ Adaptive Algae Image Segmentation Based on Morphological Watersheds”, IEEE ,2009.
10. Chen Wei-bin, Zhang Xin, “A New Watershed Algorithm for Cellular Image Segmentation based on Mathematical Morphology”, IEEE:International Conference on Machine Vision and Human-machine Interface,2010.
11. Ms. S.Malathi , Ms. S.Uma

5-8

http://blueeyesintelligence.org/2checkout_download.html

3.

Authors:

Mohammed Hussein Baqir

Paper Title:

Generation of PWM Inverter Based on a Microcontroller at Feeding Induction Motor

Abstract: The steps of process of generated PWM are implemented by using a microcontroller 89c51 and using personal computer (PC). In this project the unipolar modulation technique is presented to overcome many problems. The generated pulses are fed to six pulses at bridge inverter consist of six (MOSFET) transistors. The fundamental operation of PWM is depending upon mathematical relationships between the voltage and the frequency (V/ F) constant value, this relationship gives low harmonics that takes place the modulation process. The control signal of PWM inverters is generated by comparing a reference sinusoidal waveform and triangular waveform of modulating signal. The dc link is constant value, and modulation index is variable according to the varying output voltage of the inverter.

Keywords: PC, MOSFET, V/F.PWM

References:

[1] C.W. Lander “Power Electronic”, ten editions. London: McGraw Hillbook Company, 20012.
[2] G.K. Dubey, “Power Semiconductor Controlled Drives”, nine editions USA. Prentice Hall, International Inc.2011.
[3] A. Zucckerbeger and A. Alexandrovitz, “Dertermination of Commutation Sequence With a View to Eliminating Harmonics in Microprocessor Controlled PWM Voltage Inverter”, IEEE.Trans. Ind. Electron .Vol. IECI 28, No.4, NOV. 20011.
[4] Bowes, S.R., and Midoum, A., “Suboptimal Switching Strategies For Microprocessor Controlled PWM Inverter Drives”, IEEE Proc., Electric power application, Vol. 132, pt. B, No. 3, May 2011, P.P. 133-148.2011.
[5] J.E. Gilliam, J.A. Hould Worth, L. Hadley, “Variable Speed Induction Motor With Integral PWM Inverter”. IEEE Conference APEC, 2011, P.P 92-100.
[6] J.A. Houlds worth, W.B. Rosink, “Introduction to PWM speed control system for three phase ac motor”, Electronic components and applications, Vol. 2, No. 2, 2010.
[7] Intel corporation, “Microcontroller Handbook”, 2010.
[8] You Lee and Y. Yith Sun, “Adaptive Harmonic Control in PWM Inverter With Fluctuating Input Voltage”, IEEE. Trans. Ind Electron, Vol. IE-33 No. 1, FEB, 2010.

9-14

http://blueeyesintelligence.org/2checkout_download.html

4.

Authors:

M. S. Srividya, Shobha G.

Paper Title:

A Survey on: Underwater Video Processing for Detecting and Tracking Moving Objects

Abstract: Oceanographers need to study, analyse and interpret the biological and physical characteristics of marine organisms in the waterbed and sea floor. Images and Videos are important source of information and aids for their study. However, there are unique set of constraints in underwater environment that have limited our ability to process underwater images. Some of the important constraints in underwater images are associated with the physics of the light and attenuation of the electromagnetic spectrum. Processing issues also need to be dealt for the required application. Some of the research issues underwater have been the tasks associated with reconstructing three-dimensional information about the world from its two-dimensional Projections. In this paper the techniques of underwater video processing for detecting and tracking moving objects are discussed, analysed and compared.

Keywords: Video Processing, Detection, Tracking, Affine Transformation, Feature Extracion

References:

[1] SamanPoursoltan, Russell Brinkworth, Matthew Sorell “Biologically-inspired Video Enhancement Method For Robust Shape Recognition,” University of Adelaide, Australia, IEEE, 2013.
[2] Prabhakar C J & Praveen Kumar P U. “Feature Tracking of Objects in Underwater VideoSequences.”, Kuvempu University, India, ACEEE 2012.
[3] SaeedVahabiMashak, BehnamHosseini, S.A.R. Abu-Bakar.“Background Subtraction for Object Detection under Varying Environments.”UniversitiTeknologi Malaysia, IJCISIMA, 2012
[4] M. Weber, M. Welling, and P. Perona. “Detecting ,Tracking And Counting Fish In Low Quality Unconstrained Underwater Videos” University of Edinburgh, Edinburgh, UK.
[5] “Underwater ColorConstancy :Enhancement of Automatic Live Fish Recognition” M. Chambah, D. Semani, A. Renouf, P. Courtellemont, A. Rizzi, Université de La Rochelle, France, Dept. of Information Technology – University of Milano/Italy
[6] CodrutaOrnianaAncuti, CosminAncuti, Tom Haber and Philippe Bekaert “FUSION-BASED RESTORATION OF THE UNDERWATER IMAGES,” Belgium, IEEE, 2011.
[7] MohamedAmer, Emil Bilgazyev “Fine-grained Categorization of Fish Motion Patterns in Underwater Videos”, Oregon State University, ICCV, 2011
[8] PiotrJasiobedzki, Stephen Se, Michel Bondy, and Roy Jakola,“Underwater 3D mapping and pose estimation for ROVoperations”, OCEANS 2008, pp. 1-6, September 2008.
[7] Li Ma, KaihuaWu,L. Zhu, “Fire smoke detection in videoimages Using Kalman filter and Gaussian Mixture Colormodel”, International Conference on Artificial Intelligence andComputational Intelligence, 2010
[8] Li Xu, Feihu Qi, Renjie Jiang, YunfengHao, Guorong Wu,“Shadow Detection and Removal in Real Images: A Survey”Shanghai JiaoTong University, P.R. China, June 2006.
[9] J Shin, S Kim, et all, “Optical flow-based real-time object tracking using non-prior training active feature model”Computer Vision and Image Understanding, June 2005,Volume 98, Issue 3, Pages 462-490.
[10] B.Hosseini, SaeedVahabiMashak and A.S.R Abu Bakar,”Human Movement Based on Rule based classifier” Asia Modelling Symposium, May 2010.
[11] [14] S.S. Beauchemin and J.L. Barron, “The Computation of Optical Flow”, ACM Computing Surveys, 1995 Vol. 27,No.3, PP 43-467.
[12] S.Y. Chien, S.Y. Ma and L.G. Chen ,” Efficient moving objectsegmentation algorithm using background registrationtechnique” IEEE Trans Circuits Syst. Video Technol, JULY2002
[13] Hu Fuyuan, Zhang Yanning, et all, “A New Method of MovingObject Detection and Shadow Removal” JOURNAL OFELECTRONICS (CHINA), July 2007
[14] Dongxiang Zhou, Hong Zhang and NilanjanRay,“TextureBased Background Subtraction”, Proceedings of theInternational Conference on Information and Automation,Zhangjiajie China, IEEE Conference, June 20 -23, 2008.
[15] YunlongGuo, Bo Yang, Yangyang Ming, Aidong Men, “AnEffective Background Subtraction Under the Mixture ofMultiple Varying Illuminations”,Second InternationalConference on Computer Modeling and Simulation, IEEEConference, 2010.
[16] V. Brandou, A. G. Allais, M. Perrier, E. Malis, P. Rives, J.Sarrazin, and P. M. Sarradin, “3D Reconstruction of NaturalUnderwater Scenes Using the Stereovision System IRIS”, OCEANS 2007 – Europe , pp. 1-6, 2007.
[17] David G Lowe, “Distinctive image features from scale-invariantkeypoints”, International Journal of Computer Vision, vol. 60(2),pp. 91-110, 2004.
[18] David G Lowe, “Object recognition from local scale-invariantfeatures”, Proceedings of the International Conference on ComputerVision, vol. 2, pp. 1150-1157

15-17

http://blueeyesintelligence.org/2checkout_download.html

5.

Authors:

Juhi Sharma, Anuradha Taleja, Kshitiz Saxena

Paper Title:

Analysis of File Compression Based on Amazon EC2 Cloud Platform

Abstract: The advent and wide adoption of cloud computing has brought a new revolution in the field of IT. As consumers using cloud for data storage either in the SaaS, PaaS or IaaS deployment model are increasing-they realize the necessity of file compression. It becomes imperative to understand the scenarios where transition to the Cloud is beneficial. In our research, we have demonstrated that for small businesses – making a move to the cloud is not a good approach as less demanding applications can run well even on stand-alone machines, however as the data size grows – making a move towards cloud can yield higher availability. In this paper we evaluate and compare the performances of virtual machines running in cloud with stand-alone (unvirtualized) machine.

Keywords: Cloud computing, Amazon EC2, File Compression, Performance Analysis

References:

[1] Amazon Web Services (2011). Eucalyptus open-source cloud computing infrastructure -an overview. technical report, eucalyptus, inc.
[2] Chee, B. and Franklin Jr, C. (2009). Cloud computing: technologies and strategies of the ubiquitous data center. CRC.
[3] Chorafas, D. and Francis, T. . (2011). Cloud computing strategies. CRC Press. D, J. and Murari, K. and Raju, M. and RB, S. and Girikumar, Y. (2010). Eucalyptus Beginner’s Guide – UEC Edition.
[4] Ghoshal, D., Canon, R., and Ramakrishnan, L. Understanding i/o performance of virtualized cloud environments. Godard, S. (2004). Sysstat:System performance tools for the Linux OS.
[5] He, Q., Li, Z., and Zhang, X. (2010). Study on cloud storage system based on distributed storage systems. In Computational and Information Sciences (ICCIS), 2010 International Conference on, pages 1332–1335. IEEE.
[6] Hovestadt, M., Kao, O., Kliem, A., and Warneke, D. (2011). Evaluating adaptive compression to mitigate the effects of shared i/o in clouds. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on, pages 1042–1051. IEEE.
[7] Hugos, M. and Hulitzky, D. (2010). Business in the Cloud: What Every Business Needs to Know About Cloud Computing. Wiley.
[8] Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., and Epema, D. (2010).
[9] Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing. IEEE Transactions on Parallel and Distributed Systems, pages 1–16.
[10] Krintz, C. and Calder, B. (2001). Reducing delay with dynamic selection of compression formats. In High Performance Distributed Computing, 2001. Proceedings. 10th IEEE International Symposium on, pages 266–277. IEEE.
[11] Lilja, D.J. (2005). Measuring computer performance: a practitioner’s guide. CambridgeUniv Pr.
[12] Miyamoto, T., Hayashi, M., and Tanaka, H. (2009). Customizing network functions for high performance cloud computing. In Network Computing and Applications, 2009. NCA 2009. Eighth IEEE International Symposium on, pages 130–133. IEEE.
[13] Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., and Zagorodnov, D. (2009). The eucalyptus open-source cloud-computing system. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 124–131. IEEE Computer Society.
[14] Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., and Epema, D. (2010).A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, pages 115–131.
[15] Ozsoy, A. and Swany, M. (2011). Culzss: Lzss lossless data compression on cuda. In Cluster Computing (CLUSTER), 2011 IEEE International Conference on, pages 403–411. IEEE.

18-20

http://blueeyesintelligence.org/2checkout_download.html

6.

Authors:

Pravin Kawle, Avinash Hiwase, Gautam Bagde, Ekant Tekam, Rahul Kalbande

Paper Title:

Modified Advanced Encryption Standard

Abstract: In today’s world most of the communication is done using electronic media. Data Security is widely used to ensure security in communication, data storage and transmission. Security of multimedia data is an imperative issue because of fast evolution of digital data uses the permutation step, taking from Data Encryption Standard (DES) algorithm. Theoretical analysis and experimental results prove that this technique provides high speed as well as fewer exchanges or transfer over unsecured network. Multimedia data security is achieved by methods of cryptography, which deals with encryption of data. Standard symmetric encryption algorithms provide better security for the multimedia data. But applying symmetric key encryption algorithm on more complex multimedia data (mostly images); we might face the problem of computational overhead. To overcome that problem, we analyze the Advanced Encryption Standard (AES) and modify it, to reduce the calculation of algorithm and for improving the encryption performance. In modified AES algorithm instead of using Mixcolumne overheads on data. Modified-AES algorithm is a fast lightweight encryption algorithm for security of multimedia data. All above advantages make algorithm highly suitable for the images and plaintext transfer as well , than the AES algorithm.

Keywords: Advanced Encryption Standard (AES), Cryptography, DES, and Symmetric Key Algorithms.

References:

[1] Shtewi, A.M.”An Efficient Modified Advanced Encryption Standard (MAES) adapted for image cryptosystems” IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.2,
[2] ShashiMehrotra Seth, 2Rajan Mishra,” Comparative Analysis Of Encryption Algorithms For Data Communication”, IJCST Vol. 2, Issue 2, June 2011 pp.192-192.
[3] Gurjeevan Singh, Ashwani Kumar Singla,K.S. Sandha, ”Through Put Analysis Of Various Encryption Algorithms”, IJCST Vol. 2, Issue 3.
[4] Behrouzan A. Forouzan (2010), Cryptography & Network Security, TMH Publisher, ISBN: 9780070660465.
[5] Bruce Schneier (2009), Applied Cryptography, John Wiley & Sons Publisher, ISBN:9780471117094

21-23

http://blueeyesintelligence.org/2checkout_download.html

7.

Authors:

Sunilkumar R. Kumbhar, H. M. Dange

Paper Title:

Performance Analysis Of single Cylinder Diesel Engine, Using Diesel Blended with Thumba Oil

Abstract: Depletion of nonrenewable source of energy (fossil fuels like petroleum, coal and natural gas) many researchers are looking forward to find an alternative source of renewable energy. One such alternative to diesel fuel is biodiesel. Biodiesel can be defined as fuel comprising of mono alkyl ester of long chain of fatty acid derived from vegetable oil or animal fat. So, there is an increasing interest in India to search for suitable alternative fuels that are environmental friendly. Environmental concerns and limited amount of petroleum resources have caused interests in the development of alternative fuels for internal combustion (I.C.) engines. As an alternative, biodegradable, renewable and sulphur free biodiesel is receiving increasing attention. The use of biodiesel is rapidly expanding around the world, making it imperative to fully understand the impacts of biodiesel on the diesel engine combustion process and pollutant formation. Therefore, in this study, the performance test of a variable compression diesel engine with neat diesel fuel and biodiesel mixtures was carried out. Experiments are carried out by using dual biodiesel blends and compared it with diesel fuel characteristics. The literatures were focused on single biodiesel and its blends. So far a very few dual biodiesel blends of oils have been tried on diesel engine leaving a lot of scope in this area. This paper investigated the performance and emission characteristics of various dual biodiesel blends (mixture of biodiesel and diesel fuel) of Thumba biodiesel on a single cylinder variable compression ratio diesel engine having bore diameter 87.50mm, developed power 3.5kw at 1500rpm, compression ratios 12 to 18, stroke length 110mm, water cooled engine. The biodiesel blends of B10% (combination of Diesel 90% by volume, biodiesel 10% by volume ) and B20% (combination of Diesel 80% by volume, biodiesel 20% by volume) gave better brake thermal efficiency and lower brake specific fuel consumption than other biodiesel blends. The blends of B10% and B20% have superior emission characteristics than other blends and closer to diesel values. From the experimental results obtained, Thumba oil blends are found to be a promising alternative fuel for compression ignition engines. At CR 18 BTE and BSFC of Thumba B10, B20 and BP of Thumba B40 showed better performance.CO, HC, CO2 of B100 of Thumba biodiesel showed less emission percentage/ppm, for NOx emission B10 and B20 of Thumba, biodiesel showed less emission ppm.

Keywords: Biodiesel, Blends, Thumba Biodiesel, Engine performance, Emission Characteristics

References:

1. K. Pramanik. “Properties and use of Jatropha curcas oil and diesel fuel blends in C.I engines”, Journal of Renewable Energy, 239-248, (2003).
2. F.K.Forson, E.K Oduro, E.Hammond-Donkoh, “Performance of jatropha oil blends in a diesel engine”, Journal of Renewable Energy, 1135-1145, (2004).
3. J.Narayana Reddy, A. Ramesh “Parametric studies for improving the performance of Jatropha oil-fuelled compression ignition engine”, Journal of Renewable Energy, 1994-2016, (2006).
4. H.Reheman, S.V.Ghadge “Performance of diesel engine with biodiesel at varying compression ratio and ignition timing”, Fuel, Volume 87, Issue 12, 2659-2666, (2008).
5. Jehad A. A. Yamin, Nina Sakhnin, Ahmed Sakhrieh, M Hamdan “Performance of C.I. engines using biodiesel as fuel”, GCREEDER 2009, Amman‐Jordan, (2009)
6. GVNSR Ratnakara Rao, V. Ramachandra Raju and M. Muralidhara Rao “Optimising the compression ratio of diesel fuelled CI engine”, ARPN Journal of Engineering and Applied Sciences, APRIL 2008
7. Indraj Singh and Vikas Rastogi “Performance Analysis of a Modified 4- Stroke Engine Using Biodiesel Fuel for Irrigation Purpose”, International Journal of Applied Environmental Sciences (2009), 229–242.
8. Amit Pal, S S Kacggwaha, S. Maji and M K G Bahu, “Thumba (Citrullus Colocyntis) seed oil: A sustainable source of renewable energy for biodiesel production “Journal of scientific and industrial research” , Vol 69, May 2010.
9. T. Elango1, T. Senthil kumar “Performance and emission characteristics of CI engine fuelled with non edible vegetable oil and diesel blends”, Journal of Engineering Science and Technology, Vol. 6, No. 2 , 240 – 250, (2011) .
10. A.M. Liaquat, M.A. Kalam, H.H. Masjuki, M.H. Jayed “Engine Performance and Emissions Analysis using “Envo Diesel” and Coconut Biodiesel Blended Fuel as Alternative Fuels”, 2nd International Conference on Environmental Science and Technology, Vol.6, 2011.
11. Shiv Lal, V. K. Gorana1, N. L. Panwar “A Comparative Study of Thumba Seed Bio Diesel”, Journal of Environmental Protection, Vol 2, 454-459, 2011.
12. K. Kalyani Radha, S. Naga Sarada, K. Rajagopal and E.L. Nagesh“Performance and emission characteristics of CI engine operated on vegetable oils as alternate fuels”, International Journal of Automotive and Mechanical Engineering (IJAME), Vol 4, 414-427, July-December 2011.
13. H. M. Dharmadhikari1, Puli Ravi kumar ,S. Srinivasa Rao “Performance and emissions of CI engine using blends of biodiesel and diesel at different injection pressures”,International Journal of Applied Research in Mechanical Engineering (IJARME), Vol 2, Issue 2, 2012.
14. Suvendu Mohanty, Dr. Om prakash “Analysis Of Exhaust Emission Of Internal Combustion Engine Using Biodiesel Blend”,International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 5, May 2013.
15. Sivaramakrishnan Kaliamoorthy, and Ravikumar Paramasivam“ Investigation on performance and emissions of a biodiesel engine through optimization techniques”, Vol 17, No 1, 179-193, 2013.
16. Mekalilie Benjamin Bol, T. K. Bhattacharya and H. I. Mohammed “Studies on compatible fuel properties of soybean ethyl ester and its blends with diesel for fuel use in compression ignition engines”.

24-30

http://blueeyesintelligence.org/2checkout_download.html

8.

Authors:

Harish Kumar, Anil Kumar Solanki, Anuradha Taluja

Paper Title:

Artificial Neural Network Approach for Student’s Behavior Analysis

Abstract: India is one of the leading countries in the word for technical education and management education. A study shows that in India e-education is growing with a massive growth. The students find relevant information from various e-education web sites. Numbers of pages are indexed on www and finding the desired information is not an easy task. For solving this problem we need an approach which helps in finding precise solution as per students need. In this paper we also use Web log analysis via neural network structure. We are using a neural approach with fuzzy clustering that shows the analysis of web logs, depends on the performance of the clustering the number of requests. Clustered data is used to analyze with neural approach for effective web site modification and behavior analysis. The proposed model use neural network approach with a firing rule to discover and analyze useful knowledge from the available Web log data.

Keywords: A study shows that in India e-education is growing with a massive growth.

References:

[1] Ajith Abraham, “Business Intelligence from Web Usage Mining” Journal of Information & Knowledge Management, Vol. 2, No. 4 (2003) 375-390.
[2] Jos´e Borges, Mark Levene “An Average Linear Time Algorithm for Web Usage Mining” Sept 2003.
[3] Kumar Harish, Solanki A.K “Adaptive Markov Chain For Next Page Access Prediction” Vol. 9 No. 7 JUL 2011 Aug 25, 2011 by IJCSIS .
[4] Renáta Iváncsy, and Sándor Juhász, “Analysis of Web User Identification Methods” International Journal of Electrical and Computer Engineering 2:3 2007.
[5] . Suresh ,Madana ,A.RamaMohan Reddy, “Improved FCM algorithm for Clustering on Web Usage Mining” K IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 1, January 2011 ISSN (Online): 1694-0814
[6] Soumi Ghosh ,Sanjay Kumar Dubey “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms” , International Journal of Advanced Computer Science and Applications, Vol. 4, No.4, 2013
[7] V Chitra, Dr. Antony “An Enhanced Clustering Technique for Web Usage Mining” ,International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 4, June – 2012
[8] Michal Munka, Martin `Drlíka Procedia “Impact of Different Pre-Processing Tasks on Effective Identification of Users’ Behavioral Patterns in Web-based Educational System” Computer Science 4 (2011) 1640–1649 International Conference on Computational Science, ICCS 2011.
[9] Marquardt, Becker and D. Ruiz “A Pre-Processing Tool forWeb Usage Mining in the Distance Education Domain” . In Proceedings of the International Database Engineering and Applications, IDEAS 2004, pp. 78-87.
[10] Wang X., Abraham A. and Smith K.A.,, “Web Traffic Mining Using a Concurrent Neuro-Fuzzy Approach” In Proceedings Second International Conference on Hybrid Intelligent Systems, Chile, IOS Press Amsterdam, The Netherlands, 2002.
[11] Olfa Nasraoui and Christopher Petene. “Combining Web Usage Mining and Fuzzy Inference for Website Personalization”
[12] ”Harish Kumar, Dr. Anil Kumar Solanki “Effective Cleaning of Educational website usage patterns and predicting their next visit,International journal of computer applications Volume 53 Nov 12.
[13] Archana N Boob “ Fuzzy Clustering: An Approach for Mining Usage Profiles from Web” International Journal of Computer Technology & Applications, Vol 3 (1),329-331.
[14] Jaykumar, Kamlesh Patel, “A survey on web usage mining with neural network and proposed solutions on several issues” journal of information, knowledge and research in computer engineering, ISSN: 0975 – 6760 nov 12 to oct 13 volume – 02, issue – 02 page 330.
[15] Valishali A. Zilpe, Dr. Mohammad Atique “Neural network apprach for web usage mining”, ,ETCSIT, published in IJCA [2011]

31-35

http://blueeyesintelligence.org/2checkout_download.html

9.

Authors:

Fatima Faydhe AL-Azzawi

Paper Title:

Minimization the Effect of Different Channel Model on QAM and PAM Systems in Term of Bit Error Rate

Abstract: Quadrature amplitude modulation (QAM) has been widely used in adaptive modulation because of its efficiency in power and bandwidth while Pulse Amplitude Modulation (PAM) used for high rate data link where transmitting more bits is a symbol time, QAM and PAM systems are both tested under AWGN, Rayleigh fading, Rician Fading channels as comparison performance in term of BER between the two systems, to improve the performance of both systems convolutional code, diversity order and K-factor are used, where these techniques decrease the BER on both systems.

Keywords: QAM and PAM, fading channel, Convolutional AWGN channel, QAM in rician channel.

References:

[1] M.S. Richer et al,” The ATSC digital television system”, proc. IEEE, vol.94, pp.37-43, Jan, 2006.
[2] Cho, K., and Yoon, D., “On the general BER expression of one- and two-dimensional amplitude modulations”, IEEE Trans. Commun., vol. 50, no. 7, July 2002, pp. 1074-1080.
[3] Proakis, J. G., Digital Communications, 4th Ed., McGraw-Hill, 2001.
[4] Simon, M. K , Hinedi, S. M., and Lindsey, W. C., Digital Communication Techniques – Signal Design and Detection, Prentice-Hall, 1995.
[5] Simon, M. K., and Alouini, M. S., Digital Communication over Fading Channels – A Unified Approach to Performance Analysis, 1st Ed., Wiley, 2000
[6] Vo Nguyen Quoc Bao, Hyung Yun Kong, Seong Wook Hong, ‘Performance Analysis of M -PAM and M -QAM with Selection Combining in Independent but Non-Identically Distributed Rayleigh Fading Paths’, IEEE, 2008 .
[7] Daniel J. Ryan, ‘Blind Detection of PAM and QAM in Fading Channels’ , IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 3, MARCH 2006.

36-39

http://blueeyesintelligence.org/2checkout_download.html

10.

Authors:

Sandeep Rankawat, J.S.Purohit, D.R.Godara, S.K.Modi

Paper Title:

Scattering Measurement Due To Foliage of Western Rajasthan Region at 35 Ghz

Abstract: Scattering phenomenon due to foliage plays an important role in millimeter band as the wavelength approaches to the order of the size of the obstacles. It is important to estimate the propagation attenuation due to scattering when the arid zone foliage leaves and twigs size affect adversely in millimeter band signal propagation. A 35 GHz trans-receiver link system was used to measure the attenuation through trees due to scattering pattern of tree foliage of western rajasthan region. Measurements were made to study the angular variations of the positioning of receiver unit around the target foliage. The measurements, which were made for HH polarization configurations over a wide range of the azimuth angle, provide a quantitative reference for the design of high speed data communication links and use of millimeter-wave bistatic radar, point to point communication systems.

Keywords: A35 GHz, millimeter wave, 35 GHz, scattering, Foliage, Western Rajasthan

References:

[1]D.R.Godara,Dr.J.S.Purohit,SandeepRankawat,S.K.Modi “Effect of Foliage Length on Signal Attenuation in Millimeter Band at 35Ghz Frequency” IJCA ISSN: 0975-8887, Volume-84, DEC2013.
[2]D.R.Godara,Dr.J.S.Purohit,SandeepRankawat,S.K.Modi “Propagation Attenuation Due to Foliage at 35Ghz” IJECT ISSN: 2230-9543, Volume-4, Issue-4, OCT-DEC2013.
[3] D.R.Godara, S.K.Modi, Rupesh Kumar Rawat “Experimental studies on millimeter wave scattering from ground and vegetation at 35 GHz” IJSCE ISSN: 2231-2307, Volume-1, Issue-6, January2012.
[4] Akira Ishimaru, “Wave Propagation and Scattering in Random Media and Rough Surfaces”, Proceedings of the IEEE, Vol 79, No. 10, October 1991
[5] Hao Xu,Theodore S. Rappaport, Robert J. Boyle,James H. Schaffner, “Measurements and Models for 38-GHz Point-to-Multipoint Radiowave Propagation”,IEEE journal on selected areas in communications, Vol. 18, No. 3, March 2000
[6] Rogers, N. C., A. Seville, J. Richter, D. Ndzi, N. Savage,R. Caldeirinha, A. Shukla, M. O. AlNuaimi, K. H. Craig, E. Vi-lar, and J. Austin, “A generic model of 1-60 GHz radio propa-gation through vegetation” , Tech. Report, Radiocommunications Agency, May 2002.
[7] Wang, F. and K. Sarabandi, “An enhanced millimeter-wave foliage propagation model”, IEEE Trans. Antennas Propag., Vol. 53, No. 7, 2138-2145, 2005.
[8] “An Improved Propagation Model for Wireless Communications.”, Majeed, M. and, Tjuata, S. (1996). IEEE International Conference on Communications, Converging Technologies for Tomorrow’s Applications, Vol. 1, pp. 292-296, ICC 96.

40-41

http://blueeyesintelligence.org/2checkout_download.html

11.

Authors:

Neha Chandrima, Sunil Phulre, Vineet Richharia

Paper Title:

Wormhole Detection using RBS based Ad-hoc on-Demand Multipath Distance Vector as Routing Protocol in MANETs

Abstract: Mobile Adhoc Networks (MANETs) is a collection of mobile nodes. Due to globalization and revolution in communication technology, growth in the deployment of wireless and mobile communication networks have been noticed. Several challenges and issues are raised by researchers working in the field of Dynamic topology and infrastructure less MANETs. Such facility generates easy installation of Ad hoc networks and provides node stability without loss of connection. In such facility packet dropping is a serious challenge for quality performance of MANETs. Exhaustive literature survey reveals that MANETs generally suffer from security attacks such as black hole attack, wormhole attack and malicious attack due to the packet dropping problems. For the minimization attack and packet dropping researchers have provided the solutions like authentication, reputation based scheme, certification based scheme, passive feedback scheme, ack-based scheme etc. In certification based scheme, a problem of huge overhead due to extra authentication of packet is noticed. Also, the problem of issuing certificate and authentication of certificate at each node are the great challenge to be resolved. To resolve these challenges, Entropy based reference-broadcast synchronization (RBS) scheme is used in which nodes send reference beacons to their neighbors using physical-layer broadcasts. The characteristic entropy changes are used to construct a threshold based detector for fast worms. Proposed scheme is implemented in NS2 simulator and its result shows the effect of normal behavior and improvement of performance after application of this scheme. In this, 50 nodes are used for simulation process and performance is evaluated by packet delivery ratio and normalized load using ad-hoc on-demand multipath distance vector (AOMDV) as routing protocol. This entropy based scheme, successfully minimizes the communication cost by reducing the number of overhead packets, removes the node ambiguity in broadcast synchronization process and minimizes packet dropping also.

Keywords: AOMDV Protocol, Dynamic Topology, Mobile Ad-hoc Networks, Reference-broadcast Synchronization

References:

[1] Shuyao Yu, Youkun Zhang , Chuck Song and Kai Chen” A security architecture for Mobile Ad Hoc Networks” Proceedings of the II ACM Mobi HOC, 2001
[2] T. Fahad, D. Djenouri and R. Askwith. “On detecting Packets Droppers in MANET: a Novel Low Cost Approach”, Proceeding. III International Symposium on Information Assurance and Security, Manchester, UK August 2007.
[3] Tony Larsson, Nicolas Headman; A report on “Routing Protocols in Wireless Ad Hoc Networks: A Simulation Study”1998.
[4] M. Amitabh, “Security and quality of service in ad hoc wireless networks”, Cambridge University Press I edition, March 2008.
[5] T R Andel and A Yasinsac, “Surveying Security Analysis Techniques in MANET Routing Protocols”, IEEE Communication Surveys & Tutorials, 9,4 pp 70-84, Fourth Quarter 2007
[6] Mahesh K. Marina and Sameer . Das “Ad hoc on-demand multipath distance vector routing” wireless communications and mobile computing, 2006; 6:969–988
[7] E.A.Mary Anita, V.Thulasi Bai, E.L.Kiran Raj, B.Prabhu, “Defending against Worm Hole Attacks in Multicast Routing Protocols for Mobile Ad hoc Networks”, in proceeding of IEEE communication 2011
[8] Jeremy Elson, NLewis Girod and Deborah Estrin,” Fine-Grained Network Time Synchronization using Reference Broadcasts” in proceeding of IEEE communication 2011.
[9] Adel Saeed Alshamrani” PTT: Packet Travel Time Algorithm in Mobile Ad Hoc Networks”, in proceeding of IEEE 2011
[10] Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto, “Detecting Wormhole Attack on MAODV-based Mobile Ad Hoc Networks by Dynamic Learning Method.” in proceeding of IEEE 2011.
[11] Y. Zhang, W. Lou, W. Liu and Y. Fang, “A secure incentive protocol for mobile ad hoc networks”, Wireless Networks journal, 13(5): pp569-582, October 2007.
[12] Ning Song and Lijun Qian, Xiangfang L WINLAB “Wormhole Attacks Detection in Wireless Ad Hoc Networks: A Statistical Analysis Approach” Parallel and Distributed Processing Symposium, Proceedings, 19th IEEE International ,April 2005.
[13] Jaiswal R and Sharma S, “ Relative cluster entropy based wormhole detection using AOMDV in adhoc networks” Computational intelligence and communication networks (CICN) 2012 Proceeding of Fourth International conference, held at Mathura, Nov.2012, pp 747-752

42-46

http://blueeyesintelligence.org/2checkout_download.html

12.

Authors:

Tasher Ali Sheikh, Arindum Mukherjee

Paper Title:

Designing an Appropiate Order of Fir Filter Using Windowing Method

Abstract: This paper the authors mainly concern with the best order of the FIR filter using Boxcar windowing method. Simulation results give encouraging results regarding best order of the FIR Filters among the orders. A new method of implementation of best order of FIR Filter is proposed using MATLAB SIMULINK. Interesting results are obtained by comparing the different order filter’s cut-off frequency to the assumption fixed 1000 Hz using Boxcar window. This novel concept of finding of the best order of the FIR filter using different order in Boxcar windows method that has been verified through MATLAB simulation results.

Keywords: FIR, MATLAB SIMULINK, DSK5416 Kit, TMSC32054 hardware.

References:

1. J G Proakis and D M Malonakis, ‘Digital Signal Processing: Principles, Algorithms and Applications, Prentice Hall, Englewood Cliffs, NJ, 1996.
2. B Porat, ‘A Course in Digital Signal Processing’, John Wiley & Sons, 1997.
3. S K Mitra, ‘Digital Signal Processing: A Computer Science based Approach’, McGraw Hill Publications.
4. Oppenheim, Schfar and Buck, ‘Discrete Time Signal Processing’, Pearson Publication.
5. L. B. Jackson, J. F. Kaiser and H. S. McDonald, “An approach to the implementation of digital filters”, IEEE Trans. Audio Electroacoust., vol. AU-16, pp.413 -421 1968.
6. 6) C. M. Rader and B. Gold, “Digital filter design techniques in the frequency domain”, Proc. IEEE, vol. 55, pp.149 -171 1967.

47-50

http://blueeyesintelligence.org/2checkout_download.html

13.

Authors:

A. Lakshmana Rao, B. Neela Rao, R. Santhi Kumar

Paper Title:

Some EOQ Model for Weibull Deterioration Items with Selling Price Dependent Demand

Abstract: In this paper we develop and analyze an inventory model assumption that deterioration rate follows Weibull two parameter distributions with selling price dependent demand. Here it is assumed that demand rate is a function of selling price. With shortage and without shortage both cases have been taken care of in developing the inventory models. Shortages are fully backlogged whenever they are allowed. Through numerical examples the results are illustrated. The sensitivity analysis for the model has been performed to study the effect changes of the values of the parameters associated with the model.

Keywords: EOQ model; deteriorating items; shortage; selling price dependent demand; Weibull distribution.

References:

[1] Aggarwal, S.P., “A note on an order-level inventory model for a system with constant rate of deterioration”, Opsearch, 15(1978), pp: 84-187.
[2] Burwell T.H., Dave D.S., Fitzpatrick K.E., Roy M.R., “ Economic lot size model for price-dependent demand under quantity and freight discounts”, International Journal of Production Economics, 48(2)(1997), pp: 141-155.
[3] Chakrabarti, et al, “An EOQ model for items Weibull distribution deterioration shortages and trended demand an extension of Philip’s model. Computers and Operations Research, 25(1997), pp: 649-657.
[4] Chakraborti, T., and Chaudhuri, K.S., “An EOQ model for deteriorating items with a linear trend in demand and shortages in all cycles”, International Journal of Production Economics, 49(1997), pp: 205-213.
[5] Chung, K., and Ting, P., “A heuristic for replenishment of deteriorating items with a linear trend in demand”, Journal of the Operational Research Society, 44(1993), pp: 1235-1241.
[6] Covert, R.P., and Philip, G.C., “An EOQ model for items with Weibull distribution deterioration.AIIE Transactions”, 5(1973), pp: 323-326.
[7] Dave, U., and Patel. L.K., “(T, S) policy inventory model for deteriorating items with time proportional demand”, Journal of the Operational Research Society. 32(1981),pp: 137-142.
[8] Deb, M., and Chaudhuri. K.S., “An EOQ model for items with finite rate of production and variable rate ofdeterioration”, Opsearch, 23(1986), pp:175-181.
[9] Fujiwara, O., “EOQ models for continuously deteriorating products using linear and exponential penalty costs”, European Journal of Operational Research, 70(1993), pp:104-14.
[10] Ghare, P.M., and Schrader, G.F., “An inventory model for exponentially deteriorating items”, Journal of Industrial Engineering, 14(1963), pp:238-243.
[11] Giri, B.C., and Chaudhuri, K.S., “Heuristic models for deteriorating items with shortages and time-varying demand and costs”, International Journal of Systems Science, 28(1997), pp:53-159.
[12] Goh, M., EOQ models with general demand and holding cost functions”, European Journal of Operational Research, 73(1994), pp:50-54.
[13] Hariga, M., “Optimal EOQ models for deteriorating items with time varying demand”, Journal of Operational Research Society, 47(1996),pp: 1228-1246.
[14] Hariga, M.A., and Benkherouf, L., “Optimal and heuristic inventory replenishment models for deteriorating items with exponential time-varying demand”, European Journal of Operational Research. 79(1994), pp: 123-137.
[15] Jalan, A.K., and Chaudhuri, K.S., “Structural properties of an inventory system with deterioration and trended demand”, International Journal of systems Science, 30(1999), pp: 627-633.
[16] Jalan, A.K., Giri, R.R., and Chaudhuri, K.S., “EOQ model for items with Weibull distribution deterioration shortages and trended demand”, International Journal of Systems Science. 27(1996),pp: 851-855.
[17] Mishra, R.B., “Optimum production lot-size model for a system with deteriorating inventory”, International Journal of Production Research, 3(1975), pp: 495-505.
[18] Mondal, B., Bhunia, A.K., Maiti, M., “An inventory system of ameliorating items for price dependent demand rate”, Computers and Industrial Engineering, 45(3)(2003), pp: 443-456.
[19] Muhlemann, A.P. and Valtis-Spanopoulos, N.P., “A variable holding cost rate EOQ model”, European Journal of Operational Research,4(1980), pp: 132-135.
[20] Shah, Y.K., and Jaiswal, M.C., “An order-level inventory model for a system with constant rate of deterioration”, Opsearch, 14(1977), pp:174-184.
[21] Su, C.T., et al, “An inventory model under inflation for stock-dependent consumption rate and exponential decay”, Opsearch, 33(1996), pp: 72-82.
[22] Van Der Veen, B., “Introduction to the Theory of Operational Research”, Philip Technical Library, Springer-Verlag, New York, 1967.
[23] Wee, H.M., “A deterministic lot-size inventory model for deteriorating items with shortages and a declining market”, Computers and Operations, 22(1995), pp: 345-356.
[24] Weiss, H.J., “Economic Order Quantity models with nonlinear holding cost”, European Journal of Operational Research, 9(1982), pp: 56-60.
[25] You, S.P., “Inventory policy for products with price and time-dependent demands”, Journal of the Operational Research Society, 56(2005), pp: 870-873.
[26] C. K. Tripathy., et al, ”An Inventory Model for Weibull Deteriorating Items with Price Dependent Demand and Time-Varying Holding Cost”, Applied Mathematical Sciences, 4(2010), pp: 2171 – 2179.
[27] Sushil Kumar., et al, “An EOQ Model for Weibull Deteriorating Items With Price Dependent Demand”, IOSR Journal of Mathematics, 6(2013), pp: 63-68.

51-56

http://blueeyesintelligence.org/2checkout_download.html

14.

Authors:

Abednego Oswald Gwaya, Sylvester Munguti Masu, Githae Wanyona

Paper Title:

An Evaluation of Clients Role towards Efficiency in Projects Execution in Kenya

Abstract: Clients play a crucial role in construction projects: they provide the sites, arrange for financing, invest their funds and define the scope. While projects are being executed; they coordinate with consultants and contractors, dictate the level of scope change management and ensure regular flow of construction funds either individually, as corporate and or through commitment to financiers. The relationship of the client with other team members and or the level of coordination can affect the performance of construction projects. If clients are going to appreciate and promote the practice of project management then they are going to get value for their money; considering the fact that project management was introduced to ensure efficiency in performance of construction projects. This paper examines the role of clients in ensuring the efficiency of construction projects performance in Kenya

Keywords: Project Management, Efficiency, Teamwork, Project Planning And Project Execution.

References:

1. Burke, R. (2007). Introduction to Project Management. Burke Publishing, USA.
CII, 1997. Alignment during pre-project planning: A key to project success. Implementation resource 113-3.
2. CIT 1996. Benchmarking best practice report: Briefing and design, Construct IT Centre of excellence, Salford, UK (ISBN: 1-900491-33-8).
3. Kerzner, H. (2013). Project management: A systems approach to planning, scheduling and controlling. Wily & Blackwell.
4. Muchungu, P. K.(2012). The contribution of human factors in the performance of construction projects in Kenya. Unpublished Phd. Thesis. University of Nairobi.
5. Murray, J.P., Hudson, J., Ganeson, R.N. and Toft, B., 1990. An expert system approach to Client briefing. Building Economics and Construction Management: subject D: Expert system (2), pp. 538-548.
6. Newman, R.,Bacon, V. and Dawson, S. 1990. Brief formulation and design of building. Report at Oxford Polytechnic.
7. Rowing, J., Nelson, M. and Perry, K., 1987. Project objective setting by owners and contractors, University of Texas at Austin, source document 31, the University of Texas at Austin, the construction Industry Institute.

57-63

http://blueeyesintelligence.org/2checkout_download.html

15.

Authors:

Abednego Oswald Gwaya, Sylvester Munguti Masu, Githae Wanyona

Paper Title:

A Critical Analysis of the Causes of Project Management Failures in Kenya

Abstract: The success of a project would normally be measured by the extent to which the predetermined targets set by the Client have been met, whether it performs the function it was intended to meet satisfactorily and if it solves an identified problem within the stipulated time, cost and quality standards. To meet the objectives, the project will require effective planning control through the application of project management systems (Muchungu,2012). Developed economies have made use of project management in meeting the stated objectives. For an effective project management to apply; developed economies have made use of project management modelling to enable track and monitor project performance. There is need for developing economies to emulate the approaches of developed economies. Problems identified with the existing models prompted a discussion on the need to reconfigure the measurement process and the measures used. For this to be achieved, it is imperative that causes of project management failures be identified, analyzed, and or solutions or the way forward suggested. This paper therefore critically analyses the causes of project management failures in Kenya. A survey approach was used on a sample size of 500 members of which 312 members were responsive. The response rate was 62.4%.

Keywords: Project Management, Project failures, Construction Problems, Performance Measurement Systems

References:

1. Beatham, S., Anumba, C., and Thorpe, T., Hedges, I. (2004), “KPIs: a critical appraisal of their use in construction, Benchmarking”, An International Journal. Vol. 11 No. 1, 2004. pp. 93-117. Benchmarking the Government Client stage 2 study (1999), as quoted in “improving performance: project evaluation and benchmarking”, OGC (2007).
2. Chimwaso, D.K. (2000), “An Evaluation of Cost Performance of Public Project Case of Botwana”, Conference Proceedings, Construction Industry Development in The New Millennium. 2nd International Conference on Construction Industry Development and 1st Conference of CIB TG 29 on Construction in Developing Countries, Singapore.
3. Du Plessis, C. D. (2002), Agenda 21 for Sustainable Construction in Developing Countries –A discussion document.
4. Egan, J (1998) Rethinking Construction, Department of the Environment, Transport and the Regions, http://www.construction.detr.gov.uk. Accessed on 12th March 2013.
5. Gichunge, H. (2000). Risk Management in The Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi.
6. Hamza, R. A., 1995. Some observations on the management of quality among construction professionals in the UK. Construction Management and Economics, 14, pp. 485-495.
7. Hillebrandt, P. (2000), Economic Theory and the Construction Industry, Third Edition, Macmillan, London.
8. Koskela, L. and Howell, G., (2002a), “The Underlying Theory of Project Management is Obsolete”, Proceedings of the PMI \Research Conference, 293-302.
9. Latham, M. (1994), Constructing the team, Joint Review of Procurement and Contractual Arrangement in the United Kingdom. Design, Drawing and Print Services.
10. Le-Hoai, L, Lee,Y.D., Lee, J.Y. (2008), “Delay and Cost Overruns in Vietnam Large Construction Projects: A Comparison with other selected Countries”, KSCE Journal of Civil Engineering, 12(6):367-377
11. Lock, D.(2007). Project Manament, 9th ed. Gower Publishing Company. 101 Cherry Street. Burlington. United States of America.
12. Kerzner, H. (2013). Project management: A systems approach to planning, scheduling and controlling. Wily & Blackwell.
13. Makulwasaatudom, A., Emsley, M., Sinthawanarong, K. (2003), “Critical Factors Influencing Construction Productivity in Thailand”, Second International Conference on Construction in the 21st Century (CITC-II) “Sustainability and Innovation in Management and Technology”, 10-12 December, Hong Kong.
14. Masu, S.M. (2006).An Investigation Into The Causes and Impact of Resource Mix Practices in The Performance of Construction Firms in Kenya. Unpublished Phd. Thesis. University of Nairobi.
15. Mbatha, C.M. (1986). Building contract performance “A Case Study of Government Projects in Kenya”. Unpublished M.A. Thesis. University of Nairobi.
16. Muchungu, P. K.(2012). The contribution of human factors in the performance of construction projects in Kenya. Unpublished Phd. Thesis. University of Nairobi.
17. Mutijwaa, P., and Rwelamila, D (2007), “Project Management Competence in Public Sector Infrastructure Organisation”, Construction Management and Economics, Vol. 25, pp55-66
18. Talukhaba, A.A. (1988). Time and Cost Performance of Construction Projects. Unpublished M.A. Thesis. University of Nairobi.
19. World Bank (1994), World Development Report 1994: Infrastructure for Development, World Bank, Washinton, D.C
20. Yisa, S., Edwards, D.J. (2002), Evaluation of Business Strategies in the UK Construction Engineering Consultancy, Measuring Business Excellence, Vol. 6, No.1.
21. Zhang, Y., Zhang, Y, Zhang, L. (2003), “Study on Reasons for Delays in Civil Engineering Project in China”, Conference Proceeding, “Sustainability and Innovation in Management and Technology”, 10¬12 November 2003, Hong Kong.

64-69

http://blueeyesintelligence.org/2checkout_download.html

16.

Authors:

Abednego Oswald Gwaya, Sylvester Munguti Masu, Githae Wanyona

Paper Title:

Development of Appropriate Project Management Factors for the Construction Industry in Kenya

Abstract: The construction industry is a crucial sector for the growth of any economy. It is the sector involved with erection, repair and demolition of buildings and Civil Engineering structures in an economy (Hillebrandt, 2000). According to the Kenya National Bureau of statistics (KNBS; 2012) the construction industry contributed 3.8%, 4.1 %, 4.3% and 4.1 % towards Gross Domestic Product (GDP) for the years 2008, 2009, 2010 and 2011 respectively. This is an average of 4.1 % as compared to 10% for the developed economies (Hillebrandt, 2000). Project management was introduced as a solution to the perennial problems of cost, time and quality in execution of construction projects. But the much touted benefits are not always achieved leaving clients with a lot of disappointments. It can be argued that the traditional project management variables have been inadequate in the assessment and control of construction projects. This paper set out to develop the most appropriate project management variables for Kenya to enable achieve an efficient and effective construction industry. A survey approach covering a sample of 500 members; randomly selected from the population was utilized.

Keywords: Project Management Variables, Lagging Measures, Leading Measures, Project Success, Project Management Models.

References:

1. Atkinson, R. (1999), “Project Management: Cost, Time and Quality, Two Best Guesses and A Phenomenon, Its Time to Accept Other Success Criteria”, International Journal of Project Management, 17 (6)337-42
2. Beatham, S., Anumba, C., and Thorpe, T., Hedges, I. (2004), “KPIs: a critical appraisal of their use in construction, Benchmarking”, An International Journal. Vol. 11 No. 1, 2004. pp. 93-117.
3. Chan, A.P.C and Chan, A.P.L., (2004), “Key Performance Indicators for Measuring Construction Success Benchmarking”, An International Journal Vol.11 No. 2, 2004 Pp. 2003-221.
4. De wit, A. (1988), “Measurement of project Success”, International of Project Management, 6 (3),164-170, Butterworth & co (Publishers) Ltd.
5. Bitici, U.S., 1994. Measuring your way to profit. Management Accounting, 14(3), PP. 141-151.
6. Chinyio, P., Olomolaiye, P. and Kometa, S., 1998. Needs based methodology for classifying construction Clients and selecting contractors. Construction Management and Economics, 16, PP 91-98.
7. Davenport, D.M. and Smith, P., 1995. Assessing the effectiveness of Client participation in construction Projects. 1ST RICS conference, 8-9 September, Edinburgh, UK, PP. 17-28.
8. Gichunge, H. (2000). Risk Management in The Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi.
9. Harvey, R.C., and Ashworth, A., 1997. The construction industry of Great Britain, London: Laxtons.
10. Hillebrandt, P. (2000), Economic Theory and the Construction Industry, Third Edition, Macmillan, London.
11. Latham, M. (1994), Constructing the team, Joint Review of Procurement and Contractual Arrangement in the United Kingdom. Design, Drawing and Print Services.
12. Lim, K.C., Mohammed, A.Z., (1999). “Criteria of Project Success: An Exploration Re-Examination”. International journal of project management, Vol. 17 No.4, pp.243-8,
13. Love, P.I., 1996. Fast building: An Australian prospective. Proceedings of CIB-W92, procurement system symposium, north meets south, developing ideas, Durban, South Africa, 14-17 January, pp. 329-343.
14. Klagegg, O. J., Samset, K., and Magnussen, O.M.(2005), “Improving Success in Public Investment Projects: Lessons from Givernemen Initiative in Norway to Improve Quality at Entry”, a paper presented at the 19th IPMA World Congress, 2005.
15. Kometa, S.T., Olomolariya, P. O., and Harris, F.S. 1994. Attributes of UK construction Clients influencing project consultant’s performance. Construction Management and Economics, 12, 433-443.
16. Masu, S.M. (2006).An Investigation Into The Causes and Impact of Resource Mix Practices in The Performance of Construction Firms in Kenya. Unpublished Phd. Thesis. University of Nairobi.
17. Mbatha, C.M. (1993). An analysis of Building Procurement Systems, Features and Conception of An Appropriate Project Management Systems for Kenya. PhD Thesis. University of Wuppertal, Germany.
18. Morris, P. W. and Hough, G, H (1987), “The Anatomy of Projects”, John Wiley and Sons, New York (1987),
19. Muchungu, P. K.(2012). The contribution of human factors in the performance of construction projects in Kenya. Unpublished Phd. Thesis. University of Nairobi
20. Murray, M.D., Tookey, J.E., Langford, D.A, Hardcastle, C. (2002), “Construction Procurement Systems: Don’t Forget Murphy’s Law”, Paper submitted at the International Sympossium of the Working Committee, CIB W92 (Procurement Systems).
21. Patanakul , P. and Milosevic, D. (2009), “The Effectiveness in Managing a group of Multiple Projects: Factors of influence and Measurement Criteria”, International Journal of Project Management Vol.27, pp 216-233. Robbins, S.P. (2005), Organisational Behaviour, 11th ed., Prentice Hall, New Jersey.
22. Sadeh, A., Dvir, D., Shenhar, A. (2000), “The Role of Contract Type in Success of R & D Defence Projects under Increasing Uncertainty”, Project Management Journal, Vol. 31, No. 3, pp14-21
23. Shenhar, A.J., Levy, O., Dvir, D. (1996), “Towards a typological theory of Project Management”, Research Policy 25(4), 607-632.
24. Shenhar, A.J., Levy, O., Dvir, D. (1997), “Mapping the dimensions of project Success”, Project Management Journal 8 (2) 5-13.
25. Shenhar, A.J., Tishler, A, Dvir, D., Lipovetsky, S., Lechler, T. (2002), “Refining the Search for Project Success Factors: A Multivariate, Typological Approach”, R & D Management 32, 2. Blackwell Publishers.
26. Suh, N. P. (2001), Axiomatic Design: Advances and Applications, Oxford University Press.
27. Talukhaba, A.A. (1999). An investigation into The Factors Causing Construction Project Delays in Kenya. Case Study of High-rise Building Projects in Nairobi. Unpublished PhD. Thesis. University of Nairobi.
28. Vandevelde, A., Dierdonck, R.V., Debackere, K. (2002), “Practitioners View on Project Performance: A Three-Polar Construct”, Vlerick Leuven Gent Management School Fellows, R., Liu, A (2005), Research Methods for Construction. Blackwell Publishing, pp. 3-34
29. Walker D.H.T. (1994). An investigation into Factors that Determine Building Construction Time Performance. PhD Thesis: Royal Melbourne Institute of Technology Australia.
30. Wanyona G. (2005). Risk management in the cost planning and control of building projects. The case of Quantity Surveying profession in Kenya. Unpublished PhD Thesis. University of Cape Town.
31. Willard, B.K (2005) Project Success: Looking Beyond Traditional Metrics, Max’s Project Management Wisdom.

70-76

http://blueeyesintelligence.org/2checkout_download.html

17.

Authors:

V.N. Ivanov, GIL-OULBE Mathieu

Paper Title:

Some Aspects of the Geometry of Surfaces with a System of Flat Coordinate Lines

Abstract: The geometry of surfaces with system of flat coordinate lines is investigated. The general vector equation of surfaces obtained. On the basis of which formulas of the main quadratic forms are obtained. The condition at which performance the system of flat coordinate lines will be system of the main lines of curvature is obtained. The subclass of normal surfaces – surfaces with system of flat coordinate lines in the normal planes of directrix is studied. As an example on the basis of the general formulas the equation of Monge’s ruled surfaces is obtained. Drawings of surfaces of Monge with various flat and spatial directrices and various flat generatrices are given.

Keywords: Surfaces with system of flat coordinate lines, the vector equation, coefficients of quadratic forms, lines of main curvatures, normal surfaces, Monge’s ruled surfaces

References:

1. Vygodsky M. I. Differential geometry. – M, L. : GITTL, 1949. -512 pages
2. Kagan V. F. Bases of the theory of surfaces. M, L. : OGIZ, 1947, t. 1 . – 512 pages; 1948, t. 2 . – 408 pages.
3. Milinsky V.I. Differential geometry.  L.: Publishing house of “Kubuch”, 1934.  332 pages.
4. Rashevsky P.K. Course of differential geometry. M, L. : GITTL, 2004. –428 pages.
5. Krivoshapko S. N., Ivanov V. N., Encyclopedia of analytical surfaces. – M: Book house “Librokom”, 2010. – 556 pages.
6. Krivoshapko S. N. Torsion surfaces and shells//Directory. M: UDN publishing house, 1991. 288 pages.
7. Ivanov V. N. Architectural compositions on the basis of Koons’s surfaces. Structural mechanics of engineering designs and constructions. – 2007 . – No. 4. – Page 5-10.
8. Krivoshapko S. N., Shambina S. L. Investigation of surfaces of velaroidal type with two sets of sinusoids on the ring plan. Structural mechanics of engineering designs and constructions. – 2009 . – No. 4. – Page 9-12.
9. Ivanov V. N. Geometry of cyclic surfaces//Collection scientific works of graduate students of engineering faculty.  Edition VIII.  М: UDN, 1971.  Pages 137-142.
10. Ivanov V. N. The theory of calculation of shells in the form of cyclic surfaces//Reports of scientific and technical conference of engineering faculty. M. : UDN, 1971.  Pages 27-29.
11. Ivanov V. N. Some questions of the theory of surfaces with set of flat coordinate lines//Calculation of shells of construction designs.  M: UDN, 1977.  Page 37-48.
12. Ivanov V. N., Rizvan Muhammad. Geometry of Monge’s carved surfaces and designing of shells//Structural mechanics of engineering designs and constructions: Interuniversity collection of scientific works, 11 Edition .  M: ASV publishing house, 2002.  Pages 27-36.
13. Ivanov V. N., Nasr Yunes Abbusha. Architecture and designing of shells in the form of wavy, umbrella and Ioakhimstal’s channel surfaces //Installation and special works in construction. – M: 2002 . – Pages 21-24.
14. Ivanov V. N. Geometry and designing of hells on the basis of surfaces with system of coordinate lines in the bunch planes//Spatial structures of buildings and constructions / Collection of scientific works of MOO “Spatial Structures”. – Edition 9 . – M: “Nine PRINT”, 2004. – Pages 26-35.
15. Ivanov V. N. Spherical curves and geometry of surfaces on the basic sphere//Modern problems of geometrical modeling / Materials of the Ukrainian-Russian scientific and practical conference, Kharkov, April 19-22, 2005 – Kharkov: 2005. – Pages 114-120
16. Ivanov V. N. Designing of shells using parabolo-sinusoidal surfaces//Structural mechanics of engineering designs and constructions, No. 2, 2005. – Pages 15-25.
17. G. Monge. Appendix of the analysis to geometry. – M: ONTI, 1936.

77-82

http://blueeyesintelligence.org/2checkout_download.html

18.

Authors:

G. Senthil Kumar, R. Amutha

Paper Title:

Capacity Enhancement of MCCDMA Systems through MAI Cancellation Using Switched Interleaving Technique and Correlation Reconstruction based MRC with Diversity Gain

Abstract: The multi-carrier code division multiple access (MCCDMA) is a attractive choice for the future wireless systems where multiplexing and diversity in space, time and frequency are joined together through multiple input multiple output (MIMO) and space time block coding (STBC) in order to increase user capacity. In this study, the orthogonal complete complementary (OCC) spreading codes are employed along with unique spreading modulation for synchronous downlink transmission of MCCDMA systems. However, MC-CDMA suffers from multiple access interference (MAI) due to frequency-selective fading and multipath effects. To mitigate this MAI effect and achieve diversity gain, adaptive switched interleaving technique is proposed based on the concept of Minimum Distance Conditional Bit Error Rate (MDCBER) criterion. Furthermore, simple correlation reconstruction based maximal ratio combing (CRMRC) scheme is introduced at the receiver to compensate the diversity fading and suppress the effect of MAI at the receiver. The proposed technique modifies interleaving pattern adaptively for STBC encoded sequence of the users to reconstruct the orthogonality among the users to counteract the fading and multipath effects. For this purpose only a quantized detail of the Squared Minimum Distance Estimation (SMDE) is enough at the users such that the associated overhead is nominal compared to perfect channel state information (CSI) at the users. The simulation result shows that the MCCDMA system using switched interleaving technique along with CRMRC scheme achieves significant performance improvement in terms of BER. It also shows that the proposed technique reduces the MAI and increases user capacity.

Keywords: Multicarrier code division multiple access (MCCDMA), multiple access interference (MAI), switched interleaving, combining schemes, orthogonal complete complementary (OCC) Codes

References:

1. S. Hara and R. Prasad, “Design and performance of multicarrier CDMA system in frequency selective Rayleigh fading channels,” IEEE Transactions on Vehicular Technology, vol. 48, no. 5, pp. 1584-1595, Sep. 1999.
2. K. Fazel and S. Kaiser, “Multi-Carrier and Spread Spectrum System,” Second Edition, John Wiley and Sons, 2008.
3. S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Selected Areas in Communication, vol. 16, no. 8, pp. 1451-1458, Oct. 1998.
4. M. El-Hajjar and Lajos Hanzo, “Multifunctional MIMO systems: A combined diversity and multiplexing design perspective,” IEEE Communication Magazine, pp. 73- 79, Apr. 2010
5. H. Chenggao, N. Suehiro, T. Hashimoto, “A systematic framework for the construction of optimal complete complementary codes,” IEEE Trans. Inf. Theory, vol. 57, no. 9, pp. 6033–6042, Sept. 2011.
6. Mario E. Magaña , Thunyawat Rajatasereekul , Daniel Hank, and Hsiao-Hwa Chen, “Design of an MC-CDMA system that uses complete complementary orthogonal spreading codes,” IEEE Trans. on Vehicular Technology, vol. 56, no. 5, pp. 2976-2989, Sept. 2007
7. S.Verd´u, “Multiuser Detection, “Cambridge University Press, 1998.
8. Lu, H.-Y, “A family of minimum mean square error multiuser detectors for three-domain spread multi-carrier direct sequence-code division multiple access,” Communications, IET, vol. 6, no. 6, pp. 587 – 598, 2012.
9. Yan Z, “QoS-based space-frequency prefiltering for TDD MC-CDMA systems in slowly time-varying channels,” Wireless Communication Mobile Computing, vol. 9, pp. 1113-1124, 2009
10. B. M. Masini and, A. Conti, “Adaptive TORC detection for MCCDMA wireless systems,” IEEE Transactions on Communications, vol. 57, no. 11, pp. 3460–3471, 2009
11. S. Chen, A. Livingstone, and L. Hanzo, “Minimum bit-error rate design for space-time equalization-based multiuser detection,” IEEE Trans. Commun., vol. 54, no. 5, pp. 824_832, May 2006.
12. Katsutoshi Kusume, Gerhard Bauch, and Wolfgang Utschick, “IDMA vs. CDMA: Analysis and Comparison of Two Multiple Access Schemes,” IEEE Transactions on Wireless Communications, vol. 11, no. 1, pp.78-87, January 2012.
13. M.S. Alouini, M.K. Simon, “Performance of generalized selection combining over Weibull fading channels,” Wireless Communication Mobile Computing, vol. 6, pp. 1077-1084, 2006.
14. Wei-Xiao Meng, Si-Yue Sun, Hsiao-Hwa Chen, and Jin-Qiang Li, “Multi-User Interference Cancellation in Complementary Coded CDMA with Diversity Gain,” IEEE Wireless Commn. letters, vol. 2, no. 3, pp. 303-306, June 2013.
15. J. Harshan and B. Sundar Rajan, “On two-user Gaussian multiple access channels with finite input constellations,” IEEE Trans. Inf. Theory, vol. 57, no. 3, pp. 1299–1327, Mar. 2011.
16. G.Senthilkumar and G.Nagarajan, “Performance Enhancement of MCCDMA Using Orthogonal Complete Complementary Codes,” Proceedings of the International Conference on Communications, Networking and Signal Processing (ICCNASP-2013), India, Sep. 2013
17. Tain-Sao Chang, Cherng-Chang Yen, Ya-Yin Yang and Jyh-Horng Wen, “Pre-filtering technique for MAI cancellation in MC-CDMA systems,” Wirel. Commun. Mob. Comput, vol. 3, pp. 63–71, 2013.

83-87

http://blueeyesintelligence.org/2checkout_download.html

19.

Authors:

Ali Mahdi Hammadi, HaidarAkram Hussein

Paper Title:

Comparative Investigation of Graded Index Optical Fiber Characteristics by Using Different Materials

Abstract: Influence of different materials on graded index optical fiber systems was studied, these materials(channel 1 has a core made from PMMA+ and cladding of carbon fluorine polymer,channel 2has a core made from Polystyrene PS++ and cladding of Polymer PMMA+,the third channel has the core made from Silica with cladding of Silicon resin), were examined for their effect on quality factor ,total output power (dBm) and intermodal dispersion at the receiver.All the channels operated at the same optical transmitted with non return-to-zero (NRZ).Performance study was done for variable lengths of fiber 1, 2, 3, 4 km for these different material channelsby simulating a model of communication system. Optisystem software has been used for this simulation.Results reveal the ability of improving the graded index fiber characteristics by using these materials and theoptimum effect concluded at(20dBm) input powerby using the third channel material i.e.the quality factor and total output power were increased to reach(374.2 ) and (-9.073 dBm) respectively and intermodal dispersion decreased to reach(11.822 ns/km).

Keywords: Graded index fiber, Polymer Optical Fiber, Intermodal dispersion.

References:

[1]. R. Dangel, C. Berger, R. Beyeler, L. Dellmann, M. Gmür, R. Hamelin, F. Horst, T. Lamprecht, T. Morf, S. Oggioni, M. Spreafico, and B. J. Offrein, “Polymer waveguide based board level optical interconnect technology for datacom applications,” IEEE Trans. Adv. Packag. 31(4), 759–767 (2008).
[2]. X. Wang, W. Jiang, L. Wang, H. Bi, and R. T. Chen, “Fully embedded board-level optical interconnects fromwaveguide fabrication to device integration,” J. Lightwave Technol. 26(2), 243–250 (2008).
[3]. R. N. HAWARD, B. M. MURPHY and E. F. T. WHITE. In Proceedings of Conference on Fracture, Brighton. 1969.Paper 45.
[4] Y. Koike, “High-bandwidth graded-index polymer optical fiber,” Polymer,vol. 32, no. 10, pp. 1737–1745, 1991.
[5] Y. Koike, T. Ishigure, and E. Nihei, “High-bandwidth graded-index polymer optical fiber,” J. Lightwave Technol., vol. 13, pp. 1475–1489,July 1995.
[6] J. Senior,“ Optical Fiber Communications,” Principles and Practice, 1st, Prentice Hall International,Inc. 1985
[7]. M Rajesh,“Fabrication and characterisation of polymer optical fibers for smart sensing and optical amplification,”International School of Photonics, Cochin University of Science & Technology Cochin, INDIA www.photonics.cusat.edu July 2006.
[8]. J. Brandrup, E.H. Immergut, D.R. Bloch and E.A. Grulke,“ Polymer Handbook,”2 Volume ,Wiley, 2003
[9]. A Ravve ,“ Principles of Polymer Chemistry,” Springer, 2000
[10] W.Daum.J.Krause,P.Zamzow,O.Ziemann,“POF fordatacommunication,”,2000.
[11] J. Senior, Optical Fiber Communications: Principles and Practice, 1st, Prentice Hall International,Inc. 1985
[12]. A. Tagaya, Y. Koike, T. Kinoshita, E. Nihei, T. Yamamoto, and K. Sasaki,”Polymer optical fiber amplifier”, App Phy Left. 63,883-884,1993
[13] T. Barake, “A Generalized Analysis of Multiple-Clad Optical Fibers with Arbitrary Step-Index Profiles and Applications” M.Sc thesis, Virginia Polytechnic Institute and State University, 1997.
[14] G. P. Agrawal, Fiber-Optic Communications Systems, 3rd ed. John Wiley & Sons, Inc., 2002.
[15] R. M. Gayliard and S. Karp, Optical Communication system, John wiely and sonce ING.1979.
[16] MohamadHasrulAriffin Bin MohdBadri, “A Cost Effective Broadband ASE Light Source Based FTTH”, thesis, page 20-26.
[17] Chong Wing Keong, K R Subramanian, V K Dubey, “Optical Fiber System for Video and Telemetry Signal.” Singapore Polytechnic, Electronics and CommEngineering, School of Electrical and Electronic Engineering, Nanyang Technology University, Singapore.

88-94

http://blueeyesintelligence.org/2checkout_download.html

20.

Authors:

Vineet Kosaraju, Rishabh Jain

Paper Title:

Augmenting the SIFT Descriptor Set to Create a Navigation Assistant for the Visually Impaired

Abstract: The issue of mobility is considered among the foremost of concerns for visually impaired individuals. This work develops a Navigation Assistant that aids the visually impaired with mobility in a known environment such as a household, by combining the principles behind Scale-Invariant Feature Transform (SIFT) and Content-Based Image Retrieval (CBIR). Furthermore, this work proposes and tests several techniques such as Color Segmentation, Difference of Images, and Contrast Manipulation to improve the Navigation Assistant. Using a video stream that was obtained from a small camera that the user carries, key objects in the household were identified using the software developed. The Navigation Assistant created using the enhancements was markedly improved in both accuracy and speed from the control Assistant, and is therefore applicable to real-life usage by visually impaired individuals.

Keywords: CBIR, SIFT, Color Segmentation, Difference of Images.

References:

[1] “Statistical Facts about Blindness in the United States (2011)”,

Internet: https://nfb.org/factsaboutblindnessintheus, 2011 [Sept. 09, 2013].
[2] Frick, KD., Gower, EW., Kempen, JH., et al.: “Economic Impact of Visual Impairment and Blindness in the United States,”

Archives of Ophthalmology, Vol. , no. 125.4, pp. 544-50, 2007.
[3] “Facts and Figures about Issues around Sight Loss”, Internet:

https://www.actionforblindpeople.org.uk/about-us/media-centre/facts-and-figures-about-issues-around-sight-loss, 2008 [Sept. 09, 2013].
[4] “Free White Cane Program”, Internet: https://nfb.org/free-cane-program [Sept. 09, 2013].
[5] Lowe, DG.: “Distinctive Image Features from Scale-Invariant Keypoints” Int. J. Comput. Vision, vol. 60.2, pp. 91–110, 2004.
[6] Young, S.: “MIT Technology Review: What it’s like to see again with an artificial retina”, Internet: http://www.technologyreview.com/news/514081/can-artificial-retinas-restore-natural-sight/, 2013 [Sept. 09, 2013].
[7] Browne, D.: “Towards a Mobility Aid for the Blind” Image Vision Comput. Nz, pp. 275–79, 2003.
[8] “Location Awareness Programming Guide”, Internet: https://developer.apple.com/library/ios/documentation/userexperience/conceptual/

LocationAwarenessPG/CoreLocation/CoreLocation.html, 2012 [Sept. 09, 2013].
[9] McDaniel, T., Kahol, K., Villanueva, D., et al.: “Integration of RFID and computer vision for remote object perception for individuals who are blind” in Proceedings of the 2008 Ambi-Sys workshop on Haptic user interfaces in ambient media systems (HAS ’08). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, Belgium, Article 7.
[10] Kain, A., Macon, MW.: “Spectral voice conversion for text to-speech synthesis” In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Vol.1, pp. 285-288, May 1998.
[11] Van Kleek, M.: “Evaluating the Stability of SIFT Keypoints across Cameras”, tech. rep., Agent-based Intelligent Reactive Environments MIT CSAIL.
[12] Ran, L., Helal, S., Moore, S.: “Drishti: An Integrated Indoor/Outdoor Blind Navigation System and Service”, In Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications (PerCom.04), pp. 23-30, 2004.
[13] Adrien Auclair, LC., Vincent, N.: “How to use sift vectors to analyze an image with database templates” tech. rep., University Paris-Descartes, vol. 4918, pp. 224-236, 2008
[14] Bakken, T.: “An Evaluation of the SIFT Algorithm for CBIR”, tech. rep., Telenor R&I Research Note, 16 Aug. 2007.
[15] [Gabsi, N., Clerot, F., Hebrail, G.: “Efficient Trade-Off between Speed Processing and Accuracy in Summarizing Data Streams”, In Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining – Volume Part I, pp. 342-53, 2010.

95-100

http://blueeyesintelligence.org/2checkout_download.html

21.

Authors:

R. Manikandan, R. Arulmozhiyal, M. Sangeetha

Paper Title:

Sensorless Speed Control of FSTPI Fed Brushless DC Motor Drive Using Terminal Voltage Sensing Method

Abstract: This paper presents the design to control the speed of permanent magnet brushless DC motor using sensorless method. In conventional operation Hall sensors are used to detect the position of the rotor windings and according to which the six switches of the voltage source inverter are gated. In sensorless method, zero crossing point of back EMF of line voltage differences are measured, which will give the information about the phase which is to be energized. The zero crossing instant of back EMF waveforms is estimated indirectly from the measurement of three terminal voltages of the motor, from which correct commutation instants of the six switches are estimated. A direct phase current control method is used to control the phase currents in four switches. Cost reduction is achieved by elimination of three Hall Effect position sensors and the numbers of power switches are reduced to four instead of six. An algorithm for four switch topology is developed with the third winding connected to the neutral of the supply line and performance of the developed sensorless technique is demonstrated by simulation. The hardware implementation is done by using PIC16F877 and the voltage and virtual hall signals are verified.

Keywords: brushless DC motor, four switch inverter, sensorless control.

References:

[1] P.Pillay and R.Krishnan, “Modelling of permanent magnet motor drives”, Industrial Electronics, IEEE Transactions on, vol. 35, pp. 537-541,1988.
[2] Mingyao Lin, Weigang Gu, Wei Zhang,Qiang Li, “Design of Position Detection Circuit for Sensorless Brushless DC Motor”.
[3] B.k.Lee,T.H.Kim and M.Ehsani “On the feasibility of four switch three-phase BLDC motor drives for low cost commercial applications:Topology and control”IEEE Trans.Power Electron vol. 18, no. 1, pp.164–172, Jan. 2003.
[4] Halvaei Niasar, H. Moghbelli and A. Vahedi, “A Low-Cost Sensorless Control for Reduced-Parts, Brushless DC Motor Drives,” IEEE Transactions on Industry Applications, 2008.
[5] T.J.E. Miller, “Brushless permanent magnet and reluctance motor drive”, Oxford, 1989.
[6] Halvaei Niasar, H. Moghbelli and A. Vahedi, “A Novel Sensorless Control Method for Four-Switch, Brushless DC Motor Drive without Phase Shifter”, IEEE Transactions on Power Electronics, Vol. 23, No. 6, November 2008.
[7] J.P. Johnson, M. Ehsani, Y. Guzelgunler; “Review of Sensorless Methods for Brushless DC Motor”, IEEE Industry Applications Conference, 1999, Vol. 1, pp. 143 –150.
[8] Halvaei Niasar, H. Moghbelli and A. Vahedi, “Implementation of four-switch brushless dc motor drive based on TMS320LF2407 DSP”, 2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 24-27 November 2007, Dubai, United Arab Emirates, PP 332-335.
[9] Halvaei Niasar, H. Moghbelli and A. Vahedi, “Modeling, simulation and implementation of four-switch brushless dc motor drive based on switching functions”, IEEE Transactions on Industry Applications, 2009, pp 682-687.

101-106

http://blueeyesintelligence.org/2checkout_download.html

22.

Authors:

Hadi T. Ziboon, Muhannad Y. Muhsin

Paper Title:

Design and Implementation of Multilevel QAM Band pass Modems (8QAM, 16QAM, 32QAM and 64QAM) for WIMAX System Based on SDR Using FPGA

Abstract: The objective of this paper is to simulate, design and implementation of a proposed system of multilevel bandpass QAM modems (modulation/demodulation) schemes with selectable technique between them based on Software Defined Radio (SDR) using FPGA for WIMAX system. These modems are 8QAM, 16QAM, 32QAM and 64QAM. MATLAB-Simulink tool and M-files are used to design these modems.“Simulink HDL Coder” is used to convert all files to VHDL Codes for hardware implementation using FPGA Altera-Cyclone II Family DE2 board. Simulink HDL Coder proves the capabilities to generate Hardware Description Language (HDL) code to MATLAB model (Simulink and M-file) for complex units of proposed system. The complex units are converted to simple units compatible with Simulink HDL Coder. The experimental results show that there is coincidence between transmitted and received data with average time delay of (0.35-0.40µsec) for different data rate (1.5-3Mbps).

Keywords: FPGA, SDR, Simulink HDL Coder, VHDL and WIMAX.

References:

[1] F. Kasperski, O. Pierrelee, F. Dotto and M. Sarlotte, ″High Data Rate Fully Flexible SDR Modem Advanced Configurable Architecture & Development Methodology″, IEEE Design, Automation & Test in Europe Conference & Exhibition, pp. 1040 – 1044, September 2009.
[2] R. Muzammil, M.S. Beg and M.M. Jamali ″ Design and Implementation of BPSK Transmitter and Receiver for Software Defined Radio on a Model Based Development Platform″, IEEE Symposium on Industrial Electronics and applications, pp.89_94, September 2011.
[3] Z. Zhao, Y. Shen and Y. Bai, ″Design and Implementation of the BPSK Modem Based on Software Defined Radio″, IEEE First International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 780_784, 2011.
[4] A. Karmakar and A. Sinha, ″A Novel Architecture of a Reconfigurable Radio Processor for Implementing Different Modulation Schemes″, IEEE Computer Research and Development (ICCRD) 3rd International Conference, pp. 115_119, 2011.
[5] Y. H. Chye, M. F. Ain and N. M. Zawawi, ″Design of BPSK Transmitter Using FPGA with DAC″, IEEE 9th Malaysia International Conference on Communications, pp. 451_456, December 2009.
[6] S. W. Shaker, S. H. Elramy and K. A. Shehata, ″FPGA Implementation of a Reconfigurable Viterbi Decoder for WIMAX Reciver″, IEEE International Conference on Microelectronics, pp. 264_267, 2009.
[7] Y. Yang, J. Liu, Q. Zhang and C. Xiu ″Performance Analysis for Rectangular QAM Modulation with Arbitrary Bits-to-Symbols Mapping over Rician Channel″, IEEE International Conference, pp. 1007_1010, 2010.
[8 A. Goldsmith, ″ Wireless Communications″, Cambridge University Press, 2005.
[9] S. O. Leary, ″Understanding Digital Terrestrial Broadcasting″, Artech House Boston London, INC., 2000.
[10] J. G. Andrews, A. Ghosh and R. Muhamed, ″Fundamentals of WiMAX Understanding Broadband Wireless Networking″, Pearson Education, Inc., 2007.
[11] ″Simulink HDL Coder″, the Mathwork Inc., 2011.

107-116

http://blueeyesintelligence.org/2checkout_download.html

23.

Authors:

Oday Mohammed Abdulwahhab

Paper Title:

Improvement of the MATLAB/Simulink Photovoltaic System Simulator Based on a Two-Diode Model

Abstract: This paper presents modeling and simulation for the PV cells, modules, and array, and building of blocks based on MATLAB / Simulink software package. The main aim of this work is the improvement of the PV solar cell simulator based on a two-diode model. This model is more accurate than the single diode model especially at low insolation levels; therefore this will gives more accurate prediction of the performance of the solar cell system. Most of the inputs to the simulator are given in the PV module datasheet, and the other (i.e. Rp and Rs) are estimated by an iteration method. The simulation model makes use of the two-diode model basic circuit equations of PV solar cell, taking the effect of sunlight irradiance and cell temperature into consideration on the output current V-I and output power V-P characteristics. The output current and power characteristics of PV module and PV array are simulated using the proposed model. The simulator is verified by applying the model to Kyocera Solar KC200GT and Solarex MSX-64 PV Modules.

Keywords: Modeling, PV module/ Array, Simulation, MATLAB/Simulink

References:

[1] S.Chowdhury, S.P.Chowdhury, G.A. Taylor, and Y.H.Song ,“Mathematical Modeling and Performance Evaluation of a Stand-Alone Polycrystalline PV Plant with MPPT Facility”, IEEE, 2008.
[2] Huan-Liang Tsai, Ci-Siang Tu, and Yi-Iie Su, “Development of Generalized Photovoltaic Model Using MATLAB/SIMULINK”, Proceedings of the World Congress on Engineering and Computer Science WCECS, San Francisco, USA, 2008.
[3] Zainal Salam, Kashif Ishaque, and Hamed Taheri, “An Improved Two-Diode Photovoltaic (PV) Model for PV System”, 978-1-4244-7781-4/10/2010, IEEE.
[4] Zainal Salam, Kashif Ishaque, and Hamed Taheri,”Accurate MATLAB Simulink PV System Simulator Based on a Two-Diode Model”, Journal of Power Electronics, Vol. 11, No. 2, March 2011.
[5] Atul Gupta and Venu Uppuluri Srinivasa, “Design, Simulation and Verification of Generalized Photovoltaic Cells Model Using First Principles Modeling”, ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 01, Feb 2012.
[6] S. Sheik Mohammed, “Modeling and Simulation of Photovoltaic module using MATLAB/ Simulink”, International Journal of Chemical and Environmental Engineering, volume 2, No.5, October 2011.
[7] Haitham S.Dawood, “Analytical Equivalent Circuit of High-Irradiated Conventional Silicon Solar Cell Performance”, Iraqi Journal of Applied Physics, Vol. (5), No. (4), October 2009.
[8] H. Bourdoucen and A. Gastli, “Analytical Modeling and Simulation of Photovoltaic Panels and Array”, The Journal of Engineering Research, Vol. 4, No. 1, 2007.

117-122

http://blueeyesintelligence.org/2checkout_download.html

24.

Authors:

Omran Malik Omer Awad, Awad Hag Ali Ahmed, Abdelmonem M. Ali Artoli

Paper Title:

Stochastic Simulation Efficiency of Parallel CFD Solver on Elastic Cloud Environment

Abstract: Computational fluid dynamics applications become crucial for scientist to understand various Natural phenomenon. These applications require high performance computing resources that most small academic institutions cannot afford. Elastic cloud clusters are best suited environment for those small academic institutions to gain high performance computing power and enable researchers to explore new trends in scientific computing with reasonable cost. This work aims to study the parallelism efficiency; in term of communication time and execution time for a highly optimized parallel lattice Boltzmann solver on elastic cloud clusters. On these elastic clusters we have found that the lattice Boltzmann implementation is fully adaptive, highly flexible and cost effective to use for solving complex large fluid mechanical systems.

Keywords: Computational Fluid Dynamics, Elastic Cloud Computing, Multi-core Programming, Lattice Boltzmann.

References:

[1] S. Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer and D. Epema, “A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing,” in Cloud Compting , Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, 2010.
[2] “AWS Amazon Elastic Compute Cloud (EC2) – Scalable Cloud Servers:,” 2013. [Online]. Available: http://aws.amazon.com/ec2/.
[3] “Cloud Services – Windows Azure,” Microsoft Co. Ltd., 2013. [Online]. Available: http://www.windowsazure.com/en-us/services/cloud-services/.
[4] M. J. H. Ferziger, “Computational Methods for Fluid Dynamics,” 1996.
[5] P. Wesseling, “Principles of Computational Fluid Dynamics,” 2001.
[6] Y. Yan, Flow and particle transport by the Lattice Boltzmann Method, New York: ProQuest, 2008.
[7] X. He and L.-S. Luo, “Lattice Boltzmann model for the incompressible Navier-Stokes equation,” vol. Journal of Statistical Physics, no. 88, 1997.
[8] A. M. Artoli, “Mesoscopic Computational Haemodynamics,” PHD Thesis, 2003.
[9] A. M. Artoli, D. Kandhai, H. J. Hoefsloots, A. G. Hoekstra and P. M. Sloot, “Lattice BGK simulations of flow in a symmetric bifurcation,” Future Generation Computer Systems, vol. 20, no. 6, pp. 909-916, 2004.
[10] M. Geveler, D. Ribbrock, D. Goddeke and S. Turek, “Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors,” Facing the Multicore-Challenge, vol. 6310, 2010.
[11] H. Chen, S. Chen and W. H. Matthaeus, “Recovery of the Navier-Stokes equations using a lattice-gas Boltzmann method,” The American Physical Society, vol. 45, no. 8, 1992.
[12] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt and A. Warfield, “Xen and the art of virtualization,” in Proceedings of the Nineteenth ACM Symposium on Operating systems principles, New York, USA, 2003.
[13] S. Succi, The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, USA : Oxford University Press, 2002.
[14] A. M. Artoli, D. Kandhai, H. C. Hoefsloot and A. G. Hoekstra, “Lattice Boltzmann, a Robust and Accurate Solver for Interactive Computational Hemodynamics,” Computational Science, vol. 2657, pp. 1034-1043, 2003.

123-128

http://blueeyesintelligence.org/2checkout_download.html

25.

Authors:

A. Mukesh Kumar, B. Nupur Sharma, Shreesh Kumar Shrivastava

Paper Title:

Online Social Networks: Privacy Challenges and Proposed Security Framework for Facebook

Abstract: Social networking sites facilitate and develops a virtual community for people to share their thoughts, interest activities or to increase their horizon of camaraderie. With the advancement in Social networking sites or we can refer it as with online social networks the user can connect to each other. With all these options available on Social Networking sites it creates a virtual world for the users. Social Networking sites go upwards because of all these reasons. In recent years indicates that for many people they are now the mainstream communication technology. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as disclosure of information, identity thefts etc. Privacy practices in social networking sites often appear convoluted as information sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which has its own robust set of Privacy policies. Yet they are also prone to various privacy issues and attacks. In this work we examine the currently existing privacy policies of Facebook since 2013 and then provide with certain new policies that enhance and strengthen the existing ones. We conclude with a discussion of how these educts may be integrated into the design of Facebook like Social Networking sites systems to facilitate interaction while enhancing the individual privacy.

Keywords: Social networking sites (SNS’s), Cyber Stalking, Cyber Bullying.

References:

[1] MARK Hachman (Aoril 23,2012). “Facebook Now Totals 1.20 billion Users, Profits Slip”. PCMag.com. Retrieved September 24, 2013.
[2] A. Ho, A4. Maiga, and E. Aimeur, “Privacy protection issues in social networking sites,”IEEE/Acs International Conference on Computer Systems and Applications 2009 (AICCSA 2009),PP.271-278,Country,2009
[3] Aimeur, E.; gambus,S.; Ai Ho; , “UPP: User Privacy Policy for Social Networking Sites,”Internet and Web Applications and Services,2009. ICIW ’09.Fourth International Conference on vol., no., pp.267-272,24-28 May 2009.
[4] C.Zhang, J.Sun,X.Zhu and Y.Fang,” Privacy and security for online social networks: challenges and opportunities,” IEEE Network, vol.24,no.4,pp.13-18,2010
[5] Stutzman, F.and Kramer-Duffield, J. Friends only: Examining a Privacy Enhancing behaviour in Facebook. In Proc. CHI’10.ACM Press, 2010.1553—1562.
[6] Thomas,K., Grier, C., and Nicol,D.M.unFriendly: Multi party privacy risks in social networks.In proceedings of the 10th international conference on Privacy enhancing technologies(2010),Soringer-Verlag,pp.236
[7] http://www.facebook.com\privacy
[8] Ai Ho; Maiga, A.; Aimeur, E.; , “Privacy protection issues in social networking sites,” Computer Systems and Applications, 2009. AICCSA 2009.IEEE/ACS International Conference on vol.,no., pp.271-278, 10-13 May 2009
[9] Xi Chen; Shuo Shi; ,”A Literature review of Privacy Research on Social Network Sites,” Multimedia Information Networking and Security,2009.MINES’09.International Conference on, vol.1,no.,pp.93-97,18-20 Nov.2009
[10] http://www.privacyawarenessweek.org
[11] DotRights Social Networking Page, www.dotrights.org/social-networking
[12] SeyedHossein Mohtasebi and Ali Dehghantanha,” A Mitigation Approach to the Malwares Threats of Social Network Services,” Muktimedia Information Networking and Security,2009. MINES’09. International Conference on, vol.1,no.,pp.448-459,2011
[13] Mohammad Mannan, Paul C. Van Oorschot,” privacy-Enhanced Sharing of Personal Cpntent on the Web,” Security And Privacy- Misc” , pp.487-496,April 21-25,2008 Beijing, China
[14] Privacy Policy Facebook (2011), www.facebook.com/policy.php
[15] Chi Zhang; Jinyuan Sun;, ”Privacy and Security for Online Social networks:Challenges and Opportunities,” IEEE Network,Aug.2010
[16] Vorakulpipat, Marks, Rezgui, “ Security and Privacy Issues in Social Networking Sites from User’s Viewpoint,”IEEE Network,Jun.2011
[17] P.Kodeswaran, and E.Viegas, “Towards A privacy preserving Policy Based Infrastructure for Social Data Access To enable Scientific Research,”2010 Eighth Annual International Conference on Privacy,Security and Trust,Jun 2010
[18] I. Polakis and G. Kontaxis,” Using Social Networks to harvest Email Addresses,” In Proc.CHI”10. ACM Press,2010
[19] C.Squicciarini and M.Shehab,” Privacy policies for shared content in social network sites,” In Proc.Chi’10.Acm Press,30 June 2010
[20] Yabing Liu and P. Gummadi, “Analyzing Facebook Privacy Settings:User Expectations vs. Reality,” In Proceedings of the 10th international conference on Privacy enhancing technologies(2011)
[21] P.Joshi anf C Kuo,” Security and Privacy in Online Social Networks: A Survey”,IEEE Network,2011
[22] D Michslopoulos and I Mavridis,” Surveying Privacy Leaks Through Online Social Networks”, 2010 14th panhellenic Conference on Information,2010.

129-133

http://blueeyesintelligence.org/2checkout_download.html

26.

Authors:

Binamrata Baral, Sandeep Gonnade, Toran Verma

Paper Title:

Image Segmentation and Various Segmentation Techniques – A Review

Abstract: Image Segmentation has been an area for a long time which is providing opportunities for the intellectual communities to do research work. Image segmentation is a process of partitioning an image into meaningful regions. There exist many digital image segmentation techniques which are currently applied on different fields. These image segmentation techniques need comparative analysis for further development and modifications for continuous and consistent improvement. Hence, in this paper an overview of image segmentation and its present techniques is presented which demands a lot of research work.

Keywords: Image, Image Segmentation, Segmentation Techniques.

References:

[1] P. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson Education, Asia.
[2] Jay Acharya, Sohil Gadhiya and Kapil Raviya, “Segmentation Techniques for Image Analysis: A Review”, International Journal of Computer Science and Management Research, Vol 2 Issue 1, January 2013, Pg. 1218-1221.
[3] Dzung L. Pham, Chenyang Xu and Jerry L. Prince, “A Survey Of Current Methods In Medical Image Segmentation”, Annual Review of Biomedical Engineering, January 19, 1998.
[4] Rajvi Parikh and Dr Hitesh shah, ”A Survey on Computer Vision Based Diagnosis for Skin Lesion Detection”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 2, March 2013, Pg. 431-437.
[5] Nadia Smaoui and Souhir Bessassi, “A developed system for melanoma diagnosis”, International Journal of Computer Vision and Signal Processing, 3(1), 10-17(2013), Pg. 10-17.
[6] Aswin.R.B, J. Abdul Jaleel and Sibi Salim, “Implementation of ANN Classifier using MATLAB for Skin Cancer Detection”, International Journal of Computer Science and Mobile Computing, ICMIC13, December- 2013, Pg. 87-94.
[7] Ayesha Khalid Khan, Gulistan Raja and Ahmad Khalil Khan, ”Implementation of Marker based Watershed Image Segmentation on Magnetic Resonance Imaging”, Life Science Journal 2013; 10(2): 115-118. (ISSN: 1097-8135), Pg. 115-118.
[8] E. Nadernejad, S. Sharifzadeh and H. Hassanpour, “Edge Detection Techniques: Evaluations and Comparisons”, Applied Mathematical Sciences, Vol. 2, 2008, no. 31, Pg. 1507 – 1520.
[9] Rajesh Dass, Priyanka, Swapna Devi, “Image Segmentation Techniques”, IJECT Vol. 3, Issue 1, Jan. – March 2012.
[10] Arpit Maheshwari, Sachin Sonawane and Shashikant Patil, “Performance Overview, Comprehensive Assessment and Review of Image Segmentation Techniques for Natural Images”, Current Trends in Technology and Science, ISSN : 2279-053. Volume : II, Issue : VI, Pg. 367-373.
[11] Ramadevi, Y., B. Kalyani, and T. Sridevi, “Synergy between Object Recognition and Image Segmentation”, International Journal on Computer Science & Engineering (2010).
[12] Vishal B. Langote and Dr. D. S. Chaudhari, “Segmentation Techniques for Image Analysis”, International Journal of Advanced Engineering Research and Studies (IJAERS)/Vol. I/ Issue II/January-March, 2012.
[13] Pedro F. Felzenszwalb and Daniel P. Huttenlocher, ” Efficient Graph-Based Image Segmentation”, International Journal of Computer Vision 59(2), 167–181, 2004.
[14] Bo Peng and Olga Veksler, “Parameter Selection for Graph Cut Based Image Segmentation”, In: British Machine Vision conference (2008).
[15] S. Vicente, V. Kolmogorov, and C. Rother, “Graph cut based image segmentation with connectivity priors”. In CVPR, 2008.
[16] Mehmet Sezgin and Bulent Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–165 (January 2004).
[17] Dmitrij Csetverikov, “Basic algorithms for digital image analysis: a course“, http://ssip2003.info.uvt.ro/lectures/chetverikov/shape_analysis.pdf.
[18] Prerna Pachunde, Prof.Vikal.R.Ingle and Prof. Dr Mahindra. A. Gailwad, “Segmentation of Color Images Using Genetic Algorithms: A Survey”, IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE) ISSN: 2278-1676 Volume 1, Issue 6 (July-Aug. 2012), PP 09-12.
[19] M.E. Farmer and D. Shugars. “Application of genetic algorithms for wrapper-based image segmentation and classification”. In IEEE Congress on Evolutionary Computation, pages 1300–1307, July 2006.
[20] Catalin Amza, ”A Review on Neural Network –Based Image Segmentation Techniques”, De Montfort University, Mechanical and Manufacturing Engg., The Gateway Leicester, LE1 9BH, United Kingdom, 1-23.
[21] A. Gavlasova, A. Prochazka, M. Mudrova, “Wavelet based image segmentation”, in Proceedings of the 14th Annual Conference Techincal Computing, Prague, 2006, pp.1-7.
[22] Dongwook Cho, and Tien D. Bui, “Image Inpainting Using Wavelet-based Inter- and Intra- scale Dependency”, IEEE Transactions Image Processing, 978-1-4244-2175-6, 2008.
[23] Vaibhav V Nalawade and Sachin D Ruikar, “Image Inpainting Using Wavelet Transform“, IJAET/Vol.II/ Issue IV/October-December, 2011/302-307.
[24] M.Ceylan, O.N.Ucan, Y.Özbay, R.Jennane, G.Aufort, C.L.Benhamou, “Comparison of discrete wavelet transform and complex wavelet transform in hybrid skeletonization based on cvann”, İstanbul Aydın Üniversitesi, Fen Bilimleri Dergisi, 1, 27-51, ( Üniversite Dersgisi ).
[25] Divya.D, Sushma P.S, “FPGA Implementation of a Distributed Canny Edge Detector”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume- 1, Issue- 5, July-2013.
[26] Heimann, Tobias, and Hervé Delingette, “Model-based Segmentation”, Biomedical Image Processing, Springer Berlin Heidelberg, 2011. 279-303.

134-139

http://blueeyesintelligence.org/2checkout_download.html

27.

Authors:

Edmore Chikohora, Obeten O. Ekabua

Paper Title:

Feature Extraction Techniques in Remote Sensing Images: A survey on Algorithms, Parameterization and Performance

Abstract: Remote Sensing Images (RSI) employs various Feature Extraction Techniques (FET) that implement different algorithms to extract features from a query image. In this paper, we provide a critical and comprehensive survey on algorithms implemented by different FET, their parameter selection strategies and performance. The survey is divided into three parts where initially three FET are selected and the algorithms they implement are analysed. Secondly, their parameter selection strategies are surveyed and finally a critical analysis on performance based on literature results obtained is provided with some concluding remarks.

Keywords: Remote Sensing, Feature Extraction, Convolution Mask, Mahalanobis Distance, Gaussian Envelope, Frequency Harmonic.

References:

[1] Henrique Momm and Greg Easson, Feature Extraction from High-Resolution Remotely Sensed Imagery using Evolutionary Computation. Mississippi, USA: Prof. Eisuke Kita, 2011.
[2] J.G Daugman, “Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Optical Society of America, vol. 2, pp. 1160-1169, 1985.
[3] P Moreno, A Benardino, and J Santos-Victor, “Gabor Parameter Selection for Local Feature Detection,” in IBPRIA 2nd Iberian Conference on Pattern Recogniton and Image Image Analysis, Portugal, 2005.
[4] J Yang, L Liu, T Jiang, and Y Fan, “A modified Gabor Filter Design Method for Fingerprint Image Enhancement,” Pattern Recognition Letters, no. 24, pp. 1805 – 1817, January 2003.
[5] S Fejes and F Vaida, “Simplified Adaptive Approach To Efficient Morphological Image Analysis,” 2004.
[6] E Saffor and A Salama, “Objective Evaluation of Mathematical Mophology Edge Detection on Computed Tomography (CT) Images,” International Journal of Medical Science and Engineering, vol. 7, no. 9, pp. 631 – 635, 2013.
[7] D Ze-Feng, Y Zhou-Ping, and X You-Lun, “High Probability Impulse Noise-Removing Algorithm Based on Mathematical Morphology,” IEEE Signal Processing Letters, vol. 14, no. 1, pp. 31-34, January 2007.
[8] G Joseph, Fundamentals of Remote Sensing, 2nd edition. India: Universities press private Ltd., 2005.
[9] M.S Moran, Y Inoue, and Barnes E.M, “Opportunities and Limitations for image-based Remote Sensing in Precision Crop Management.,” Remote Sensing environment, vol. 61, pp. 319 – 346, January 1997.
[10] Pesaresi, Kolbeinn A Benediktsson J.A, “Classification and Feature Extraction for Remote Sensing Images from Urban Based on Morphological Transformations,” IEEE Transactions on Geo-Science and Remote Sensing, vol. 41, no. 9, September 2003.
[11] Lee C., “Feature Extraction Based on Decision Bounderies,” IEEE Trans. Pattern Anal. Machine Intell, vol. 15, pp. 388-400, April 1993.
[12] Suruliandi A. Jeyabharathi D., “Perfomance Analysis of Feature Extraction and Classification Techniques in CBIR,” International Conference on Circuits, Power and Computing Technologies, pp. 1211 – 1214, 2013.
[13] Mehta K., Arora S. Basal A., “Face Recognition using PCA and LDA Algorithms ,” Advancded Computing and Communication Technologies (ACCT), Jan 2012.

140-144

http://blueeyesintelligence.org/2checkout_download.html

28.

Authors:

Joydeep Sengupta, Girish Chandra Ghivela, Monojit Mitra

Paper Title:

Dynamic Characterization and Noise Analysis of 4H-SiC Impatt Diode at Ka Band

Abstract: The microwave as well as the small signal noise properties on a one dimensional n+npp+ DDR structure 4H-SiC IMPATT Diode have been studied using advanced computer simulation program developed by us and compared at different frequency of Ka band by taking the area of the diode as . Also the theory for the diode current noise associated with the electron hole pair generation and recombination in the space charge region of the diode is presented. This paper can help to know about the small signal behavior as well as noise behavior of IMPATT diode along with power density at the Ka band and will be helpful for designing the 4H-SiC based IMPATT diode depending upon the microwave applications.

Keywords: Impact ionization, efficiency, mean square noise voltage, quality factor, noise spectral density, power density, noise measure.

References:

[1] J.B. Casady and R.W. Johnson, Status of silicon carbide as a wide-bandgap semiconductor for high-temperature applications: a review Solid-State Electron., 39(10), p. 1409-1422 (1996).
[2] P.G. Neudeck, Progress in silicon carbide semiconductor electronics technology, J. Electron. Mater., 24 (4), p. 283-288 (1995).
[3] Joydeep Sengupta, Dr. Monojit Mitra “Comparison of Performance of Impatt Diode for Various Materials” IJSER Vol 4,Issue 6, June 2013 Edition (2229-5518).
[4] H. Eisele and G. I. Haddad, “Active Microwave Devices in Modern Semiconductor Devices Physics”, S. M. Sze , Ed. New York: Wiley, 1998, pp. 343–407.
[5] D.N.Datta,S.P.Pati,J.P.Banerjee,B.B.Pal and S.K.Roy,”Computer Analysis of DC Field and Current density profiles of DAR Impatt Diode”,IEEE Trans. on Electron Devices ,Vol.ED-29,No.11,Nov.1982.
[6] S. K. Roy, M. Sridharan, R. Ghosh, and B. B. Pal, “Computer method for the dc field and carrier current profiles in the IMPATT device starting from the field extremum in the depletion layer” Proceedings of the 1st Conference on Numerical Analysis of Semiconductor Devices (NASECODE I), J. H. Miller, Ed., Dublin, Ireland, 266-274 (1979).
[7] S.M Sze and R.M Ryder, “Microwave Avalanche Diodes”,Proceedings of the IEEE, Volume: 59, PP: 1140,1971.
[8] Hermann K. Gummel and J.L Blue, “A small-signal theory of avalanche noise in IMPATT diodes”,IEEE Transaction. Electron Devices, Volume: 14, PP: 569, 1967.
[9] Mukherjee M, Roy S K “Wide band gap III-IV nitride based avalanche transit time diode in terahertz regime: studies on the effects on punch through on high frequency characteristics and series resistance of the device”Current Applied Physics 10(2010) 646-651.
[10] Eisele H and Haddad G I,” Active microwave Devices Microwave Semiconductor Device Physics” S M Sze (New York) p 343, ed:1997.
[11] S.M.Sze,”Physics of Semiconductor Devices”,1969,John Wiley and sons.
[12] J.K.Mishra, G.N.Dash, S.R.Pattanaik, I.P.Mishra,”Computer Simulation Study on the noise and millimeter wave properties of InP/GaInAs hetero junction double avalanche region IMPATT didoe”, Solid-state electronics,vol-48.pp:401-408,2004.
[13] G.N.Dash,J.K.Mishra and A.K.Panda,”Noise in mixed tunneling Avalanche transit time (MITATT) diodes”,Solid-state electronics,vol-39(10),pp:1473-1479,Jan-1996.
[14] A.K. Panda, D. Pavlidis, and E. Alekseev, Noise characteristics of GaN-based IMPATTs,IEEE Trans. Electron Devices, 48, p. 1473-1475,2001.
[15] A. Reklaitis and L. Reggiani, Monte Carlo investigation of current voltage and avalanche noise in GaN double-drift impact diodes , J. Appl. Phys., 97, 043709 (2005).
[16] J.K.Mishra, A.K.Panda, and G.N.Dash,”An Extremely low noise hetero junction IMPATT”, IEEE Trans. on Electron Devices ,Vol.ED-44,No.12,Dec.1997.
[17] T.Misawa,”IMPATT diodes”, in semiconductor and semimetals, vol.7,Newyork,Academic Press.1971,partB.

145-149

http://blueeyesintelligence.org/2checkout_download.html

29.

Authors:

Anchal Katyal, Amanpreet Kaur, Jasmeen Gill

Paper Title:

Punjabi Speech Recognition of Isolated Words Using Compound EEMD & Neural Network

Abstract: Automatic Speech recognition and conversion of speech to text is a work which has proved its importance for decades. A lot of work has already been done in this contrast. This paper focuses on the Punjabi speech and the conversion of speech to text using advanced system voice recognition pattern. This paper also focuses on the optimization of the EEMD process by combining EEMD process with the Neural Network. Neural Network has been found to be friendly in the contrast of compounding different algorithms to it and it produces significant results. This paper also focuses on the future works to be considered in the same field.

Keywords: ASR, EEMD, Neural Network, Acoustical Models, Neural Identifier, Data Acquisition.

References:

[1] Preeti Saini, “Automatic Speech Recognition”, A Review International Journal of Engineering Trends and Technology- Volume4Issue2- 2013.
[2] WiqasGhai, “Literature Review on Automatic Speech Recognition”, International Journal of Computer Applications (0975 – 8887)Volume 41– No.8, March 2012.
[3] Yuqiang Qin,“EEMD-Based Speaker Automatic Emotional Recognition,” Chinese Mandarin Appl. Math.Inf. Sci. 8, No. 2, 617-624 (2013).
[4] Akshay S. Utane, “Emotion Recognition through Speech Using Gaussian Mixture Model and Support Vector Machine “, International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May-2013.
[5] Shing-Tai Pan, “Robust Speech Recognition by DEEMD with A Codebook Trained by Genetic Algorithm”, Journal of Information Hiding and Multimedia Signal Processing, October 2012.
[6] Wu, S., Falk, “Automatic recognition of speech emotion using long-term spectro-temporal features,” Proc. Internat. Conf. on Digital Signal Processing, 1-6 (2009).
[7] Geoffrey Hinton, Li Deng, “Neural Networks for Acoustic Modeling in Speech Recognition”.
[8] H. Hermansky, D. P. W. Ellis, “Tandem connectionist feature extraction for conventional EEMD systems,” Proceedings of ICASSP, Los Alamitos, CA, USA, 2000, vol. 3, pp. 1635–1638, IEEE Computer Society.
[9] H. Bourlard, N. Morgan, “Connectionist Speech Recognition: A Hybrid Approach”, Kluwer Academic Publishers, Norwell, MA, USA, 1993.
[10] L. Deng, “Computational models for speech production,” in Computational Models of Speech Pattern Processing, pp. 199–213. Springer- Verlag, New York, 1999.
[11] L. Deng, “Switching dynamic system models for speech articulation and acoustics,” in Mathematical Foundations of Speech and Language Processing, pp. 115–134. Springer-Verlag, New York, 2003.
[12] A. Mohamed, G. Dahl, and G. Hinton, “Deep belief networks for phone recognition,” in NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009.
[13] A. Mohamed, G. Dahl, “Acoustic modeling using deep belief networks,” IEEE Transactions on Audio, Speech, and Language Processing,, vol. 20, no. 1, pp. 14–22, jan. 2012.
[14] D. E. Rumelhart, G. E. Hinton, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
[15] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of AISTATS, 2010, pp. 249–256.
[16] D. C. Ciresan, U. Meier, “Deep, Big, Simple Neural Nets for Handwritten Digit Recognition,” Neural Computation, vol. 22, pp. 3207–3220, 2010.
[17] G. E. Hinton , R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.
[18] H. Larochelle, D. Erhan, “An empirical evaluation of deep architectures on problems with many factors of variation,” in Proceedings of the 24th international conference on Machine learning, 2007, pp. 473–480.
[19] J. Pearl, “Probabilistic Inference in Intelligent Systems: Networks of Plausible Inference”, Morgan Kaufmann, 1988.
[20] G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Computation, vol. 14, pp. 1771–1800, 2002.
[21] G. E. Hinton, “A practical guide to training restricted boltzmann machines,” Tech. Rep. UTML TR 2010-003, Department of Computer Science, University of Toronto, 2010.

150-154

http://blueeyesintelligence.org/2checkout_download.html

30.

Authors:

Begared Salih Hassen

Paper Title:

Peak to Average Power Ratio Reduction in OFDM Systems Using Clipping and Filtering Technique

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) has been currently under intense research for broadband wireless transmission due to its robustness against multipath fading. However OFDM signals have a problem with high Peak-to-Average power ratio (PAPR) and thus, a power amplifier must be carefully manufactured to have a linear input-output characteristic or to have a large input power back-off. The drawbacks of high peak to average power ratio (PAPR) can outweigh all the potential benefits of Orthogonal Frequency Division Multiplexing (OFDM) signals. In this Paper, a sophisticated PAPR reduction technique, named Iterative Clipping and filtering (ICF) is proposed for OFDM system. By considering the example of OFDM, with Quadrature Phase-Shift Keying (QPSK) mapping, simulation results under Matlab environment show that the proposed method performs well in reducing PAPR..

Keywords: OFDM, High Peak to Average Power Ratio, Iterative Clipping and Filtering.

References:

1. Z. Cheng, D. Dahlhaus: Time versus Frequecy Domain Channel Estimation for OFDM Systems with Antenna Arrays, Proc. of the 6th International Conference on Signal Processing, ICSP’02, vol. 2, pp 1340-1343, Aug. 2002
2. Jeffrey G. Andrews, Ph.D., Arunabha Ghosh, Ph.D,Rias Muhamed., ed, Fundamentals of WiMAX Understanding Broadband Wireless Networking. Theodore S. Rappaport, Series Editor, ed. P.H.C.E.a.E.T. Series. 2007
3. Y.-J. Wang, W. Chen. A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals. IEEE Trans. Wireless Commun.; vol. 9, no. 10, pp. 2994-2999, Oct. 2010.
4. L. J. Cimini Jr. and N. R. Sollenberger. Peak-to-average power ratio reduction of an OFDM signal using partial transmit sequences. IEEE Comm. Lett.; vol. 4, no. 3, pp. 86-88, Mar. 2000.
5. Physical Channels and Modulation (Release 9); 3GPP TS 36.211 v9.1.0, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); 2010.
6. T.Jiang, W.Xiang, P.C.Richardson, J.Guo, and G. Zhu, “PAPR Reduction of OFD Signals Using Partial Transmit Sequences with Low Computational Complexity,” IEE Transactions on Broadcasting, Vol.53, No.3, September 2007, pp.719-724.
7. C.-P. Li, S.-H. Wang, and C.-L. Wang. Novel Low-Complexity SLM Schemes for PAPR Reduction in OFDM Systems. IEEE Trans. Signal Processing; vol. 58, no. 5, pp. 2916-0921, May. 2010.
8. Y.-C. Wang and Z.-Q. Luo. Optimized Iterative Clipping and Filtering for PAPR Reduction of OFDM Signals. IEEE Trans. Commun.; vol. 59, no. 1, pp. 33-37, Jan. 2011.
9. X. Zhu, G. Zhu, and T. Jiang. Reducing the peak-to-average powerra- dio using unitary matrix. IET Commun.; vol. 2, no. 2, pp. 161-171, 2009.
10. D.Guel and J.Palicot, “FFT/IFFT Pair based Digital Filtering for the Transformation of Adding Signal PAPR Reduction Techniques in Tone Reservation Techniques,” Fifth International Conference on Wireless and Mobile communications, (ICWMC 2009), August 2009.
11. Y.Wu and W. Y. Zou, “Orthogonal frequency division multiplexing: A multi-carrier modulation scheme,” IEEE Trans. Consumer Electronics, vol. 41, no. 3, pp. 392–399, Aug. 1995.
12. J. Tellado, “Peak to Average Power Ratio Reduction for Multicarrier Modulation,” PhD thesis, University of Stanford, Stanford, 1999.

155-158

http://blueeyesintelligence.org/2checkout_download.html

31.

Authors:

Bhawna Jyoti, Aman Kumar Sharma

Paper Title:

Test Case Suite Reduction of High Dimensional Data by Automatic Subspace Clustering

Abstract: Mostly, testing techniques are designed for data which are having low dimensional space and less intention is paid to the testing of high dimensional data. In this paper, data undergoes a process of dimensionality reduction by principal component analysis (PCA) which leads to the automate subspace clustering of data. The combination of distributed based approach and coverage based approach is used to test the test cases sampled from each cluster formed. The contribution of this paper is related to the dimensionality reduction as well as test case suite reduction by discovering patterns in software testing in a rigorous manner.

Keywords: Dimensionality reduction using PCA, clustering, the test suite minimization.

References:

[1] Mark Shtern and Vassilios Tzerpos,” Clustering Methodologies for Software Engineering ”in Hindawi Publishing Corporation Advances in Software Engineering ,Volume 2012, Article ID 792024, 18 pages, doi:10.1155/2012/792024.
[2] Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan,” Automatic Subspace Clustering Of High Dimensional Data, ”in Data Mining and Knowledge Discovery, 11, 5–33, 2005, Springer Science ,Inc. Manufactured in The Netherlands.
[3] Lilly Raamesh, Lilly Raamesh,” An Efficient Reduction Method for Test Cases,”in International Journal of Engineering Science and Technology”,Vol. 2(11), 2010, 6611-6616.
[4] Mamta Santosh,Rajvir Singh,”Test Case Minimization By Generating Requirement Based Mathematical Equations,”in International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 6, June – 2013
[5] Rajvir Singh and Mamta Santosh,” Test Case Minimization Techniques : A Review,” in International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 12, December – 2013.
[6] Saeed Parsa and Alireza Khalilian,” A Bi-objective Model Inspired Greedy Algorithm for Test Suite Minimization,” FGIT ’09 Proceedings of the 1st International Conference on Future Generation Information Technology,2009.
[7] Saeed Parsa and Alireza Khalilian,” On the Optimization Approach towards Test Suite Minimization,” in International Journal of Software Engineering and Its Applications,Vol. 4, No. 1, January 2010.
[8] Shin Yoo & Mark Harman,” TR-09-09: Regression Testing Minimisation, Selection and Prioritisation – A Survey,” in Technical report TR-09-09, Department of Computer Science, King’s College London, 2009.
[9] Mary Jean Harrold ,Rajiv Gupta And Mary Lou Soffa,” A Methodology for Controlling the Size of aTest Suite,” 1993 ACM 1049 -331X/93 /’O7OO-O27O $01.50 Julv 1993, Pages 270–285.
[10] Alireza Khalilian and Saeed Parsa,” Bi-criteria Test Suite Reduction by Cluster Analysis of Execution Profiles,” in International Federation for Information Processing, 2012, CEE-SET 2009, LNCS 7054, pp. 243–256, 2012.
[11] Tajunisha1 and Saravanan “An efficient method to improve the clustering performance for high dimensional data by Principal Component Analysis and modified K-means,”in International Journal of Database Management Systems ( IJDMS ), Vol.3, No.1, February 2011.
[12] Imola K. Fodor “A survey of dimension reduction techniques” UCRL-ID-148494, May 9, 2002.
[13] Siripong Roongruangsuwan, jirapun Daengdej,” Test Case Prioritization Techniques” in Journal of Theoretical and Applied Information Technology,© 2005 – 2010 JATIT & LLS.
[14] Book: An Introduction to Neural Networks, Simon Haykin, Prentice Hall, 1999.

159-162

http://blueeyesintelligence.org/2checkout_download.html

32.

Authors:

H. Faumia, B. Abirami, K. Muthulakshmi, M. Kasthuri

Paper Title:

A Knowledge Based Graphical Authentication Using X and Y Coordinates

Abstract: Graphical authentication is more efficient and more securable. An important usability goal for knowledge based authentication systems is to support user in selecting passwords for higher security in the sense of being from an expanded effective security space. Here we are using x and y coordinates to choose a picture password. Additionally the pictures in x and y coordinates are changing randomly and hence it is difficult to find the original picture. In our base paper the approaches used are “Persuasive cued click points”,” Cued click points” and “Pass points”. Main tools used here are pccp, viewport and that is used for password creation. Viewport approaches are used to memorable user chosen password and secure system generated random password that are difficult to remember. The pccp creating a less guessable password is the easiest course of action. In order to avoid “Shoulder surfing” algorithm we are going for AES (Advanced encryption standard).

Keywords: Authentication, Security, Graphical Passwords, Knowledge-based.

References:

[1] S. Chiasson, P. van Oorschot, and R. iddle, “Graphical Password Authentication Using Cued Click Points,” Proc. European Symp. Research in Computer Security (ESORICS), pp. 359-374, Sept. 2007
[2] L. O’Gorman, “Comparing Passwords, Tokens, and Biometrics for User Authentication,” Proc. IEEE, vol. 91, no. 12, pp. 2019-2020, Dec. 2003.
[3] S. Wiedenbeck, J. Waters, J. Birget, A.Brodskiy, and N. Memon, “Pass Points: Design and Longitudinal Evaluation of a Graphical Password System,” Int’l J. Human-Computer Studies, vol. 63, nos. 1/2, pp. 102-127, 2005.
[4] S. Wiedenbeck, J. Waters, J. Birget, A. Brodskiy, and N. Memon, “Authentication Using Graphical Passwords: Effects of Tolerance and Image Choice,” Proc. First Symp. Usable Privacy and Security (SOUPS), July 2005.
[5] P.C. van Oorschot, A. Salehi-Abari, and J. Thorpe, “Purely Automated Attacks on PassPoints-Style Graphical Passwords,” IEEE Trans. Information Forensics and Security, vol. 5, no. 3, pp. 393- 405, Sept. 2010.
[6] S. Chiasson, E. Stobert, A. Forget, R. Biddle, and P. van Oorschot, “Persuasive Cued Click-Points: Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism,” Technical Report TR-11-03, School of Computer Science, Carleton Univ., Feb. 2011.
[7] A. Forget, S. Chiasson, and R. Biddle, “Shoulder-Surfing Resistance with Eye-Gaze Entry in Click-Based Graphical Passwords,” Proc. ACM SIGCHI Conf. Human Factors in Computing Systems (CHI), 2010.
[8] B. Pinkas and T. Sander, “Securing Passwords against Dictionary Attacks,” Proc. Ninth ACM Conf. Computer and Comm. Security (CCS), Nov. 2002.
[9] Alireza Pirayesh Sabzevar and Angelos Stavrou, “Universal Multi-Factor Authentication Using Graphical Passwords,” in IEEE International Conference on Signal Image Technology and Internet Based Systems, 2008.
[10] Ahmad Almulhem, “A Graphical Password Authentication System,” in 978-0-9564263-7/6/$25.00 IEEE, 2011
[11] S. Man, D. Hong, and M. Mathews, “A shoulder-surfing resistant graphical password scheme,” in Proceedings of International conference on security and management. Las Vegas, NV, 2003
[12] Birget, J.C., D. Hong, And N. Memon, “Graphical Passwords Based On Robust Discretization” IEEE Trans. Info. Forensics And Security, 1(3), September 2006.
[13] Thorpe, J. and P.C. van Oorschot. Human-Seeded Attacks and Exploiting HotSpots in Graphical Passwords. 16th USENIX Security Symposium, 2007.
[14] Zhi Li, Qibin Sun, Yong Lian, and D. D. Giusto, 2005, „An Association-Based Graphical Password Design Resistant to Shoulder Surfing Attack‟, IEEE International Conference on Multimedia and Expo (ICME).

163-165

http://blueeyesintelligence.org/2checkout_download.html

33.

Authors:

Abhishek Sengupta, Sarika Saxena, Gaurav Singh, Priyanka Narad, Ayushi Yadav, Monendra Grover

Paper Title:

A Computational Systems Biology Approach to Decipher Significant Intricacies of Dihydrolipoamide Dehydrogenase Deficiency in Human

Abstract: The alpha-ketoglutarate dehydrogenase complex (KGDHC) is elemental in mitochondria, and its deficiency is associated with a number of neurological disorders like Alzheimer’s disease, Parkinson’s disease and Dihydrolipoamide dehydrogenase deficiency (DLDD). The molecular mechanisms underlying the age dependent loss of brain KGDHC activity remain incomprehensible. In order to disentangle this phenomenon, a kinetic model is developed representing correlations between three aspects namely the reduced AKGDHC activity; mitochondrial ATP generation and increased pyruvate concentration. The kinetic model centralizes on the mitochondrial-derived ATP production and is distributed into cytosol and mitochondria. The model revealed a decline in ATP production with lowered enzyme concentration. With the effect, the concentration of pyruvate was increased resulting in its excretion which is a characteristic feature of DLDD. In agreement with the previous literature the model simulations confirmed the decline in reaction fluxes and NADH level. The finding suggests that reduction of pyruvate is the rate limiting step of the TCAC which is supported by past bibliographic findings. Since ATP production is also affected by NADH production rate hence it can be safely assumed that decrease in NADH also causes Change in ATP production rate. Change in pyruvate concentration on changing the concentration of AKGDH also underpins the importance of the studied enzyme in DLDD. It is clearly indicated by simulations that AKGDH deficiency can cause increase in pyruvate concentration.

Keywords: Alpha-Ketoglutarate Dehydrogenase Complex, Dihydrolipoamide Dehydrogenase Deficiency, Kinetic Model

References:

[1] Reddy PH (2007) Mitochondrial dysfunction in aging and Alzheimer’s disease: strategies to protect neurons. Antioxid Redox Signal, 9: 1647–58.
[2] Blass JP, Brown AM. Lower activity of Krebs cycle enzymes than of electron transport in human brain: disease implications. Neurobiol. Aging. 2000;21 Suppl:81.
[3] Chinopoulos C, Adam-Vizi V. (2006) Calcium, mitochondria and oxidative stress in neuronal pathology. Novel aspects of an enduring theme. FEBS J, 273: 433–450
[4] Cooney GJ, Taegtmeyer H, Newsholme EA. (1981) Tricarboxylic acid cycle flux and enzyme activities in the isolated working rat heart. Biochem J, 200: 701–703.
[5] Felipe A. Beltrán, Aníbal I. Acuña, María Paz Miró and Maite A. Castro (2012). Brain Energy Metabolism in Health and Disease, Neuroscience – Dealing With Frontiers, Dr. Carlos M. Contreras (Ed.), ISBN: 978-953-51-0207-6, InTech.
[6] Gancedo C and Serrano R. (1989) Energy-yielding metabolism, pp. 205–209 in The Yeasts, Vol. 3, Ed. 2, edited by A. H. Rose and J. S. Harrison. Academic Press, San Diego.
[7] Gibson GE, Starkov A, Blass JP, Ratan RR, and Beal MF. (2010) Cause and Consequence: Mitochondrial Dysfunction Initiates and Propagates Neuronal Dysfunction, Neuronal Death and Behavioral Abnormalities in Age Associated Neurodegenerative Diseases. Biochim Biophys Acta, 1802(1): 122–134.
[8] Sengupta A and Saxena S. (2014) A Computational Model of Mitochondrial Beta-Oxidation Highlighting the Implications on Uremia Disease in Human. IJSCE, Vol. 3, No.6: 188-192.
[9] Kim Y, Kim Y, Hwang O and Kim DJ. (2012) Pathology of Neurodegenerative Diseases, Brain Damage – Bridging Between Basic Research and Clinics, Dr. Alina Gonzalez-Quevedo (Ed.), ISBN: 978-953-51-0375-2
[10] Lai JC, Walsh JM, Dennis SC & Clark JB. (1977) Synaptic and non-synaptic mitochondria from rat brain: isolation and characterization. J. Neurochem, 28: 625–631.
[11] Lyubarev AE, Kurganov BI. Supramolecular organization of tricarboxylic acid cycle enzymes.Biosystems. 1989;22:91–102. [PubMed]
[12] Mazziotta JC, Phelps ME, Pahl JJ, Huang SC, Baxter LR, Riege WH, Hoffman JM, Kuhl DE, Lanto AB, Wapenski JA. (1987) Reduced cerebral glucose metabolism in asymptomatic subjects at risk for Huntington’s disease. N Engl J Med, 12; 316(7): 357-62
[13] Mclain AL, Szweda PA, and Szweda LI. (2011) α-Ketoglutarate dehydrogenase: AN mitochondrial redox sensor. Free Radic Res, 45(1): 29–36.
[14] Moreno-Sanchez R, Hogue BA, Hansford RG. (1990) Influence of NAD-linked dehydrogenase activity on flux through oxidative phosphorylation. Biochem J, 268: 421–428.
[15] Navarro A and Boveris A. (2009) Brain mitochondrial dysfunction and oxidative damage in
[16] Nunomura A, Perry G, Aliev G, Hirai K, Takeda A, Balraj EK, et al., (2001) Oxidative damage is the earliest event in Alzheimer disease. J Neuropathol Exp Neurol, 60(8): 759–767.
[17] Parkinson’s disease. J Bioenerg Biomemb, 41(6): 517-21.
[18] Sandstrom PA, Tebbey PW, Van Cleave S, Buttke TM. (1994) Lipid hydroperoxides induce apoptosis in T cells displaying a HIV-associated glutathione peroxidase deficiency. J Biol Chem, 269(2): 798–801.
[19] Santa-Cruz LD, Ramírez-Jarquín UN and Tapia R. (2012) Role of Mitochondrial Dysfunction in Motor Neuron Degeneration in ALS, Amyotrophic Lateral Sclerosis, Prof. Martin Maurer (Ed.), ISBN: 978-953-307-806-9
[20] Schapira, A. H. (2008). Mitochondrial dysfunction in neurodegenerative diseases. Neurochem Res, 33(12): 2502-2509
[21] Shi Q, Xu H, Yu H, Zhang N, Ye Y, et al., (2011) Inactivation and reactivation of the mitochondrial α-ketoglutarate dehydrogenase complex. J Biol Chem, 286: 17640–17648.
[22] Shimohama S. (2000) Apoptosis in Alzheimer’s disease–an update. Apoptosis, 5(1):9-16.
[23] Sundaram S, Tripathi A and Gupta DK. (2010) Metabolic modeling of Rosmarinic acid biosynthetic pathway. Bioinformation, 5: 168-172.
[24] Tretter L, Adam-Vizi V. (2000) Inhibition of Krebs cycle enzymes by hydrogen peroxide: a key role of [alpha]-ketoglutarate dehydrogenase in limiting NADH production under oxidative stress. J Neurosci, 20: 8972–8979.
[25] Tretter L, Adam-Vizi V. (2005) Alpha-ketoglutarate dehydrogenase: a target and generator of oxidative stress. Phil. Trans. R. Soc. B, 360: 2335–2345
[26] Turrens JF, Alexandre A and Lehninger AL. (1985) Ubisemiquinone is the electron donor for superoxide formation by complex III of heart mitochondria. Archives of biochemistry and biophysics, Vol.237, No.2: 408-414.

166-170

http://blueeyesintelligence.org/2checkout_download.html

34.

Authors:

Wasim Arif , Dhrubjun Nath Saikia, S.Baishya

Paper Title:

An Adaptive Spectrum Sensing Model for Cognitive Radio Application

Abstract: The problem of underutilization of spectrum may be solved by allowing the Secondary Users (SU) to use the spectrum allocated to the Primary Users(PU) (license holder of a spectrum band) when they are not using it, without causing any harmful interference to the PUs. Cognitive Radio (CR) technology promises to solve the problem of spectrum underutilization and spectrum crowding. The cognitive users employ their cognitive abilities, to adaptively change the radio parameters as per the radio environment, to communicate, without harming the primary users. In this paper Cyclostationary Feature Detection method has been taken as the detection method as it not only can detect the signal under low SNR but can also detect different features of the signal such as modulation type, carrier frequencies etc.. Energy Detection or radio meter can only detect presence or absence of the primary signal whereas a Matched Filter system requires extensive knowledge about the channel and the signals that are to be identified. The signals exhibiting cyclostationarity includes Spectral Correlation Function which is a data analysis algorithm that measures how the properties of a spectra varies, position to position, in a two dimensional spectral line map . We have proposed an adaptive sensing mechanism for an effective and efficient detection of spectrum hole. The signal SNR is determined in the first phase and appropriate sensing technique is adopted by the system based on the SNR level in the second phase confirming the radio parameters for CR.

Keywords: Cycle Frequency, Cyclic Autocorrelation Function, Cyclostationarity, Spectral Correlation Function, Spectrum Sensing.

References:

[1] FCC, B Spectrum Policy Task Force, ET Docket 02-135, Nov. 2002.
[2] M. McHenry, E. Livsics, T. Nguyen, and N. Majumdar, BXG dynamic spectrum access field test results, IEEE Commun. Mag.,vol. 45, pp. 51–57, Jun. 2007.
[3] J. Mitola and G. Q. Maguire, “Cognitive radio: Making software radios more personal,” IEEE Pers. Commun. , vol. 6, Aug. 1999, pp. 13–18.
[4] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Select. Areas Commun., vol. 23, Feb. 2005, pp. 201-220.
[5] Paul D. Sutton. “Cyclostationary signature inpractical cognitive radio application”. IEEE journalon Selected Areas In Communications, VOL. 26, NO.1, January 2008.
[6] J. Mitola III, “Cognitive radio an integrated agent architecture for software defined radio”, KTH Royal Institute of Technology, Stockholm, Sweden, 2000.
[7] Qiwei Zhang, Andre B.J. Kokkeler and Gerard J.M.Smit, “A reconfigurable radio architecture for cognitive radio in emergency network”, Proceedings of 9th European Conference on Wireless Technology.
[8] K. Kim, I.A. Akbar, K. K. Bae, J. S. Um, C. M. Spooner, and J. H. Reed, “Cyclostationary approaches to signal detection and classification in cognitive radio,” in Proc. IEEE DySpan, 2007, pp. 212–215.
[9] W. A. Gardner, Statistical Spectral Analysis: A Non probabilistic Theory. Englewood Cliffs, NJ: Prentice-Hall, 1988.
[10] G. K. Yeung and W. A. Gardner, “Search efficient methods of detection of cyclostationary signals,” IEEE Trans. Signal Processing , vol. 44, no. 5, pp. 1214–1223, May 1996.
[11] Paul D. Sutton. “Cyclostationary signature inpractical cognitive radio application”. IEEE journalon Selected Areas In Communications, VOL. 26, NO.1, January 2008
[12] William A. Gardner, “Spectral Correlation of Modulated Signals-Part-I-Digital Modulation”, IEEETrans. on Commun. ,Vol.35,no.6, pp-595-601.
[13] ArtemT kachenko, DanijelaCabric, and Robert W.Brodersen, “Cyclostationary Feature Detector Experiments using Reconfigurable BEE2”, in 2ndIEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks ,2007
[14] Dong-Chan Oh and Yong-Hwan Lee, “Energy detection based spectrum sensing for sensing error minimization in Cognitive Radio networks” International journal of Communication Networks and Information Security, vol. 1, no. 1, April 2009.
[15] W. M. Gardner, “Measurement of Spectral Correlation,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. ASSP-34, No. 5, Oct. 1986.

171-176

http://blueeyesintelligence.org/2checkout_download.html

35.

Authors:

Fakhreldeen Abbas Saeed

Paper Title:

Using WASSEC to Evaluate Commercial Web Application Security Scanners

Abstract: The web application security has currently become a very significant area of scholarship, the best way to deal with it is to use web application security scanner to discover the architectural weaknesses and vulnerabilities in the web application. The goal of this paper is to use The Web Application Security Scanner Evaluation Criteria (WASSEC) to compare and contrast the Commercial Web Application Security Scanners, and show the differences between them. We used six factors to do this compression (Protocol Support, Authentication, Session Management, Crawling, Parsing and Testing). The study shows that Acunetix WVS, Ammonite and Burp Suite Professional are the most suitable ones because they have 0.831325, 0.771084 and 0.73494 averages respectively. As the result of this study and depend on the information about the Commercial Web Application Security Scanner we collected; the Acunetix WVS, Burp Suite Professional and Ammonite are the best respectively. So the web developer or administrator can use them together or choose one.

Keywords: Web Application Security Scanner, WASSEC, Evaluation.

References:

[1] Jan-Min Chen and Chia-Lun Wu, “An Automated Vulnerability Scanner for Injection Attack Based on Injection Point”, Proceedings of the 2010 IEEE Symposium on Security and Privacy, 2010, pp.
[2] J. Bau, E. Bursztein, D. Gupta, and J. Mitchell, “State of the Art: Automated Black-Box Web Application Vulnerability Testing”, Proceedings of the 2010 IEEE Symposium on Security and Privacy, 2010, pp.332-345.
[3] WASSEC project, Web Application Security Consortium (http://www.webappsec.org).
[4] http://en.wikipedia.org/wiki/Information_security
[5] http://en.wikipedia.org/wiki/Web_application_security.
[6] Y.-W Huang, F. Yu, C. Huang, C.-H.Tsai, D.-T.Lee, and S.-Y Kuo, “Securing Web Application code by static analysis and runtime protection”, Proc. 13th Int. Conf. on World Wide Web (WWW’04), NY, USA. ACM, pp. 40-52.
[7] V.B. Livshits and M. S. Lam, “Finding security errors in Java program with static analysis”, Proc. 14th Usenix Security Symposium, Baltimore, MD, USA, 2005.
[8] N. Jovanovic, C. Kruegel, and E. Kirda, “Static analysis for detecting taint-style vulnerabilities in web applications”, Journal of Computer Security, 18 (2010), pp. 861-907.
[9] Y. Xie, A. Aiken, “Static detection of vulnerabilities in scripting languages”, Proc. 15th USENIX Security Symposium, 2006, pp. 179-192
[10] G. Wassermann and Z. Su, “Sound and precise analysis of web applications for injection vulnerabilities”, SIGPLAN Notices, vol 42, n06, 2007, pp.32-41.
[11] M. S. Lam, M. Martin, B. Livshits, and J. Whaley, “Securing Web Applications with static and dynamic information flow tracking”, Proc . of the 2008 ACM SIGPLAN Symposium on Partial evaluation and semantics based program manipulation (PEPM’08), New York, NY, USA : ACM, 2008, pp. 3-12.
[12] T. Pietraszek, C.V. Berghe, “Defending against injection attacks through context sensitive string evaluation”, Recent Advances in Intrusion Detection (RAID-2005), Seattle, WA, USA, 2005.
[13] C. Kruegel, G. Vigna, “Anomaly Detection of Web-based Attacks”, Proc. of the 10th ACM Conference on Computer and Communication Security (CCS’03, October 2003), pp. 251-261.
[14] E. Kirda, C. Kruegel, G. Vigna, and N. Jovanovic, “Noxes: A client-side solution for mitigating cross-side scripting attacks”, 21st ACM Symposium on Applied Computing (SAC2006), Dijon, France, 2006.
[15] K.Stefan, E. Kirda, C. Kruegel and N. Jovanovic,“SecuBat: a web vulnerability scanner”, Proc. of the 15th int. conf. on World Wide Web (WWW ’06), Edinburgh, Scotland, 2006.
[16] Zoran Djuric, ”A Black-box Testing Tool for Detecting SQL Injection Vulnerabilities”, Proceedings of the 2010 IEEE Symposium on Security and Privacy, 2010, pp.216-221.
[17] A. Doup´e, M. Cova, and G. Vigna, “Why Johnny Can’t Pentest: An Analysis of Black-box Web Vulnerability Scanners”, July 2010
[18] J. Fonseca, M. Vieira, and H. Madeira, “Testing and Comparing Web Vulnerability Scanning Tools for SQL Injection and XSS Attacks”, prdc, 13th Pacific Rim International Symposium on Dependable Computing (PRDC 2007), 2007, pp.365-372
[19] Fakhreldeen Abbas Saeed, “Comparing and Evaluating Open Source E-learning Platforms”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-3, Issue-3, July 2013,pp.244-249.¬¬¬¬¬¬
[20] Price and Feature Comparison of Web Application Scanners (http://www.sectoolmarket.com/) Last updated: 27/08/2012.

177-181

http://blueeyesintelligence.org/2checkout_download.html

36.

Authors:

Ahmad Hamza Al Cheikha, Ruchin Jain

Paper Title:

Generation of Orthogonal Sequences by Walsh Sequences

Abstract: Walsh sequences of the order 2k, k positive integer, form an additive group generated by Rademacher sequences set of k-order. Except the zero sequences, Walsh formed an orthogonal set of The present work allows generating orthogonal sequences with length 2k , 22k, l.2k respectively, by using Walsh sequences and theirs complements.

Keywords: Walsh sequences, Rademacher sequences, Orthogonal sequences.

References:

[1] BEAUCHAMP, K. Applications of Walsh And Related Functions. Academic
[2] WALSH, J.L. A Closed Set of Normal Orthogonal Functions. Amer. J. Math.
[3] BYRNES, J.S.; SWICK. Instant Walsh Functions. SIAM Rever., VOL. 12 1970, 131.
[4] LEE, D.S.; MILLER, L.E. CDMA Engineering Hand Book. Artech House, Boston,London, 1998. 48 – 70
[5] MAC WILLIAMS, F.J.; SLOANE N.G.A. The Theory of Error-Correcting Codes. North-Holland, Amsterdam, 1978
[6] YANG, S.C. CDMA RF System Engineering. Artech House, Boston and London, 1998. 80 – 110
[7] AL-CHEIKHA, A. Generation of Sets of Sequences Isomorphic to Walsh Sequences. Qatar Uni. Sci. J. VOL 25, 2005. 16-30
[8] YANG K, S. C.; KIM, Y.; KUMAR, P.V. Quasi-orthogonal Sequences for Codes-Division Multiple Access Systems. IEEE Trans. On Information Theory, VOL 46, 2000. 982-993

182-184

http://blueeyesintelligence.org/2checkout_download.html

37.

Authors:

Rahul Sharma, Rahul Atri, Preet Kanwar Singh Rekhi, Sukhvinder Singh Malik, Mandeep Singh Arora

Paper Title:

Dimensioning Tracking Area for LTE Network

Abstract: Mobility management (MM) is one of the main functions in mobile networks. It aims to track the user equipment (UEs) and to allow calls, other mobile phone services to be delivered to UEs. For any mobility protocol there are two separate problems to be solved. One is location management (or sometimes called reachability), which keeps track of the positions of a UE in the mobile network. The other one is handover management (or sometimes called session continuity), which makes it possible for a UE to continue its sessions while moving to another cell and changing its access point. This document focuses on the location management problems. Tracing UEs in a mobile network is the key task in location management. Tracking Area (TA) in LTE is a logical grouping of cells in a network. TA is almost the same concept as the Location Area (LA). In configuring TAs, a key consideration is to minimize the total amount of signaling overhead.

Keywords: LTE, Tracking Area, Paging Capacity TA list.

References:

[1] Lte – The Umts Long Term Evolution from Theory To Practice 2nd Edition by Stefania Sesia , Issam Toufik, Matthew Baker
[2] 3GPP TS 36.213 “Evolved Universal Terrestrial Radio Access (E-UTRA) Physical layer procedures”.
[3] 3GPP TS 36.221 “Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) protocol specification”
[4] 3GPP TS 36.300 “Evolved Universal Terrestrial Radio Access (E-UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRAN).
[5] 3GPP TS 24.301: “Non-Access-Stratum (NAS) protocol for Evolved Packet System”.
[6] 3GPP TS 24.302: “Access to the 3GPP Evolved Packet Core (EPC) via Non-3GPP Access Networks”.
[7] 3GPP TS 36.331: “Evolved Universal Terrestrial Radio Access (E-UTRAN); Radio Resource Control (RRC) Protocol Specification”
[8] 3GPP TS 36.401: “Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Architecture Description”
[9] 3GPP TS 36.413: “Evolved Universal Terrestrial Radio Access Network (E-UTRAN); S1 Application Protocol (S1AP).

185-190

http://blueeyesintelligence.org/2checkout_download.html

38.

Authors:

Badgude Puja, Ghorpade Hemlata, Ghadge Yogita, More Supriya

Paper Title:

Image and Sound Based Authentication of User

Abstract: An image and sound based authentication of a user,it means we will authenticate the users of a system by a password that they entered through a sequence of images. And also having some special sound effects associated with a password. In the previous days, Pass Point was the technique,used to give image based password.But it requires five clicks on one image.This will take more memory to store the clicks and become easy for attacker to guess. To overcome that drawback CCP technique was developed which takes a sequence of five images and one click on each image. In addition, a user has to select sound signature with respective each click on image to easily recall the password. After addition a persuasive feature to CCP, we will get PCCP,which was used to address an issue of hotspots.While creating an image based password a complete image is dimmed except viewport area.A user has to click within this viewport. This viewport area can be repositioned randomly by using shuffle button. In the proposed work, we aimed to build more interactive, user-friendly and secure email system by creating strong and difficult password to guess by attacker.

Keywords: CCP and PCCP technique, system architecture, working of proposed system.

References:

[1] S.Singh, G.Agrawal ”Integration of sound signature in graphical password authentication system” Invertis University Bareilly,India, January 2011.
[2] S. Chiasson, E. Stobert, A. Forget, R. Biddle, and P. van Oorschot, “Persuasive cued click-points: Design, implementation and evaluation of a knowledge-based authentication mechanism,” School of Computer Science, Carleton University ,Tech. Rep. TR-11-03, February 2011.
[3] Chippy.T, R.Nagendran “Defence Against Large Scale Online Password Guessing Attacks By Using Persuasive Click Points” International Journal of Communications and Engineering Volume-3, 01 March 2012.
[4] S.Chiasson,JaykumarSrinivasan, Robert Bibble, P.C. van Oorschot “Centered Discretization with Application to Graphical Passwords” Ottawa,Canada Carleton University.

191-192

http://blueeyesintelligence.org/2checkout_download.html

39.

Authors:

Laxmi Shankar Awasthi, Himanshu Pathak, Parth Singhal

Paper Title:

Cloud Computing: Analyzing Security Issues & Need of Prevention against Vulnerabilities

Abstract: Cloud computing, is fast growing Technology having its originations from Distributed computing technology. Cloud computing is a collaboration of different computing technologies and it has many advantages in the field of data storage, high expansibility, high reliability, virtualization, and low price service. Cloud computing has massive effects how we store and access our personal and business data. It is a matter of great concern to secure personal information and data from attacks like DDos, MIMA and data theft. And therefore it is extremely important to understand the existing security issues and risks in cloud computing. This paper analyses the above mentioned existing security issues and protection against threats in cloud computing. This paper is to quantify the need of security between networks in cloud computing, therefore minimizes the vulnerabilities.

Keywords: DDos, man in the middle attack, packet sniffing.

References:

[1] Survey of network-based defence mechanisms countering the DoS and DDoS problems.
[2] Trusted cloud computing with secure resources and data coloring 1080-7801/10/$26 2010 by IEEE.
[3] http://www.dummies.com/how-to/content/common-network-attack-strategies-packet-sniffing.html
[4] http://www.symantec.com/threatreport/topic.jsp?id=threat_activity_trends&aid=data_breaches_that_could_lead [9]. Above the Clouds: A Berkeley View of Cloud Computing”
[5] Denz and Taylor Journal of Cloud Computing: Advances, Systems and Applications 2013, 2:17http://www.journalofcloudcomputing.com/content/2/1/17
[6] CLOUD COMPUTING AND SECURITY ISSUES IN THE CLOUD International Journal of Network Security & Its Applications (IJNSA), Vol.6, No.1, January 2014

193-194

http://blueeyesintelligence.org/2checkout_download.html

40.

Authors:

Tarik Anouari, Abdelkrim Haqiq

Paper Title:

A QoE-Based Scheduling Algorithm for UGS Service Class in WiMAX Network

Abstract: To satisfy the increasing demand for multimedia services in broadband Internet networks, the WiMAX (Worldwide Interoperability for Microwave Acces) technology has emerged as an alternative to the wired broadband access solutions. It provides an Internet connection to broadband coverage area of several kilometers in radius by ensuring a satisfactory quality of service (QoS), it’s an adequate response to some rural or inaccessible areas. Unlike DSL (Digital Subscriber Line) or other wired technology, WiMAX uses radio waves and can provide point-to-multipoint (PMP) and point-to-point (P2P) modes. In parallel, it’s observed that in the opposite of the traditional quality evaluation approaches, nowadays, current researches focus on the user perceived quality, the existing scheduling algorithms take into account the QoS and many other parameters, but not the Quality of Experience (QoE). In this paper, we present a QoE-based scheduling solution in WiMAX network in order to make the scheduling of the UGS connections based on the use of QoE metrics. Indeed, the proposed solution allows controlling the packet transmission rate so as to match with the minimum subjective rate requirements of each user. Simulation results show that by applying various levels of mean opinion score (MOS) the QoE provided to the users is improved in term of throughput, jitter, packet loss rate and delay.

Keywords: WiMAX, QoE, QoS, UGS, NS-2.

References:

[1] Z. Abichar, Y. Peng and J. Morris Chang, “WiMax: The Emergence of Wireless Broadband”, IT Professional, Vol. 8, Issue. 4, pp. 44-48, Doi:10.1109/MITP.2006.99, July-Aug. 2006.
[2] M. Alreshoodi, J. Woods, “Survey on QoE\QoS Correlation Models for Multimedia Services”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013.
[3] T. Anouari and A. Haqiq, “Analysis of VoIP and Video Traffic over WiMAX Using Different Service Classes”, Journal of Mobile Multimedia, Vol. 9, No.3&4, pp. 230-241, 2014.
[4] A. Belghith, L. Nuaymi “Design and Implementation of a QoS-included WiMAX Module for NS-2 Simulator”, SIMUTools 2008, Marseille, France, March 3-7, 2008.
[5] P. Brooks, B. Hestnes,”User measures of quality of experience: Why being objective and quantitative is important”. IEEE Network 24(2): pp. 8–13, 2010.
[6] P. Calyam, P. Chandrasekaran, G. Trueb, N. Howes, R. Ramnath, D. Yu, Y. Liu, L. Xiong, &D. Yang, “Multi-Resolution Multimedia QoE Models for IPTV Applications”, Volume 2012, Article ID 904072, 13 pages doi:10.1155//904072, 2012.
[7] H. Du, C. Guo, Y. Liu& Y. Liu,(2009)“Research on Relationships between QoE and QoS based on BP Neural Network”, In: Proceedings of IC-NIDC, pp. 312-315, 2009.
[8] P. Frank & J. Incera, “A neural network based test bed for evaluating the quality of video streams in IP networks”, 0-7695-2569-5/06 © IEEE, Proceedings of the Electronics, Robotics and Automotive Mechanics Conference (CERMA’06), 2006.
[9] S. R. Gulliver and G. Ghinea. “The Perceptual and Attentive Impact of Delay and Jitter in Multimedia Delivery”. IEEE Transactions on Broadcasting, 53(2): pp. 449–458, June 2007.
[10] IEEE 802.16-2004, “IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems”, October, 2004.
[11] IEEE standard 802. 16-2005, “IEEE standard for Local and Metropolitan Area Networks-Part16: Air Interface for Fixed and Mobile Broadband wireless Access systems Amendment 2”, February 28, 2006.
[12] ITU-T Rec. P.10/G.100, Amendment 2, “New definitions for inclusion in Recommendation ITU–T P.10/G.100,” July 2008.
[13] ITU-T Recommendation P.800,” Methods for subjective determination of transmission quality”. http://www.itu.int/, Geneva, 08/1996
[14] V. Machado, C. Oliveira , A. Marcelino, S. Carlos, N. Vijaykumar, C. Hirata, “A New Proposal to Provide Estimation of QoS and QoE over WiMAX Networks, An approach based on computational intelligence and discrete-event simulation”, 978-1-4673-0279-1©IEEE, 2011.
[15] Marc Greis, “Tutorial for Network Simulator NS”, http://www.scribd.com/doc/13072517/tutorial-NS-full-byMARC-GREIS.
[16] C. H. Muntean, “Improving learner quality of experience by content adaptation based on network conditions”, Computers in Human Behavior, 24(4), pp. 1452-1472, 2007.
[17] D. Norman and S. Draper, “User centered system design: New perspectives on human-computer interaction”. L. Erlbaum Associates, 1986.
[18] P. Rengaraju, C.H. Lung, A. Srinivasan, R.H.M. Hafez, “Qos Improvements in Mobile WiMAX Networks”, AHU J. of Engineering & Applied Sciences, Vol. 3, Issue 1, pp. 107-118, 2010.
[19] Seamless and secure mobility.
http://www.nist.gov/itl/antd/emntg/ssm_tools.cfm

195-199

http://blueeyesintelligence.org/2checkout_download.html

41.

Authors:

S. Y. Amdani

Paper Title:

Study of Energy Efficient Wireless Sensor Network Protocols

Abstract: Wireless Sensor Networks are networks of large number of tiny, battery powered sensor nodes having limited on-board storage, processing, and radio capabilities. Nodes sense and send their reports toward a processing center which is called sink or base station. Since this transmission and reception process consumes lots of energy as compare to data processing, Designing protocols and applications for such networks has to be energy aware in order to prolong the lifetime of the network. Generally, real life applications deal with Heterogeneity rather than Homogeneity. In this paper, a protocol is proposed, which is heterogeneous in energy. We first completely analyze the basic distributed clustering routing protocol LEACH (Low Energy Adaptive Clustering Hierarchy), and SPIN focused on energy consumption.

Keywords: Security Protocols, Network Security, Energy efficient, Authentication, Wireless Sensor Networks

References:

[1]. J.P. Walters, Z. Liang, W. Shi, and V. Chaudhary,“Wireless Sensor Network Security: A Survey,” TechnicalReport MIST-TR-2005-007, July, 2005.
[2]. J.P. Walters, Zh. Liang, W. Shi, V. Chaudhary, Security in Distributed, Grid, and Pervasive Computing, Chapter 17, CRC Press, 2006.
[3]. A. Perrig, R. Szewczk, J.D. Tygar, V. Wen, D.E. Culler, SPINS: Security Protocols for Sensor Networks, Wireless Networking, Vol. 8, No. 5, pp. 521-534, Sept 2002.
[4]. A. Perrig, R. Canneti, J. D. Tygar, D. Song, The TESLA Broadcast Authentication Protocol, Crypto Bytes, Vol. 5, No. 2, pp. 2-13, 2002.
[5]. D. Boyle, T. Newe, Security Protocols for use with Wireless Sensor Networks: A Survey of Security Architectures, Proceedings of the 3rd International Conference on Wireless and Mobile Communications, Guadeloupe, French Caribbean, pp. 54, 04-09 March 2007.
[6]. Sun Limin, Li Jianzhong, Chen Yu, ―Wireless Sensor Networks, Tsinghua publishing company Beijing, 2005.
[7]. Wendi Rabiner, Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, Energy-efficient Communication Protocol for Wireless MicrosensorNetworks, In: Proc. of 33rd Annual Hawaii Inter Cord on System Sciences, Hawaii, USA: IEEE Computer Society, 2000.
[8]. Li Han, LEACH-HPR: An Energy Efficient Routing Algorithm for heterogeneous WSN IEEE 2010.
[9]. Vivek Mhatre and Catherine Rosenberg, Homogeneous Vs Heterogeneous Clustered Sensor Networks:A Comparative Study In Proceeding of IEEE International Conference on Communications(ICC), 2004, PP. 3646-3651.
[10]. Georgious Smaragdakis, Ibrahim Matta and Azer Bestavors, ―SEP: A Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks. In Proceding of the International Workshop on SANPA, 2004.
[11]. Wendi Rabiner Heinzelman, Amit Sinha. Alice Wang and Anatha P. Chandrakasan, ―Energy Scalabel Algorithms and Protocols for Wireless Micro sensor Networks, 2000.
[12]. J. Al-Karaki, and A. Kamal, Routing Techniques in Wireless Sensor Networks: A Survey., IEEE Commun-ications Magazine, vol 11, no. 6, Dec. 2004, pp. 6-28.
[13]. W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, An Application-Specific Protocol Architecture for Wireless Microsensor Networks. In IEEE Transactions on Wireless Communications vol. 1(4), 2002, pp. 660-670.
[14]. Yan Li, Yan Zhong Li, Energy-Efficient clustering Routing algorithm based on LEACH, Journal of Computer Applications, 2007.
[15]. Ben Alla Said, Ezzati Abdellah, Abderrahim Beni Hssane, Moulay Lahcen Hasnaoui, ―Improved and Balanced LEACH for heterogeneous wireless sensor networks (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 08, 2010, 2633-2640
[16]. Zeenat Rehena, Sarbani Roy, Nandini Mukherjee ,”A Modified SPIN for Wireless Sensor Networks”, 978-1-4244 8953-4/11 2011 IEEE.

200-202

http://blueeyesintelligence.org/2checkout_download.html

42.

Authors:

B. Venkatesh, P. Prakash

Paper Title:

Extracting Semantic Structure of Web Pages Using Graph Grammar Induction Algorithm

Abstract: With the appearance of the web, it’s fascinating to interpret and extract helpful data from the net. One major challenge in internet interface interpretation is to get the semantic structure underlying an internet interface. Several heuristic approaches have been developed to get and cluster semantically related interface objects. However, those approaches cannot solve the problem of non similarity satisfactorily and don’t seem to be ready to tag the participant role of every object. Distinct from existing approaches, this paper develops a sturdy and formal approach to ill interface semantics mistreatment graph grammars induction. Due to the distinct capability of spatial specifications within the abstract syntax, the spatial graph grammar induction algorithm (SGGI) is chosen to perform the semantic grouping and interpretation of divided screen objects. Instead of analyzing HTML supply codes, we tend to apply an economical image processing technology to acknowledge atomic interface objects from the screenshot of an interface and manufacture a spatial graph, which records vital spatial relations among recognized objects. A spatial graph is a lot of taciturn than its corresponding document object model structure and, thus, facilitates interface analysis and interpretation. Supported the spatial graph, the SGGI parser recovers the graded relations among interface objects.

Keywords: Content extraction, Image Segmentation, Graph Grammar Induction Algorithm, Spatial Parsing.

References:

[1] H. Ahmadi and J. Kong, “Efficient web browsing on small screens,”in Proc. ACM Int. Working Conf. Adv. Visual Interfaces, 2008, pp. 23–30.
[2] F. Ashraf, T. Ozyer, and R. Alhajj, “Employing clustering techniques for automatic information extraction from HTML documents,” IEEE Trans. Syst., Man, Cybern.—Part C: Appl. Rev., vol. 38, no. 5, pp. 660–673, Sep.2008.
[3] F. Ashraf and R. Alhajjt, “ClusTex: Information extraction from HTML pages,” in Proc. 21st Int. Conf. Adv. Inf. Netw. Appl. Workshops, May 2007, pp. 355–360.
[4] K. Ates, J. Kukluk, L. Holder, D. Cook, and K. Zhang, “Graph grammar induction on structural data for visual programming,” in Proc. IEEE 18th Int. Conf. Tools Artif. Intell., Nov. 2006, pp. 232–242.
[5] K. Ates and K. Zhang, “Constructing VEGGIE: Machine learning for context-sensitive graph grammars,” in Proc. IEEE 19th Int. Conf. Tools Artif. Intell., Oct. 2007, pp. 456–463.
[6] S. Baluja, “Browsing on small screens: Recasting web-page segmentation into an efficient machine learning framework,” in Proc. World Wide Web, 2006, pp. 33–42.
[7] O. Boiman, E. Shechtman, and M. Irani, “In defense of nearest-neighbor based image classification,” in Proc. IEEE Conf. Comput. Vision Pattern Recogn., Jun. 2008, pp. 1–9.
[8] Y. Borodin, J. Mahmud, and I. V. Ramakrishnan, “Context browsing with mobiles—When less is more,” in Proc. 5th Int. Conf. Mobile Syst., Appl. Services, 2007, pp. 3–15.
[9] R. Burget, “Visual HTML document modeling for information extraction,”in Proc. Reconfigurable Architectures Workshop, 2005, pp. 17–24.
[10] J. Kong, K. Zhang, and X. Q. Zeng, “Spatial graph grammar for graphic user interfaces,” ACM Trans. Human-Comput. Interaction, vol. 13, no. 2, pp. 268–307, 2006.
[11] N. Milic-Frayling and R. Sommerer, “SmartView: Flexible viewing ofweb page contents,” presented at the Proc. of the 11th World Wide Web Conf. (poster paper), New York, 2002.
[12] S. Zheng, R. Song, and J. Wen, “Template-independent news extraction based on visual consistency,” in Proc. 22nd Nat. Conf. Artif. Intell., 2007, vol. 2, pp. 1507–1512.
[13] Opera Software ASA. (2008). [Online]. Available: http://www.opera.com/products/mobile/smallscreen.
[14] Y. D. Yang and H. J. Zhang, “HTML page analysis based on visual cues,” in Proc. 6th Int. Conf. Document Analysis Recognit., 2001, pp. 859–864.
[15] Y. Chen, W. Y. Ma, and H. J. Zhang, “Detecting web page structure foradaptive viewing on small form factor devices,” in Proc. WorldWide Web,2003, pp. 225–233.
[16] F. Paterno and G. Zichittella, “Desktop-to-mobile web adaptation through customizable two-dimensional semantic redesign,” in Proc. 3rd Int. Conf.Human-Centered Softw. Eng., 2010, pp. 79–94.
[17] D. Cai, S. Yu, J. Wen, and W. Ma, “Extracting content structure for Webpages based on visual representation,” in Proc. 5th Asia Pac. Web Conf., 2003, pp. 406–417.
[18] J. Kong, K. L. Ates, K. Zhang, and Y. Gu, “Adaptive mobile interfacesthrough grammar induction,” in Proc. IEEE 20th Int. Conf. Tools Artif. Intell., 2008, pp. 133–140.
[19] N. Mavridis, W. Kazmi, and P. Toulis, “Friends with faces: How social networks can enhance face recognition and vice versa,” in ComputationalSocial Networks Analysis: Trends, Tools and Research Advances.Berlin, Germany: Springer-Verlag, 2009.
[20] M. Labsk´y, V. Sv´atek, O. ˇSv´ab, P. Praks, M. Kr´atk´y, and V. Sn´aˇsel, “Information extraction from HTML product catalogues: from source code and images to RDF,” in Proc. IEEE/WIC/ACMInt. Conf.Web Intell., 2005, pp. 401–404.
[21] T. L. Wong and W. Lam, “Adapting web information extraction knowledge via mining site-invariant and site-dependent features,” ACM Trans.Internet Technol., vol. 7, no. 1, art no. 6, 2007.
[22] X. Y. Xiao, Q. Luo, D. Hong, H. Fu, X. Xie, andW. Y. Ma, “Browsing on small displays by transforming web pages into hierarchically structured subpages,” ACM Trans. Web, vol. 3, no. 1, art no. 4, 2009.
[23] G. Hattori, K. Hoashi, K. Matsumoto, and F. Sugaya, “Robust web page segmentation for mobile terminal using content-distances and page layout information,” in Proc. 16th Int. Conf. World Wide Web, 2007, pp. 361–370.
[24] J. Chen and K. Xiao, “Perception-oriented online news extraction,” in Proc. 8th ACM/IEEE-CS Joint Conf. Digital Libraries, 2008, pp. 363–366.

203-207

http://blueeyesintelligence.org/2checkout_download.html

43.

Authors:

Sayani Palit, Madhumita Mukherjee

Paper Title:

Comparative Study of Delay and Power Dissipation of a Low Power CMOS BPSK Modulator Circuit

Abstract: In this paper we have presented a BPSK modulator using low power CMOS technology. The key design issues in VLSI circuit design are power and delay. Thus in this paper we have focused on LP CMOS with different technologies(16nm, 22nm ,32nm,and 45nm) with the help of TANNER EDA Tool. The value of model parameters are used from Predictive Technology Model(PTM). The T-SPICE simulation results indicate that there is a 59% deduction in Dynamic power For 16nm technology compare to 45nm technology keeping supply voltage constant whereas there is 74.64% reduction in power delay product in 16nm technology compare to 45nm technology.

Keywords: BPSK, LP CMOS, Power dissipation, PTM

References:

[1]. Bong-Young Chung, Charles Chien, Henry Samueli,”Performance Analysis of an All-Digital BPSK Direct-Sequence Spread-Spectrum IF Receiver Architecture”, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 11, NO. 7, SEPTEMBER 1993.
[2]. S.-M. Moon, J.-W. Yu and M.-Q. Lee,” Performance Analysis of an All-Digital BPSK Direct-Sequence Spread-Spectrum IF Receiver Architecture”, ELECTRONICS LETTERS 19th November 2009 Vol. 45 No. 24.
[3]. Han Yan, Jose Gabriel Macias-Montero, Ate Akhnoukh, Leo C. N. de Vreede John R. Long, Joachim N. Burghartz, “An Ultra-Low-Power BPSK Receiver and Demodulator Based on Injection-Locked Oscillators”, IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 59, NO. 5, MAY 2011.
[4]. ASU, Berkeley Predictive Technology Model (BPTM) Dept. of EE, Arizona State Univ., Tempe, AZ, 2006 [Online].
[5]. Sanjay Sharma, “Digital Communication”,S.K Katariya and sons,2009.
[6]. R.Saleh,”Design and Technology Trends”, Dept. of ECEUniversity of British Columbia,[Online].
[7]. S.M Kang,Y.Leblebici,”CMOS Digital Integrated Circuit”,The McGraw-hill Companies,3rd Edition,1999.
[8]. Issam S. Abu-Khater, Student Member, IEEE, Abdellatif Bellaouar, and M. I. Elmasry,” Circuit Techniques for CMOS Low-PowerHigh-Performance Multipliers”, IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL 31, NO 10, OCTOBER 1996.
[9]. Eitan N. Shauly,” CMOS Leakage and Power Reduction in Transistors and Circuits: Process and Layout Considerations”, J. Low Power Electron. Appl. 2012.
[10]. Hung-Chi Wang#\ Jyh-Ching Juang#2, Chun-Lin LU,” A CMOS BPSK Transmitter with a Monitoring Demodulator Embedded”, Proceedings of Asia-Pacific Microwave Conference 2010.

208-211

http://blueeyesintelligence.org/2checkout_download.html

44.

Authors:

Priti V. Jasud, Manish D. Katkar, S. D. Kamble

Paper Title:

Authentication Mechanism for Smart Grid Network

Abstract: The Smart Grid is formed by many sub-networks such as the Home Area Network (HAN), service providers, transmission, distribution, and bulk generation, operations and market which are at risk and can be attacked remotely. Smart grids that is supports data aggregation and access control. Data can be aggregated by home area network (HAN), building area network (BAN), and neighboring area network (NAN) in such a way that the privacy of customers is protected. Smart grid designing a mutual authentication scheme and a key management protocol. This paper proposes an efficient scheme that mutually authenticates a smart grid and an authentication server in SG by decreasing the number of steps in the secure remote password protocol. In this paper we propose an efficient key management protocol based on our enhanced identity-based cryptography for secure SG communications using the public key infrastructure. The improved efficiency for key management is realized by periodically refreshing all public/ private key pairs as well as any multicast keys in all the nodes using only one newly generated function broadcasted by the key generator entity. We show that the proposed mechanisms are resilient against insider attackers performing serious attacks such as man-in-the-middle or impersonation during device authentication. Further, the proposed authentication mechanisms are intuitive and require no (or minimum) user effort.

Keywords: Enhanced identity-based, key management, cryptography (EIBC), smart grid (SG) mutual authentication, and secure remote password (SRP).

References:

1. Z. Fan, P. Kulkarni, S. Gormus, C. Efthymiou, G. Kalogridis, M. Sooriyabandara, Z. Zhu, S. Lambotharan, and W. H. Chin, “Smart grid communications: Overview of research challenges,solutions, and standardization activities,” IEEE Commun. Surveys Tuts., vol. 15, no. 1, pp. 21–38,2013.
2. J.Wang and V. Leung, “A survey of technical requirements and Consumer application standards for IP-based smart grid AMI network,” in Proc. ICOIN, 2011, pp. 114–119.
3. H. Nicanfar, P. Jokar, and V. Leung,“Smart grid authentication and key management for unicast and multicast communications, ” in Proc. IEEE PES ISGT, 2011, pp. 1–8.
4. D. Cooper, S. Santesson, S. Farrell, S. Boeyen, R. Housley, and W. Polk, “Internet X. 509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile,” Internet Engineering Task Force, Fremont, CA, USA, 2008.
5. M. Amin, “Challenges in reliability, security, efficiency, and resilience of energy infrastructure: Toward smart self-healing electric power grid,” in Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, Jul. 2008, pp. 1–5.
6. Metke and R. Ekl, “Security technology for smart grid networks,” Smart Grid, IEEE Transactions on, vol. 1, no. 1, pp. 99 –107, Jun. 2010.
7. Z. Fadlullah, N. Kato, R. Lu, X. Shen, and Y. Nozaki, “Towards secure targeted broadcast in smart grid,” IEEE Commun. Mag., vol. 50, no. 5, pp. 150–156, May 2012 [Online]. Available: http://bbcr.uwaterloo.ca/h8liang/sg/Papesg commx.pdf
8. J. Xia and Y. Wang, “Secure key distribution for the smart grid,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1437–1443, Sep. 2012.
9. M. Fouda, Z. M. Fadlullah, N. Kato, R. Lu, and X. S. Shen, “A light-weight message authentication scheme for smart grid communications,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 675–685, 2011.
10. S. R. Rajagopalan, L. Sankar, S.Mohajer, and H. V. Poor, “Smartmeter privacy: A utility-privacy framework,” Proc. IEEE Smart Grid Comm, 2011.

212-215

http://blueeyesintelligence.org/2checkout_download.html

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Volume-4 Issue-2

Download Abstract Book

S. No

Volume-4 Issue-2, May 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Nihar Ranjan, Neha Thombare, Pallavi Deshmukh, Simantini Patil, Shailesh Jain

Paper Title:

Personalized Image Search

Abstract: Personalized Search is a feature in which when a user is logged into a account, all of his or her searches on Personal Search are recorded into Web History. Then, when a user performs a search, the search results are not only based on the relevancy of each web page to the search term, but the service also takes into account what websites the user previously visited through search results to determine which search results to determine for future searches, to provide a more personalized experience. The feature only takes effect after the user has performed several searches, so that it can be calibrated to the user’s tastes. Social sharing websites like facebook, twitter, YouTube they are allowing user to comment, tag, like and unlike the shared documents or images. Rapid Increase in the search services for social websites has been developed.

Keywords:
Personalized Search, Tagging, Topic Model


References:

1. Learn to Personalized Image Search from the Photo Sharing Websites Jitao Sang, Changsheng Xu, Senior Member, IEEE, Dongyuan Lu
2. B. Smyth, “A community-based approach to personalizing web search,”Computer, vol. 40, no. 8, pp. 42–50, 2007.

3. Personalized Search on Flickr based on Searcher’s Preference Prediction Dongyuan Lu, Qiudan Li

 

1-3

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

2.

Authors:

Bipin Pandey, Rituraj Jain

Paper Title:

Soft Computing Based Approaches for Software Testing: A Survey

Abstract: Software testing is the process of validation and verification of the software product which in turn deliver the reliable and quality oriented software product to users with lower maintenance cost, and more accurate and reliable results. Software testing effectiveness always depends on issues like generated test cases, prioritization of test cases etc. These issues demands on effort, time and cost of the testing. Many academicians and researchers are using soft computing based approached for better accuracy in testing. The aim of this research paper is to evaluate and compare soft computing approaches to do software testing and determine their usability and effectiveness.

Keywords:
Black Box Testing, Fuzzy Logic, Genetic Algorithms, Neural Network, Soft Computing, Software Testing, Tabu Search, White Box testing


References:

1. Dr. Velur Rajappa, Arun Biradar, Satanik Panda “Efficient software test case generation Using Genetic algorithm based Graph theory” International conference on emerging trends in Engineering and Technology, pp. 298-303, IEEE (2008).
2. Praveen Ranjan Srivastava and Tai-hoon Kim “Application of Genetic algorithm in software testing”, International Journal of software Engineering and its Applications, vol.3, No.4, pp. 87-96 (2009).

3. André Baresel , Hartmut Pohlheim , Sadegh Sadeghipour, Structural and functional sequence test of dynamic and state-based software with evolutionary algorithms, Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII, July 12-16, 2003, Chicago, IL, USA

4. O. Buehler and J. Wegener. Evolutionary functional testing of an automated parking system. In International Conference on Computer, Communication and Control Technologies and The 9th International Conference on Information Systems Analysis and Synthesis, Orlando, Florida, USA, 2003.

5. H. P. Schwefel and R. Manner, editors, Parallel Problem Solving From Nature, pages 176–185. Springer-Verlag, October 1990

6. D E Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, Reading, 1989.

7. J Holland, “Adaptation in Natural and Artificial Systems”, MIT Press, Cabmridge, MA, 1975.

8. D Koza, “Genetic Programming, On the Programming of Computers by Means of Natural Selection”, MIT Press, Cambridge, MA, 1992.

9. P von Laarhoven and E Aarts, “Simmulatd Annealing: Theory and Applications, Mathematics and its Applications” Kluwer, Dordrecht, 1987.

10. http://www.softcomputing.net.in/ (Access on 15th Feb, 2014)

11. Chattopadhyay S (2006), “Soft Computing Techniques in combating the complexity of the atmosphere-a review”, Arxiv preprint nlin/0608052.

12. David E. Goldberg (1989)” Genetic Algorithm in Search, Optimization and Machine Learning”, Pearson Education-India.

13. M. Melanie, “An Introduction to Genetic Algorithms, Massachusetts”, MIT Press, 1999.

14. K. F. Man , K. S. Tang and S. Kwong “Genetic algorithms: Concepts and applications”, IEEE Trans. on Industrial Electronics, vol. 43, no. 5, pp.519 -534 1996

15. S. Sabharwal, R. Sibal and C. Sharma, “Applying genetic algorithm for prioritization of test case scenarios derived from UML diagrams”, International Journal of Computer Science Issues (IJCSI), vol. 8, No. 2, pp. 433-444, May 2011.

16. Radim Belohlavek and George J. Klir. “Concepts and fuzzy logic”, Cambridge Mass. London: MIT Press, 2011

17. http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html, (Access on 18th Feb, 2014)

18. Maureen Caudill, “Neural networks primer, part I”, AI Expert, v.2 n.12, p.46-52, Dec. 1987

19. Zupan, J. (1994) “Introduction to artificial neural network (ANN) methods: what they are and how to use them” Acta Chim. Slov. 41, 327–352.

20. Naresh Chauhan, “Software Testing: Principles and Practices”, Oxford University Press, 2010.

21. Paul C. Jogersen, “Software testing: A craftsman approach” 3rd edition, CRC presses, 2008.

22. http://khannur.com/stb6.2.htm (access date: 19th Feb, 2014)

23. Z. Bo and W. Chen, “Automatic generation of test data for path testing by adaptive genetic simulated annealing algorithm,” in Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on, 2011, pp. 38-42.

24. Praveen Ranjan Srivastava et. al., “Generation of test data using Meta heuristic approach” IEEE, 2008, pp.19 – 21.

25. Ribeiro, J. C. B., Zenha-Rela, M. A., and de Vega, F. F. (2008a), “A Strategy for Evaluating Feasible and Unfeasible Test Cases for the Evolutionary Testing of Object-Oriented Software” In Proceedings of the 3rd international workshop on Automation of Software Test (AST ’08) , pages 85–92, Leipzig, Germany. ACM.

26. Girgis, “Automatic test generation for data flow testing using a genetic algorithm”, Journal of computer science, 11 (6), 2005, pp. 898 – 915.

27. Yeresime Suresh et. al, “A Genetic Algorithm based Approach for Test Data Generation in Basis Path Testing” The International Journal of Soft Computing and Software Engineering, Vol. 3, No. 3, Special Issue [SCSE’13], March 2013

28. Premal B. Nirpal and Kale K.V.(2010), “Comparison of Software Test Data for Automatic Path Coverage Using Genetic Algorithm”, Internal Journal of Computer Science and Engineering Technology, Vol. 1, Issue 1.

29. Jasmine Minj Lekhraj Belchanden, “Path Oriented Test Case Generation for UML State Diagram using Genetic Algorithm” International Journal of Computer Applications (0975 – 8887) Volume 82 – No 7, November 2013

30. Eugenia Díaz , Javier Tuya , Raquel Blanco , José Javier Dolado, “A tabu search algorithm for structural software testing”, Computers and Operations Research, v.35 n.10, p.3052-3072, October, 2008

31. Francisca Emanuelle et. al., “Using Genetic algorithms for test plans for functional testing”, 44th ACM SE proceeding, 2006, pp. 140 – 145.

32. Mark Last et. al., “Effective black-box testing with genetic algorithms”, Lecture notes in computer science, Springer, 2006, pp. 134 -148.

33. Chartchai Doungsaard, Keshav Dahal, Alamgir Hossain, and Taratip Suwannasart, 2007, “An Automatic Test Data Generation from UML State Diagram using Genetic Algorithm”, The proceedings of the Second International Conference on Software Engineering Advances.

34. H. Bhasin, S. Gupta, M. Kathuria, “Regression testing using fuzzy logic”, International Journal of Computer Science and Information Technology (IJCSIT), 4(2), pp. 378-380, 2013.

35. Abhas Kumar, “Dynamic Test Case Generation using Neural Networks”, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.3110&rep=rep1&type=pdf (Access on 20th Feb, 2014)

 

4-8

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

Manisha P. Khorgade, Shweta Hajare, P.K.Dakhole

Paper Title:

Structural level designing of Processing Elements using VHDL

Abstract: This paper involves structural design and development of processing elements using Hardware Description Language (HDL) using Altera or Xilinx softwares and implements them on Field Programmable Gate Arrays (FPGAs). In this paper, we will simulate and synthesize the various parameters of processing elements by using VHDL on Xilinx ISE 13.1 and target it for SPARTAN 6 FPGA board. The output is displayed by means of Liquid Crystal Display (LCD) interface. The state of each output bit is shown by using Light Emitting Diodes (LED). The processor can perform 2n number of operations where n is the control bit. More number of designs can be implemented on FPGA as per user’s needs.

Keywords:
FPGA, XILINX ISE 13.1, SPARTAN 6.


References:

1. V. Khorasani, B. V. Vahdat, and M. Mortazavi, “Design and implementation of floating point ALU on a FPGA processor”, IEEE International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 772-776, 2012.
2. Suchita Kamble, Prof .N. N. Mhala, “VHDL Implementation of 8-Bit ALU”, IOSR Journal of Electronics and Communication Engineering (IOSRJECE), ISSN : 2278-2834 Volume 1, Issue 1 (May-June 2012), PP 07-1.

3. Prof. S. Kaliamurthy & Ms. U. Sownmiya, “VHDL design of arithmetic processor”, Global Journals Inc. (U.S.A), November 2011.

4. S.Kaliamurthy, R.Muralidharan, “VHDL Design of FPGA Arithmetic Processor” International Conference on Engineering and ICT, 2007.

5. Charles H. Roth, Jr., “Digital system design using VHDL”, PWS publishing company, 2006.

6. B.Stephen Brown, V. Zvonko, “Fundamentals Of digital logic with VHDL Design”, 2nd Edition, McGraw Hill International Edition, 2005.

7. Bryan H. Fletcher, “FPGA Embedded Processors”,

8. Embedded Systems Conference San Francisco 2005

9. ETP-367.

10. J. Bhaskar, “VHDL Primer”, Pearson Education, 3rd edition, 2000.

11. Douglas L.Perry, “VHDL”, tata mc grawhill, international edition 1999.
12. Module 4: Design of Embedded Processor, Lesson 20: Field Programmable Gate Arrays and Applications, Version 2, EE IIT Kharagpur.

 

9-13

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

4.

Authors:

Maitham Ali Naji

Paper Title:

Implementation of Encryption Data Table by Using Multi-Keys

Abstract: The paper presents an encryption application that is able to work with data access table. In this paper the Caesar method is developed to generate one key to each record. The length of the key is computed from first word of record. The record after encryption will be stored in separate line in text file that separate each field by semicolon, this process will continue until the end of table.

Keywords:
Caesar Cipher, Database Encryption, Text File,and Visual Basic.


References:

1. Lee, K, H., “Basic Encryption and Decryption”, on line document
2. http://www.apprendre-en-ligne.net/crypto/bibliotheque/PDF/Kwang.pdf

3. Freeman J., Neely R., and Megalo L., “Developing Secure Systems”, IEEE Journal of Computer and Communication, Vol. 89, PP. 36-45, 1998.

4. “The Basics of Cryptography”, on line documents ftp://ftp.pgpi.org/pub/pgp/6.5/docs/english/IntroToCrypto.pdf

5. Fernandez EB, Summers RC, Wood C, “Database Security and Integrity”, Addison-Wesley, Massachusetts, 127, 1980.

6. M. Bellare, A. Desai, E. Jokipii, P. Rogaway, “A Concrete Security Treatment of Symmetric Encryption”, In Proceedings of the 38th Symposium on Foundations of Computer Science, IEEE, 1997.

7. O. Goldreich, “Foundations of Cryptography”, Cambridge University, Press, 2003.

8. Stalling W., “Cryptography and Network Security Principles and Practices”, Printice Hill publishing, PP. 36, 2005.

 

14-17

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

5.

Authors:

Sunil Kumar Mishra, Vishakha Nandanwar, Eskinder Anteneh Ayele, S.B. Dhok

Paper Title:

FPGA Implementation of Single Precision Floating Point Multiplier using High Speed Compressors

Abstract: Floating point multiplier is one of the vital concerns in every digital system. In this paper, the concepts of High speed compressors are used for the implementation of a High speed single precision binary Floating point multiplier by using IEEE 754 standard. Since compressors are special kind of adder which is capable to add more number of bits at a time, the use of these compressors makes the multiplier faster as compared to the conventional multiplier. For Mantissa calculation, a 24×24 bit multiplier has been developed by using these compressors. Owing to these high speed compressors, the proposed multiplier obtains a maximum frequency of 1467.136MHz. It is implemented using Verilog HDL and it is targeted for Xilinx Virtex-5 FPGA.

Keywords:
Compressors, Floating point multiplier, Mantissa, IEEE754 standard, Verilog HDL.


References:

1. Dandapat, S. Ghosal, P. Sarkar, D. Mukhopadhyay, “A 1.2-ns16×16-Bit Binary Multiplier Using High Speed Compressors”, International Journal of Electrical and Electronics Engineering, 2010.
2. Shubhajit Roy Chowdhury, Aritra Banerjee, Aniruddha Roy, Hiranmay Saha,”Design, Simulation and Testing of a High Speed Low Power 15-4 Compressor for High Speed Multiplication Applications”, First International Conference on Emerging Trends in Engineering and Technology, 2008.

3. Jeevan, S. Narender, Dr C.V. Krishna Reddy, Dr K. Sivani,”A High Speed Binary Floating Point Multiplier Using Dadda Algorithm”,IEEE,2013.

4. Loucas Louca, Todd A. Cook, William H. Johnson, “Implementation of IEEE Single Precision Floating Point Addition and Multiplication on FPGAs”, IEEE,1996.

5. Shaifali, Sakshi, “ FPGA Design of Pipelined 32-bit Floating Point Multiplier”, International Journal of Computational Engineering & Management, Vol. 16, 5th September 2013.

6. IEEE 754-2008, IEEE Standard for Floating-Point Arithmetic, 2008.

7. Mohamed Al-Ashrafy, Ashraf Salem, Wagdy Anis, “An Efficient Implementation of Floating Point Multiplier”, IEEE, 2008.

8. Guy Even, Silvia M. Mueller, Peter-Michael Seidel,” A dual precision IEEE floating-point multiplier”, INTEGRATION the VLSI journal, pp167-180, 2000.

9. M. Morris Mano, “Digital Design”,3rd edition, Prentice Hall,2002

 

18-23

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

6.

Authors:

Wojciech Wodo, Lucjan Hanzlik, Konrad Zawada

Paper Title:

USB Keyboard Security Unit

Abstract: Every user has its specific rhythm of typing which could be used as a biometrics in order to build some kind of “footprint” – unique profile. If somebody gets to know this profile, legitimate user is endangered by tracking and being impersonated. That is the way typing rhythm must be protected. We designed a hardware-based device in order to protect the identity of the individual during usage of keyboard (typing). The unit is plugged between the keyboard and the personal computer and works as an interface modifying data on the fly in the model “man in the middle”. Thanks to these modifications, an adversary who eavesdrops communication between a legitimate user and workstation gets practically no information about the “keystroking identity” of user. The security unit is based on two microprocessors: AVR AT90USB1287 working as USB Host – simulating workstation and AVR Atmega88 working as USB Device – simulating virtual keyboard. In the paper we present technical details of the security unit including electronic schemes and PCB referring to previously designed protection algorithms and results of performed efficiency tests as well.

Keywords:
biometrics, security and privacy protection, microprocessors and microcomputers, user interfaces, human factors in software design.


References:

1. Giot, R., El-Abed, M., Hemery, B., and Rosenberger, C. (2011). Unconstrained Keystroke Dynamics Authentication with Shared Secret. In Computers & Security 30(6-7), pages 427-445.
2. Fridman, A.; Stolerman, A.; Acharya, S.; Brennan, P.; Juola, P.; Greenstadt, R., and Kam, M., (2013). Decision Fusion for Multi-Modal Active Authentication. In IT Professional 15(4), pages 29-33.

3. Zhong, Y., Deng, Y., and Jain, A. K. (2012). Keystroke dynamics for user authentication. In CVPR Workshops, pages 117–123.

4. Klonowski, M., Syga, P., and Wodo, W. (2012). Some remarks on keystroke dynamics – global surveillance, retrieving information and simple countermeasures. In SECRYPT, pages 296–301.

5. Hanzlik, L., and Wodo, W., (2013). Identity Security in Biometric Systems Based on Keystroking. In SECRYPT, pages 524–530.

6. Universal Serial Bus (USB), Device Class Definition for Human Interface Devices (HID), 2001, [Online]

7. http://www.usb.org/developers/devclass_docs/HID1_11.pdf

8. Documentation for 8-bit Atmel Microcontroller with 64/128 Kbytes of ISP Flash and USB Controller

9. AT90USB646,AT90USB647,AT90USB1286,AT90USB1287, 2012, [Online] http://www.atmel.com/Images/doc7593.pdf

10. Documentation for 8-bit Atmel Microcontroller with 4/8/16K Bytes In-System Programmable Flash ATmega48/V, ATmega88/V, ATmega168/V, 2011, [Online]

11. http://www.atmel.com/images/doc2545.pdf

12. LUFA Library Documentation, 2013, [Online]

13. http://www.fourwalledcubicle.com/files/LUFA/Doc/130303/html

14. V-USB, A Firmware-Only USB Driver for the AVR, [Online]

15. http://vusb.wikidot.com

24-27

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

7.

Authors:

V.Sivaranjani, J.Umamaheswari

Paper Title:

Comparison of Repeating Pattern Extraction Techniques for Audio Pitch Detection

Abstract: Music separation methods are more demanding and complex, demanding system “training,” user designation of special music features, and audio processing time to support their complicated frameworks. Pattern extraction from music strings is an complex problem. The repeated sequence extracted from music strings can be used as features for music extracted or compared. various works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. Present the REpeating Pattern Extraction Technique (REPET), a novel and simple approach for separating the repeating “background” from the non-repeating “foreground” in a mixture. The basic idea is to identify the periodically repeating segments in the audio, compare them to a repeating segment model derived from them, and extract the repeating patterns via time-frequency masking. But in proposed system doesn’t support the Small rhythmic patterns, but rhythmic patterns are essential for the balance of the music, and can be a way to identify a song. And enhanced a method to extract a monophonic rhythmic signature from a symbolic polyphonic score. To go beyond the simple extraction of all time intervals between onsets we select notes according to their length (short and long extractions) or their intensities (intensity+/− extractions). Once the frequency is calculated, now use dynamic programming to compare several sequences of audio.

Keywords:
Pitch extraction, musical information retrieval, audio mining, pitch tracking, pattern extraction, audio segments.


References:

1. Jonathan T. Foote, “An Overview of Audio Information Retrieval”, In Multimedia Systems, vol. 7 no. 1, pp. 2-11, ACM Press/Springer-Verlag, January 1999..
2. Kirthika, P. ; Chattamvelli, R. ,“A review of raga based music classification and music information retrieval (MIR)”, Engineering Education: Innovative Practices and Future Trends (AICERA), 2012 ,Digital Object Identifier: 10.1109/AICERA.2012.6306752 Publication Year: 2012 , Page(s): 1 – 5

3. Krishnaswamy, A, “Application of pitch tracking to South Indian classical music”, Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on 19-22 Oct. 2003.

4. Lew, Michael S.; Sebe, Nicu; Djeraba, Chabane; Jain, Ramesh , “Content- based multimedia information retrieval: State of the art and challenges” , ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 1, February 2006, Pages 1–19..

5. Parag Chordia, “Automatic raag classification of pitch tracked performances using pitch-class and pitch-class dyad distributions”, In Proceedings of International Computer Music Conference, 2006.

6. https://ccrma.stanford.edu/~pdelac/research/MyPublishedPapers/icmc_2001-pitch_best.pdf

7. http://www.musipedia.org

8. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629434/

9. http://www.riaa.org

10. http://www.shazam.com

11. http://en.wikipedia.org/wiki/Audio_mining and shazam

 

28-30

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

8.

Authors:

Rama Shanker, Shambhu Sharma, Uma Shanker, Ravi Shanker, Tekie Asehun Leonida

Paper Title:

The Discrete Poisson-Janardan Distribution with Applications

Abstract: In the present paper a discrete Poisson-Janardan distribution (PJD), of which the Sankaran’s (1970) discrete Poisson-Lindley distribution (PLD) is a particular case, has been obtained by compounding Poisson distribution with the Janardan distribution of Shanker et al (2013). The first four moments of this distribution have been obtained and the estimation of its parameters using the method of maximum likelihood and the method of moments has been discussed. The distribution has been fitted to some data-sets to test its goodness of fit and its fitting of two data sets has been presented.

Keywords:
Poisson-Lindley distribution, Janardan distribution, compounding, moments, estimation of parameters, goodness of fit.


References:

1. Beall, G. (1940): The fit and significance of contagious distributions when applied to observations on larval insects, Ecology, Vol. 21, 460-474
2. Cochran, W. G. (1952): The test of goodness of fit, Annals of Mathematical Statistics, Vol. 23, pp. 315- 345.

3. Cochran, W.G. (1954): Some methods for strengthening the common tests, Biometrics, Vol. 10, pp. 417 – 451.

4. Ghitany, M. E., and Al-Mutairi, D.K. (2009): Estimation Methods for the discrete Poisson-Lindley distribution, Journal of Statistical Computation and Simulation, Vol.79 (1), 1 – 9.

5. Kemp, C.D. and Kemp, A.W. (1965): Some properties of the Hermite distribution, Biometrika, Vol. 52, 381-394.

6. Lindley, D. V. (1958): Fiducial distributions and Bayes theorem, Journal of Royal Statistical Society, Ser. B, Vol.20, 102-107

7. Sankaran, M. (1970): The discrete Poisson-Lindley distribution, Biometrics, Vol. 26, 145-149.

8. Shanker, R., Sharma, S., Shanker, U., and Shanker, R. (2013): Janardan distribution and its Applications to Waiting times data, Indian Journal of Applied Research, Vol. 3, Issue 8, pp. 500 – 502.

31-33

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

9.

Authors:

Le Ngoc Son, Daji Ergu, Pham Xuan Kien

Paper Title:

A New Approach for Dealing with Uncertain Degree in Group Judgment Aggregation using Triangular Intuitionistic Fuzzy Numbers

Abstract: The goal of this paper is to propose a new approach for aggregating group judgment using the triangular intuitionistic fuzzy number (IFN). The original group decision making (GDM) problems are converted to a triangular intuitionistic fuzzy decision making model by adding one simple conversion step which generates triangular IFNs from the mean and deviation of group judgment values to the process of GDM methods and inherits existing techniques. Using of triangular IFNs to express group judgment aggregation values keeps completely the information after aggregating and reflects evaluation more truthfully. Consequently, the application of the proposed model helps improve the efficiency and accuracy of GDM methods. In addition, an illustrative example is also presented in order to put this process in detail and comparing with conventional methods.

Keywords:
Group decision making, triangular intuitionistic fuzzy set, group judgment, group aggregation.


References:

1. Morais, Danielle C., and Adiel Teixeira de Almeida. “Group decision making on water resources based on analysis of individual rankings.” Omega 40.1 (2012): 42-52.
2. Ju, Yanbing, and Aihua Wang. “Emergency alternative evaluation under group decision makers: A method of incorporating DS/AHP with extended TOPSIS.” Expert Systems with Applications 39.1 (2012): 1315-1323.

3. Chaudhuri, Atanu, Bhaba Krishna Mohanty, and Kashi Naresh Singh. “Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data.” International Journal of Production Research 51.10 (2013): 2790-2804.

4. Sanayei, Amir, S. Farid Mousavi, and A. Yazdankhah. “Group decision making process for supplier selection with VIKOR under fuzzy environment.” Expert Systems with Applications 37.1 (2010): 24-30.

5. Zadeh, Lotfi A. “Fuzzy sets.” Information and control 8.3 (1965): 338-353.

6. Atanassov, Krassimir T. “Intuitionistic fuzzy sets.” Fuzzy sets and Systems 20.1 (1986): 87-96.

7. Xu, Zeshui, and Ronald R. Yager. “Some geometric aggregation operators based on intuitionistic fuzzy sets.” International journal of general systems 35.4 (2006): 417-433.

8. Xu, Zeshui. “Intuitionistic fuzzy aggregation operators.” Fuzzy Systems, IEEE Transactions on 15.6 (2007): 1179-1187.

9. Wei, Guiwu. “Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making.” Applied Soft Computing 10.2 (2010): 423-431.

10. Zhao, Hua, et al. “Generalized aggregation operators for intuitionistic fuzzy sets.” International Journal of Intelligent Systems 25.1 (2010): 1-30.

11. Yu, Xiaohan, and Zeshui Xu. “Prioritized intuitionistic fuzzy aggregation operators.” Information Fusion 14.1 (2013): 108-116.

12. Sadiq, Rehan, and Solomon Tesfamariam. “Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP).” Stochastic Environmental Research and Risk Assessment 23.1 (2009): 75-91.

13. Zhao, Hua, et al. “Generalized aggregation operators for intuitionistic fuzzy sets.” International Journal of Intelligent Systems 25.1 (2010): 1-30.

14. Li, Deng-Feng. “TOPSIS-based nonlinear-programming methodology for multiattribute decision making with interval-valued intuitionistic fuzzy sets.” Fuzzy Systems, IEEE Transactions on 18.2 (2010): 299-311.

15. Li, Jinquan, et al. “The relationship between similarity measure and entropy of intuitionistic fuzzy sets.” Information Sciences 188 (2012): 314-321.

16. Boran, F. E., K. Boran, and T. Menlik. “The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS.” Energy Sources, Part B: Economics, Planning, and Policy 7.1 (2012): 81-90.

17. Wan, Shu-Ping, Qiang-Ying Wang, and Jiu-Ying Dong. “The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers.” Knowledge-Based Systems 52 (2013): 65-77.

18. Atanassov, Krassimir T. Intuitionistic fuzzy sets. Physica-Verlag HD, 1999.

19. Xu, Zeshui. “Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment.” Information Sciences 168.1 (2004): 171-184.

20. Mendel, Jerry M. “Fuzzy sets for words: why type-2 fuzzy sets should be used and how they can be used.” presented as two-hour tutorial at IEEE FUZZ, Budapest, Hongrie (2004).

21. Chan, Felix TS, and Hing Kai Chan. “An AHP model for selection of suppliers in the fast changing fashion market.” The International Journal of Advanced Manufacturing Technology 51.9-12 (2010): 1195-1207.

22. Sanayei, Amir, S. Farid Mousavi, and A. Yazdankhah. “Group decision making process for supplier selection with VIKOR under fuzzy environment.” Expert Systems with Applications 37.1 (2010): 24-30.

23. Devi, Kavita. “Extension of VIKOR method in intuitionistic fuzzy environment for robot selection.” Expert Systems with Applications 38.11 (2011): 14163-14168.

 

34-39

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

10.

Authors:

Yousif I. Al Mashhadany, Eman Huassan, Aseel Najeeb

Paper Title:

Design and Implementation of GUI Package for the Muscle Diseases Recognition Based on EMG Signals

Abstract: An artificial neural network (ANN) provides a comprehensive and specialized service for the diagnosis and care of muscle diseases. Medical consultations are offered at the neuromuscular clinics, which are staffed by neurologists with special expertise in muscle diseases. This work presents the design and implementation of muscle diseases detection based on real electromyography (EMG) signals. This paper consists of three main parts. The first part presents the measurement of the signals of real human arm muscles (EMG signal). The data are then rearranged and recorded using EMGLab software. Surface electrodes are used to measure the real EMG signals. The suitable features of signal are extracted for classification. The second part applies signal requirements, such as filtering amplification and normalization, using MATLAB or any software. Muscle diseases were classified using an ANN package based on the features of EMG signals, amplitude of signals, and period of signals to identify the diseases. The third part explains the design of the graphical user interface based on MATLAB to implement the classification on real EMG signals. Satisfactory results are obtained from numerous executions with different cases of human arm muscles, thus ensuring the feasibility of this design for practical implement in hospitals or private clinics.

Keywords:
Electromyography (EMG) signals; Graphical User Interface (GUI); EMGLab software.


References:

1. K. G. Murphy, “Effective Information Display and Interface Design for Decomposition-based Quantitative Electromyography” , M.Sc thesis, University of Waterloo, Canada, 2002.
2. N. BU, “ EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network ”, Journal of Intelligent Information Systems, 21:2, 113–126, 2003.

3. Toshio Tsuji1 ” Pattern classi”cation of time-series EMG signals using neural networks”, international journal of adaptive control and signal processing , 2000.

4. Madeleine M. Lowery, ” A Multiple-Layer Finite-Element Model of the Surface EMG Signal”, MAY 2002.

5. O. Bida, “ Influence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimation ”, M.Sc Thesis , worcester polytechnic institute ,Electrical Engineering , January 2005.

6. M. B. I. Reaz, ” Techniques of EMG signal analysis detection, processing, classification and applications”, March 23, 2006.

7. Anne K. G. Murphy, ” Effective Information Display and Interface esign for Decomposition-based Quantitative Electromyography, thesis, 2002.

8. Luca Mesin and Dario Farina, ” Simulation of Surface EMG Signals Generated by Muscle Tissues With Inhomogeneity Due to Fiber Pinnation”, Sep. 2004.

9. Andrew Hamilton, ” Physiologically Based Simulation of Clinical EMG Signals”, FEBRUARY 2005.

10. M. Z. Al-Faiz, Yousif. I. Al-Mashhadany, “Human Arm Movements Recognition Based on EMG Signal”, MASAUM Journal Of Basic and Applied Sciences (MJBAS) Volume 1 Issue 2, PP 164-171, (September 2009).

11. Yousif. I. Al-Mashhadany, “Measurement of human leg joint angle through motion based on electromyography (EMG) signal”, The Engineering Conference of Control, Computers and Mechatronics, ECCCM2011, university of Technology, 30-31, January, 2011

12. Yousif I. Al-Mashhadany, “Design and Analysis of Virtual Human Arm Driven by EMG Signal”, BOOK , ISBN: 978-3-8433-7973-1, 2011, LAP LAMBERT Academic Publishing GmbH & Co. KG, 2011

13. Yousif Al Mashhadany, Nasrudin Abd Rahim, “Real-Time Controller for Foot-Drop Correction by Using SEMG Sensor”, Proc IMechE Part H: J Engineering in Medicine, 227(4) 373–383. Jan, 2013,( Q2 ISI Journal)(I.F.=1.208)
14. N. A. Shrirao, N. arender, P, Reddy, “ Neural network committees for finger joint a ngle estimation from surface EMG signals”, BioMedica l Engineeri ng OnLine 8 :2, 2009.
15. V. R . Mankar, A. A. Ghatol, “Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal ”,Hindawi Publishing Corporation Advances in Artificial Neural Systems Volume 2009, Article ID 942697, 9 pages doi:10.1155, 2009.

16. O. A. Alsayegh, “ EMG Based Human Machine Interface System” , IEEE Transaction of Biomedical Engineering, 0-7803-6535-4/00/ pp 925-928, 2000.

17. M. M. Lowery, N. S. Taflove, “ A Multiple Layer Finite-Element Model of the Surface EMG Signal ” , IEEE Transaction of Biomedical Engineering, Vol. 49, no.5, PP 446-454, May 2002.

18. L. Mesin, D. Farina, “ Simulation of Surface EMG Signals Generated by Muscle Tissues with Inhomogeneity Due to Fiber Pinnation ”, IEEE Transaction on Biomedical Engineering Vol. 51, no. 9, PP 1521-1529, September 2004.

19. Motion Lab Systems, Inc, “A software user guide for EMG Graphing and EMG Analysis EMG Analysis”, Updated Thursday, February 26, 2009.

20. EMGLAB software Version 0.9 User’s Guide, “The MathWorks, at www.mathworks.com, May 2008.

 

40-44

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

11.

Authors:

I A. Kamani C Samarasinghe, Saluka Kodituwakku, Roshan D. Yapa

Paper Title:

Understanding the Internet Usage Habits of the Students of University of the Visual & Performing Arts through Data Mining

Abstract: This Data mining has been a commonly used tool in the telecommunication sector. This is due to the useful insights that it can provide to assess the user preferences and optimize the service offerings consistent with user expectations. Data mining provides the required insights to the Internet and data usage habits of the students of the University of the Visual & Performing Arts (UVPA). It is clear that the data usage demand has been accelerating over the past few years and mobile data usage has been in the forefront of growth. Reduced prices as well as increased Internet usage options along with benefits achieved from the attributes of increased speed had augmented the usage of the data services. The study was a quantitative study and the information was collected from a random set of students who had registered with the University for their Degree program. The information collected had been processed consistent with the requirements to gain insight on the Internet usage habits of the students. The study represents data of 133 students who are from 17 districts of the country. The findings indicate that 83% of the households are Internet ready and Close to 50% of the households possess desktops followed by over 40% of households employing smartphones, which facilitate Internet access. Meanwhile, several students use dongles for Internet connectivity. The Internet is utilized for various purposes; where the purposes of online education and social networking are the two prominent areas.

Keywords:
Data mining, Telecommunication, University of the Visual & Performing Arts.


References:

1. Baxter, G., (2003). Challenge and Change in the Information Society. Journal of Documentation. Vol. 59, Iss: 6, pp.731 – 734.
2. Elragal, A. and El-Gendy, N., (2013). Trajectory data mining: integrating semantics. Journal of Enterprise Information Management. Vol. 26, Iss: 5, pp.516 – 535.

3. Gargano, M.L. and Raggad, B. G., (1999). Data mining – a powerful information creating tool. OCLC Systems & Services. Vol. 15, Iss: 2, pp.81 – 90.

4. Jareevongpiboon, W. and Janecek, P., (2013). Ontological approach to enhance results of business process mining and analysis. Business Process Management Journal. Vol. 19, Iss: 3, pp.459 – 476.

5. Lee, S.J. and Siau, K., (2001). A review of data mining techniques. Industrial Management & Data Systems. Vol. 101, Iss: 1, pp.41 – 46.

6. Mutula, S. M., (2002). Current developments in the Internet industry in Botswana. Electronic Library. The, Vol. 20, Iss: 6, pp.504 – 511.

7. Nemati, H. R. and Barko, C.D., (2003). Key factors for achieving organizational data-mining success. Industrial Management & Data Systems. Vol. 103, Iss: 4, pp.282 – 292.

8. Ozgulbas, N. and Koyuncugil, A.S. (2006). Application of Data Mining Method for Financial Profiling. Social Responsibility Journal. Vol. 2, Iss: 3/4, pp.328 – 334.

9. Sharma, S. Goyal, D. P. and Mittal, R. K., (2007). Evaluation model for data mining software: an empirical investigation of ICICI bank. Journal of Advances in Management Research. Vol. 4, Iss: 2, pp.63 – 68.

10. Telecommunication Regulations Commission (TRC), (2013). Statistics. [Online] Available

11. at:<http://www.trc.gov.lk/index.php/information/statistics.html> (Accessed March, 2014).

12. Viktor, H. L. and Arndt, H., (2006). Combining data mining and human expertise for making decisions, sense and policies. Journal of Systems and Information Technology. Vol. 4, Iss: 2, pp.33 – 56

 

45-48

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

12.

Authors:

Rohini.A.Maind, Alka Khade, D.K.Chitre

Paper Title:

Image Copy Move Forgery Detection using Block Representing Method

Abstract: As one of the most successful applications of image analysis and understanding, digital image forgery detection has recently received significant attention, especially during the past few years. At least two trend account for this: the first accepting digital image as official document has become a common practice, and the second the availability of low cost technology in which the image could be easily manipulated. Even though there are many systems to detect the digital image forgery, their success is limited by the conditions imposed by many applications. Most existing techniques to detect such tampering are mainly at the cost of higher computational complexity. In this paper, we present an efficient and robust approach to detect such specific artifact. Firstly, the original image is divided into fixed-size blocks, and discrete cosine transform (DCT) is applied to each block, thus, the DCT coefficients represent each block. Secondly, each cosine transformed block is represented by a circle block and four features are extracted to reduce the dimension of each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks will be matched by a preset threshold value. In order to make the algorithm more robust, some parameters are proposed to remove the wrong similar blocks. Experiment results show that our proposed scheme is not only robust to multiple copy-move forgery, but also to blurring or nosing adding and with low computational complexity.

Keywords:
Didgital forencics copy-move forgery circle block duplicated region


References:

1. A. Fridrich, et al., Detection of Copy-move Forgery in Digital Images, 2003.
2. Y. Huang, et al., Improved DCT-based detection of copy-move forgery in images,Forensic Science International 206 (1–3) (2011) 178–184.

3. Popescu and H. Farid, Exposing digital forgeries by detecting duplicate image regions, Dept. Computer. Sci. Dartmouth College, Tech.Rep. TR2004 515, 2004.

4. Mahdian, S. Saic, Detection of copy-move forgery using a method based on blur moment invariants, Forensic Science International 171 (2007) 180–189.

5. Li Jing, and Chao Shao,” Image Copy-Move Forgery Detecting Based on Local Invariant Feature Journal Of Multimedia,Vol.7,No.1, February 2012.

6. Vincent Christlein,” An Evaluation of Popular Copy-Move ForgeryDetection Approaches”, IEEE Transactions On Information Forensics And Security, 2011.

7. S. Bayram, H.T. Sencar, N. Memon,” An efficient and robust method for detecting copy-move forgery”, in: IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Press, New York, 2009.

8. X. Pan, S. Lyu,” Detecting image region duplication using SIFTfeatures”, in: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010, 2010, 1706–1709.

9. Frank Y. Shih and Yuan Yuan,”A Comparison Study on Copy-Cover Image Forgery Detection”,The Open Artificial Intelligence Journal, 2010, 4, 49-54.

10. Preeti Yadav, YogeshRathore, Aarti Yadav,” DWT Based Copy-Move Image Forgery Detection”, International Journal of Advanced Research in Computer Science an Electronics Engineering Volume 1, Issue 5, July 2012

11. Hwel-Jen Lin, Chun-We Wang,” Fast Copy-Move Forgery Detection”, WSEASTransactions on SIGNAL PROCESSING, May 2009.

12. B.L.Shivakumar1 and Lt. Dr. S.SanthoshBaboo,” Detection of Region Duplication Forgery in Digital Images Using SURF”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.

13. Sevinc Bayram, Taha Sencar, and Nasir Memon, “An efficient and robust method for detecting copy-move forgery,” in Proceedings of

14. Yanjun Cao a,*, TiegangGao,” A robust detection algorithm for copy-move forgery in digital images”,Forensic Science International 214 2012.

 

49-53

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

13.

Authors:

Rahul Ravindran, Riya Suchdev, Yash Tanna

Paper Title:

Heterogeneous Parallel Programming

Abstract: This paper presents Heterogeneous Parallel Computing (HPC), which is a well-orchestrated and co-ordinated effective use of a suite of diverse high performance machines to provide super-speed processing for computationally demanding tasks with diverse computational needs. GPUs are accoutered with a much more throughput oriented design as compared to that of the CPUs thus making them a powerful alternative to boast overall performance. It is now used all the way from mobile computing to supercomputing, like in Blue Star Super Computers. Upcoming Exascale and Petascale systems have embraced even heterogeneity in order to overcome power limitations. This paper also illustrates programming example using CUDA C to demonstrate the efficiency achieved in problems like matrix multiplication using a more heterogeneous approach as compared to that of sequential approach. It also explains how Heterogeneous Parallel Programming is a plausible, novel technique which allows to exploit inherent capabilities of a wide range of computational machines to solve computationally intensive problems that have several types of embedded parallelism by breaking it into separate modules. This paper also puts light on the challenges and concerns which exist when programming in HPC environment and some techniques to alleviate them.

Keywords:
About four key words or phrases in alphabetical order, separated by commas.


References:

1. Jacques A. Piennar,Srimat Chakradar and Anand Ragunathan “automatic generation of software peipeline for heterogeneous parallel systems”
2. Ashfaq A. Khokar “Heterogeneous Computing:Challenges and opportunities”

3. T. Berg and H.J. Siegel, “Instruction Execution Trade-offs for SIMD vs. MIMD vs. Mixed-Mode Parallelism,’’ Proc. Int’l Parallel Processing Symposium (IPPS), IEEE CS Press. Los Alamitos. Calif., Order NO. 2167. 1991, pp. 301-308.

4. Khokhar et al.. “Heterogeneous Supercomputing: Problems and Issues,” Proc. Workshop on Heterogeneous Processing, IEEE CS Press, Los Alamitos. California Order No. 2702. 1992. pp. 3-12.

5. R. Freund. “Optimal Selection Theory for Superconcurrency.” Proc. 89 Super- computing, IEEE CS Press, Los Alamitos, Calif., Order No. M2021 (microfiche), 1989. pp. 13-17.

6. The Multi-core Dilemma white paper by CITO Research

7. Heterogeneous supercomputing: Problems and issues Ashfaq Khokhar, Viktor Prasanna, Muhammad Shaaban, Cho-Li Wang

8. Instruction set innovations for the convey HC-1 by TM Brewer

 

54-59

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

14.

Authors:

Daulat Singh, Rakesh Shrivastava, Dinesh Varshney

Paper Title:

Identification and statistical analysis of the problems associated with Edusat based distance learning with special reference to Madhya Pradesh Bhoj (Open) University, Bhopal

Abstract: This Paper is an attempt to understand the problems associated with education imparted in distance learning mode by Bhoj Open University, Bhopal, Madhya Pradesh. The paper also deals with the tentative possible solutions to minimise the problems. A study was conducted on 80 students of 2centres of different regions and data was collected related to hub and 40 SIT’s (i.e. BER (Bit Error Rate), Bandwidth, number of operation days, number of recorded and live lectures telecasted) to understand the nature of the various problems. A detailed analysis was performed using SPSS 22.0 on the primary data collected. On the basis of observations and interpretation of the analysis the present study attempts to categories the problems and suggests possible solutions to make the education imparted though edusat satellite more effectively.

Keywords:
Satellite dedicated for education, SIT – Satellite. Interactive terminals, BER – Bit Error Rate, Bandwidth – the amount of data that can be carried from one point to another in given time period. (Usually a second).


References:

1. Keegan, D. Foundations of Distance Education. Rutledge Taylor and Francis Group, New York, 10-50, 1996.
2. Carter, A. Interactive distance education: Implications for the adult learner. International Journal of Instructional media , 28 (3), 249-261, 2001.

3. Teaster, P., & Blieszner, R. Promises and pitfalls of the interactive television approach to teaching adult development and aging. Educational Gerontology, 25 (8), 741-754, 1999.

4. Daulat Singh, Shiv Kumar, Rakesh Shrivastava, Dinesh Varshney, Edusat Satellite Based Education: Study of Scope for Enhancement of Audio-Video Quality- A Case Study of Madhya Pradesh Bhoj (Open) University, International Journal of Soft Computing and Engineering (IJSCE) 2, 11, 2012

 

60-69

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

15.

Authors:

Sreelatha P, P Pradeep Kumar, S V Mohankumar

Paper Title:

A Lab VIEW Based Extended (10, 5) Binary Hamming Code Generator for Telecommanding Applications

Abstract: The paper presents the design details of an extended binary Hamming code generator for generation of codewords suitable for remote applications needing telecommands. It is required that these telecommand codes maintain a minimum Hamming distance of three. For the present application, a suitable (10, 5) Extended Hamming code generator is designed for 5 data bits, which generates a corresponding 10 bit codeword for each data word. The design implemented in LabVIEW is detailed here along with a distance table showing the Hamming distance between the generated codes.

Keywords:
Extended Hamming code, Hamming distance, error correction, SEC-DED, LabVIEW.


References:
1. Proakis J.G., Digital Communications, 4th Edition, McGraw Hill Co., 2001.
2. Forouzan, Behrouz A., Data Communications and Networking, 4th Edition, McGraw Hill Higher Education, 2007.

3. Hall J.I., “Notes on Coding Theory”, http://www.mth.msu.edu/~jhall/classes/codenotes/coding-notes.html

4. Bhattacharya D. K. and S Nandi, “Theory and design of SEC-DED-AUED codes”, IEE Proceedings on Computers and digital techniques, Vol. 145, Issue 2, pp 121-126, 1998.

5. Bhattacharya D. K. and S Nandi , “An Efficient Class of SEC-DED-AUED Codes”, Proceedings of the Third International Symposium I-SPAN97, pp 410-416, 1997.

6. www.ni.com

 

70-73

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

16.

Authors:

Shama Kousar Jabeen.A, B.Arthi

Paper Title:

Software Effort Estimation for Size Proxy Metric Framework Modelling using Software Estimation Models and Neuro Fuzzy Logic Approach

Abstract: As software grew in size and requirements it also successively grew in complexity and cost. Evaluating size estimates accurately at an initial stage in the software conglomeration is of high priority. Conventional techniques have the problems of uncertainty and precision during the evaluation of size estimates. Software engineering cost models and estimation techniques are used for a number of purposes. In our work we have compared the results using three function point based effort estimation models. We have also compared MMRE, MMER, MRE, MER values by training the dataset using neuro-fuzzy logic based machine learning aapproach which overcomes the problems present in the traditional methods. In this paper effort estimates has been obtained by modeling and training the size metric framework. The dataset trained in our work is for 100 projects .

Keywords:
size metric, fuzzy logic software effort ,software engineering, cost estimation models, MMRE ,MER,MRE, MMER.


References:

1. B. Boehm, Software Engineering Economics, Prentice-Hall,City:Englewood Cliffs,State:N.J., 1981.
2. B.Boehm,C.Abts,S.Chulani,”Software development Cost Estimation approaches-a survey”,in J.C.Baltzer, Annals Of Software Engineering,Vol.10,Science Publishers,pp.177-205,Issue ¼.

3. International Function Point Users Group (IFPUG) <http://www.ifpug.org/>

4. J. Wong, D. Ho, L.E. Capretz, An investigation of using Neuro-Fuzzy with software size estimation, in: ICSE Workshop on Software Quality, WOSQ’09, 2009, pp. 51–58.

5. K. Srinivasan and D. Fisher, “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Transactions on Software Engineering, 21 (2), 1995, pp. 126-136.

6. K.K. Shukla, Neuro-genetic prediction of software development effort,Information & Software Technology 42 (10) (2000) 701–713.

7. L.Putnam and W.Myers,”Measures For Excellence”,Yordon Press,1992

8. M.W.Nisar,W.Yong-Ji,M.Elahi,“Software Effort Effort Estimation Using Fuzzy Logic –A survey”, In Fifth International Conference on Fuzzy Systsms and Knowledge Discovery,FKSD’08, pp-421-427, 2008

9. Moataz A.Ahamed, Irfan A.Ahmad, Jarallah S.Alghamdi,”Probabilistic Size Proxy For Software Effort prediction: A framework”, King Fahd University Of Petroluem and Minerals,Dhahran 31261,Saudi Arabia, pp 241-251, 2013.

10. Moataz A.Ahmad, Zeeshan Muzaffar, ”Handling Imprecision and uncertanty in software development effort prediction : A Type-2 fuzzy logic based framework”, Journal Of Information and Software Technology(IST),Vol.51,No.3,pp.92-109,http://dx.doi.org/10.1016/j.infsof.2008.09.004.2009

11. R.S.Pressman, Software Engineering,”A Practioner’s Approach”, Mcgraw Hill, ,pp-674-702,2001.

12. S.H.Kan,Metrics and Models in Software Quality Engineering,Second Edition,Pearson Education Inc,2002.

13. Sandeep Kad,Vinay Chopra,“Software development Effort Estimation using Soft Computing”,International Journal Of Machine Learning and Computing (IJMLC),V2.186,Vol.2, No.5,pp.548-551, ISSN: 2010-3700,DOI:10.7763,October 2012.

14. Shama Kousar Jabeen.A and Mrs.B.Arthi,”A Proposal On Size Metric Framework Modelling For Software Effort Estimation Models Using Neuro Fuzzy Logic Approach”Proceedings of fourth International Conference On ”Advance Computing,Control Systems,Machnies and Embedded Technology”,(ICACT),pp-1372-1376,ISBN:978-93-80757-74-2.

15. Shama Kousar Jabeen.A and Mrs.B.Arthi,”Size Proxy Metric Framework Modeling Of Software Effort Estimation in Soft Computing”, Journal Of Emerging Technolgies,Vol:8,pp:1-5,Special Issue-III,Feb 2014.

16. Visual Use Case Tool, <http://www.technosolutions.com/topteam-use-case.html>.

 

74-79

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

17.

Authors:

Neha Khanduja, Simmi Sharma

Paper Title:

Performance Analysis of CSTR using Adaptive Control

Abstract: In industry nowadays the control of chemical process is important task. Mostly all the chemical processes are highly nonlinear in nature and this causes instability of process. This paper presents the performance evaluation on the application of model reference adaptive control with various types of command inputs in a process plant. In the design of model reference adaptive control (MRAC) scheme, adaption law have been developed based on MIT and Lyapunov rule. This paper deals with basic simulation studies of the Continuous Stirred Tank Reactor (CSTR). The mathematical model is developed from material balances. Numerical mathematics is used for steady-state analysis and dynamic analysis which is usually represented by a set of differential equations.A simulation is carried out using Mat Lab and Simulink to control the process system using the adaptive control algorithm. It is also concluded that the adaptive controller will be superior to the conventional controller even without parameters change in the process. In a real world situation, these parameters could be estimated by using simulations or real execution of the system. It may be possible to improve the performance of the adaptive controller by further modifying the adaptation law or by incorporating parameter identification into the control.

Keywords:
Process control – CSTR ; Adaptive controller; MIT rule,Lyapunov Rule.


References:

1. Rahul Upadhyay, Rajesh Singla,”application of adaptive control in a process control”,2nd international conference on education technology and computer(ICETE),2010.(IEEE).
2. R.Aruna,M.Senthil Kumar, “Adaptive Control for interactive thermal process “proceedings of ICTECT,2011.(IEEE)

3. KarlJ.Astrom and Bjorn Witten mark,Adaptivecontrol,secondedition,Pearson Education, 2001.

4. K. S. Narendra, L. S. Valavani, Stable Adaptive Controller Design ­ Direct Control. IEEE Trans. Auto. Control, vol.23, pp. 570-583, Aug. 1978.

5. R.Aruna,M.SenthilKumarD.Babiyola,”Intelligence based and model based controller to the interactive thermal process “international conference on VLSIcommunicationand instrumentation(ICVCI),2011.

6. K.Prabhu,Dr.V.MuraliBhaskaran,”optimization of control loop using adaptive method”, International Journal Of Engineering and Innovative Technology,Volume1,Issue3,March 2012.

7. S.Lakshminarayanan, RaoRaghurajK.S.Balaji, “CONSIM-MS Excelbasedstudentfriendly simulatorforteachingProcess control theory”, Proceedings ofthe11thAPCCHEcongress,August27-30, 2006

8. Jiri vojtesec,petrdostal,”simulation analysis of continuous stirred tank reactor”, proceeding 22nd Europeanconference on modeling and simulation(ECMS),2006.

9. ComanAdrian,AxenteCorneliu,BoscoianuMircea,”the simulation of adaptive system using MIT rule”,10thinternational conference on mathematical methods and computational technique in electrical engineering(MMACTEE),2008.

10. Dr. M.J.Willis,”continuous stirred tank reactor models”,.Deptt. ofChemical and Process Engineering, University of Newcastle,March2010.

11. K.Prabhu,Dr.V.MuraliBhaskaran,”optimization of control loop using adaptive method”, International Journal Of Engineering and Innovative Technology,Volume1,Issue3,March 2012.

12. S.Jegan,K.Prabhu,”Temperature control of CSTR process using adaptive control”, International Conference on Computing and Control Engineering(ICCCE),2012.

 

80-84

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

18.

Authors:

J. Hussain, Lalthlamuana

Paper Title:

Unicode Mizo Character Recognition System Using Multilayer Neural Network Model

Abstract: The current investigation presents an algorithm and software to detect and recognize pre-printed mizo character symbol images. Four types of mizo fonts were under investigation namely – Arial, Tohoma, Cambria, and New Times Roman. The approach involves scanning the document, preprocessing, segmentation, feature extraction, classification & recognition and post processing. The multilayer perceptron neural network is used for classification and recognition algorithm which is simple and easy to implement for better results. In this work, Unicode encoding technique is applied for recognition of mizo characters as the ASCII code cannot represent all the mizo characters especially the characters with circumflex and dot at the bottom. The experimental results are quite satisfactory for implementation of mizo character recognition system.

Keywords:
Character Recognition, Neural Network, Multi-Layer Perceptron, and Unicode.


References:

1. Vivek Shrivastava and Navdeep Sharma, “Artificial Neural Network based Optical Character Recognition”, Signal & Image Processing: An International Journal (SIPIJ), vol.3, No.5, October, 2012.
2. Mohanad Alata and Mohammad Al-Shabi, “Text Detection and Character Recognition using Fuzzy Image Processing”, Journal of Electrical Engineering, vol.57, No.5, 2006, p258-267.

3. Pritpal Singh and Sumit Budhiraja, “Feature Extraction and Classification Techniques in OCR Systems for Handwritten Gurmukhi Script-A Survey”, International Journal of Engineering Research and Applications (IJERA), vo.1, Issue 4, 2011, pp.1736-9622.

4. Seethalakshmi R, Sreeranjani T.R., and Balachandar T., “Optical Character Recognition for Printed Tamil Text using Unicode”, Journal of Zhejiang University Science, 2005 6A(11):1297-1305, September, 2005.

5. Pramod J Simha and Suraj K V, “Unicode Optical Character Recognition and Translation Using Artificial Neural Network”, International Conference on Software Technology and Computer Engineering (STACE-2012), 22nd July 2012, Vijayawada, Andhra Pradesh, India.

6. Kauleshwar Prasad, Devrat C. Nigam, Ashmika Lakhotiya, Dheeren Umre, “Character Recognition using Matlab’s Neural Network Toolbox”, International Journal of u- and e- Service, Science and Technology, Vol. 6, No. 1, February, 2013

7. Md. Mahbub Alam, Dr. M. Abul Kashem, “A complete Bangla OCR System for Printed Characters”, International Journal of Computer and Information Technology, Vol. 01, Issue 01, July, 2010.

8. Madhup Shrivastava, Monika Sahu, and Dr. M.A. Rizvi, “Artificial Neural Network Based Character Recognition using Backpropagation” International Journal of Computers & Technology, vol. 3, No. 1, Aug, 2012

9. Om Prakash Sharma, M.K.Ghose, Krihna Bikram Shah and Benoy Kumar Thakur, “Recent Trends and Tools for Feature Extraction in OCR Technology”, International Journal of Soft Computing and Engineering (IJSCE), volume-2, Issue-6, January, 2013.

10. Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, The Neural Network ToolboxTM 7 User’s Guide. 3 Apple Hill Drive, Natick, MA: The Mathwork Inc., 2010

11. S.N. Sivanandam, S. Sumathi, S.N. Deepa, Introduction to Neural Networks using Matlab 6.0, Tata McGraw-Hill, 2006

 

85-89

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

19.

Authors:

Khushwant Kaur, Swimpy Pahuja

Paper Title:

A Brief Study of Data Mining

Abstract: Data mining plays a significant role on human activities and has become an essential component in various fields of human life. It is the knowledge discovery process which analyzes the large volumes of data from various perspectives and summarizes it into useful information. Data mining is greatly inspired by advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities. In this paper, we have discussed the concept of data mining, its tools and techniques, its applications and advantages/disadvantages from beginning of the term to present scenario.

Keywords:
Data Mining, Tools, Techniques, Applications


References:

1. Heikki, Mannila. 1996. Data mining: machine learning, statistics, and databases, IEEE.
2. Piatetsky-Shapiro, Gregory. 2000. The Data-Mining Industry Coming of Age. IEEE Intelligent Systems.

3. Salmin, Sultana et al. 2009. Ubiquitous Secretary: A Ubiquitous Computing Application Based on Web Services Architecture , International Journal of Multimedia and Ubiquitous Engineering Vol. 4, No. 4, October, 2009.

4. Hsu, J. 2002. Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century, The Proceedings of the 19th Annual Conference for Information Systems Educators (ISECON 2002), ISSN: 1542-7382.

5. Z. K. Baker and V. K.Prasanna. 2005. Efficient Parallel Data Mining with the Apriori Algorithm on FPGAs. In Submitted to the IEEE International Parallel and Distributed Processing Symposium (IPDPS ’05).

6. Jing He.2009. Advances in Data Mining: History and Future, Third international Symposium on Information Technology Application, 978-0-7695-3859-4/09 IEEE 2009 DOI 10.1109/IITA.2009.204.

7. Han, J., & Kamber, M. 2001. Data mining: Concepts and techniques .Morgan-Kaufman Series of Data Management Systems. San Diego: Academic Press.

8. http://en.wikipedia.org/wiki/Data_mining

9. http://www.statsoft.com/textbook/stdatmin.html

90-91

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

20.

Authors:

R. Vasundhara Devi, S. Siva Sathya, Mohane Selvaraj Coumar

Paper Title:

Multi- Objective Genetic Algorithm for De Novo Drug Design

Abstract: Genetic algorithms, can be used to solve NP-hard problems in various domains, including computer-aided drug design (CADD). As design & development of a drug molecule takes a number of man years and is also an expensive process, use of computer-aided techniques could help to reduce the time required and the cost of developing drugs. De novo drug design (DNDD) is one of the CADD technique used to design drug-like molecules virtually from smaller fragments/building blocks. This paper proposes a multi-objective genetic algorithm for the de novo design of novel molecules similar to a known reference molecule, possessing drug-like properties from a given set of input fragments and reference molecules. It could be used to design a variety of other virtual drug-like molecules by varying the input fragments and reference molecules based on the user requirement.

Keywords:
computer-aided drug design, de novo drug design, multi-objective genetic algorithm.


References:

1. R. Ng. Drugs: From disocovery to approval. 2nd ed. New Jersey: John Wiley & Sons, Inc., 2009, pp. 1-52.
2. T.T. Talele, S.A. Khedkar, A.C. Rigby, Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr. Top. Med. Chem., 10, 2010, 127-141.

3. D.E. Clark, What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov., 1, 2006, pp. 103-110.

4. G. Schneider, U. Fechner. Computer-based de novo design of drug-like molecules. Nature Rev. Drug Discov., 4, 2005, pp. 649-663.

5. K. Loving, I. Alberts, W. Sherman, Computational approaches for fragment-based and de novo design. Curr. Top. Med. Chem., 10, 2010, pp. 14-32.

6. C.A. Nicolaou, C. Kannas, E. Loizidou, Multi-objective optimization methods in de novo drug design. Mini Rev. Med. Chem., 12, 2012, pp. 979-987.

7. D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-Wesley Longman Publishing Co., Inc, 1989.

8. K. Deb, Multi-objective optimization using Evolutionary algorithms. London: Wiley, 2001.

9. C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 23, 1997, pp. 3-25.

10. C. A. Lipinski. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol., 1, 2004, pp. 337-341.

11. N. Brown, Chemoinformatics – An introduction for Computer Scientists. ACM Computing Surveys, 41, 2009, 8.

12. T. Tanimoto. An Elementary Mathematical theory of Classification and Prediction. “IBM Internal Report,” IBM technical report series, 1957.

13. E. Pihan, L. Colliandre, J.F. Guichou and D. Douguet. e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design, Bioinformatics, 28, 2012, pp. 1540-1541.

14. C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, E. Willighagen. The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J. Chem. Inf. Comput. Sci., 43, 2003, pp. 493-500.

15. C. Steinbeck, C. Hoppe, S. Kuhn, M. Floris, R. Guha, E. L. Willighagen. Recent developments of the chemistry development kit (CDK) — an open-source java library for chemo- and bioinformatics. Curr. Pharm. Des., 12, 2006, pp. 2111-2120.

16. https://www.chemaxon.com/products/marvin/

17. http://www.drugbank.ca/

18. M.S. Coumar, C.Y. Chu, C.W. Lin, H.Y. Shiao, Y.L. Ho, R. Reddy, et al. Fast-forwarding hit to lead: aurora and epidermal growth factor receptor kinase inhibitor lead identification. J. Med. Chem., 53, 2010,

 

92-96

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

21.

Authors:

Hadi Razmi

Paper Title:

Investigation of the AVR Output Voltage Limits in Power System Voltage Stability Assessment

Abstract: Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power systems. The accurate representation of the voltage instability phenomena requires a detailed model of power system components (generators, transformers, loads, etc.). On the other hand, reactive power generation limits have a significant effect on voltage collapse. In general, the system equations change non-smoothly when these limits are encountered. This paper presents a continuation-based method to steady-state voltage stability analysis that considered complete model of power system and the automatic voltage regulator (AVR) output voltage limits that indirectly control the reactive power generation limits. Results are provided for the New England 39-bus power system model. By comparing results obtained through this method and the continuation power flow (CPF) method, it is concluded that for design and developing the power systems, using proposed method seems a better approach due to its higher accuracy.

Keywords:
steady-state voltage stability, automatic voltage regulator (AVR) output voltage limit, power system differential-algebraic equation (DAE) model.


References:

1. G. M. Huang, N. C. Nair, “Voltage stability constrained load curtailment procedure to evaluate power system reliability measures,” In: Proceedings of IEEE/PES Winter Meeting, New York, 2002.
2. Tiranuchit, R. J. Thomas, “A posturing strategy against voltage instability in electric power systems,” IEEE Trans. Power Syst., vol. 3, 1998, pp. 87–93.

3. P. Kessel, H. Glavitch, “Estimating the voltage stability of a power system,” IEEE Trans. Power Deliver., vol. 1, 1986, pp. 346–354.

4. N. Amjady, “Dynamic voltage security assessment by a neural network based method,” Electr. Power Syst. Res., vol. 66, 2003, pp. 215–226.

5. F.M. Echavarren, E. Lobato, L. Rouco, “Steady-state analysis of the effect of reactive generation limits in voltage stability,” Electric Power Systems Research, vol. 79, 2009, pp. 1292–1299.

6. Y. Kataoka, Y. Shinoda, “Voltage stability limit of electric power systems with generator reactive power constraints considered,” IEEE Trans. Power Syst., vol. 20, 2005, pp. 951–962.

7. V. Ajjarapu, Computational techniques for voltage stability assessment and control, Springer, 2007.

8. P. Kundur, Power system stability and control, McGraw-Hill, 1994.

9. C. Z. De Souza, C. A. Canizares, V. H. Quintana, “New techniques to speed up voltage collapse computations using tangent vectors,” IEEE Trans. Power Syst., vol. 12, 1997, pp. 1380–1387.

10. A. Gharaveisi, M. Rashidinejad, A. Mousavi, “Voltage security evaluation based on perturbation method,” Electr. Power Energy Syst., vol. 31, 2009, pp. 227–235.

11. W. C. Rheinbolds, Numerical analysis of parameterized nonlinear equations, New York: John Wiley & Sons Interscience, 1986.

12. W. C. Rheinbolds, J. V. Burkradt, “A locally parameterized continuation process,” ACM Trans. Math. Software, vol. 9, 1983, pp. 215–235.

13. R. Seydel, From equilibrium to chaos, Elsevier, New York, 1988.

97-101

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

22.

Authors:

Alireza Asadi

Paper Title:

Inducing Stepping- Like Movement by Controlling Movement Primitive Blocks Using Intraspinal Microstimulation

Abstract: Recently, intraspinal microstimulation (ISMS) has been developed as a potential technique for restoring the motor function in paralyzed limbs. It has been shown that that there are functional units in the spinal cord (i.e., motor pools, motor primitives) that generates a specific motor output by selecting a specific pattern of muscle activation. Dynamics identification of these spinal primitives is a critical issue in rehabilitation the motor function using spinal microstimulation. In this paper, we have triggered the motor primitives by electrical microstimulation of the interneuron networks within the spinal cord. The major challenge in generating Walking cycles is finding suitable patterns to stimulate each primitive. By using EMG of normal walking we have tuned patterns of each primitive but this procedure is too time-consuming, thus we have applied closed-loop control using neuro-adaptive fuzzy sliding mode control. The results show both procedures can reconstruct walking, But in closed-loop procedure we tune little controller parameters once. Whereas in open loop procedure for each animal different pattern must be find.

Keywords:
functional electrical stimulation, intraspinal Microstimulation, movement primitives, neuro-Fuzzy sliding mode.


References:

1. C. Tai, C. J. Robinson. “Isometric torque about the knee joint generated by microstimulation of the cat L6 spinal cord,” IEEE Trans. Rehabil. Eng, vol. 7, no. 1, March 1999, pp. 46-55.
2. Lau, L. Guevremont, V. K. Mushahwar “Strategies for Generating Prolonged Functional Standing Using Intramuscular Stimulation or Intraspinal Microstimulation,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 15, no. 2, JUNE 2007, pp. 273 – 285.

3. V. K. Mushahwar, K.W. Horch, “Selective activation of muscle groups in the feline hindlimb through electrical microstimulation of the ventral lumbo-sacral spinal cord,” IEEE Trans. Rehabil. Eng, vol. 8, no. 1, March 2000, pp. 11 – 21.

4. V. K. Mushahwar, K.W. Horch, “Intraspinal Microstimulation Generates Locomotor-Like and Feedback-Controlled Movements,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 10, no. 1, March 2002, pp. 68 – 81.

5. S.F. Giszter, F.A. Mussa-Ivaldi, E. Bizzi ,” Convergent force fields organized in the frog’s spinal cord,” J. Neurosci, vol. 13,1993, pp. 467–491.

6. F.A. Mussa-Ivaldi, S.F. Giszter, E. Bizzi, “Linear combinations of Primitives in vertebrate motor control,” Proc. Natl. Acad. Sci. vol. 91 , 1994, pp. 7534–7538.

7. M.C. Tresch, P.Saltiel, E. Bizzi, “The construction of movement by the spinal cord,” Nat. Neurosci, vol. 2, no. 2, 1999, pp. 162–167.

8. M.C. Tresch, V.C.K. Cheung, A.d’Avella, “Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets,” J. Neurophysiol, vol.95, 2006, pp. 2199–2212.

9. A.R. Asadi, A.Erfanian, “adaptive neuro-Fuzzy sliding mode control of multi-joint movement using Intraspinal microstimulation,” J neural sys and rehab eng, vol. 20, no.4, July 2012, pp. 499 – 509

10. A. K. Thota, S. CarlsonWatson, E. J. Knapp, B. T. Thompson, and R. Jung, “Neuromechanical control of locomotion in the rat,” J. Neurotrauma, vol. 22, no. 4, Apr. 2005, pp. 442–465.

102-105

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

23.

Authors:

M.Vamsi Krishna Reddy, Md Ali Hussain

Paper Title:

Content Based Filtering In Social Networking Sites Using Web Apllication

Abstract: In consideration to the today’s globalized world everybody in the society are being addicted in using the Social Networks Sites.The basic problem that we are gonna be seen in using these sites is “Lack Of Privacy”. Till today, Social Networks Sites provide little support to this requirement. To sort out this problem, in this project we are proposing a system which will provide the indirect control to the users of these sites .This proposed model can be achieved through a modern rule-based system, that allows administrators to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of content-based filtering.

Keywords:
Social Networks Sites, Content-based filtering, Machine Learning, Rule-based system


References:

1. Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati and Moreno Carullo, “a system to filter unwanted messages from osn userwalls,” IEEE Trans. Knowledge and Data Eng., vol. 25, no. 2, february 2013.
2. M. Chau and H. Chen, “A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482-494, 2008.

3. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-Based Filtering in On-Line Social Networks,” Proc. ECML/PKDD Workshop Privacy and Security Issues in Data Mining and Machine Learning (PSDML ’10), 2010.

4. S. Pollock, “A Rule-Based Message Filtering System,” ACM Trans. Office Information Systems, vol. 6, no. 3, pp. 232-254, 1988.

5. R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204.

6. Nicholas J.Belkin and W.Bruce Croft ” Information filtering and information retrieval: Two sides of the same coin?” in Communications of the ACM, DEC1992 V35 N12 P29(10).

7. BrainWhitworth notes on” The Social Requirements of Technical Systems”Massey University-Auckland,NZ.

 

106-108

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

24.

Authors:

S. Adebayo Daramola, Ademola Abdulkareem, K. Joshua Adinfona

Paper Title:

Efficient Item Image Retrieval System

Abstract: Content based image retrieval system is a very effective means of searching and retrieving similar images from large database. This method is faster and easy to implement compare to text based image retrieval method. Ability to extract discriminative low level feature from these images and use them with appropriate classifier is factor in determining retrieval result. In this work efficient item image retrieval system is proposed. The system utilizes Haar wavelet transform, Phase Congruency and Support Vector Machine. Haar wavelet transform acted on image to form four sub-images. Texture feature is extracted from smaller image blocks from detailed bands and it was combined with shape feature from approximation band to form feature vector. Feature distance margin is achieved between query image and images in the database using Support Vector Machine (SVM). The effectiveness of the system is confirmed from output retrieval results.

Keywords:
Content, Texture, shape, Support Vector Machine, Phase Congruency


References:

1. Neha Jain, Sumit Sharma, Ravi Mohan Sairam, “Content Base Image Retrieval using Combination of Color, Shape and Texture Features”, International Journal of Advanced Computer Research Volume-3 Number-1 Issue-8, 2013, pp. 70 -77.
2. P.S Hiremath and Jagadeesh Pujari, “Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement”, International Journal of Computer Science and Security, Volume (1): Issue (4), pp.25 – 35.

3. Monika Jain, 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, 2011, pp.23-39.

4. Manimala Singha and K.Hemachandran, “ Content Based Image Retrieval using Color and Texture”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, 2012, pp.39-56.

5. A.Komali , R.Veera Babu, “An Efficient Content Based Image Retrieval System for Color and Shape Using Optimized K-Means Algorithm”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 8, 2013, pp. 203-207.

6. Arun K.S, Hema P Menon, “Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and Fourier Descriptors”, International Journal of Recent Trends in Engineering, Vol 2, No. 2, 2009, pp.35-39.

7. Ch.Srinivasa Rao , S. Srinivas Kumar , B.N.Chatterji , “Content Based Image Retrieval using Contourlet Transform”, ICGST-GVIP Journal, Volume 7, Issue 3, 2007, pp.9 – 15.

8. Swapna Borde, Udhav Bhosle, “Image Retrieval using Contourlet Transform”, International Journal of Computer Applications , Volume 34, No.5, November 2011 pp.37-43.

9. Sujata T Bhairnallykar, V.B.Gaikwad, “Content based Medical Image Retrieval with SVM Classification and Relevance Feedback”, International Conference & workshop on Advanced Computing 2013 (ICWAC 2013) pp.25 – 29.

10. T. Dharani, I. Laurence Aroquiaraj, “Content Based Image Retrieval System Using Feature Classification with Modified KNN Algorithm, International Journal of Computer Treds and Technology, Vol.4, Issue 7, 2013, pp.2008 – 2013.

11. Anju Maria, Dhanya S, “Amalgamation of Contour, Texture, Color, Edge, and Spatial features for Efficacious Image Retrieval”, International Journal of Research in Engineering and Technology, Volume: 03 Issue: 02, 2014, pp. 674- 680.

12. M. S. Shirdhonkar and Manesh B. Kokare, “Handwritten Document Image Retrieval”, International Journal of Modeling and Optimization, Vol. 2, No. 6, 2012, pp.693-696 .

 

109-113

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

25.

Authors:

Heena Sharma, Navdeep Kaur Kaler

Paper Title:

A Synthesized Approach for Comparison and Enhancement of Clustering Algorithms in Data Mining for Improving Feature Quality

Abstract: K-Means and Kohonen SOM clustering are two major analytical tools for unsupervised forest datasets. However, both have their innate disadvantages. Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the databases, which is beyond human being’s capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly; in addition, it is sensitive to outliers. SOM We present an improved approach to combined merits of the two and discard disadvantages.

Keywords:
Clustering, K-means, Kohonen SOM, Data Mining


References:

1. Balaji,S., .Srivatsa, S.K. (2012)” Decision Tree induction based classification for mining Life Insurance Databases” IRACST – International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 2, No.3, June 2012 pp 699-704.
2. Dan,Ji, Jianlin,Qiu, Xiang, Gu,Li,Chen, Peng, He (2010)” A Synthesized Data Mining Algorithm Based on Clustering and Decision Tree” 2010 10th IEEE International Conference on Computer and Information Technology (CIT 2010) 978-0-7695-4108-2/10 © 2010 IEEE ,pp 2722-2728

3. Fayyad, Piatetsky-Shapiro, Smyth, “From Data Mining to Knowledge Discovery: An Overview” in Fayyad, Piatetsky-Shapiro, Smyth, Uthurusamy, Advances in Knowledge Discovery and Data Mining ,AAAI Press / The MIT Press, Menlo Park, CA, 1996, pp.1-34.

4. Hatamlo, Abdolreza, Abdullah, Salwani (2011) ”Two stage algorithm for clustering” in Data Mining and Optimisation Research Group, Center for Artificial Intelligence Technology Int’ Conf. Data Mining DMIN 2011, pp 135-139.

5. Huo, Jianbing, Wang, Xizhao, Lu, Mingzhu, Chen, Junfen (2006) ” Induction of Multi-stage decision tree” 2006 IEEE International Conference on Systems, Man, and Cybernetics October 8-11, 2006, Taipei, Taiwan pp 835-839

6. .Kanellopoulos,Y. , Antonellis, P., Tjortjis,C., Makris,C., Tsirakis, N. (2011) ” k-attactors a partitional clustering algorithm for numeric data analysis”Applied Artificial Intelligence, 25:97–115, 2011 Copyright 2011 Taylor & Francis Group, LLC pp 97-115.

7. Kristensen, Terje, Jakobsen, Vemund (2011)” Three Different Paradigms for Interactive Data Clustering” Int’ Conf. Data Mining DMIN 2011 pp-3-9.

8. Li, Xiangyang, Ye, Nong (2006) ”A Supervised Clustering and Classification Algorithm for Mining Data With Mixed Variables” IEEE Tranactionon systems, man and Cybernetics-part systems and humans, VOL. 36, NO. 2, MARCH 2006 pp 396-406

9. Lin, Zetao, Ge, Yaozheng, Tao, Guoliang (2005) “Algorithm for Clustering Analysis of ECG Data” Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005 pp-3857-3860

10. Mao,Guojun ,Yang, Yi (2011)” A micro-Cluster based Ensemble Approach for Classifying Distributed Data Streams” 2011 23rd IEEE International Conference on Tools with Artificial Intelligence pp-753-759.

11. Shi, Yong, Meisner, Jerry (2011) ”An Approach to Selecting Proper Dimensions for Noisy Data” Int’ Conf. Data Mining DMIN 2011 pp 172-175.

12. Silwattananusarn, Tipawan , Tuamsuk, Kulthida (2012) ” Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012” International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, September 2012 pp 13-24.

13. Ramamohan, Y., Vasantharao, K., Chakravarti, C. Kalyana, Ratnam, A.S.K. (2012)” A Study of Data Mining Tools in Knowledge Discovery Process” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012 pp 191-194.

 

114-117

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

26.

Authors:

AhmadHamza Al Cheikha

Paper Title:

Matrix Representation of Groups In the Finite Fields GF(2n)

Abstract: The representation of mathematical fields can be accomplished by binary rows (or columns) of a binary triangular matrix as the Hamming’s matrices, but this representation don’t show the basic product properties of the fields, that is the nonzero elements of the fields forms a cyclic multiplicative group.In this paper we show that the elements of the fields GF(2n), and their subgroups, can represent as square matrices by m – sequences, which satisfies the product properties as a cyclic group.

Keywords:
Galois fields, m-sequences,bcyclic groups, Orthogonal sequences.


References:

1. Yang K , Kg Kim y Kumar l. d ,“Quasi – orthogonal Sequences for code – Division Multiple Access Systems ,“ IEEE Trans .information theory ,
2. Vol. 46 NO3, 200, PP 982-993

3. Jong-Seon No, Solomon W& Golomb, “Binary Pseudorandom Sequences For period 2n-1 with Ideal Autocorrelation,” IEEE Trans. Information Theory,Vol. 44 No 2, 1998, PP 814-817

4. Lee J.S &Miller L.E, ” CDMA System Engineering Hand Book, ” Artech House. Boston, London,1998.

5. Yang S.C,”CDMA RF System Engineering,” Artech House.Boston-London,1998.

6. LIDL,R.& PILZ,G., ”Applied Abstract Algebra,” Springer – Verlage New York, 1984.

7. Lidl, R. & Niderrreiter, H., “ Introduction to Finite Fields and Their Application,” Cambridge university U SA, 1994.

8. Thomson W. Judson , “Abstract Algebra: Theory and Applications ,” Free Software Foundation,2013.

9. FRALEIGH,J.B., “A First course In Abstract Algebra, Fourth printing. Addison-Wesley publishing company USA,1971.

10. Mac WILIAMS,F.G& SLOANE,N.G.A., “The Theory Of Error- Correcting Codes,” North-Holland, Amsterdam, 2006.

11. KACAMI,T.&TOKORA, H., “Teoria Kodirovania,” Mir(MOSCOW), 1978.

12. David, J., “Introductory Modern Algebra,” Clark University USA, 2008.SLOANE,N.J.A., “An Analysis Of The Stricture And Complexity Of Nonlinear Binary Sequence Generators,” IEEE Trans. Information Theory Vol. It 22 No 6,1976, PP 732-736.

 

118-125

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

27.

Authors:

Gul Ahmad, Tariq Rahim Soomro, Mohammad Nawaz Brohi

Paper Title:

XSR: Novel Hybrid Software Development Model (Integrating XP, Scrum & RUP)

Abstract: Software industries are progressively adopting the agile development practices of customized models such as Extreme Programming (XP) or Scrum or Rational Unified Process (RUP). Scrum and Extreme Programming (XP) are frequently used agile models, whereas Rational Unified Process (RUP) is one popular classic plan driven software development methodology. Both agile and plan driven models have their own merits & demerits such as XP has good engineering practices, team collaboration and on the other hand weak documentation, poor performance in medium & large scale projects. Scrum is based on project management practices. RUP model has some limitations such as impractical for small and fast paced projects, tendency to be over budgeted, condemn rapid changes in requirements. This research paper based on propose novel hybrid framework XSR by combining strengths of Scrum, XP and RUP by suppressing their limitations to produce high quality software.

Keywords:
eXtreme Programming (XP), Scrum, Rational Unified Process (RUP), XP Scrum RUP (XSR) \

References:
1. M. Salman Bashir, M. Rizwan Jameel Qureshi, HYBRID SOFTWARE DEVELOPMENT APPROACH FOR SMALL TO MEDIUM SCALE PROJECTS: RUP, XP & SCRUM, 2012, Sci.Int.(Lahore), 24(4),381-384, 2012
2. Lamia Nassif, Jessy, Nadine Ghanem, & Pedro Maroun Eid, Extreme Programming, March 2002, Software Engineering CSC 423 B – MWF 11-12

3. Zaigham Mushtaq, M. Rizwan Jameel Qureshi, Novel Hybrid Model: Integrating Scrum and XP, I.J. Information Technology and Computer Science, 2012, 6, 39-44

4. M. Grant, “Introduction to Extreme Programming”. http://www.xprogramming.com

5. Sillitti and G. Succi, “The Role of Plan-Based Approaches in Organizing Agile Companies,” Cutter IT Journal, Vol. 19, No. 2, 2006, pp. 14-19.

6. “Extreme Programming Official Website”. http://www.extremeprogramming.org/map/project.html

7. Schwaber K, Beedle M. Agile Software Development with Scrum. Prentice Hall, USA, 2001.

8. Abrahamson P, Salo O, Ron K. Agile Software Development Methods: Reviews and Analysis. VTT Electronics, 2002.

9. Maria P. Sadra D., Casper L. Distributed Agile Development: Using Scrum in Large Projects. In: Proceedings of IEEE International Conference on Global Software Engineering, Bangalore, India, August 2008, 87-95.

10. Brent Barton, Evan Campbel, Ken. Implementing a Professional Services Organization Using Type C Scrum. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Hawaii, 2007, 275 a-275 a.

11. P. Kruchten, “The Rational Unified Process—An Introduction,” 2nd Edition, Addison-Wesley, 2000.

12. P. Kroll and P. Kruchten, “Rational Unified Process Made Easy: A Practitioner’s Guide to the RUP,” Addison Wesley, Boston, 2003.

13. Sadaf Un Nisa, M. Rizwan Jameel Qureshi, Empirical Estimation of Hybrid Model: A Controlled Case Study, I.J. Information Technology and Computer Science, 2012, 8, 43-50 Published Online July 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2012.08.05

14. GUL AHMAD ,TARIQ RAHIM SOOMRO, MOHAMMAD NAWAZ BROHI, Agile Methodologies: Comparative Study and Future Direction, European Academic Research , Feb 2014

15. Szalvay V. An introduction to agile software development, Retrieved June 2012 from http//www.danube.com/docs/Intro_to_Agile.pdf, 2004

16. Marchesi M, Mannaro K, Uras S, Locci M. Distributed Scrum in Research Project Management. In: Proceedings of the 8th International Conference on Agile processes in software engineering and extreme programming, Como, Italy,2007.240–244.

17. Kamlesh V, Ahmad S. Evaluating Evolutionary Prototyping for Customizable Generic Products in Industry. M.S. Thesis, School of Engg. Blekinge Inst. Tech. (Ronneby, Sweden), 2008

18. Scott W. Ambler, Disciplined Agile Delivery: An introduction,, April 2011, http://public.dhe.ibm.com/common/ssi/ecm/en/raw14261usen/RAW14261USEN.PDF

19. J.Grey, The development of a hybrid agile project management methodology, June 2011, Potchefstroom Campus of the North-West University

20. Juyun Cho, A HYBRID SOFTWARE DEVELOPMENT METHOD FOR LARGE-SCALE PROJECTS: RATIONAL UNIFIED PROCESS WITH SCRUM, Issues in Information Systems,2-2009

21. Ghulam Rasool, Shabib Aftab, Shafiq Hussain, Detlef Streitferdt, eXRUP: A Hybrid Software Development Model for Small to Medium Scale Projects, Sept 2013, Journal of Software Engineering and Applications.

22. Jiang, Armin Eberlein. Towards a framework for understanding the relationships between classical software engineering and agile methodologies. In: Proceedings of the 2008 international workshop on Scrutinizing agile practices or shoot-out at the agile corral, Germany, May 2008, 9-14.

23. Jeff S, Anton V, Jack B, Nikolai P. Distributed Scrum: Agile Project Management with Outsourced Development Teams. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Hawaii, 2007, 274a-274a, 2007.

24. Y. Dubinskyl, O. Hazzanz and A. Keren, “Introducing Extreme Programming into a Software Project at the Israeli Air Force,” Proceedings of the 6th International Conference on Extreme Programming and Agile Processes in Software Engineering, Sheffield, 18-23 June

25. ScrumUP A Visual Blog on IT Improvement using Scrum, XP & RUP, May 20, 2011 2011, http://blog.scrumup.com/2011/05/comparing-methods.html

 

126-130

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

28.

Authors:

Kirti B. Satale, Anagha P. Khedkar

Paper Title:

Analysis of Different Speed Controllers and Implementation of Novel Speed Controller using FPGA for BLDC Motor

Abstract: BLDC motor controller has significant importance because of inherent properties of motor like high efficiency, low noise operation, maintenance free, etc. Variety of speed controllers like PID, fuzzy PID and adaptive fuzzy PID are reviewed. In adaptive fuzzy PID controller, to have online control, gains of fuzzy PID controller are changed with change in error. After simulating and comparing speed characteristic of different speed controllers, a novel speed controller having less rise time and overshoot will be implemented using FPGA.

Keywords:
BLDC motor, Fuzzy logic, PID controller.


References:

1. M.V. Ramesh, J. Amarnath, S. Kamakshaiah, G. S. Rao, “ Speed Control of Brushless DC Motor by Using Fuzzy Logic PI Controller”, ARPN journal of engineering and applied science, vol. 6, No. 09, pp. 55-62, Sept. 2011.
2. Y. Wu, H. Jiang, M. Zou, “The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor”, Elsevier, International conference on applied science and industrial engineering, pp. 1311-1318, 2012.

3. B. Park, T. Kim, D. Hyun, “Fuzzy Back EMF Observer for improving performance of Sensorless Brushless DC Motor Drive”, IEEE, pp. 674-978,2006.

4. J. Wang, C. Wang, Y. Chang, C. teng, “Intelligent Control of High-speed Sensorless Brushless DC Motor for Intelligent Automobiles”, IEEE, International conference on systems, man and cybernetics, pp. 3394-3398, 2008.

5. T. Siong, B. Ismail, S. Siraj, M. Mohmmed, “Fuzzy Logic Controller for BLDC Permanent Magnet Motor Drives”, IEEE, International journal of electrical and computer sciences, vol. 11, No. 02, pp. 12-17, April 2011.

6. N. Parhizkar, M. Shafiei, , M. Kauhshahi, “Direct Torque Control of Brushless DC Motor Drive with Reduced Starting Current using Fuzzy Logic Controller”, IEEE, International conference on uncertainty reasoning and knowledge engineering, pp. 129-132, 2011.

7. Rajan, R. Raj, S. Vasantharathna, “Fuzzy Based Reconfigurable Controller for BLDC Motor”, IEEE, International conference on computing, communication and networking technologies, 2010.

8. P. Shreekala, A. Sivasubramanian, “Speed Control of Brushless DC Motor with PI and Fuzzy Logic Controller using Resonantpole Inverter”, IEEE PES innovative smart grid technologies, 2011.

9. W. Yuanxi, Yu Yali, Z. Guosheng, S. Xiaoliang, “Fuzzy Auto-adjust PID Controller Design of Brushless DC Motor”, Elsevier, International conference on medical physics and biomedical engineering, pp. 1553-1559, 2012.

10. R. Arulmozhiyal, “Design and Implementation of Fuzzy PID Controller for BLDC Motor using FPGA”, IEEE, International conference on power electronics, drives and systems, Dec. 2012.

11. R. Kandiban, R. Arulmozhiyal, “Speed Control of BLDC Motor using Adaptive Fuzzy PID Controller”, Elsevier, International conference on modelling, optimisation and computing, pp.306-313, 2012.

12. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller- Part I”, IEEE Transaction on systems, man and cybernetics, vol. 20, No. 02, pp. 404-418, March 1990.

13. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller- Part II”, IEEE Transaction on systems, man and cybernetics, vol. 20, No. 02, pp. 419-435, March 1990.

14. J. E. Miller, “Brushless Permanent Magnet and Reluctance Motor Drives”, Oxford university press.

 

131-134

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

29.

Authors:

Manpreet Kaur, Chirag Sharma

Paper Title:

Improved Method for Segmentation of Real-time Image of Printed Documents

Abstract: The investment possibility of making a vast database of archive picture has left an enormous need for vigorous approaches to get to the data. Up to date engineering has made it conceivable to handle, process, transmit and store computerized pictures productively. Thusly, the measure of visual data is expanding at a quickening rate in numerous different provision zones. To completely misuse this picture recovery methods are needed. Archive picture recovery frameworks could be used in numerous associations which are utilizing record picture databases widely. The paper proposes a strategy to concentrate and recover the pictures from a printed archive.

Keywords:
Document image, retrieval, segmentation, image extraction


References:

1. Hyung Il Koo, Segmentation and Rectification of Pictures in the Camera-Captured Images of Printed Documents, IEEE
2. AndrásBarta and IstvánVajk, Document Image Analysis by Probabilistic Network and Circuit Diagram Extraction, Informatica29

3. (2005) 291–301

4. MohammadrezaKeyvanpour and Reza Tavoli, Document Image Retrieval: Algorithms, Analysis and Promising Directions, International Journal of Software Engineering and Its Applications, Vol.7,No.1,January, 2013

5. M. N. S. S. K. Pavan Kumar and C. V. Jawahar, Information Processing from Document Images

6. KonstantinosZagoris, KavallieratouErgina , Nikos Papamarkos, A Document Image Retrieval System

7. Manesh B. Kokare, M.S.Shirdhonkar, Document Image Retrieval: An Overview, 2010 International Journal of Computer Applications (0975– 8887) Volume 1–No. 7

8. Jilin Li, Zhi-Gang Fan, Yadong Wu and Ning Le, Document Image Retrieval with Local Feature Sequences, 2009 10th International Conference on Document Analysis and Recognition

9. REZA TAVOLI, Classification and Evaluation of Document Image Retrieval System, WSEAS TRANSACTIONS on COMPUTERS, Issue 10, Volume 11, October 2012, E-ISSN:2224-2872

10. Mrs.Waykule J.M, Ms. Patil V.A, Region Filling and Object Removal by Exemplar-Based Image In painting, International Journal of Scientific & Engineering Research Volume 3, Issue 1, January-2012 ISS N 2229-5518

11. Muhammad Waseem Khan, A Survey: Image Segmentation Techniques, International Journal of Future Computer and Communication, Vol. 3, No. 2, April 2014

12. Rajwantkaur, Sukhpreetkaur (2013), “Object Extraction and Boundary Tracing Algorithms for Digital Image Processing: Comparative Analysis: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering 3(5), May – 2013, pp. 263-268, ISSN: 2277 128X

13. Manjusha Singh, AbhishekMisal (2013),”A Survey Paper on Various Visual Image Segmentation Techniques”, International Journal of Computer Science and Management Research Vol 2 Issue 1 January 2013 ISSN 2278-733X

14. Toru Tamakia, Tsuyoshi Yamamurab and Noboru Ohnishi, “Image segmentation and object extraction based on geometric features of regions”, IS&T/SPIE Conf. on VCIP’99, SPIE Vol.3653, Part Two, pp.937

15. Atsushi Yamashita, Toru Kaneko, and Hajime Asama, Precise Extraction of Visual Information from Imagesby Image Processing Techniques, SUPEN

16. 2013

 

135-138

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

30.

Authors:

A.G. Ovskiy

Paper Title:

Instrumented System for the Solution of Static Problems on the Theory of Elasticity for a Multilayer Elastic Foundation

Abstract: The article presents an instrumented system developed by the author on the basis of analytical methods. The essence of analytical methods is given in the text. The compute kernel of the instrumented system is represented by Maxima computer mathematics. Examples of instrumented system operation constitute the fully automated development of analytical solutions of static problems on the theory of elasticity for a multilayer elastic foundation in two-dimensional and three-dimensional setting.

Keywords:
computer mathematics system, instrumented system, preprocessor, theory of elasticity.


References:

1. Vlasov V.Z. Beams, plates and covers on elastic foundation. / V. Vlasov, N. Leontyev – Moscow: FIZMATGIZ, 1960. – 491 p.
2. Gorshkov A.G. Theory of elasticity and plasticity / Gorshkov A.G., Starovoytov E.I., Talakovskiy D.V.; Textbook for higher educational establishments. – M.: FIZMATLIT, 2002. – 416 p.

3. Polianin A.D. Reference book on linear equations of mathematical physics / Polianin A.D. – M. FIZMATLIT, 2001. – 576 p.

4. Ovskiy A.G. Application of Maple system in the implementation of Vlasov’s initial functions method / Ye.Ye. Galan, Ovskiy A.G., V.A. Tolok // Journal of Zaporozhye National University: Collection of Scientific Articles. Physics and Mathematics Sciences. – Zaporozhye: ZNU. – 2008. – No. 1. – P. 16-26.

5. Ovskiy A.G. Application of Maple computer mathematics system for substantiation of orthogonality law for direct and inversion matrices built by Vlasov V.Z. / A.G. Ovskiy, V.A. Tolok // Journal “Radioelectronics, Computer Science. Management”. – Zaporozhye. ZNTU. – 2008. – No. 1. – P. 78-85.

6. Ovskiy A.G. Modeling a scheme of solution of elasticity theory three-dimensional problem within the Maple system / A.G. Ovskiy, V.O. Tolok // Hydroacoustic journal. – 2008. No. 3. – P. 88-97.

7. Ovskiy A.G. Preprocessor for solution of static two-dimensional and three-dimensional problems on the theory of elasticity. / A.G. Ovskiy, V.A. Tolok // Information technologies of modeling and management. – Voronezh. – 2014. – No. 85. – P. 47-58.

 

139-143

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

31.

Authors:

Edmore Chikohora, Obeten O. Ekabua

Paper Title:

A Genetic Approach to Parameterization of Feature Extraction Algorithms in Remote Sensing Images

Abstract: Genetic Algorithms (GA) are an adaptive heuristic search algorithm found on the evolutionary ideas of natural selection. In this paper, we propose an adaptive heuristic based on the Gabor Filter (GF) to generate useful solutions to optimization of parameter selection strategies for Feature Extraction Algorithms (FEA) in Remote Sensing Images. Experiments were done using computer simulations and a critical analysis on performance of the heuristic algorithm is done in a comparative manner with the rest of the algorithms.

Keywords:
Average Ranking, Square Error, Local Extrema, Phenotype, Genotype.


References:

1. M. Henrique and G. Easson, Feature Extraction from High-Resolution Remotely Sensed Imagery using Evolutionary Computation. Mississippi, USA: Prof. Eisuke Kita, 2011.
2. E. Chikohora and O. O. Ekabua, “Feature Extraction Techniques in Remote Sensing Images: A survey on Algorithms, Parameterization and Perfomance,” International Journal of Soft Computing and Engineering, vol. 4, no. 1, pp. 140-144, March 2014.

3. P. Moreno, A. Benardino, and J. Santos-Victor, “Gabor Parameter Selection for Local Feature Detection,” in IBPRIA 2nd Iberian Conference on Pattern Recogniton and Image Image Analysis, Portugal, 2005.

4. J. Yang, L. Liu, T. Jiang, and Y. Fan, “A modified Gabor Filter Design Method for Fingerprint Image Enhancement,” Pattern Recognition Letters, no. 24, pp. 1805 – 1817, January 2003.

5. H. N. Al-Duwaish, “Parameterization and Compensation of Friction Forces Using Genetic Algorithms,” in Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE, Phoenix, AZ , 1999, pp. 653-655.

6. C. M. Keet. (2002, May) Homepage of Maria (Marijke) Keet : Genetic Algorithms – An Overview. [Online]. Http://www.meteck.org/gaover.html

7. P. B. Brazdil and C. Soares, “A Comparison of Ranking Methods for Classification Algorithm Selection,” in Machine Learning: ECML 2000, R. L. Mántaras, Ed. Porto, Portugal: Springer Berlin Heidelberg, 2000, pp. 63-75.

8. W. M. Spears and V. Anand, “A Study Of Crossover Operators In Genetic Operators,” in Methodologies for Intelligent Systems, W. Z. Ras and M. Zemankova, Eds. Charlotte, N. C, USA: Springer Berlin Heidelberg, 1991, pp. 409-418.

9. M. E. Famer, S. Bapna, and A. K. Jain, “Large Scale Feature Selection Using Modified Random Mutation Hill Climbing,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume:2 ) , 2004, pp. 287-290.

 

144-149

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

32.

Authors:

Abdulaziz S. Al-Aboodi

Paper Title:

Monte Carlo Simulation on Estimation of Contact Pressure at Tubular Exchanger

Abstract: The strength of tube-to-tubesheet joints is influenced by many factors such as method of attachment, details of construction, and material properties. The strength of tube-tubesheet joints is measured in terms of the force required to pull or push the tube out of the hole in which it was expanded or by the radial interfacial residual contact pressure. In this paper, Monte Carlo method was conducted to estimate the tube/tubesheet mean interfacial pressure and its standard deviation using experimental sample data Sampling repetition were conducting to estimate the mean and standard deviation. Finally, a linear relation between force and contact pressure were introduced with the coefficient of determination, R2 of 0.9895..

Keywords:
Monte Carlo, Simulation, Contact pressure, Tubular Exchanger.


References:

1. Grimison, E. D., Lee, G. H., “Experimental Investigation of Tube Expansion”, Transaction of The ASME, July 1943, pp. 497-505.
2. Scott, D. A., Wolgemuth, G. A., Aikin, J. A., “Hydraulically Expanded Tube-to-Tubesheet Joints”, Journal of Pressure Vessel Technology, Vol. 106, Feb. 1984, pp. 104-109.

3. Jawad, M. H., Clarkin, E. J., Schuessler, R. E., “Evaluation of Tube-to-Tubesheet Junctions” Journal of Pressure Vessel Technology, Vol. 109, Feb. 1987, pp. 19-26.

4. Sherburne, P. A., Hornbach, D. J., Ackeman, R. A., Mcllree, A. R., “Residual Stresses in OTSG Tube Expansion Transitions” 8th Water Reactors, Proceeding, Aug 10, 1997, Amelia Island, Florida, NACE, TMS.

5. Shuaib, A. N., Merah, N., Khraisheh, M. K., Allam, I. M., Al-Anizi, S. S., “Experimental Investigation of Heat Exchanger Tubesheet Hole Enlargement” Journal of Pressure Vessel Technology, Vol. 125, Feb. 2003, pp. 19-25.

6. Lee, Jae Bong; Park, Jai HakView Profile; Kim, Hong-Deok; Chung, Han-Sub; Kim,Tae Ryong, “Statistical assessment of integrity in steam generator tubes considering uncertainty of nde”, Key Engineering Materials 326-328 (2006): 545-548.

7. Wu, Gary,”A probabilistic-mechanistic approach to modeling stress corrosion cracking propagation in Alloy 600 components with applications”, University of Maryland, College Park, ProQuest, UMI Dissertations Publishing, 2011. 1501253.

8. Mao, Dan, “Bayesian modeling of pitting corrosion in steam generators”, University of Waterloo (Canada), ProQuest, UMI Dissertations Publishing, 2007. MR35285.

9. Vincent, Brady, “A probabilistic assessment technique for shell-and-tube heat exchanger inspection”, University of New Brunswick (Canada), ProQuest, UMI Dissertations Publishing, 2007. MR56487.

 

150-152

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

33.

Authors:

Ipsa Das, Md Imran Alam, Jayanti Dansana

Paper Title:

A Survey on Location Based Services in Data Mining

Abstract: Data privacy has been the primary concern since the distributed database came into the picture. More than two parties have to compile their data for data mining process without revealing to the other parties. Continuous advancement in mobile networks and positioning technologies have created a strong challenge for location-based applications. Challenges resembling location-aware emergency response, location-based advertisement, and location-based entertainment. Privacy protection in pervasive environments has attracted great interests in recent years. Two kinds of privacy issues, location privacy and query privacy, are threatening the security of the users. The novel combined clustering algorithm for protecting location privacy and query privacy, namely ECC, is discussed. ECC applies an iterative K-means clustering method to group the user requests into clusters for providing location safety while utilizing a hierarchical clustering method for preserving the query privacy.

Keywords:
Location Based Services (LBSs), K-Anonymity, Location K-Anonymity, Clustering, Clustering Cloak


References:

1. B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” Mobile Computing, IEEE Transactions on, vol. 7, no. 1, pp. 1–18, 2008.
2. L. Yao, C. Lin, X. Kong, F. Xia, and G. Wu, “A clustering based location privacy protection scheme for pervasive computing”, in Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing. IEEE Computer Society, 2010, pp.
719–726.

3. Chi Lin, Guowei Wu, Lin Yao, Zuosong Liu”A Combined Clustering Scheme for Protecting Location Privacy and Query Privacy in Pervasive Environments”, 2012
IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications

4. M. Gruteser and D. Grunwald, “Anonymous Usage of Location- Based Services through Spatial and Temporal Cloaking”, Proc. ACM Int’l Conf. Mobile Systems, Applications, and Services (MobiSys ’03), 2003.

5. L. Liu, “From Data Privacy to Location Privacy”, VLDB ’07: Proceedings of the 33rd international conference on Very large data bases, ACM Press, Sep. 2007, pp. 1429-1430.

153-158

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

34.

Authors:

Vaibhav Ingle, Nilesh Swami, Mahesh Shelke, Saurabh Kataria, Chhaya Varade

Paper Title:

Web Services and Security

Abstract: The vision of a landscape of heterogeneous web services deployed as encapsulated business software assets in the Internet is currently becoming a reality as part of the Semantic Web. When pro-active agents handle the context-aware discovery, acquisition, composition, management of applications services and data, ensuring the security if customers data become a principle task. In this paper we propose neoteric way web services and security. A methodology based on type-based information flow to control the security of dynamically computed data and their proliferation to other web services. The approach is based on the following trine guidelines: (1)The business and security concern of integrated web services are separated and building them independently.(2)Runtime modification of integrated web services.(3)Providing compartmentalization so that one service can not affect another. We are developing flight system to demonstrate the feasibility of our approach.

Keywords:
The business and security concern of integrated web services are separated and building them independently, Runtime modification of integrated web services, Providing compartmentalization so that one service can not affect another.


References:

1. Benslimane, D.; Dustdar, S.; Sheth, A. (2008). “Services Mashups: The New Generation of Web Applications”. IEEE Internet Computing 10 (5): 13–15.doi:10.1109/MIC.2008.110
2. Maler, Eve. “Minutes of 9 January 2001 Security Services TC telecon”. security-services at oasis-open mailing list. Retrieved 7 April 2011.

3. Bob Atkinson, et. al.: Web Services Security (WS-Security) http://www.oasis-open.org/committees/tc home.php?wg abbrev=wss.

4. Anne Anderson, “WS-XACML:Authorization and Privacy Policies for Web Services” https://www.oasis-open.org

5. F. Paci, E. Bertino, and J. Crampton, “An Access-Control Framework for WS-BPEL,” International Journal of Web Services Research, vol. 5, no. 3, pp. 20–43, 2008.

159-161

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

35.

Authors:

Swati D. Nikam, Sachin D. Ruikar

Paper Title:

A Method of Color Image Denoised and Enhanced Using Wavelet Transform

Abstract: The objective of the image enhancement is to remove the noise. Real color images are images with noise. In traditional image enhancement algorithms color images are firstly converted to gray images. These algorithms enhanced noise while they enhanced image. In this paper wavelet transform is used for color image enhancement. Wavelet transform is an efficient tool to represent a multi resolution analysis of an image. A novel method of color image enhancement based on Hue invariability in HIS color pattern is presented here.

Keywords:
Image enhancement, HIS color space, Wavelet transforms.


References:

1. Wang Ping, Cheng Hao, Lou Yingxin. “Color Image Enhancement Based on Hue Invariability”. Journal. Journal of Image and Graphics.2007 IEEE.
2. SHI Meihong, LI Yonggang, ZHANG Junying. “Novel method of color image enhancement” Journal. Computer Application, 2004 IEEE.

3. Zhang Yujun, Image Engineering. Tsinghua University Press Beijing.1999 IEEE.

4. Kartik Sau, Amitabha Chanda, Milan Pal, “Color Image Enhancement based on Wavelet Transform and Human Visual System”. IEEE 2011.

5. Zhang Yanhong, Hou Dewen. “An Image Enhancement Algorithm Based on Wavelet Frequency Division and Bi-histogram Equalization”, Journal. Computer Application and Software 2007 IEEE.

6. S. Daly, “The visible differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision, A. B.Watson, Ed., chapter 14, pp. 179–206, MIT Press, Cambridge, Mass, USA, 1993.

7. L .Meylan, Tone mapping for high dynamic range images, Ph.D. thesis, EPFL, Lausanne, Switzerland, July 2006.

8. Meylan and S. S¨usstrunki, “Bio-inspired color image enhancement,” in Human Vision and Electronic Imaging Conference, vol. 5292 of Proceedings of SPIE, pp. 46–56, San Jose, Calif, USA, 2004.

9. Ruan Qiuqi. Digital Image Processing, Publishing House of Electronics Industry, Beijing 2001.

10. Jinyong Cheng, Caixia Liu, – “Novel Method of Color Image Enhancement Based on Wavelet Analysis”, 2008 IEEE.

11. Shaohua Chen and Azeddine Beghdadi, – “Natural Enhancement of Color Image”, 2010 IEEE.

12. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing, Prentice Hall, Upper Saddle River, NJ, USA, 2004 IEEE.

13. Han Xiaowei. “The research on Color image processing key technology”. North Eastern University, 2005. 1.1.

14. Zhang Yanhong, Hou Dewen. “An Image Enhancement Algorithm Based on Wavelet Frequency Division a Bi-histogram Equalization”, Journal. Computer Application and Software. 2007.11, 24(11), pp.159-161

15. Jobson D J, Rahman Z U, Woodell G A. The statistics of visual representation [C] // Processing of SPIE Visual Information processing XI. Washington: SPIE Press, 2002:25-35 438

16. V. Buzuloiu, M. Ciuc, M. R. Rangayyn & C. Vartan. Adaptive neighborhood histogram equalization of color images. International journal of electron Image. 10(2), 2001, 445-459.

17. P. E. Trahanias, & A. N. Venetsanopoulos, Color image enhancement through 3-D histogram equalization Proc. 11th IAPR conference on pattern recognition, The Hague, Netherlands, 1992, 545-548.

18. B. A. Thomas. R. N. Strickland, & J. J. Rodriguez, Color image enhancement using spatially adaptive saturation feedback. Proc. 4th IEEE conf. on image processing, Santa Barbara, CA, USA, 1997, 30-33

19. Gupta, & B. Chanda, A hue preserving enhancement scheme for a class of color images, Pattern recognition Letters, 17(2), 1996, 109-114

20. Kartik Sau, Amitabha Chanda, – “Color Image Enhancement Based on Wavelet Transform and Human Visual System”, IEEE2011.

21. S. Chen and A. Beghdadi, “Natural rendering of color image based on retinex” in Proceedings of the IEEE International Conference on Image Processing (ICIP ’09), Cairo Egypt, November 2009.

 

162-165

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

36.

Authors:

Pallavi Grover, Sonal Chawla

Paper Title:

Evaluation of Ontology Creation Tools

Abstract: Representation of distributed information, with a well defined meaning understandable for different parties, is the major challenge of Semantic Web. Several solutions have been built up. Use of Ontologies is one of the solutions to challenges faced by semantic web. This paper highlights importance of ontologies. This paper has three fold objectives. Firstly the paper throws light on how a semantic web based tool helps producing information using ontologies. Secondly, paper highlights the importance of ontology. Lastly a comparison of various tools for ontology development has been presented on various parameter

Keywords:
Ontology Ontology Tools, RDF, Semantic Web


References:

1. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6245779
2. http://en.wikipedia.org/wiki/Semantic_Web

3. http://www.rdfabout.com/intro/#Introducing RDF

4. Ontology Development Tools for ontology-based knowledge management, S.Youn, D.McLeod, University of Southern California, 2006

5. http://www2012.wwwconference.org/proceedings/ companion/p595.pdf

6. Kapoor, B., Sharma, S., “A Comparative Study Ontology Building Tools for Semantic Web Applications”, International Journal 1, July (2010), 1-13.

7. Kalyanpur, A., Parsia, B., Sirin, E., Grau, B.C., Hendler, J., “Swoop: A „Web‟ Ontology Editing Browser”, Mind, July 2005, 1-20.

8. Gruber, T. R. “A translation approach to portable ontology specification. Knowledge Acquisition” 5(2), 199-220, 1993.

9. Snae. C and Brueckner. M. Ontology-Driven E-Learning System Based on Roles and Activities for Thai Learning Environment. Interdisciplinary Journal of Knowledge

 

166-169

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

37.

Authors:

Amlan Raychaudhuri, Arkadev Roy, Ashesh Das, Gourav Kumar Shaw, Pratik Kumar Mitra

Paper Title:

Moving Object Detection using Differential Evolution

Abstract: Moving Object detection is the process of detecting a change in position of an object relative to its surroundings or the change in the surroundings relative to an object. Different complex algorithms are employed to detect a moving object in a video. It has large number of applications in video surveillance and other security systems that are used to process video information. We have achieved it using Differential Evolution (DE). The proposed method is successfully tested over two video sequences.

Keywords:
Clustering, Differential Evolution, Moving Object Detection, Temporal video segmentation.


References:

1. K. Skifstad, R. Jain and C. Ramesh, “Illumination independent change detection for real world image sequences”, Computer Vision, Graphics and Image Processing 46(3), pp. 387–399, (1989).
2. Y. Z. Hsu, H. H. Nagel and G. Rekers, “New likelihood test methods for change detection in image sequences”, Computer Vision, Graphics and Image Processing, 26(1), pp. 73–106, (1984).

3. T. Aach, A. Kaup and R. Mester, “Statistical model-based change detection in moving video”, Signal Processing 31, pp. 165–180, (1993).

4. K. McKoen, R. Navarro-Prieto, B. Duc, E. Durucan, F. Ziliani and T. Ebrahimi, “Evaluation of video segmentation methods for surveillance applications”, EUSIPCO 2000, Tampere, Finland, (Sept. 2000).

5. P. Villegas, X. Marichal and A. Salcedo, “Objective evaluation of segmentation masks in video sequences”, WIAMIS’99 Workshop, Berlin, Germany, (May 1999).

6. M. Wollborn and R. Mech, “Procedure for objective evaluation of VOP generation algorithms”, Fribourg, ISO/IEC JTC1/SC29/WG MPEG97/2704, (1997).

7. E. Durucan and T. Ebrahimi, “Change Detection and Background Extraction by Linear Algebra”, Proceedings of the IEEE 89(10), (October 2001).

8. K. V. Price, “An Introduction to Differential Evolution”, New Ideas in Optimization, McGraw-Hill, London, pp. 79-108, (1999).

9. R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization 11, pp. 341–359, (1997).

10. S. Z. Li, “Markov Random Field Modeling in Image Analysis”, New York: Springer, (2001).

11. E. Y. Kim and K. Jung, “Genetic Algorithms for video Segmentation”, Pattern Recognition 38(1), pp. 59-73, (2005).

12. E. Y. Kim and S. H. Park, “Automatic video Segmentation using genetic algorithms”, Pattern Recognition Letters 27(11), pp. 1252-1265, (Aug. 2006).

13. S. W. Hwang, E. Y. Kim, S. H. Park and H.J. Kim, “Object Extraction and Tracking using Genetic Algorithms”, Proceedings of International Conference on Image Processing 2, pp. 383-386, (2001).

14. A. Raychaudhuri and M. De,”A Novel Approach of Detection of Moving Objects in a Video”, International Journal of Advanced Research in Computer and Communication Engineering 2(11), pp. 4485 – 4488, (2013).

 

170-173

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

38.

Authors:

Safaa ERRIHANI, Said ELFEZAZI, Khalid BENHIDA

Paper Title:

IT Project Management According To the PMBoK Adaptation and Application in a Set of Computing Projects in a Moroccan Public Body

Abstract: With the growth of the computing projects and the limited visibility by the administrators regarding the use of the resources as well as the technologies which lead to the abundance of projects, along with the trend to the subcontracting in external suppliers, the project management becomes the key function (office) for the success of all the technical projects. So the Project Management Body Of Knowledge (Guide PMBOK)) of PMI (Project Management Institute) is in phase to become an essential tool for the practitioners in all organizations and business sectors.The main axis of this article is the PMBoK’s analysis and modeling in order to reach a uniform model of project management. The obtained model will be projected on a set of projects within a Moroccan public department. Let’s recall that the PMBoK defines the project management in terms of: integration, scope, time, cost, quality, human resources, communication, risk, procurement and the stakeholders of the project.

Keywords:
IT project, Modeling, PMBOK, Project Management..


References:

1. foad.refer.org/IMG/pdf/INTRODUCTION_module_6.pdf
2. John M. Nicholas ET Herman Steyn “Project Management for Business, Engineering, and Technology “, principals and practices, 3rd edition, Elsevier 2008

3. Le guide de gestion de projets- Introduction Cadre amélioré pour la gestion des projets de technologie de l’information. Bureau de dirigeant principal de l’information Secrétariat du Conseil du Trésor du Canada. Février 2002

4. A guide to the project management body of knowledge (PMBOK GUIDE) – Fourth edition ANSI/PMI 99-001-2008

5. http://www.piloter.org/

6. A guide to the project management body of knowledge 3rd Ed ANSI/PMI 99-001-2004

7. The principals of project management by Meri Williams 2008 SitePoint

8. Project management for Dummies 3rd edition by Stanley E. Portny 2010 Wiley Publishing

9. C. Dumont ‘’ITIL pour un service informatique optimal’’, Eyrolles, 2007

10. Introduction à ITIL V3 et au cycle de vie des services, Pascal Delbrayelle, (www.itilfrance.com), juillet 2011

11. http://www.best-management-practice.com

12. the official introduction to the ITIL service lifecycle. Office of Government Commerce (OCG) 2007

13. E. Delbaldo, ‘’CMMI light’’, éditions AFNOR, 2008

14. M.Lamnabhi, ‘’Evaluer avec CMMI’’, éditions AFNOR, 2008

15. CMMISM for Systems Engineering/Software Engineering, Version 1.02 (CMMI-SE/SW, V1.02, CMMI Product Development Team, SEI Joint Program Office

16. D. Moisand & F. Garnier de Labareyre ‘’CobiT : Pour une meilleure gouvernance des systèmes d’information’’ Paris, Eyrolles, 2009

17. http://itil.fr/COBIT/

18. Cobit 1.4, IT Governance Institute, 2007

19. C.v. Wangenheim, D. A. da Silva, L. Buglione, R. Scheidt, R.Prikladnicki ‘’Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK ’’ Information and Software Technology, (52), 7, 749-757, 2010

20. Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK 2008, Information and Software Technology 52 (2010) 749–757, 2010 Elsevier

21. Successful Project Management Third Edition, Larry Richman, 2011 American Management Association

22. The Complete Project Management Methodology and Toolkit Gerard M. Hill ISBN: 978-1-4398-0154-3

 

174-182

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

49.

Authors:

Safaa ERRIHANI, Said ELFEZAZI, Khalid BENHIDA

Paper Title:

IT Project Management According To the PMBoK Adaptation and Application in a Set of Computing Projects in a Moroccan Public Body

Abstract: With the growth of the computing projects and the limited visibility by the administrators regarding the use of the resources as well as the technologies which lead to the abundance of projects, along with the trend to the subcontracting in external suppliers, the project management becomes the key function (office) for the success of all the technical projects. So the Project Management Body Of Knowledge (Guide PMBOK)) of PMI (Project Management Institute) is in phase to become an essential tool for the practitioners in all organizations and business sectors.The main axis of this article is the PMBoK’s analysis and modeling in order to reach a uniform model of project management. The obtained model will be projected on a set of projects within a Moroccan public department. Let’s recall that the PMBoK defines the project management in terms of: integration, scope, time, cost, quality, human resources, communication, risk, procurement and the stakeholders of the project.

Keywords:
IT project, Modeling, PMBOK, Project Management..


References:

1. foad.refer.org/IMG/pdf/INTRODUCTION_module_6.pdf
2. John M. Nicholas ET Herman Steyn “Project Management for Business, Engineering, and Technology “, principals and practices, 3rd edition, Elsevier 2008

3. Le guide de gestion de projets- Introduction Cadre amélioré pour la gestion des projets de technologie de l’information. Bureau de dirigeant principal de l’information Secrétariat du Conseil du Trésor du Canada. Février 2002

4. A guide to the project management body of knowledge (PMBOK GUIDE) – Fourth edition ANSI/PMI 99-001-2008

5. http://www.piloter.org/

6. A guide to the project management body of knowledge 3rd Ed ANSI/PMI 99-001-2004

7. The principals of project management by Meri Williams 2008 SitePoint

8. Project management for Dummies 3rd edition by Stanley E. Portny 2010 Wiley Publishing

9. C. Dumont ‘’ITIL pour un service informatique optimal’’, Eyrolles, 2007

10. Introduction à ITIL V3 et au cycle de vie des services, Pascal Delbrayelle, (www.itilfrance.com), juillet 2011

11. http://www.best-management-practice.com

12. the official introduction to the ITIL service lifecycle. Office of Government Commerce (OCG) 2007

13. E. Delbaldo, ‘’CMMI light’’, éditions AFNOR, 2008

14. M.Lamnabhi, ‘’Evaluer avec CMMI’’, éditions AFNOR, 2008

15. CMMISM for Systems Engineering/Software Engineering, Version 1.02 (CMMI-SE/SW, V1.02, CMMI Product Development Team, SEI Joint Program Office

16. D. Moisand & F. Garnier de Labareyre ‘’CobiT : Pour une meilleure gouvernance des systèmes d’information’’ Paris, Eyrolles, 2009

17. http://itil.fr/COBIT/

18. Cobit 1.4, IT Governance Institute, 2007

19. C.v. Wangenheim, D. A. da Silva, L. Buglione, R. Scheidt, R.Prikladnicki ‘’Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK ’’ Information and Software Technology, (52), 7, 749-757, 2010

20. Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK 2008, Information and Software Technology 52 (2010) 749–757, 2010 Elsevier

21. Successful Project Management Third Edition, Larry Richman, 2011 American Management Association

22. The Complete Project Management Methodology and Toolkit Gerard M. Hill ISBN: 978-1-4398-0154-3

 

183-187

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

40.

Authors:

Adhar Vashishth, Bipan Kaushal, Abhishek Srivastava

Paper Title:

Caries Detection Technique for Radiographic and Intra Oral Camera Images

Abstract: In the modern times, caries is one of the most prevelent disease of the teeth in the whole world. A large percentage of population is affected by them. Dentists try their level best to identify the problem at an earlier stage, but, with poor dentist to patient ratio , the problem becomes compounded. To provide them a helping hand various machines and techniques are developed. Prominent among them is the DIFOTI(digital imaging fiber-optic transillumination) technique, but it requires very expensive machinery to work with which can not be afforded by most of the dentists. We are proposing a method that can provide the needed diagnostic help without requiring the kind of machinery currently in use. We have used image processing technique to identify the caries that provide the dentists with the precise results about caries and the area affected. This method can detect caries in radiographic images as well as in intra oral camera images. This will not only help in countering the low man power problem but will also provide an accurate and cost effective method in identifying and treating caries.

Keywords:
binarization, caries, mask, RGB plane, MATLAB.


References:

1. Stefan Oprea, Costin Marinescu, Ioan Lita, Mariana Jurianu, Daniel Alexandru Vişan, Ion Bogdan Cioc “Image Processing Techniques used for Dental X-Ray Image Analysis “ , IEEExplore , E-ISBN: 978-1-4244-3974-4.
2. Grace F. Olsen, Susan S. Brilliant, David Primeaux, and Kayvan Najarian “An Image-Processing Enabled Dental Caries Detection System” , Published in Complex Medical Engineering, 2009. CME, E-ISBN : 978-1-4244-3316-2

3. Supaporn Kiattisin, Adisorn Leelasantitham, Kosin Chamnongthai and Kohji Higuchi “ A Match of X-ray Teeth Films Using Image Processing Based on Special Features of Teeth”, Published in SICE Annual Conference 2008, E-ISBN : 978-4-907764-29-6.

4. “Dental Caries: The Disease and Its Clinical Management”, 2nd Edition by Ole Fejerskov and Edwina Kidd.

5. Paul Fotek, DMD, Florida Institute for Periodontics & Dental Implants Published in Medical Plus, A service of the U.S. National Library of Medicine National Institutes of Health.

6. “Digital Image Processing Using MATLAB” by Rafael C. Gonzalez.

7. Programming in MATLAB ®: A problem-solving approach by Ram N Patel and Ankush Mittal.

8. World statistics published by WHO in 2013

9. By Brett Shoelson, PhD,Email: brett.shoelson@mathworks.com

 

188-190

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

41.

Authors:

Ameya V. Mane, Yogesh Ankurkar, Pratik K. Bajaria

Paper Title:

Cooperative Mobile Robotics

Abstract: “Cooperative control” is a term which is used to capture those problem areas in which some type of repetition of identical or non-identical subsystems, which are interconnected together, occurs. Such systems are often found in nature, i.e. in the motion of clusters of birds, fish, insects, etc. moving together, in the cell structure of mammals and life-forms, and also in the man-made systems such as in transportation systems. In such systems, a decentralized control configuration is often applied to control the overall system, so that some common objective is achieved. In this paper two non-linear models in multi-agent systems are proposed. These models operate on the principles of distributed control and cascade control.

Keywords:
About four key words or phrases in alphabetical order, separated by commas.


References:

1. David Payton, Mike Daily, Regina Estowski, Mike Howard And Craig Lee, “Pheromone Robotics”, Autonomous Robots 11, 319–324, 2001.
2. Y. Uny Cao, Alex S. Fukunaga, Andrew B. Kahng, “Cooperative Mobile Robotics: Antecedents and Directions”, Autonomous Robots 4, 7–27 (1997)

3. Maziar E. Khatir, Edward J. Davison, “Cooperative Control of Large Systems”, Block Island Workshop, Post workshop volume 2003.

4. Marios M. Polycarpou, Yanli Yang, Kevin M. Passino, “Cooperative Control of Distributed Multi-Agent Systems”, IEEE Control Systems Magazine (June 2001).

5. Po Wu, Panos J. Antsaklis, “Distributed Cooperative Control System Algorithms – Simulations and Enhancements”, ISIS-2009-001, April 2009

 

191-194

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

42.

Authors:

Sarah Bal, Anmol Kalra, Rishi Kumar

Paper Title:

Motioned Facial Recognition from Live Feed for Surveillance Solutions

Abstract: The paper focuses on how face recognition can be done on live video stream (using a webcam-inbuilt or USB attached).The live video is checked for any human face. If a human face is detected, a rectangular box is formed around the face. If nothing is found for the face detection method, a text box showing the error is presented in front of the user. If the face is detected this face is then matched with the already saved database which was priorly created having images of different faces. This is the training database which is then matched with the face image extracted from the live video stream. Initially the project shows the process of face detection and matching procedure from images and then proceeds to face recognition and matching through a live video streaming. The live video here considered is the webcam, the face is detected through the webcam and if any match is found from the train database previously stored in the computer or the device is found then both the detected image and the current image are displayed on the graphical user interface. The GUI being made consists of three axes windows, one showing the continuous live streaming of video, the second shows the screenshot or singular frame of the face detected in the live stream and the third has the image got from the database that somewhat matches to the current image being displayed. The two databases are there, one the train database where the images of different faces of people are stored which would then be used for matching from live video stream for the purpose of security and authentication. The test database consists of the images that are being received from the live video stream, the video stream as soon as it detects the face of human, takes the snapshot of the frame and saves it to the test database, these images are then checked for authentication by matching them with the images in the train database.

Keywords:
The test database consists of the images that are being received from the live video stream, the video stream as soon as it detects the face of human, takes the snapshot of the frame and saves it to the test database, these images are then checked for


References:

1. Study of Moving Object Detection and Tracking for Video Surveillance, International Journal of Advanced Research in Computer Science and Software Engineering
2. Real Time Motion Detection in Surveillance Camera Using MATLAB, International Journal of Advanced Research in Computer Science and Software Engineering, Iraqi National Cancer Research Center ,Baghdad University, Iraq

3. A Video-based Face Detection and Recognition System using Cascade Face Verification Modules, Ping Zhang, Department of Mathematics and Computer Science, Alcorn State University, USA

4. A Surveillance System based on Audio and Video Sensory Agent cooperating with a Mobile Robot, The University of Padua, Italy

5. Face Recognition using Eigenfaces, Mathew.A.Turk and Alex.P.Pentland, Vision and Modeling Group, The Media Laboratory, Massachusetts Institute of Technology

6. Performance evaluation of object detection algorithms for video surveillance, Jacinto Nascimento, Member, IEEE andJorge Marques

7. Face recognition using multiple eigenface subspaces, P.Aishwarya and Karnan Marcus, Journal of Engineering and Technology Research Vol. 2(8), pp. 139-143, August 2010

8. Development of a real-time face recognition system for access control, Desmond E. van Wyk, James Connan, Department of Computer Science, University of Western Cape, South Africa

9. Face Recognition and Retrieval in Video, Caifeng Shan

10. Image-based Face Detection and Recognition: “State of the Art”, Faizan Ahmad , Aaima Najam and Zeeshan Ahmed

11. OBJCUT for Face Detection, Jonathan Rihan, Pushmeet Kohli, and Philip H.S. Torr, Oxford Brookes University, UK

12. Design Of Efficient Face Recognition Based On Principle Component Analysis Using Eigenfaces Method, Mr.A.R.Sejani

13. Eigenfaces for Recognition, Alex Pentland and Mathew Turk, MIT

14. An Improved Face Detection Method in Low-resolution Video, Chih-Chung Hsu and Hsuan T. Chang Photonics and Information Laboratory Department of Electrical Engineering National Yunlin University of Science & Technology Douliu Yunlin, 64045 Taiwan ROC

15. Biometrics and Face Recognition Techniques, International Journal of Advanced Research in Computer Science and Software Engineering, Renu Bhatia

16. Biometrics- Fingerprint Recognition, International Journal of Information & Computation Technology, Sarah Bal and Anmol Kalra

17. Face Detection and Tracking in a Video by Propagating Detection Probabilities, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 10, OCTOBER 2003, Ragini Choudhury Verma, Cordelia Schmid, and Krystian Mikolajczyk

 

195-201

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

43.

Authors:

S.Y.S Hussien, H.I Jaafar, N.A Selamat, F.S Daud, A.F.Z Abidin

Paper Title:

PID Control Tuning VIA Particle Swarm Optimization for Coupled Tank System

Abstract: This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD) of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then, the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) method. Simulation is conducted within Matlab environment to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error (SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID tuning methods.

Keywords:
Coupled Tank System (CTS), Particle Swarm Optimization (PSO), PID Controller, PID Tuning Method.


References:

1. M. Abid, “Fuzzy Logic Control of Coupled Liquid Tank System”, International Conference on Information and Communication Technologies, 27-28 August 2005, Karachi, Pakistan, pp. 144-147.
2. M. F. Rahmat and S.M. Rozali, “Modelling and Controller Design for a Coupled-Tank Liquid Level System: Analysis & Comparison”, Journal of Technology, vol. 48 (D), June. 2008, pp. 113-141.

3. H. Abbas, S. Asghar, S. Qamar, “Sliding Mode Control for Coupled-Tank Liquid Level Control System”, International Conference on Frontiers of Information Technology, 17-19 Dec. 2012, Islamabad, Pakistan, pp. 325-330.

4. K. O. Owa, S. K. Sharma, R. Sutton, “Optimised Multivariable Nonlinear Predictive Control for Coupled Tank Applications”, IET Conference on Control and Automation, 4-5 June 2013, Birmingham, England, pp. 1-6.

5. N. A. Selamat, N. A. Wahab, and S. Sahlan, “Particle Swarm Optimization for Multivariable PID Controller Tuning”, 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, 8-10 March 2013, Kuala Lumpur, Malaysia, pp. 170-175.

6. H. I. Jaafar, Z. Mohamed, A. F. Z. Abidin and Z. A. Ghani, “PSO-Tuned PID Controller for a Nonlinear Gantry Crane System”, 2012 IEEE International Conference on Control System, Computing and Engineering, 23-25 Nov. 2012, Penang, Malaysia, pp. 515-519.

7. H. I. Jaafar, S. Y. S. Hussien, N. A. Selamat, M. S. M. Aras and M. Z. A. Rashid, “Development of PID Controller for Controlling Desired Level of Coupled Tank System”, International Journal of Innovative Technology and Exploring Engineering, vol. 3 (9), Feb. 2014, pp. 32-36.

8. M. Khairuddin, A. S. A. Dahalan, A. F. Z. Abidin, Y. Y. Lai, N. A. Nordin, S. F. Sulaiman, H. I. Jaafar, S. H. Mohamad, N. H. Amer, “Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank System”, Advanced Materials Research, vol. 903, Feb. 2014, pp. 321-326.

9. Coupled-Tank Liquid Level Computer-Controlled Laboratory Teaching Package: Experimental and Operation (Service) Manual, Augmented Innovation Sdn. Bhd., Kuala Lumpur, Malaysia.

10. J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, WA, 27 Nov. – 1 Dec. 1995, pp. 1942-1948.

11. Q. Bai, “Analysis of Particle Swarm Optimization Algorithm”, Computer and Information Science, vol 3 (1), February 2010, pp. 180-184.

202-206

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

44.

Authors:

D.Ashok Kumar, P.Samundiswary

Paper Title:

Design and Study of Enhanced Parallel FIR Filter Using Various Adders for 16 Bit Length

Abstract: Now a day’s parallel Finite Impulse Response (FIR) filter plays very important role in the Digital Signal Processing (DSP) based applications. FIR filters are one of the most widely used fundamental filters in the DSP systems. The parallel FIR filters are derived from FIR digital filter. In this paper, design and study of enhanced parallel FIR filter with various adders using the structure of Fast FIR Algorithm (FFA) based FIR filter and symmetric convolution based FIR filter structures considering 2-parallel and 3-parallel filters is done. These entire filter structures are also designed using Ripple Carry Adder (RCA), Carry save Adder (CSA) and Carry Increment Adder (CIA) by replacing the existing adders with the input bit length and coefficient length of 16-bits. Then the performance metrics of the above two structures is done by designing using Verilog HDL. Further, they are simulated and synthesized using Xilinx ISE 13.2 for Vertex family device of speed -12.

Keywords:
Parallel FIR filter, FFA, symmetric convolution, Ripple Carry Adder (RCA), Carry Save Adder (CSA), Carry Increment Adder (CIA).


References:

1. S.Balasubramaniam, R.Bharathi, “Performance analysis of parallel FIR digital filter using VHDL,” International Journal of Computer Applications, vol.39, no.9, pp.1-6, February 2012.
2. Yu-Chi Tsao and Ken Choi, “Area Efficient of parallel FIR digital filter structures for Symmetric Convolutions based on Fast FIR algorithm,” IEEE Transactions on VLSI systems, vol.20, no.2, pp.366-371, February 2012.

3. Yu-Chi Tsao and Ken Choi, “Area Efficient VLSI Implementation for parallel Linear-Phase FIR digital filters of odd length based on Fast FIR algorithm,” IEEE Transactions on Circuits and Systems, vol.59, no.6, pp.371-375, June 2012.

4. D.A.Parker, K.K. Parhi, “Low-area/power parallel FIR digital filter implementations,” Journal of VLSI Signal Processing Systems, vol. 17, no. 1, pp. 75– 92, Sep 1997.

5. J. G. Chung, K. K. Parhi, “Frequency-spectrum-based low-area low power parallel FIR filter design,” Journal of European Association for Signal Processing Application Signal Processing, doi:10.1155/S1110865702205077, pp. 944–953, Jan 2002.

6. K.K.Parhi, VLSI Digital Signal Processing Systems: Design and Implementation. New York: Wiley, 1999.

7. C. Cheng and K. K. Parhi, “Hardware efficient fast parallel FIR filter structures based on iterated short Convolution,” IEEE Transactions on Circuits Systems. I, Regular Papers, vol. 51, no. 8, pp. 1492–1500, Aug. 2004.

8. C. Cheng, K. K. Parhi, “Further complexity reduction of parallel FIR filters,” Proceedings of IEEE International Symposium on Circuits and Systems, USA, vol. 2, pp. 1835–1838, May 2005.

9. C.Cheng , K.K.Parhi, “Low cost parallel FIR structure with two stage parallelism”, IEEE Transactions on Circuits and Systems.I, Regular Papers, vol.54, no: 2, pp.280-290, Feb 2007.

10. Xilinx13.4, “Synthesis and Simulation Design Guide”, UG626 (v13.4) January 19, 2012.

11. Xilinx 13.1, “RTL and Technology Schematic Viewers Tutorial”, UG685 (v13.1), March 1, 2011.

12. Xilinx, “7 Series FPGAs Configurable Logic Block”, UG 474 (v 1.5), August 6, 2013.

13. Xilinx 12.4, “ISim User Guide”, UG660 (v 12.4), December 14, 2010.

14. Ashok kumar, Maroju Saikumar and Dr. P.Samundisary, “ Design and study of modified parallel FIR filter using Fast FIR algorithm and Symmetric convolution”, Proceedings of IEEE International Conference on Information Communications and Embedded Systems, Chennai, 27-28, Feb-2014.

207-213

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

45.

Authors:

Sasikumar Gurumurthy, T. Niranjan Babu, G. Siva Shankar

Paper Title:

An Approach for Security and Privacy Enhancing by Making Use of Distinct Clouds

Abstract: Security challenges are the major concern when we considering the acceptence of cloud service. A lot of research activities regarding to cloud security resulting in an amount of application and targeting the cloud security threats. The cloud concept comes with a new set of unique features, techniques and architectures. This paper is related to security and privacy enhancing by making use of multiple distinct clouds. Based on the different cloud architecture, the security and privacy capabilities can be approximated. Cloud computing refers to applications and services that run on a distributed network using virtualized resources and accessed by common internet protocols. In this paper, we are introduced different clouds for encryption, decryption and storage process.

Keywords:
Security, Privacy, Multicloud, data partitioning.


References:

1. P. Mell and T. Grance, “The NIST Definition of Cloud Computing, Version 15,” Nat’l Inst. of Standards and Technology, Information Technology Laboratory, vol. 53, p. 50, http://csrc.nist.gov/groups/ SNS/cloud-computing/, 2010.
2. F. Gens, “IT Cloud Services User Survey, pt.2: Top Benefits & Challenges,” blog, http://blogs.idc.com/ie/?p=210, 2008.

3. Gartner, “Gartner Says Cloud Adoption in Europe Will Trail U.S. by at Least Two Years,” http://www.gartner.com/it/page. jsp?id=2032215, May 2012.

4. J.-M. Bohli, M. Jensen, N. Gruschka, J. Schwenk, and L.L.L. Iacono, “Security Prospects through Cloud Computing by Adopting Multiple Clouds,” Proc. IEEE Fourth Int’l Conf. Cloud Computing (CLOUD), 2011.

5. D. Hubbard and M. Sutton, “Top Threats to Cloud Computing V1.0,” Cloud Security Alliance, http://www. cloudsecurityalliance.org/topthreats, 2010.

6. M. Jensen, J. Schwenk, N. Gruschka, and L. Lo Iacono, “On Technical Security Issues in Cloud Computing,” Proc. IEEE Int’l Conf. Cloud Computing (CLOUD-II), 2009.

7. T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, “Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third- Party Compute Clouds,” Proc. 16th ACM Conf. Computer and Comm. Security (CCS ’09), pp. 199-212, 2009.

8. N. Gruschka and L. Lo Iacono, “Vulnerable Cloud: SOAP Message Security Validation Revisited,” Proc. IEEE Int’l Conf. Web Services (ICWS ’09), 2009.

9. M. McIntosh and P. Austel, “XML Signature Element Wrapping Attacks and Countermeasures,” Proc. Workshop Secure Web Services, pp. 20-27, 2005.

10. J. Kincaid, “Google Privacy Blunder Shares Your Docs without Permission,” TechCrunch, http://techcrunch.com/2009/03/07/ huge-google-privacy-blunder-shares-your-docs-withoutpermission/, 2009.

 

214-217

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

46.

Authors:

Sasi Kumar Gurumurthy, T. Siva Shankar, Niranjan Babu

Paper Title:

Monitoring Company Status on Single dashboard by using GRC

Abstract: In this paper we are going to describe how to organize a company based records securely. The company contains several module such as audit, asset, policy and so on. Nowadays every company maintains their records using XL Sheet, So we need to enter our each and every data manually. This contains several setbacks such as litter of time, entering and recoup time also gets high, high manpower required, not secured and even we may also enter our data in wrong fields. Here this system provides full security of maintaining company relevant records and creating an application for maintaining company records by using fully role based access manner. In which only authorized user can access respective action and suppose unauthorized user trying to access someone’s data at that point of time it sends an alert message to respective authorized user. Overall company status can be seen in an single dashboard and based upon that status we can act accordingly. Means each and every module such as asset, audit, policy and so on status can be monitored in a single dashboard at a same time.

Keywords:
GRC, Dash board, Asset ,policy, Risk business continuity management, audit ,standards.


References:

1. Racz, N. ; Tech. Univ. Vienna, Vienna, Austria ; Weippl, E. ; Seufert, A., Governance, Risk & Compliance (GRC) Software – An Exploratory Study of Software Vendor and Market Research Perspectives, 284 (5) (2011) 1–10.
2. Racz, N. ; Inst. of Software Technol. & Interactive Syst., Tech. Univ. Vienna, Vienna, Austria ; Weippl, E. ; Bonazzi, R, IT Governance, Risk & Compliance (GRC) Status Quo and Integration: An Explorative Industry Case Study, Services (SERVICES), 2011 IEEE World Congress on 4-9 July 2011.

3. Nissen, V. ; Dept. of Service Inf. Syst. Eng., Univ. of Technol. Ilmenau, Ilmenau, Germany ; Marekfia, W. Towards a Research Agenda for Strategic Governance, Risk and Compliance (GRC) Management – Vol. 2, No.1 pp.1 – 6

4. N. Racz, E. Weippl, and A. Seufert, “A process model for integrated IT governance, risk, and compliance management,” Databases and Information Systems, Proc. of the Ninth International Baltic Conference (DB&IS 2010), Riga University Press, Jul. 2010, pp. 15570.

5. IT Policy Compliance Group, “2008 Annual Report. IT Governance, Risk, and Compliance,” Retrieved 10 November, 2010, from http://www.itpolicycompliance.eom/pdfs/ITPCGAnnualReport2008.p df, 2008.

6. ISO/IEC, “38500 Corporate governance of information technology,” 2008.

7. COSO, “Enterprise Risk Management Framework,” Retrieved 5 July, 2010, from http://www.coso.org. 2004.

8. F. Caldwell, P.E. Proctor, and M. Nicolett, “EMC Buys Archer for Enhanced IT GRC Capabilities,” Retrieved 23 May, 2010, from http://www.gartner.com/DisplayDocument?ref=clientFriendlyUrl&id=1275214. 2010.

9. N. Racz, E. Weippl, and A. Seufert, “A frame of reference for research of integrated Governance, Risk & Compliance (GRC),” Communications and Multimedia Security, 11th IFIP TC 6/TC 11 Int. Conf. (CMS 2010), Springer, Jun. 2010, pp.106-117.

10. C. McClean, “The Forrester Wave: Enterprise Governance, Risk, and Compliance Platforms, Q3 2009,” Retrieved 7 July, 2009, from http://img.en25.com/Web/OpenPages/Forrester-wave-ent-gov-risk-compl.pdf. 2009.

 

218-220

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

Volume-4 Issue-3

Download Abstract Book

S. No

Volume-4 Issue-3, July 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Ahmad Hamza Al Cheikha

Paper Title:

Matrix Representation of Groups In the Finite Fields GF(pn)

Abstract: The representation of mathematical fields can be accomplished by binary rows (or columns) of a binary triangular matrix as the Hamming’s matrices, but this representation don’t show the basic product properties of the fields, that is the nonzero elements of the fields forms a cyclic multiplicative group. In this paper we show that the elements of the fields GF(pn), and their subgroups, can represent as square matrices by m – sequences, which satisfies the product properties as a cyclic group.

Keywords:
Galois fields, m-sequences, cyclic groups, Orthogonal sequences.


References:

1. Yang K , Kg Kim y Kumar l. d ,“Quasi – orthogonal Sequences for code – Division Multiple Access Systems ,“IEEE Trans .information theory, Vol. 46 NO3, 200, PP 982-993
2. Jong-Seon No, Solomon. W & Golomb, “Binary Pseudorandom Sequences For period 2n-1 with Ideal Autocorrelation, ”IEEE Trans. Information Theory, Vol. 44 No 2, 1998, PP 814-817

3. Lee J.S &Miller L.E, ”CDMA System Engineering Hand Book, ”Artech House. Boston, London,1998.

4. Yang S.C,”CDMA RF System Engineering, ”Artech House.Boston-London,1998.

5. LIDL,R.& PILZ,G.,”Applied Abstract Algebra,” Springer – Verlage New York, 1984.

6. Lidl, R.& Niderreiter, H., “Introduction to Finite Fields and Their Application,” Cambridge university USA, 1994.

7. Thomson W. Judson, “Abstract Algebra: Theory and Applications ,” Free Software Foundation,2013.

8. FRALEIGH,J.B., “A First course In Abstract Algebra, Fourth printing. Addison-Wesley publishing company USA,1971.

9. Mac WILIAMS,F.G& SLOANE,N.G.A., “The Theory Of Error- Correcting Codes,” North-Holland, Amsterdam, 2006.

10. KACAMI,T.&TOKORA, H., “TeoriaKodirovania,”Mir(MOSCOW), 1978.

11. David, J., “Introductory Modern Algebra, ”Clark University U. S. A, 2008.

12. SLOANE,N.J.A., “An Analysis Of The Stricture And Complexity Of Nonlinear Binary Sequence Generators,” IEEE Trans. Information TheoryVol. It 22 No 6,1976, PP 732-736.

13. Al Cheikha A. H. “ Matrix Representation of Groups in the finite Fields GF(2n) ,” International Journal of Soft Computing and Engineering, Vol. 4, Issue 2, May 2014, PP 118-125.

1-6

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

2.

Authors:

Ketki Thakre, Nehal Chitaliya

Paper Title:

Dual Image Steganography for Communicating High Security Information

Abstract: The recent growth in computational power and technology has propelled the need for highly secured data communication. One of the best techniques for secure communication is Steganography-a covert writing. It is an art of hiding the very existence of communicated message itself. The process of using steganography in conjunction with cryptography, called as Dual Steganography, develops a sturdy model which adds a lot of challenges in identifying any hidden and encrypted data. Using cryptographic techniques to encrypt data before transmission may forestall any type of security problems. But the camouflaged appearance of encrypted data may arouse suspicion. Therefore using steganography inside steganography, give rise to improved version of dual steganography which will provide better security. This paper presents a technique for hiding data with two level of security to embed data along with good perceptual transparency and high payload capacity. Here the secret data is not restricted to images only but also applicable to any text, audio or video.

Keywords:
Cryptography, Dual Steganography, LSB Technique, Steganography


References:

1. Shailender Gupta, Ankur Goyal and Bharat Bhushan, “Information Hiding Using Least Significant Bit Steganography and Cryptography” International Journal Modern Education and Computer Science, vol. 6, pp. 27-34, 2012
2. Kanzariya Nitin K, Nimavat Ashish V., “Comparison of Various Images Steganography Techniques” International Journal of Computer Science and Management Research, vol. 2,pp. 1213-1217, 2013

3. Arvind Kumar, Km. Pooja, “Steganography- A Data Hiding Technique” International Journal of Computer Applications, vol. 9, pp. 19-23, 2010

4. Pratap Chandra Mandal, “Modern Steganographic technique: A survey”, International Journal of Computer Science & Engineering Technology, vol. 3, pp. 444-448, 2012

5. T. Sharp, “An implementation of key-based digital signal Steganography”, Proc. Information Hiding Workshop, Springer, vol. 2137, pp. 13-26, 2001

6. Johnson, Neil F., “Steganography”, IRM Conference, 2000

7. Johnson, N.F and Jajodia, S., “Exploring Steganography: Seeing the Unseen”, IEEE Computer Journal, vol. 31, pp. 26-34, 1998

8. H. Arafat Ali “Qualitative Spatial Image Data Hiding for Secure Data Transmission” International Journal on Graphics, Vision and Image Processing, vol. 7, pp. 35-43, 2007

9. Himanshu Gupta, Prof. Ritesh Kumar, Dr. Soni Changlani, “Enhanced Data Hiding Capacity Using LSB-Based Image Steganography Method”, International Journal of Emerging Technology and Advanced Engeneering,vol.3,pp. 212-214,2013

10. Mr. Vikas Tyagi, Mr. Atul kumar, Roshan Patel, Sachin Tyagi, Saurabh Singh Gangwar “Image Steganography Using Least Significant Bit With Cryptography” Journal of Global Research in Computer Science, vol. 3, pp. 53-55 , 2012

11. Shilpa Gupta, Geeta Gujral and Neha Aggarwal, “ Enhanced Least Significant Bit algorithm For Image Steganography”, International Journal of Computational Engineering & Management, vol. 15, pp. 40-42, 2012

12. T. morkel , J.h.p. eloff , M.s. olivier “An overview of image Steganography”, Information and computer security architecture research group ,pp. 1-11, 2005

13. Tanmay Bhattacharya, Nilanjan Dey and S. R. Bhadra Chaudhuri, “A Novel Session Based Dual Steganographic Technique Using DWT and Spread Spectrum” International Journal of Modern Engineering Research, vol. 1, pp. 157-161, 2012

14. K.Sakthisudhan, P.Prabhu, “Dual Steganography Approach for Secure Data Communication” International Conference on Modeling, Optimization and Computing, Elsevier, Procedia Engineering, vol. 38, pp. 412-417, 2012

15. Rosziati Ibrahim and Teoh Suk Kuan, “Steganography Algorithm to Hide Secret Message inside an Image”, Computer Technology and Application, vol. 2, pp. 102-108, 2011

16. Weiqi Luo, Jiwu Huang, Fangjun Huang, “Edge Adaptive Image Steganography Based on LSB Matching Revisited”, IEEE Transactions on Information Forensics and Security, vol. 5, pp. 201-214, 2010

17. Mazen Abu Zaher, “Modified Least Significant Bit (MLSB)” Computer and Information Science, vol. 4, pp. 60-67, 2011

18. Silman, J., “Steganography and Steganalysis: An Overview”, SANS Institute, pp. 1-8, 2001

19. Ronak Doshi, Pratik Jain, Lalit Gupta, “Steganography and Its Applications in Security” International Journal of Modern Engineering Research, vol. 2, pp.4634-4638, 2012

20. Udit Budhiaa, Deepa Kundura. “Digital video steganalysis exploiting collusion sensitivity”, Proc. of SPIE. vol. 5403, pp. 210-221,2004

21. Shabir A. Parah, Javaid A. Sheikh, Abdul M. Hafiz and G.M. Bhat, “Data hiding in scrambled images: A new double layer security data hiding technique” Computers and Electrical Engineering, Elsevier,vol. 40,pp. 70-82, 2014

22. Phad Vitthal S.,Bhosale Rajkumar S.,Panhalkar Archana R., “ A Novel Security for Secret Data using Cryptography and Steganography” International Journal Computer Network and Information Security, vol. 2, pp. 36-42,2012

 

7-12

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

Srividya M. S., Hemavathy R., Shobha G.

Paper Title:

Underwater Video Processing for Detecting and Tracking Moving Object

Abstract: In this paper, we present a vision system capable of analyzing underwater videos for detecting and tracking moving object. The video processing system consists of three subsystems, the video texture analysis, object detection and tracking modules. Moving object detection is based on adaptive Gaussian mixture model. The tracking was carried out by the application of the Kalman algorithm that enables the tracking of objects. Unlike existing method, our approach provides a reliable method inwhich the moving object is detected in unconstrained environments and under several scenarios (murky water, algae on camera lens, moving plants, low contrast, etc.). The proposed approach was tested with 20 underwater videos, achieving an overall accuracy as high as 85%.

Keywords:
Video Processing, Detection, Tracking, Gaussian Mixture Model, Kalman Filtering


References:

1. SamanPoursoltan, Russell Brinkworth, Matthew Sorell “Biologically-inspired Video Enhancement Method For Robust ShapeRecognition,” University of Adelaide,
Australia, IEEE, 2013.

2. Prabhakar C J & Praveen Kumar P U. “Feature Tracking of Objects in Underwater VideoSequences.”, Kuvempu University,India, ACEEE 2012.

3. SaeedVahabiMashak, BehnamHosseini, S.A.R. Abu-Bakar.“Background Subtraction for Object Detection under VaryingEnvironments.”UniversitiTeknologi Malaysia, IJCISIMA, 2012

4. M. Weber, M. Welling, and P. Perona. “Detecting ,Tracking And Counting Fish In Low Quality Unconstrained Underwater Videos” University of Edinburgh, Edinburgh, UK.

5. “Underwater ColorConstancy :Enhancement of Automatic Live Fish Recognition” M. Chambah, D. Semani, A. Renouf, P. Courtellemont, A. Rizzi, Université de La
Rochelle, France, Dept. of Information Technology – University of Milano/Italy.

6. CodrutaOrnianaAncuti, CosminAncuti, Tom Haber and Philippe Bekaert “FUSION-BASED RESTORATION OF THE UNDERWATER IMAGES,” Belgium, IEEE, 2011.

7. MohamedAmer, Emil Bilgazyev “Fine-grained Categorization of Fish Motion Patterns in Underwater Videos”, Oregon StateUniversity, ICCV, 2011

8. PiotrJasiobedzki, Stephen Se, Michel Bondy, and Roy Jakola,“Underwater 3D mapping and pose estimation for ROVoperations”, OCEANS 2008, pp. 1-6, September 2008.

9. Li Ma, KaihuaWu,L. Zhu, “Fire smoke detection in videoimages Using Kalman filter and Gaussian Mixture Colormodel”, International Conference on Artificial Intelligence andComputational Intelligence, 2010

10. Li Xu, Feihu Qi, Renjie Jiang, YunfengHao, Guorong Wu,“Shadow Detection and Removal in Real Images: A Survey”Shanghai JiaoTong University, P.R. China, June 2006.

11. J Shin, S Kim, et all, “Optical flow-based real-time object tracking using non-prior training active feature model”Computer Vision and Image Understanding, June 2005,Volume 98, Issue 3, Pages 462-490.

12. B.Hosseini, SaeedVahabiMashak and A.S.R Abu Bakar,”Human Movement Based on Rule based classifier” Asia ModellingSymposium, May 2010.

13. S.S. Beauchemin and J.L. Barron, “The Computation of Optical Flow”, ACM Computing Surveys, 1995 Vol. 27,No.3, PP 43- 467.

14. S.Y. Chien, S.Y. Ma and L.G. Chen ,” Efficient moving objectsegmentation algorithm using background registrationtechnique” IEEE Trans Circuits Syst. Video Technol, JULY2002

15. Hu Fuyuan, Zhang Yanning, et all, “A New Method of MovingObject Detection and Shadow Removal” JOURNALOFELECTRONICS (CHINA), July 2007

16. Dongxiang Zhou, Hong Zhang and NilanjanRay,“TextureBased Background Subtraction”, Proceedings of theInternational Conference on Information and Automation,Zhangjiajie China, IEEE Conference, June 20 -23, 2008.

17. YunlongGuo, Bo Yang, Yangyang Ming, Aidong Men, “AnEffective Background Subtraction Under the Mixture of MultipleVarying Illuminations”,Second InternationalConference on Computer Modeling and Simulation, IEEEConference, 2010.

18. V. Brandou, A. G. Allais, M. Perrier, E. Malis, P. Rives, J.Sarrazin, and P. M. Sarradin, “3D Reconstruction oNaturalUnderwater Scenes Using the Stereovision System IRIS”, OCEANS 2007 – Europe , pp. 1-6, 2007.

19. David G Lowe, “Distinctive image features from scaleinvariantkeypoints”, International Journal of Computer Vision, vol.60(2),pp. 91-110, 2004.

20. David G Lowe, “Object recognition from local scaleinvariantfeatures”, Proceedings of the International Conference on ComputerVision, vol. 2, pp. 1150-1157, 1997.

 

13-16

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

4.

Authors:

Dipali Rojasara, Nehal Chitaliya

Paper Title:

Real Time Visual Recognition of Indian Sign Language using Wavelet Transform and Principle Component Analysis

Abstract: Sign language is a mean of communication among the deaf people. Indian sign language is used by deaf for communication purpose in India. Here in this paper, we have proposed a system using Euclidean distance as a classification technique for recognition of various Signs of Indian sign Language. The system comprises of four parts: Image acquisition ,pre processing, Feature Extraction and Classification. 31 signs including A to Z alphabets & one to five numbers were considered in this paper.

Keywords:
Indian Sign Language (ISL), Principle Component Analysis (PCA), Sign Language Recognition (SLR)


References:

1. Deepika Tewari, Sanjay Kumar Srivastava; “ A Visual Recognition of Static Hand Gestures in Indian Sign Language based on Kohonen Self- Organizing Map Algorithm” ; International Journal of Engineering and Advanced Technology , Vol-2, Issue-2, December 2012.
2. Kenny Morrison, Stephen J. McKenna; “An Experimental Comparison of Trajectory-Based and History-Based Representation for Gesture Recognition”; International Gesture Workshop,2004.

3. P. Kakumanu, S. Makrogiannis, N. Bourbakis; “Asurvey of skin-color modeling and detection methods”; Elsevier, The journal of the pattern recognition society, 40 ,1106 – 1122, 2007.

4. Y. Wang, B. Yuan;”A novel approach for human face detection from color images under complex background”, Pattern Recognition vol. 34 ,2001.

5. Ketki. P.Kshirsagar, Dharmpal Doye; “ Object Based key Frame Selection for Hand Gesture recognition”; International Conference on Advances in Recent Technologies in Communication and Computing, 2010.

6. R. Rojas ; “Neural Networks”; Springer-Verlag, Berlin, 1996.

7. Siddharth S. Rautaray , Anupam Agrawal; “Vision based hand gesture recognition for human computer interaction: a survey”; Springer Science+Business Media Dordrecht, November 2012.

8. Joyeeta Singha, Karen Das; “ Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique”;International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013

9. Rashmi D. Kyatanavar, Prof. P. R. Futane;“Comparative Study of Sign Language Recognition Systems”; International Journal of Scientific and Research Publications,Vol. 2, 2012.

10. Anirudh Garg; “Converting American Sign Language To Voice Using RBFNN”; Master’s Thesis, Computer Science, Faculty of San Diego State University, Summer 2012

11. Bhawna Gautam; “Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transform”; Master’s Thesis, Computer Science and Engineering, NIT Rourkela, 2010

12. Henrik Birk, Thomas Baltzer Moeslund;“Recognizing Gestures From the Hand Alphabet Using PrincipalComponent Analysis”; Master’s Thesis, Laboratory of Image Analysis, Aalborg University, Denmark, October 1996

13. M.K. Bhuyan, “FSM-based Recognition of Dynamic Hand Gestures via Gesture Summarization using Key Video Object Planes”, International Journal of Computer and Communication EngineeringVol.6, 2012.

14. Vaishali S. Kulkarni, Dr. S.D.Lokhande;“Appearance Based Recognition of American Sign Language Using Gesture Segmentation”; International Journal on Computer Science and

15. Engineering, Vol. 02, No. 03 ,2010.

16. Brian L. Pulito, Raju Damarla, Sunil Nariani, ” 2-D Shift Invariant image Classification Neural Network, which overcomes Stability, Plasticity Dilemma”, International Joint Conference on Neural Network, San Deigo, Vol 2,1990.

17. Jong Bae Kim,Hye Sun Park,Min Ho Park,Massimo Piccardi,’Background subtraction techniques: a review’,Systems, Man and Cybernetics, ,IEEE InternationalConference, vol.4,2004.

18. Murthy, G. R. S. and Jadon, R. S. A Review of Vision Based Hand Gestures Recognition. International Journal. Of Information Technology and Knowledge Management,Vol. 2,2009.

19. N. Ibraheem, M. Hasan, R. Khan, P. Mishra, “comparative study of skin color based segmentation techniques”, Aligarh Muslim University, A.M.U., Aligarh, India,2012.

20. K. Burgers, et al., A Comparative Analysis of Dimension Reduction Algorithms on Hyperspectral Data, 2009

21. M. A. amin and H. Yan, “Sign Language Finger Alphabet Recognition from Gabor-PCA Representation of hand gestures”, International Conference on Machine Learning and Cybernetics, 2007.

22. Shikha Gupta et.al. , “Static Hand Gesture Recognition Using Local Gabor Filter”, International Symposium on Robotics and Intelligent Sensors,elsevier Procedia Engineering 41 , 2012 .

23. Moharir PS. Pattern recognition transforms. New York: Wiley; 1992.

24. S.S. Tamboli1 et.al. “Image Compression Using Haar Wavelet Transform”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, 2013

 

17-20

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

5.

Authors:

Amit Kumar Rohit, N. G. Chitaliya

Paper Title:

A Novel Approach for Content based Mri Brain Image Retrieval

Abstract: With increasing amount in neuro patients which increases workload on small group of radiologists, a new system is needed that help radiologists for getting essential information like types of image, extraction of tumor and retrieve the similar images for references to take treatment planning for neuro patient. In this paper, a new content based MRI brain image retrieving method is to be designed using Discrete Wavelet Transform based feature extraction, Support Vector Machine based classifier and Euclidean Distance based matching method. New tumor detection method is to be designed using Incremental Supervised Neural Network and Invariant moments.

Keywords:
Content Based Image Retrieval (CBIR); Discrete Wavelet Transform (DWT); Euclidean Distance; Incremental Supervised Neural Network (ISNN); Invariant Moment; Support Vector Machine (SVM).


References:

1. Hatice Cinar Akakin and Metin N. Gurcan, “Content-Based Microscopic Image Retrieval System for Multi-Image Queries”, IEEE Transaction on Information Technology in Biomedicine, Vol. 16, No. 4, pp 758-768, 2012.
2. R.Guruvasuki, A. Josephine Pushpa Arasi, “MRI Brain Image retrieval using Multi Support Vector Machine Classifier”, International Journal of Advanced Information Science and Technology, Vol. 10, No 10, pp 29-36, 2013.

3. Mohanpriya S., Vadivel M, “Automatic Retrieval of MRI Brain Image using Multiqueries System”, IEEE Conference, pp 1099-1103, 2013.

4. Lidiya Xavier, Thusnavis B. , Newton D.R. , “Content Based Image Retrieval Using Texture Features Based On Pyramid- Structure Wavelet Transform” , IEEE Conference, pp 79-83, 2011.

5. B.Ramasubramanian, G. Praphakar, S. Murugeswari, “ A Novel Approach for Content Based Microscopic Image Retrieval system Using Decision Tee Algorithm”, International journal of scientific& engineering research, Vol. 4, No 6, pp 584-588, 2013.

6. Yudong Zhang, Zhengchao Dong, LenanWua, ShuihuaWanga, “A hybrid method for MRI brain image classification”, Elsevier journal Expert system and Application, Vol. 20, No 2, pp 10049-10053 ,2011.

7. Sandeep Chaplot , L.M. Patnaik , N.R. Jagannathan, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Elsevier journal on Biomedical Signal Processing and Control, Vo.1,No 1,pp 86 -92 ,2006.

8. Hashem Kalbkhania, Mahrokh G. Shayesteha, BehroozZali-Vargahan , “Robust algorithm for brain magnetic resonance image (MRI)classification based on GARCH variances series”, Elsevier journal on Biomedical Signal Processing and Control, Vol. 8, No 6 , pp 909-919, 2013.

9. Z. Iscan, Z. DokurandT. Olmez, “Tumor detection by using Zernike moments on segmented magnetic resonance brain images”, Elsevier Journal of Expert system and Application, Vol. 37, No 3, pp 2540-2549, 2010.

10. Bogdam M. , “ Neural Network Architectures and learning ” , IEEE Conference , pp 7802-7852 ,2003.

11. M. M. Rahman, P. Bhattacharya, and B. C. Desai, “A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback”, IEEE Transaction on Information Technology in Biomedicine, Vol. 11, No. 1, pp. 58–69, 2007.

12. Dr. Fahui long, Dr. Hongjiang Zhang and Prof. David Dagan Feng, “Fundamental of image Retrieval”, Available at: www.cse.iit.ernet.in.

13. R.C. Gonzalez, R.E. Woods, “Digital Image Processing, second edition, Pearson Education, Ch. Wavelet and Multi resolution Processing”, pp. 349–408 , 2004.

14. Dustin Boswell, “Introduction to Support Vector Machines”, Available at: http://www.work.caltech.edu/~boswell/IntroToSVM.pdf

15. Harvard Medical School, Web: data available at: http://med.harvard.edu/AANLIB/

16. Mehdi Lofti, Ali Solimani, Aras Dargazany, Hooman Afzal, MojtabaBandarabadi, “Combinig Wavelet Transform and Neural Networks for Image Classification”, IEEE, 41st Southeastern Symposium on System Theory, pp 15-17, 2009.

17. ShenFurao, Tomotaka Ogura, Osamu Hasegawa, “An Enhanced Self Organizing Incremental Neural network For Online Unsupervised learning”, Elsevier Journal on Neural Network, Vol. 20, No 8, pp 893-903, 2007.

18. M.Kanimozhi, C.H. HimaBindu, “Brain MR Image Segmentation Using Self Organizing map”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No 10,pp 3968-3973, 2013.

19. El- Shayed A El Dahshan, Tamel Hosny, Abdel- badech M. Salem, “Hybrid intelligent techniques for MRI brain images classification”, Elsevier Journal of Digital Signal Processing, Vol. 20 , No 2 ,pp 433-441,2010.

20. Amir Ehsan Lashkari, “A Neural network based Method for Brain Abnormality Detection in MR Images using Zernike Moments and Geometric moments”,
International Journal of Computer Appliction, Vol. 4, No 7,pp 1-8, 2010.

21. Brain Tumor Facts & Statistics: http://www.sdbtf.org/facts-bout-bt.html

22. Cancer mortality in India: a nationally representative survey:

23. http://www.thelancet.com/journals/lancet/article/PIIS01406736%2812%29603584/abstract

 

21-29

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

6.

Authors:

Dalip Singh, Gulshan Taneja

Paper Title:

Reliability and Cost-Benefit Analysis of a Power Plant Comprising two Gas and one Steam Turbines with Scheduled Inspection

Abstract: A reliability model for a power generating system comprising two gas turbines and one steam turbine is developed wherein scheduled inspection is done at regular interval of time for maintenance. Initially, all the three units i.e. two gas turbines as well as one the steam turbine are operating and working of the system is called the working at full capacity. On failure of one of the gas turbines with steam turbine working, the system works at reduced capacity. If both the gas turbines get failed, the system goes to down state, whereas on failure of steam turbine, the system may be kept in the up state with one of the gas turbines working or put to down state according as the buyer of the power so generated is ready to pay higher amount or not and this is working in single cycle. Three types of scheduled inspection, that is, minor, path and major are done in this order at regular intervals of time for maintenance. System is analyzed by making use of semi – Markov processes and regenerative point technique. Various measures of system effectiveness such as mean time to system failure, availability at full capacity, availability at reduced capacity, availability in single cycle, expected down time, expected time for minor, path and major inspection, busy period for repair and expected number of visits have been obtained. Cost- benefit analysis has been carried out. Graphical study has been made and interesting conclusions are drawn

Keywords:
Power Plant comprising Two Gas Turbines and One Steam Turbine, Scheduled Inspection, Reliability, Cost-Benefit


References:

1. G. Taneja, D.V. Singh and Amit Minocha, “Profit evaluation of a 2-out of -3 unit system for an ash handling plant wherein situation of system failure did not arise,” Pure and Applied Mathematika Sciences , 28(2007),195-204.
2. S.M. Rizwan, V. Khurana and G.Taneja, “Reliability analysis of a hot standby industrial system,” International Journal of modeling and Simulation, 30 (2010), 315-322.

3. N. Padmavathi, Rizwan S.M., Pal Anita and Taneja G., “Reliability analysis of an evaporator of a desalination plant with online repair and emergency shutdowns,” Aryabhatta Journal of Mathematics &Informatics, 4(2012), 1-12.

4. Sukhbir Singh, Rahul Rishi, G. Taneja and Amit Manocha, “Reliability and availabilty analysis of database system with standby unit provided by the system provider,” Int. J. Of Soft Computing and Engg., 3(2013), 235-237.

5. Su Baohe, “On a two-dissimilar unit system with three modes and random check,” Microelectronics Reliab. , 37(1997), 1233-1238.

6. R.K. Tuteja, U. Vashistha and G.Taneja, “Cost-benefit of a system where operation and some times repair of main unit depends on sub-unit,” Pure Appl. Math. Sci., LIII (2001), 41-61.

7. B. Parashar and G.Taneja, “Reliability and profit evaluation of a PLC hot standby system based on a master slave concept and two types of repair facilities,” IEEE Trans Reliab, 56(2007), 534-539.

8. Goyal, D.V.Singh & G.Taneja, “Reliability and cost-benefit analysis of a system comprising one big unit and two small identical units with priority for operation/repair to big unit,” Mathematical Science,Iran,5(2011), 235-248.

9. Dalip Singh and Gulshan Taneja, “Reliability analysis of a power generating system through gas and steam turbines with scheduled inspection,” Aryabhatta Journal of Mathamatics & Informatics, 5(2013), 373-380.

30-37

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

7.

Authors:

P. Saraswathi, M. Prabha

Paper Title:

Multi-Objective Evolutionary Algorithm for Routing in Wireless Mesh Networks

Abstract: Wireless Mesh Networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self organized networks in places where wired infrastructures are not available. Routing in Wireless Mesh Networks has multi-objective nonlinear optimization problem with some constraints. This problem has been addressed by considering Quality of Service parameters such as bandwidth, packet loss rates, delay, path capacity and power consumption. Multi-objective evolutionary algorithms can find multiple Pareto optimal solutions in one single run. This paper uses multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem. Simulation results show that our proposed algorithm can generate well-distributed Pareto optimal solutions.

Keywords:
Multi-objective Optimization, Evolutionary Algorithm, NSGA and Routing.


References:

1. Jose Maria A. Pangilinan and Gerrit K. Janssens. (2007). Evolutionary Algorithms for the Multi-objective Shortest Path Problem. World Academy of Science, Engineering and Technology, 25, 205-210.
2. Kalyanmoy Deb. (2001). Multi-objective Optimization using Evolutionary Algorithms. New York: John Wiley & Sons., 2001.

3. Kalyanmoy Deb & Santosh Tiwari. (2008). Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of OperationalResearch, 185(3), 1062-1087.

4. Omar Al Jadaan, Lakishmi Rajamani, & C. R. Rao. (2008). Non-dominated ranked genetic algorithm for Solving multi-objective optimization Problems: NRGA. Journal of Theoretical and Applied Information Technology, 60-67.

5. Rath, K. & S. N. Dehuri. (2006). Non-dominated Sorting Genetic Algorithms for heterogeneous Embedded System Design. Journal of ComputerScience, 2 (3),
288-291.

6. Salman Yussof & Ong Hang See. (2005). QoS Routing for Multiple Additive QoS Parameters using Genetic Algorithm. Proc. International Conference on Communication, 1, 99-104.

7. Salman Yussof & Ong Hang See. (2007). Finding Multi-Constrained Path Using Genetic Algorithm. Proc. IEEE International Conference on Telecommunications
8. Srinivas, N. & Deb, K. (1994). Multi-objective Optimization using Non-dominated Sorting in Genetic Algorithm. Evolutionary Computation, 2(3), 221-248.
9. Srinivas, N. & Kalyanmoy Deb. (2007). Multi-objective Optimization Using non-dominated Sorting in Genetic Algorithms, Evolutionary computation, 2(3) 221-248.

10. Sriram, R., Manimaran, G., & Siva Ram Murthy, C. (1998). Preferred link based delay-constrained least-cost routing in wide area networks. Computer Communications, 21, 1655–1669.

11. Chang Wook & Ramakrishna, R.S. (2002). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transaction Evolutionary Computation, 6(6), 566-579.

12. George N. Rouskas & Ilia Baldine. (1997). Multicast Routing with End-to-End Delay and Delay Variation Constraints. IEEE Journal on SelectedAreas in Communications, 15(3),346-356.

13. Ha Chen & Baolin Sun. (2005). Multicast Routing Optimization Algorithm with Bandwidth and Delay Constraints Based on GA. Journal of Communication and Computer, 2(5), 63-67.

 

38-41

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

8.

Authors:

Teressa T. Chikohora

Paper Title:

A Study of the Factors Considered when Choosing an Appropriate Data Mining Algorithm

Abstract: A lot of data is generated and collected in today’s organizations .Data mining has helped a lot of businesses to extract knowledge from data and use it to make decisions and gain competitive advantage. Businesses now analyse the data to make business decisions. Various algorithms may be used to analyse the data, however some of them do not yield useful knowledge. Choosing the appropriate algorithm remains a problem given the diversity in available algorithms. There are many algorithms, making it difficult for analysts and researchers who may not know which algorithm will be suitable for their needs. As a way of optimizing the chances of extracting useful knowledge, this study focuses on how the data analysts and researchers may choose appropriate algorithms that will yield desired knowledge. A number of factors to be considered when selecting an algorithm are discussed to help analysts in choosing appropriate algorithms.

Keywords:
algorithm, factors, tool, data mining


References:

1. Alexander, D. (n.d.): Data Mining [online] http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/ (accessed 13/12/2013 234 p.m.)
2. Anon (2012): Data Mining Algorithms (Analysis Services – Data Mining) [online] http://technet.microsoft.com/en-us/library/ms175595.aspx (accessed on 08/01/2014) 2012.

3. Anon, (2008): Oracle Data Mining Concepts 11g Release 1 (11.1).

4. Anon, (n. d.): An overview of Data Mining Techniques.

5. Brown, M. (2012): Data Mining Techniques [online] http://www.ibm.com/developerworks/library/ba-data-mining-techniques/ (accessed 23/12/2013).

6. Cheeseman, P. and R. W. Oldford (1994): Selecting models from data. LNStats 89, Springer.

7. Chung, M.H. and P. Gray (1999): Special Section Data mining. Journal of Management Information Systems Volume 16 No.1.

8. Fournier-Viger, P. (2013): What are the steps to implement a data mining algorithm? [Online] http://data-mining.philippe-fournier-viger.com/what are the steps to implement a data mining algorithm (accessed 30/01/2014)

9. Gibert, K., Sànchez-Marrè, M. and V. Codina (2010): Choosing the Right Data Mining Technique: Classification of Methods and Intelligent Recommendation.

10. Silltow, J. (2006): Data Mining 101: Tools and Techniques online] http://www.theiia.org/intAuditor/itaudit/archives/2006/august/data-mining-101-tools-and-techniques/ (accessed on 13/12/2013 at 409pm).

11. Miquel Sànchez-Marrè, Karina Gibert and Ignasi Rodríguez-Roda (n.d.): GESCONDA: A Tool for Knowledge Discovery and Data Mining in Environmental Databases.

12. Parthasarathy, S. (n.d.): CIS 674 Introduction to Data Mining.

13. Witten, I. H. and E. Frank (2005): Data Mining Practical Machine Learning

 

42-45

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

9.

Authors:

Tamanna Jagga, Jyoti Mann

Paper Title:

Secure Digital Image Steganography

Abstract: Steganography is the art of hiding the fact that communication is taking place. It is the science that hides the information in an appropriate cover carrier like image, text, audio and video media. Established businesses have adopted steganography for covert communication, artists have done the same for intellectual property protection from consumers and advertising agencies. This work proposes a DWT and Arnold Transform based Steganographic technique. Arnold transform is a significant technique of image encryption. Results show that proposed algorithm has good perceptual invisibility.

Keywords:
Alpha Blending, Arnold Transformation, DWT, Steganography


References:

1. Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006
2. Liu Tie-yuan, Chang Liang, Gu Tian-long, “Analyzing the Impact of Entity Mobility Models on the Performance of Routing Protocols in the MANET”, 3rd International Conference on Genetic and Evolutionary Computing, 14-17 Oct. 2009, pp.56-59.

3. Bhavyesh Divecha, Ajith Abraham, Crina Grosan and Sugata Sanyal, “Analysis of Dynamic Source Routing and Destination-Sequenced Distance-Vector Protocols for Different Mobility models”, First Asia International Conference on Modeling and Simulation, AMS2007, 27-30 March, 2007, Phuket, Thailand.
Publisher: IEEE Press, pp. 224-229.

4. M.F. Sjaugi, M. Othman, M.F.A. Rasid, “Mobility models towards the performance of geographical-based route maintenance strategy in DSR”, IEEE International Symposium on Information Technology, ITsim 2008 , Vol. 3, 26-28 Aug. 2008, pp. 1-5.

5. S. Gowrishankar, S. Sarkar, T.G.Basavaraju, “Simulation Based Performance Comparison of Community Model, GFMM, RPGM, Manhattan Model and RWP-SS Mobility Models in MANET,” First International Conference on Networks and Communications (NETCOM ’09), 27-29 Dec. 2009, pp.408-413.

6. Jonghyun Kim, Vinay Sridhara, Stephan Bohacek, “Realistic mobility simulation of urban mesh networks”, Journal of Ad Hoc Networks, Vol. 7, Issue 2, March 2009, Publisher: Elsevier, pp. 411-430.

7. Kalra, G.S., R. Talwar and H. Sadawarti,” Blind Digital Image Watermarking Robust Against Histogram Equalization”, Journal of Computer Science 8 (8): 1272-1280, 2012.

8. Zhenjun Tang and Xianquan Zhang,” Secure Image Encryption without Size Limitation Using Arnold Transform and Random Strategies”,Journal Of Multimedia, Vol. 6, No. 2, April 2011

9. Lingling Wu, Jianwei Zhang, Weitao Deng, Dongyan He,” Arnold Transformation Algorithm and Anti-Arnold Transformation Algorithm”, The 1st International
Conference on Information Science and Engineering (ICISE2009), pp. 1164- 1167, IEEE 2009

10. Po-Yueh Chen*and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, International Journal of Applied Science and Engineering, pp. 275-290, vol.- 4,Issue- 3, 2006

11. S.Arivazhagan, W.Sylvia Lilly Jebarani, M.Shanmugaraj,” An Efficient Method for the Detection of Employed Steganographic Algorithm using Discrete Wavelet Transform”, pp. 1-6, Second International conference on Computing, Communication and Networking Technologies,2010 IEEE.

12. Nilanjan Dey, Sourav Samanta, Anamitra Bardhan Roy,” A Novel Approach of Image Encoding and Hiding using Spiral Scanning and Wavelet Based Alpha-Blending Technique”, Int. J. Comp. Tech. Appl., Vol. 2 (6), pp. 1970-1974, 2011

13. Pratibha Sharma, Shanti Swami,” Digital Image Watermarking Using 3level Discrete Wavelet Transform”, Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), pp. 129- 133, 2013

 

46-49

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

10.

Authors:

Virender Kadyan, Ritu Aggarwal

Paper Title:

Performance Analysis and Designing of Technique for Enhancement of Fingerprints based on the Estimated Local Ridge Orientation and Frequency

Abstract: Fingerprint identification is a growing and popular biometric identification technology. It includes two steps one is fingerprint verification and other is fingerprint recognition. Both of them use minutiae, such as end points and bifurcation points, as features. Therefore, how to appropriately extract minutiae from fingerprint images becomes an important step in fingerprint identification. Extracting features from fingerprints is an essential step in fingerprint verification and recognition. Many algorithms for this issue have been developed recently. on. The goal of this paper is to develop a system that can be used for fingerprint verification through extracting and matching minutiae. To achieve good minutiae, initially, extraction is done in fingerprints with varying quality, then preprocessing in form of image enhancement. Many methods have been joined to build a minutia extractor and a minutia matcher. A fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency, is be implemented in this paper. Performance of the new developed system is then evaluated using visual analysis and goodness index value of enhanced image.

Keywords:
Enhancement, Fingerprint, Ridges, Valleys


References:

1. Jianwei Yang, Lifeng Liu, Tianzi Jiang, Yong Fan, “A modified Gabor filter design method for fingerprint image enhancement” Pattern Recognition Letters 24 (2003) 1805–1817.
2. L.Hong,., Y.Wan., A.K Jain,., 1998. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. Pattern Anal. Machine Intell. 20 (8), 777–789.

3. D.Gabor,., 1946. Theory of communication. J. IEE 93, 429–457.

4. C. Gottschlich, “Fingerprint growth prediction, image preprocessing and multi-level judgment aggregation” Ph.D. thesis, Univ. Göttingen, Göttingen, Germany, 2010.

5. C. Gottschlich, P. Mihailescu, and A. Munk, “Robust orientation field estimation and extrapolation using semilocal line sensors,” IEEE Trans. Inf. Forensics Security, vol. 4, no. 4, pp. 802–811, Dec. 2009.

6. C. Gottschlich, T. Hotz, R. Lorenz, S. Bernhardt, M. Hantschel, and A. Munk, “Modeling the growth of fingerprints improves matching for adolescents,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp. 1165–1169, Sep. 2011.

7. Carsten Gottschlich, “Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012.

8. Kulvir Singh, Sahil Sharma, Rakesh K. Garg, “Visualization of latent fingerprints using silica gel G:A new technique” ,Egyptian Journal of Forensic Sciences (2013) 3, 20–25.

 

50-53

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

11.

Authors:

Marwa Sharawi, E. Emary, Imane Aly Saroit, Hesham El-Mahdy

Paper Title:

Flower Pollination Optimization Algorithm for Wireless Sensor Network Lifetime Global Optimization

Abstract: As wireless sensor networks still struggling to extend its lifetime, nodes` clustering and nomination, or selection of cluster head node are proposed as solution. LEACH protocol is one of the oldest remarkable clustering approaches that aim to cluster the network`s nodes and randomly elects a cluster head for each cluster. It selects cluster heads but it is not responsible for proper clustering formation. In this paper we use the Flower Pollination Optimization Algorithm (FPOA) to propose a WSN energy aware clustering formation model based on the intra-cluster distances. The objective is to achieve the global optimization for WSN lifetime. Simulation results and performance analysis show that applying flower pollination optimization on WSNs clustering is more efficient. It is effectively balance power utilization of each sensor node and hence extends WSN lifetime comparatively with the classical LEACH approach.

Keywords:
Wireless Sensor Network; Energy-aware algorithm; Flower Pollination Optimization Algorithm; Hierarchical routing protocol.


References:

1. W. B. Heinzelman, Application-Specific Protocol Architectures for Wireless Networks, PhD thesis, Massachusetts Institute of Technology, June 2000.
2. Mishra, Neeraj Kumar, Vikram Jain, and Sandeep Sahu. “Survey on Recent Clustering Algorithms in Wireless Sensor Networks.” International Journal of Scientific and Research Publications 3, no. 4 (2013).

3. Gupta, Abhimanyu Kumar, and Rupali Patro. “Study of Energy Ecient Clustering Algorithms for Wireless Sensor Network.” PhD diss., 2013.

4. Walker, M.: How flowers conquered the world, BBC Earth News, 10 July 2009. http://news.bbc.co.uk/earth/hi/earth news/newsid 8143000/8143095.stm

5. Oily Fossils provide clues to the evolution of flowers, Science Daily, 5 April2001. http://www.sciencedaily.com/releases/2001/04/010403071438.htm

6. Glover, Beverly J. Understanding flowers and flowering: an integrated approach. Oxford: Oxford University Press, 2007.

7. Pavlyukevich, Ilya. “Lévy flights, non-local search and simulated annealing.” Journal of Computational Physics 226, no. 2 (2007): 1830-1844.

8. Waser, N.M., Flower constancy: definition, cause and measurement. The American Naturalist, 127(5), 1986, pp. 596-603.

9. Yang, Xin-She. “Flower pollination algorithm for global optimization.” In Unconventional Computation and Natural Computation, pp. 240-249. Springer Berlin Heidelberg, 2012.

10. Yang, Xin-She. “Flower pollination algorithm for global optimization.” In Unconventional Computation and Natural Computation, pp. 240-249. Springer Berlin Heidelberg, 2012.

11. Reynolds A. M. and Frye M. A., Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search, PLoS One, 2, e354 (2007)

12. Heinzelman, W.; Chandrakasan, A.; Balakrishnan, H. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences, Hawaii, HI, USA, 2000; pp. 1–10.

 

54-59

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

12.

Authors:

R. Balaji, B.Mohamed Faizal

Paper Title:

Design of Shunt Active Filters based on Phase Locked Loop and PI Controller

Abstract: This paper presents a active filter topology and its control technique. Active power filter topology is the most efficient way to compensate reactive power and lower order harmonics generated by non linear loads. The shunt active power filter was consider to be the most basic configuration for the APF. Different techniques have been applied to obtain a control signal for the active filters. One technique is Phase Locked Loop controller combined with PI controller, where the current waveform injected by the active filter is able to compensate the reactive power and load current harmonics. Here the simulation has been carried out through the MATLAB SimPowerSystems Toolbox and the results are tabulated. With the proposed control strategy the total harmonic distortion is reduced to a great level and hence the power factor is also improved there by towards power quality enhancement.

Keywords:
Active power filters, harmonics, Power quality, Phase Locked Loop.


References:

1. Salmerón and S. P. Litrán, “Improvement of the electric power quality using series active and shunt passive filters” IEEE Trans. Power Del.,vol.25, no.2,pp1058, April 2010.
2. F. Z. Peng and D. J. Adams, “Harmonics sources and filtering approaches,” in Proc. Industry Applications Conf., Oct. 1999, vol. 1, pp.448–455.

3. J. C. Das, “Passive filters-potentialities and limitations,” IEEE Trans. Ind. Appli., vol. 40, no. 1, pp. 232–241, Jan. 2004.

4. Subhashish Bhattacharya, “An universal active Power filter controller system,”IEEE Trans 2009.

5. H. L. Ginn, III and L. S. Czarnecki, “An optimization based method for selection of resonant harmonic filter branch parameters,” IEEE Trans .Power Del., vol. 21, no. 3, pp. 1445–1451, Jul. 2006.

6. Liqing Tong, “A new control strategy for series in series hybrid active power filter,” IEEE trans pp1553-1556 , 2008.

7. H. Akagi, “Active harmonic filters,” Proc. IEEE, vol. 93, no. 12, pp.2128–2141, Dec. 2005.

8. Ahad Kazami,” A Reference Detection algorithm for series active power filters, aimed at current harmonics and reactive power compensation,”Proc. IEEE pp 1761-1766, 2007

9. J. W. Dixon, G. Venegas, and L. A. Moran, “A series active power filter based on a sinusoidal current-controlled voltage-source inverter,” IEEETrans. Ind. Electron., vol. 44, no. 5, pp. 612–620, Oct. 1997.

10. M. Salehifer,” Hybrid active filter for harmonic suppression and reactive power compensation,”IEEE Trans .Oct 1999.

11. F. Z. Peng, H. Akagi, and A. Nabae, “A novel harmonic power filter,” in Proc. IEEE/PESC, Apr. 1988, pp. 1151–1159.

12. F. Z. Peng, H. Akagi, and A. Nabae, “A new approach to harmonic compensation in power systems-a combined system of shunt passive and series active filters,” IEEE Trans. Ind. Appl., vol. 26, no. 6, pp.983–990, Nov./Dec. 1990.

13. J.G. Pinto” A combined series active filter and passive filters for harmonics, unbalances and flicker compensation,” IEEE Trans.pp 54-59, 2007.
14. Karuppanan. P and Kamala Kanta Mahapatra ” A Novel Active Power Line Conditioners using PLL synchronization and PI Controller ,”International Conference on Future Engineering Trends(ICFET-2010) .

 

60-63

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

13.

Authors:

Anju Pratap, C. S. Kanimozhiselvi, R. Vijayakumar, K. V. Pramod

Paper Title:

Soft Computing Models for the Predictive Grading of Childhood Autism- A Comparative Study

Abstract: Artificial intelligence technique is a problem solving method, by simulating human intelligence where reasoning is done from previous problems and their solutions. Soft computing consists of artificial intelligence based models that can deal with uncertainty, partial truth, imprecision and approximation. This article discusses about the performance of some soft computing models for the predictive grading of childhood autism. Now a day’s, childhood autism is a common neuro-psychological developmental problem among children. Early and accurate intervention is needed for the correct grading of this disorder. Result demonstrates that soft computing techniques provide acceptable prediction accuracy in autism grading by dealing with the uncertainty and imprecision.

Keywords:
soft computing, autism, naïve bayes model, neural network, classifier combination model.


References:

1. Cohen, Ira L., Vicki Sudhalter, Donna Landon-Jimenez, and Maryellen Keogh. “A neural network approach to the classification of autism.” Journal of autism and developmental disorders 23, no. 3 , 1993 ,pp. 443-466.
2. Arthi, K., and A. Tamilarasi. “Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.” International Journal of developmental neuroscience 26, no. 7, 2008, pp. 699-704.

3. Florio, T., Einfeld, S., Tonge, B., & Brereton, A.” Providing an Independent Second Opinion for the Diagnosis of Autism Using Artificial Intelligence over the Internet”. Counseling, Psychotherapy, and Health, 5(1), The Use of Technology in Mental Health Special Issue, 5, no.1,2009, pp.232-248..

4. Kannappan, Arthi, A. Tamilarasi, and Elpiniki I. Papageorgiou. “Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder.” Expert Systems with Applications 38, no. 3 ,2011,pp.1282-1292.

5. Pratap, Anju, and C. S. Kanimozhiselvi. “Application of Naive Bayes dichotomize