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Volume1 Issue3, July 2011, ISSN: 22312307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Subhransu Padhee, Abhinav Gautam, Yaduvir Singh, and Gagandeep Kaur

Paper Title:

A Novel Evolutionary Tuning Method for Fractional Order PID Controller

Abstract: PID controller is a well known controller which is used in most control applications. Around 90% control applications use PID controller as the controlling element. The tuning of PID controller is mostly done using ZeiglerNichols tuning method. But there are some inherent drawbacks of ZieglerNichols based tuning. For the optimal tuning of controller, the tuned values have to be changed using computer simulation to meet the process needs. In PID controller the derivative and the integral order are in integer. Fractional order PID (FOPID) is a special kind of PID controller whose derivative and integral order are fractional rather than integer. The key challenge of designing FOPID controller is to determine the two key parameters λ (integral order) and μ (derivative order) apart from the usual tuning parameters of PID using different tuning methods. Both λ and μ are in fraction which increases the robustness of the system and gives an optimal control. This paper proposes a novel tuning method for tuning λ and μ of FOPID using genetic algorithms.
Keywords: Fractional order PID, genetic algorithms, PID, ZieglerNichols
References:
1. J S Saini, M Gopal and A P Mittal, “Genetic algorithm based PID tuner,” IE(I) Journal, vol. 85, pp. 216221, 2005
2. JunYi Cao, Jin Liang and BingGang Cao, “Optimization of fractional PID controllers based on genetic algorithms,” Proc. 4th Int. Conf. Machine Learning and Cybernetics, 2005, pp. 56865689
3. F Gao and H Q Tong, “A novel optimal PID tuning and online tuning based on particle swarm intelligence,” Proc. Int. Conf. Sensing, Comput. and Automation, 2006, pp. 182186
4. JunYi Cao and BingGang Cao, “Design of fractional order controller based on particle swarm optimization,” Int. J. Control, Automation and Systems, vol. 4, no. 6, 2006, pp. 775781
5. Schlegel Milos , C Ech Martin, “The fractional order PID controller outperforms the classical one,” Proc. 7th Int. Sci. & Tech. Conf. Process Control, 2006, pp. 17
6. Dingyu Xue, Chunna Zhao, YangQuan Chen, “Fractional order PID control of a DC motor with elastic shaft: Acase study,” Proc. 2006 american control conference, 2006, pp. 31823187
7. Majid Zamani, Masoud KarimiGhartemani, Naseer Sadati, “FOPID controller design for robust performance using particle swarm optimization,” Fractional Calculus and Applied Analysis, vol. 10, no. 2, 2007, pp. 169187
8. Deepyaman Maiti, Sagnik Biswas and Amit Konar, “Design of a fractional order PID controller using particle swarm optimization technique,” Proc. ReTIS’08, 2008.
9. Chuang Zhao, Xiangde Zhang, “The application of fractional order PID controller to position servomechanism,” Proc. 7th World Congress Intelligent Control and Automation, 2008, pp. 33803383
10. Tushar Jain and M.J Nigam, “Optimization of PDPI controller using swarm intelligence,” Intl. J.Computational Cognition, vol. 6, no. 4, 2008, pp. 5559
11. Andrezej Dzielinski and Dominik Sierociuk, “Simulation and experimental tools for fractional order control education,” Proc. IFAC’08, 2008.
12. Varsha Bhambhani and YangQuan Chen, “Experimental study of fractional order PI controller for water level control,” Proc. IEEE Conf. Decis. Cont., 2008, pp. 17911796.
13. Ramiro S Barbosa, J A Tenreiro Machado, Isabel S Jesus, “On the fractional PID control laboratory servo system,” Proc. 17th world congress IFAC, 2008, pp. 15273
15278
14. Varsha Bhambahani, Yangquan Chen, Dingyu Xue, “Optimal fractional order proportional integral controller for varying time delay systems,” Proc. 17th world congress IFAC, 2008, pp. 49104915
15. Mithun Chakraborty, Deepyaman Maiti and Amit Konar, “The application of stochastic optimization algorithms to the design of a fractionalorder PID controller,” 2008 IEEE region 10 colloquium, 3rd ICIIS, 2008, pp. 16
16. HyoSung Ahn, Varsha Bhambhani and YangQuan Chen, “Fractionalorder integral and derivative controller design for temperature profile control,” in Proc. 2008 CCDC2008, pp. 47674771
17. D. Devaraj and B. Selvabala, “Real coded genetic algorithm and fuzzy logic approach for realtime tuning of proportionalintegralderivative controller in automatic
voltage regulator system,” IET Gener. Transm. Distrib., vol. 3, issue 7, 2009, pp. 641649
17.Bijoy K. Mukherjee and Santanu Metia, “Fractional order modeling and GA based tuning for analog realization with lossy capacitors of a PID controller,” Proc. Int. Multi Conf. of Engg. Comput. Sci., vol II, 2009
18. Ying Luo and YangQuan Chen, “Fractional order [PD] controller for robust motion control: Tuning procedure and validation,” 2009 American control conf., 2009, pp. 14121417
19. Li Meng, Dingyu Xue, “Design of an optimal fractionalorder PID controller using multiobjective GA optimization,” Proc. Chinese Contr. Desi. Conf., 2009, pp. 38493853
20. Ivo Petras, “Fractionalorder feedback control of a DC motor,” J. Elect. Engg., vol. 60, no. 3, 2009, pp. 117128
21. Arijit Biswas, Swagatam Das, Ajith Abraham, Sambarta Dasgupta, “Design of fractional order PIλDμ controller with improved differential evolution,” Engineering Applications of Artificial Intelligence, 22, 2009, pp. 343350
22. Venu Kishore Kadiyala, Ravi Kumar Jathoth, Sake Pothalaiah, “Design and implementation of fractional order PID controller for aerofin control system,” Proc. 2009 World Congress NaBIC, 2009, pp. 696701
23. Mohamed Karim Bouafoura, Naceur Benhadj Braiek, “PID controller design for integer and fractional plants using piecewise orthogonal functions,” Commun Nonlinear Sci Number Simulat 15, 2010, pp. 12671278
24. Ammar A Aldair, and Weiji J Wang, “Design of fractional order controller based on evolutionary algorithm for a full vehicle nonlinear active suspension system,” Int. J. Contr Automation, vol. 3, no. 4, 2010, pp. 3346
25. Juan J. Gude and Evaristo Kahoraho, “analysis and performance comparison of PID and fractional PI controllers,” Proc. 9th Int. Symps. Dynamics and Control of Process Systems, 2010, pp. 503508
26. Fabrizio Padula, Antonio Visioli, “Tuning rules for optimal PID and fractional order PID controllers,” Journal of Process Control, 21, 2011, pp. 6981

19

2.

Authors:

Abhishek Katariya, Amita Yadav, Neha Jain

Paper Title:

Performance Elevation Criteria for OFDM under AWGN Fading Channel using IEEE 802.11a

Abstract: In this paper, the BER performance of OFDMBPSK 16 –QAM and 64–QAM system over AWGN fading channel has been reported. Orthogonal Frequency Division Multiplexing (OFDM) is a key technique for achieving high data rates and spectral efficiency requirements for wireless communication systems. However in fading environments the bit error rate (BER) increases. The performance can be improved by using some kind of channel coding. This form of OFDM is called codedOFDM (COFDM). This paper presents a modeling and simulation of OFDM based on IEEE 802.11a standard. The flexibility of this model is because fading parameters compared to only one in AWGN fading model, which helps to analyze the severity of fading more deeply and this model is versatile enough to represent fading. Finally simulations of OFDM signals are carried with AWGN faded signal to understand the effect of channel fading .The performance of OFDM is compared in terms of BER vs SNR for different modulation formats.
Keywords: BER, OFDM Additive white Gaussian noise (AWGN), BPSK ,QAM
References:
1. L. Zou, “Automatic Detection of the Guard Interval Length in OFDM System”, Journal of Communications, vol. 1, no. 6, pp. 2832, Spt. 2006.
2. Z. Du, J. Cheng, and N. C. Beaulieu, "Accurate ErrorRate Performance Analysis of OFDM on FrequencySelective Nakagamim Fading Channels", IEEE Transactions on Communications, vol. 54, no. 2, , Feb. 2006.
3. Y. Mostofi, and D. C. Cox, "ICI Mitigation for Pilot Aided OFDM Mobile Systems", IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO. 2, MARCH 2005.
4. N. Yuan, "An Equalization Technique for High Rate OFDM Systems", M.Sc Thesis, University of Saskatchewan Saskatoon, 2003. A. Kandangath "Simulation of frequencyflat fading Channels", EEE558 Wireless Communications, Project 1, 2003.
5. E. Lawrey, "The suitability of OFDM as a modulation technique for wireless telecommunications, with a CDMA comparison", in partial fulfillments of the requirements for the Degree of Bachelor of Engineering with Honours in Computer Systems Engineering at James Cook University. 1997
6. A. Kandangath "Simulation of frequencyflat fading Channels", EEE558 Wireless Communications, Project 1, 2003. 4.IEEE 802.11A SPECIFICATIONS
7. K.Thenmozhi, V.Prithiviraj, “Suitability of Coded Orthogonal Frequency Division Multiplexing (COFDM) for Multimedia Data Transmission in Wireless Telemedicine Applications,” in IEEE Conference on Computational Intelligence and Multimedia Applications,2007, vol. 4, Dec.2007, pp.288 – 292.
8. T. Rappaport, “Wireless Communications Principles and Practice”, Prentice Hall, 1996.
9. R. Novka, and W. Krzymien, “Diversity Combining Options for spread Spectrum OFDM System in Frequency Selective Fading Channels”, IEEE Communication Society WCNC, 2005.
10. Shinsuke Hara, and Prasad R., Multicarrier Techniques for 4G Mobile Communications,” Artech House, Boston. London, (2003).
11. Hanzo L., Munster M., Chol B. J., and Keller T., “OFDM and MCCDMA for Broadband Multiuser Communications, WLANs and Broadcasting,” John Wiley & Sons, (2003):
12. Lingzhi Cao. N.C Beaulieu, “A simple efficient method for generating independent Nakagamim fading samples,” in Vehicular Technology Conference,IEEE 61st publication, vol.I,2005,pp. 44 47.
13. Iskander, CyrilDaniel, “A MATLABbased ObjectOriented Approach to Multipath Fading Channel Simulation”, a MATLAB Central submission available in www.mathworks.com.
14. S.Elnoubi, S.A. Chahine, H.Abdallah, “BER performance of GMSK in Nakagami fading channels,” in IEEE TwentyFirst National Radio Science Conference,March 2004,pp.C13 18.
15. Li Tang , Zhu Hongbo , “Analysis and simulation of Nakagami fading channel with MATLAB,” in IEEE AsiaPacific Conference on Environmental Electromagnetics, 2003,pp.490 494.

1013

3.

Authors:

Archana Shende, Subhash Mishra , Shiv Kumar

Paper Title:

Comparison Of Different Parameters Used In Gmm Based Automatic Speaker Recognition

Abstract: The performance of Speaker recognition systems has improved due to recent advances in speech processing techniques but there is still need of improvement. In this paper we present the comparison of different parameters used in automatic speech recognition system to increase the accuracy of the system. The main goal here is a detailed evaluation of the parameters used in Automatic speech recognition system such as window type, MFCC frame size, number of Gaussian mixtures and GMM & VQ/GMM technique .In this paper we propose a decision function by using vector quantization techniques to decrease the training model for GMM in order to reduce the processing time.
Keywords: Gaussian Mixture Model (GMM), Mel Frequency Cepstral Coefficient (MFCC), Speaker Identification (SI), Speaker Verification (SV), Vector Quantization (VQ).
References:
1. CAMPBELL, Joseph P.,Jr.” Speaker Recognition: A Tutorial”.Proceedings of IEEE, vol. 85,no. 9, pp. 14371462, September 1997.
2. D. Reynolds, "An overview of automatic speaker recognition technology," in Proc. Int. Conf. on Acoust. Speech and Signal Process (ICASSP 2002), Orlando, FL,
2002, pp. 40724075.
3. F. Bimbot, J.F. Bonastre, G. Gravier, I. Chagnolleau, S. Meignier, T.Merlin, J. Garcia, D. Delacrètaz, and D. Reynolds, "A tutorial on text independent speaker verification," Eurasip Journal on Applied SignalProcess., vol. 4, pp. 430451, 2004.
4. Vlasta Radová and Zdenek Svenda, "Speaker Identification Based on Vector Quantization", Proceedings of the Second International Workshop on Text, Speech and Dialogue, Vol. 1692,1999, Pages: 341 344.
5. TEXT INDEPENDENT AUTOMATIC SPEAKER RECOGNITION‟ Othman O. Khalifa, S. Khan, Md. Rafiqul Islam, M. Faizal and D. Dol Electrical and Computer Engineering, International Islamic University Malaysia, 3rd International Conference on Electrical & Computer Engineering ICECE 2004, 2830 December 2004, Dhaka,
Bangladesh ISBN 9843218044 561.
6. Reynolds, D. A. and Rose, R. C. "Robust textindependent speaker identification using Gaussian mixture speaker models. IEEE Trans.Speech Audio Process. 3, 1995, pp 72–83.
7. “Text independent speaker verification using GMM” Charles B.de Lima,Abraham Alcaim and Jose A.Apoloniyaro Jr.
8. “SPEAKER IDENTIFICATION USING MEL FREQUENCY CEPSTRAL COEFFICIENTS “565 Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbani Md. Saifur Rahman, 3rd International Conference on Electrical & Computer Engineering ICECE 2004, 2830 December 2004, Dhaka, Bangladesh ISBN 9843218044
9. “GMM BASED SPEAKER IDENTIFICATION USING TRAININGTIMEDEPENDENT NUMBER OF MIXTURES” 1998 IEEE Chakib Tadjt , Pierre Dumouchelt~ and Pierre Ouellett tEcole de Technologie Supkrieure  Electrical Engineering, 1100 rue Notre Dame Quest Montr6ai (Qc)  H3C 1K3  Canada
10. “Vector Quantization Decision Function for Gaussian Mixture Model Based Speaker Identification”.2008 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS2008) Swissôtel Le Concorde,Bangkok,Thailand .

1418

4.

Authors:

Vijender Singh, Deepak Garg

Paper Title:

Survey of Finding Frequent Patterns in Graph Mining: Algorithms and Techniques

Abstract: Graphs become increasingly important in modeling complicated structures, such as circuits, images, chemical compounds, protein structures, biological networks, social networks, the web, workflows, and XML documents. Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing and text retrieval with the increasing demand on the analysis of large amounts of structured data; graph mining has become an active and important theme in data mining.
Keywords: Subgraphs, Graph Mining, gSpan
References:
1. J. R. Ullmann, “An algorithm for subgraph isomorphism”. J. ACM, 23, 1976, pp. 3142.
2. R. Agrawal, R. Srikant, “Fast Algorithms for mining association rules. In the proc. Of the 20th Int. conf. on very large databases (VLDB), 1994.
3. D. J. Cook and L. B. Holder, “Substructure discovery using minimum description length and background knowledge” Journal of Artificial intelligence Research, 1, 1994, 231255.
4. Holder, L. B. Holder, Cook, D. J. Cook, Djoko, S. Djoko, “Substructure Discovery in the SUBDUE system”, In Proc. AAAI’94 Workshop knowledge Discovery in Databases (KDD’94), pp 169180.
5. X. wang, J.T.L. Wang, D. Shasha, B. Shapiro, S. Dikshitulu I.Rigoutsos, K. Zhang, “Automated discovery of active motifs in three dimensional molecules”, In Proc. Of the 3rd int. conf. on knowledge discovery and data mining, 1997.
6. H. Blockeel, L.D. Raedt, “Topdown induction of firstorder logic decision trees”, Artificial Intelligence, 101, 1998, pp. 285297.
7. S. Chakrabarti, B. Dom, P. Indyk, “Enhanced hypertext categorization using hyperlinks” ACM, (SIGMOD’98), 1998, pp. 307318.
8. L. Dehaspe, H. Toivonen, R. D. King, “Finding frequent substructures in chemical compounds”. In 4th Int. conf. on knowledge Discovery and Data mining, 1998.
9. A. Inokuchi, T. Washio, H. Motoda, “An Aprioribased Algorithm for Mining Frequent substructures from Graph Data. In proc. 2000 European Symp. Principle of Data mining and knowledge Discovery (PKDD’00), 1998, pp. 1323.
10. T. Calders, J. Wijsen, “On Monotone mining Languages”, In proc. Of international workshop on database programming Languages(DBPL), 2001, pp. 119132.
11. S. Kramer, L.D. Raedt, C. Helma, “Molecular feature mining in HIV data”, In Proc.ational conf. on of the 7th ACM SIGKDD International conf. on knowledge discovery and data mining, 2001, pp. 136143
12. M. Kuramochi, G. Karypis, “ Freovequent Subgraph Discovery “, In Proc 2001 Int. conf. Data mining(ICDM’01).
13. J. Pei, J. Han, B. MortazaviAsl, H. Pinto, “PrefixSpan: Mining Sequential Pattern Growth.” In proc. 2001 int. conf. Data Engineering (ICDE’01), 2001, pp. 215224.
14. T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Satamota, and S. Arikawa, “Efficient substructure discovery from large semistructured data.” In proc. 2002 SIAM Int. conf. Data mining(SDM’02), 2002, pp. 158174.
15. C. Borgelt and M.R. Berthold, “Mining molecular fragments: Finding relevant substructures of molecules.” In Proc. 2002 int. conf. Data Mining (ICDM’02), PP. 211218.
16. X. Yan, and J. Han, “gSpan: GraphBased Substructure Pattern Mining.” In Proc. 2002 Int. conf. Data mining, 2002, pp. 721724.
17. M.J. Zaki, “Efficiently Mining Frequent trees in a forest.” In Proc. 2002 ACM SIGKDD Int. conf. knowledge Discovery and Datamining(KDD’02), 2002, pp. 7180.
18. M.Deshpande, M. Kuramochi, G. Karypis, “Automated approaches for classifying structures.” In Proc. 2002 workshop on Data mining in Bioinformatics (BIOKDD’02), 2002, pp. 1118.
19. C. Cooper and A. Frieze, “A general model of web graph.” 2003.
20. S. Dzeroski, “MultiRelational Data mining: An Introduction” SIGKOD Explore. Newsl, 5(1), 2003, pp.116.
21. L. Getoor, “Link Mining: A new data mining challenge.” SIGKDD Explorations, (5), 2003, pp.8489.
22. J. Huan, W. Wang and J. Prins, “Efficient Mining of frequent Subgraph in the Presence of Isomorphism.” In Proc. 2003 int. conf. Data mining (ICDM’03), 2003, pp. 549552.
23. T. Washio and H. Motoda, “State of the art of Graph based data mining.” SIGKDD Explorations, (5), 2003, pp. 5968.
24. X. Yan, J. Han and R. Afshar, “CloSpan: Mining Closed Sequential patterns in Large Datasets.” In Proc. 2003 SIAM Int. conf. Data mining (SDM’03), 2003, pp. 166177.
25. X. Yan and J. Han CloseGraphs: Mining Closed Frequent Graph Patterns. In proc. 2003 ACM SIGKDD Int. conf. knowledge Discovery and Data Mining (KDD’03), 2003, pp. 286295.
26. X. Yin and J. Han, “ CPAR: Classification based on Predictive Association Rules. In Proc. 2003 SIAM Int. conf. Data Mining (SDM’03), 2003, pp. 331335.
27. J. Huan, W. Wang, J. Prins and J. Yang,”Spin: mining maximal frequent subgraphs from graph Databases”, KDD04 Seattle, Washington, USA, 2004.
28. J. Huan, W. Wang, D Bandyopadhyay, J. Snoeyink, J. Prins and J. Tropsha,” A. Mining Sapitial Motifs from Protein Structure Graphs. In Proc. 8th int. conf. Research in computational Molecular Biology (RECOMB), 2004, pp. 308315.
29. M. Koyuturk, A. Grama, and W. Szpankowski. “An Efficient algorithm for detecting frequent subgraphs in biological networks.” Bioinformatics, (20), 2004, pp. i200i207.
30. T. Meinl, C. Borgelt and M.R. Berthold, “ Discriminative closed fragment mining and perfect extensions in MoFa.” 2004.
31. X. Yin, J. Han, J.Yang and P.S. Yu, “CrossMine: Efficient Classification Across Multiple Database Relations. In Proc. 2004 int. conf. Data Engineering (ICDE’04), 2004, pp. 399410.
32. X. Yan, P.S. Yu, and J. Han. “ Graph Indexing: A Frequent Structurebased approach.” In Proc. 2004 ACM SIGKDD Int. conf. management of Data, 2004, pp.335346.
33. H. Hu, X. Yan, Y. Huang, J. Han and X. J. Zhou, “Mining coherent dense subgraphs across massive biological networks for functional discovery.” In proc. 2005 Int. conf. Intelligent system for molecular Biology (ISMB’05), 2005, pp. 213221.
34. X.L. Li, S.H. Tan, “Interaction graph mining for protein complexes using local clique merging.” Genome Informatics, 16(2),2005, pp. 260269.
35. X. Yan, P.S. Yu, J. Han, “Substructure similarity search in graph databases.” In proc. 2005 ACMSIGMOD Int. conf. Management of Data (SIGMOD’05), 2005, pp. 766777.
36. X. Yin, J. Han, P.S. Yu, “CrossRelational Clustering with User’s Guidance. In Proc. 2005 ACM SIGKDD Int. conf. knowledge Discovery and Data mining (KDD’05), 2005, 344353.
37. D. Chakrabarti, C. Faloutsos, “Graph mining: Laws, Generators, and Algorithm.” ACM computing survey, 38(2), 2006, pp. 169.
38. S. Maji, S. Mehta, “A Netflow distance between labeled graphs applications in chemoinformatics.” www.cs.berkeley.edu., 2008.
39. A. Krasky, A. Rower, J. Schroeder, P.M. Selzen,“A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs.” Elsevier, genomics, 2006, pp.18.
40. T. Meinl, M. Worlein, O. Urzova, , I. Fischer, M. Philippsen, “The ParMol package for frequent subgraph mining. Electronics communication of ESST 1, 2006.
41. T. Meinl, M. Worlein, I. Fischer, M. Philippsen “Mining Molecular datasets on Symmetric Multiprocessor systems. 2006.
42. Tsuda, K. Kudo, T. Clustering Graphs by weighted substructure mining. Proc. Of 23rd Int. conf. on machine learning, ACM, 148:953960, 2006.
43. J. K. Wegner, H. Frohlich, H.M. Mielenz, A. Zell, “ Data and graph mining in chemical space for ADME and activity data sets.” WileyVCH, 25(3),2006, 205206.
44. C. Merkwith, M. Ogorzalek, “Applying CNN to chemoinformatics.” IEEE Xplore, 2007, 29182921.
45. X. Dong, K.E. Gilbert, R. Guha, R. Heiland, J. Kim, M.E. Pierce, G.C. Fox, D.J. Wild, “Web Service Infrastructure for chemoinformatics.” J. Chem. Inf. Model, 47, 2007, pp. 13031307.
46. J. Rhodes, S. Boyer, J. Kreulex, Y. Chen, P. Ordonez, “Mining patents using molecular similarity search.” Pacific symp. On Biocomputing, 12, 2007, pp. 304315.
47. P. Bogdanov, “Graph searching, indexing, mining and modeling for Bioinformatics, cheminformatics and Social network.” 2008.
48. A.M. Fahim, G. Saake, A.M. Salem, F. A. Torkey, M.A. Ramadan, “Kmens for spherical clusters with large variance in sizes.” WorldAcademy of science, Engineering & Tech., 45, 2008, pp. 177182.
49. Godeck, R.A. Lewis, “Exploiting QSAR models in lead optimization.” Curr. Opin. Drug Discov Devel, 11(4), 2008, pp. 569575.
50. R. Guha, S.C. Schurer, “Utilizing high throughput screening data for predictive toxicology models: Protocols and Application to MLSCN assays.” J. comput. Aided Mol Des 22(67), 2008, pp. 367384.
51. Hubler, C. Kriegel, H.P. Borgwardt, K. Ghahramani, Z. Metropolis Algorithms for Representative Subgraph sampling. IEEEXplore, 2008, pp. 283292.
52. T.Karunaratne, H. Bostrom, “Using background knowledge for graph based learning: a case study in chemoinformatics.” Springer, Artificial Inteligence, (6), 2008, pp. 151153.
53. W.W.M.Lam, K.C.C. Chan, “A Graph mining algorithm for classifying chemical compounds." IEEE Int. conf. on Bioinformatics and Biomedicine, 2008.
54. S. Maji, S. Mehta, “A Netflow distance between labeled graphs applications in chemoinformatics.” www.cs.berkeley.edu., 2008.
55. K. Tsuda, K. Kurihara, “Graph Mining with variational Dirichlet process mixture models.” SIAM, 432442, 2008.
56. L. Schietget, F. Costa, J. Ramon, L.D. Raedt, “Maximum common subgraph mining: A Fast and effective Approach towards feature generation.” In Proc. At SRLMLGILP, Leven, Belgium, 2009.
57. C. Jiang, F. Coenen, M. Zito, M. “Frequent Subgraph mining on Edge Weighted Graphs.” 2010.
58. O. Spjuth, E.L. Willighagen, R. Guha, M. Eklund, J.E.S. Wikberg, “Toward interoperable and reproducible QSAR analysis: Exchange of data sets.” Journal of
cheminformatics, 2:5, 2010.
59. X. Yang, S. Parthasarthy, P. Sadayappan, “ Fast Mining Algorithms of Graph data on GPUs.” ACM, KDDLDMTA’10, 2010.

1923

5.

Authors:

Abhishek Katariya, Neha Jain, Amita Yadav

Paper Title:

Performance Elevation Criteria Of Rs Coded Ofdm Transmission Over Noisy Channel

Abstract: OFDM has recently been applied widely in wireless communication system due to its high data rate transmission capability with high bandwidth efficiency. Error control codes are used to protect information from errors that can occur during transmission Reed Solomon (RS) codes is one of the most important and best known classes of non binary [5].In the present study a simulink of RS are done using single carriers transceivers, to observe the performance of RS code and choose the effective parameters that can improve OFDM system. Evaluation of Bit Error Rate (BER) performance for RS code as a function of code rate and block size are tested. This testing is applied to single carrier system with different channel model using QPSK technique. BER performance over Rayleigh channel and Rician channels are done respectively. In this paper, the system throughput of a working OFDM system has been enhanced by adding RS coding. The bit error rate (BER) performance degradation of OFDM system is investigated in different channel. We consider the effects of BER both in Rayleigh and Rician. The environment theoretical approximate calculation method is also derived of BER for binary using quaternary phase shift keying (QPSK) modulation schemes in OFDM systems. In addition, the method of BER calculation is validated by simulinks. In this paper Improvement of performance elevation criteria of RS coded OFDM TRANSMISSION over noisy channel is analyzed using simulink. The Simulink Communication tool and basic library tools are use with standard values in IEEE802.11A.
Keywords: BER, OFDM, RS Code, Fading Channels, QPSK, Simulinks
References:
1. Ramjee Prasad, “OFDM for Wireless Communications systems”, Artech House Publishers, 2004.
2. L. Hanzo, M. Munster, B.J. Choi, T. Keller, “OFDM & MCCDMA for Broadband Multiuser Communications, WLANs and Broadcasting” John Wiley Publishers, 2003.
3. Omar AlAskary, “Coding and Iterative Decoding of Concatenated Multilevel Codes for the Rayliegh Fading Channel”, Ph. D Thesis in Radio Communication Systems, Stockholm, Sweden, 2006.
4. IEEE 802.11 detailed documentation available at IEEE website http://standards.ieee.org/getieee802
5. C. Xu(2007), “Soft Decoding Algorithm for RSCC Concatenated Codes in WiMAX System”, IEEE 65th Conference on Vehicular Technology VTC 2007, pp. 740742.
6. Bernard Sklar (2001), “Digital Communication Fundamentals and Applications”, 2nd edition, Prentice Hall Inc.
7. L. Zou, “Automatic Detection of the Guard Interval Length in OFDM System”, Journal of Communications, vol. 1, no. 6, pp. 2832, Spt. 2006.
8. O.Daoud, A.R AlQawasmi, “Efficient Performance Of Cofdm Based DVBT,” in IEEE International MultiConference on Systems, Signals and Devices, 2009, pp. 14.
9. Zhu Qi Li Hao Feng Guangzeng “performance analysis of channel estimaton on for LDPC – coded OFDM system in multipath fading channel” JOURNAL OF ELECTRONICS (CHINA), Vol.23 No.6 Nov 2006.
10. S. Roman, “Coding and Information Theory”. Springer Verlag, 1992. [11] L. Biard and D. Noguet (2008), “Reed Solomon Codes for Low Power Communication”, Journal of Communications, vol. 3, no. 2, pp. 1321.
11. M. K. GUPTA, VISHWAS SHARMA, “To improve bit error rate of turbo coded OFDM transmission over noisy channel, Journal of Theoretical and Applied Information Technology © 2005 – 2009 JATIT
12. John G. Proakis, Masoud Salehi, “communication system using MATLAB” Thomson Asia Pvt. Ltd., Singapore, 2003.
13. T. Rappaport, “ Wireless Communications Principles and Practice”, Prentice Hall, 1996.
14. Iskander, CyrilDaniel, “A MATLABbased ObjectOriented Approach to Multipath Fading Channel Simulation”, a MATLAB Central submission available in www.mathworks.com.

2428

6.

Authors:

Md. Robiul Hoque, Md. RashedAlMahfuz

Paper Title:

A New Approach in Spatial Filtering to Reduce Speckle Noise

Abstract: Speckle noise gives a grainy appearance in radar, SAR (Synthetic Aperture Radar), MRI (Magnetic Resonance imaging) images as well as ultrasound medical images. It reduces the image contrast, which has a direct negative effect on texturebased analysis of the imageries. In this paper, a new approach for speckle reduction technique has been proposed and then its performances on simulated (image with artificial speckle noise) imageries with other existing filtering methods has been compared. It has been found that, the proposed technique has better performance than others.
Keywords: Image enhancement, Noise Reduction, Spatial Filter, Speckle Noise
References:
1. M. Simard, “ Extraction of information and speckle noise reduction in SAR images using the wavelet transform,”Geoscience and Remote Sensing Symposium Proceedings, 1998 IEEE International, Volume 1, p. 46, Seattle, WA, USA, Jul 1998.
2. R. K. Raney, “Rada Fundamentals: Technical Perspective. Chapter 2 in Principles and Applications of Imaging Radar, Manual of Remote Sensing,”, Third Edition, Volume 2, ASPRS, John Wiley and Sons Inc., Toronto, 1998.
3. Rafael C. Gonzalez and Richard E. Woods “Digital Image Processing,” Second Edition, ISBN 81780862298, 2002.
4. L. Gagnon and F.D. Smaili: 'Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavelets', SPIE Proc. #2759, pp. 1424,1996.
5. M. Mansourpour, M.A. Rajabi, and J.A.R. Blais, “Effect and Performance of Speckle Noise Reduction Filter On Active RADAR and SAR Images,” ISPRS Volume Number: XXXVI1/W41, Ankara, Turkey, Feb 2006.
6. K. Thangavel, R. Manavalan, and I. Laurence Aroquiaraj, “Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study,” ICGSTGVIP Journal, ISSN 1687398X, Volume (9), Issue (III), June 2009.
7. L. Gagnon and F.D. Smaili: “Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavele,” SPIE Proc. #2759, pp. 1424,1996.

2932

7.

Authors:

Supriya Khaitan, Supriya Raheja

Paper Title:

Finding Optimal Attack Path Using Attack Graphs: A Survey

Abstract: As the traditional methods, the result of vulnerability scanning can’t directly reflect complex attack routes existing in network, so the concept of attack graph comes. After analyzing host computer, devices link relation and the characteristic of attack, the model of network security status was built. Attack graphs are one of the important tools for analyzing security. A lot of research has been done on issues such as scalable and time efficient ways of generation of attack graphs. The intent of this paper is to study different ways to generate an attack graph and to provide future scope for research on these attack graphs.
Keywords: Attack Graph, Attack Path, Network Security, optimal attack path.
References:
1. F.Cuppens and R. Ortalo, ―LAMBDA: A Language to Model a Database for Detection of Attacks,‖ Recent Advances in Intrusion Detection (RAID) 2000, Lecture Notes in Computer Science 1907, H. Debar, L. Me, and F. Wu, Eds., Berlin: Springer Verlag, 2001.
2. R. Ritchey and P. Amman,‖Using Model Checking to Analyze Network Vulnerabilities‖, Proceedings of the IEEE Symposium on Security and Privacy, pp. 156165, 2000.
3. L. P. Swiler, C. Phillips, D. Ellis, and S. Chakerian, ―ComputerAttack Graph Generation Tool,‖ Proceedings of the Second DARPA Information Survivability Conference & Exposition (DISCEX II), Los Alamitos, California, vol. II, pp. 307321, IEEE Computer Society, 2001.
4. Xiaochun Xiao, Tiange Zhang, Huan Wang, Gendu Zhang ―A ComponentCentric Access Graph Based Approach to Network Attack Analysis,‖ in International Seminar on Future Information Technology and Management Engineering pp.171176, 2008
5. Jan Magott, Marek Woda ―Evaluation of SOA security metrics using attack graphs,‖ Third International Conference on Dependability of Computer Systems DepCoSRELCOMEX pp. 277284 ,IEEE Computer Society , 2008
6. P. Ammann, D. Wijesekera, and S. Kaushik, ―Scalable, GraphBased Network Vulnerability Analysis,‖ Proceedings of the 9th ACM Conference on Computer and Communications Security, New York: ACM Press, 2002, 217–224.
7. Marcel Frigault, Lingyu Wang ‖ Measuring Network Security Using Bayesian NetworkBased Attack Graphs ‖ Computer Software and Applications, 2008. COMPSAC'08. 32nd Annual IEEE International, pp.698703, 2008
8. M. Artz, NETspa, A Network Security Planning Architecture, M.S. Thesis, Cambridge: Massachusetts Institute of Technology, May 2002.
9. Nessus, ―Nessus Security Scanner,‖ [10] O. Sheyner, S. Jha, J. M. Wing, R. P. Lippmann, and J. Haines, Automated Generation and Analysis of Attack Graphs,‖ in 2002 IEEE Symposium on Security and Privacy. Oakland, California, 2002.
10. B. Schneier, ―Attack Trees,‖ Dr. Dobbs Journal, December, 1999.
11. T. Tidwell, R. Larson, K. Fitch, and J. Hale, ―Modeling Internet Attacks,‖ Proceedings of the Second Annual IEEE SMC Information Assurance Workshop, United States Military Academy, West Point, New York, June 2001: IEEE Press, 2001, pp. 54–59.
12. A. Wool, ―A Quantitative Study of Firewall Configuration Errors,‖ IEEE Computer, vol. 37, pp.62–67, 2004.
13. D. Turner, S. Entwisle, O. Friedrichs, D. Hanson, M. Fossi, D. Ahmad, S. Gordon, P. Szor, E. Chien, F. Perriot, and P. Ferrie, ―Symantec Internet Security Threat Report, Trends for January 1,2004–June 30, 2004,‖ vol. VI, September 2004.
14. G. Cohen, M. Meiseles, and E. Reshef, ―System and Method for Risk Detection and Analysis in a
15. Computer Network,‖ USA: Skybox Security Ltd., 2004.
16. S. Jajodia, S. Noel, and B. O’Berry, ―Topological Analysis of Network Attack Vulnerability,‖ Managing Cyber Threats: Issues, Approaches and Challenges, Kumar, Kluwer Academic Publisher, 2003.
17. J. Dawkins and J. Hale, ―A Systematic Approach to MultiStage Network Attack Analysis,‖Proceedings of the Second IEEE International Information Assurance Workshop (IWIA’04), IEEE Computer Society, 2004.
18. ]T.Zhang, MingZeng, Dong, Liang Sun,‖ An Effective method to generate Attack Graph‖ Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005
19. Vaibhav Mehta, Constantinos Bartzis, Haifeng Zhu, ‖ Ranking Attack Graphs‖, IEEE Computer Society, 2006.
20. Somak Bhattacharya, S. K. Ghosh ―An Attack Graph Based Risk Management Approach of an Enterprise LAN‖ ‖ Journal of Information Assurance and Security 119127, 2008
21. Yue Chen, Barry Boehm Luke Sheppard ―Value Driven Security Threat Modeling Based on Attack Path Analysis‖ Proceedings of the 40th Hawaii International Conference on System Sciences , 2007
22. Irina Petreska, Igor Tomovski, Eugenio Gutierrez ― Application of modal analysis in assessing attack vulnerabilityof complex networks‖ Commun Nonlinear Sci Numer SimulatScience Direct pp. 10081018,2009
23. N Ghosh, S Nanda, S.K Ghosh ,―A quantative approach towardsdetection of an optimal attack path in wireless network using modified PSO technique‖ , IEEE proceedings , 2009

3336

8.

Authors:

Arun Kumar Gandhi , S.S.Tyagi

Paper Title:

Security Enhancement in Elliptic Key Cryptography Using Character Based Method

Abstract: It is widely recognized that data security will play a central role in the design of future IT system. Elliptic key cryptography recently gained lot of attention in industries when we talk about security. This paper discusses the basic operation how ECC is more secure than other public key cryptosystems and also how security can be enhanced in ECC by using characterbasedmethod.
Keywords: Elliptic Key Cryptography, Security
References:
1. Darrel Hankerson, Julio Lopez Hernandez, Alfred Menezes, Software Implementation of Elliptic Curve Cryptography over Binary Fields, 2000.
2. M. Brown, D. Hankerson, J. Lopez, A. Menezes, Software Implementation of the NIST Elliptic Curves Over Prime Fields, 2001.
3. Certicom, Standards for Efficient Cryptography, SEC 1: Elliptic Curve Cryptography, Version 1.0, September 2000.
4. Certicom, Standards for Efficient Cryptography, SEC 2: Recommended Elliptic Curve Domain Parameters, Version 1.0, September 2000
5. Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, Handbook of Applied Cryptography, CRC Press, 1996
6. I. F. Blake, G. Seroussi, and N. P. Smart. Elliptic curves in cryptography, volume 265 of London Mathematical Society Lecture Note Series.Cambridge University Press, Cambridge, 2000. Reprint of the 1999 original.
7. D. Shanks. Class number, a theory of factorization, and genera. In 1969 Number Theory Institute (Proc. Sympos. Pure Math., Vol. XX, State Univ. New York, Stony Brook, NY, 1969), pages 415440. Amer. Math. Soc., Providence, RI, 1971.
8. J. H. Silverman. The arithmetic of elliptic curves, volume 106 of Graduate Texts in Mathematics. SpringerVerlag, New York, 1986.
9. W. Trappe and L.Washington. Introduction to cryptography with coding theory, (2nd ed.). Prentice Hall, Upper Saddle River, NJ, 2006.
10. L. C. Washington. Elliptic curves. Number theory and cryptography, (2nd ed.). Chapman & Hall/CRC, New York, NY, 2008.
11. A. S. Tanenbaum, "Modemn Operating Systems", Prentice Hall, 2003.[3] M. J. B. Robshaw, "Block Ciphers", Technical Report, RSA Laboratories, Number TR  601, July 1994.
12. H.M. Heys and S.E. Tavares, "On the Security of the CAST Encryption Algorithm," Proceedings of the Canadian Conference on Electrical and Computer Engineering, Halifax, Nova Scotia, Sep 1994, pp. 332335.
13. R. Rivest, ―The encryption algorithm,‖ in Fast Software Encryption, ser. LNCS, vol. 1008. SpringerVerlag, 1995, pp. 86–96.
14. M.A. Viredaz and D.A. Wallach, ―Power Evaluation of a Handheld Computer: A Case Study,‖ WRL Research Report, 2001/1.
15. P. Ruangchaijatupon, P. Krishnamurthy, ''Encryption and Power Consumption in Wireless LANsN,‘‘ The Third IEEE Workshop on Wireless LANs  September 2728, 2001 Newton, Massachusetts.

3741

9.

Authors:

Priti Narwal, S.S. Tyagi

Paper Title:

Density Based Protocol For Head Selection In Wireless Sensor Networks

Abstract: In wireless sensor networks nodes have several limitations such as limited battery life, low computational capability, short radio transmission range and small memory space. However, the most severe constraint of the nodes is their limited energy resource because they cease to function when their battery has been depleted. To reduce energy usage in wireless sensor networks, many clusterbased algorithms have been proposed. Among those proposed, LEACH (Low Energy Adaptive Clustering Hierarchy) is a wellknown clusterbased sensor network architecture. In this paper, subtractive clustering technique, which is an enhancement in the basic LEACH protocol to deal with its severe shortcomings with respect to handling of node‘s nonuniform and time variant energy distribution.
Keywords: Clustering, Density based clustering, LEACH protocol, Subtractive clustering, Wireless Sensor Networks
References:
1. Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella, Energy Conservation in Wireless Sensor Networks: a Survey, Ad Hoc Networks, 2009.
2. W. R. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An ApplicationSpecific protocol Architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, October, 2002.
3. W. R. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks,” in Proceedings of the ACM MobiCom‟99.
4. C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Network,” IEEE J. Select. Area Commun., vol 15, pp. 12651275, Sept 1997.
5. J. H. Ryu, S. Song, and D. H. Cho, “EnergyConserving Clustering Scheme for Multicasting in Twotier Mobile AdHoc Networks,” Electron. Lett., vol. 37, pp. 1253 1255, Sept 2001.
6. T. C. Hou and T. J. Tsai, “Distributed Clustering for Multimedia Support in Mobile Multihop Ad Hoc Network,” IEICE Trans. Commun., vol. E84B, pp. 760 770, Apr 2001.
7. Wendi Rabiner Heinzelman, Anantha Chanfrakasan, and Hari Balakrishnam, “EnergyEfficient Communication Protocol for Wireless Microsensor Networks,” 2000 IEEE The Hawaii International Conference on System Sciences Proceeding, January 2000.
8. A. Manjeshwar and D. P. Agarwal, “APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks,” Proc.Int‟l. Parallel and Distrib. Proc. Symp., 2002, pp. 195– 202.
9. P. Tillapart, T. Thumthawatworn, P. Pakdeepinit, T. Yeophantong, S. Charoenvikrom and J. Daengdey, “Method for Cluster Heads Selection in Wireless Sensor Network,” Proc. of the 2004 IEEE Aerospace Conference, Big Sky, Montana, USA, 2004.
10. C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” in Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM), 2000.
11. D. Ganesan et al., „„HighlyResilient, EnergyEfficient Multipath Routing in Wireless Sensor Networks,‟‟ Mobile Computing and Communications Review, Vol. 1, No. 2, 2002.
12. R. Kannan, et al., „„SensorCentric Quality of Routing in Sensor Networks,‟‟ Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom‟03), Vol. 1, Apr. 2003.
13. B. Krishnamachari, D. Estrin, S. Wicker, „„Modelling DataCentric Routing in Wireless Sensor Networks,‟‟ Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom‟02), New York, June 2002.
14. N. Bulusu et al., „„Adaptive Beacon Placement,‟‟ Proceedings of the 21st International Conference on Distributed Computing Systems (ICDCS‟21), Phoenix, AZ, Apr. 2001, pp. 489ff.
15. P. Bergamo, G. Mazzini, „„Localization in Sensor Networks with Fading and Mobility,‟‟ Proceedings of the 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC‟02), Vol. 2, Sept. 2002.
16. Q. Li et al., „„Distributed Algorithms for Guiding Navigation Across a Sensor Network,‟‟ Proceedings of the 9th ACM International Conference on Mobile Computing and Networking (MobiCom‟03), San Diego, CA, Sept. 2003.

4246

10.

Authors:

Roli Pradhan, KK Pathak, VP Singh

Paper Title:

Application of BPNN in the analysis of SBI’s Credit Capacity

Abstract: During the existing business scenario much need exists for a system that can predict the failure of any firm with accuracy much before the bankruptcy actually occurs. Credit decisions by commercial banks are based to a large extent on the financial statements provided by corporate borrowers as monitored using financial ratios suggesting their financial position. This paper uses the tailored backpropagation neural network endeavors to predict the financial ratios expressing the position of a firm to regulate the bankruptcy and assess the credit risks. It first estimates the financial ratio for a firm from 20012008 to the train the BPNN and uses the estimates of the year 2009 and 2010 values for the validation process. Finally it dwells to draw predictions for the period 20112015 and emphasizes the growing role of BPNN application based prediction models for banking sector with a case study of State Bank of India. We conclude with practical suggestions on how best to integrate models and research into policy making decisions. Along with establishing the ratios, analysis regarding the bankruptcy status of the firm is also analysed. The basic Z Score value of the firm from 20012008 has been used to predict the Z Score values upto 2015.
Keywords: Neural Networks, Credit lending, Credit Capacity, BPNN
References:
1. ALTMAN, EDWARD [1968]: "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, Vol 13, n.4, September, pp. 589609.
2. ALTMAN, EDWARD [1984]: "The success of business failure prediction models: an international survey", Journal of Banking and Finance, pp. 171198.
3. ATIYA, AMIR [2001]: ―Bankruptcy prediction for credit risk usingneural networks: a survey and new results‖, IEEE Transactions on Neural Networks, Vol. 12, n.4, July, pp. 929935.
4. BEAVER, WILLIAM H.; MCNICHOLS, MAUREEN F.; RHIE, JUNG WU [2005]: ―Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy‖, Review of Accounting Studies, n.10, pp. 93122.
5. BHARATH SREEDHAR T., SHUMWAY TYLER, (2008)‖ Forecasting Default with the Merton Distance to Default Model‖, The Review of Economic Times, Volume21, Issue3,Pp. 13391369.
6. CAMPBELL, STEVEN V. [1996]: ―Predicting bankruptcy reorganization for closely held firms‖, Accounting Horizons, Vol. 10, n. 3, September, pp. 1225.
7. COLLINS, ROBERT A.; GREEN, RICHARD [1982]: ―Statistical methods for bankruptcy forecasting‖, Journal of Economics and Business, n. 34, pp. 349354.
8. DEAKIN, EDWARD [1972]: "A discriminant analysis of predictors of business failure", Journal of Accounting Research, Spring , pp. 167179. • EDMINSTER, ROBERT O. [1972]: "An empirical test of financial ratio analysis for small business failure prediction", Journal of Financial and Quantitative Analysis, March, pp. 1477 1493.
9. GORDON, M. J. [1971]: "Towards a theory of financial distress", Journal of Finance, n. 26, May, pp. 347356.
10. HILLEGEIST, STEPHEN; KEATING, ELIZABETH; CRAM, DONALD, LUNDSTEDT, KYLE [2004]: ―Assessing the probability of bankruptcy‖, Review of Accounting Studies, n.9, pp. 534.
11. KEASEY, KEVIN; MC.GUINESS, PAUL [1990B]: ―The failure of U.K. industrial firms for the period 19761984, logistic analysis and entropy measures‖, Journal of Business, Finance and Accounting, Vol. 17, n. 1, Spring, pp. 119135.
12. MENSAH, WAW H. [1983]: ―The differential bankruptcy predictive ability of specific price level adjustments: some empirical evidence‖, The Accounting Review, Vol. LVIII, n. 2, April, pp. 228246.
13. MENSAH, YAW H. [1984]: "An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study", Journal of Accounting Research, Vol. 22, n.1, Spring, pp.380395.14. MEYER, PAUL A.; PIFER, HOWARD W. [1970]: "Prediction of bank failures", The Journal of Finance, Vol.25, n.4, September, pp. 853868.
15. MERTON, ROBERT C., "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates", Journal of Finance, Vol. 29, No. 2, (May 1974), pp. 449470
16. OHLSON, JAMES A. [1980]: "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, Vol 18, n.1, Spring, pp. 109131.
17. SCOTT, JAMES [1981]: ―The probability of bankruptcy‖, Journal of Banking and Finance, n. 5, pp. 317344.
18. SHUMWAY, TYLER [2001]: ―Forecasting bankruptcy more accurately: a simple hazard model‖, Journal of Business, Vol 74, n.1, pp.101124.
19. STEVENS, DONALD L. [1973]: ―Financial characteristics of merged firms: a multivariate analysis‖, Journal of Financial and Quantitative Analysis, March, pp. 149158.
20. WILCOX, JARROD W.[1971]: ―A simple theory of financial ratios as predictors of failure‖. Journal of Accounting Research, Autumn, pp. 389395.
21. ZHANG, GUOQIANG; HU, MICHAEL Y.; PATUWO, EDDY B.; INDRO, DANIEL C. [1999]: ―Artificial neuronal networks in bankruptcy prediction: general framework and cross validation analysis‖, European Journal of Operational Research, n. 116, pp. 1632.
22. ZMIJEWSKI, MARK E. [1984]: ―Methodological issues related to the estimation of financial distress prediction models‖, Journal of Accounting Research, Vol. 22, supplement, pp. 5982.

4754

11.

Authors:

Anand Tamrakar, Kamal K. Mehta

Paper Title:

Analysis of Effectiveness of Web based ELearning Through Information Technology

Abstract: Advancements of technology help in providing effective decisionmaking and problem solving in many aspects of life. Amongst the available technologies, Information Technology (IT) is one of the leading technologies in effectively utilizing the scarce resources to encounter the gap between solutions provided by existing methodologies and demands of society. Effectiveness of education system depends on the degree of quality it exhibits. Traditional learning models like classroom teaching and distance learning have their own limitations. It is increasingly becoming hard to maintain the standards of education due to limitations of infrastructure, finance and other resources including skilled manpower. Learning empowered through Information Technology (IT), to some extent ensures high quality learning by providing necessary information This project , study the effectiveness of elearning as it relates to the level of elearning experience It is clearly observed that, eLearning systems emphasise on quality and effective presentation of information.This paper has a hypothesis (regarding effectiveness of web based elearning and other learning methods) using a varying statistics and statistical methods performed on data. It includes a hypothesis statement and details for the performance of the hypothesis test on the data. The paper also includes an analysis of variance (ANOVA) computation for the data and an interpretation of the results.
Keywords: ANOVA, elearning, semantic web ,ontologies
References:
1. Welsh, E.T., C.R. Wanberg; K.G. Brawn and M.J. Simering, 2003. Elearning emerging uses, empirical results and future direction. International J. Training and Develop. 7(4): 245288.
2. Janicki, T., Steinberg, G. (2003). Evaluation of a computerSupported Learning System, Decision Sciences the Journal of Innovative Education, 1, 2 (Sept.), 203223.
3. Drucker, P. (2005), “Need to Know: Integrating eLearning with High Velocity Value Chains”, A Delphi Group White
Paper,http://www.delphigroup.com/pubs/whitepapers/20001213elearningwp. pdf
4. European Journal of Open, Distance and eLearning EURODL, Date of publication: 28.05.2009. http://www.eurodl.org/article=359 The Influence of Experience, Ability and Interest on elearning Effectiveness
5. Vrasidas, C., & McIsaac, M. (2000). Principles of pedagogy and evaluation of Web based learning, Educational Media International, 37(2), 105111.
6. Collis, B (1998). New didactics for university instruction: why and how? Computers & Education, 31 pp. 373393, Collis, B and Moonen, J (2001).
7. Collis, B.. (1998) New didactics for university instruction: why and how? Computers and Education, 31, 373393. Collis, B. & Anderson, R.E. (1994).
8. Barker, Ph. (2000),”Designing Teaching Webs: Advantages, Problems and Pitfalls. Educational Multimedia,
9. elearning  A Review of Literature Barron, Tom (2000a) “The future of digital learning.” elearning May/June 2000. Vol. 1, No.2, pp. 467. Barron, Tom (2000b)
10. 5 Jun 2010, Abernathy, D. J. (1998). The WWW of distance learning: Who does what and where? Training and Development 52 (9) p. 2930.
11. Reseach in Education ,Ninth Edition by John W.Best,James V.Kahn,second impression 2006.
12. Testing Group Di®erences using Ttests, ANOVA, and Nonparametric Measures, Department of sychology University of Alabama348 Gordon Palmer Hall,jan 2003 .

5559

12.

Authors:

R. HariKumar, T.Vijaya Kumar

Paper Title:

Estimation of Drowsiness and Classification of Epilepsy Risk Levels from EEG Signals Using Chaos Theory

Abstract: Chaos in nonlinear dynamical systems has become a widelyknown phenomenon and its presence has been identified in many different systems in virtually all the fields of science. In medical world, analyzing chaos in the brain and explore its dynamics is a challenging task to every individual. In this paper, an effective and a practical method for exploring such brain activities are studied. This paper relates a method to analyze an Electroencephalogram (EEG) using Correlation Dimension (CD) for drowsiness estimation in sleep onset and epilepsy. Dimension is a critical property since, it indicates how many independent state variables are required to reproduce the system dynamics in state space and this in turn indicates how many state variables should be included in a mathematical model of the system. Aside from this practical issue, the dimension is an indicator of the degree of "complexity" of a system and tracking any changes in dimension due to pathology or other manipulations to the system is a useful diagnostic criterion. For many chaotic systems, accurate calculation of the CD from measured data is difficult because of very slow convergence as the scale size is reduced. This paper proposes a method for collecting data at large scales, creating the time series and determining the possibility of constructing an attractor for establishing the deterministic character of dynamics of the underlying system.
Keywords: EEG Signals, Chaos, Sleep onset, Epilepsy, Correlation Dimension.
References:
1. T. Gasser, "General characteristics of the EEG as a signal in EEG Informatics‖, A Didactic Review of Methods and Applications Of EEG Data Processing, A. Remond, Ed. Amsterdam, The Netherlands: Elsevier, 1977, pp : 3756.
2. Obermaier (2001), ―Hidden markov Models for online classification of single trial EEG data‖, Pattern Recognition Letters, vol. 22, no.1299–1309,.
3. R.Harikumar, B.Sabarish Narayanan (2003), ―Fuzzy Techniques for Classification of Epilepsy risk level from EEG Signals‖, Proceedings of IEEE Tencon–2003, Bangalore, India, pp 209213.
4. Babloyantz A, Salazar JM, Nicolis C, ―Evidence of chaotic dynamics of brain activity during the sleep cycle‖, Phys Lett A 1985; 111: 152–156.
5. R. S. Shaw, ―The Dripping Faucet as a Model Chaotic System‖, Aerial Press, Santa Cruz, A (1985).
6. Takens, F. (1981), ― Dynamical Systems and Turbulence‖, Warwick, Lecture Notes in Mathematics, eds. Rand, D. A. & Young, L. S. (Springer, Berlin), Vol. 898, pp. 366381.
7. Mauricee.Cohen, Donnal. Hudson, Prakash C.Deedwania Etal (1996) ―Applying Continuous chaotic modeling to cardiac signal analysis‖, IEEE EMB magazine, PP 97102.
8. Russel , C Eberhart, ―Chaos Theory for Biomedical Engineer‖, IEEE EMB magazine, pp4145.
9. Leonidas D, Iasemidis & J. Chris Sackellares, ―Chaos Theory and Epilepsy‖, The Neuroscientist 2:118126, 1996
10. Hornero R, Alonso A, Jimeno N, Jimeno A., López M, ―Estimation of Correlation Dimension to Evaluate Cognitive Performance in Schizophrenic Patients Using a New Computer Technique‖, Nonlinear Dynamics, Psychology, and Life Sciences, Volume 03, Number 1, January 1999 , pp. 4963(15).
11. R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharthi, Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals, I.E . India Journal of Inter disciplinary panels, May 2005, 86(1):917.
12. JC Roux, RH Simoyi, HL Swinney (1983), Observation of a strange attractor‖. Physica D 8:157266.
13. M Casdagli, S Eubank, JD Farmer, J Gibson (1991), ―State space reconstruction in the presence of noise‖, Physica D 51:5298.
14. M Shelhamer, S Zalewski (2001), ―A new application for timedelay reconstruction: detection of fastphase eye movements‖, Physics Letters A 291:349354.
15. MB Kennel, R Brown, HD Abarbanel (1992), ―Determining embedding dimension for phasespace reconstruction using a geometrical construction‖, Physical Review A 45:34033411.
16. P Grassberger, I Procaccia (1983) ―Measuring the strangeness of strange attractors‖, Physica D 9:189208.
17. A Babloyantz, A Destexhe (1986), ―Lowdimensional chaos in an instance of epilepsy‖, Proceedings of the National Academy of Sciences of the USA 83:35133517.

6065

13.

Authors:

H.S. Behera

Paper Title:

Segmentation and Classification using Heuristic HRSPSO

Abstract: In this paper, a Heuristic Hybrid Rough Set Particle Swarm optimization (HRSPSO) Algorithm is proposed for partitioning a digital image into different segments that is more meaningful and easier to analyze segmentation andClassification. The heuristic HRSPSO algorithm has been implemented with a novel method in MATLAB platform considering 50 iterations and 20 particles. The experimental study and performance evaluation show that Heuristic HRSPSO optimization Algorithm is observed to be having optimal solution with smallest DB (DaviesBouldin) index and it converges after fifteenth iterations.
Keywords: Image Processing, Clustering, Image Segmentation, Classification, DaviesBouldin index, Rough Set, Particle Swarm Optimization, Hybrid Rough Set Theory, Image Pixel Classification, Fuzzy C Means (FCM).
References:
1. Pawlak, Z., Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht, 1991.
2. Davies, D.L., Bouldin, D.W., A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227, 1979.
3. Eberhart, R. C., Shi, Y.: Particle swarm optimization: Developments, applns and resources, In Proceedings of IEEE International Conference on Evolutionary Computation, vol. 1,8186, 2001.
4. Swagatam Das1, Ajith Abraham2 and Subir Kumar Sarkar1 “A Hybrid Rough Particle Swarm Algorithm for Image Pixel Clasification”
5. Kennedy, J, Eberhart R. Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, (1995) 19421948.
6. Jain, A.K., Murty M.N., and Flynn P.J. (1999): Data Clustering: A Review, ACM Computing Surveys, Vol 31, No. 3, 264323.
7. Trivedi M. M, Bezdek J. C, Lowlevel segmentation of aerial images with fuzzy clustering, IEEE Trans.on Systems, Man and Cybernetics, Volume 16, Issue 4 July,
1986.
8. L.O. Hall, I.B. Özyurt, J.C. Bezdek, Clustering with a genetically optimized approach, IEEE Trans. Evo Comp. 3 (2)(1999) 103–112.
9. Bandyopadhyay, S., and Maulik, U., (2002) Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition, 35, 11971208.
10. Lingras, P., West, C., Interval set clustering of Web users with rough kmeans. Technical Report No. 2002002, Department of Mathematics and Computer Science, St. Mary’s University, Halifax, Canada, 2002.

6669

14.

Authors:

Sumit Sahu, Bharti Dongre, Rajesh Vadhwani

Paper Title:

Web Spam Detection Using Different Features

Abstract: Spamming is any deliberate action solely in order to boost a web page’s position in search engine results, incommensurate with page’s real value. Web Spam is the Web pages that are the result of spamming. Web spam is the deliberate manipulation of search engine indexes. It is one of the search engine optimization methods. Implementing web spam on a search engine reduces the redundant and nondesirable results. In our paper we discuss the features which are responsible for web page ranking. We also discuss the results of the different classification techniques on our dataset which we process from the WEBSPAMUK2006 Dataset. We are also proposing a feature which will help in the web spam detection.
Keywords: web spam; feature selection; classification technique; N gram algorithm;
References:
1. LUCA BECCHETTI, CARLOS CASTILLO, DEBORA DONATO, RICARDO BAEZA YATES, STEFANO LEONARDI ―Link Analysis for Web Spam Detection‖
2. Manuel Egele , Clemens Kolbitsch , Christian Platzer, ―Removing web spam links from search engine results‖ in SpringerVerlag France 2009 J Comput Virol (2011) 7:51–62
3. Wei Wang , Guosun Zeng , Daizhong Tang ―Using evidence based content trust model for spam detection‖ in Expert Systems with Applications 37 (2010) 5599–5606, Science Direct.
4. JunLin Lin ―Detection of cloaked web spam by using tagbased methods‖ in Expert Systems with Applications 36 (2009) 7493–7499, Science Direct.
5. Luca Becchetti, Carlos Castillo, Debora Donato, Stefano Leonardi, and Ricardo Baeza Yates. Linkbased characterization and detection of web spam. In Proceedings of the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2006
6. C. Castillo, D. Donato, A. Gionis, V. Murdock, and F. Silvestri. Know your neighbors: web spam detection using the web topology. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 423–430, 2007.
7. Alexandros Ntoulas, Marc Najork, Mark Manasse, and Dennis Fetterly. Detecting spam web pages through content analysis. In Proceedings of the 15th International World Wide Web Conference (WWW), pages 83–92, Edinburgh, Scotland, 2006.
8. Gilad Mishne, David Carmel, and Ronny Lempel. Blocking blog spam with language model disagreement. In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), Chiba, Japan, 2005.
9. A. A. Benczúr, I. Bíró, and K. Csalogány. Detecting nepotistic links by language model disagreement. In Proceedings of the 15th International World Wide Web Conference (WWW), 2006.
10. GuangGang Geng, ChunHeng Wang, QiuDan Li, Lei Xu and XiaoBo Jin.‖ Boosting the Performance of Web Spam Detection with Ensemble UnderSampling Classification‖.
11. Manuel Egele, Christopher Kruegel, Engin Kirda ―Removing Web Spam Links from Search Engine Results‖.
12. Lourdes Araujo and Juan MartinezRomo ―Web Spam Detection: New Classification Features Based on Qualified Link Analysis and Language Models‖ in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 3, SEPTEMBER 2010.
13. Juan MartinezRomo, Lourdes Araujo. ―Retrieving Broken Web Links using an Approach based on Contextual Information‖.
14. J. Abernethy, O. Chapelle, and C. Castillo, ―Webspam identification through content and hyperlinks,‖ in Proc. Fourth Int. Workshop on Adversarial Information Retrieval on the Web (AIRWeb), Beijing, China, 2008, pp. 41–44.
15. András A. Benczúr, Károly Csalogány, Tamás Sarlós, Máté Uher―SpamRank – Fully Automatic Link Spam Detection Work in progress‖ in Proc. First Int. Workshop on Adversarial Information Retrieval on the Web (AIRWeb, Chiba, Japan, 2005, pp. 25–38
16. Jay M. Ponte and W. Bruce Croft, ―A Language Modeling Approach to Information Retrieval‖ in Proc. 21st Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR’98), New York, 1998, pp. 275–281, ACM.
17. C. Castillo, D. Donato, L. Becchetti, P. Boldi, S. Leonardi, M. Santini, and S. Vigna, ―A reference collection for web spam,‖ SIGIR Forum, vol. 40, no. 2, pp. 11–24, 2006.
18. P. Boldi, B. Codenotti, M. Santini, and S. Vigna ―UbiCrawler: a scalable fully distributed web crawler. Software, Practice and Experience‖, 34(8):711–726, 2004.
19. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue). Available: http://www.(URL)
20. J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com
21. (Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given. Available: http://www.(URL)
22. R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880. Available: http://www.halcyon.com/pub/journals/21ps03vidmar

7073

15.

Authors:

H. S. Hota, Ramesh Pratap Singh

Paper Title:

A minmax Approach for Improving the Accuracy of Effort Estimation of COCOMO

Abstract: This study proposed to extend the Constructive Cost Model (COCOMO) by incorporating the concept of minmax approach to estimation. Formal effort estimation models like Constructive Cost Model (COCOMO) are limited by their inability to manage uncertainties and imprecision surrounding software projects early in the development life cycle. A minmax approach is suggested to rectify data uncertainties and modeling errors. The proposed method of minmax is used to improve the accuracy of effort estimation of COCOMO and its result have been compared with the gradient descent, robust fuzzy clustering, kmean clustering methods of estimation. It has been observed that the proposed method have lowest and steady state absolute estimate error AE(k) and mean absolute estimate error MAE(k) for different value of k(time series) and different stepsize s.
Keywords: Sugeno fuzzy inference system, minmax method, Constructive Cost Model (COCOMO), effort estimation, Absolute Estimate Error, Mean Absolute Estimate Error.
References:
1. Steve McConnell. Rapid development: taming wild software schedules. Microsoft Press, 1996.
2. Mohit Kumar et al, A MinMax Approach to fuzzy clustering Estimation and Identification, IEEE Transaction on fuzzy system, vol.14, No. 2, PP 248262, 2006.
3. B.W. Boehm, Software Engineering Economics, Englewood Cliffs, NJ, Prentice Hall, 1981.
4. Iman Attarzadeh, Siew Hock Ow, Improving the Accuracy of Software Cost Estimation Model Based on a New Fuzzy Logic Model, World Applied Science, Vol. 8(2), pp. 177184, 2010.
5. C.S. Reddy and KVSVN Rao, Improving the accuracy of effort estimation through fuzzy set representation of size, Journal of Computer Science, Vol 5, pp 451455, 2009.
6. M. Jorgensen, B. Faugli, T. Gruschke, Characteristics of software engineers with optimistic prediction, Journal of Systems and Software, Vol. 80, Issue. 9, 2007, pp. 14721482.
7. C.L. Martin, J.L. Pasquier, M.C. Yanez, T.A. Gutierrez, Software Development Effort Estimation Using Fuzzy Logic: A Case Study, IEEE Proceedings of the Sixth Mexican International Conference on Computer Science (ENC‟05), 2005, pp. 113120.
8. M.T. Su, T.C.Ling, K.K.Phang, C.S.Liew, P.Y.Man, Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model, Malaysian Journal of Computer Science, Vol. 20, No. 2, 2007, pp. 199207.
9. A. Heiat, Comparison of artificial neural network and regression models for estimating software development effort, Information and Software Technology, Vol. 44, Issue 15, 2002, pp. 911922.
10. X. Huang, Danny Ho, J. Ren, L.F. Capretz, “Improving the COCOMO model using a neurofuzzy approach”, Applied Soft Computing , Vol.7, Issue 1, 2007, pp. 2940.
11. A. R. Gray, S. G. MacDonell, Applications of Fuzzy Logic to Software Metric Models for Development Effort Estimation, Fuzzy Information Processing Society 1997 NAFIPS’ 97, Annual Meeting of the North American, 21 – 24 September 1997, pp. 394 – 399.
12. P. Thrift, Fuzzy logic synthesis with genetic algorithms, in proc., 4th Int. Conf. on Genetic Algorithms, pp 509513, 1991.
13. Fei Z, Liu X (1992) fCOCOMO: fuzzy constructive cost model in software engineering. In: IEEE international conference on fuzzy systems, pp 331–337.
14. D. Simon, Design and rule base reduction of a fuzzy filter for the estimation of Motor currents, Int. Jou. of Approx. Reason, Vol. 25, pp 145167, 2000.
15. J. A. Roubos and M. Setnes, Compact fuzzy models through complexity reduction and evolutionary optimization, in proc. IEEE Int. Conf. fuzzy systems, San Antonio, TX, pp 762767, 2000.
16. J. S. R. Jang, ANFIS: Adaptivenetworkbased fuzzy inference systems IEEE Trans. Syst., Man, Cybernetics, Vol. 23, No. 3, pp 665685, 1993.
17. M. Setnes et al, Rulebased modeling precision and transparency, IEEE Trans. Syst. Man. Cybern. C. Apple. Rev., Vol. 28, No. 1, pp 165169, 1998.
18. J. S. R. Jang, C. T. Sun and E. Mizutani, Neurofuzzy and soft computing: a computational approach to learning and machine intelligence, Upper Saddle River, NJ: PrenticeHall 1997.
19. M. Kumar et al, Robust Adaptive Fuzzy Identification of TimeVarying Processes with Uncertain Data. Handling Uncertainties in the Physical Fitness Fuzzy Approximation with Real World Medical Data: An Application, Fuzzy Optimization and Decision making, Vol. 2, No.3, pp 243259, 2003.
20. J. C. Bezdeck, Pattern Recognition with fuzzy objective function algorithms, New York: Plenum, 1981.
21. L. X. Wang and J. M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. Syst., Man, Cybernetics, Vol. 22, No. 6, pp 14141427, 1992.
22. D. Nanck and R. Kruse, A Neurofuzzy approach to obtain interpretable fuzzy systems for function approximation, in proc. IEEE Int. Conf. fuzzy systems 1998 (FUZZIEEE‟98), Anchorage, AK, pp 11061111, May 1998.
23. Menzies, T., 2005. Promise software engineering repository. http://promise.site.uottawa.ca/SERepository/
24. B. Boehm, E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B. Steece, A. W. Brown, S. Chulani, and C. Abts, Software Cost Estimation with Cocomo II. Prentice Hall, 2000.

7479

16.

Authors:

Rita Yadav, Danvir Mandal

Paper Title:

Optimization of Artificial Neural Network for Speaker Recognition using Particle Swarm Optimization

Abstract: This paper proposes a particle swarm optimization (PSO) based optimization technique for Artificial Neural Network weights optimization for speaker recognition. PSO is a search algorithm, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The particle swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for optimization in that particle swarms will discover best optimized value as they fly within the subset space. Combining the ANN and PSO algorithms improves the performance as compared to that ANN alone.
Keywords: Artificial Neural Network (ANN), Feature Extraction, Matlab, Mel Frequency Cepstral Coefficient, Particle Swarm Optimization, Speaker Recognition.
References:
1. D.O. Shaughnessy, ―Speaker Recognition, ASSP Magazine, IEEE Signal Processing Magazine, Vol. 3, No. 4, Part. 1, pp. 417, October 1986.
2. J. P. Campbell, JR, “Speaker Recognition: A Tutorial”, Proceedings of the IEEE, Vol. 85, No. 9, September 1997.
3. A.E. Rosenberg, ―Automatic speaker verification: A review, Proc IEEE, vol. 64(4), pp. 47587, Apr. 1976.
4. H. Gish, and M.Schmidt, ―Textindepent speaker identification, IEEE Signal Process. Mag., vol. 18, pp.1832, Oct. 2002.
5. Sirko Molau, Michael Pitz, Ralf Schlu¨ ter, and Hermann Ney, ―Computing MelFrequency Cepstral Coefficients on the Power Spectrum‖ Lehrstuhl fu¨ r Informatik VI, Computer Science Department, RWTH Aachen – University of Technology, 52056 Aachen, Germany.
6. T. Kohonen, ―Selforganization and Associative Memory SpringerVerlag, Berlin New York, 1988a.
7. B.D. Ripley, ―Neural Networks and Related Methods for Classifications Journal of Royal Statistics Society, B56,pp. 409456,1994.
8. (PSO Tutorial), Xiaohui Hu. Available: http://www.swarmintelligence.org/tutorials.php
9. J. Kennedy and R. Eberhart, "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks (ICNN'95), Vol. IV, pp.19421948, Perth, Australia, 1995.
10. Campell J.P. and Jr., ―Speaker recognition: a tutorial Proceeding of the IEEE, Vol 85, pp. 14371462, 1997.
11. Y.Yan, M. Fanty, and R. Cole, "Speech Recognition Using Neural Networks with Forwardbackward Probability Generated Targets", Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Munich, April 1997.
12. Shukla, Anupam, Tiwari, Ritu, ―A novel approach of speaker authentication by fusion of speech and image features using Artificial Neural Networks, Int. J. of Information and Communication Technology 2008Vol.1,No.2 pp . 159 – 170.
13. R. P. Lippmann, ―Review of Neural Networks for Speech Recognition, Neural Computation, Vol. 1, No. 1, pp. 138, 1989.
14. Ehab F., M. F. Badran, Hany Selim “Speaker Recognition Using Artificial Neural Networks Based on Vowel phonemes Electrical Engineeling Department, Assiut University.
15. Zahorian, S. A., ―Reusable BinaryPaired Partitioned Neural Networks for TextIndependent Speaker Identification, Proc. ICASSP99, pp. II: 849 852, 1999.
16. Zebulum, R.S. Vellasco, M. Perelmuter, G. Pacheco, ‗A comparison of different spectral analysis models for speech recognition using neural networks Departamento de Engenharia Eletrica, PUC, Rio de Janeiro, Brazil; IEEE 1996.
17. A. Abraham, H. Guo, and H. Liu, ―Swarm intelligence: Foundations, perspectives and applications, swarm intelligent systems, in Studies in Computational Intelligence. Berlin, Germany: Springer Verlag, pp. 325, 2006.
18. www23.Homepage.Villanova.edu/Varadarajan…./PSO_meanderline .ppt

8084

17.

Authors:

Tarun Chopra, Jayashri Vajpai

Paper Title:

Classification of Faults in DAMADICS Benchmark Process Control System Using Self Organizing Maps

Abstract: This paper presents a new approach for classification of faults in a process control system with complex overlapping fault classes. It is based on the application of Self Organising Maps that possess the capability of efficient unsupervised learning. Using the SOM training process, the proposed approach derives a set of neurons by considering process monitoring dataset comprising of multiple measured attributes. This set of neurons constitutes the multilayered SOM, in which each neuron corresponds to a class of faults. The neurons with similar attribute values are spatially arranged in adjoining localities, to set up an exploratory linkage between the SOM and the fault dataset. The performance of the proposed method is found to be satisfactory for fault diagnosis in the DAMADICS Benchmark Process Control System, even for the overlapping fault classes that pose considerable difficulty to other classification approaches applied by researchers.
Keywords: Artificial Neural Networks, DAMADICS Benchmark Process Control System, Fault Diagnosis, Self Organizing Maps.
References:
1. T. Kohonen, “SelfOrganizing Maps,” SpringerVerlag Berlin, Heideberg, 1997.
2. Michal Bartys et al, “Introduction to the DAMADICS actuator FDI benchmark study” Control Engineering Practice 14 , pp. 577–596, 2006
3. Bocaniala et al, “On the applicability of stateoftheart fault diagnosis methodologies to simple and complex systems”, The annals of “DUNAREA DE JOS” University Of Galati Fascicle III, 2005 ISSN 1221454X, Electrotehnics, Electronics, Automatic Control, Informatics
4. Dustegor et al, “Structural analysis of fault isolability in the DAMADICS benchmark”, Control Engineering Practice 14 , pp. 597–608, 2006.
5. Puig et al, “Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 621–633, 2006.
6. Supavatanakul et al, “Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem”, Control Engineering Practice 14 , pp. 609–619, 2006.
7. Previdi et al , “Modelfree actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 635–644, 2006.
8. Bocaniala et al , “Application of a novel fuzzy classifier to fault detection and isolation of the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 653–699, 2006.
9. Uppal et al , “A neurofuzzy multiplemodel observer approach to robust fault diagnosis based on the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 699–717, 2006.
10. Witczak et al , “A GMDH neural networkbased approach to robust fault diagnosis: Application to the DAMADICS benchmark problem”, Control Engineering Practice 14 , pp. 671–683, 2006.
11. Calado et al , “FDI approach to the DAMADICS benchmark problem based on qualitative reasoning coupled with fuzzy neural networks”, Control Engineering Practice 14 , pp. 685–698, 2006.
12. Almeida et al , “Fault Detection and Diagnosis in the DAMADICS Benchmark Actuator System –A Hidden Markov Model Approach” Proceedings of the 17th World Congress, The International Federation of Automatic Control,Seoul, Korea, July 611, 2008.
13. Juha Vesanto,”Data Exploration Process Based on the Self−Organizing Map”, Mathematics and Computing Series No. 115, 1999.
14. Vesanto J., Himberg J., Alhoniemi E., Parhankangas J., “Selforganizing map in Matlab: the SOM Toolbox”, In Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 3540, November 1617, 1999.

8590

18.

Authors:

Siddesh.G.K, K.N.Muralidhara, Manjula.N.Harihar

Paper Title:

Routing in Ad Hoc Wireless Networks using Soft Computing techniques and performance evaluation using Hypernet simulator

Abstract: An adhoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination, due to the limited range of each mobile host’s wireless transmissions. Much effort has gone into mobile adhoc network (MANET) research over the past decade. Yet, even today, mobile adhoc networking is seen as a relatively new area of research. The reason for this can be traced to the fact that the maturity in truly understanding these networks is still alarmingly low and actual deployment of these networks rare. This paper presents a protocol for routing in ad hoc networks that uses soft computing techniques like neural networks, fuzzy logic and genetic algorithm. The simulation has been performed using hyper net simulator for various existing protocols like proactive routing , reactive routing, power aware routing protocol, hybrid routing . Our protocol uses soft computing techniques protocol for establishing the link between the nodes in minimum time. The results of our experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a large extent. The simulation results are obtained using hypernet simulator and are also compared with the results obtained using NS2.
Keywords: Routing Protocol, MANET, Neural Nets, Fuzzy Logic, Genetic Algorithm, Soft Computing, hyper net.
References:
1. F. Baker, "An outsider's view of MANET," Internet Engineering Task Force document ,17 March 2002.
2. C. Barrett et al., "Characterizing the Interaction Between Routing and MAC Protocols in Adhoc Networks," Proc. MobiHoc 2002 , pp. 92103
3. J. Broch et al., "A Performance Comparison of MultiHop Wireless Ad Hoc Network Routing Protocols," Proc. Mobicom '98.
4. D. Cavin et al., "On the accuracy of MANET simulators," Proc. ACM Workshop on Princ. Mobile Computing (POMC'02), Oct. 2002, pp. 3843.
5. K.W. Chin, et al., "Implementation Experience with MANET Routing Protocols," ACM SIGCOMM Computer Communications Review, Nov. 2002, pp. 4959.
6. M. S. Corson et al., "InternetBased Mobile Ad Hoc Networking," IEEE Internet Computing, JulyAugust 1999, pp. 6370
7. C. Elliott and B. Heile, "SelfOrganizing, SelfHealing Wireless Networks," Proc. 2000 IEEE Int'l Conf. on Personal Wireless Comm., pp. 355362.
8. L. M. Feeney, "A Taxonomy for Routing Protocols in Mobile Ad Hoc Networks," Swedish Institute of Computer Science Technical Report T99/07, October 1999.
9. M. Frodigh, et al, "Wireless Ad Hoc Networking: The Art of Networking without a Network," Ericsson Review, No. 4, 2000.
10. David F. Bantz and Fr´ed´eric J. Bauchot. Wireless LAN design alternatives. IEEE Network, 8(2):43–53, March/April 1994.
11. Vaduvur Bharghavan, Alan Demers, Scott Shenker, and Lixia Zhang. MACAW: A media access protocol for wireless LAN’s. In Proceedings of the SIGCOMM ’94 Conference on Communications Architectures, Protocols and Applications, pages 212–225, August 1994.
12. Robert T. Braden, editor. Requirements for Interne thosts—communication layers. Internet Request For Comments RFC 1122, October 1989.

9197

19.

Authors:

Tarun Chopra, Jayashri Vajpai

Paper Title:

Fault Diagnosis in Benchmark Process Control System Using Stochastic Gradient Boosted Decision Trees

Abstract: Decision trees create an easily understood structure for evaluating complex decisions. Tree Boost models often have a degree of accuracy that cannot be obtained using a large, singletree model. Tree Boost models are adaptable, easy to interpret and often equal to or superior to any other predictive functions including neural networks. In this paper, the performance of the proposed approach based on Stochastic Gradient Boosted Decision Trees based method is demonstrated on the DAMADICS benchmark problem. An attempt has been made to improve the performance of fault diagnosis task on DAMADICS benchmark.
Keywords: Fault Diagnosis, Stochastic Gradient Boosted Decision Trees, DAMADICS
References:
1. Friedman, Jerome H., “Greedy Function Approximation: A Gradient Boosting Machine” Technical report, Dept. of Statistics, Stanford University. 1999.
2. S.M.Weiss and C.A. Kulikowski, “Computer Systems that Learn: Classification and Prediction Methods from Statistics”, Neural Nets, Machine Learning, and Expert Systems, 1991.
3. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J.Stone. “Classification and Regression Trees”. Pacific Grove: Wadsworth, 1984.
4. M. Mehta, R. Agrawal, and J. Rissanen.,“SLIQ: A fast scalable classifier for data mining” In Proc. Of EDBT, 1996.
5. T.S. Lim, W.Y. Loh, and Y.S. Shih, “An empirical comparison of decision trees and other classification methods” TR 979, Department of Statistics, UW Madison, June 1997.
6. Quinlan, J. R., “Induction of decision trees. In Machine Learning” pp. 81–106, 1986.
7. Chen,K.,LU,R.,Wong,C.K.,Sun,G.,Heck,L., and Tseng,B.L. Trada, “Tree based ranking function adaptation”. In CIKM, pp. 1143– –1152, 2008
8. Zheng,Z.,Chen,K.,Sun,G., and Zha, H. “A regression framework for learning ranking functions using relative relevance judgments”. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval , pp. 287–294,2007.
9. Friedman, J. H., “Stochastic gradient boosting”. Comput. Stat. Data Anal. 38,4 , pp. 367–378, 2002.
10. Michal Bartys et al, “Introduction to the DAMADICS actuator FDI benchmark study” Control Engineering Practice 14 , pp. 577–596, 2006
11. Jan M. Koscielnya, Micha Bartys, Pawe Rzepiejewski Jose Sa da Costa, “Actuator fault distinguishability study for the DAMADICS benchmark problem “, Control Engineering Practice 14 , pp. 645652, 2006
12. Phillip H. Sherrod, “DTREG Predictive Modeling Software”, 2003.

98101

20.

Authors:

Sridevi.V, Jayanthy.T

Paper Title:

Design of Active Filters using CNTFET Opamp

Abstract: The main objective of this paper is to describe the work on modeling, performance benchmarking for nanoscale devices and circuits using Carbon Nano Tube Field Effect Transistors (CNTFETs), with the aim of guiding nanoscale device and circuit design. First, a compact model has been developed for MOSFET like Carbon Nanotube Field Effect Transistor (CNTFET) which posses high fabrication feasibility and superior device performance. The design aspects of a high performance CNTFET based operational amplifier are presented. This CNTFET opamp is used for the realization of various active filters and the simulation studies have been done using Hspice.
Keywords: Active Filter, Carbon Nanotube Field Effect Transistor, Hspice, MOSFET, Op amp.
References:
1. Arijit Raychowdhury, Saibal Mukhopadhyay and Kaushik Roy, ―Modeling of Ballistic Carbon Nanotube Field Effect Transistors for Efficient Circuit Simulation‖, ICCAD’03,pp.487 490, November 1113, 2003.
2. H.S. P. Wong, ―Beyond the Conventional Transistor, IBM J. Research & Development, March/May, pp. 133168, 2002.
3. T. Skotnicki, J. A. Hutchby, T.J. King, H.S. P. Wong, F. Beouff, ―The Road to the End of CMOS Scaling, IEEE Circuits and Devices Magazine, pp. 16 – 26, 2005.
4. Peng, N., H. Li and Q. Zhang, 2009. ―Nano Scale contacts between carbon Nanotubes and metallic Pads,‖ Acsnano, 10: 101625. 11.
5. Tans, S., A. Verschaerea and C. Dekker, ―Room temperature transistor based on a SWCN,‖ Science, 397: 4952, 1998.
6. Martel, R., T. Schmidt, H. Shea and T. Avouris, ―Sand MWCNTFET Si transistors,‖ Applied physics Letter, 73: 24 4749, 1998.
7. S. Heinze, J. Tersoff, R. Martel, V. Derycke, J.Appenzeller, and P.Avouris, ―Carbon nanotubes as Schottky barrier transistors, Phys. Rev.Lett., vol. 89, no. 10, pp. 106 8011–106 8014, 2002.
8. Fabien Pr´egaldiny et.al.,―Design Oriented Compact Models for CNTFETs, IEEE Trans. Elec.dev., 2006.
9. A. Bahari, G. Arasteh and S. Taghizadeh, NanometerScaled Contacts in CNTFETs, World Applied Sciences Journal 7 (Supplement 1):1821, 2009
10. J. Deng and H.S P. Wong, ―A Compact SPICE Model for Carbon Nanotube Field Effect Transistors Including NonIdealities and Its Application — Part I: Model of the Intrinsic Channel Region, Submitted to IEEE Transactions on Electron Devices, 2007.
11. J. Deng and H.S P. Wong, ―A Compact SPICE Model for Carbon Nanotube Field Effect Transistors Including NonIdealities and Its Application — Part II: Full Device Model and Circuit Performance

102106

21.

Authors:

Phalgun Pandya, Mandeep Singh

Paper Title:

Morphology Based Approach To Recognize Number Plates in India

Abstract: Automatic number plate recognition is a realtime embedded system which automatically recognizes the license number of vehicles. Such systems require the localization of number plate area in order to identify the characters present on it. This paper presents an approach based on simple but efficient morphological opening and closing operations for localization of Indian number plates. After localization the skew correction of the number plate is also done for effective segmentation of characters. The number plate skew correction greatly affects the accuracy of the character extraction. The character extraction is based on template matching approach. Our proposed algorithm has been tested on 100 samples and is found to be robust to detect vehicle license plates as well as extracting the numbers with an overall accuracy of 90%.
Keywords: Character Recognition, Template Matching, Morphology, LPR, ANPR.
References:
1. Prathamesh Kulkarni, Ashish Khatri, Prateek Banga, Kushal Shah “A Feature Based Approach for Localization of Indian Number Plates” IEEE International Conference on Information Technology, pp: 157162, 2009.
2. Velappa Ganpathy and Wen Lik Dennis LUI “A Malaysian Vehicle License Plate Localization and Recognition System” Journal of Systemics, Cybernetics and Informatics, ISSN 16904524, Vol. 6, No. 1, 2008.
3. P. V. Suryanarayana, Suman K. Mitra, Asim Banerjee and Anil K. Roy “A Morphology Based Approach for car license Plate” IEEE Indicon 2005 Conference, Chennai, India, 1113 Dec. 2005.
4. C.Nelson Kennedy Babu, Krishnan Nallaperumal “A License Plate Localization using Morphology and Recognition” In Proceedings of India Conference, Volume 1, pp.34 – 39, 2008.
5. Yassin M. Y. Hasan and Lina J. Karam, “Morphological text extraction from images” IEEE Trans. Image Process. 9(11), pp. 1978–83, 2000.
6. JuiChen Wu · JunWei Hsieh · YungSheng Chen. “Morphology Based text line Extraction” Machine Vision and Applications, pp.195–207, 2008.
7. Kaushik Deb, Andrey Vavilin, JungWon Kim, and KangHyun Jo “Vehicle License Plate Tilt Correction Based on the Straight Line Fitting Method and Minimizing Variance of Coordinates of Projection Points” International Journal of Control, Automation, and Systems 8(5):, pp. 975984, 2010.
8. N. Otsu, “A Threshold Selection Method from Gray Level Histograms”. IEEE Transactions on Systems, Man, And Cybernetics, vol. 9, pp.6266, 1979.
9. Leonard G. C. Hamey, Colin Priest “Automatic Number Plate Recognition for Australian Conditions” Proceedings of the Digital Imaging Computing Techniques And
Applications(DICTA) 2005
10. Shishir Kumar, Shashank Agrawal and Kumar Saurabh “License Plate recognition system for indian vehicles” International Journal of Information Technologyand Knowledge Volume 1 no.2 Management JulyDecember 2008
11. Hanchuan Peng, Fuhui Long, and Zheru Chi, “Document Image Recognition Based on Template Matching of Component Block Projections” IEEE Transactions On Pattern Analysis And Machinev Intelligence, Vol. 25, No. 9,September 2003
12. M.I. Khalil. ”Car plate Recognition Using Template matching Method” International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010
13. Rule no.50,51,76 and 77, Indian Central Motor Vehicle Act,1989
14. George Nagy “At the Frontiers of OCR”. Proceedings of the IEEE,vol.80 no.7, July 199215. Kumar Parasuraman, P.Vasantha Kumar “An Efficient Method for Indian Vehicle License Plate Extraction and Character Segmentation” IEEE International Conference on Computational Intelligence and Computing Research, 2010.
16. Gonzalez R. C., Woods R. E., and Eddins S. L., “Digital Image Processing ”, 3rd edition, Pearson Education(Singapore) Pvt. Ltd, 2009.

107112

22.

Authors:

Abdullah Al Masud, Hossain Md. Shamim, Amina Akhter

Paper Title:

Performance Analysis of AQM Schemes in Wired and Wireless Networks based on TCP flow

Abstract: TCP is the main protocol that carried the traffic in a reliable way. IP based network always facing congestion because of increasing traffic. To control congestion and implement QOS in TCP we perform a comparative study between different active queue management techniques. Queue management is an important part to provide for better utilization of buffer. Our objective is to perform a comparative study of RED, DropTail, REM and PI in wired and wireless network based on the number of TCP flows and how they perform in congested link.
Keywords: AQM, DROPTAIL, PI, RED, REM, TCP.
References:
1. B. Braden, D. Clark, J. Crowcroft, B. Davie, S. Deering, D. Estrin, S. Floyd, V. Jacobson, G. Minshall, C. Partridge, L. Peterson, K. Ramakrishnan, S. Shenker, J. Wroclawski, and L. Zhang. RFC 2309: ―Recommendations on queue management and congestion avoidance in the Internet‖, Apr. 1998.
2. D. Clark and W. Fang. ―Explicit allocation of best effort packet delivery service‖. IEEE/ACM Transactions on Networking, 6(4):362–373, 1998.
3. S. Floyd and V. Jacobson. ―Random Early Detection Gateways for Congestion Avoidance. IEEE/ACM Transactions on Networking, 1(4):397–413, Aug. 1993.
4. T. Bonald, et al., ―Analytic Evaluation of RED Performance, in Proceedings of IEEE INFOCOM, 2000.
5. S. Floyd, R. Gummdi, and S. Shenker, ―Adaptive RED : An algorithm for increasing the robustness of RED,‖
6. Athuraliya, S.—Li, V. H.—Low, S. H.—Yin, Q.: ―REM: Active Queue Management‖. IEEE Network, Vol. 15, Issue 3, pp. 48–53, May 2001.
7. Hollot, C. V.—Misra, V.—Towsley, D.—Gong, W.: ―On Designing Improved Controllers for AQM Routers Supporting TCP Flows. In Proceedings of IEEE INFOCOM 2001, Vol. 3, 2001, pp. 1726–1734, Anchorage, AK.
8. Archan Misra, Teunis Ott, and John Baras, ―The window distribution of multiple TCPs with random loss queues,‖ in Proceedings of IEEE GLOBECOM ’99, Nov. 1999.
9. Claudio Casetti and Michela Meo, ―A new approach to model the stationary behavior of TCP connections,‖ in Proceedings of IEEE INFOCOM 2000, Mar. 2000.
10. Robert Morris, ―TCP behavior with many flows,‖ in Proceedings of IEEE International Conference on Network Protocols, October 1997.

113117

23.

Authors:

M.Suneetha, S. Sameen Fatima

Paper Title:

Corpus based Automatic Text Summarization System with HMM Tagger

Abstract: The rapid growth of the data in the Internet has overloaded the user with enormous amounts of information which is more difficult to access huge volumes of documents. Automatic text summarization technique is an important activity in the analysis of high volume text documents. Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. In this paper a frequent term based text summarization technique with HMM tagger is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The model consists of four stages. In first stage, the system decomposes the given text into its constituent sentences, assigning the POS (tag) for each word in the text and stores the result in a table. The second stage removes the stop words, stemming the text and applying lemmatization. Feature term identification is done in third stage. Finally each sentence is ranked depending on feature terms. This stage reduced the amount of the sentences in the summary in order to produce a qualitative summary.
Keywords: Text Summarization, HMM Tagger, Brown Corpus, POS tagging.
References:
1. Vishal Gupta , Gurpreet Singh LehalKuceral., A Survey of Text Summarization Extractive, Journal of Emerging Technologies in Web Intelligence, vol. 2, no. 3, august 2010
2. TechniquesComputational Analysis of PresentDay American English. Brown University Press, Providence, RI
3. Hongyan Jing, Sentence Reduction for Automatic Text Summarization, Proceedings of the sixth conference on Applied natural language processing, Seattle, Washington, pp.310 – 315, 2000.
4. Kai ISHIKAWA et. al.; “Trainable Automatic Text Summarization Using Segmentation of Sentence”; Multimedia Research Laboratories, NEC Corporation 41Miyazaki MiyamaekuKawasakishi Kanagawa 2168555, 2003.
5. Church, K.W., A Stochastic parts program and noun phrase parser for unrestricted text. Proceedings 1st Conference on Applied Natural Language Processing, ANLP, pp. 136–143. ACL, 1988.
6. Brill, E. A Simple RuleBased Partofspeech Tagger. Proceedings 3rd Conference on Applied Natural Language Processing, ANLP, pp. 152–155. ACL, 1992.
7. Brill, E. Automatic Grammar Induction and Parsing Free Text: A Transformation based Approach. Proceedings 31st Annual Meeting of the Association for Computational Linguistics, 1993
8. Brill, E. Transformation–based error–driven learning and natural language processing: A case study in artofspeech tagging. Computational Linguistics 21(4): 543–565. 1995a
9. Daelemans, W., Zavrel, J., Berck, P. and Gillis, S.MBT: A memorybased partofspeech tagger generator. Proceedings 4th Workshop on Very Large Corpora, pp. 14–27. Copenhagen, Denmark, 1996.
10. Goldstein, J., et al.: Summarizing Text Documents: Sentence Selection and Evaluation Metrics. In: Proceedings of ACM SIGIR Conference 1999.
11. Ratnaparkhi, A. A maximum entropy partofspeech tagger. Proceedings 1st Conference on Empirical Methods in Natural Language Processing, EMNLP, 1996.
12. L. Bahl and R. L. Mercer, PartOfSpeech assignment by a statistical decision algorithm, IEEE International Symposium on Information Theory, pages: 88  89, 1976.
13. D. Cutting, J. Kupiec, J. Pederson and P. Sibun, A practical PartOfSpeech Tagger, In proceedings of the Third Conference on Applied Natural Language Processing, pages: 133  140, ACL, Trento, Italy, 1992.
14. Brown Tagset, available online at: http://www.scs.leeds.ac.uk/amalgam/tagsets/brown. html
15. Edmundson, H.P. New Methods in Automatic Extraction. Journal of the ACM 16(2), 264–285, 1968.
16. Ferranpla and Antoniomol i n a, Improving partofspeech tagging using lexicalized HMMs, Cambridge University Press, Natural Language Engineering 10 (2): 167–189, 2004
17. Rafeeq AlHashemi, Text Summarization Extraction System (TSES) Using Extracted Keywords, International Arab Journal of eTechnology, Vol. 1, No. 4, June 2010 pp 164168
18. I. Mani and M. Maybury. Advances in Automatic Text Summarization. MIT Press, ISBN 0262133598, 1999.
19. Marcus, M. P., Marcinkiewicz, M. A. and Santorini, B. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19(2), 1993.
20. Cutting, D., Kupiec, J., Pederson, J. and Sibun, P. A practical partofspeech tagger. Proceedings 3rd Conference on Applied Natural Language Processing, ANLP, pp. 133–140. ACL, 1992.

118123

24.

Authors:

Tony Gladvin George, N.Malmurugan

Paper Title:

UEP based on Proximity Pilot Subcarriers in OFDM

Abstract: A novel UnequalErrorProtection method is proposed that utilizes the subcarrier positions relative to pilot subcarriers in an OFDM multicarrier frame. With the available physical layer techniques, a prioritized encoding strategy based on the characteristics of the channel fading effects on the data subcarriers, those are in close proximity to the pilot subcarriers, for layered video is developed. The strategy is to efficiently map the bit streams of various priorities into the subcarriers with assisted information on their individual error recovery probability. The proposed technology maintains a minimum QoS for all periods outside outage since the high priority layer is guaranteed to be transmitted under BER constraints. At lower SNR scenarios this difference between the pilot proximate data subcarriers are more distinctive.
Keywords: Video Transmission, Unequal Error Protection, Proximity Pilot Subcarriers, OFDM.
References:
1. J. Hagenauer, “Ratecompatible punctured convolutional codes (RCPC codes) and their applications,” IEEE Trans. Commun., vol. 36, no. 4, pp. 389400, April 1998.
2. L. P. Kondi, F. Ishtiaq, and A. K. Katsaggelos, “Joint sourcechannel coding for scalable video,” in Proc. SPIE Conf. On Image and Video Communications and Processing'00, San Jose, CA, USA, April 2000, pp. 324335.
3. N. Thomos, N. V. Boulgouris, and M. G. Strintzis, “Wireless image transmission using turbo codes and optimal unequal error protection,” IEEE Trans. Image Processing, vol. 14, no. 11, pp. 18901901, November 2005.
4. M. Khansari, A. Jalali, E. Dubois, and P. Mermelstein, “Low bit rate video transmission over fading channels for wireless microcellular systems,” IEEE Trans. Circuits Syst. Video Technol., vol. 6, no. 1, pp. 111, February 1996
5. S. O'Leary, “Hierarchical transmission and COFDM systems,” IEEE Trans. Broadcast., vol. 43, no. 2, pp. 166174, June 1997.
6. H. Zheng and K. J. R. Liu, “Robust image and video transmission over spectrally shaped channels using multicarrier modulation,” IEEE Trans. Multimedia, vol. 1, no. 1, pp. 88103, March 1999.
7. ETSI, Digital Video Broadcasting (DVB); Framing structure, channel coding and modulation for digital terrestrial television, EN 300 744 V1.5.1., November 2004
8. A. Albanese, J. Blomer, J. Edmonds, M. Luby, and M. Sudan, “Priority Encoding Transmission,” IEEE Trans. Inform. Theory, vol.42, pp.17371744, Nov. 1996.
9. R. Puri and K. Ramchandran, “Multiple Description Source Coding through Forward Error Correction Codes,” in Proc. Asilomar Conference on Signals, Systems, and Computers, Asilomar, CA, Oct. 1999.
10. N. Thomos, N.V. Boulgouris, and M.G. Strintzis, “Wireless Image Transmission Using Turbo Codes and Optimal Unequal Error Protection” IEEE Trans. Image Proc., vol. 14, no. 11, pp. 18901901, Nov. 2005.
11. C. Berrou and A. Glavieux, “Near Optimum Error Correcting Coding and Decoding: Turbo Codes,” IEEE Trans. Commun., vol.44, no. 10, pp. 12611271, Oct. 1996.
12. Yanzhuo Ma, Yilin Chang, “A CrossLayer H.264/AVC Video Transmission Method Over Wireless OFDM”, IEEE Cross Layer Design, 2007. IWCLD '07. International Workshop on Issue Date: 2021 Sept. 2007
13. Yan sun, Xiaowen Wang, K.J. Ray Liu, “A Joint Channel Estimation and Unequal Error Protection Scheme for Video Transmission in OFDM Systems”, Proceedings. 2002 International Conference on Image Processing. Volume 1, 2225 Sept. 2002 Page(s):I549  I552

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