International Journal of Innovative Technology and ... · Director of SVS Group of Institutions,...

International Journal of Innovative

Technology and Exploring Engineering

ISSN : 2278 - 3075Website: www.ijitee.org

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Exploring Innovation

www.ijitee.org

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EXPLORING INNOVA

TION

Volume-2 Issue-6, MAY 2013

Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd. Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.

Editor In Chief

Dr. Shiv K Sahu Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT)Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal(M.P.), India Dr. Shachi Sahu Ph.D. (Chemistry), M.Sc. (Organic Chemistry)Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal(M.P.), India Vice Editor In Chief

Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran Prof.(Dr.) Anuranjan Misra Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University, Noida (U.P.), India Chief Advisory Board

Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Dr. Uma Shanker Professor & Head, Department of Mathematics, CEC, Bilaspur(C.G.), India Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea Dr. Vinita Kumari Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., India Dr. Kapil Kumar Bansal Head (Research and Publication), SRM University, Gaziabad (U.P.), India Dr. Deepak Garg Professor, Department of Computer Science and Engineering, Thapar University, Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of TechniEducation (ISTE), Indian Science Congress Association Kolkata. Dr. Vijay Anant Athavale Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India Dr. T.C. Manjunath Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru Dr. Kosta Yogeshwar Prasad Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, RajkotGujarat, India Dr. Dinesh Varshney Director of College Development Counceling, Devi Ahilya University, Indore (MUniversity, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India Dr. P. Dananjayan Professor, Department of Department of ECE, Pondicherry Engineering College, P Dr. Sadhana Vishwakarma Associate Professor, Department of Engineering Chemistry, Technocrat Institute of Technology, Bhopal(M.P.), India Dr. Kamal Mehta Associate Professor, Deptment of Computer Engineering, Institute of Technology, Dr. CheeFai Tan Faculty of Mechanical Engineering, University Technical, Malaysia Melaka, Malaysia Dr. Suresh Babu Perli Professor & Head, Department of Electrical and Electronic Engineering, Narasaraopeta E

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT) Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal(M.P.), India

Chemistry) Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal(M.P.), India

Professor, Faculty of Civil Engineering, University of Tabriz, Iran

Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University,

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Professor & Head, Department of Mathematics, CEC, Bilaspur(C.G.), India

Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea

Intelligence Engineering & Sciences Publication Pvt. Ltd., India

Head (Research and Publication), SRM University, Gaziabad (U.P.), India

Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India, Senior Member of IEEE, Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of TechniEducation (ISTE), Indian Science Congress Association Kolkata.

Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India

Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India

Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, Rajkot-Morbi Highway, Gauridad, Rajkot,

Director of College Development Counceling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India

Professor, Department of Department of ECE, Pondicherry Engineering College, Pondicherry,India

of Engineering Chemistry, Technocrat Institute of Technology, Bhopal(M.P.), India

Associate Professor, Deptment of Computer Engineering, Institute of Technology, NIRMA University, Ahmedabad (Gujarat), India

, University Technical, Malaysia Melaka, Malaysia

Head, Department of Electrical and Electronic Engineering, Narasaraopeta Engineering College, Guntur, A.P., India

Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal(M.P.), India

Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University,

Patiala (Punjab), India, Senior Member of IEEE, Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of Technical

Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India

560045, Karnataka, India

Morbi Highway, Gauridad, Rajkot,

.P.), Professor, School of Physics, Devi Ahilya University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India

ondicherry,India

of Engineering Chemistry, Technocrat Institute of Technology, Bhopal(M.P.), India

NIRMA University, Ahmedabad (Gujarat), India

ngineering College, Guntur, A.P., India

Technical Advisory Board

Dr. Mohd. Husain Director MG Institute of Management & Technology, Banthara, Lucknow (U.P.), India Dr. T. Jayanthy Principal, Panimalar Institute of Technology, Chennai (TN), India Dr. Umesh A.S. Director, Technocrats Institute of Technology & Science, Bhopal(M.P.), India Dr. B. Kanagasabapathi Infosys Labs, Infosys Limited, Center for Advance Modeling and Simulation, Infosys Labs, Infosys Limited, Electronics City, Bangalore, India Dr. C.B. Gupta Professor, Department of Mathematics, Birla Institute of Technology & Sciences, Pilani (Rajasthan), India Dr. Sunandan Bhunia Associate Professor & Head,, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India Dr. Jaydeb Bhaumik Associate Professor, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, Dr. Rajesh Das Associate Professor, School of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India Dr. Mrutyunjaya Panda Professor & Head, Department of EEE, Gandhi Institute for Technological Development, Bhubaneswar, Odisha, India Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lu Dr. Haw Su Cheng Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya Dr. Hossein Rajabalipour Cheshmehgaz Industrial Modeling and Computing Department, Faculty of Computer Science and InformatiMalaysia (UTM) 81310, Skudai, Malaysia Dr. Sudhinder Singh Chowhan Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India Dr. Neeta Sharma Professor & Head, Department of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India Dr. Ashish Rastogi Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India Dr. Santosh Kumar Nanda Professor, Department of Computer Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa), India Dr. Hai Shanker Hota Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India Dr. Sunil Kumar Singla Professor, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India Dr. A. K. Verma Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India Dr. Durgesh Mishra Chairman, IEEE Computer Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India Dr. Xiaoguang Yue Associate Professor, College of Computer and Information, Southwest Forestry University, Kunming

Director MG Institute of Management & Technology, Banthara, Lucknow (U.P.), India

Panimalar Institute of Technology, Chennai (TN), India

Technology & Science, Bhopal(M.P.), India

Infosys Labs, Infosys Limited, Center for Advance Modeling and Simulation, Infosys Labs, Infosys Limited, Electronics City,

tics, Birla Institute of Technology & Sciences, Pilani (Rajasthan), India

Associate Professor & Head,, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West

Associate Professor, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal,

Associate Professor, School of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India

Professor & Head, Department of EEE, Gandhi Institute for Technological Development, Bhubaneswar, Odisha, India

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lu

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya

Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi

Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India

nt of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India

Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India

Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa),

Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India

nt of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India

Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India

Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis

Associate Professor, College of Computer and Information, Southwest Forestry University, Kunming

Infosys Labs, Infosys Limited, Center for Advance Modeling and Simulation, Infosys Labs, Infosys Limited, Electronics City,

tics, Birla Institute of Technology & Sciences, Pilani (Rajasthan), India

Associate Professor & Head,, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West

Associate Professor, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

Associate Professor, School of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India

Professor & Head, Department of EEE, Gandhi Institute for Technological Development, Bhubaneswar, Odisha, India

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya

on Systems, Universiti Teknologi

Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India

nt of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India

Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa),

nt of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India

Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India

Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis

Associate Professor, College of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China

Dr. Nirmala Mungamuru Associate Professor, Department of Computing, School of Engineering, Adama Science and Technology University, Ethiopia Dr. Srilalitha Girija Kumari Sagi Associate Professor, Department of Management, Gandhi Institute of Technology and Management Dr. Vishnu Narayan Mishra Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev DumRoad, Surat (Gujarat), India Dr. Yash Pal Singh Director/Principal, Somany (P.G.) Institute of Technology & Management, Garhi Bolni Road , Rewari Haryana, India. Dr. Sripada Rama Sree Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh. India. Dr. Rustom Mamlook Associate Professor, Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman. Middle East. Managing Editor

Mr. Jitendra Kumar Sen International Journal of Innovative Technology and Exploring Engineering Editorial Board

Dr. Saeed Balochian Associate Professor, Gonaabad Branch, Islamic Azad University, Gonabad, Iratan Dr. Mongey Ram Associate Professor, Department of Mathematics, Graphics Era University, Dehradun, India Dr. Arupratan Santra Sr. Project Manager, Infosys Technologies Ltd, Hyderabad (A.P.) Dr. Ashish Jolly Dean, Department of Computer Applications, Guru Nanak Khalsa Institute & Management Studies, Yamuna Nagar (Haryana), India Dr. Israel Gonzalez Carrasco Associate Professor, Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain Dr. Guoxiang Liu Member of IEEE, University of North Dakota, Grand Froks, N.D., USA Dr. Khushali Menaria Associate Professor, Department of Bio-Informatics, Mau Dr. R. Sukumar Professor, Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar, Tamilnadu, India Dr. Cherouat Abel Professor, University of Technology of Troyes, Franc Dr. Rinkle Aggrawal Associate Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India Dr. Parteek Bhatia Associate Professor, Deprtment of Computer Science & Engineering, Thapar University, Patiala (Punjab) Dr. Manish Srivastava Professor & Head, Computer Science and Engineering, Guru Ghasidas Central University, Bilaspur Dr. B. P. Ladgaonkar Assoc. Professor&Head, Department of Electronics, Shankarrao Mohite Mahavidyalaya, Akluj, Dr. E. Mohan Professor & Head, Department of Computer Science and Engineering, Pallavan College of Engineering, KanchipuramIndia

Associate Professor, Department of Computing, School of Engineering, Adama Science and Technology University, Ethiopia

Associate Professor, Department of Management, Gandhi Institute of Technology and Management, India

Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dum

of Technology & Management, Garhi Bolni Road , Rewari Haryana, India.

Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem,

Associate Professor, Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman. Middle East.

International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Associate Professor, Gonaabad Branch, Islamic Azad University, Gonabad, Iratan

Associate Professor, Department of Mathematics, Graphics Era University, Dehradun, India

Manager, Infosys Technologies Ltd, Hyderabad (A.P.)-500005, India

Dean, Department of Computer Applications, Guru Nanak Khalsa Institute & Management Studies, Yamuna Nagar (Haryana), India

r, Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain

Member of IEEE, University of North Dakota, Grand Froks, N.D., USA

Informatics, Maulana Azad National Institute of Technology (MANIT), Bhopal

Professor, Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar, Tamilnadu, India

Professor, University of Technology of Troyes, France

Associate Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India

Associate Professor, Deprtment of Computer Science & Engineering, Thapar University, Patiala (Punjab)

Professor & Head, Computer Science and Engineering, Guru Ghasidas Central University, Bilaspur (C.G.), India

Assoc. Professor&Head, Department of Electronics, Shankarrao Mohite Mahavidyalaya, Akluj, Maharashtra, India

Professor & Head, Department of Computer Science and Engineering, Pallavan College of Engineering, Kanchipuram

Associate Professor, Department of Computing, School of Engineering, Adama Science and Technology University, Ethiopia

, India

Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dumas

of Technology & Management, Garhi Bolni Road , Rewari Haryana, India.

Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem,

Associate Professor, Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman. Middle East.

Dean, Department of Computer Applications, Guru Nanak Khalsa Institute & Management Studies, Yamuna Nagar (Haryana), India

r, Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain

lana Azad National Institute of Technology (MANIT), Bhopal (M.P.), India

Associate Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India

Associate Professor, Deprtment of Computer Science & Engineering, Thapar University, Patiala (Punjab), India

(C.G.), India

Maharashtra, India

Professor & Head, Department of Computer Science and Engineering, Pallavan College of Engineering, Kanchipuram, Tamilnadu,

S. No

Volume-2 Issue-6, May 2013, ISSN: 2278-3075 (Online) Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors: Minavathi, Murali .S, Dinesh M. S.

Paper Title: Dual Modality: Mammogram and Ultrasound Feature Level Fusion for Characterization of Breast Mass

Abstract: detection of abnormalities in breast is done in different phases using different modalities and different biomedical techniques. These techniques and modalities are able to furnish morphological, metabolic and functional information of breast. Integrating these information assists in clinical decision making. But it is difficult to retrieve all these information from single modality. Multimodal techniques supply complementary information for improved therapy planning. This work concentrates on early detection of breast cancer which characterises the breast mass as malignant or benign by investigating the features retrieved from dual modalities: mammograms and Ultrasound. Architectural distortion (AD) with Spiculated mass is an important finding for the early detection of breast cancer. Such distortions can be classified as spiculation, retraction, and distortion which can be detected in mammograms. Spiculated masses carry a much higher risk of malignancy than calcifications or other types of masses. The proposed approach is based on the fusion of two modalities at feature extraction level with Z-Score Normalization technique to improve the performance of dual modality. Gabor filters are used to retrieve texture features from region of interest (ROI) of mammograms. Shape and structural features are retrieved from ROI’s of Ultrasound. In addition to that some other discriminative features like denseness texture feature, standard deviation, entropy and homogeneity are also extracted from ROI’s of both modalities. Feature level fusion is then achieved by using a simple concatenation rule. Finally classification is done using Support vector machine (SVM) classifiers to classify breast mass as malignant or benign. Receiver operating characteristic curves (ROC) are used to evaluate the performance. SVM classifiers achieved 95.6% sensitivity in characterising the breast masses using the features retrieved from two modalities. Keywords: Mammogram, Ultrasound, Spiculated mass, Architectural distortion, Dual modality, SVM, Feature level fusion, Z-score Normalization. References: 1. M. A, Wirth, Nonrigid Approach to Medical Image Registration: Matching Images of the Breast , Ph.D. Thesis, RMIT University,

Elbourne, Australia, 2000. 2. R. Gupta and P. E. Undrill, The use of texture analysis to delineate suspicious masses in mammography, Phys. Med. Biol., vol. 40, pp. 835–

855, 1995 . 3. Mehul P. Sampat,Gary J. Whitman, Alan C. Bovik, and Mia K. Markey, “ Comparison of Algorithms to Enhance Spicules of Spiculated

Masses on Mammography”, Journal of Digital Imaging, Vol 0, No 0 (Month), 2007: pp 1-8. 4. Kopans,” Breast Imaging”, New York: Lippincott-Raven Publishers, 1998. 5. Minavathi, Murali. S and M.S.Dinesh, Model based approach for Detection of Architectural Distortions and Spiculated Masses in

Mammograms, International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397 Vol. 3 No. 11 November 2011page no: 3534.

6. Minavathi, Murali. S and M.S.Dinesh, curvature and shape analysis for the detection of spiculated masses in breast ultrasound images, IJMI International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 4, 2011, pp-333-339.

7. M. A, Wirth, Nonrigid Approach to Medical Image Registration: Matching Images of the Breast, Ph.D. Thesis, RMIT University, Melbourne, Australia, 2000.

8. Marnell Jameson, Ultrasound as a breast cancer test is becoming more accepted, Los Angeles Times , 000037057 June 14 , 2004. 9. E.A Sickles, R.A. Filly, P.W. Callen, Breast detection with sonography and mammography, AJR, 140:843-845, 1983. 10. H. Gholam Hosseini, A. Alizad and M. Fatemi, Integration of Vibro-Acoustography ImagingModality with the TraditionalMammography,

Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2007, Article ID 40980, 8 pages doi:10.1155/2007/40980.

11. Gian Luca Marcialis, Fabio Roli, Luca Didaci(2009) Pattern Recognition 42(11): 2807- 2817. 12. R. Brunelli, D. Falavigna, "Person identification using multiple cues," IEEE Transactions on Pattern Analysis and Machine Intelligence

1995. 13. J. KITTLER, R. P.W. DUIN, "The combining classifier: to train or not to train," in Proceedings of the International Conference on Pattern

Recognition, vol. 16, no. 2, pp. 765–770, 2002. 14. Hassan Solimanv, Abdelnasser Saber Mohamed, Ahmed Atwan, Feature Level Fusion of Palm Veins and Signature Biometrics ,

International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol: 12 No: 01 28. 15. Han-ling ZHANG , Fan YANG, Multimodality Medical Image Registration Using Hybrid Optimization Algorithm, 2008 International

Conference on BioMedical Engineering and Informatics, 978-0-7695-3118-2/08 $25.00 © 2008 IEEE DOI 10.1109/BMEI.2008.108. 16. Andrzej Krol, Member, IEEE, Ioana L. Coman Member, IEEE, James A. Mandel, Karl Baum, Min Luo, David H. Feiglin, Edward D.

Lipson, and Jacques Beaumont1, Inter-Modality Non-Rigid Breast Image Registration Using Finite-Element Method, 0-7803-8257-9/04/$20.00 © 2004 IEEE.

17. Francis Arena M.D, Thomas DiCicco, Azad Anand, M.D. , multi-modality data fusion aids early detection of breast cancer Using conventional technology and advanced digital infrared Imaging, Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA • September 1-5, 2004, 0-7803-8439-3/04/$20.00©2004 IEEE.

18. Minavathi, Murali. S, M. S. Dinesh, Detection of Architectural Distortions with Spiculations in Mammograms by analyzing the structure of Showsmammary glands , Proceedings of Fifth Indian International Conference on Artificial Intelligence(IICAI) , Tumkur, Dec-2011, page no: 218-230.

19. Yuji Ikedoa, Daisuke Fukuokab, Takeshi Haraa, Hiroshi Fujitaa, Etsuo Takadac, Tokiko Endod, and Takako Moritae, Computerized mass detection in whole breast ultrasound images: Reduction of false positives using bilateral subtraction technique, Medical Imaging 2007, Proc. of SPIE Vol. 6514, 65141T, (2007) • 1605-7422/07.

20. Steve R. Gunn, Support Vector Machines for Classification and Regression, Technical Report, University of Southampton, 1998. 21. Ravishankar Rao and Ramesh C, “Computerized Flow Field Analysis: Oriented Texture Fields” , 0162-8828 , 1992 IEEE. 22. Minavathi, Murali. S, M. S. Dinesh, Classification of Mass in Breast Ultrasound Images using Image Processing Techniques, International

journal of computer applications, march 2012. 23. J. Suckling, D. R. Dance, E. Moskovic, D. J. Lewis, and S. G. Blacker, Segmentation of mammograms using multiple linked self-

organizing neural networks, Med. Phys., vol. 22, no. 2, pp. 145–152, Feb. 1995. 24. N. Karssemeijer, Automated classification of parenchymal patterns in mammograms, Phys. Med. Biol., vol. 43, no. 2, pp. 365–378, Feb.

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25. R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, and A. F. Frère, Segmentation of mammograms: Identification of the skin boundary, pectoral muscle, and fibroglandular disc, in IWDM 2000: Proc. 5th Int. Workshop Digital Mammography, M. J. Yaffe, Ed., Madison, WI, 2001, pp.

26. F. Georgsson, Algorithms and techniques for computer aided mammographic screening, Ph.D. dissertation, Umeå Univ., Dept. Comput. Sci., Umeå, Sweden, 2001.

27. P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 629–639, July 1990.

28. M. Mignotte and J. Meunier, “A multiscale optimization approach for the dynamic contour-based boundary detection issue,” Comput. Med. Imag. Graph., vol. 25, no. 3, pp. 265–275, May–Jun. 2001.

29. F. Heitz, P. Perez, and P. Bouthemy, “Multiscale minimization of global energy functions in some visual recovery problems,” CVGIP: Image Understanding, vol. 59, pp. 125–134, 1994.

30. Tourassi GD, Delong DM, Floyd CE Jr (2006): A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 1(5),1299-1312.

31. Rangayyan RM, Ayres FJ (2008): Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis , Int J CARS (2008) 2:347-361.

32. Luan Jiang, MSc, Enmin Song, PhD, Xiangyang Xu, MSc, Guangzhi Ma, MSc, Bin Zheng, PhD: Automated Detection of Breast Mass Spiculation Levels and Evaluation of Scheme Performance1, Acadamic Radiology , Vol 15 , No 12, Dec 2008, 15:1534-1544 .

33. Burhenne L J W, Wood S A, D’Orsi C J, Feig S A, Kopans D B, O’Shaughnessy L F, Sickles E A, Tabar L, Vyborny C J and Castellino R A Radiol. 215 554-562.

34. Evans W P, Burhenne L J W, Laurie L, O’Shaughnessy K F and Castellino R A 2002 Radiol. 225 182-189. 35. L. Liberman et al. : The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment

categories, AJR. American Journal of Roentgenology. 171 (1), 35 (1998). 36. N. R. Mudigonda, R. M. Rangayyan, and J. E. L. Desautels, “Detection of breast masses in Mammograms by density slicing and texture

flow-field analysis”, IEEE Trans. Med. Imag., vol. 20, no. 12, pp. 1215–1227, December 2001. 37. Julia E. E. de Oliveira , Thomas M. Deserno and Arnaldo de A. Araujo, ” Breast Lesions Classification applied to a reference Database “,

IEEE and E-MEDISYS 2008 2nd International Conference: E- Medical Systems. 38. Cheng X.-Y., Akiyama’ I., Itoh K., Wang Y.,Taniguchi N. and Nakajima M. (1997) 0-8186- 8183-7/97, IEEE. 39. Horsch K., Giger M. L., Venta L. A. and Vyborny C. J. (2001) Med. Phys., vol. 28, no. 8, pp. 1652–1659. 40. Madabhushi A. and Metaxas D. N. (2003) IEEE Trans. Med. Imag., vol. 22, no. 2, pp. 155–169. 41. Yuji Ikedoa, Daisuke Fukuokab, Takeshi Haraa, Hiroshi Fujitaa, Etsuo Takadac, Tokiko Endod, and Takako Moritae (2007) Medical

Imaging 2007, Proc. of SPIE Vol. 6514, 65141T. 42. Dar-Ren Chena, T. Ruey-Feng Changb, Chii- Jen Chenb, Ming-Feng Hob, Shou-Jen Kuoa, Shou-Tung Chena, Shin-Jer Hungc, Woo

Kyung Moon (2005) Journal of Clinical Imaging, 29, 235–245. 43. Yuji Ikedo , Takako Morita , Daisuke Fukuoka , Takeshi Hara , Bert Lee , Hiroshi Fujita , Etsuo Takada , Tokiko Endo (2009) Int J CARS,

4:299–306.

2.

Authors: Rajiv Dahiya, A. K. Arora, V. R. Singh

Paper Title: RF Energy Harvesting for Hybrid Applic ation: Review and Analysis

Abstract: Wireless Sensor node is one of the important technologies for the recent world applications like military, agriculture, health etc. But the major problem associated with these nodes is their battery dependence. The Battery Dependence for such nodes responds to the life time of the complete node.Failure of network due to node failure occurs when the battery completely depleted. To beware from such conditions for critical conditions there is one more option of Energy harvesting. In this paper we have discussed about many kinds of energy harvesting procedures. RF energy harvesting and related work discussion are the major issues which we have discussed in this paper. The Power transmitted and received, DC power output, efficiency enhancement due to multipliers or charge pumps are the areas which have been discussed in this paper. Some protocols and routing techniques are also discussed in this paper. Keywords: WSN, RF Energy harvesting, Energy harvesting techniques, Ambient energy harvesting. References:

1. N. Tesla, “The transmission of electric energy without wires,” in 13th Anniversary Number of the Electrical World and Engineer, 1904. 2. J. Curty, M. Declercq, C. Dehollain, and N. Joehl, Design and Optimization of Passive UHF RFID Systems. New York: Springer, 2007. 3. J. A. Paradiso, “Systems for human-powered mobile computing,” inProc. 43rd Design Automation Conf. (DAC), Jul. 24–28, 2006, pp.645–

650. 4. K.-W. Fan, Z. Zheng, and P. Sinha. Steady and fair rate allocationfor rechargeable sensors in perpetual sensor networks.In SenSys ’08,

pages 239–252, 2008. 5. S. M. Jose, J. O. Mur-mir, R. Amirtharajah, A. P. Ch, andJ. H. Lang. Vibration-to-electric energy conversion. IEEETransactions on Very

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SensorNetworks, Berlin, Germany,pp. 46–49, 2004. 10. I. W. Marshall, C. Roadknight, I. Wokoma, L. Sacks.Self-organizing Sensor Networks. In Proceedings ofUbiNet, London, UK, [Online],

Available:http://wwwdse.doc.ic.ac.uk/Projects/UbiNet/ws2003/papers/marshall.pdf, 2003. 11. R. Beckwith, D. Teibel, P. Bowen. Unwired Wine: SensorNetworks in Vineyards. In Proceedings of IEEE Sensors,IEEE Press, vol. 2, pp.

561–564, 2004. 12. A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, J. Anderson.Wireless Sensor Networks for Habitat Monitoring.In Proceedings of the

1st ACM International Workshop onWireless Sensor Networks and Applications, ACM, Atlanta,Georgia, USA, pp. 88–97, 2002. 13. P. Juang, H. Oki, Y. Wang, M. Martonosi, L. S. Peh, D.Rubenstein. Energy-efficient Computing for Wildlife Tracking:Design Tradeoffs

and Early Experiences with ZebraNet.In Proceedings of the 10th Architectural Supportfor Programming Languages and Operating Systems, ACM,San Jose, USA, vol. 36, no. 5, pp. 96–107, 2002.

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18. S. H. Yang, P. Frederick. SafetyNET – A Wireless SensorNetwork for Fire Protection and Emergency Responses.Measurement and Control, vol. 39, no. 7, pp. 218–219, 2006.

19. C. Kappler, G. Riegel. A Real-world, Simple Wireless SensorNetwork for Monitoring Electrical Energy Consumption.In Proceedings of European Workshop on WirelessSensor Network, Lecture Notes in Computer Science, Springer-Verlag, vol. 2920, pp. 339–352 2004.

20. G. Z. Yang. Body Sensor Networks, Springer-Verlag, Germany,2006. 21. N. F. Timmons, W. G. Scanlon.Analysis of the Performanceof IEEE 802.15.4 for Medical Sensor Body Area Networking.In Proceedings of

the 1st Annual IEEE CommunicationsSociety Conference on Sensor and Ad Hoc Communicationsand Networks, IEEE Press, pp. 16–24, 2004.

22. I. Stojmenovic. Handbook of Sensor Networks: Algorithmsand Architectures, John Wiley & Sons Inc, Hoboken, NewJersey, USA, 2005. 23. J. L. Gonzalez, A. Rubio, F. Moll. Human Powered PiezoelectricBatteries to Supply Power to Wearable ElectronicDevices.International

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pp. 86–88, 2003. 25. S. Roundy, D. Steingart, L. Frechette, P.Wright, J. Rabaey.Power Sources for Wireless Sensor Networks.Lecture Notesin Computer

Science, Springer-Verlag, vol. 2920, pp. 1–17,2004. 26. L. Mateu, F. Moll. Review of Energy Harvesting Techniquesand Applications for Microelectronics. In Proceedings of SPIE – The

International Society for Optical Engineering, VLSI Circuits and Systems II, SPIE Press, vol. 5837, pp.359–373, 2005. 27. T. von Büren. Body-worn Inertial Electromagnetic Microgenerators, Ph.D. dissertation ETH 16466, Swiss FederalInstitute of Technology,

Zurich, Germany, 2006. 28. A. H. Sellers, P. J. Robinson. Contemporary Climatology,Longman Scientific & Technical, Essex, UK, 1986. 29. J. L.Monteith, M. H. Unsworth. Principles of Environmental Physics, Edward Arnold, London, UK, 1990. 30. S. J. Roundy. Energy Scavenging for Wireless Sensor Nodeswith a Focus on Vibration to Electricity Conversion, Ph.D.dissertation,

University of California, Berkeley, USA, 2003. 31. K. Finkenzeller. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification,John Wiley & Sons, 2003. 32. T. Starner. Human-powered Wearable Computing. IBM Systems Journal, vol. 35, no. 3, pp. 618–629, 1996. 33. Highway Energy Systems Ltd., [Online], Available:http://www.hughesresearch.co.uk/, March 6, 2008. 34. M. Trew, T. Everett. Human Movement: An IntroductoryText, Churchill Livingstone, 2001. 35. S. J. Roundy, P. K. Wright, J. Rabaey. A Study of LowLevel Vibrations as a Power Source for Wireless SensorNodes.Computer

Communications, vol. 26, no. 11, pp.1131–1144, 2003. 36. H. P. Tan, Z. A. Eu, and W. K. Seah, “Impact of Power Control in Wireless Sensor Networks Powered by Ambient Energy Harvesting for

Railroad Health Monitoring,” in Proc. of the 2nd Intl Workshop on Applications of Ad hoc and Sensor Networks (AASNET), Bradford, UK, May 26-29 2009.

37. Z. A. Eu, H. P. Tan, and W. K. G. Seah, “Routing and relay node placement in wireless sensor networks powered by ambient energy harvesting,”in Proc. of the IEEE Wireless Communications & NetworkingConference (WCNC), Budapest, Hungary, Apr 5-8 2009.

38. Z. A. Eu, W. K. G. Seah, and H. P. Tan, “A Study of MAC Schemes forWireless Sensor Networks Powered by Ambient Energy Harvesting,” inProc. of the Fourth Intl Wireless Internet Conference (WICON 2008), Maui, Hawaii, USA, Nov 17-19 2008.

39. FCC Codes of Regulation, Part 15 [Online]. Available: http://www. access.gpo.gov/nara/cfr/waisidx03/ 40. Gianni Giorgetti, Alessandro Cidronali, Sandeep K.S. Gupta, Gianfranco Manes,” Exploiting Low-Cost Directional Antennas in 2.4

GHzIEEE 802.15.4 Wireless Sensor Networks”, IEEE2011, pp:2098-2101

3.

Authors: Vinod Nagashetti, Mahadevaraju.G.K, T.S.Muralidhar, Aamir Javed, Dwijendra Trivedi, Khum Prasad Bhusal

Paper Title: Biosorption of Heavy Metals from Soil by Pseudomonas Aeruginosa

Abstract: Pseudomonas aeruginosa is known to accumulate metal in their cell by the process called Biosorption. This property is used here for removal of metals from the soil to reduce its toxicity. Soil sample collected from Durgapur, West Bengal was mixed with different concentration of metals; chromium, copper & zinc in ppm level and was inoculated with bacterial sample. After incubation the metals were extracted and were compared with different standards using spectrophotometric and titration based methods. Metals are indispensable constituents of approximately one third of all proteins. As such, metals are involved in virtually all biological processes, including metabolism, energy transduction, gene expression, cell signalling, and formation of endo- and exoskeletons, and electron transfer. Among the techniques suitable for the quantification of metal ions in soil sample, inductively coupled plasma mass spectrometry are likely to be the most widely employed. However, although these techniques are reliable and sensitive, they suffer from the limitation of being rather costly (considering instrument acquisition and maintenance), time-consuming (with respect to sample preparation), and not always readily available. Therefore a general spectrophotometric and titration based analysis is been performed. The results are quite promissable, Metal absorbed by pseudomonas aeruginosa i.e., Cr, Zn and Cu in 1000 ppm were 1.07mg, 1.33mg and 0.67mg when compared to control soil sample in 1000 ppm with metals, but without Pseudomonas aeruginosa , compared were 3.7mg respectively. The present study depicts that bacteria removes chromium efficiently and this could be used for industrial waste management and other environmental contaminants. Keywords: Biosorption, Pseudomonas aeruginosa, chromium, copper & zinc in ppm level, Industrial waste Management. References: 1. Kabata-Pendias A., Pendias H., Biogeochemistry Of Trace Elements, Pwn, Warszawa, 1999 (In Polish). 2. Kolembkiewicz I., Chromium In Soils And Some As- Pects Of Its Analysis, Laboratory Apparatus. Research, 3, 9 1999 (In Polish). 3. Cwietlik R., Trojanowska M. Fractionation Of Chromium In Soil Heavily Polluted By Tannery Wastes. Case Study Of The Radom

Region (Poland). Polish Journal Of Environmental Studies. 13, Sup. Iv, 19, 2004. 4. Dudka S., Evaluation Of Total Content Of Macro- And Mi- Croelements In The Surface Layer Of Soil In Poland, Iung Pu³Awy, R (293), 1,

1992 (In Polish). 5. Directive Of The Minister Of The Environment Of. 9.09.2002. Journal Of Law No 165, Item 1359 (In Polish). 6. Jankiewicz B., Ptaszyñski B., Turek A., Spec- Trophotometric Determination Of Copper (Ii) In Samples Of Soil From Selected Allotment

Gardens In Lodz, Polish J. Environ. Studies, 8, 35, 1999. 7. Jankiewicz B., Ptaszyñski B., Wieczorek M., Spectrophotometric Determination Of Cadmium (Ii) In Samples Of Soil From Selected

Allotment Gardens In Lodz, Polish J. Environ. Studies, 9, 83, 2000. 8. Turek A., Verpoucke G., Ptaszyñski B., Jan- Kiewicz B., Determination Of Bioavailable Copper (Ii) In The Soil, Chem. Ekol. Ing., 857,

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2000. 9. Jankiewicz B., Ptaszyñski B., Wieczorek M., Spectrophotometric Determination Of Lead In Soil Of Allotment Gardens In Lodz, Polish J.

Environ. Studies, 10, 123, 2001. 10. Ramakrishna, W. (2011). Effect Of Multiple Metal Resistant Bacteria From Contaminated Lake Sediments On Metal Accumulation And

Plant Growth. Journal Of Hazardous Materials, 189(1-2), 531-539. Doi: 10.1016/J.Jhazmat.2011.02.075 11. Guo, H., Luo, S., Chen, L., Xiao, X., Xi, Q., Wei, W., ... Chen, J. (2010). Bioremediation Of Heavy Metals By Growing Hyperaccumulaor

Endophytic Bacterium Bacillus Sp. L14. 12. Bioresource Technology, 101(22), 8599-8605. Doi: 10.1016/J.Biortech.2010.06.085

4.

Authors: Malgireddy Ravi, Palarapu Sravan Kumar, Devireddy Sathish

Paper Title: Three-Stage 18 level Hybrid Cascaded Multilevel Inverter Using a Hybrid Method

Abstract: This paper proposes a new hybrid algorithm for three stage 18 level cascaded multilevel inverter. The inverter consists of main high-voltage, medium-voltage and low-voltage stages connected in series from the output The high-voltage stage is made of a three-phase conventional inverter to reduce dc source cost and losses. The medium- and low voltage stages are made of three-level inverters constructed using cascaded H-bridge units. The aim of the proposed algorithm is to avoid the undesirable high switching frequency for high and medium-voltage stages despite the fact that the inverter’s dc sources are selected to maximize the inverter levels by eliminating redundant voltage states. Switching algorithms of the high- and medium-voltage stages have been developed to assure fundamental switching frequency operation of the high-voltage stages and not more than few times this frequency for the medium-voltage stage. The low-voltage stage is controlled using SVM to achieve the reference voltage vector exactly and to set the order of dominant harmonics as desired. The realization of this control approach has been enabled by considering the vector space plane in the state selection rather than individual phase levels. The simulation results shows the effectiveness of the proposed strategy in terms of computational efficiency as well as the capability of the inverter to produce very low distorted voltage with low switching losses. Keywords: Inverters, harmonics, pulse width modulation (PWM), space vector modulation (SVM). References: 1. N. Celanovic, D. Boroyevich, D. “A fast space vector modulation algorithm for multilevel three-phase converters”, IEEE Trans. Ind. Appl.,

37(2), pp. 637-641, (2001) 2. R. Kanchan, M. Baiju, K. Mohapatra, P. Ouseph, K. Gopakumar. “Space vector PWM signal generation for multilevel inverters using only

the sampled amplitudes of reference phase voltages”, IEE Electric Power Appl., 152(2), pp. 297 – 309, (2005) 3. A. Massoud, S. Finney, B. Williams. “Mapped hybrid spaced vector modulation for multilevel cascaded-type voltage source inverters”, IET

Power Elec., 1 (3), pp. 318- 335, (2008). 4. B. McGrath, D. Holmes, T. Lipo. “Optimized space vector switching sequence for multilevel inverters”, IEEE Trans. Power Elect., 18(6),

pp. 1293-1301, (2003). 5. J. I. Leon, S. Vazquez, S. Kouro, L. G. Franquelo, J. M. Carrasco, J. Rodriguez. “Unidimensional modulation technique for cascaded

multilevel converters”, IEEE Trans. Indst. Elect., 56 (8), pp. 2981-2986, (2009). 6. Y. Liu, F. Luo. “Trinary hybrid 81-level multilevel inverter for motor drive with zero common-mode voltage”, IEEE Trans. Ind. Elect., 55,

(3), pp. 1014-21. (2008). 7. C. Rech, J. R. Pinheiro, “Hybrid multilevel converters: unified analysis and design considerations”, IEEE Trans. Indus. Electr. , 54(2),

pp.1092-1104, (2007). 8. Y. S. Lai and F. S. Shyu, “Topology for hybrid multilevel inverter,” Proc. Inst. Elect. Eng.—Elect. Power Appl., vol. 149, no. 6, pp. 449–

458, Nov. 2002. 9. M. A. Pérez, P. Cortés, and J. Rodríguez, “Predictive control algorithm technique for multilevel asymmetric cascaded H-bridge inverters,”

IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4354–4361, Dec. 2008. 10. Y. Liu and F. Luo, “Trinary hybrid 81-level multilevel inverter for motor drive with zero common-mode voltage,” IEEE Trans. Ind.

Electron., vol. 55, no. 3, pp. 1014–1021, Mar. 2008. 11. S. Kouro, P. Lezana, M. Angulo, and J. Rodríguez, “Multicarrier PWM with dc-link ripple feedforward compensation for multilevel

inverters,” IEEE Trans. on Power Electron., vol. 23, no. 1, pp. 52–59, Jan. 2008. 12. N. Celanovic and D. Boroyevich, “A fast space-vector modulation algorithmfor multilevel three-phase converters,” IEEE Trans. Ind.

Appl.,vol. 37, no. 2, pp. 637–641, Mar./Apr.. 13. B. P. McGrath, D. G. Holmes, and T. Lipo, “Optimized space vector switching sequences for multilevel inverters,” IEEE Trans. Power

Electron., vol. 18, no. 6, pp., Nov. 2003. 14. Y. Liu and F. Luo, “Trinary hybrid 81-level multilevel inverter for motor drive with zero common-mode voltage,” IEEE Trans. Ind.

Electron., vol. 55, no. 3, pp. 1014–1021, Mar. 2008. 15. M. S. A. Dahidah and V. G. Agelidis, “Selective harmonic elimination PWM control for cascaded multilevel voltage source converters: A

generalized formula,” IEEE Trans. Power Electron., vol. 23, no. 4, pp. 1620– 1630, Jul. 2008. 16. Y. Liu, H. Hong, and A. Q. Huang, “Real-time calculation of switching angles minimizing THD for multilevel inverters with step

modulation,” IEEE Trans. Ind. Electron., vol. 56, no. 2, pp. 285–293, Feb. 2009. 17. J. I. Leon, S. Vazquez, S. Kouro, L. G. Franquelo, J. M. Carrasco, and J. Rodriguez, “Unidimensional modulation technique for cascaded

multilevel converters,” IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 2981– 2986, Aug. 2009. 18. J. I. Leon, S. Vazquez, J. Sanchez, R. Portillo, LFranquelo, J. Carrasco, and E. Dominguez, “Conventional space-vector modulation

techniques versus the single-phase modulator for multilevel converters,” IEEE Trans. Ind. Electron., vol. 57, no. 7, pp. 2473–2482, Jul. 2010.

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5.

Authors: Xia Xu, Hui Chang, Hongchao Kou, Zhao Yang, Jinshan Li

Paper Title: Simulation of Temperature Field Distribution in Melting TiAl Alloy by PAM Process

Abstract: In this paper, the distribution of melt temperature field during Plasma Arc Cold Hearth Melting (PAM) process in TiAl alloy is investigated by computational simulation using the software FLUENT, and the effects of buoyancy, surface tension and process parameters on the melt temperature distribution of TiAl alloy in the second refining hearth were studied. The results show that the buoyancy and the surface tensions strongly affected the melingt temperature field distribution and they are the main cause to drive the fluid flow, thus the chemical composition uniformity of the melt can be improved through controlling the temperature field. The temperature increases as the power of the plasma gun is increased. Adjusting torch moving pattern and scanning frequency can be used to change the distribution of temperature field by controlling the residence time and the overheat of the melt.

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Keywords: Melt temperature field distribution, PAM, Scanning frequency, Torch moving pattern. References: 1. S.V. Patankar, “Computational modeling of flow and heat transfer in industrial applications,” International Journal of Heat and Fluid Flow,

vol. 23, no. 3, pp. 222-231, June 2002. 2. J.M. Cai, J.M. Ma, M.Y. Meng, Z.X. Li, C.X. Cao, “Hard Alpha Defect in Titanium Alloys and its Control Using Plasma Arc Cold Hearth

Melting Technique,” Failure Analysis and Prevention, vol. 2, no.2, pp. 51-57, May 2007. 3. J.R. Wood, “Melting and casting of gamma titanium aluminide ingots,” Gamma Titanium Aluminides 2003, pp. 227-232, 2003, Gamma

Titanium Aluminides 2003. 4. J.P. Bellot, E. Hess, and D. Ablitzer, “Aluminum volatilization and inclusion removal in the electron beam cold hearth melting of Ti

alloys,” Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science, vol. 31, no. 4, pp. 845-854, Aug 2000.

5. H.V. Zhuk, P.A. Kobryn, and S.L. Seniatin, “Influence of heating and solidification conditions on the structure and surface quality of electron-beam melted Ti-6Al-4V ingots,” Journal of Materials Processing Technology, vol. 190, no. 1-3, pp. 387-392, July 2007.

6. S.C. Chu, S.S. Lian, “Numer ical analysis of temperature distribution of plasma arc with molten pool in plasma arc melting,” Computational Materials Science, vol. 30, no. 3-4, pp. 441-447, August 2004.

7. K.B. Bisen, M. Arenas, N. EI-Kaddah, V.L. Acoff, “Computation and validation of weld pool dimensions and temperature profiles for gamma TiAl,” Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, vol. 34A, no. 10, pp. 2273-2279, October 2003.

8. R.H. Zhang, Seiji Katayama, Naitou Yasuaki, D. Fan, “Numerical simulation of the laser welding by using the Rotary-Gauss body heat source model,” Electric Welding Machine, vol. 37, no. 5, pp. 51-54, May 2003.

9. A. Powell, J. Van Den Avyle, B. Damkroger, J. Szekely, U. Pal, “Analysis of multicomponent evaporation in electron beam melting and refining of titanium alloys,” Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science, Vol. 28, no. 6, pp. 1227-1234, Dec 1997.

10. L. Ma, “The three dimensional numerical simulation of temperature and fluid Flow fields of plasma arc welding,” Tianjin University, pp. 11-21, 2006.

11. “Fluent 6.3 User’s Guide,” Fluent Inc., pp. 1793-1806, 2006. 12. Y.Y. Zhao, R.M. Ward, T.P. Johnson, “Numerical modeling of electromagnetic stirring of liquid pool in plasma arc melting,”

Electromagnetic Processing of Materials, Proc of the International Congress on Electromagnetic Processing of Materials, May 1997. 13. Xia Xu, master degree candidate for Materials Science, mainly engaged in the studies of melting and refining TiAl alloy

6.

Authors: Hari Kumar Choudhari, Akhilesh A. Waoo, P. S. Patheja, Sanjay Sharma

Paper Title: Performance Evaluation of Zig Bee Using Multiple Input Single Output (MISO) Architecture in the Secured Environment

Abstract: ZigBee is a protocol stack created specifically for control of sensor networks which is built on IEEE 802.15.4, this standard works for low data rate wireless personal area networks (WPAN). The IEEE 802.15.4 standard works on PHY and MAC Layer. The IEEE 802.15.4 has become the preferable PAN system for wireless sensor networks and many software and hardware platforms are based on it. The implementation and performance analysis of this standard is needed to understand the conceptual limitations of it. This standard is designed for low data rate, low power consumption, long battery life. The implementation & performance analysis of this standard is needed to understand the conceptual limitations of it. In this paper we have tried to improve Error rate performance and other affected factors of Zigbee Personal Area Network (PAN) using multiple transmitters on existing system with OQPSK modulation in AWGN channel environment..Here in ZigBee we are applying MISO architecture with ECC 160 Encryption/Decryption method. The idea to use both of the schemes to apply in the IEEE 802.15.4 standard comes from that ECC 160 has features of low power consumption, more enough security and very less complexity as compare to RSA 1024, AES and IBE. The Multi transmitter scheme consumes high power compare to existing system. Hence we got a solution for that to save some amount of energy in the ZigBee Upper Layer. From the simulation results we have found that our proposed multiple transmitters scheme (MISO) gives very better results on low SNR values comparatively with the existing Single Input Single Output (SISO) approach together with the ECC 160 Encryption / Decryption in ZigBee based wireless sensor network. Keywords: MISO, ZIGBEE, Physical Layer, OQPSK, BER, SNR. ECC 160 References: 1. J. Stankovic, I. Lee, A. Mok, R. Rajkumar, “Opportunities and Obligations for Physical Computing Systems”, in IEEE Computer, Volume

38, Nov, 2005. 2. The Economist, “When everything connects”, April 28th – May 4th, 2007. 3. N. Aakvaag, M. Mathiesen, and G. Thonet, “Timing and power issues in wireless sensor networks, an industrial test case”, In Proceedings

of the 2005 International Conference on Parallel Processing Workshops (ICPPW). IEEE, 2005. 4. S A Bhatti (Student), I A Glover, “Performance Evaluation of IEEE 802.15.4 Receiver in the Presence of Broadband Impulsive Noise”

IEEE Student Application Paper 2011. 5. Minakshmi Roy, H.S. Jamadagni, “Cancellation of Zigbee interference in OFDM based WLAN for multipath channel”, ACEEE Int. J. on

Network Security, Vol. 02, No. 01, Jan 2011. 6. Nilesh Shirvoikar, Hassanali Virani, Dr. R.B. Lohani, “Performance Evaluation of Zigbee Using Matlab Simulation”, in IJIIT, vol. 1 issue 3

2012-13. 7. IEEE Std. 802.15.4, Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal

Area Networks (WPANs), IEEE, New York, N.Y., 2006. 8. Alnuaimi, Mariam work on “Performance Evaluation of IEEE 802.15.4 Physical Layer Using MatLab/Simulink” Innovations in

Information Technology, 2006, in Coll. of Inf. Technol., UAE Univ., Al-Ain , page 1 – 5. 9. Arvinderpal S. Wander†, Nils Gura‡, Hans Eberle‡, Vipul Gupta‡, Sheueling Chang Shantz‡†University of California, Santa Cruz ‡Sun

Microsystems Laboratories “Energy Analysis of ECC for Wireless Sensor Network”. 10. International Journal of EngineeringResearch & Technology (IJERT) ISSN: 2278-0181 Vol. 1 Issue 3, May – 2012” Elliptic Curve

Cryptography ( ECC ) for Security in Wireless Sensor Network.”. 11. Int. J. Security and Networks, Vol. 1, Nos. 3/4, 2006 “Elliptic Curve Cryptography-Based Access Control in Sensor Networks” 12. Journal of Communication and Computer 9 (2012) 712-720 Hilal Houssain1, Mohamad Badra2 and Turki F. Al-Somani1 “Software

Implementations of Elliptic CurveCryptography in Wireless Sensor Networks” 13. 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops “Analytical study of

implementation of Elliptical Curve Cryptography for Wireless Sensor Networks” Pritam Gajkumar Shah, Xu Huang, Dharmendra Sharma,Faculty of Information Sciences and Engineering,University of Canberra, Act 2601-Australia

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14. Research Paper IEEE Wireless Technology and Applications (ISWTA), 2012 IEEE Symposium on Date of Conference: 23-26 Sept. 2012 Author(s): Rosli, R. Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia Yusoff, Y.M.; Hashim, H.Page(s): 187 - 191 Product Type: Conference Publications.

15. ZigBee Alliance “ www.zigbee.org “

7.

Authors: Chougule Mahadeo Annappa, Kallurkar Shrikant Panditrao

Paper Title: Integration of Quality Function Deployment and Value Engineering in Furniture Manufacturing Industry for Improvement of Computer Work Station

Abstract: Manufacturing operations in the Indian furniture industry are currently very competitive. It is necessary for entrepreneurs to improve the quality of their products and to develop processes including quality management to obtain new product design and development. Quality function deployment technique can be used in the processing development of new Computer work station to improve customer satisfaction. The use of QFD would enlarge the chance of success, produce higher quality products, and decrease the cost. The Computer Work Station was selected for this research because of its high sales number and it is most complicated compared to other furniture products. The ultimate goal of this research is to design and produce a new type of Computer Work Station. Important ratings of customer requirements are increased to some extent by effective implementation of Value Engineering with QFD. The result revealed that the average satisfaction values for all new types of computer work station are increased over those of the current computer work station from 1131 to 1956 (54.45%). Also design target values are increased from 1138 to 1988 (79.14%). Therefore with QFD approach, there is significant increase in average customer satisfaction between the current and the new designs. Keywords: Quality Function Deployment, Value Analysis, Productivity Improvement, Cost Improvement, Furniture Industry, Computer Work Station References: 1. Chatree Homkhiew, Thanate Ratanawilai and Klangduen Pochana, Application of a quality function deployment technique to design and

develop furniture products. 2. Yunia dwie, nurcahyanie moses and Singgih budi santosa, Quality function deployment by creative industries research institute. 3. Ignacio cariaga, tamer el-diraby and hesham osman Integrating value analysis and quality function deployment for evaluating design

alternatives. 4. Davood Gharakhani and Javad Eslami, Determining customer needs priorities for improving service quality using QFD 5. Sivadas aniyan t.s. promod v.r., Quality function deployment in manufacturing industry (improving the Existing sb cnc 40/60 slant bed

turning centre in hmt, kalamassery). 6. Irem dikmen, Talat birgonul and semiha kiziltas, Strategic use of quality function deployment (QFD) in the Construction industry 7. Robin Rawlings-Quinn,Quality function deployment (QFD): a case study 8. Chee-cheng chen, Application of quality function deployment in the semiconductor industry: A case study 9. Jim Diemsey, QFD to direct value engineering in design of brake 10. Marvin e. Gonzalez, Gioconda Quesada and Terry bahill, Improving product design using quality function deployment: the school Furniture

case in developing countries. 11. K. Yegenegi , m.Arastim Mousakhani, The integration of QFD technique and value engineering and its Applying in a healthcare center 12. R.umesh sundar, G. mohan kumar, Application of quality function deployment method and fuzzy logic for improving the design

characteristics in FRP cooling tower-case study.

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8.

Authors: D.Harihara Santosh, U.V.S. Sitarama Varma, K.S.K Chaitanya Varma, Meena Jami, V.V.N.S Dileep

Paper Title: Absolute Moment Block Truncation Coding For Color Image Compression

Abstract: In this paper Color image data compression using absolute moment block truncation coding scheme (AMBTC) is implemented. This compression technique reduces the computational complexity and achieves the optaimum minimum mean square error and PSNR. It is an improvised version of BTC, obtained by preserving absolute moments. AMBTC is an encoding technique that preserves the spatial details of digital images while achieving a reasonable compression ratio. The simulation results obtained indicate that both the computational complexity of and the reconstructed image quality obtained using AMBTC algorithm are better than those obtainable with other existing BTC algorithms. Keywords: Absolute moment block truncation coding, computational complexity. References: 1. Rafael C. Gonzalez ,Richard E. Woods Digital image compression 2nd edition 2. Doaa Mohammed, Fatma Abou-Chadi (Senior Member, IEEE.) Image “Compression Using Block Truncation Coding” ,journal in selected

areas of telecommunication (JSAT),Ffebruary edition, 2011. 3. M. Ghanbari "Standard Codecs: Image Compression to Advanced 4. Video Coding" Institution Electrical Engineers , ISBN: 0852967101, 5. 2003 , CHM , 430 pages 6. Pasi Fränti, Olli Nevalainen and Timo Kaukoranta Department of Computer Science, University of Turku Compression of Digital Images

by Block Truncation coding : a survey © The Computer Journal, 37 (4), 308-332, 1994 7. S. K.Kapde and Mr. S.V.Patil Image compression using BTC and AMBTC(IJARCSEE)Volume 1, Issue 10, December 2012 8. Chen-Kuei Yang, Member, IEEE, Ja-Chen Lin, and Wen-Hsiang Tsai 9. Color Image Compression by Moment-Preserving and Block Truncation Coding Techniques, IEEE TRANSACTIONS ON

COMMUNICATIONS, VOL. 45, NO. 12, DECEMBER 1997. 10. K.Somasundaram and I.Kaspar Raj ,An Image compression Scheme based on Predictive and Interpolative Absolute Moment Block

Truncation Coding , GVIP Journal, Volume 6, Issue 4, December, 2006 11. K.Somasundaram and I.Kaspar Raj ,Low Computational Image Compression Scheme based on Absolute Moment Block Truncation

Coding, World Academy of Science, Engineering and Technology 19 2006 12. G. Lu and T. L. Yew, “Image compression using quad tree partitioned iterated function systems,” Electronic Letters, VOL. 30, NO.1, pp.

23-24, Jan. 1994. 13. Reversible Image Watermarking using Bit Plane Coding andLifting Wavelet Transform by S. Kurshid Jinna, Dr. L. Ganesan , IJCSNS

International Journal of Computer Science and Network Security, VOL.9 No.11, November 2009 14. Yu-Chen Hu, “Low complexity and low bit-rate image compression scheme based on Absolute Moment Block Truncation Coding”, Vol.

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42 No. 7 (2003) pp 1964-1975. 15. Wen Jan Chen, Shen Chuan Tai ,Bit rate reduction techniques for Absolute Block Truncation Coding, journal for Chinese institute of

engineers ,vol. 22,no 5,pp661-667(1999) 16. Y V Ramana, and H Eswaran, “A new algorithm for BTC image bit plane coding,” IEEE Trans on Comm, 32(10), 1984, pp. 1148-1157. 17. M.D.Lema , and O.R.Mitchell, “Absolute Moment Block Truncation Coding and its Application to Color images, “ IEEE Trans. On

Communications, Vol. 32, pp. 1148-1157,1984.

9.

Authors: P.Ram Kishore Kumar Reddy, P. Nagasekhara Reddy, M. Ramachandra Rao

Paper Title: Sizing of a Right BLDC Motor for CNC Feed Drive

Abstract: This paper provides a technical aspects and design factors for selection of a right Brushless Direct Current (BLDC) motor for a CNC feed drive.To meet the most demanding requirements of CNC machine tools, robots and transfer lines in terms of productivity, accuracy and dynamic performance, brushless DC drives are very widely used. These drives employ brushless torque motors which are of high torque, low speed direct drive type. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. This paper presents a procedure for selecting and sizing a right torque motor for a CNC feed drive when the relevant application data is available. The duty cycle characteristics of the motor are presented to study certain parameters from thermal considerations. Expressions for acceleration torque and acceleration time are developed to study their effect on the high dynamic performance. In this paper the procedure is illustrated in the selection of Z-axis feed motor for CNC lathe with an example. Keywords: Brushless DC motors, sizing, PWM, CNC Drive, acceleration/deceleration curve, speed and torque. References: 1. G. Sakthival, T.S. Anandhi and S.P. Natarjan. 2010. Real time implementation of DSP based Fuzzy logic controller for Speed control of

BLDC motor. International Journal of Computer Applications (0975-8887). 10(8). 2. K. Naga Sujatha, K. Vaisakh and Anand. G. 2010. Artificial Intelligence based speed control of brushless DC motor. IEEE 978-1-4244-

6551-4/10. 3. Dr.M.Ramachandra Rao “Axis servomotor selection for NC application” machine tool engineer-January 1978. 4. P. Yedamale, Brushless DC (BLDC) Motor Fundamentals. Chandler, AZ: Microchip Technology, Inc., last access; March 15, 2009 5. M.V.Ramesh1, J.Amarnath2, S.Kamakshaiah3, B.Jawaharlal4, Gorantla.S.Rao “Speed Torque characteristics of Brushless DC motor in

either direction on load using ARM controller”, Journal of Energy Technologies and Policy, Vol.2, No.1, 2011. 6. P.Pillay and R.krrishnan. “Modelling, simulation and analysis of a Permanent magnet brushless DC motor drive”, IEEE Transaction on

Industrial Applicantions, Vol26, pp124-129,2002. 7. John M. Rhodes, “Practical Considerations in the interaction of machine structure and Control System” Machine Tool Engineer, General

Electric Company U.S.A, July-October, 1973. 8. Jack Thomas “Applying Electric Servos and Steppers to numerical Control” The seventh annual meeting and technical conference of the

Numerical Control Society, April 8-10, 1970, Boston, Massachusetts.

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Authors: Anish Gupta, K. B. Singh, R. K. Singh

Paper Title: Study of WEBCRAWLING Polices

Abstract: Web crawler is a software program that browses WWW in an automated or orderly fashion, and the process is known as web crawling. A web crawler creates the copy of the visited pages so that when required later on, it will index the pages and processing becomes faster. This paper discuss the various techniques of the web crawling through which search becomes faster. In this paper studied has been done on the various issues important for designing high performance system. The performances and outcomes are determined by the given factors under the summarization criteria. Keywords: Web crawler, WWW - World Wide Web, URL - Universal resource locator, OPIC (On-line page importance computation), MIME – (Multipurpose Internet mail extension). References: 1. Gulli, A.; Signorini, A. (2005). "The indexable web is more than 11.5 billion pages". Special interest tracks and posters of the 14th

international conference on World Wide Web. ACM Press.. pp. 902–903. doi:10.1145/1062745.1062789. 2. Lawrence, Steve; C. Lee Giles (1999-07-08). "Accessibility of information on the web". Nature 400 (6740): 107. Bibcode

1999Natur.400..107L. doi:10.1038/21987. PMID 10428673. 3. Menczer, F. (1997). ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery. In D. Fisher,

ed., Machine Learning: Proceedings of the 14th International Conference (ICML97). Morgan Kaufmann 4. Menczer, F. and Belew, R.K. (1998). Adaptive Information Agents in Distributed Textual Environments. In K. Sycara and M. Wooldridge

(eds.) Proc. 2nd Intl. Conf. on Autonomous Agents (Agents '98). ACM Press 5. Chakrabarti, S., van den Berg, M., and Dom, B. (1999). Focused crawling: a new approach to topic-specific web resource discovery.

Computer Networks, 31(11–16):1623–1640. 6. Pinkerton, B. (1994). Finding what people want: Experiences with the WebCrawler. In Proceedings of the First World Wide Web

Conference, Geneva, Switzerland. 7. Pant, Gautam; Srinivasan, Padmini; Menczer, Filippo (2004). "Crawling the Web". In Levene, Mark; Poulovassilis, Alexandra. Web

Dynamics: Adapting to Change in Content, Size, Topology and Use. Springer. pp. 153–178. ISBN 978-3-540-40676-1. Retrieved 2009-03-22.

8. Cothey, Viv (2004). "Web-crawling reliability". Journal of the American Society for Information Science and Technology 55 (14): 1228–1238. doi:10.1002/asi.20078.

9. Jian Wu, Pradeep Teregowda, Madian Khabsa, Stephen Carman, Douglas Jordan, Jose San Pedro Wandelmer, Xin Lu, Prasenjit Mitra, C. Lee Giles, Web crawler middleware for search engine digital libraries: a case study for citeseerX, In proceedings of the twelfth international workshop on Web information and data management Pages 57-64, Maui Hawaii, USA, November 2012.

10. Jian Wu, Pradeep Teregowda, Juan Pablo Fernández Ramírez, Prasenjit Mitra, Shuyi Zheng, C. Lee Giles , The evolution of a crawling strategy for an academic document search engine: whitelists and blacklists, In proceedings of the 3rd Annual ACM Web Science Conference Pages 340-343, Evanston, IL, USA, June 2012.

11. Cho, Junghoo; Hector Garcia-Molina (2000). "Synchronizing a database to improve freshness". Proceedings of the 2000 ACM SIGMOD international conference on Management of data. Dallas, Texas, United States: ACM. pp. 117–128. doi:10.1145/342009.335391. ISBN 1-58113-217-4. Retrieved 2009-03-23.

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12. Jr, E. G. Coffman; Zhen Liu, Richard R. Weber (1998). "Optimal robot scheduling for Web search engines". Journal of Scheduling 1 (1): 15–29. doi:10.1002/(SICI)1099-1425(199806)1:1<15::AID-JOS3>3.0.CO;2-K.

13. Cho, J. and Garcia-Molina, H. (2003). Effective page refresh policies for web crawlers. ACM Transactions on Database Systems, 28(4). 14. Koster, M. (1995). Robots in the web: threat or treat? ConneXions, 9(4). 15. Heydon, Allan; Najork, Marc (1999-06-26) (PDF). Mercator: A Scalable, Extensible Web Crawler. Retrieved 2009-03-22.[dead link] 16. Dill, S., Kumar, R., Mccurley, K. S., Rajagopalan, S., Sivakumar, D., and Tomkins, A. (2002). Self-similarity in the web. ACM Trans.

Inter. Tech., 2(3):205–223. 17. Shkapenyuk, V. and Suel, T. (2002). Design and implementation of a high performance distributed web crawler. In Proceedings of the 18th

International Conference on Data Engineering (ICDE), pages 357-368, San Jose, California. IEEE CS Press.

11.

Authors: Vijay Prakash Singh, Anubhuti Khare

Paper Title: A Multiple Access Technique for Differential Chaos Shift Keying

Abstract: Various chaos-based digital communications techniques have been proposed recently. Among them, differential chaos shift keying (DCSK) allows the receiving end to decode factors. In this paper we also use AWGN channel and see how it effects the BER, the signal using non coherent detection. This paper proposes and analyses a multiple access scheme for DCSK. A simple 1-dimensional iterative map has been used to generate the chaotic signals for all users. Bit error probabilities have been derived numerically for different number of users and computer simulations have been performed to verify the results and also compare the BEP for different spreading factors. Keywords: Chaos-based communications, exact bit error rate (BER), multiple access. References: 1. M.P. Kennedy, “Chaotic communications: from chaotic synchronization to FM-DCSK,” Proceedings, 6th International Specialist

Workshop on Nonlinear Dynamics of Electronics Systems (NDES 98), Budapest, Hungary, pp. 31-40, July 1998. 2. G. Kolumban, M.P. Kennedy and G. Kis, “Multilevel differential chaos keying,” Proceedings, 5th International Specialist Workshop on

Nonlinear Dynamics of Electronics Systems, Moscow, Russia, June 1997, pp. 191-196. 3. F. C. M. Lau, M. M. Yip, C. K. Tse, and S. F. Hau, “A multiple access technique for differential chaos shift keying,” IEEE Trans Circuits

Syst. I, vol. 49, pp. 96–104, Jan. 2002. 4. F. C. M. Lau and C. K. Tse, Chaos-Based Digital Communication Systems: Operation, Analysis and Evaluation, 1st ed. Heidelberg,

Germany: Springer-Verlag, 2003. 5. A. J. Lawrance and G. Ohama, “Exact calculation of bit error rates in communication systems with chaotic modulation,” IEEE Trans.

Circuits Syst. I, vol. 50, pp. 1391–1400, Nov. 2003. 6. G. Mazzini, G. Setti, and R. & Rovatti, “Chaotic complex spreading sequences for asynchronous DS-CDMA—I: System modeling and

results,” IEEE Trans. Circuits Syst. I, vol. 44, pp. 937–947, Oct. 1997. 7. “Chaotic complex spreading sequences for asynchronous DS-CDMA—II: Some theoretical performance bounds,” IEEE Trans.Circuits

Syst. I, vol. 45, pp. 496–506, Apr. 1998. 8. T. Geisel and V. Fairen, “Statistical properties of chaos in Chebyshev maps,” Phys. Lett., vol. 105A, no. 6, pp. 263–266, 1984.

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12.

Authors: Muthukumarasamy.R, Mukesh.M.V, Tamilselvi.M1, Singarasubramanian.S.R, Chandrasekaran.A, Sabeen.H.M

Paper Title: Shoreline Changes Using Remotesensing Andgisenvironment: A Case Study of Valinokkam to Thoothukudi Area, Tamilnadu, India

Abstract: Coastal erosion is a worldwide difficulty distressing almost every country throughout the world having a shoreline. This complexity is probable due to the global warming, sea-level rise and the impact is the global problem. An attempt is made to study the erosion and accretion along the Valinokkam and Thoothukudi coast. Landsat ETM 1992, ETM 2000, ETM SLC OFF 2005, ETM SLC OFF 2010 and ETM SLC OFF 2012 data is utilized and comparison with toposheet no NC44-3 and NC44-13(1920) as baseline data. It is estimated that erosion during the period 1992 to2000 was 369m, 2000 to 2005 was573m, 2005to2010was 172m and 2010 to2012 with 305m respectively. The accretion during the period 1992 to 2000 was1258m, 2000 to 2005 was 120m. 2005 to 2010 were 531m, and 2010 to2012 were 366m correspondingly. Simultaneous erosion and accretion were also observed in specific geographical areas in Hare Island as sand spits below southern harbour breaker water and urban coast. In these areas the accretion dominates, suggesting the coast as a pro-grading coast. To verify the change, wave pattern and its dynamics were also studied using land sat image 2012. Keywords: Remote sensing & GIS, Landsat and ETM, shoreline erosion, deposition, Valinokkam, Thoothukudi References: 1. Amaro, A.C., Duesen, H. and Belchers, G. (2002 b) Modeling cliff development in South Wales: Savigear re-viewed. Z. Geomorph.

N.F.,Vol. 28, pp. 405-426. 2. Boutiba M, Bouakline S (2011) Monitoring shoreline changes using digital aerial photographs, quick-bird image and DGPS topographic

survey: Case of the east coast of Algiers, Algeria [J]. European Journal of Scientific Research, 48(3): 361-369 3. Capobianco, M., Devriend, H.J., Nicholls, R. J., Stive, M. J. (1999) Coastal Area Impact and Vulnerability Assessment: The point of view a

morphodynamic modeler.Jour.Coastal Research., Vol.15, No.3, pp.701-716. 4. Chandrasekar, N., Anil Cherian, Rajamanickam, M. andRajamanickam, G.V. (2000, b) Coastal landform mapping between Tuticorin and

Vaippar using IRS - IC Data. Ind. Jour. Geom., Vol.5, No. 1 and 2, pp. 114-120. 5. Charatkar (2004) A study of erosion and accretion along Gulf of Khambat, Gujarat coast using remote sensing and GIS[C]. AFITA/WCCA,

Joint Congress on Agriculture, Bangkok, Thailand 6. Griffiths,c.j.(1988) the impact of sand extraction from seasonal streams on erosion of kunduchibeach, In: Beach erosion along kunduchi

beach, north of Dar es salaam. A report for NEMC by Beach Erosion Monitoring Committee, 55pp. 7. Griffths, C.J.&Lwiza, K.M.(1998) physical backround of the study area.In:Beach erosion along Kunduchi beach, north of dare s salaam. A

report for NEMC by Beach Erosion Monitoring Committee. 55pp. 8. K. Selvavinayagam (2008) shoreline change monitoring in tuticorin coast using remote sensing and gistools. 9. Loveson V. J., Rajamanickam G. V. ndAnbarasu K. (1990), Remote sensing application in the study of sea level variation along the

Tamilnadu coast, India, In: Sea level variation and its impacts on coastal environments, (ed) Rajamanickam G.V., Tamil University, Tanjavur, pp. 179-196.

10. Meijeriink, M.J. (1971) Reconnaissance survey of quaternary Geology of Cauvery delta. Jour Geol.Soc. India. Vol.12, pp.113-124. 11. Nayak, S.R. and Sahai, B. (1985) Coastal geomorphology: a case study of the Gulf of Khambhat (Cambay). Int. Jour. Remote Sensing.,

Vol. 6, pp. 559-567.

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12. Nayak, S.R., (2002). Application of remote sensing to coastal zone management in India. Proc. Int. Sympo. Remote Sensing and Environment Monitoring and ISRS Annual Convension held at Hyderabad, India from December 3-6, 2002.

13. PrabhakarRao, P., Nair, M., and Raju, D.V.(1985) Assessment of the role of Remote Sensing in monitoring shoreline changes, A case study of the Kerala Coast, Intl. Jour.RemoteSensing, Vol.6, No.3 & 4, pp.549-558.

14. Rajamanickam, M. (2006) Remote sensing and GIS applications on beach placer minerals evaluation along the coast between Kallar and Vember, unpublished Ph.Dthesis, Tamil University.

15. VedastMakota, Rose sallema, and Charles Mahika (2004) Monitoring Shoreline Change Using Remote sensing and GIS: A Case Study of Kunduchi Area, Tanzania. Western Indian Ocean J.Mar.Sci. Vol. 3, No.1, pp.1-10.

16. Vital, H. (2003 a) Applications of Remote Sensing for monitoring and evaluation of coastal morphodynamic on the north-eastern coast of Brazil.EOS Transactions, American Geophysical Union. ISSN 0096-394. pp. 67-89.

17. Vital, T.L., Isobe, M. Igarashi, H. Sasaki, T.O. and Horikawa, K. (2003 b) Discussion of Sand Bypassing Simulation Using Synthetic Long shore Transport Data; Second Series, CETA 82-4. U.S. Army Corps of Engineers, Waterways Experiment Station, Coastal Engineering research Centre, Vicksburg, Mississipi. pp. 56-67.

13.

Authors: C.Nagaraju, B.Srinu, E. Srinivasa Rao

Paper Title: An efficient Facial Features extraction Technique for Face Recognition system Using Local Binary Patterns

Abstract: Imaging in life and materials sciences has become completely digital and this transformation of visual imagery in to mathematical constructs has made it common place for researchers to utilize computers for their day to day image analysis tasks. The main objective of the paper is extracting the facial features of an image. In this paper presents a survey on the recent use of Local Binary Patterns (LBPs) for face recognition. It is becoming a popular technique for face representation. In the existed system we are using LBP. It is a non-parametric kernel which summarizes the local special structure of an image and it is invariant to monotonic gray-scale transformations. Here, we describe the LBP technique and different approaches proposed in the literature to represent and to recognize faces but it is having some limitations like it is not suitable for shadow images and low contrasted images. To overcome those problems in this project we are proposing 2D principles of component analysis (2D-pca) to extract the facial features of an image. Here we are using our own data bases to extract the facial features. Keywords: LBP, Extended LBP, PCA kerne References: 1. A.S.Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Illumination cone models for face recognition under variable

lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 643–660, Jun. 2001. 2. R.Basri and D. Jacobs, Lambertian Reflectance and Linear Subspaces, NEC Research Inst. Tech. Rep. 2000-172R, 2000, Tech. Rep. 3. R.Basri and D.W. Jacobs,“Lambertian reflectance and linear sub-spaces,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 218–

233, Feb. 2003. 4. Bhabatosh Chanda and Dwijest Dutta Majumder, 2002, Digital Image Processing and Analysis. 5. R.W.Jr. Weeks,(1996). Fundamental of Electronic ImageProcessing Bellingham SPIE Press 6. A.K.Jain, Fundamentals of Digital Image Processing.Englewood Cliffs, NJ: Prentice Hall, 1989. 7. R.M. Haralick, and L.G. Shapiro, Computer and RobotVision, Vol-1, Addison Wesley, Reading, MA, 1992. 8. R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, McGraw-Hill International Edition, 1995. 9. W. K. Pratt, Digital image processing, Prentice Hall, 1989. 10. A.C. Bovik, Digital Image Processing Course Notes, Dept. ofElectrical Engineering, U. of Texas at Austin, 1995. 11. D. W. Jacobs, P. N. Belhumeur, and R. Basri, “Comparing images under variable illumination,” in Proc. IEEE Conf. Computer Vision and

Pattern Recognition, 1998, pp. 610–617. 12. J. Tumblin and G. Turk, “LCIS: A boundary hierarchy for detail-pre¬serving contrast reduction, in ACM SIGGraph, 1999, pp. 83–90. 13. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Generative models for recognition under variable pose and

illumination,” in Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, 2000, pp. 277–284.

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14.

Authors: S. U. Kadam, P. D. Khawale

Paper Title: Network Security Design Based on Internet Information System

Abstract: The network information security use is closely related to the field of e-commerce applications. The Network security Evaluation, is a hot issue, in development of computer and network. Without trust, most business operators and clients may decide to decline use of the Internet and revert back to traditional methods of doing business. The encryption technology of e-commerce platform for network security, implementing information encryption and transmission on the network by using DES algorithm. Cryptography is allows secure storage of sensitive data on any computer. Keywords: network security; encryption; security; e-commerce References: 1. Hu Xiangdong,Wei Qinfang. “Application of Cryptography Tutorial” Beijing: Electronic Industry Press, 2005:172-220 2. Nayunipatruni Suman , “Network Security Evaluation Based on Information Security” ,International Journal of Wisdom Based Computing,

Vol. 1 (2), August 2011 3. Xi Jianrong, “Network Security Platform Design Based on WWW Information System” , International Conference on Machine Vision and

Human-machine Interface 2010 4. Xie Xiren.” Computer Networks (4th edition)” Beijing: Electronic Industry Press, 2006:280-330 5. Wang YanPing, Zhang Yue. “Windows network and communications programming” Beijing: People's Posts & Telecom Press, 2006:51 –

80 6. Wang Zhengjun “Visual C++ 6.0 programming from entry to the essence” Beijing: People's Posts & Telecom Press, 2006:200 - 220 7. Zhang Hongqi. “Information network security” Tsinghua University Press, 2002:81 - 94 8. Katsikas.SK, Lopez.j., Pernul.G, “Trust, privacy and security in digital business” Computer System Science and Engineering Volume 20,

Issue 6, November 2005, Pages 391-399 9. Yu Shaojun. “E-Commerce Security Analysis and data encryption technology” China's management of information technology (Integrated

version), 2007 10. ShiHua “Network transmission of data technology research” Scientific and technical information, 2006

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15. Authors: Puneeth C N, Shetty Santosh Kumar, Sachin R Naik, Bhaskara Rao N.

Paper Title: Two of Three Threshold Visual Cryptography in Block Truncation Coding Scheme with Error Diffusion

Abstract: A new method of embedding a secret binary data in the Block Truncation Coded image shares is presented. The proposed scheme implements two of three threshold visual encryption. Error reduction is achieved by Floyd-Steinberg Error diffusion. Keywords: Block Truncation Coding, Error Diffusion, Secret Sharing, Threshold Visual Cryptography. References: 1. Nimrod Peleg, “Block Truncation Coding” cs.haifa.ac.il/~nimrod/Compression/Image/I5btc2005.pdf. 2. Pasi Fränti, Olli Nevalainen and Timo Kaukoranta,” Compression of Digital Images by Block Truncation Coding: A Survey”. The

Computer Journal, 37 (4), 308-332, 1994. 3. Doaa Mohammed, Fatma Abou-Chadi, “Image Compression Using Block Truncation Coding”, Cyber Journals: Multidisciplinary Journals

in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), February Edition, 2011. 4. Block Truncation Coding - Wikipedia, the free encyclopedia en.wikipedia.org/wiki/Block_Truncation_Coding. 5. Jing-Ming Guo and Yun-Fu Liu,” High Capacity Data Hiding for Error Diffused Block Truncation Coding”, IEEE transactions on image

processing, vol. 21, no. 12, december pp.4808-4818, 2012. 6. Error diffusion - Wikipedia, the free encyclopedia en.wikipedia.org/wiki/Error_diffusion. 7. Halftoning by Error Diffusion www.ece.utexas.edu/~bevans/projects/.../talks/ErrorDiffusion.html

16.

Authors: S.Saagari, P.Devi Anusha, Ch.Lakshmi Priyanka, V.S.S.N.Sailaja

Paper Title: Data Warehousing, Data Mining, OLAP and OLTP Technologies Are Essential Elements to Support Decision-Making Process in Industries

Abstract: This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, applications and the architecture of Data Warehousing. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. Keywords: Data Warehousing, OLAP, OLTP, Data Mining, Decision Making and Decision Support References: 1. Devlin, B. & Murphy, P. (1988) An Architecture for a Business and Information System, IBM Systems Journal, 27 (1), 60-80. 2. Inmon, W.H. (1996) Building the Data Warehouse, Second Edition, New York: John Wiley & Sons. 3. Kimball, R. (1996) The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses, New York: John Wiley

& Sons. 4. Gardner, S.R. (1998) Building the Data Warehouse, Communications of the ACM , 41 (9), 52-60. 5. Simon, A. (1998) 90 Days to the Data Mart, New York: John Wiley & Sons. 6. Frolick, M.N. & Lindsey, K. (2003) Critical Factors for Data Warehouse Failure, Journal of Data Warehousing, 8 (1). 7. Hwang, H.-G., Kua, C.-Y., Yenb, D. C., & Chenga, C.-C. (2002) Critical factors influencing the adoption of data warehouse technology: a

study of the banking industry in Taiwan, Decision Support Systems, In Press, Corrected Proof. 8. Finnegan, P. & Sammon, D. (1999) Foundations of an Organisational Prerequisites Model for Data Warehousing, Proceedings of the 7th

European Conference on Information Systems (ECIS 1999), Copenhagen, June. 9. O’Donnell, P., Arnott, D., & Gibson, M. (2002) Data warehousing development methodologies: A comparative analysis, Working Paper,

Melbourne, Australia: Decision Support Systems Laboratory, Monash University. 10. Meyer, M. & Winter, R. (2001) Organization of Data Warehousing in Large Service Companies: A Matrix Approach Based on Data

Ownership and Competence Centers, Journal of Data Warehousing, . 11. Kachur, R. (2000) Data Warehouse Management Handbook, Paramus: Prentice Hall. 12. Adelman, S. & Moss, L. (2000) Data Warehouse Project Management, Boston: Addison-Wesley. 13. Auth, G. (2003) Prozessorientierte Organisation des Metadatenmanagements für Data- Warehouse-Systeme, Doctoral Thesis, University of

St. Gallen, Bamberg: Difo-Druck (in German). 14. Bolman, L.G. & Deal, T.E. (2003) Reframing Organizations: Artistry, Choice, and Leadership, San Francisco: Jossey-Bass. 15. Mueller-Stewens, G. (2003) Die Organisation als Gegenstand von Veränderungsprozessen, in H. Oesterle & R. Winter (Eds.), Business

Engineering: Auf dem Weg zum Unternehmen des Informationszeitalters (pp. 133-145), Berlin: Springer (in German). 16. Wixom, B.H. & Watson, H.J. (2001) An Empirical Investigation of the Factors Affecting Data Warehouse Success, MIS Quarterly, 25 (1),

17-41. 17. Little, R. & Gibson, M. (1999) Identification of Factors Affecting the Implementation of Data Warehousing, Proceedings of the 32nd

Annual Hawaii International Conference on System Sciences

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17.

Authors: Ramkrishna Das, Saurabh Dutta

Paper Title: An Approach Of Bitwise Private-Key Encryption Technique Based On Multiple Operators And Numbers Of 0 And 1 Counted From Binary Representation Of Plain Text’s Single Character

Abstract: Private-key cryptography is a cryptographic system that uses the same secret key to encrypt and decrypt messages. The Existing Private-key cryptography systems are complex and not full proof in respect of security concern as the distribution of the private-key without interpretation is very hard to achieve. Here, we have proposed an idea to increase the security of Private-key encryption technique. We have focused on the secret procedure to retrieve secret value from the private-key rather than securing the actual private-key value. The encryption is done by the secret value derived from the private-key. The secret value is being derived by making arithmetic operation between the user defined base value and a decimal value. That decimal value is derived by

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performing arithmetic operation between number of ‘0’ and number of ‘1’counted from 8 bit representation of a plain text’s character. The operators are being supplied by the user. As the numbers of ‘0’ and ‘1’ are different for several numbers of character, so we get different secret private-key value for several numbers of character in the plain text. Thus the security is increased. Keywords: Arithmetic operators, Counting of ‘0’ and ‘1’ from binary representation of a character, Private-key Encryption, Stream Cipher. References: 1. J. K. Mandal, S. Dutta, “A 256-bit recursive pair parity encoder for encryption ”, Advances D -2004, Vol. 9 nº1, Association for the

Advancement of Modeling and Simulation Techniques in Enterprises (AMSE, France), www. AMSE-Modeling.org, pp. 1-14 2. William Stallings, Cryptography and Network security: Principles and practice (Second Edition), Pearson Education Asia, Sixth Indian

Reprint 2002. 3. Atul Kahate (Manager, i-flex solution limited, Pune, India), Cryptography and Network security, Tata McGraw-Hill Publishing Company

Limited. 4. Mark Nelson, Jean-Loup Gailly, The Data Compression Book. BPB Publication 5. Saurabh Dutta, “An Approach towards Development of Efficient Encryption technique”, A thesis submitted to the University of North

Bengal for the Degree of Ph.D., 2004.

18.

Authors: Aiswarya.V.S, Jemimah Simon

Paper Title: Diagnosis of Alzheimer’s disease in Brain Images using Pulse Coupled Neural Network

Abstract: Alzheimer’s disease is most commonly occurring type of disease in elderly patients. An automatic computer-aided diagnosis tool that supports the interpretation of functional brain images is proposed in this paper for the diagnosis of the Alzheimer’s disease. This new technique is based on Pulse Coupled Neural Network (PCNN) for image classification. In Alzheimer’s disease diagnosis mainly two databases are selected: a Single photon emission computed tomography (SPECT) database and Positron emission tomography (PET) images, both contains details for Alzheimer’s disease patients(AD) and healthy references (NOR). The major steps in detection of Alzheimer’s disease are feature extraction, feature reduction and classification of these features for making correct decision. The features from the images are extracted using wavelet packet transform (WPT). The reduction & selection of the most relevant features is done using non-negative matrix factorization (NMF). The resulting sets of data, which contain a reduced number of features, are classified by means of a Pulse Coupled Neural Network - based classifier for decision. This novel technique provides high classification accuracy and also reduces time consumption compared to existing methods. Keywords: Alzheimer’s disease, wavelet packet tree, positron emission tomography (PET), single photon emission computed tomography (SPECT), pulse coupled neural network (PCNN). References: 1. I.Álvarez, J.M.Górriz, J.Ramírez, D.Salas, M.López, C.G.Puntonet, and F. Segovia, “Alzheimer’s diagnosis using Eigen brains and support

vector machines,”-2009 2. “Breast cancer diagnosis system based on machine learning techniques”-2012 3. R. Chaves, J. Ramréz, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Álvarez, and F. Segovia, “SVM-based computer-aided diagnosis of the

Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting,”-2008 4. Eckhorn R., Bauer R., Jordan W., Brosch M., Kruse W., Munk M.,and Reitboeck H. J., ”Coherent oscillations: A mechanism of feature

linking in the visual cortex”, Biol. Cybern -1998 5. I.ElNaqa,Y.Yang,M.Wernick,N.Galatsanos,andR.Nishikawa,“A support vector machine approach for detection of microcalcifications,”-

2002. 6. G. Fung and J. Stoeckel, “SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information,”-2007 7. Hualiang Zhuang, Kay-Soon Low, Senior Member, IEEE, and Wei-Yun Yau, Senior Member, IEEE, “Multichannel Pulse-Coupled-Neural-

Network-BasedColor Image Segmentation for Object Detection”- 2012 8. I. A. Illán, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez, M. M. López,F. Segovia, R. Chaves, M. Gómez-Rio, and C. G. Puntonet, “18F-FDG

PET imaging analysis for computer aided Alzheimer’s diagnosis,” -2011 9. D. D. Lee and S. Seung, “Algorithms for non-negative matrix factorization,”-2001 10. Lindsay I Smith “A tutorial on Principal Components Analysis”-2002 11. M. López, J. Ramírez, J. M. Górriz, I. Alvarez, D. Salas-Gonzalez, F.Segovia, R. Chaves, P. Padilla, and M. Gómez-Río, “Principal

component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease,”-2011 12. M. López, J. Ramírez, J. M. Górriz, I. Álvarez, D. Salas-Gonzalez, F.Segovia, and R. Chaves, “Automatic tool for the Alzheimer’s disease

diagnosis using PCA and Bayesian classification rules,”-2009 13. M. Mignotte and J. Meunier, “Three-dimensional blind deconvolution of SPECT images,” - 2000. 14. Own H. S. and Aboul Ella Hassanien, ”Image registration based in multiresolution local contrast entropy in wavelet transform domain”, -

2002 15. Ruhi Sarikaya, Brayan L. Pellom, John H.L.Hansen, “Wavelet Packet Transform Features With Application to Speaker Identification”. 16. Suge Wang, Deyu Li, Yingjie Wei, and Hongxia Li “A Feature Selection Method Based on Fisher’s Discriminant Ratio for Text Sentiment

Classification”-2009 17. X. Ye, X. Lin, J. Dehmeshki, G. Slabaugh, and G. Beddoe, “Shape-based computer-aided detection of lung nodules in thoracic CT images,”-

2009.

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19.

Authors: Er. Lakhan Nagpal, Arvind Dewangan, Er. Sandeep Dhiman, Er. Sumit Kumar

Paper Title: Evaluation of Strength Characteristics of Concrete Using Crushed Stone Dust as Fine Aggregate

Abstract: The purpose of this study was to investigate the possibility of using crushed stone dust as fine aggregate partially or fully with different grades of concrete composites. The suitability of crushed stone dust waste as fine aggregate for concrete has been assessed by comparing its basic properties with that of conventional concrete. Two basic mixes were selected for natural sand to achieve M25 and M30 grade concrete. The equivalent mixes were obtained by replacing natural sand by stone dust partially and fully. The test result indicates that crushed stone dust waste can be used effectively used to replace natural sand in concrete. In the experimental study of strength

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characteristics of concrete using crushed stone dust as fine aggregate it is found that there is increase in compressive strength , flexural strength and tensile strength of concrete. Keywords: concrete, strength, fine aggregate, crushed stone dust. References: 1. Prakash Rao D.S. and Gridhar V. 2004. Investigation on Concrete with Stone crusher dust as Fine aggregate. The Indian concrete Journal.

pp. 45-50. 2. Sahu A.K., Sunil Kumar and Sachan A.K. 2003. Quarry Stone Waste as Fine aggregate for concrete. The Indian Concrete Journal. pp. 845-

848. 3. Ilangovan R. and Nagamani K. 2006. Studies on Strength and Behavior of Concrete by using Quarry Dust as Fine Aggregate. CE and CR

Journal, New Delhi. October. pp. 40-42. 4. Ilangovan R. and Nagamani K. 2006. Application of quarry Rock dust as fine aggregate in concrete construction. National Journal on

construction Management: NICMR. Pune. December. pp. 5-13. 5. Ilangovan, R.; Nagamani, K., and Kumarasamy, K.,“Studies on strength and behaviour of concrete by using crushed rock dust as fine

aggregate,” Civil Engineering and Construction Review, October 2006, pp. 924-932.

20.

Authors: Sandeep Dhiman, Arvind Dewangan, Er. Lakhan Nagpal, Sumit Kumar

Paper Title: Permeability Behavior of Self Compacting Concrete

Abstract: Self compacting concrete (SCC) is the new category of high performance concrete characterized by its ability to spread and self consolidate in the formwork exhibiting any significant separation of constituents. Elimination of vibration for compacting concrete during placing through the use of Self Compacting Concrete leads to substantial advantages related to better homogeneity, enhancement of working environment and improvement in the productivity by increasing the speed of construction. Understanding of this concrete flow property is of interest to many researchers. Flow properties of concrete at green stage are significantly governed by paste content, aggregate volume and admixture dosage. The flow properties of concrete is characterized in the fresh state by methods used for Self compacting concrete, such as slump-flow, V-funnel and L- box tests respectively. The number of trail mixtures are used and tests such as Slump Flow, V-Funnel, L-box etc. are conducted for their permissible limits, then the final proportions of ingredients and admixtures have been finalized for M30 , M 40 , M 50 and M 60 grade Concretes . In the present experimental investigation the main concentration is focused on permeability properties of self compacting concrete mixes. Keywords: Permeability, Self Compacting Concrete References: 1. Ganesan N, Indira P.V & SanthoshKumar P.T, “Durability aspects of steel fibre-reinforced SCC”, The Indian Concrete Journal, May 2006,

pp 31-37. 2. Jagadesh Vengala & Ranganath .R.V . “Effect of Fly Ash on Long term Strength in High Strength Self Compacting Concrete”. International

Conference on Recent Trends In Concrete Technology and Structures. INCONTEST 2003 Coimbatore , 10-12, September,2003, pp 341-347. 3. Naveen Kumar C , Kiran V. John , Jagadesh Vengala & Ranganth R.V , Self-Compacting Concrete with fly ash and metakaolin”, The Indian

Concrete Journal, April 2006 , pp 33-39. 4. Dr.P.Srinivasa Rao, Seshadri sekhar T “Strength properties of glass fibre self compacted concrete” . Journal of Institution of Engineers , vol

88, Feb 2008, pp 61-65. 5. Dr.P.Srinivasa Rao , Seshadri Sekhar. T “Compressive ,Split tensile strength , Flexural strength of self compacted concrete” . Journal of

Future Engineering & Technology, vol 3, Aug-Oct 2007, pp 78-82. 6. EFNARC, “Specifications and guidelines for self compacting concrete”, www.efnarc.org

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21.

Authors: S. D. Bhagwat, Vinod Jain

Paper Title: EEG Data Sets Signal Processing Using Wavelet Transforms

Abstract: Sensing is fundamental to all measurements, and its quality depends on many factors such as size, material used, etc. Physiological sensors measure core body temperature, ambulatory blood pressure, blood oxygen etc. Sensitive medical equipment EEG (Electroencephalography) measures electricity levels over areas of the human brain scalp. Data acquisition and processing of these voltages and signals involves lot of processing time. It is possible to expand the signal in a series of wavelets. Then we can join the advantages of the wavelet transform with the atomic decomposition of signal. Wavelet analysis provides a timescale description of any finite energy signal. Essentially, it is a successive decomposition of the signal in different scales. At each step, the corresponding details are separated, providing useful information for detecting and characterizing short time phenomena or abrupt changes of energy. This paper studies wavelet transforms for EEG data processing Keywords: Sensor, EEG, Neurological Disorder, Wavelets References: 1. Dhanjoo Ghista, et. al., “A Closed EEG Feedback System and its Clinical Applications in Treating Neurological Disorders”, Gordon and

Breach Science Publishers Ltd., pp. 119-134, Vol. 2 1978 2. "Brain Scanning." Encyclopedia Britannica. 2008. Encyclopedia Britannica Online. (7/19/2008) http://www.search.eb.com/eb/article-

9016179 3. Akihiko Murai, Kosuke Kurosaki, Katsu Yamane and Yoshihiko Nakamura, “Computationally Fast Estimation of Muscle Tension for

Realtime Bio-feedback” 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, pp. 6546-6549, Sept 2009 4. T. Barber, Biofeedback and self control, Aldine, New York 1971 5. Insight in to Wavelets, K P Soman and Ramachandran, ebook, Prentice Hall India 6. Ingrid Daubechies, Ten lectures on Wavelets, CBMS NSF Regional Conference Series in Applied Mathematics, June 1992

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22. Authors: Nithya Menon, S.Praveena

Paper Title: BECAN: A Bandwidth Efficient Cooperati ve Authentication Scheme for Wireless Sensor Networks

Abstract: This paper propose a bandwidth-efficient cooperative authentication (BECAN) scheme for filtering injected false data in Wireless sensor Networks. Sensor node could be easily compromised as the attacker can gain control obtain key values and change the properties of the node. This results in an false report to sink and energy waste in en-route nodes. The proposed BECAN scheme can save energy by early detecting and filtering the most of injected false data with less time and difficulty at the en-route nodes. In addition,only a very small amount of injected false data needs to be checked by the sink, thereby reduceing the burden on sink. To filter the false data, the BECAN scheme adopts cooperative neighbor router (CNR)-based filtering mechanism. Hence it achieves not only high filtering probability but also high reliability. Keywords: Injected false data,Wirelesss sensor network, compromised sensor node , cooperative neighbor router References: 1. X. Lin, R. Lu, and X. Shen, “MDPA: Multidimensional Privacy- Preserving Aggregation Scheme for Wireless Sensor Networks,” Wireless

Comm. and Mobile Computing, 2010. 2. X. Lin, “CAT: Building Couples to Early Detect Node Compromise Attack in Wireless Sensor Networks,” IEEE GLOBECOM 2009. 3. V.C. Giruka, M. Singhal, J. Royalty, and S. Varanasi, “Security in Wireless Sensor Networks,” Wireless Comm. and Mobile Computing,Jan.

2008. 4. K. Ren, W. Lou, Y. Zhang, “Multi-User Broadcast Authentication in Wireless Sensor Networks,” IEEE Sensor Ad Hoc Comm. Networks

(SECON ’07), June 2007. 5. K. Ren, W. Lou, and Y. Zhang, “LEDS: Providing Location-Aware End-to-End Data Security in Wireless Sensor Networks,” IEEE

INFOCOM ’06, Apr. 2006. 6. Y. Zhang, W. Liu, W. Lou, and Y. Fang, “Location-Based Compromise-Tolerant Security Mechanisms for Wireless Sensor Networks,”

IEEE J. Selected Areas in Comm. 2006. 7. F. Ye, H. Luo, S. Lu, and L. Zhang, “Statistical En-Route Detection and Filtering of Injected False Data in Sensor Networks,” IEEE

INFOCOM ’04, Mar. 2004. 8. S. Zhu, S. Setia, S. Jajodia, and P. Ning, “An Interleaved Hop-by- Hop Authentication Scheme for Filtering of Injected False Data in Sensor

Networks,” IEEE Symp. Security and Privacy, 2004 9. L. Eschenauer and V.D. Gligor, “A Key-Management Scheme for Distributed Sensor Networks,” Ninth ACM Conf. Computer and Comm.

Security, 2002

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23.

Authors: Sunita, Amrita Priyam, Parikshit Munda

Paper Title: Numerical Characterization of Gene Sequences Based on Chaos Principle

Abstract: In this paper new approach is being proposed to numerically represent a gene. This can be further used to build a model that can search a gene more accurately and in less time than present methods. The method consists of three parts: primary sequence was reduced to a few of binary sequences, based on the classifications of the four nucleic acid bases. Then, by using a encoding rule, binary sequences were converted into DNA signal, which were used as input vector to calculate six embedded phase space fractal dimension(PSFD) as new invariants for the DNA primary sequences. Using these invariants, similarities among the primary sequences for one gene belonging to 10 different species is being computed. Keywords: chaos, fractals, genes, phase space,dna References: 1. Eddy, S. R., What is dynamic programming? Nature Biotechnology, 22, 909-910 (2004) 2. He P, Wang J, Numerical Characterisation of DNA Primary Sequence, Internet Electronic Journal of Molecular Design 2002, 1, 668-674 3. Lahiri T, Kumar U, Mishra H, Subrata S, Roy A D, Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial

infarction, Journal of Scientific & Industrial Research, 68, 886-870 (2009). 4. Stephen H. Kellert, In the Wake of Chaos: Unpredictable Order in Dynamical Systems, University of ChicagoPress, 1993,p32, ISBN0-226-

42976-8. 5. http://en.wikipedia.org/wiki/Fractal. 6. Yiming WEI, Ying FAN and Weixuan XU, “Nonlinear Dynamic Analysis for Inundated Area of Flood Disaster in China”, Institute of Policy

and Management, Chinese Academy of Sciences, P.O.Box 8712, Beijing 100080, and P.R. China, pp.692-696. 7. Schuster, H.G. “Deterministic Chaos: An Introduction”, Physik-Verla GmbH, 1984. 8. http://www.genome.jp/kegg/genes.html

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24.

Authors: Sumit Kumar, Arvind Dewangan, Lakhan Nagpal, Sandeep Dhiman

Paper Title: Significance of Silica Fume in Enhancing the Quality of Concrete

Abstract: In the Refractories world three decades ago, very few people were acquainted to silica fume and its usage. After a few years, it was used as an additive to brick. When added to high Alumina brick, mullite was formed in the matrix of the brick on firing, giving the brick good volume stability, strength and chemical resistance. When used in basic brick, high hot strengths resulted, at least at 2700oF, which was about the limit of what could be tested. At the time it was only logical that silica fume would be used in brick not castables. Brick were used for all critical applications, no one would have considered using castables. In this paper we shall discuss the anufacturing, properties of silica fume and its effect on concrete after addition. Today’s refractory castables have gone beyond having “brick-like properties” to actually out performing brick in many applications. Silica fume has played a major role in this transformation. Keywords: Silica, Concrete, High-performance, Smelting, Structures, Strength. References: 1. Effect of silica fume on properties of concrete by Preeti P Patel and Elizabeth George and Deepa A Sinha. 2. Silica Fume by Gary M. Gapinski and 3. John Scanlon.

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4. Silica Fume Concrete: A solution to Steel Reinforcement Corrosion in Concrete by J.T. Wolsiefer, Sr. 5. Two decades of Ready Mixed- High performance Silica Fume Concrete by Eckart R. Buhler.

25.

Authors: Venkatareddy Chilakala, Polisetti Lakshmi Manikanta, Samuel Eda, Medikonda Girija Prasad

Paper Title: Design of Variable Pitch Punching Tool

Abstract: Punching tool is used to cut and create blanks in a sheet metal with a certain pitch. The present day punching machine consists of a punching tool with constant pitch. The idea of this paper is to design variable punching tool with pitch varying from min to max pitch (40-180mm) using die with guidelines. The above design solves the problem of installing new tool for different pitches. Due to the variable punching tool the productivity of the company increases drastically and the setup time for the different punching tool to the machine also decreases. The initial cost of installation decreases to a maximum extent. Keywords: compound/combination die ;Progressive die ;punch ;die setdie- shoe ;guide plate; shank holder; punch holder ;guide rails; stripper pilot; guidepost/bush；screw; knock-out；dowel ;backing plateblank holder References: 1. Metals Handbook (Forming), ASM, Vol. 14, 1988. 2. C.W. Wegst, Stahlshl Ussel, Verlag stahlschlussel wegst Gmbh, 1989. 3. C. Donaldson, G. H. Lecain and V.C. Goold, Tool Design, McGraw- Hill, 1943. 4. F. W. Wilson, P. D. Harvey and C. B. Gump, Die Design Handbook, McGraw-Hill, 1955. 5. J.R. Paquin, Die Design Fundamentals, Industrial Press INC., 1962. 6. D. wolff, Mechanical Desktop 6.0 Quick Reference, McGraw-Hill, 2000. 7. R. Cheng, Master Mechanical Desktop, Auto Desk Press, 1997.

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26.

Authors: B.D.Deshmukh, P.W.Ingle, S.V.Bhaskar

Paper Title: An Experimental Investigation to Study Energy Absorption Capabilities of Foam Filled Tubes In Oblique Loading Using Taguchi Method

Abstract: An Experiment is conducted to determine the effect of various parameters on performance of tube in oblique loading and optimize its crushing in the given environment..The Experiment was designed using Taguchi technique and the parameters like material density , tube thickness ,foam density and angle of loading were selected which have more influence on energy absorption ,specific energy absorption and specific total efficiency based on the available literature. The energy absorption was measured from the load –displacement graph obtained from UTM . The results obtained from this experimental process were analyzed using ANOVA and the empirical formulae predicting the energy absorption, specific energy absorption and specific energy efficiency were determined for tube under oblique loading.L16 array was used ,which is a full factorial array and conducted experiments using tube material, tube wall thickness, filler material density and angle of loading as the 4 factors with 2 levels , low and high. Keywords: ANOVA, Energy Absorption(EA), Interaction, Optimization, Taguchi method. References: 1. A.E. Ismail, Parit Raja, Batu Pahat, “ Energy Absorption Of Foam-Filled Steel Extrusion Under Quasi Static Oblique Loading”,

International Journal of Engineering and Technology, Vol. 5, No. 1, 2008, pp. 11-24. 2. Mark Allen,Dev Barpanda,Rifat Tabakovic and Jay Tudor, “Improving Vehicle Stiffness and Crashworthiness Utilizing A New

Syntactic Polyurethane Foam Technology”,SAE 2003-01- 1569. 3. Thornton PH. “Energy Absorption By Foam Filled Structures”. SAE Technical Paper Series; 1980: 800081. 4. Lampinen BE, Jeryan RA.” Effectiveness Of Polyurethane Foam In Energy Absorbing Structures.” SAE Technical Paper Series; 1982:

820494. 5. S.A. Meguid , M.S. Attia, A. Monfort “On The Crush Behaviour Of Ultralight Foam-Filled Structures” Materials And Design , 2004 ,

25,183–189. 6. P. Raju Mantena , Richa Mann , “Impact And Dynamic Response Of High-Density Structural Foams Used As Filler Inside Circular

Steel Tube”, Composite Structures , 2003 , 61,291–302. 7. Sigit P.Santosa , Tomasz Wierzbicki , Arve G.Hanssen , Magnus Langseth , “Experimental And Numerical Studies Of Foam-Filled

Sections”. Int J Impact Eng 2000;24:509–34. 8. A.G. Hanssen, M. Langseth, O.S. Hopperstad ,“Static And Dynamic Crushing Of Square Aluminium Extrusions With Aluminium Foam

Filler”, International Journal of Impact Engineering, 2000 ,24 :347-383. 9. Hanssen AG, Langseth M, Hopperstad OS, “Optimum Design For Energy Absorption Of Square Aluminium Extrusions With Aluminium

Foam Filler”,Int. J. Mech. Sci., 2001,43:153-176. 10. A.G. Hanssen, M. Langseth, O.S. Hopperstad, “Static And Dynamic Crushing Of Circular Aluminium Extrusions With Aluminium

Foam Filler”, International Journal of Impact Engineering, 2000, 24 :475-507. 11. L. Aktay , A.K. Toksoy , M. Guden , “Quasi-Static Axial Crushing Of Extruded Polystyrene Foam-Filled Thin-Walled Aluminum Tubes:

Experimental And Numerical Analysis,” Materials and Design, 2006,27:556–565. 12. Halit Kavi , A. Kaan Toksoy , Mustafa Guden, “Predicting Energy Absorption In A Foam-Filled Thin-Walled Aluminum Tube Based On

Experimentally Determined Strengthening Coefficient”, Materials and Design ,2006 , 27 : 263–269. 13. Sujit Chalipat, Sarang Kshirsagar, Ajit Gokhale “Application Of Light Weight Structural Foams For Crashworthiness Of In-Production

Passenger Cars”, SAE 2005–26–327. 14. Zhibin Li , JilinYu , Liuwei Guo “Deformation And Energy Absorption Of Aluminum Foam-Filled Tubes Subjected To Oblique

Loading”, International Journal of Mechanical Sciences 2012, Vol-54 pp 48–56. 15. Phillip .J.Ross, “Taguchi Techniques for Quality Engineering”,Second Edition,1996. 16. Paul Mathews, “Design of Experiments with MINITAB”,New Age International(P)Ltd,First Indian Sub- Continent Edition,2010

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27.

Authors: Shital Solanki, H.B.Jethva

Paper Title: Modified Back Propagation Algorithm of Feed Forward Networks

Abstract: The Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real time problems in world. It is highly suitable for the problems which involve large amount of data and there is no relationships found between the outputs and inputs. However BPNN possesses a problem of slow convergence and convergence to the local optimum. Over the years, many improvements and modifications of the BP learning

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algorithm have been reported to overcome these shortcomings. In this paper, a modified backpropagation algorithm (MBP) based on minimization of the sum of the squares of errors is proposed and implemented on benchmark XOR problem. Implementation results show that MBP outperforms standard backpropagation algorithm with respect to number of iterations and speed of connvergence. Keywords: Back propagation, convergence, feed forward neural networks, training, local minima, learning rate and momentum References: 1. Kosko, B.: Neural Network and Fuzzy Systems. 1sted., Prentice Hall of India (1994) 2. Krasnopolsky, V. M. and Chevallier, F.Some Neural Network application in environmental sciences. Part II: Advancing Computational

Efficiency of environmental numerical models. In: Neural Networks. (eds.), vol. 16(3-4), pp. 335--348. (2003) 3. Coppin, B.: Artificial Intelligence Illuminated,” Jones and Bartlet Illuminated Series, USA, Chapter 11, pp. 291-- 324. (2004) 4. Basheer, I. A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application.” J. of Microbiological

Methods, 43(1), 03--31 (2000) 5. Zheng, He., Meng, Wu., Gong, B.: Neural Network and its Application on Machine fault Diagnosis,” In: ICSYSE 1992, 17-19

September, pp. 576--579, (1992) 6. Rumelhart, D. E., Hinton, G.E., Williams, R. J.: Learning Internal Representations by error Propagation. J. Parallel Distributed

Processing: Explorations in the Microstructure of Cognition. (1986) 7. Lee, K., Booth, D., and Alam, P. A.: Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean

Firms. J. Expert Systems with Applications. 29, 1- -16 (2005) 8. Zweiri, Y.H., Seneviratne, L D., Althoefer, K.: Stability Analysis of a Three-term Back-propagation Algorithm. J. Neural Networks. 18,

1341- -1347 (2005) 9. Shao, H., and Zheng,H.: A New BP Algorithm with Adaptive Momentum for FNNs Training,” In: GCIS 2009, Xiamen, China, pp. 16--

20 (2009) 10. Rehman, M. Z., Nawi, N.M., Ghazali, M. I.: Noise-Induced Hearing Loss (NIHL) Prediction in Humans Using a Modified Back

Propagation Neural Network, In: 2nd International Conference on Science Engineering and Technology, pp. 185--189 (2011) 11. Swanston, D. J., Bishop, J.M., and Mitchell, R. J.: Simple adaptive momentum: New algorithm for training multilayer Perceptrons. J.

Electronic Letters. 30, 1498--1500 (1994) 12. Mitchell, R. J., On Simple Adaptive Momentum. In: CIS 2008, London, United Kingdom, pp.01--06 (2008) 13. M. Z. Rehman , N. M. Nawi “ Improving the Accuracy of Gradient Descent Back Propagation Algorithm (GDAM) on Classification

Problems” (IJNCAA) 1(4): 838-847ns, (2011) (ISSN: 2220-9085) 14. Anil K Ahlawat,Ankit Gupta,Aniruddha Gupta,Gaurav Malik,Rahul Ramchandani “A Variant Of Back Propagation Algorithm For

Feed Forward Network”,2005 15. V.V.Joseph ,Rajapandian, N.Gunaseeli Modified Standard Backpropagation Algorithm With Optimum Initialization For Feedforward

Neural Networks” JISE,GA,USA,ISSN:1934-9955,vol.1,no.3, july 2007 16. S.P.Kosbatwar, S.K.Pathan “Pattern Association for character recognition by Back-Propagation algorithm using Neural Network

approach” (IJCSES) Vol.3, No.1, February 2012 [16] 17. J. Kamruzzaman, S.M. Aziz A Note on ActivatioFunction in Multilayer Feedforward Learning Neural Networks, 2002. IJCNN '02.

Volume 3 Proceedings of the 2002 International Joint Conference

28.

Authors: Revathy R, A.Illayarajaa

Paper Title: Efficient Load Re Balancing Algorithm for Distributed File Systems

Abstract: This system presents an innovative idea in cloud computing. In a giant cloud we are able to add thousands of nodes together. The main aim is to allot files to those nodes while not creating significant load to any of the nodes, for that files square measure partitioned off into completely different modules. Another objective is to cut back the network inconsistencies and network traffic attributable to the unbalancing of hundreds. The reduction of network inconsistency can result in maximization of network information measure in order that {so many|numerous|such a big amount of, such a giant amount of, such a lot of} large applications will run in it. Because of quantifiability property we are able to add, delete, update new nodes in order that it supports heterogeneousness of the system. To enhance the potential of nodes we tend to use Distributed file system in Cloud Computing Applications. Keywords: Cloud Computing, distributed Hash tables, load rebalancing. References: 1 J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” in Proc. 6th Symp. Operating System Design and

Implementation (OSDI’04), Dec. 2004, pp. 137–150. 2 G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels,

“Dynamo: Amazon’s Highly Available Key-value Store,” in Proc. 21st ACM Symp. 3 Hadoop Distributed File System, “Rebalancing Blocks,” http://developer.yahoo.com/hadoop/tutorial/module2.html#rebalancing. 4 HDFSFederation, http://hadoop.apache.org/common/docs/r0.23.0/hadoop-yarn/hadoop-yarn-site/Federation.html. 5 D. Karger and M. Ruhl, “Simple Efficient Load Balancing Algorithms for Peer-to-Peer Systems,” in Proc. 16th ACM Symp. Parallel

Algorithms and Architectures (SPAA’04), June 2004, pp. 36–43.

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29.

Authors: Soham H. Gandhi, D. R. Anekar, Mahevash A. Shaikh, Ajinkya A. Salunkhe

Paper Title: Security Metric for Object Oriented Class Design- Result Analysis

Abstract: It is difficult to detect vulnerabilities in the operational stage of software, because the security concern are not addressed or known sufficiently early during software development. Accessibility (data encapsulation) and interaction (cohesion) related software metrics can be measured during the earlier phases of software development. The most importance of software measurement has led to the development of new software measure. To satisfy security requirement, it is important to protect data from unauthorized disclosure of information and alteration of information. Taking security early phase of a system development should have an impact on reducing many software vulnerabilities. A new methodology has been proposed in this paper to check accessibility and interaction of class design. These metrics allow designer of system to discover and fix the security of various alternative of class designs. We also mention the analysis of these metrics. These observations show that security design metrics can be used as

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early indicators of vulnerability in software. Keywords: Class diagram, Software measurement, Vulnerability, Security Metrics, Data encapsulation, Cohesion, Model file parser. References: 1 Krishan K Aggarwal, Yogesh Singh, Jitender Kumar Chhabra. An Integrated Measure of SoftwareManitainability. Proceedings Annual

Reliability and Security Symposium. 2002; 235-241. 2 Steve Counsell, Stephen Swift, Jason Crampton “The interpretation and Utility of Three Cohesion Metrics for Object – Oriented Design”

(ACM Transactions on SE & Methodology, Vol. 15, No. 2, April 2006) 3 I. Chowdhury, B. Chan, and M. Zulkernine, "Security metrics for source code structures," in Proceedings of the Fourth International

Workshop on Software Engineering for Secure Systems Leipzig, Germany: ACM, 2008. 4 Hoglund, G. and McGraw, G., Exploiting Software: How to Break Code. Boston: Addison-Wesley, 2004. 5 Finding Accessibility and Interaction vulnerability of Rational Rose Class Design Using Design Metrics Soham H. Gandhi, D. R. Anekar,

Mahevash A. Shaikh, Ajinkya A. Salunkhe 6 Pankaj Jalote. An Integrated to Software Engineering. 2nd Edition. Narosa Publication Home. 2002. 7 Krsul, I. V., Software Vulnerability Analysis, PhD Thesis, West Lafayette, Purdue University, 1998. 8 M HarrySneed, Agnes Kapose. A Study on the Effect of Reengineering Upon Software Security.IEEE Press. 1990; 91-99. 9 J. H. Saltzer and M. D. Schroeder, "The protection of information in operating systems," in Proceedings of the IEEE, 1975, pp. 1278-1308. 10 M. Bishop, Computer Security: Art and Science. Boston: Addison-Wesley, 2003. 11 Alshammari, Bandar and Fidge, Colin J. and Corney, Diane (2009) “Security metrics for object-oriented class designs”. In: QSIC 2009

Proceedings of: Ninth International Conference on Quality Software, August 24-25, 2009, Jeju, Korea. (In Press). 12 J. Bansiya, "A Hierarchical Model for Quality Assessment of Object-Oriented Designs," Ph.D. Thesis, University of Alabama in Huntsville,

1997.

30.

Authors: Sanjay S. Gharde, Vidya A. Nemade, K. P. Adhiya

Paper Title: Design And Implementation of Special Symbol Recognition System using Support Vector Machine

Abstract: Recognition of printed mathematical symbols is a subject of growing interest to automatically convert scientific paper documents into electronic form. Several methods have been proposed for recognition of printed and handwritten symbols. Symbol recognition in mathematical expressions is also difficult because there is a large character set with a variety of font styles and font sizes. Generally, in many applications, it is necessary to copy the contents from some original documents which may be in PDF like format. While accessing the data from that document, if it encounters symbols it remains unread in the copied document. So it is very difficult to read the original document. In proposed work, Discrete Cosine Transform method is used to extract the features of the symbol and Support Vector Machine, which performs the classification task used as classifier. Support Vector Machine provides the high accuracy at the time of classification. Keywords: Symbol Recognition, Discrete Cosine Transform, Support Vector Machine. References: 1. http://en.wikipedia.org/wiki/Symbol 2. L. P. Cordella, M. Vento, “Symbol recognition in documents: a collection of techniques?” International Journal on Document Analysis and

Recognition (IJDAR),pp 73-78, Springer Varlag 2000. 3. Xiaofang Xie , “On the Recognition of Handwritten Mathematical Symbols”, Thesis submitted at London, Ontario, Dec 2007. 4. http://www.scribd.com/doc/60245721/English-Character- Recognition -System-Using-matlab. 5. Alexander Wong and William Bishop, “Robust Hough-Based Symbol Recognition Using Knowledge-Based Hierarchical Neural

Networks”. 6. GONG Xin, LI Cuiyun, PE Jihon, XIE Weixin, “HMM Based Online Hand-Drawn Graphic Symbol Recognition”,ICSP'02 Proceedings,pp-

1067-1070, 2002 IEEE. 7. T. Cheng, J. Khan, H. Liu and D. Y. Y. Yun, “A Symbol Recognition System”,pp-918-921, 1993 IEEE. 8. Jisheng Liang’ Ihsin T. Phillips Vikram Chalana’ Robert Haralick, “A Methodology for Special Symbol Recognitions”, 2000 IEEE. 9. Xue-Dong Tian, Li-Na Zuo, Fang Yang, Ming-Hu Ha,” An Improved Method Based On Gabor Feature For Mathematical Symbol

Recognition”, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007, IEEE.

10. Heloise Hse, A. Richard Newton, “Sketched Symbol Recognition using Zernike Moments”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04) IEEE.

11. Nafiz Arica and Fatos T. Yarman-Vural, “An Overview of Character Recognition Focused on of Character Recognition Focused on Off-Line Handwriting” pp 216-233, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, VOL. 31, NO. 2, MAY 2001 IEEE.

12. Myer Blumenstein, “Intelligent Techniques for Handwriting Recognition” Thesis submitted in December 2000. 13. Nasien ,Habibollah Haron, Siti Yuhaniz, “Support Vector Machine for English handwritten character recognition” 2010 second

International conference on Engineering and applications 2010 IEEE. 14. Anita Pal & Dayashankar Singh, ”Handwritten English Character Recognition Using Neural Network”,, International Journal of Computer

Science & Communication Vol. 1, No. 2, July-December 2010, pp. 141-144. 15. G.A. Papakostas, D.E. Koulouriotis and E.G. Karakasis, “Efficient 2-D DCT Computation from an Image Representation Point of View”

,pp 21-34. 16. Daniela Stan Raicu, “ Tutorial 2: Image Feature Extraction” Visual Computing Workshop: Image Processing , DePaul University May 21,

2004 17. Sai Charan K., “A Block DCT based Printed Character Recognition System “ Dissertation submitted at Prashanthi Nilayam, March 2006. 18. J.Pradeep, E.Srinivasan, S.Himavathi, ”Neural Network based Handwritten Character Recognition system without feature extraction”, pp

40-44, ICCCET 2011,March. 2011 IEEE. 19. Jesse Hansen, “A Matlab Project in Optical Character Recognition (OCR) 20. Shailedra Shrivastava , Sanjay S. Gharde “Support Vector Machine for Handwritten Devanagari Numeral Recognition” International

Journal of Computer Applications Oct 2010. 21. Sukhpreet Singh, Ashutosh Aggarwal, Renu Dhir, “Use of Gabor Filters for Recognition of Handwritten Gurmukhi Character”,

International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5, May 2012, pp 324-240.

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31. Authors: Anoosha Movva, Singavarapu Navya, Alisha

Paper Title: Reliable Testing of Capacitors

Abstract: A capacitor is an electronic device that consists of two conducting plates bearing opposite electronic charges, having dielectric (insulator) medium as their separator. Different capacitors may make use of different dielectric. Mostly include air, paper, and plastic, polyester and polystyrene. A potential difference across the conducting plates causes the formation of a static electric field in the dielectric. Capacitor stores energy between the two conductors where the electric field is present. Charging is the process allows the capacitor to store energy. The capacitor has a wide range of application in electronic circuits with the purpose of restricting direct current (DC) and allowing alternating current (AC) to pass through. The device is basically built for storing energy and for releasing all the energy at once. The capacity of this device to hold an electrical charge is termed as 'capacitance' and it is measured by Farads. For the measurement of electric charge between the two conducting plates is measured in the reliable methodology is described below. Keywords: Capacitance, Conducting Plates, Dielectric, Electric Field. References: 1. Standard Capacitor of 1 X 10E-5; UST, GST and GSTg testing modes 2. B. S. Rawal, M. Kahn, and W. R. Buessem, Grain Boundary Phenomena in Electronics Ceramics in Advances in Ceramics, Vol. 1, p. 172-

188, 1981. 3. D. Hennings and G. Rosenstein, J. Am. Ceram. Soc., 67, 249-254 (1984). 4. Y. Mizuno, T. Hagiwara, H. Chazono, and H. Kishi, J. Euro. Ceram. Soc., 21, 1649-1652 (2001). 5. X. Xu, P. Pinceloup, J. Beeson, A. Gurav, and G.Y. Yang, p373-376, 12th US-Japan Seminar on Dielectric and Piezoelectric Ceramics, Nov.

6-9, 2005, Annapolis, MD, USA. 6. X. Xu, et al., p179-188, CARTS USA 2007, Alburquerque, NM, USA. 7. P. Pinceloup, et al., p459-466, CARTS USA 2006, Orlando, FL, USA. 8. A. S. Gurav, X. Xu, P. Pinceloup, M. Sato, A. Tajuddin, C. Randall and G. Yang, 13th US-Japan Seminar on Dielectric and Piezoelectric

Ceramics, Nov. 2-5, 2007, Awaji Island, Japan. 9. T. Prokopowicz and A. Vaskas, “Research and Development, Intrinsic Reliability, Subminiature Ceramic Capacitors,” Final Report, ECOM-

9705-F, 1969 NTIS AD-864068 10. G. Maher, “Highly Accelerated Life Testing of K-4500 Low Fired X7R Dielectric,” Proceedings of the Passive Components for Power

Electronics Workshop. April 26-27, 2000, Penn State University. Also presented in parts at the US-Japan Seminar on Dielectric Studies November, 1999, Okinawa, Japan.

11. M.J. Cozzolino, B. Wong, L.S. Rosenheck, “Investigation of Insulation Resistance Degradation in BG Dielectric Characteristic, MIL-PRF-55681 Capacitors,” CARTS 2001 pp. 254-264.

12. J.L. Paulsen, E.K. Reed, “Highly Accelerated Life Testing of KEMET Base Metal Electrode (BME) Ceramic Chip Capacitors,” CARTS 2001, pp. 265-270.

13. M. Randall, A. Gurav, D. Skamser, J. Beeson, “Lifetime Modeling of Sub 2 Micron Dielectric Thickness BME MLCC,” CARTS 2003.

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32.

Authors: Bindu U. Kansara, B.R. Parekh

Paper Title: Dispatch, Control Strategies and Emissions for Isolated Wind-Diesel Hybrid Power System

Abstract: Depleting oil reserves and the growing concerns of global warming, have made it compulsory to seek alternative in form of environment friendly technologies like renewable energy sources. The advantage of hybrid power systems is the combination of the continuously available diesel power and locally available, pollution-free wind energy. With the Wind-Diesel hybrid power system, the annual diesel fuel consumption can be reduced and, at the same time, the level of pollution can be minimized. A proper control and dispatch strategy has to be developed to take full advantage of the wind energy during the periods of time it is available and to minimize diesel fuel consumption. The paper presents two dispatch strategies (i) load following and (ii) Cycle charging along with different system controls. For the proposed system, load following dispatch strategy along with its system control performs better than the cycle charging strategy. Keywords: Wind Turbine, Diesel Generator, Distributed Generation, HOMER References: 1. Rocha M. da., Landa Noronha G., Paula Cardoso A. ,et. al. “Feasibility study for Hybrid Electric Generating with Wind-Diesel and Grid

Resources “ RIO 9 – World Climate and Energy Event , 17-19 March, 2009, Brazil 2. Manwel J. F., McGowan J. G., Abdulwahid U., “Simplified Performance model for Hybrid Wind-diesel System” Renewable Energy

Laboratory, MA, USA. 3. BJindal A.K., Gole A.M., Muthumuni D., “Modeling and Performance Analysis of an Integrated Wind/Diesel Power System for Off Grid

Locations,” 15th National Power System Conference (NPSC), IIT Bombay, December 2008. 4. Lasseter R. H., Paigi P. “Microgrid : A Conceptual Solution” PESC’04, Germany, 20-25 June 2004 5. Getting Started Guide for HOMER Version 2.1, April 2005 6. Shahid S. M., El-Amin I, Ahmed F., “Potential of Off-Grid Wind/Diesel hybrid System for Electrification of a remote Settlement in Saudi

Arabia” Wind Engineering, 28 (5) , 621-628, 2004. 7. Frye Jack A. “Performance Objective Design of a Wind-Diesel Hybrid Energy System for Scott Base, Antarctica”, Master’s Thesis, 2006. 8. http://www.academia.edu/1485012/Dispatch_Strategy_and_Model_for_Hybrid_Photovoltaic_and_Trigeneration_Power_Systems accessed

on 12 March, 2013 9. J.K. Kaldellis, An integrated model for performance simulation of hybrid wind–diesel systems Renewable Energy 32 (2007) 1544–156

science direct 10. Survey on Microgrid Control Strategies Wei Huanga, Miao Lua*, Li Zhangb elsevier

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33.

Authors: M.Arun, K.Somasundara Vinoth

Paper Title: Design and Development of Laminated Aluminum Glass Fiber Drive Shaft for Light Duty Vehicles

Abstract: A drive shaft, also known as a propeller shaft or cardan shaft, it is a mechanical part that transmits the torque generated by a vehicle's engine into usable motive force to propel the vehicle. Now a day’s two piece steel shaft are mostly used as a drive shaft. The two-piece steel drive shaft consists of three universal joints, a center supporting bearing and a bracket, which increases the total weight of an automotive vehicle and decreases fuel efficiency. This work deals with the replacement of conventional two piece steel drive shafts with a one piece Hybrid

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Aluminum E glass/epoxy composite drive shaft for an automotive application. The basic requirements considered here are torsional strength, torsional buckling and bending natural frequency. A hybrid of Aluminum and E-glass/epoxy as in which the aluminum has a role to transmit the required torque, while the E-Glass epoxy composite increases the bending natural frequency . An experimental study was carried out to study the static torsion capability .Four cases were studied using aluminum tube wounded by different layers of composite materials. Results obtained from this study show that increasing the number of layers would enhance the maximum static torsion approximately 66% for [+45/-45]3s laminates higher than the pure aluminum and mass reduction of 42% compared with of steel drive shaft. A one-piece hybrid composite full drive shaft is optimally analyzed using Finite Element Analysis Software and simulation results were compared with the existing steel drive shaft . Keywords: One-piece hybrid aluminum/composite drive shaft, Static torque capability, buckling torque capability ,bending natural frequency, E-glass fiber ,Static Analysis , Modal Analysis, ANSYS. References: 1. Lee, D.G., Kim, H.S., Kim, J.W., and Kim, J.K. 2004. Design and manufacture of an automotive hybrid aluminum/composite drive shaft. 2. Kim H. S. and Lee, D. G. (2005), Optimal design of the press fit joint for a hybrid aluminum/composite drive shaft, Composite Structure,

in press. 3. Jin Kook Kim.Dai GilLee, and Durk Hyun Cho, 2001, “Investigation of Adhesively Bonded Joints for Composite Propeller shafts”, Journal

of Composite Materials, Vol.35, No.11, pp.999-1021. 4. Cho D. H. and Lee D. G. (1997), Manufacturing of co-curing aluminum composite shafts with compression during co-curing operation to

reduce residual thermal stresses, J. of composite Materials, 32:1221-1241. 5. Shokrieh M. M, Hasani K.and Lessard L. B. (2004), Shear buckling of a composite drive shaft under torsion, Composite Structure, 64: 63-

69. 6. Bang, K. G. and Lee, D. G. (2000), Design of carbon fiber composite shaft for high-speed air spindles, Composite Structures, 55: 247-259 7. Kim, J. K, Lee, D. G., and Cho D. H., (2001), Investigation of Adhesively Bonded Joints for Composite Propeller Shafts. J. Composite

material, 35: 999-1019. 8. Rangaswamy,T & Vijayarangan, S. & Chandrashekar, R.A. & Venkatesh, T.K. & Anantharaman, K. (2002) “Optimal design and

analysis of automotive composite drive shaft”, International Symposium of Research Students on Materials Science and Engineering, 2004, 19.

9. Lee, D. G., Kim J. W. and Hwang H. Y., (2004). Torsional fatigue characteristics of Aluminum/Composite co-cured shafts with axial compressive preload, J. of Composite Materials, 38:737-756.

10. PSGCT, Design Data. India.1995 11. Timoshenko, S. P., Gere, J. M. Theory of Elastic Stability. McGraw-Hill, NY, 1963:pp. 500 – 509. 12. Rao, S. S. Mechanical Vibrations. Addision-Wesely Publishing Company, NY: pp. 537 – 541. 13. Cho DH, Lee DG, Choi JH. Manufacturing of one-piece automotive drive shafts with aluminum and composite materials.Compos Struct

1997;38:309–19. 14. Lalanne M, Berthier O, Hagopian JD, Nelson FC. Mechanical Vibration for Engineers. New York: John Wiley and Sons; 1983. p. 102–5. 15. Gibson RF. Principles of the Composite Material Mechanics. New York: McGraw-Hill; 1994. p. 110–2. 16. Mallick PK. Fiber-Reinforced Composites. New York: Marcel Dekker; 1988. p. 2–3.

34.

Authors: S.Pratheesh Kumar, S.R.Devadasan

Paper Title: Standardization of Inspection Processes Incorporated in a Precision Machining Centre

Abstract: This paper discusses about the gauge repeatability and reproducibility (R&R) studies which are carried out in measurement system analysis (MSA) to find out whether the measurement system variations are within the standard limit or not. Variations of readings in measurement system used in inspection process are due to measuring instrument, method of measurement, material, measurement standards, man and environment. This paper discusses how a measurement system used in inspection process can be standardized so that all the variations in measurement systems are eliminated in an effective manner. The repeatability and reproducibility (R&R) value obtained as a result shows that the measurement system variations in inspection process are within the standard limit. Keywords: Measurement System Analysis (MSA), Repeatability and Reproducibility (R&R.) References: 1. Stavrianidis P., Rennie J., “Improving Management of Technological Risk: Reliability Certification of Safety Systems”, ISA Transactions,

Vol.35, 1996, pp. 127-136. 2. Khalifa M., Khan F., Haddara M., “Reliability based inspection of nickel-based alloy welds in boiling water reactor environment”,

Reliability Engineering and System Safety, Vol.95, 2010, pp.494-498. 3. Gallwey T.J., “Evaluation and control of industrial inspection: Part 1 – guidelines for the practitioner”, Industrial Ergonomics, Vol.22,

1998, pp. 37-49. 4. Gallwey T.J., “Evaluation and control of industrial inspection: Part 2 – The scientific basis for the guide”, Industrial Ergonomics, Vol.22,

1998, pp. 51-65. 5. Kruth J.P., Bartscher M., “Computed tomography for dimensional metrology”, CIRP Annals - Manufacturing Technology, vol.60, 2011,

pp. 821-842. 6. Yadong L.I., Peihua G.U., “Free-form surface inspection techniques state of the art review”, Computer-Aided Design,vol.36, 2004, pp.

1395-1417. 7. Jonathan E., Hengan O.U., “A virtual inspection framework for precision manufacturing of aerofoil components”, Computer-Aided Design,

vol.44, 2012, pp. 858-879. 8. Rucki M., Barisic B., “Air gauges as a part of the dimensional inspection systems”, Measurement, vol.43, 2010, pp. 83-91. 9. Zhao H., Kruth J.P., “Automated dimensional inspection planning using the combination of laser scanner and tactile probe”, Measurement,

vol.45, 2012, pp. 1057-1066. 10. Zhao H., Marquez J., Kruth J.P., “Inspection of free-form shaped parts”, Robotics and Computer-Integrated Manufacturing, vol.21, 2005,

pp. 421-430. 11. Zhao Y., Kramer T., “Dimensional metrology interoperability and standardization in manufacturing systems”, Computer Standards &

Interfaces, vol.33, 2011, pp. 541-556. 12. Zhang S.G., Ajmal A., “A feature-based inspection process planning system for co-ordinate measuring machine (CMM)”, Journal of

Materials Processing Technology, vol.107, 2010, pp. 111-118. 13. Hunter R., Perez J., “Modeling the integration between technological product specifications and inspection process”, Journal of Materials

Processing Technology, vol.191, 2007, pp. 34-38.

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14. Gerhard L., Nahse U., Marquez J., “Automatic Execution Of Inspection Plans For Knowledge-Based Dimensional Measurements Of Micro- And Nanostructured Components”, Metrology for a Sustainable Development, vol.23, 2006, pp. 17-22.

15. Bleys P., “Inspection planning by laser scanner”, Measurement Analysis, vol.35, 2012, pp. 75-81. 16. Nashman B.M., Yoshimi H.L., Rippey W., “A unique sensor fusion system for coordinate measuring machine tasks”, Advanced

Manufacturing, vol.53, 1997, pp. 145-156. 17. Mohib A., Azab A., Maraghy E.H., “Feature-based hybrid inspection planning: a mathematical programming approach”, Measurement

Analysis, vol. 22, 2009, pp. 13-29. 18. Agaplou J.S., “Assuring the Day-to-Day Accuracy ofCoordinate Measuring Machines - A Comparison of Tools and Procedures”,

Manufacturing Systems Research Laboratory, vol.9, 2007, pp. 109-120. 19. Kramer T.R., Huang H., Messina E., Proctor F.M., Scott H., “A feature-based inspection and machining system”, Computer-Aided Design,

vol.33, 2001, pp. 653-669. 20. Dhanish P.B., Mathew J., “Effect of CMM point coordinate uncertainty on uncertainties in determination of circular features”,

Measurement, vol.39, 2006, pp.522-531.

35.

Authors: Karthika N, Sangari A, Umamaheswari R

Paper Title: Performance Analysis of Multi Level Inverter with DC Link Switches for Renewable Energy Resources

Abstract: The main objective for a grid‐tied Photo Voltaic (PV) inverter is to feed the harvested energy from PV panel to the grid with high efficiency and high power quality. This paper reveals a novel modulation scheme called single carrier Phase Opposition Disposition (POD) Pulse Width Modulation (PWM) technique for H Bridge Multi Level Inverter topology for the solar plants that can account for voltage profile fluctuations among the panels during the day and the performance is studied by comparing total harmonic distortion and switching losses at different switching frequencies. The operating principle and performance of the proposed inverter is verified through simulation studies. Keywords: PV system, Pulse Width Modulation, THD, Simulation. References: 1. M. G. Villalva, J. R. Gazoli, E. Ruppert F, “Modeling and Circuit-Based Simulation of Photovoltaic Arrays”, Brazilian Journal of Power

Electronics, Vol. 14, No. 1, pp. 35-45 2009. 2. Ho-Dong Sun, Honnyong Cha, Heung-Geun Kim, Tae-Won Chun, Eui-Cheol Nho ,”Multi-level Inverter Capable of Power Factor Control

with DC Link Switches”, IEEE Transaction, pp. 1639-1643, 2012,. 3. Johan H. R. Enslin, , and Peter J. M. Heskes, “,Harmonic Interaction Between a Large Number of Distributed Power Inverters and the

Distribution Network” IEEE Transactions on Power Electronics, vol. 19, no. 6, november 2004 4. F. Z. Peng, J-S Lai, “Multilevel Converters – A New Breed of Power Converters,” IEEE Transactions on Industry Applications, Vol.32,

No.3, pp.509- 517, May/June 1996. 5. D.A.B. Zambra, C. Rech and J.R. Pinheiro, “Comparison of Neutral- Point Clamped,Symmetrical, and Hybrid Asymmetrical Multilevel

Inverters”, IEEE Transaction Industrial Electronics, vol. 57, no.7, pp 2297-2306, 2010. 6. G. Grandi, C. Rossi, D. Ostojic and D. Casadei, “A New Multilevel Conversion Structure for Grid-Connected PV Applications”, IEEE

Transaction Industrial Electronics, vol. 56, no. 11, pp.4416- 4426, 2009. 7. E. Villanueva, P. Correa and M. Pacas, “Control of a Single-Phase Cascaded H-Bridge Multilevel Inverter for Grid-Connected Photovoltaic

Systems”, IEEE Transaction Industrial Electronics, vol. 56, pp. 4399-4406, 2009. 8. Jose I. Leon, Sergio Vazquez, Samir Kouro, Leopoldo G. Franquelo, Juan M. Carrasco, and Jose Rodriguez, “Unidimensional

Modulation Technique for Cascaded Multilevel Converters”, IEEE Transaction on Industrial Electronics, vol. 56, no. 8, August 2009. 9. Yu Liu, Alex Q. Huang, Wenchao Song, Subhashish Bhattacharya, and Guojun Tan, “Small-Signal Model-Based Control Strategy for

Balancing Individual DC Capacitor Voltage in Cascade Multilevel Inverter-Based STATCOM” IEEE Transaction on Industrial Electronics, vol. 56, no. 6, June 2009.

10. O. Lopez, R. Teodorescu and J. Doval-Gandoy, "Multilevel Transformerless Topologies for Single-Phase Grid-Connected Converters" IEEE. IECON , pp. 5191-5196, 2006.

11. Brendan Peter McGrath, “Multicarrier PWM strategies for Multilevel Inverters”, IEEE Transaction Industrial Electronics, vol. 49, no. 4, pp. 858-867, 2002.

12. Pradyumn K. Chaturvedi, Shailendra Jain, Pramod Agarwal, Rajesh K.Nemaand Kaushal K.Sao, “Switching Losses and Harmonic Investigations in Multi Level Inverters”, IETE Journal of Research, vol. 54, no. 4, pp.297-307,2008.

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36.

Authors: Kavitha Murugeshan, Neeraj RK

Paper Title: Discovering Patterns to Produce Effective Output through Text Mining Using Naïve Bayesian Algorithm

Abstract: Text mining has been an unavoidable data mining technique. There are different methods for text mining, One of the most successful will be mining using the effective patterns. Here a Naïve Bayesian algorithm is being used for discovering of patterns, since this will be the most appropriate one for classifying positive and negative documents. The usual results will not be in an optimized manner. The prescribed method makes the output arranged in a particular order. Keywords: Text mining, Pattern discovery, Pattern Taxonomy References: 1. Ning Zhong, Yuefeng Li, and Sheng-Tang Wu. “Effective Pattern Discovery” .2012-IEEE 2. K. Aas and L. Eikvil, “Text Categorization: A Survey,” Technical Report Report NR 941, Norwegian Computing Center, 1999. 3. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” Proc. 20th Int’l Conf. Very Large Data

Bases (VLDB ’94), pp. 478-499, 1994. 4. H. Ahonen, O. Heinonen, M. Klemettinen, and A.I. Verkamo, “Applying Data Mining Techniques for Descriptive Phrase Extraction in

Digital Document Collections,” Proc. IEEE Int’l Forum on Research and Technology Advances in Digital Libraries (ADL ’98), pp. 2-11, 1998.

5. R. Baeza-Yates and B. Ribeiro-Neto,Modern Information Retrieval. Addison Wesley, 1999. 6. N. Cancedda, N. Cesa-Bianchi, A. Conconi, and C. Gentile, “Kernel Methods for Document Filtering,” TREC, trec.nist.gov/

pubs/trec11/papers/kermit.ps.gz, 2002. 7. N. Cancedda, E. Gaussier, C. Goutte, and J.-M. Renders, “WordSequence Kernels,” J. Machine Learning Research, vol. 3, pp. 1059-1082,

2003. 8. M.F. Caropreso, S. Matwin, and F. Sebastiani, “Statistical Phrasesin Automated Text Categorization,” Technical Report IEI-B4-07-2000,

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Instituto di Elaborazione dell’Informazione, 2000. 9. C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995. 10. S.T. Dumais, “Improving the Retrieval of Information fromExternal Sources,” Behavior Research Methods, Instruments, andComputers,

vol. 23, no. 2, pp. 229-236, 1991. 11. J. Han and K.C.-C. Chang, “Data Mining for Web Intelligence,”Computer, vol. 35, no. 11, pp. 64-70, Nov. 2002. 12. J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. ACM SIGMOD Int’l Conf. Management of

Data (SIGMOD ’00), pp. 1-12, 2000. 13. Y. Huang and S. Lin, “Mining Sequential Patterns Using GraphSearch Techniques,” Proc. 27th Ann. Int’l Computer Software and

Applications Conf., pp. 4-9, 2003.

37.

Authors: Uthaya Vasanthan.S, Karthikeyan.B, Senthil Kumar.M

Paper Title: Flexi Sort Algorithm

Abstract: The main objective of this project is to develop a sorting algorithm which forms an integral part of any domain, irrespective of the place in which the algorithm is used. And the scope of this system is not limited to a specific user group. Any user who wishes to perform sorting operation can make use of the system, given that the user has prior knowledge on basic computer and complier operation. Keywords: Any user who wishes to perform sorting operation can make use of the system. References: 1. Sartaj Sahni, Data structures, Algorithms and Applications in Java, McG‐Hill. 2. Robert Sedgewick, Algorithm in C++, Third edition, Addison Wesley. 3. Herbert Schildt, The complete Reference Java 2, Fifth edition, McG‐Hill.

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38.

Authors: Nilanjan Das, Ramkrishna Das

Paper Title: An Approach of Secured Ecommerce Transaction Model without Using Credit or Debit Card

Abstract: E-Commerce or e-business consists of the buying and selling of products or services over computer networks including Internet. The amount of trade conducted electronically has grown with widespread Internet usage. Security of transaction process in E-Commerce is more difficult to implement and there is no privacy of information as the information passes through the internet may be accessed by strangers. In this paper we proposed an idea for secure e-commerce transaction. In this mechanism customer buy products from seller through online without using their credit or debit card details. The payment is done between seller and customer bank account. The purchased amount is being verified by customer bank from the customer and also by the seller bank from the seller. The purchased amount is being transferred from customer account to seller account after the proper verification of the amount from both the seller and the customer end. This new idea is more secured compared to existing online payment system as we are making transaction between the seller and customer bank account without using the credit or debit card. Keywords: E-Commerce Security, Online Fund Transfer, Verification of Amount from seller and customer. References: 1. R. Kalakota A. B.Whinston “Electronic Commerce”, Pearson Education, 1997,pp 3-12. 2. E Turbun, D.King, J. Lee, T.Peng, Liang D.Debborrah,”Electronic Commerce 2010”, Pearson Education, 2010,pp . 3. J.Schmuller “Teach yourself UML In 24 Hours”.SAMS, 2004,pp 448-451. 4. Satti, M.M; Garner, B.J;Nagrial,M.H ,(2002,25-28 Nov) on Information security standards for e-businesses IEEE Communication Systems,

pp 641-645 vol.2.Available:http://www.ieexplore.ieee.org 5. Zhang Yifei,(20-22 aug,2010) ,“Research on online payment pattern and security strategy of e-commerce” ,IEEE Internet Technology and

Applications pp 1-4. Available :http://www.ieexplore.ieee.org 6. https://en.wikipedia.org/wiki/E-commerce_payment_system., E-commerce payment system

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39.

Authors: R. Satya Prasad, V. Goutham, N. Pawan Kumar

Paper Title: Estimation of Failure Count Data Using Confidence Interval

Abstract: A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. Confidence intervals are usually calculated so that this percentage is 95%, Confidence limits are the lower and upper boundaries / values of a confidence interval, that is, the values which define the range of a confidence interval. The upper and lower bounds of a 95% confidence interval are the 95% confidence limits. The method described here is based on Goel Okomoto (GO) model, Confidence Interval (CI) and parameter estimation is maximum likehood (ML). It uses historical sample test data to predict how many residual defects are there in the software system and the estimated range being calculated from a given set of sample data to achieve at least 95% confidence level. Keywords: Confidence interval(CI) , Failure intensity funtion, Goel-Okumoto model(GO), Interval Estimation, Maximum likelihood estimator (MLE), Paramenter estimation,Sof twar Reliability. References: 1. A. L. Goel, K. Okumoto, ”Time-dependent error-detection rate model for software reliability and other performance measures”. IEEE

Trans. Reliab. 1979, R-28, pp. 206-211. 2. Knafl, G.J.(1992),Solving maximum likelihood equations for two-parameter software reliability 3. models using grouped data. Proceedings., Third International Symposium on Software Reliability Engineering (205-213). 4. M. Kimura, S. Yamada, S. Osaki, ”Statistical Software reliability prediction and its applicability based on mean time between failures”.

Mathematical and Computer Modelling, 1995, Volume 22, Issues 10-12, Pages 149-155. 5. Lyu M.R(1996),Handbook of software Reliability Engineering,McGraw-Hill,NewYork. 6. J. D. Musa, A. Iannino, K. Okumoto, “Software Reliability: Measurement Prediction Application”. McGraw-Hill, New York. 1987. 7. M. Ohba, “Software reliability analysis model”. IBM J. Res. Develop. 1984, 28, 428-443.

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8. System software reliability, Springer, 2006, H. Pham. 9. Xie, M and Wohlin, C., 1997. “A Practical Method for the Estimation of Software Reliability Growth in the Early Stage of Testing”. IEEE

Computer Society

40.

Authors: Awadhesh Kumar, Neeraj Tyagi, Vinay Kumar, Prabhat Singh

Paper Title: A Study of Backbone Based Approaches Use for Data Dissemination in Wireless Sensor Network: A Survey

Abstract: Wireless sensor networks consist of sinks, events, and a large number of tiny, multifunctional and battery-powered sensor nodes. Thousands of the sensor nodes are randomly distributed over a vast field to self-organize a large-scale wireless sensor network. The sensor nodes monitor some events in surrounding environments, such as temperature, humidity, sound, vibration, presence of objects, and so on. In Wireless Sensor Networks, data dissemination to multiple mobile sinks consumes a lot of energy. Various grid-based data dissemination schemes have been proposed over the years to reduce the energy consumption in Wireless Sensor Networks. Energy is one of the most important aspects for designing a data dissemination protocol for the applications such as battle-field monitoring, habitat monitoring etc. Keywords: Data Centric protocols, Hierarchical protocols References: 1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, Y., and E. Cayirci, “Wireless sensor networks: A survey”, Computer Networks (Elsevier)

Journal, vol. 38, no. 4, pp. 393 – 422, Mar. 2002. 2. I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “A survey on wireless multimedia sensor networks”, Computer Networks (Elsevier), vol.

51, no. 4, pp. 921 – 960, Mar. 2007. 3. F. Hu and S. Kumar, “QoS considerations for wireless sensor networks in telemedicine”, in Proceedings of 2003 Intl. Conf. on Internet

Multimedia Management Systems, Orlando, Florida, pp. 323 – 334, Sept. 2003. 4. B. Girod, A. Aaron, S. Rane, and D. Rebollo - Monedero, “ Distributed video coding ”, Proceedings of the IEEE , vol. 93 , no. 1 , pp. 71 –

83, Jan. 2005. 5. J. Reed, “Introduction to Ultra Wideband Communication Systems”, Prentice Hall , Englewood Cliffs, NJ , June 2005. 6. S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, “ Localization via ultra-wideband radios ”,

IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70 – 84, July 2005. 7. Soochang Park, Euisin Lee, Fucai Yu, and Sang-Ha Kim, “Scalable and Robust Data Dissemination in Large-scale Wireless Sensor

Networks”, IEEE transaction on Consumer Electronics, Vol. 56, No. 3, August 2010. 8. Haiyun Luo, Fan Ye, Jerry Cheng , Songwu Lu, Lixia Zhang, “TTDD: two-tier data dissemination in large-scale wireless sensor networks”,

Kluwer Academic Publishers’ Wireless Networks, vol. 11, issue 1-2, pp.161-175, January 2005. 9. T.P. Sharma, R.C. Joshi, Manoj Misra, “GBDD: Grid Based Data Dissemination in Wireless Sensor Networks” Advanced Computing and

Communications, 2008. ADCOM 2008, pp 234 – 240, 2008 10. E. B. Hamida and G. Chelius, “Line-Based Data Dissemination Protocol for Wireless Sensor Networks with Mobile Sink,” EEE ICC, May

2008. 11. E. Hamida and G. Chelius, “Strategies for Data Dissemination to Mobile Sinks in Wireless Sensor Networks,” IEEE Wireless

Communications, Vol. 15, Iss. 6, pp. 31-37, Dec. 2008. 12. Z. zhou, X. Xiang, and X. Wang, “An Energy-Efficient Data- Dissemination Protocol in Wireless Sensor Networks,” IEEE WoWMoM,

Jun. 2006. 13. Xuan, H. L., & Lee, S., “A coordination-based data dissemination protocol for wireless sensor networks,” In Proceedings of the Sensor

Networks and Information Processing Conference, pp. 13–18, 2004. 14. J. N. Al-Karaki, and A. E. Kamal, “Routing Techniques in Wireless Sensor Network: A Survey,” IEEE Wireless Communications, pp.1-37,

2004. 15. K. Akkaya, and M. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,” In: Ad Hoc Networks, vol. 3, pp.325-349,

2005. 16. M. I. Abd-El-Barr, M. M. Al-Otaibi, and M. A. Youssef, “Wireless Sensor Networks-Part II: Routing Protocols and Security issues,”

Saskatoon, IEEE, pp.69-72, May 2005. 17. W. Heinzelman, K. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks”,

Proceeding of the 5th Int. Conf. on Mobile Computing and Networking (MobiCom), Seattle, USA, pp.174-185, August 1999.

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41.

Authors: Tahirou DJARA, Marc Kokou ASSOGBA, Amine NAÏT-ALI, Antoine VIANOU

Paper Title: Comparison of Harris Detector and Ridge Bifurcation Points in the Process of Fingerprint Registration using Supervised Contactless Biometric System

Abstract: Most of the matching or verification phases of the contact based fingerprint verification systems utilize minutiae to find the matched pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and twisting of fingers during enrollment, this process can cause the minutiae features to be distorted from the original. Contact-based fingerprint systems have some drawbacks due to skin elasticity, inconsistent finger placement, contact pressure, small sensing area, environment conditions and sensor noise... In this paper, we present two fingerprint registration algorithms based on Harris Detector and Minutiae matching for a Contactless Biometric system approach. The performances of the algorithms are evaluated using statistical mean and variance. Based on the above measures, the most efficient registration algorithm is pointed out. Keywords: Contactless Biometrics, Fingerprint, Harris Detector, Ridge Bifurcation, Registration, Variance, Mean. References: 1. S. Pankanti, S. Prabhakar, A.K. Jain, On the individuality of fingerprints, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24 (8) 1010-1025. 2. Lifeng Liu, Tianzi Jiang, Jianwei Yang, and al. Fingerprint Registration by Maximization of Mutual Information, IEEE Transactions on

image processing, May 2006, Vol. 15, No. 5, pp. 1100-1110. 3. C. Serief, Robust feature points extraction for image registration based on the nonsubsampled contourlet transform., International Journal

Electronics Communication. 63 (2) (2009) 148-152. 4. Parziale, G., Santana, E-D., Hauke, R. The Surround Imager : A Multi-camera Touchless Device to acquire 3D Rolled-Equivalent

Fingerprints. ICB2006, LNCS 3832, 2006, pp. 244-250. 5. B. Hiew, A. Teoh, and Y. Pang, Touch-less fingerprint recognition system, june 2007, pp. 24-29. 6. S. Mil’shtein, J. Palma, C. Liessner, M. Baier, A. Pillai, and A. Shendye, Line scanner for biometric applications, may 2008, pp. 205-208. 7. T. Djara, M. K. Assogba, A. Nait Ali. Caractérisation spatiale des empreintes de l'index en analyse biométrique. Actes du CARI 2010.

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Yamoussoukro. pp :501-508. 8. C. Harris and M. Stephens. "A combined corner and edge detector," in Proceedings of the 4th Alvey Vision Conference, pp. 147-151, 1988 9. H. Moravec. "Rover visual obstacle avoidance," in Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp. 785-

790, 1981. 10. H. Moravec. "Visual mapping by a robot rover," in Proceedings of the 6th International Joint Conference on Artificial Intelligence, pp. 598-

600,1979. 11. R. J. Prokop and A. P. Reeves, A survey of moment-based techniques for unoccluded object representation and recognition, Computer

Vision, Graphics, and Image Processing. Graphical Models and Image Processing, September 1992, vol. 54, No. 5, pp. 438-460. 12. R. Mukundan and K. R. Ramakrishnan, Moment Functions in Images Analysis. Theory and Applications, World Scientific, September

1998, 164pp. 13. W. Y. Kim and P. Yuan, A practical pattern recognition system for translation, scale, and rotation invariance, in Proceedings of the

Conference on Computer Vision and Pattern Recognition, pages 391-396, Los Alamitos, CA, Etats-Unis. IEEE Computer Society Press, 1994

14. L. Van Gool and T. Moons and D. Ungureanu, Affine/photometric invariants for planar intensity patterns, in Proceedings of the 4th European Conference on Computer Vision, 1996, pp. 642-651.

15. A. Khotanzad and Y. H. Hong, Invariant image recognition by Zernike moments, IEEE Trans. Pattern Anal. Mach. Intell., May 1990, vol. 12, No. 5, pp.489-497.

16. P. A. ELSEN, E. J. D. POL and M. A. VIERGEVER, Medical image matching a review with classification, IEEE engineering in medecine and biology,march 1993, pp26-39.

17. M. A. Fischler and R. C. Bolles. Random Sample Consensus : A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, June 1981, 24(6) :381-395.

18. Sunglok Choi, Taemin Kim and Wonpil Yu, Performance Evaluation of RANSAC Family. BMVC 2009 doi :10.5244/C.23.81 19. http://spiral.univ-lyon1.fr/mathsv/cours/stats/chap7/c7p4/c7p4.html 20. http://www.cmi.univ-mrs.fr/~kowalski/downloads/Enseignement/2008_2009/MASS/MSHE02/pdf/chapitre2.pdf 21. http://stat.ethz.ch/R-manual/R-patched/library/stats/html/var.test.html

42.

Authors: Harsh Rai,Abhinav Bharti , Rakesh Singh,Neeraj Kr.Prasad

Paper Title: Energy Efficienct Coal Gasification for IGCC Power Plant

Abstract: Today we are facing the problems regarding conventional energy sources as they are depleted drastically and limited reserves. This paper enumerates technical feasibility and assessment of the integrated gasification combined cycle (IGCC) power plant which is useful for the conventional coal-fired power plant to enhance their plant performance. IGCC is one more advanced coal combustion technology available now a day to improve overall cycle efficiency of the system, for generation of electricity. Keywords: Conventional energy sources, power plant, coal References: 1. Shozo Kaneko,et.al., "250MW AIR BLOWN IGCC DEMONSTRATION PLANT PROJECT",Proceeding of the ICOPE-03(2003),3-

pp163~167. 2. Christopher Higman, Maarten van der Burgt,"Gasifaication"(2003), pp126-128. 3. Narimitsu Araki and Yoshiharu Hanai: Bulletin of Japan Energy, 75-9, (1996) pp839-850. 4. Technical papers of Gasification Technologies Conference 1998-2000. (http://www.gasification.org). 5. Technical papers of Ist International Conference on Green Power - The need for the 21st century (12-14 Februray,1997 New Delhi) 6. Technical papers of Indo European Seminar on Clean Coal Technologies (1997 New Delhi) 7. International Energy Agency, 2007, Indicators for Industrial Energy Efficiency and CO2 Emissions: A Technology Perspective, Paris,

France, Washington, DC 8. Proceedings of the Seminar on Texaco Gasification For Refining in the 21st Century (New Delhi April,1998) 9. Various international journals such as Power Engineering International, Power, Modern Power System, Gas Turbine World etc. 10. http://envfor.nic.in/cpcb/newsletter/coal/ccombs.html 11. www.oecd.org/env/cc 12. www.coalindia.nic.in/coalreservesindia.htm 13. www.diehariandian.com/infra/poer.htm

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43.

Authors: Mohan.G, Madhu.M

Paper Title: Automation of Business Process by Optimization of Data Extraction and Loading

Abstract: An Automobile industry is transitioning to a new electronic enterprise product lifecycle management (PLM) environment using the WindchillPDMLink 10.0 application from Windchill 5.0. The development teams were using software called EIS, which does not have proper workflow. Even though new software exists the users were using the legacy EIS database. Data migration between EIS and WindchillPDMLink 10.0 has to be performed parallel. This involved a mechanism of downloading the data from EIS database and upload into Windchill database. In order to extract the data from EIS Database, SQL Scripting language is used .The data to be retrieved is specified in the script itself and then execution is carried out. The output obtained is saved as .csv file format and when this file gets executed in Windchill server, data gets loaded in the Windchill database. If the output obtained from EIS database exceeds the limit of character in SQL work sheet, then the remaining output gets stored in another .csv file. Complication starts while merging files. This data migration effort required considerably longer time than was expected due to an unexpectedly high percentage of errors in the extracted configurations. Although several iterations of extraction scripts were developed to reduce the number of errors, a significant percentage of configurations continued to display problems. Hence to optimize the data extraction methodology as well as to overcome the drawbacks of the EIS data extraction and loading, the system named as "Optimization of Data Extraction and Loading" is designed to improve quality and to reduce time of the data extraction and loading between databases. Keywords: Data migration, PLM upgradation, Product Lifecycle Management(PLM), Windchill. References:

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1. Mariano Ceccato, Thomas Roy Dean, Paolo Tonella, “Recovering structured data types from a legacy data model with overlays , Science direct Information and Software Technology 51 (2009) 1454–1468, 10 May 2009.

2. J. Bisbal, D. Lawless, B. Wu, J. Grimson, “Legacy information systems”: issues and directions, Software, IEEE 16 (5) (1999) 103–111, doi:10.1109/52.795108.

3. Rapid Application Development (RAD) for Data Migration — White Paper Solutions by Premier International, Applaud_White_Paper.pdf 2004.

4. A Roadmap to Data Migration Success-White Paper by SAP. 5. M. Aboulsamh, J. Davies, Specification and verification of model-driven data migration, Model and Data Engineering (2011) 214–225. 6. A.V. Aho, J.D. Ullman, Universality of data retrieval languages, in: Proceedings of Principles of Programming Languages, ACM, 1979, pp.

110–119. 7. Farouk Belkadi, Nadège Troussier, Collaboration based on product lifecycles interoperability for extended enterprise, Int J Interact Des

Manuf (2010). 8. Hongjun Lu and Ling Feng , Integrating database and world wide web technologies , Baltzer Science Publishers , (1998) 73–86. 9. Jesus Bisbal and Jane grimson, “Database sampling with functional dependencies”, science direct Information and software technology,

43(2001) 607-615. 10. C. Ou-Yang and M. C. Cheng,"Developing a PDM/MRP integration framework to evaluate the influence of engineering change on

inventory scrap cost”, Int J Adv Manuf Technol (2003)22:161–174 DOI 10.1007/s00170-002-1454-4. 11. Hong-Taek Ju, Mi-Joung Choi and James W. Hong, “EWS-Based Management Application Interface and Integration Mechanisms for Web-

Based Element Management”, Journal of Network and Systems Management, Vol. 9, No. 1, 20011064-7570/ 01/ 0300-0031. 12. Gregory Follen , Chan Kim, Isaac Lopez , Janche Sang And Scott Townsend, “A CORBA-Based Distributed Component Environment for

Wrapping and Coupling Legacy Scientific Codes” , Kluwer Academic Publishers. Manufactured in The Netherlands, Cluster Computing 5, 277–285, 2002.

13. Andres S. Orrego • Gregory E. Mundy, A study of software reuse in NASA legacy systems Innovations System Software Engineering (2007) 3:167–180 DOI 10.1007/s11334-007-0027.

14. Takahiro Hara, Kaname Harumoto,Masahiko Tsukamoto, Member, and Shojiro Nishio, “Database Migration:A New Architecture for Transaction Processing in Broadband Networks”, IEEE transactions on knowledge and data engineering, vol. 10, no. 5, september/october 1998 839.

15. Katarina Grolinger AND Miriam A.M. Capretz, “A unit test approach for database schema evolution” ,Science direct Information and Software Technology 53 (2011) 159–170.

44.

Authors: A.R Kulkarni, D.S Bormane

Paper Title: An Improved Hybrid Face Recognition Based on PCA and Subpattern Technique

Abstract: In this paper a new technique for face recognition Based on PCA is implemented .Subpattern PCA (SpPCA) Is actually an improvement over PCA. It was found to give Better results so in this paper Integration of Different SpPCA methods with PCA was done and found to get Improvement in recognition accuracy. Keywords: Principle Component Analysis (PCA, Subpattern PCA (SpPCA), SpPCA I, SpPCAII References: 1. T. Young, K-S Fu, Handbook of pattern recognition and image processing, Academic Press, 1986. 2. M. Turk, A. Pentland, Eigenfaces for recognition, Journal Cognitive Neuroscience, 3(1) (1991)71-86. 3. G. Loizou, S.J. Maybank, The nearest neighbor and the Bayes error rates, IEEE Trans. Patt. Anal. & Mach. Intell., vol.9 (1987)254-262,. 4. [4] Seema Asht, Rajeshwar Dass, Dharmendar, “Pattern Recognition Techniques”, International Journal of Science, Engineering and

Computer Technology, vol. 2, pp. 87-88, March 2012. 5. M. Kirby, L. Sirovich, “Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces”, IEEE Transactions on

Pattern Analysis and Machine Intelligence, vol. 12, No. 1, pp. 103-108, January 1990. 6. M. Turk and A. Pentland. “Face recognition using eigenfaces”. In Proceedings of the IEEE Conference on Computer Vision and Pattern

Recognition, 1991 7. Face Recognition : National Science and Technology Council (NSTC) , Committee on Technology, Committee on Homeland and National

Security, Subcommittee on biometrics. 8. N. Sun, H. Wang, Z. Ji, C. Zou, and L. Zhao, "An efficient algorithm for Kernel two-dimensional principal component analysis," Neural

Computing & Applications, vol.17, pp.59-64, 2008. 9. Q. Yang and X. Q. Ding, "Symmetrical Principal Component Analysis and Its Application in Face Recognition," Chinese Journal of

Computers, vol.26, pp.1146–1151, 2003. 10. J. Yang and D. Zhang, "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition," IEEE

Trans. Pattern Analysis and Machine Intelligence, vol.28, pp.131- 137, 2004. 11. K. R. Tan and S. C. Chen, "Adaptively weighted subpattern PCA for face recognition," Neurocomputing, vol.64, pp.505-511, 2005.

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45.

Authors: Implementation of Multiplier using Vedic

Paper Title: Poornima M, Shivaraj Kumar Patil, Shivukumar , Shridhar K P , Sanjay H

Abstract: Vedic mathematics is the name given to the ancient Indian system of mathematics that was rediscovered in the early twentieth century from ancient Indian sculptures (Vedas). This paper proposes the design of high speed Vedic Multiplier using the techniques of Vedic Mathematics that have been modified to improve performance. A high speed processor depends greatly on the multiplier as it is one of the key hardware blocks in most digital signal processing systems as well as in general processors. Vedic Mathematics has a unique technique of calculations based on 16 Sutras. This paper presents study on high speed 8x8 bit Vedic multiplier architecture which is quite different from the Conventional method of multiplication like add and shift. Further, the Verilog HDL coding of Urdhva tiryakbhyam Sutra for 8x8 bits multiplication and their FPGA implementation by Xilinx Synthesis Tool on Spartan 3 kit have been done and output has been displayed on LED’s of Spartan 3 kit . Keywords: Architecture, Ripple Carry (RC) Adder, Multiplication, Vedic Mathematics, Vedic Multiplier (VM), Urdhava Tiryakbhyam Sutra References: 1. Jagadguru Swami, Sri Bharati Krisna, Tirthaji Maharaja, “Vedic Mathematics or Sixteen Simple Mathematical Formulae From the

Veda, Delhi (1965)”, Motilal Banarsidas, Varanasi, India, 2. M. Morris Mano, “Computer System Architecture”, 3rd edition, Prientice-Hall, New Jersey, USA, 1993, pp. 346-348. 3. H. Thapliyal and H.R Arbania. “A Time-Area-Power Efficient Multiplier and Square Architecture Based On Ancient Indian Vedic

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Mathematics”, Proceedings of the 2004 International Conference on VLSI (VLSI’04), Las Vegas, Nevada, June 2004, pp. 434-439. 4. P. D. Chidgupkar and M. T. Karad, “The Implementation of Vedic Algorithms in Digital Signal Processing”, Global J. of Engg.

Edu,Vol.8, No.2, 2004, UICEE Published in Australia. 5. Thapliyal H. and Srinivas M.B, “High Speed Efficient NxN BitParallel Hierarchical Overlay Multiplier Architecture Based on Ancient

Indian Vedic Mathematics”, Transactions on Engineering, Computing and Technology, 2004, Vol.2. 6. Harpreet Singh Dhillon and Abhijit Mitra, “A Reduced– BitMultipliction Algorithm for Digital Arithmetics” InternationalJournal of

Computational and Mathematical Sciences 7. Honey Durga Tiwari, Ganzorig Gankhuyag, Chan Mo Kim and Yong Beom Cho, “Multiplier design based on ancient Indian Vedic

Mathematician”, International SoC Design Conference, pp. 65- 68, 2008. 8. Parth Mehta and Dhanashri Gawali, “Conventional versus Vedic mathematics method for Hardware implementation of a multiplier”,

International conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 640-642, 2009. 9. Ramalatha, M.Dayalan, K D Dharani, P Priya, and S Deoborah, “High Speed Energy Efficient ALU Design using Vedic Multiplication

Techniques”, International Conference on AdvancesIn Computationa Tools for Engineering Applications (ACTEA) IEEE, pp. 600-603, July15-17, 2009.

10. Sumita Vaidya and Deepak Dandekar, “Delay-Power Performance comparison of Multipliers in VLSI Circuit Design”, International Journal of Computer Networks & Communications (IJCNC), Vol.2, No.4, pp 47-56, July 2010.

11. S.S.Kerur, Prakash Narchi, Jayashree C N, Harish M Kittur and Girish V A “Implementation of Vedic Multiplier For Digital Signal ” International conference on VLSI communication & instrumentation

12. Pushpalata Verma, K. K. Mehta” Implementation of an Efficient Multiplier based on Vedic Mathematics Using EDA Tool” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-5, June 2012

46.

Authors: Sarita, Jyoti Hooda, Shweta Chawla

Paper Title: Design and Implementation of Low Power 4:1 Multiplexer using Adiabatic Logic

Abstract: The main and highly concerned issue in the low power VLSI design circuits is Power dissipation. The basic approaches that we used for reducing energy/power dissipation in conventional CMOS circuits include reducing the supply voltages, on decreasing node capacitances and minimize the switching activities with efficient charge recovery logic. The Adiabatic switching technique based upon the energy recovery principle is one of the techniques which is widely used to achieve low power VLSI design circuits. In the following paper the power dissipation of various adiabatic circuits is calculated and then simulated using T-SPICE tool. From the results of calculation it is observed that among all of the techniques used for multiplexer implementation the efficient charge recovery logic (ECRL) multiplexer exhibits the minimum power dissipation. The adiabatic logic family has been proposed by implementing PMOS and NMOS transistors as pull down network and pull up network. With the help of calculated result, it has been shown that the multiplexer used with adiabatic logic can reduce the power dissipation than conventional CMOS circuit. Keywords: Adiabatic, VLSI, ECRL, T-SPICE. References: 1. M. Pedram, "Power minimization in IC design: principles and applications," ACM Transactions on Design Automation of Electronic

Systems, 1(1): 3-56,January 1996. 2. N. Weste and K. Eshraghian, Principles of CMOS VLSI Design: A Systems Perspective, 2nd ed. New York: Addison - Wesley,1993. 3. J. S. Denker, "A review of adiabatic computing," inProc. of the Symposium on Low Power Electronics,1994, pp. 94-97. 4. Jan Rabey,Massoud Pendram, Low power Design Methodologies:5-7,Kluwer Academic Publishers,5th edition. 2002 5. PD Khandekar, S Subbaraman ,Manish Patil"Low power Digital Design Using Energy-Recovery Adiabatic Logic" International Journal of

Engineering Research and Industrial Apllications,Vol1,No. III,pp199-2081994, pp. 94-97. 6. S. Samanta "Adiabatic Computing" a contemporary review" 4th international conference on computer and devices for communication :

codec 09" Kolkata 2009. 7. S. Samanta" Power Efficient VLSI Inverter Design using Adiabatic Logic and Estimation of Power dissipation using VLSI-EDA Tool"

Special issue of International Journal of computer communication Technology. vol 2. isuue 2,3,4. pp300-303. 2010. 8. Arsalan, M. ; Shams, M. "An investigation into transistor-based adiabatic logic styles. " Circuits and Systems, 2004. NEWCAS 2004. The

2nd Annual IEEE Northeast Workshop on 20-23 June 2004 Page(s):1 – 4 9. J. Marjonen, and M. Aberg, “A single clocked adiabatic static logic- a proposal for digital low power applications,” J. VLSI Signal

Processing, Vol.27, Issue 27, Feb. 2001, pp. 253-268. 10. Lance Pick up and Scott Tyson, “Efficient Power management in 90 nanometer foundry reference flow” published in August/ September

2004 issue of chip design magazine. 11. Y. MOON AND D. K. JEONG, “An Efficient Charge Recovery Logic Circuit,” IEEE JSSC, Vol. 31, No. 04, pp. 514-522, April 1996. 12. T. INDERMAUER AND M. HOROWITZ, “Evaluation of Charge Recovery Circuits and Adiabatic Switching for Low Power Design,”

Technical Digest IEEE Symposium Low Power Electronics, San Diego, pp. 102-103, October 2002.

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47.

Authors: Nikita L. Vikhar, G.R.Bamnote

Paper Title: Misben: Reliable and Risk Free Approach of Blocking Misbehaving Users in Anonymizing Networks

Abstract: As we studied in our literature, the recent method was presented for blocking of misbehaving user in the Tor networks called as Nymble. However the limitation which we identified for Nymble is that if the Nymble manager fails, then whole security system is fails. And hence this approach is heavily vulnerable for failure risks. Thus in this paper we are presenting the new extended method for overcoming above said problems. In this paper we propose a secure Misben system, where users acquire an ordered collection of Misbens, a special type of pseudonym, to connect to Websites. Without additional information, these Misbens are computationally hard to link and hence, using the stream of Misbens simulates anonymous access to services. Web sites, however, can blacklist users by obtaining a seed for a particular Misben, allowing them to link future Misbens from the same user. Servers can therefore blacklist anonymous users without knowledge of their IP addresses while allowing behaving users to connect anonymously. Our system ensures that users are aware of their blacklist status before they present a Misben, and disconnect immediately if they a0072e blacklisted. For the risk free and reliability of proposed approach we also proposed architecture with details that if first misben manager failed to generate seed when it get complaint from server, in previous systems in that condition system get collide and then anonymizing network in trouble ,so provide solution to this problem we introduced new 2nd misben manager.

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Keywords: Anonymizing Networks, TOR, Pseudonym, Anonymous Access, 2nd Misben Manager, Misben. References: 1. A. Lysyanskaya, R. L. Rivest, A. Sahai, and S. Wolf. Pseudonym Systems. In Selected Areas in Cryptography, LNCS 1758, pages 184–

199. Springer, 1999. 2. B. N. Levine, C. Shields, and N. B. Margolin. A Survey of Solutions to the Sybil Attack. Technical Report Tech report 2006- 052,

University of Massachusetts Amherst, Oct 2006. 3. P. C. Johnson, A. Kapadia, P. P. Tsang, and S. W. Smith. Nymble: Anonymous IP-Address Blocking. In Privacy Enhancing Technologies,

LNCS 4776, pages 113–133. Springer, 2007. 4. M. Bellare, A. Desai, E. Jokipii, and P. Rogaway. A Concrete Security Treatment of Symmetric Encryption. In FOCS, pages 394–403,

1997. 5. R. Dingledine, N. Mathewson, and P. Syverson, ―Tor: The Second- Generation Onion Router,‖ Proc. Usenix Security Symp., pp.

303- 320, Aug. 2004. 6. J. Feigenbaum, A. Johnson, and P.F. Syverson, “A Model of Onion Routing with Provable Anonymity,” Proc. Conf. Financial Crypto-

graphy, Springer, pp. 57-71, 2007. 7. J.R. Douceur, “The Sybil Attack,” Proc. Int’l Workshop on Peer-to-Peer Systems (IPTPS), Springer, pp. 251-260, 2002. 8. J. Feigenbaum, A. Johnson, and P.F. Syverson, ―A Model of Onion Routing with Provable Anonymity,‖ Proc. Conf. Financial

Cryptography, Springer, pp. 57-71, 2007. 9. M. Bellare, R. Canetti, and H. Krawczyk, ―Keying Hash Functions for Message Authentication,‖ Proc. Ann. Int’l Cryptology Conf.

(CRYPTO), Springer, pp. 1-15, 1996. 10. M. Bellare, A. Desai, E. Jokipii, and P. Rogaway, ―A Concrete Security Treatment of Symmetric Encryption,‖ Proc. Ann. Symp.

Foundations in Computer Science (FOCS), pp. 394-403, 1997. 11. J. Camenisch and A. Lysyanskaya. Dynamic Accumulators and Application to Efficient Revocation of Anonymous Credentials. In

CRYPTO, LNCS 2442, pages 61–76. Springer, 2002. 12. G. Ateniese, J. Camenisch, M. Joye, and G. Tsudik, ―A Practical and Provably Secure Coalition-Resistant Group Signature,

Scheme,‖ Proc. Ann. Int’l Cryptology Conf. (CRYPTO), Springer, pp. 255-270, 2000. 13. G. Ateniese, J. Camenisch, M. Joye, and G. Tsudik, ―A Practical and Provably Secure Coalition-Resistant Group Signature, Scheme,‖

Proc. Ann. Int’l Cryptology Conf. (CRYPTO), Springer, pp. 255-270, 2000.

48.

Authors: S Kavitha Murugesan, Shanavas K A

Paper Title: Secure Image Authentication of a Grayscale Document using Secret Sharing Method and Chaotic Logistic Map with Data Repair Capability

Abstract: In this paper we proposes a new authentication method which is based on secret sharing technique and logistic map with repairing capability of data, via the use of the Portable Network Graphics image. In this approach each block of a grayscale document image generate an authentication signal, which combine with binarized block content, is transformed into several shares using Shamir secret sharing scheme. The shares generated from the binarized block contents are then embedded into an alpha channel plane. The original grayscale image combine with alpha channel plane to form a PNG image; this PNG image encrypted by using chaotic logistic map to form a stego image. Stego image received in the receiver side is decrypt and checks the authentication. If the authentication process fails then repairing is done in each tampered block, after collecting two shares from unmarked block using reverse Shamir scheme. Security of data provided by sharing of data in the alpha channel and encrypting the stego image. Keywords: Image authentication, secret sharing, data repair, PNG (Portable Network Graphics), encryption, logistic map. References: 1. M. U. Celik, G. Sharma, E. Saber, and A.M. Tekalp, “Hierarchical watermarking for secure image authentication with localization,” IEEE

Trans. Image Processing, vol.11, no.6, pp.585-595, june.2002. 2. C Yu, X Zhang “Watermark embedding in binary images for authentication”, IEEE Trans. Signal Processing, vol.01, no.07, pp.865-868,

September. 2004. 3. A. Shamir, “How to share a secret,” Commun.ACM, vol.22, no.11, pp.612-613, Nov, 1979. 4. P.Jhansi Rani, S. DurgaBhavani1^stInt’1Conf on Recent Advances in Information Technology RAIT-2012. 5. Chih-HsuanTzeng and Wen-Hsiang Tsai, “A new approach to authentication of Binary image for multimedia communication with

distortion reduction and security enhancement. IEEE communication letters VOL.7.NO.9 2003 6. H. Yang and A. C. Kot, “Binary image authentication with tampering localization by embedding cryptographic signature and block

identifier,” IEEE Signal Processing Letters, vol. 13. 7. M. Wu and B. Liu, “Data hiding in binary images for authentication and annotation,” IEEE Trans.on Multimedia, vol. 6, no. 4, pp. 528–

538, Aug. 2004. 8. Che- Wei Lee and Wen-Hsiang Tsai “A secret-sharing-based method for authentication of grayscale document images via the use of the

png image with data repair capability” IEEE Trans. Image Processing., vol.21, no.1, january.2012. 9. Niladri B. Puhan, Anthony T. S. Ho “Binary Document Image Watermarking for Secure Authentication Using Perceptual Modeling” IEEE

International Symposium on Signal Processing and Information Technology2005. 10. W.H. Tsai, “Moment-Preserving thresholding: a new approach.” Computer Vision, Graphics, and Image Processing, vol. 29, no.3, pp.377-

393, 1985.

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49.

Authors: Devendrasingh Thakore, Akhilesh R Upadhyay

Paper Title: Implementation of an Environment to Analyze Object-Oriented Software and Quality Assurance

Abstract: Software quality cannot be improved simply by following industry standards which require adaptive/upgrading of standards or models very frequently. Quality Assurance (QA) at the design phase, based on typical design artifacts, reduces the efforts to fix the vulnerabilities which affect the cost of product. For this different design metrics are available, based on its result design artifacts can be modified. But to modify or make changes in artifacts is not an easy task because these artifacts are designed by rigorous study of requirements. The purpose of this research work is to automatically find out software artifacts for the system from natural language requirement specification as forward engineering and from source code as reengineering, to generate formal models specification in exportable form that can be used by UML compliment tool to visually represent the model of system. This research work also assess these design models artifacts for quality assurance and suggest alternate designs

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options based on primary constraints given in requirement specification. Following problems are resolved in this research work 1. Automatic generation of design phase class model from natural language input 2. Automatic generation of design phase class model from already developed source code 3. Generation of secure validated deign from above generated class models with different level of security as high

low and medium with the help of different software metrics To resolve these problem there is need of automated environment which will assess generated design artifacts from natural language as forward engineering and from source code as reengineering and finally suggest and validates alternate designs options for better quality assurance. Keywords: POS Tagging, OOA, UML, Use case, actor, software quality, quality metrics, XMI. References: 1. Ali Bahrami, Chapter 6, Object Oriented Analysis Process, in Object Oriented System Development. 2. H. M. Harmain and R. Gaizauskas, CM-Builder: An Automated NL Based CASE tool, in IEEE International Conference on automated

software engineering (2000) 3. Overmyer, S. P., Benoit, L. and Owen R., Conceptual modeling through linguistic analysis using LIDA. International Conference of

Software Engineering (ICSE), (2001) 4. G.S. Anandha Mala, J. Jayaradika, and G. V. Uma, Restructuring Natrual Language Text to Elicit Software Requirements, in proceeding of

the International Conference on Cognition and Recognition (2006) 5. A Visual Analysis and Design Tool for Planning Software Reengineering”, by Martin Beck, Jonas Trumper, Jurgen Dollner 6. Security metrics for object-oriented class designs”, Alshammari, Bandar and Fidge, Colin J. and Corney, Diane 7. WordNet 2.1, last updatehttp://wordnet.princeton.edu/wordnet/, 27th October, 2010 8. “BCEL API documentation” “bcel.sourceforge.net/docs/index.html” 9. Lionel C. Briand Jie Feng Yvan Labiche," Using Genetic Algorithms and Coupling Measures to Devise Optimal Integration Test Orders"

SEKE '02, July 15-19, 2002, Ischia, Italy. ACM 1-58113-556-4/02/0700. 10. Chidamber and C. Kemerer, “A metrics suite for object oriented design,” IEEE Transactions on Software Engineering, vol. 20, pp. 476–

493,

50.

Authors: M A Khan, Tazeem Ahmad Khan, M T Beg

Paper Title: Optimization of Wireless Network MAC Layer Parameters

Abstract: Wireless communication systems either ad hoc or infrastructure mode the key challenges that must be overcome to realize the practical benefits of Quality of Service (QoS). Generally the QoS is the ability for network element to provide some level of assurance for consistent network data delivery. The ability of Wireless Local Area Network (WLAN) to support real-time services is possible with QoS. IEEE802.11 is a standardized protocol for Wireless LAN (WLAN). The access mechanism of 802.11e, referred to as Enhanced Distributed Channel Access (EDCA), assigns different types of data traffic with different priorities based on the QoS requirements of the traffic, and for each priority, uses a different set of medium access parameters to introduce QoS support. In This paper optimized network parameter of IEEE 802.11e simulation model using Qualnet 5.02 Simulator, which is popular simulation software for wireless networks Keywords: MAC, QoS, wireless LAN, EDCA. References: 1. Dajiang H. and Shen C. “Simulation Study of IEEE 802.11e EDCF,” in Proceedings of Vehicular Technology Conference, Korea, pp.685-

689, 2003. 2. Deyun G., Jianfei C., and King N., “Admission Control in IEEE 802.11e Wireless LANs,”Computer Journal of Network IEEE, vol. 19, no.

4, pp. 6-13, 2005. 3. Grilo A., Macedo M., and Nunes M., “A Scheduling Algorithm for QoS Support in IEEE 802.11e Networks,” in Proceedings of IEEE

Wireless Communications, USA, pp. 509-519, 2003. 4. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Amendment 8: Medium Access control (MAC)

Quality of Service Enhancements, IEEE Std. 802.11e-2005 (November 2005). 5. B Naga Naveen, K Manikandan, "Novel approach for Improvisation of Medium Access Protocol for Wireless Sensors Networks "IJCST

Vol. 2, ISSue 2, June 2011, page 83-88 6. Zhen-ning Kong, Danny H. K. Tsang, Brahim Bensaou, and Deyun Gao "Performance Analysis of IEEE 802.11e Contention-Based

Channel Access", IEEE Journal on Selected Areas in Communications, Vol. 22, No. 10, December 2004. 7. Qiang Ni, National University of Ireland, “Performance Analysis and Enhancements for IEEE 802.11e Wireless Networks”, IEEE Network,

August 2005. Scalable Networks Technologies http://www.scalable-networks.com. 8. Seema Aarya, "Congestion Minimization through Collision Detection in TDMA and CSMA/CA Scheme in Wireless Mobile Ad-Hoc

etwork", International Journal of Computer Applications (0975 – 8887) Volume 45– No.1, May 2012 9. Parma Nand,Dr. S.C. Sharma,"Comparative study and Performance Analysis of FSR, ZRP and AODV Routing Protocols for MANET",2nd

International Conference and workshop on Emerging Trends in Technology (ICWET) 2011, pp 14-19 10. Mohammad M. Qabajeh, Aisha-Hassan A. Hashim, Othman O. Khalifa, Liana K. Qabajeh and Jamal I. Daoud,"Performance Evaluation in

MANETs Environment",Australian Journal of Basic and Applied Sciences, 6(1): 143-148, 2012 11. Dr. Ritika, Dr. Nipur,"ROUTING ANALYSIS BASED ON PERFORMANCE COMPARISON OF AODV, DSR, DYMO, OLSR AND

ZRP IN MOBILE AD-HOC",IJRIM Volume 2, Issue 2 (February 2012) (ISSN 2231-4334)pp 356-363 12. M A Khan, T A Khan, M T Beg, “RTS/CTS Mechanism of MAC Layer IEEE 802.11a WLAN in presence of Hidden Nodes”, International

Journal of Engineering and Innovative Technology, Vol 2, Issue 5, pp 232-236, Nov, 2012 13. M A Khan, T A Khan, M T Beg, “Performance Optimization of MAC Layer IEEE802.11 WLAN using Fragmentation” VSRD

International Journal of Electrical, Electronics & Communication Engineering”, VSRD IJEECE, Dec 2012 , pp 916-920

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51.

Authors: Mintu M. Ladani,Vinit Kumar Gupta

Paper Title: A Framework for Performance Analysis of Computing Clouds

Abstract: Cloud Computing is widely spread technique which has emerged a new technology that provides large amounts of computing and data storage capacity to its users with a promise of increased scalability, better availability, and reduced administration and maintenance costs. Because the use of cloud computing environments

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increases, it becomes crucial to understand the performance of this environment. So, to assess the performance of computing clouds in terms of various metrics is of great importance, such as the overhead of acquiring and releasing the virtual computing resources, and other virtualization and network communications overheads. To address these issues, we have presented the architecture of the framework and discuss several cloud resource management alternatives. Then finally we have designed and implemented load balancing algorithm for performance increase. we state the requirements for defining such algorithm to assess the performance of computing clouds. Secondly, a new VM load balancing algorithm has been proposed and designed for an IaaS framework in cloud computing environment; i.e. ‘Modified Weighted Active Monitoring Load Balancing Algorithm’ on cloud, for the Datacenter to effectively load balance requests between the available virtual machines assigning a weight, in order to achieve better performance parameters such as response time and Data processing time. Keywords: About four key words or phrases in alphabetical order, separated by commas. References: 1. The Economist, “A Special Report on Corporate IT,” October2008,”http://www.economist.com/specialReports/showsurvey.cfm?

Issue=20081025”. 2. R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as

Computing Utilities,” CoRR, vol. abs/0808.3558, 2008. 3. T. Killalea, “Meet the virts,” Queue, vol. 6, no. 1, pp. 14–18, 2008. 4. A. Iosup and D. Epema, “GrenchMark: A Framework for Analyzing, Testing, and Comparing Grids,” in Proc. of the 6th IEEE/ACM

Intl.Symposium on Cluster Computing and the Grid (CCGrid06). IEEE Computer Society, 2006, pp. 313–320, an extended version can be found as Technical Report TU Delft/PDS/2005-002, ISBN 1387-2109.

5. Amazon, “http://aws.amazon.com/ec2/,” 2008. 6. Open Grid Forum, “Job Submission Description Language (JSDL) Specification, Version 1.0,” 2005. 7. D. Feitelson, L. Rudolph, U. Schwiegelshohn, K. Sevcik, and P. Wong, “Theory and practice in parallel job scheduling,” in 3rd Workshop

on Job Scheduling Strategies for Parallel Processing, vol. LNCS 1291, 1997, pp. 1–34. 8. D. Tsafrir, Y. Etsion, and D. G. Feitelson, “Backfilling Using System- Generated Predictions Rather Than User Runtime Estimates,” IEEE

Trans. Parallel & Distributed Syst., vol. 18, no. 6, pp. 789–803, Jun 2007. 9. S. L. Garfinkel, “An Evaluation of Amazons Grid Computing Services: EC2, S3 and SQS,” Center for Research on Computation and

Society School for Engineering and Applied Sciences, Harvard University, Tech. Rep. TR-08-07, 2007. 10. S. Garfinkel, “Commodity Grid Computing with Amazon’s S3 and EC2,” ;login, vol. 32, no. 1, pp. 7–13, 2007. 11. E. Walker, “Benchmarking Amazon EC2 for High-Performance Scientific Computing,” ;login, vol. 33, no. 5, pp. 18–23, 2008. 12. D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and D. Zagorodnov, “Eucalyptus : A Technical Report on an

Elastic Utility Computing Architecture Linking Your Programs to Useful Systems,” Department of Computer Science, University of California, Santa Barbara, Tech. Rep. 2008-10, 2008.

13. M. R. Palankar, A. Iamnitchi, M. Ripeanu, and S. Garfinkel, “Amazon S3 for science grids: a viable solution?” in DADC ’08: Proceedings of the 2008 international workshop on Data-aware distributed computing. New York, NY, USA: ACM, 2008,.

14. Karthik Paladugu Sumanth Mukk “Systematic Literature Review and Survey on High Performance Computing in Cloud” Systematic literature review (SLR) September 2012

15. By Cloud Computing Ajith Singh.“An Approach on Semi-Distributed Load Balancing Algorithm for Cloud Computing System” N Dept. of Computer Science Karpagam University M. Hemalatha Dept. of Computer Science Karpagam University,oct-2012

16. By Mariana Carroll1,, Paula Kotzé1,3,”Secure virtualization Benefits, Risks and Controls “ Alta van der Merwe Secure Virtualization 17. By Rajkumar Buyya1,2, Chee Shin Yeo1, and Srikumar Venugopal” Market-Oriented Cloud Computing: Vision, Hype, and Reality for

Delivering IT Services as Computing Utilities “ 18. By Ajay Gulati, Chethan Kumar, Irfan Ahmad “Modeling Workloads and Devices for IO Load Balancing in Virtualized Environments”

52.

Authors: R. V. Wanjari, T. C. Parshiwanikar

Paper Title: Failure of Camshaft

Abstract: Camshaft can be defined as a machine element having the curve outlined or a curved grooved, gives the predetermined specified motion to another element called the follower. In automotive field, Camshaft and its follower take importance roles to run the engine. Nowadays the car maker have developed the vary schemes of cam profile to match with the engine performance. Since the system deals with high load and high speed and many analyses have been carried out on the failure of the components. The analysis is done either by experimental or finite element analysis. The result from the finite element analysis is an approximate of the component failure. In the mean time, the software development is improving in this few decades. Keywords: Cams and followers; engine; failure of camshaft; material; valves. References: 1. J. Michalski), J. Marszalek, K. Kubiak “An experimental study of diesel engine cam and follower wear with particular reference to the

properties of the materials” Received 9 August 1999; received in revised form 17 February 2000; accepted 17 February 2000 2. Li ping, Li Ping , Li Fengjun, Cai Anke and Wei Bokang “Fracture analysis of chilled cast iron camshaft”, , Received: 2008-10-10;

Accepted: 2009-02-20 3. “Camshaft Installation and Degreeing Procedure” manual by comp cams 4. R. ˙Ipek, B. Selcuk ,“The dry wear profile of camshaft” Journal of Materials Processing Technology 168 (2005) 373–376 5. W.A. Glaeser and S.J. Shaffer, B a t t e l l e Laboratories “contact fatigue”, ASM Handbook, Volume 19: Fatigue and Fracture, ASM

Handbook Committee, p 331-336 6. Mandeep Saini and Frances E. Lockwood, The Valvoline Company and Jerry C. Wang and Carl F. Musolff, Cummins, Inc., “Contribution

of Oil Traction to Diesel Engine Cam Galling”, Society of Automotive Engineers, Inc. 7. Merriam-webster.com. 2010-08-13. Retrieved 2010-11-07.

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53.

Authors: Swasti Singhal, Monika Jena

Paper Title: A Study on WEKA Tool for Data Preprocessing, Classification and Clustering

Abstract: The basic principles of data mining is to analyze the data from different angle, categorize it and finally to summarize it. In today’s world data mining have increasingly become very interesting and popular in terms of all application. The need for data mining is that we have too much data, too much technology but don’t have useful

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information. Data mining software allows user to analyze data. This paper introduces the key principle of data pre-processing, classification, clustering and introduction of WEKA tool. Weka is a data mining tool. In this paper we are describing the steps of how to use WEKA tool for these technologies. It provides the facility to classify the data through various algorithms. Keywords: Data mining; data preprocessing, classification, cluster analysis, Weka tool etc. References: 1. “G EFFECTIVE USE OF THE KDD PROCESS AND DATA MINING FOR COMPUTER PERFORMANCE PROFESSIONALS “ by

Susan P. Imberman Ph.D. College of Staten Island, City University of New York 2. “DATA MINING TECHNIQUES CLASSIFI ATION AND PREDICTION “by Han/Kamber/Pei, Tan/Steinbach/Kumar, and Andrew

Moore MirekRiedewald 3. “CLASSIFICATION AND PREDICTION IN A DATA MINING APPLICATION “ by SERHAT ÖZEKES and A.YILMAZ ÇAMURCU

2 Istanbul Commerce University, Ragıp Gümüş pala Cad. No: 84 Eminönü 34378, Istanbul – Turkey 4. “SURVEY OF CLASSIFICATION TECHNIQUES IN DATA MINING “ byThair NuPhyu E. H. Miller, “A note on reflector arrays

(Periodical style—Accepted for publication),” IEEE Trans. Antennas Propagat., to be published. 5. DATA MINING TECHNOLOGY by Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign 6. Amazon Elastic Compute Cloud (Amazon EC2),http://aws.amazon.com/ec2/,2009. 7. David Heckerman. Bayesian Network for Data Mining. Data Mining and Knowledge Discovery, 1997:79-119.. 8. David Hand, Heikki Mannila and Padhraic Smyth. Principles of Data Mining, the MIT Press, 2001:1-5... 9. A Short Introduction to Data Mining and Its Applications Zhang Haiyang 10. Google Web Applications http://www.google.com/ 11. Ritu Chauhan, Harleen Kaur, M.Afshar Alam, “Data Clustering Method for Discovering Clusters in Spatial Cancer Databases”,

International Journal of Computer Applications (0975 – 8887) Volume 10– No.6, November 2010 12. J.R Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufman, 1993. 13. S. Kotsiantis, D. Kanellopoulos, P. Pintelas, "Data Preprocessing for Supervised Leaning", International Journal of Computer Science,

2006, Vol 1 N. 2, pp 111–117. 14. MacQueen J. B., "Some Methods for classification and Analysis of Multivariate Observations", Proceedings of 5th Berkeley Symposium on

Mathematical Statistics and Probability.University of California Press. 1967, pp. 281–297. 15. Lloyd, S. P. "Least square quantization in PCM". IEEE Transactions on Information Theory 28, 1982,pp. 129–137. 16. Manish Verma, MaulySrivastava, NehaChack, Atul Kumar Diswar and Nidhi Gupta, “A Comparative Study of Various Clustering

Algorithms in Data Mining”, International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp.1379-1384

17. Timonthy C. Havens. “Clustering in relational data and ontologies” July 2010. 18. Weka: http://www.cs.waikato.ac.nz/~ml/weka/

54.

Authors: Chandana Sukesh, Katakam Bala Krishna, P.Sri Lakshmi Sai Teja, S.Kanakambara Rao

Paper Title: Partial Replacement of Sand with Quarry Dust in Concrete

Abstract: The reduction in the sources of natural sand and the requirement for reduction in the cost of concrete production has resulted in the increased need to identify substitute material to sand as fine aggregates in the production of concretes especially in Concrete. Quarry dust, a by-product from the crushing process during quarrying activities is one of such materials. Granite fines or rock dust is a by-product obtained during crushing of granite rocks and is also called quarry dust. In recent days there were also been many attempts to use Fly Ash, an industrial by product as partial replacement for cement to have higher workability, long term strength and to make the concrete more economically available. This present work is an attempt to use Quarry Dust as partial replacement for Sand in concrete. Attempts have been made to study the properties of concrete and to investigate some properties of Quarry Dust the suitability of those properties to enable them to be used as partial replacement materials for sand in concrete. Keywords: Quarry Dust, Fly ash, Workability, Compressive strength. References: 1. Properties of fresh concrete incorporating high volume of fly-ash as partial fine sand replacement, Dan Ravin, Pg.No. 473 to 479, materials

and structures/ materiaux et construction, vol.30, October 1997. 2. Strength and durability properties of concrete containing quarry dust as fine aggregate, R.Ilangovana, N.Mahendrana and K.Nagamanib,

Pg.No. 20 to 26, ARPN Journal of Engineering and Applied Science, Vol.3,No.5, October 2008. 3. Compressive strength of concrete using lateritic sand and quarry dust as fine aggregates, Joseph O. Ukpata, Maurice E. Ephraim and

Godwin A. Akeke, Pg.No. 81 to 92, ARPN Journal of Engineering and Applied Science, Vol.7, No.1, January 2012. 4. Study of Properties of SCC using ‘Quarry Dust’ and ‘Fly Ash’, M.V. Rama Raju, K.V.Vivek, Dr.T.Siva Shankar Reddy and P. Srinivas

Reddy, Pg.No. 323 to 332, International Journal if Engineering Science Research, Vol 02, Issue 04, August-September 2011. 5. Characteristic studies on the mechanical properties of quarry dust addition in conventional concrete, A.Sivakumar and Prakash M, Journal

of civil engineering and construction technology, vol. 2(10), Pg.No. 218 to 235, October 2011. 6. Effect of fine aggregates replacement with Class F fly ash on the mechanical properties of concrete, Rafat Siddique, , Pg.No. 539 to 547,

Cement and Concrete Resarch, Pergamon, Vol. 33, 2003. 7. Use of crusehed granite fine as replacement ti river sand in concrete production, Manasseh JOEL, Pg.No. 85 to 96, Leonardo electronics

journal of practice and technologies, Issue 17, July- December 2010. 8. Comparative long term study of concrete mix design procedure for fine aggregate replacement with fly ash by minimum void method and

maximum density method, A.D.Pofale and S.V.Deo, Pg.No. 759 to 764, KSCE journal of civil Engineering (2010) 14(5). 9. Mechanical properties of structural concrete incorporating a high volume of class F fly ash as a partial fine sand replacement, Dan Ravina,

Materials and structure/ materiaux et construction, vol.31, march 1998.

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55.

Authors: Sajith A.G, Hariharan.S

Paper Title: Medical Image Segmentation Using CT Scans-A Level Set Approach

Abstract: Identification of Liver and liver tumors from CT images is of great interest to physicians and image processing researchers. In this paper a simple and clinically useful system has been developed for segmenting the liver tumor from CT images. Level set methods have been widely used in image processing for segmenting the biomedical images such as liver images. Various methods of segmentation were explored, and a few were chosen for implementation and further development. Liver Images were collected and the region of interest was selected.

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Segmentation has been performed by using Fuzzy C means algorithm followed by fine delineation using level sets. The method could clearly segment the tumor regions and their boundaries are well defined. Keywords: FCM, Level Set method, Liver tumors References: 1. Militzer.A,Friedrich-Alexander,Erlangen-Nuremberg, Hager.T, Jager.F, Tietien.C, Hornegger.J, “Automatic Detection and Segmentation of

Focal Liver Lesions in Contrast Enhanced CT Images”, IEEE: International Conference on Pattern Recognition, 2524-2527, 2010. 2. Abdel-Massieh N.H, Hadhoud M.M, Amin K.M, “Fully automatic liver tumor segmentation from abdominal CT scans”, IEEE:International

Conference on Computer Engineering and Systems, 197-202, 2010. 3. Abdel-Massieh N.H, Hadhoud M.M, Amin K.M, “Automatic liver tumor segmentation from CT scans with knowledge-based constraints”,

IEEE: 5th Cairo International Conference on Biomedical Engineering, 215-218, 2010. 4. Masuda Y, Foruzan A.H, Tateyama T, Yen Wei Chen, “Automatic liver tumor detection using EM/MPM algorithm and shape

information”, IEEE:2nd International Conference on Software Engineering and Data Mining, 692-695,2010. 5. Preiswerk F, Arnold P, Fasel B, Cattin P.C, “Robust tumour tracking from 2D imaging using a population-based statistical motion model”,

IEEE:Workshop on Mathematical Methods in Biomedical Image Analysis, 209 – 214, 2012. 6. Zhaohui Luo, “Segmentation of liver tumor with local C-V level set”, IEEE:2nd International Conference on Mechanic Automation and

Control Engineering, 7660 – 7663, 2011. 7. Ballangan C, Xiuying Wang, Dagan Feng, “Lung tumor delineation in PET-CT images based on a new segmentation energy”, IEEE:

Nuclear Science Symposium and Medical Imaging Conference, 3202 -3205, 2011. 8. Xing Zhang, Jie Tian, Dehui Xiang, Xiuli Li, Kexin Deng, “Interactive liver tumor segmentation from CT scans using support vector

classification with watershed”, IEEE:International Conference on Engineering in Medicine and Biology Society, 6005 – 6008, 2011. 9. Wu Qiu, Rui Wang, Feng Xiao, Mingyue Ding, “Research on Fuzzy Enhancement in the Diagnosis of liver tumor from B-mode Ultrasound

Images”, IEEE: International Conference on Intelligent Computation and Bio-Medical Instrumentation, 74 – 80, 2011. 10. Masuda Y, Tateyama T, Wei Xiong, Jiayin Zhou, Wakamiya M, Kanasaki S, Furukawa A, Yen Wei Chen, “Liver tumor detection in CT

images by adaptive contrast enhancement and the EM/MPM algorithm”, IEEE: International Conference on Image Processing, 1421 – 1424, 2011.

11. Sariyanni C, Asvestas P, Matsopoulos G K, Nikita K.S, Nikita A.S, Kelekis D, “A fractal analysis of CT liver images for the discrimination of hepatic lesions:A comparative study”, IEEE: Proceedings of the 23rd Annual International Conference on Engineering in Medicine and Biology Society,1557 – 1560,2001.

12. Po-Hsiang Tsui, Yin-Yin Liao, Chien-Cheng Chang, Wen-Hung Kuo, King-Jen Chang, Chih-Kuang Yeh, “Classification of Benign and Malignant Breast Tumors by 2-D Analysis Based on Contour Description and Scatterer Characterization”, IEEE:Transactions on Medical Imaging, 513 – 522, 2010.

13. Azaid S.A, Fakhr M.W, Mohamed A.F.A, “Automatic Diagnosis of Liver Diseases from Ultrasound Images”, IEEE: International Conference on Computer Engineering and Systems, 313 – 319, 2006.

14. Mougiakakou S.G, Valavanis I, Nikita K.S, Kelekis D, “Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme”, IEEE:Proceedings of the 25th Annual International Conference on Engineering in Medicine and Biology Society, 1287 – 1290, 2003.

15. E-Liang Chen, Pau-Choo Chung, Ching Liang Chen, Hong-Ming Tsai,Chein-I Chang, “An Automatic Diagnostic System for CT Liver Image Classification” ,IEEE:Transactions on Biomedical Engineering,Vol.45,No. 6, June 1998.

16. Whitehouse RW. Computed tomography attenuation measurements for the characterization of hepatic hemangiomas.Br J Radiol 1991; 64: 1019-22.

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20. Janio Szklaruk, Paul M.Silverman, Chusilp Charnsangave, “Imaging in the Diagnosis, Staging, Treatment, and Surveillance of Hepatocellular Carcinoma”, AJR 2003; 180:441-454.

21. Raghunandan, Henry D. W. Yeung, Homer A Macapinlac, Revathy B. Iyer,”Utility of PET/CT in Differentiating Benign from Malignant Adrenal Nodules in Patients with Cancer”, AJR 2008;191:1545-1551.

22. Carlos Nicolau, Ramon Vilana, Violeta Catala, Luis Bianchi, Rosa Gilabert, Angeles Garcia, Concepcio Bru,”Importance of Evaluating All Vascular Phases on Contrast Enhanced Sonography in the Differentiation of Benign from Malignant Focal Liver Lesions”,AJR 2006;186:158-167.

23. Simone Maurea, Ciro Mainolfi, Lucio Bazzicalupo, Maria Rosaria Panico, Carmela Imparato, Bruno Alfano, Mario Ziviello, Marco Salvatore,”Imaging of Adrenal Tumors Using FDG PET: Comparison of Benign and Malignant Lesions”, AJR 1999;173:25-29.

24. Ernst Rummeny, Ralph Weissleder, David D. Stark, Sanjay Saini, Carolyn C. Compton, William Bennett, Peter F. Hahn, Jack Wittenberg, Ronald A. Malt, Joseph T. Ferrucci,”Primary Liver Tumors: Diagnosis by MR imaging”AJR 1989;152:63-72.

25. Koyama T, Fletcher JG, Jhonson CD, Kuo MS, Notohara K, Burgart LJ,”Primary hepatic angiosarcoma: findings at CT and MR imaging. Radiology 2002; 222:667-73.

26. Yugang Liu, Yizhou Yu,”Interactive Image Segmentation Based on Level sets of Probabilities” IEEE: Transactions on Visualization and Computer Graphics,2012 Vol.18, No 2;202-213.

27. Chunming Li, Chenyang Xu, Chanfeng Gui, Martin D.Fox,”Level set evolution without Re-initialization:A new variational Formulation”, Proceedings of the IEEE Computer Society Conference on Computer Vision and pattern Recognition 2005.

28. Kaihua Zhang, Lei Zhang, Huihui Song, Wengang Zhou,”Active contours with selective local or global segmentation: A new formulation and level set method”Image and Vision Computing, 2010; 28:668-676.

29. Chunming Li, Rui Huang, Zhaohua Ding, J.Chris Gatenby, Dimitris N. Metaxas,”A Level Set Method for Image Segmentation in the presence of Intensity Inhomogeneities with application to MRI”,IEEE Transactions on Image Processing, Vol.20,No. 7, July 2011.

30. Bing Nan Li, Chee Kong Chui, Stephen Chang, Sim Heng Ong,”A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images”Expert Systems with applications.2012;39:9661-9668.

56.

Authors: Himanshu Maurya, Shikha Maurya, B. C. Sahana

Paper Title: Identification of a Person by Palm Geometry Using Invariant Features

Abstract: Feature extraction is one of the main topics in Computer Vision. This paper presents the extraction of features of interest from two or more images of the same and different objects and the matching of these features in adjacent images. Each of these feature vectors is supposed to be distinctive and invariant to any scaling, rotation or translation of the image. It uses the SIFT (Scale Invariant Feature Transform) technique for feature extraction from the image. The paper describes our own implementation of the SIFT algorithm and highlights potential direction for future research.

264-269

Keywords: Biometric, Features, Matching, SIFT. References: 1. D. Zhang, W. K. Kong, J. You, and M. Wong, “Online palmprint identification,” IEEE Transactions on Pattern Analysis and Machine

Intelligence, vol. 25, no. 9, pp. 1041-1050, September 2003. 2. D.D. Zhang, Palmprint Authentication. Norwell, Mass. Kluwer Academic Publishers, 2004. 3. D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003. 4. A.K. Jain, A. Ross, “A Prototype Hand Geometry based Verification System,” proc. of AVBPA, 166-171, 1999. 5. David G. Lowe, “Distinctive Image Feature from Scale-Invariants Keypoints,” International Journal of Computer Vision, vol. 60 (2), pp.

91-110, 2004. 6. Cong Geng, Xudond Jiang, “Face Recognition Using SIFT Feature,” ICIP, IEEE, 2001. 7. U. Park, S. Pankanti, A.K. Jain, “Fingerprint Verification Using SIFT Feature,” proc. of SPIE6944, 2008. 8. David G. Lowe, “Object Recognition from Local Scale-Invariant Features,” The Proceedings of the Seventh IEEE International Conference

on Computer Vision, vol.2, pp.1150-1157, 1999. 9. C. Zhang, Zhihui Gong, Lei Sun, “Improved SIFT Feature applied in image Matching,” Computer Engineering and Application, vol.2,

pp.95-97, 2008. 10. David G. Lowe, “ Local feature view clustering for 3D object recognition,” IEEE Conference on Computer Vision and Pattern

Recognition, Kauai, Hawaii, pp. 682-688, 2001.

57.

Authors: Sivagamasundari, P.Melba Mary

Paper Title: Simulation of Quasi Z-Source Converter Based Zero Voltage Electronic Loads

Abstract: Electronic loads are a family of power converters which can be used as a variable impedance load in different applications. This paper analyzes high efficiency zero-voltage electronic loads based on the quasi Z-source converter topology .The proposed topology operate as an ideal current source which enables the operation of the zero input voltage. It can achieve a high efficiency of more than 90% which reduces the system cost. . The validity of this proposed method has been studied by the PSPICE Simulation and prototype experiment. Keywords: Electronic loads, ideal current source, Z-source converter, Converter References: 1. Anderson J., Peng F. Z. Four quasi–Z–Source inverters / IEEE Power Electronics Specialists Conference (PESC’2008), 2008. – P. 2743–

2749. 2. Yuan Li, Anderson J., Peng F. Z., Dichen Liu, Quasi–Z– Source Inverter for Photovoltaic Power Generation Systems //Twenty–Fourth

Annual IEEE Applied Power Electronics Conference and Exposition (APEC’09), 2009. – P. 918–924. 3. Jong–Hyoung Park, Heung–Geun Kim, Eui–Cheol Nho,Tae–Won Chun, Jaeho Choi,Grid–Connected PV System Using a Quasi–Z–Source

Inverter // Twenty–Fourth Annual IEEE Applied Power Electronics Conference and Exposition (APEC’09), 2009. – P. 925–929. 4. W.Toke Franke, Malte Mohr, Friedrich W. Fuch Comparison of a Z–Source Inverter and a Voltage–Source Inverter Linked with a DC/DC

Boost–Converter for Wind Turbines Concerning Their Efficiency and Installed Semiconductor Power // IEEE Conf. (PESC’08), 2008. – P. 1814–1820.

5. Gajanayake C. J., Luo F. L., Gooi H. B., So P. L., Siow L.K. Extended boost Z–source inverters // IEEE Conf.(ECCE’09), 2009. – P. 3845–3850.

6. Adamowicz M., Strzelecki R., Vinnikov D., Cascaded Quasi–Z–Source Inverters for Renewable Energy Generation Systems // Ecologic Vehicles and Renewable Energies Conference (EVER’10), 2010.

7. Vinnikov D., Roasto I., Strzelecki R., Adamowicz M. Performance Improvement Method for the Voltage–Fed qZSI with Continuous Input Current // IEEE Mediterranean Electrotechn. Conf. (MELECON’10), 2010.

8. Vinnikov D., Roasto I., Jalakas T., Comparative Study of Capacitor–Assisted Extended Boost qZSIs Operating in CCM // 12th Biennial Baltic Electronic Conf. BEC’2010, 2010.

9. C.-L. Chu and J.-F. Chen, “Self-load bank for UPS testing by circulating current method,” Proc. Inst. Elect. Eng.—Electr. Power Appl., vol. 141, no. 4, pp. 191–196, Jul. 1994.

10. E. A. Vendrusculo and J. A. Pomilio, “High-efficiency regenerative electronic load using capacitive idling converter for power sources testing,” in Proc. PESC, 1996, vol. 2, pp. 969–974.

11. C. A. Ayres and I. Barbi, “A family of converters for UPS production burn-in energy recovery,” IEEE Trans. Power Electron., vol. 12, no. 4, pp. 615–622, Jul. 1997.

12. C.-E Lin, M.-T. Tsai, W.-I. Tsai, and C.-L. Huang, “Consumption power feedback unit for power electronics burn-in test,” IEEE Trans. Ind. Electron., vol. 44, no. 2, pp. 157–166, Apr. 1997.

13. M.-T. Tsai and C. Tsai, “Energy recycling for electrical AC power source burn-in test,” IEEE Trans. Ind. Electron., vol. 47, no. 4, pp. 974–976, Aug. 2000.

14. M. T. Tsai, “Comparative investigation of the energy recycler for power electronics burn-in test,” Proc. Inst. Elect. Eng.—Electr. Power Appl., vol. 147, no. 3, pp. 192–198, May 2000.

15. S.-J. Huang and F.-S. Pai, “Design and operation of burn-in test system for three-phase uninterruptible power supplies,” IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 256–263, Feb. 2002.

16. E. G. Mantingh, W. Zaaiman, and H. A. Ossenbrink, “Ultimate transistor electronic load for electrical performance measurement of photovoltaic devices using pulsed solar simulators,” in Proc. Photovolt. Energy Convers. Conf.; Conf. Rec. 24th IEEE Photovolt. Spec. Conf., 1994, vol. 1, pp. 871–873.

17. E. Duran, J. M. Enrique, M. A. Bohorquez, M. Sidrach-de-Cardona, J. E. Carretero, and J. M. Andujar, “A new application of the coupledinductors SEPIC converter to obtain I–V and P–V curves of photovoltaic modules,” in Proc. Eur. Conf. Power Electron. Appl., 2005, p. 10.

18. P. Sanchis, J. Lopez, A. Ursua, and L. Marroyo, “Electronic controlled device for the analysis and design of photovoltaic systems,” IEEE Power Electron. Lett., vol. 3, no. 2, pp. 57–62, Jun. 2005.

19. J. M. Enrique, E. Duran, M. Sidrach-de-Cardona, J. M. Andujar, M. A. Bohorquez, and J. Carretero, “A new approach to obtain I–V and P–V curves of photovoltaic modules by using DC–DC converters,” in Proc. Photovolt. Spec. Conf., 2005, pp. 1769–1772.

20. E. Duran, J. Galan, M. Sidrach-de-Cardona, and J. M. Andujar, “A new application of the buck–boost-derived converters to obtain the I–V curve of photovoltaic modules,” in Proc. PESC, 2007, pp. 413–417.

21. E. Duran, M. Sidrach-de-Cardona, J. Galan, and J. M. Andujar, “Comparative analysis of buck–boost converters used to obtain I–V characteristic curves of photovoltaic modules,” in Proc. PESC, 2008, pp. 2036–2042.

22. F. Z. Peng, “Z-source inverter,” IEEE Trans. Ind. Appl., vol. 39, no. 2, pp. 504–510, Mar./Apr. 2003. 23. F. Z. Peng, “Z-source networks for power conversion,” in Proc. APEC, 2008, pp. 1258–1265. 24. M. Shen, A. Joseph, J. Wang, F. Z. Peng, and D. J. Adams, “Comparison of traditional inverters and Z-source inverter for fuel cell

vehicles,” IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1453–1463, Jul. 2007. 25. Julio Cesar Rosas-Caro,Fang Zheng Peng,Honnyong Cha, and Craig Rogers, Z Source converter based energy recycling zero voltage

electronic loads, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 12, DECEMBER 2009,4894-4902.

270-273

58.

Authors: A. Vinoth, V. Sujathabai

Paper Title: A Pixel Based Signature Authentication System

Abstract: Signature verification is the process used to recognize an individual’s h a n d -written s i g n a t u r e . The p r o c e s s o f verifying signature is cumbersome in practice. There is a need for automatic verification system for a signature since the signature has been a means of a person’s identification through ages. Verification of a signature can be done either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper, we verify the off-line signatures by taking a boundary of the entire signature and do the pixel comparison. Signature is acquired using a scanner. Detection process is done after the data acquisition and pre-processing. Pre processing includes noise removal, grey-scale, manipulation, edge detection. Experimental results show that 50% of the accurate matching with the existing one from the data base. Signature is a behavioral biometric: it differs from finger print, face, iris recognition, because these are based on the physical properties of human beings. There is an important distinction between simple signature Comparisons anddynamic signature verification. Both can be computerized, but a Simple comparison only takes into account what the signature looks like. Dynamicsignature verification takes into account how the signature was made. With dynamic signature verification it is not the shape or look of the signature that is meaningful; it is the change in speed, pressure and timing that occur during the act of signing. Only the original signer can recreate the changes in timing and X, Y and Z (pressure). A pasted bitmap, a copy machine or an expert forger may be able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes ins X, Y and Z (pressure). The practiced and nature motion of the original signer would require repeating the pattern shown. There w i l l al ways be sli ght vari at i ons in a Person’s hand-written signature, but the consistency created by natural motion and practice over time creates a recognizable pattern that makes the hand-written signature a natural for biometric identification. Signature v e r i f i c a t i o n i s n a t u r a l a n d i n t u i t i v e . The technology is easy to explain and trust. The primary advantage that signature verification systems have over types of biometric technologies is that signatures are already accepted as the common method of identity verification. This history is trust means that people are very willing to accept a signature based verification system. Keywords: Pre-processing, data acquisition, off-line signature, edge detection, noise removal, gray-scale manipulation. References: 1. M. Ammar, Y.Yoshida, T. Fukumura, “A new effective approach for offline verification of signature by using pressure features",In

Proceedings of the International Conference on Pattern Recognition, pp 566-599,1986. 2. S.Djeziri, F.Nouboud, R.Plamondon, Extraction of signatures from check background based on a filiformity criterion, IEEE Trans.Images

Process.7 (10) (1998) 1425-1438. 3. MadasuHanmandlu, Mohd.Hafizuddin, Mohd.Yusof, Vamsi Krishna Madasu, Offline signature verification and forgery detection using

Fuzzy Modeling, Pattern Recognition 38 (2005) 341-356. 4. M.Hanmandlu, K.R.MuraliMohan, S.Chakraborty, G.Garg, “Fuzzy modeling based signature verification system”, in : Proceedings of the

sixth International Conference on Document Analysis and Recognition, USA, 2001, pp.110-114 5. M.Hanmandlu, K.R.Murali Mohan S. Chakraborty, S.Goel, D.Roy Choudhury, Unconstrained handwritten character recognition based

on Fuzzy logic, Pattern Recognition 36 (3) (2003) 603-623. 6. M.Hanmandlu, K.R. Murali Mohan. Vivek Gupta , Fuzzy Logic based character recognition, Proceedings of the International Conference

on Image Processing, Santa Barbara, USA, pp.714-717. 7. R. Plamondon, S.N SriHari, Online and Offline handwriting recognition:a comprehensive survey, IEEE Trans. Pattern Anal.Mach.Intell.22

(1) (2000)63-84. 8. C . Q u e k , R.W.Zhou, Antiforgery: a novel pseudo-outer product based fuzzy neutral network driven. Signature verification

system, Pettern Recognition Lett.23 (2002) 1795-1816. 9. R. Sabourin, R. Plamondon, G. Lorette, Offline identification with handwritten signature images: survey and perspectives, Structured Image

Analysis, Springer, New York, pp 219-234, 1992.

274-276

59.

Authors: Sushil G. Nikam A.C. Attar

Paper Title: Alternative Walling System for Low Cost Housing by Using Bamboo

Abstract: The use of bamboo as a structural construction material is gaining traction primarily because it is a rapidly growing material and thus sustainable, and it has many positive engineering attributes such as its high strength and Durability. This work takes into consideration the alternate construction material for walling system by using Bamboo . In this project the one type of bamboo-based construction is examined and experimental results are carried out, thus confirming that this type of construction is a viable alternative for walling system for low cost housing.The goal of assessing bamboo’s potential to meet regional housing needs in a low-cost, eco-friendly manner.This increased civil society awareness on bamboo’s potential as a construction material Keywords: bamboo, structure, construction, affordability, housing, materials. References: 1. Chung, K.F., Chan, S.L. (2003), “Design of Bamboo Scaffolds”, INBAR Technical Report No.23, The Hong Kong Polytechnic University,

China. 2. Grewal, J., (2009), "Bamboo Housing in Pabal", EWB-UK Research Conference 2009 at the RAEng. London, February 2009. 3. Liang, C.B. (2009), "Bamboo as a permanent Structural Component", Imperial College London, April 2009 (BEng Final Year Project

Dissertation) 4. Bambus- RWTH Aachen. Public report: Construction with Bamboo: Modern Bamboo Architecture”.2002.

277-281

60. Authors: Shobhitha Ann Job, R.Jegan, Melwin Abraham C

Paper Title: OWI-535 EDGE Robotic Arm Control Using ElectroMyoGram (EMG) Signals

Abstract: As science advances day by day, people rely upon technology for their assistance. Compared to older days, people who are handicapped and who have disabilities of limbs due to old age have been increased today. Several availabilities have been introduced by the new technology to overcome this problem. One of the best options among them is the human-assisting robot . These robots can be controlled by a human in different ways. An Electromygraphy (EMG) is a signal which is produced due to the electrical activity when muscle contracts. These signals can be used as control signals for serving the robot. In this paper, a pick and place robotic arm is controlled using the EMG signals acquired from the arm of the user in real time. EMG signals are acquired from the forearm of the user with the help of surface electrodes attached to the user’s skin. It is found to be more robust when compared to other ideas. Keywords: Electromyographic (EMG) signals, LabVIEW, Robotic arm, Root Mean Square (RMS) value References: 1. Hsiu-Jen Liu and Kuu-Young Young, “An adaptive upper-arm EMG-based Robot control system”, International Journal of Fuzzy Systems,

Vol. 12,Issue No. 3,Pg No.181-189 September 2010 2. Mai S. Mabrouk,Olfat A Kandil, “Surface Multi-Purposes Low Power Wireless Electromyography (EMG) system Design”, International

Journal of Computer Applications Vol No.41,Issue No.12,Pg No 10-16, March 2012 3. Niji Johnson,“A hand-arm robotic system for autonomous motion control”, International Journal of Emerging trends in Engineering and

Development, Vol.No3 Issue No. 2,Pg No.52-59,April-2012 4. Shyam R.Nair, “Design of a robotic arm for picking and placing an object controlled using LabVIEW”, International Journal of Scientific

and Research Publications, Vol No 2, Issue No 5,Pg No-234-238 May 2012 5. Amanpreet Kaur, “The Implementation OF Prosthetic Index Finger Based On EMG Signals”, International Journal Of Computational

Engineering Research,Vol. 2,Issue No.3,Pg.No.898-900, May-June 2012 6. Rama Krishna, “Design And Implementation Of A Robotic Arm Based On Haptic Technology”, International Journal of Engineering

Research and Applications (IJERA) Vol.No.2, Issue No.3,Pg No..3098-3103,May-June 2012 7. Anthony Mandow, Alfonso J. García-Cerezo, “Using LEGO NXT Mobile Robots With LabVIEW for Undergraduate Courses on

Mechatronics”, IEEE Transactions on education, Vol No.54,Issue No.1,Pg No.41-47,February 2011 8. Basil Hamed, “A Mimicking Human Arm with 5 DOF Controlled by LabVIEW”, International Journal of Engineering and

Technology,Vol.3,Issue No:1,Pg No.9-15,February 2011 9. Panagiotis K. Artemiadis, “EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings”, IEEE Transactions on

robotics,Vol. 26,Issue No. 2, Pg No.393-398,April 2010 10. Kostas Kyriakopoulos, “An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features”, IEEE Transactions on

information technology in biomedicine, Vol.14,Issue No.3,Pg No.582-588,May 2010

282-286

61.

Authors: Roopali Goel, Javed Ahmed

Paper Title: Contrast of Wavelets Order Behavior for Sine Signal

Abstract: Degradation of signals by noise is a very common problem in today’s world .As we know that noise is the main factor that affect the accuracy of results in the signals. It is due to the rapid growth of latest technologies .Noise is added by various factors like heavy machines, loud music, heavy vehicles and noisy engines .Noise is basically unwanted signals which cause disruption of original signals. There are various ways by which we can restore original signals from the distorted signals. Lots of research is conducted for the improvement of results .The best motivational factor to be consider is wavelet. Wavelet denoising attempts to remove the noise present in the signal while preserving the signal characteristics, regardless of its frequency content. . In this paper, the author compares the performances of Haar, Coiflet and BIORSPLINE Wavelets for different values of their order for a sine signal. The variation of correlation value with the wavelets order has been investigated. Keywords: About four key words or phrases in alphabetical order, separated by commas. References: 1. Florian Luisiera, Thierry Blua, Brigitte Forsterb and Michael Unsera a Biomedical Imaging Group (BIG), Ecole Polytechnique Fédérale

de Lausanne (EPFL), Lausanne, Switzerland Centre for Mathematical Sciences, Munich University of Technology (TUM), Munich,Germany. Which Wavelet bases are the best for image denoising ?

2. A. K. Verma, Neema Verma. A Comparative Performance Analysis of Wavelets in Denoising of Speech Signals, National Conference on Advancement of Technologies – Information Systems & Computer Networks (ISCON – 2012) Proceedings published in International Journal of Computer Applications® (IJCA).

3. Nathaniel A. Whitmal‟, Janet C.Rutledge‟, and Jonathan Cohen2.Reducing Correlated Noise in Digital Hearing Aids. 4. Adrian E. Villanueva- Luna1, Alberto Jaramillo-Nuñez1,Daniel Sanchez-Lucero1, Carlos M. Ortiz-Lima1,J. Gabriel Aguilar-Soto1, Aaron

Flores-Gil2 and Manuel May-Alarcon2.. De-Noising Audio Signals Using MATLAB Wavelets Toolbox 5. Dr. Parvinder Singh, Dinesh Singh, Deepak Seth. Reduction of Noise from Speech Signal using Haar and Biorthogonal Wavelet, ISSN:

2230-7109(,IJECT Vol. 2, ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print) Issue 3, Sept. 2011. 6. Sofia Ben Jebara, Research Unit TECHTRA Ecole Supérieure des Communications de Tunis Route de Raoued 3.5 Km, Cité El Ghazala,

2083, Ariana, Tunisia. Peridoc/Aperiodic Decomposition And Wavelet Transform for Noise Reduction Is Oesophageal Speech.M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989.

7. Michel Misiti, Yves Misiti, Georges Oppenheim, Jean-Michel Poggi. Wavelet Toolbox for use with matlab. 8. Rajeev Aggarwal, Sanjay Rathore, Jai Karan Singh, Mukesh Tiwari, Vijay Kumar Gupta, Dr. Anubhuti Khare. Noise Reduction of Speech

Signal using Wavelet Transform with Modified Universal Threshold, International Journal of Computer Applications (0975 – 8887) Volume 20– No.5, April 2011

9. Bahoura M & Rouat J, (2006). Wavelet speech enhancement based on time–scaleadaptatio,SpeechCommunicationVol.48,No.12,pp:1620-16 37. ISSN: 0167-6393.

10. Rajeev Aggarwal, Jay Karan Singh, Vijay Kr. Gupta and Dr. Anubhuti Khare. Elimination of White Noise from Speech Signal Using Wavelet TransformBy Soft and Hard Thresholding, VSRD-IJEECE, Vol. 1 (2), 2011, 62-71.

11. Davis, G, M, (2002). „Noise Reduction in Speech Applications, CRC Press LLC, ISBN 0-8493-0949-2, USA. 12. Dong E & Pu X. (2008). Speech denoising based on perceptual weighting filter, Proceedings of 9th IEE International Conference on Signal

Processing, pp: 705-708, October 26-29, Beijing. Print ISBN: 978-1-4244-2178-7. 13. Gold, B. & Morgan, N. (1999) Speech and audio signal processing: processing and perception of speech, and music, John Wiley & Sons,

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INC., ISBN: 0-471-35154-7, New York, USA. 14. Johnson M. T, Yuan X and Ren Y, (2007). Speech Signal Enhancement through Adaptive Wavelet Thresholding, Speech Communications,

Vol. 49, No. 2, pp: 123-133, ISSN: 0167-6393. 15. Képesia M & Weruaga L. (2006). Adaptive chirp-based time–frequency analysis of speech signals, Speech Communication, Vol. 48, No. 5,

pp: 474-492. ISSN: 0167-6393 16. Li N & Zhou M. (2008). Audio Denoising Algorithm Based on Adaptive Wavelet Soft-Threshold of Gain Factor and Teager Energy

Operator, Proceedings of IEEE International Conference on Computer Science and Software Engineering, Vol. 1,pp: 787-790. Print ISBN: 978-0-7695-3336-0.

17. Ioana Adam, Alexandru Isar, Jean-Marc Boucher. Complex Wavelet Transform: Application to denoising, Thesis, Politehnica University of Timisoara Universite DE RENNES 1, PhD thesis.

18. McLaughlin I (2009). Applied Speech and Audio Processing With MATLAB Examples, Cambridge University Press, ISBN-13 978-0-521-51954-0, UK.

19. Minkoff, J. (2002). Signal Processing Fundamentals and Applications for Communications and Sensing Systems, ARTECH HOUSE, INC., ISBN 1-58053-360-4, USA.

20. Roopali Goel,Ritesh Jain.Speech Signal Noise Reduction by Wavelets. International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN-2278-3075, Volume-2, Issue-4, March 2013.

21. Ritesh Jain.Analysis of different wavelets by correlation. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013.

22. S.Manikandan, “Speech enhancement based on wavelet denoising” Academic Open Internet Journalwww.acadjournal.com, Volume 17, 2006.

23. Hamid Sheikhzadeh, Hamid Reza Abutalbi, “An improved wavelet based speech enhancement system”,Proceedings of 7th European Confrence on Speech Communication and technology, Alborg, Denmark,3-7, Sept 2001, pp 1855-1857.

24. Lallouani, M. Gabrea and C.S. Gargour, “Wavelet based Speech enhancement using different threshold based denoising algorithms”, Canadian conference, Electrical and Computer Engg., May 2004.Amara Graps, “An introduction to Wavelets”, IEEE Computation Science and Engineering, Volume 2, Issue 2,June 1995, page 50-61.

25. Soon Ing Yann, “Transform based Speech Enhancement Techniques”, PhD Thesis Nanyang Technological University 2003. 26. Chris Perkins, Tobin Fricke, “Wavelets”, University of California at Berkely, 1St December 2000, pp 1-18. 27. Nikhil Rao, “Speech Compression using Wavelets”, B.E Thesis, School of Information Technology and Electrical Engineering University

of Queensland Oct 8th, 2001. 28. Yan Long, Lin Gang and Guo Jun, “ Selection Of The best Wavelet Base For Speech Signal”, Proceedings of International Symposium on

Intelligent Multimedia, Video and Speech Processing, October 20-22, 2004. 29. Qin Linmei, Hu Guangrui and Li Chongni,” A new speech enhancement method”, Proceedings of 2001 International Symposium on

Intelligent Multimedia, Video and speech processing, May 2-4 2001, Hongkong. 30. V. Balakrishnan, Nash Borges, Luke Parchment, “ Wavelet Denoising and Speech Enhancement”, Spring 2006. 31. 31. Mahesh S. Chavan, Nikos Mastorakis, INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING

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