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Volume-3 Issue-3: Published on July 05, 2013
24
Volume-3 Issue-3: Published on July 05, 2013

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Volume-3 Issue-3, July 2013, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Kalpana Bhelotkar, Sandeep B. Patil

Paper Title:

Image Watermarking using Wavelet Denoising Method

Abstract: Robust image watermarking aims to embed invisible information, typically for copyright protection applications, in images in a way that the watermark is robust against various image processing attacks. Such attacks can be divided into signal processing and geometric attacks leading to different requirements for achieving robustness against attacks. This thesis investigate approaches to robust image watermarking focusing on the type of watermarking techniques termed as “second generation watermarking". This class of watermarking schemes increases robustness against geometric attacks by including the use of the image's perceptual features into the marking/detection process. Additional focus is put on the wavelet transform and its properties relevant for applications in robust image watermarking.

Keywords:
Digital Image Watermarking, Singular Value Decomposition, Watermark Embedding Algorithm, Watermark Extracting Algorithm.


References:

1.             Ch. Shakir,” Steno Encrypted Message in Any Language for Network Communication Using Quadratic Method”, in Journal of Computer Science 6 (3), Science Publications, 2010, pp. 320-322 .
2.             Arup Kumar Bhaumik, Minkyu Choi, Rosslin J.Robles, and  Maricel O.Balitanas,”Data Hiding in Video”, in International  Journal of Database Theory and Application Vol. 2, No. 2, June  2009.

3.             Andreas Westfeld and Gritta Wolf,” Steganography in a Video Conferencing System”, Information Hiding 1998,  Springer-Verlag Berlin Heidelberg 1998 pp. 32-47.

4.             D. P. Gaikwad and Dr. S.J. Wagh, “Color Image Restoration  for Effective Steganography”, in i-manager’s, Journal on Software  Engineering, Vol. 4 l, No. 3 l , January - March  2010,  pp.65-71.

5.             D.P.Gaikwad and Dr. S.J.Wagh, “Image Restoration Based LSB Steganography for Color Image”, AISA-PACIFIC Regional Conference in ICTM-2010 on Innovations and Technology Management at Mumbai

6.             Richard E. Woods & Rafael C. Gonzalez , Digital  Image  Processing (second edition), Pearson Prentice Hall.

7.             Neil F. Johnson and Sushil Jajodia,”Exploring  Steganography: Seeing the Unseen”, George Mason University. 

8.             S. Suma Christal Mary, “Improved Protection In Video  Steganopgraphy Used Compressed Video Bitstream ,” in International Journal on Computer Science and Engineering ,Vol. 02, No. 03, 2010, pp.764-766.

9.             Saurabh Singh and Gaurav Agarwal,”Hiding image to video: A new approach of LSB replacement”, in International Journal of Engineering Science and Technology Vol.  2(12), 2010, pp.6999-7003. 

10.          Steganography on new generation of mobile phones with  image and video processing abilities, as appeared  Computational Cybernetics and Technical Informatics (ICCCCONTI), 2010 International Joint Conference on 27-29 May  2010 in Timisoara, Romania .

11.          D.-C. Wu and W.-H. Tsai ” A steganographic method for images by pixel-value differencing”, in Pattern Recognition Letters, Vol. 24,  2003, pp.1613–1626.

12.          F Hartung., B. Girod.”Steganoing of uncompressed and  compressed video”, in  Signal Processing,Special Issue on  Copyright Protection and Access Control for Multimedia  Services, 1998, 66 (3), pp. 283-301.


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2.

Authors:

M. Senthil Raja, D.Vidyabharathi

Paper Title:

Identity Disclosure Protection in Slicing for Privacy Preservation

Abstract:   In recent years privacy preservation micro data publishing has gained wide popularity. Two of the most widely used anonymization techniques are generalization and bucketization. Bucketization doesn’t prevent membership disclosure and it doesn’t apply for data that don’t have a clear distinction between quasi-identifiers and sensitive attribute. On the other hand, generalization loses high amount of data. A combination of both i.e., slicing provides better data utility but still its prone to attacks. Slicing protects the data against membership and attribute disclosure but it doesn’t provide any details about identity disclosure. To overcome this we apply k-anonymity through ranging which will improve the overall utility and privacy of data. Here the data is not lost as well as it doesn’t result in inference attacks.

Keywords:
 Anonymization, Data Privacy, Privacy Preservation, Slicing.


References:

1.                 Tiancheng Li, Ninghui Li, “Slicing: A New Approach for Privacy Preserving Data Publishing”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 3, pp. 561-574, MARCH 2012
2.                 C. Aggarwal, “On k-Anonymity and the Curse of Dimensionality,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 901-909, 2005.

3.                 Blum, C. Dwork, F. McSherry, and K. Nissim, “Practical Privacy: The SULQ Framework,” Proc. ACM Symp. Principles of Database Systems (PODS), pp. 128-138, 2005.

4.                 J. Brickell and V. Shmatikov, “The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp. 70-78, 2008.

5.                 B.-C. Chen, K. LeFevre, and R. Ramakrishnan, “Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 770-781, 2007.

6.                 H. Cramt’er, Mathematical Methods of Statistics. Princeton Univ. Press, 1948.


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3.

Authors:

Rajalakshmi Somasundaram, Tharani Thangavel

Paper Title:

An Enhanced Energy Efficient Unequal Layered Clustering Algorithm for Large Scale Wireless Sensor Networks

Abstract:    Organizing wireless sensor networks into clusters ensures the effective utilization of limited energy resources of the sensor nodes. The problem of unbalanced energy consumption and hot-spots problem remain unavoidable. To solve this problem, we propose an Enhanced Energy Efficient Unequal Layered Clustering Algorithm (EEEULCA), which leads to the uniform energy dissipation among the cluster heads. Layer close to the base station will have smaller size than from the outer, which can preserve more energy for data forwarding. Data-aggregated Unequal Layered clustering protocol is used for inter-cluster communication. Simulation result shows that, our proposed algorithm effectively balances the energy consumption and increases the network lifetime.

Keywords:
   hot-spots, network lifetime , unequal layered clustering, Wireless sensor networks.


References:

1.           V. Mhatre and C. Rosenberg, “Design guidelines for wireless sensor networks: communication, clustering and aggregation”, Ad Hoc Networks , vol. 1,  pp. 45-63,  Feb. 2004.
2.           Q. Xue, A.Ganz, ”Maximizing Sensor Network Lifetime: Analysis and Design Guides”, proceedings of  MILCOM, October 2004.

3.           S. Soro and W. Heinzelman, “Prolonging the Lifetime of Wireless Sensor Network via Unequal Clustering”,  Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (lPDPS), vol. 1,  pp. 1-8, April 2005.

4.           W. R. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, “An Application Specific Protocol Architecture for Wireless Micro-sensor Networks”, IEEE Transactions on Wireless Communications, pp. 660-670, 2002.

5.           Li C F , Ye M , Chen G H ,et aI, “An energy-efficient unequal clustering mechanism for wireless sensor networks”, Proceedings of the 2nd IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS 2005) , Washington , DC , pp. 597-604, 2005.

6.           Xinyuan Zhao , Neng Wang ,”An Unequal Layered Clustering Approach For Large Scale Wireless Sensor Networks”, 2nd  international conference on future computer and communication, vol.1, pp. 750-756, 2010.

7.           LIU Pin, Huang Ting-lei, ZHOU Xian-yan, WU Gong-xing, “An Improved Energy Efficient Unequal Clustering Algorithm of wireless sensor network”, IEEE International conference on ontelligent computing and integrated syatems, pp. 930-933, 2010.

8.           Xu Lu , Lianglun Cheng, Ning  Yang, “A  Data-aggregated Unequal Clustering Routing Protocol for Wireless Sensor Networks” , 2nd international workshop on Intelligent system and applications, pp. 1-4, may 22-23 ,2010.

9.           S J. Yang and D. Zhang, “An Energy- Balancing Unequal Clustering Protocol for Wireless Sensor Networks”, Information Technology Journal vol. 8, no. 1, pp. 57-63, 2009.

10.        O.Younis , S. Fahmy, “Heed: A hybrid, Energy-Efficient, Distributed Clustering Approach for Ad-hoc Sensor Networks”, IEEE Transactions on Mobile Computing, vol. 3,  pp. 366-379, 2004.

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4.

Authors:

Uthaya Vasanthan.S, Karthikeyan.B

Paper Title:

Density of Defect

Abstract:     The main objective of this project is to come out with a new and effective idea for measuring the quality of the software (software quality metrics).  The existing product quality metrics which is a subset of the software quality metrics focus on measuring the quality by MTTF [Mean Time To Failure] and DD    [DEFECT DENSITY].  We bring in a new idea called the “DENSITY OF DEFECT”, stressing that quality of the product can be better judged by measuring the DENSITY of the identified defect, proving that merely the number of defects will not be an effective parameter in quality estimation as stated in DEFECT DENSITY.  This project’s scope will also include how the density of defect idea can be effective enough in measuring not only the quality but also in reducing the effort of identifying and correcting the individual defect.

Keywords:
    DD--Defect Density. D(D) -- Density of Defect. LOC  --  Lines Of Code.


References:

1.          Metrics & models in Software Quality Engineering [Stephen- H kan] 2nd edition.
2.          Measuring Software Quality [Richard walts].

3.          Software Quality, theory & management 2nd edition [Alan C Gillies].

4.          Software Engineering practioner’s approach [Roger.S.Pressman].

5.          Software metrics [C.Ravindranath Pandian].

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5.

Authors:

Antony Sagaya Jeyanthi, K.C.Nishitha

Paper Title:

Optimum Security Service for Heterogeneous Multicast Receivers

Abstract: The Resource Reservation Protocol (RSVP) lets hosts request quality of (bandwidth) service for multicast applications on the Internet. As network equipment advances to provide improved bandwidth service, security service becomes the more critical problem. However, RSVP doesn’t provide a flexible mechanism to support quality of security service (QoSS). Security service RSVP extends RSVP to provide the needed mechanism for dynamically negotiating QoSS among the senders and heterogeneous receivers of multicast applications on the Internet with minimum overhead. SSRSVP provides different QoSS resolutions according to receiver nodes’ security service needs.

Keywords:
 Multicast, Quality of Security Service, Resource ReSerVation Protocol.


References:

1.             ZhengYou Xia, YunAn Hu , ”Extending RSVP for Quality of Security Service”, ”IEEE Internet Computing, vol.10, no. 2,March/April 2006,pp.51-57.
2.             L. Zhang et al., “RSVP: A New Resource Reservation Protocol,” IEEE Network, vol. 7, no. 5, Sept. 1993, pp. 8–18.

3.             R. Braden et al., Resource Reservation Protocol (RSVP) —Version 1 Functional Specification, IETF RFC 2205, Sept.1997, www.ietf.org/rfc/rfc2205.txt.

4.             R. Braden, D. Clark, and S. Shenker, Integrated Services in the Internet Architecture: An Overview, IETF RFC 1633,June1994; ww.ietf.org/rfc/rfc1633.txt.

5.             J. Wroclaw ski, The Use of RSVP with IETF Integrated Services,IETF RFC 2210, Sept.1997, www.ietf.org/rfc/rfc2210.txt.         

6.             E.C. Rosen, A. Viswanathan, and R. Callon, Multiprotocol Label Switching Architecture, IETF RFC 3031, Jan. 2001,www.ietf.org/rfc/rfc3031.txt.

7.             E. Mannie, ed., “Generalized Multiprotocol Label Switching (GMPLS) Architecture,” IETF Internet draft, work in progress, Aug. 2002.

8.             D. Awduche et al., RSVP-TE: Extensions to RSVP for LSP Tunnels, IETF RFC 3209, Dec. 2001, www.ietf.org/rfc/rfc3209.txt.

9.             L. Berger, ed., Generalized Multiprotocol Label Switching (GMPLS) Signaling Resource Reservation Protocol-Traffic Engineering (RSVP-TE) Extensions, IETF RFC 3473, Jan. 2003; www.ietf.org/rfc/rfc3473.txt.

10.          G.-S. Kuo and Po-Chang Ko, “Dynamic RSVP Protocol,” IEEE Comm., vol. 41, May 2003, pp. 130–135.

11.          F. Baker and P. Bose, “QoS Signaling in a Nested Virtual Private Network,” IETF Internet draft, work in progress, Oct.2005.

12.          B. Pratik, D. Voce, and D. Gokhale, “QoS for Aggregated Flows in VPN,” Proc. Int’l Workshop Quality of Service(IWQOS), LNCS 3552, Springer-Verlag, 2005, pp. 392–394.

13.          L. Berger and T. O’Malley, RSVP Extensions for IPSec Data Flows, IETF RFC 2207, Sept. 1997; www.ietf.org/rfc/rfc2207.txt.

14.          C. Irvine and T. Levin, “Quality of Security Service,” Proc.New Security Paradigms Workshop, ACM Press, 2000, pp.91–99.

15.          M. Handley and V. Jacobson, SDP: Session Description Protocol, IETF RFC 2327, Apr. 1998; www.ietf.org/rfc/rfc2327.txt.

16.          F. Baker, B. Lindell, and M. Talwar, RSVP Cryptographic Authentication, IETF RFC 2747, Jan. 2000.


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6.

Authors:

Sunitha Abburu, G Suresh Babu

Paper Title:

A Framework for Ontology Based Knowledge Management

Abstract:  The three concepts of information science are data, information and knowledge. The structure of one is different from another. The structure of knowledge is more complex than data and information. Knowledge management is complex for traditional information management techniques due its complex structure and difficult to achieve common structure for knowledge captured from heterogeneous sources. Ontology is a upright technology to represent knowledge. Ontology provides homogeneous structure for knowledge acquired from heterogeneous sources. It enables knowledge sharing within and among organizations.  Ontology based knowledge management provides a better support for integration of related knowledge sources and searching. The current work proposes a enhanced and clear framework for knowledge management using domain ontology. It addresses  major issues of traditional and existing ontology based knowledge management systems.    

Keywords:
 Knowledge, Knowledge Management, Knowledge Representation, Ontology.


References:

1.                Chaim Zins, “Conceptual Approaches for Defining Data, Information and Knowledge”, Journal of the American Society for Information Science and Technology, vol. 58, issue 4, pp. 479–493, 2007.               
2.                R. J. Thierauf, “Knowledge Management Systems for Business”, QUORUM Books, Westport, CT, 1999.

3.                T. H. Davenport and L. Prusak, “Working Knowledge: How Organizations Manage what they Know”, Harvard Business School Press, 2000.

4.                LI Zhiping and SUN Yu, “Ontology-based Knowledge Management in Intelligent Tutoring Systems”, In Proc. International Conference on Management and Service Science, IEEE, pp. 1-4, 2009.   

5.                Haisheng Li, Wenzheng Li, Qiang Cai and Hongzhi Liu, “A Framework of Ontology-based Knowledge Management System”, In Proc. 2nd IEEE International Conference on  Computer Science and Information Technology (ICCSIT 2009), IEEE, pp. 374 – 377    

6.                Preece, A. Flett, D. Sleeman, D. Curry, N. Meany and P. Perry, “Better Knowledge Management through Knowledge Engineering”, Intelligent Systems, IEEE, vol. 16, issue. 1, pp. 36-43, 2001.         

7.                F. Chen, F. Burstein, “A dynamic model of knowledge management for higher education development”, In Proc. International Conference on  Information Technology Based Higher Education and Training (ITHET '06), IEEE,  pp.173 – 180, 2006.

8.                J. Zhou , “Knowledge Dichotomy and Semantic Knowledge Management”, Industrial Applications of Semantic Web, Springer, vol. 188, pp. 305-316, 2005.

9.                R. Zhao and C. Zhang, “An Ontology–Based Knowledge Management Approach for E-Learning System”, In Proc. International Conference on Management and Service Science, pp.1-4, 2009. 

10.             M. Lenzerini, “Ontology-based data management”, In Proc. Of the 20th ACM international conference on Information and knowledge management (CIKM 11), ACM, pp. 5-6, 2011.                           

11.             J. Zhang, W. Zhao, G. Xie and H. Chen, “Ontology- Based Knowledge Management System and Application”, Advanced in Control Engineering and Information Science, Elsevier, pp. 1021-1029, 2011.

12.             Uszok, L. Bunch, J. M. Bradshaw, T. Reichherzer, J. Hanna and A. Frantz, “ Knowledge-Based Approaches to Information Management in Coalition Environments”, Intelligent Systems, IEEE, Vol. 28, Issue 1, pp. 34-41, 2013.  

13.             Ming-jian Zhou and Jun-cai Tao, “A framework for ontology-based knowledge management”, Business Management and Electronic Information (BMEI-2011), IEEE, vol. 4, pp. 428-431, 2011.        

14.             T. R. Gruber, “A Translation Approach to Portable Ontology Specifications”, Knowledge Acquisition, ACM, vol  5, issue 2, pp. 199−220, 1993.

15.             Kougias1, L. Seremeti and D. Kalogeras, “Ontology-Based Knowledge Management in NGEEs”, International Journal of Pure and Applied Sciences and Technology, vol. 2, issue 1, pp. 54-62, 2011.               

16.             ALAmri, “The Relational Database Layout to Store Ontology Knowledge Base”, In Proc. 2012 International Conference on Information Retrieval & Knowledge Management (CAMP), IEEE, pp. 74-81, 2012.         

17.             Oscar Corcho, Asunción Gómez-Pérez, “A Roadmap to Ontology Specification Languages”, In Proc. 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW00), pp. 80-96, 2000.

18.             Asuncio Gomez-Perez, Mariano Fernandez, Oscar Corcho, “Ontological Engineering with Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web”, Springer, 2003.

19.             Matthew Horridge, “A Practical Guide To Building OWL Ontologies Using Protégé 4 and CO-ODE Tools,” Edition 1.3, 201  

20.             NeOn toolkit: http://neon-toolkit.org/wiki/Main_Page. 

21.             TopBraid Composer, “Getting Started Guide”, version 3.0, July 18, 2011; available at http://www.topquadrant.com/docs/marcom/TBC- Getting-Started-Guide.pdf.

22.             http://www.mindswap.org/2004/ SWOOP/

23.             Gómez-Pérez, M. Fernández-López, O. Corcho, and J. Aspiréz, “WebODE: a sacalable ontological engineering workbench,” First International Conference on Knowledge Capture (K-CAP 2001) Canada, 2001.

24.             GUO Rong and WUJun, “Design and Implementation of Domain Ontology-based Oilfield Non-metallic Pipe Information Retrieval System” In Proc. 2012 International Conference on Computer Science and Information Processing (CSIP), IEEE, pp. 813-816, 2012.

25.             Sangodiah and L. E. Heng, “Integration of Data Quality Component In An Ontology-Based Knowledge Management Approach for E-Leaming System”, In Proc. 2012 International Conference on Computer & Information Science (ICCIS), IEEE, pp. 105-108, 2012.

26.             Jing FAN, Xiuying LIU, Ying SHEN and Tianyang DONG, “Ontology-based Knowledge Management for Forest Channel”, In Proc. 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), IEEE, pp. 1523-1527, 2012.

27.             Maalel, L. Mejri, H. H. Mabrouk and H. B. Ghezela, “Toward a Knowledge Management Approach Based on an Ontology and Case-based  Reasoning (CBR)”, In Proc. Sixth International Conference on Research Challenges in Information Science (RCIS), IEEE, pp. 1-6, 2012.

28.             M. Zhou and J. Tao, “A Framework for Ontology-Based Knowledge Management”, In Proc. 2011 International Conference on Business Management and Electronic Information (BMEI), IEEE, pp. 428-431, 2011.       

29.             James N. K. Liu, Yu-Lin He, Edward H. Y. Lim and Xi-Zhao Wang, “A New Method for Knowledge and Information Management Domain Ontology Graph Model”, IEEE Transactions on Systems, Man, and Cybernetics, IEEE, vol. 43, no. 1, pp. 115-127, 2013

30.             John Domingue, Dieter Fensel, James A. Hendler (Eds.), “Hand Book of Semantic Web Technologies”, 2011, ISBN 978-3-540-92912-3; doi: 10.1007/978-3-540-92913-0.                   

31.             York Sure and Rudi Studer, “On-To-Knowledge Methodology —Final Version”, EU-IST Project IST-1999-10132, 3rd September, 2002.

32.             M. Fernández-López, A. Gómez-Pérez and N. Juristo, “METHONTOLOGY: From Ontological Art Towards Ontological Engineering”, In Proc. Symposium on Ontological Engineering of AAAI. Stanford University, California, Springer, pp 33–40, 1997.

33.             H.S. Pinto, S. Staab, and C. Tempich, "DILIGENT: towards a finegrained methodology for distributed, loosely-controlled and evolving engineering of ontologies," In Proc. 16th European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, pp. 393-397, 2004.

34.             M. C. S. Figueroa, A. Gomez-Perez and M. Fernandez-Lopez, “The NeOn Methodology for Ontology Engineering”, Ontology Engineering in a Networked World, chapter 2, Springer, pp. 9-34, 2012.

35.             S. Das, S. Sundara, M. Perry, J. Srinivasan, J. Banerjee, A. Yalamanchi, “Making Unstructured Data SPARQL Using Semantic Indexing in Oracle Database “, In proc. 2012 IEEE 28th International Conference on Data Engineering (ICDE), IEEE, pp. 1405-1416, 2012

36.             S. Harris and N. Gibbins, “3Store: Efficient Bulk RDF Storage”, In Proc. 1st International Workshop on Practical and Scalable Semantic Web Systems, 2003.

37.             Chuck Murray, “Oracle Database Semantic Technologies Developer's Guide”, 11g Release 2 (11.2), May 2012.     

38.             E. Prud’hommeaux and A. Seaborne, “SPARQL query language for RDF”, Technical report, W3C Recommendation, 2008. Available on http://www.w3.org/TR/rdf-sparql-query/            


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7.

Authors:

Abel García B., Francisco R. Trejo-M., Felipe Coyotl-M., Rubén Tapia-O., Hugo Romero-T.

Paper Title:

Design and Implementation of a FLC for DC-DC Converter in a Microcontroller for PV System

Abstract:   This paper presents the design and implementation of a simple fuzzy logic controller (FLC) for a DC-DC buck converter based on the PIC18F4550 microcontroller to control the lead acid battery charging voltage in solar cells applications. For cost consideration, an inexpensive 8-bit microcontroller is selected to program and implement the FLC proportional-integral. The obtained simulation and experimental results show the viability of the controller with a variation on the load of the buck converter showing a good performance on the design of the FLC, and it has also a smooth response with a small overshoot. The DC-DC converter designed in this work can be found applications in low cost photovoltaic (PV) systems, although in the literature has been already reported this kind of devices with a better response [3-4], however these use a expensive microcontroller or its designs are very complex, and where these are not necessary for this kind of applications. Finally, a prototype PV system with 100 V/6 A has been implemented for verifying the feasibility of the CD-CD converter.

Keywords:
 DC-DC converter, fuzzy logic control, and Microcontroller.


References:

1.              Gupta, T., R.R. Boudreaux, R.M. Nelms, J.Y. Hung, “Implementation of a fuzzy controller for DCDC converters using an inexpensive 8-b microcontroller”, IEEE Trans. on Industrial Electronics, Vol. 44 No.5, pp. 661-669, 1997.
2.              Perry, A.G., Feng. Guang, Liu. Yan-Fei, P.C. Sen, “A Design Method for PI-like Fuzzy Logic Controllers for DC–DC Converter” IEEE Trans. on Industrial Electronics, Vol. 54, No. 5, pp. 2688-2696, 2007

3.              Liping, Guo, J.Y. Hung, R.M. Nelms, “Evaluation of DSP-Based PID and Fuzzy Controllers for DC–DC Converters”, IEEE Trans. on Industrial Electronics, Vol. 56, No. 6, pp. 2237- 2248, 2009.

4.              Kuo-Hsiang Cheng, Hsu Chun-Fei, Lin Chih-Min, Tsu-Tian Lee and Li Chunshien,”Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions”, IEEE Trans. on Industrial Electronics, Vol.54, No.3,pp. 1528 – 1536, 2007.

5.              He, D., R.M. Nelms,“Fuzzy logic peak current-mode control for dc-dc converters using an inexpensive 8-bit microcontroller”, Applied Power Electronics Conference and Exposition, Vol. 3, pp. 2000 – 2006, 2005.

6.              He, D., R.M. Nelms,“Fuzzy logic average current-mode control for DC-DC converters using an inexpensive 8-bit microcontroller”, IEEE Trans. on Industry Applications, Vol. 41, No. 6, pp. 1531-1538, 2005.

7.              Sarabadani, H., H. Feshki, R.D. Habibinia and Valipour Ebrahimi, “Investigating effects of Different types of membership function for fuzzyfication in control of DC-DC converters”, First International Power Engineering and Optimization Conference (PEOCO2007), 2007.

8.              Ned Mohan, Tore M. Undeland, William P. Robbins, Electronica de Potencia Convertidores, Aplicaciones y Diseño, 2nd Edition, New York, Mc Graw Hill, pp. 142-175, 1989.

9.              J. Mahdavi, A. Emadi, M.D. Bellar, and M. Ehsani, “Analysis of Power Electronic Converters Using the Generalized State-Space Averaging Approach,” IEEE Trans. on Circuit and Systems., Vol. 44, pp.767-770, 1997.

10.           Emadi, “Modeling and Analysis of Multiconverter DC Power Electronic Systems Using the Generalized State-Space Averaging Method,” IEEE Trans. on Indus. Elect., Vol. 51, No. 3, pp. 661-668, 2004.

11.           Emadi, M. Ehsani, and J.M. Miller, “Vehicular Electric Power Systems: Land, Sea, Air, and Space Vehicles,” Ed. CRC Press, pp. 520 2003

12.           Shyama M, “Digital Linear and Nonlinear Controller for Buck Converter:  Fuzzy Controlled Buck Converter”, Ed. Lambert Academic Publishing, pp. 76, 2012.


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8.

Authors:

Virendra Babanrao Magar

Paper Title:

Intelligent and Superior Vedic Multiplier for FPGA Based Arithmetic Circuits

Abstract:    The speed of a multiplier is very important to any Digital Signal Processor (DSPs). Vedic Mathematics is the earliest method of Indian mathematics which has a unique technique of calculations based on 16 Formulae.  In this paper, a high performance, high throughput and area efficient architecture of a multiplier for the Field Programmable Gate Array (FPGAs) is proposed. This paper presents the efficiency of Urdhva Triyagbhyam Vedic method for multiplication, which strikes a difference in the actual process of multiplication itself. It enables parallel generation of partial products and eliminates unwanted multiplication steps. Multiplier  architecture  is  based  on  generating  all  partial products and  their  sums  in  one  step. Chipscope VIO is used to give random inputs of desired values by user, on which proposed Vedic multiplication is performed. The proposed algorithm is modeled using VHDL i.e. Very High Speed integrated circuit hardware description language. The propagation time of the proposed architecture is found quiet less. The Xilinx Chipscope VIO generator allows us to give the runtime inputs. The Xilinx Chipscope tool will be used to test the FPGA inside results while the logic running on FPGA. The Xilinx Spartan 3 Family FPGA development board will be used for this circuit. The proposed multiplier implemented using Vedic multiplication is efficient and competent in terms of area and speed compared to its implementation using Array and Booth multiplier architectures. The results clearly indicate that Urdhava Tiryakbhyam can have a great impact on improving the speed of Digital Signal Processors.

Keywords:
 Vedic Multiplier, urdhva tiryakbhayam, High Speed, Low Power, Latency.


References:

1.             Jagadguru Swami Sri Bharati Krisna Tirthaji Maharaja, “Vedic Mathematics: Sixteen Simple  Mathematical Formulae from the Veda,” Motilal Banarasidas Publishers, Delhi, 2009, pp. 5-45.
2.             H. Thapliyal and M. B. Shrinivas and H. Arbania, “Design and Analysis of a VLSI Based High Performance Low Power Parallel Square Architecture,” Int. Conf. Algo.Math.Comp. Sc., Las Vegas, June 2005, pp. 72-76.

3.             ‘Xilinx ISE User manual’, Xilinx Inc, USA, 2007

4.             A. D. Booth, “A signed binary multiplication technique,” Q. J. Mech.Appl. Math., vol. 4,    pp.     236–240, 1951.

5.             C. S. Wallace, "A suggestion for a fast multiplier," lEEE Trans.Electronic Comput., vol. EC-\3, pp. 14-17, Dec. 1964.

6.             Sandesh S. Saokar, R. M. Banakar, Saroja Siddamal, “High Speed Signed Multiplier for   Digital Signal Processing Applications” 2012 IEEE.

7.             A.Raman, A.Kumar and R.K.Sarin, “High Speed Reconfigurable FFT Design by Vedic  Mathematics”, Journal of Computer Science and Engineering, Vol. 1, Issue 1, pp 59-63, May,2010.

8.             V Jayaprakasan, S Vijayakumar, V S Kanchana Bhaaskaran, “Evaluation of the Conventional vs. Ancient Computation methodology for Energy Efficient Arithmetic Architecture”.

9.             R. Pushpangadan, V. Sukumaran, R.Innocent, D.Sasikumar, and V.Sunder, “High Speed Vedic Multiplier for Digital Signal Processors”, IETE Journal of Research, vol.55, pp.282-286, 2009.

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9

Authors:

Mandeep Devgan, Kanwalvir Singh Dhindsa

Paper Title:

A Study of Different QoS Management Techniques in Cloud Computing

Abstract:     Cloud Services are becoming a major system for constructing distributed systems. Service-oriented architecture (SOA) is widely working in electronic business, electronic -government, automotive systems, multimedia services, process control, finance, and a lot of other domains. Quality-of-Service (QoS) is usually employed for describing the non-functional characteristics of Cloud services and employed as an important differentiating point of different Cloud services. With the prevalence of Cloud services on the Internet, Cloud service QoS management is becoming more and more important. This paper first study a distributed QoS evaluation technique for Cloud services. In this technique, users in different geographic locations collaborative with each other to evaluate the target Cloud services and share their observed Cloud service QoS information. Based on this Cloud service evaluation technique, several large-scale distributed evaluations are conducted on many real-world Cloud services and the detailed evaluation results are released for future research. Cloud service evaluation is time and resource consuming. Moreover, in some scenarios, Cloud service evaluation may not be possible (e.g., the Cloud service invocation is charged, too many service candidate, etc.). Therefore, Cloud service QoS prediction approaches are becoming more and more attractive. In order to prediction the Cloud service QoS as accurate as possible, this paper studies three prediction methods. The first prediction method employs the information of neighborhoods for making missing value prediction. The second method discusses matrix factorization techniques to enhance the prediction accuracy. The third method predicts the ranking of the target Cloud services instead of QoS values. The predicted Cloud service QoS values can be employed to build fault-tolerant service-oriented systems. In the area of service computing, the cost for developing multiple redundant components is greatly reduced, since the functionally equivalent Cloud services are provided by different organizations and are accessible via Internet. Hence, based on the predicted QoS values, this paper study two methods for building fault tolerance Cloud services. Firstly, this paper studies an adaptive fault tolerance strategy for Cloud services. Then, this paper presents an optimal fault tolerance strategy selection technique for Cloud services.

Keywords:
 QoS, Evaluation, Prediction, Active User, Ranking.


References:

1.                E. Al-Masri and Q. H. Mahmoud. Investigating cloud services on the World Wide Web. In Pro. 17th Int'l Conf. World Wide cloud (WWW'08), pages 795–804, 2008.
2.                M. Alrifai and T. Risse. Combining global optimization with local selection for efficient qos-aware service composition. In Proc. 18th Int'l Conf. World Wide cloud (WWW'09), pages 881–890, 2009.

3.                Apache. Axis2. In http://ws.apache.org/axis2, 2008.

4.                D. Ardagna and B. Pernici. Adaptive service composition in flexible processes. IEEE Trans. Software Engeering, 33(6):369–384, 2007.

5.                K. J. arvelin and J. Kekalainen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems, 20(4):422–446, 2002.

6.                Avizienis. The methodology of n-version programming.Software Fault Tolerance, M. R. Lyu (ed.), Wiley, Chichester, pages 23–46, 1995.

7.                Benatallah, M. Dumas, Q. Z. Sheng, and A. H. H. Ngu. Declarative composition and peer-to-peer provisioning of dynamic cloud services. In Proc. 18th Int'l Conf. Data Eng. (ICDE'02), 2002.

8.                S. Bilgin and M. P. Singh. A daml-based repository for qos-aware semantic cloud service selection. In Proc. 2nd Int'l Conf. cloud Services (ICWS'04), pages 368–375, 2004.

9.                P. A. Bonatti and P. Festa. On optimal service selection. In Proc. 14th Int'l Conf. World Wide cloud (WWW'05), pages 530–538, 2005.

A.               Bram. Incentives build robustness in bittorrent. In Proc. First Workshop Economics of Peer-to-Peer Systems, pages 1–5, 2003.

10.             J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. 14th      Annual Conf. Uncertainty in Arti_cial Intelli- gence (UAI'98), pages 43–52, 1998.

11.             R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.

12.             C.-L.Hwang and K.Yoon. Multiple criteria decision making. Lecture Notes in Economics and Mathematical Sys- tems, 1981.

13.             J. Canny. Collaborative filtering with privacy via factor analysis. In Proc. 25th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval (SI- GIR'02), pages 238–245, 2002.

14.             V. Cardellini, E. Casalicchio, V. Grassi, F. Lo Presti, and R. Mirandola. Qos-driven runtime adaptation of service oriented architectures. In Proc. 7th Joint Meet- ing European Software Engineering Conf. and ACM SIGSOFT Symp. Foundations of Software Engineering

15.             (ESEC/FSE'09), pages 131–140, 2009.

16.             V. Cardellini, E. Casalicchio, V. Grassi, and F. L. Presti. Flow-based service selection for cloud service composition supporting multiple qos classes. In Proc. 5th Int'l Conf. cloud Services (ICWS'07), pages 743–750, 2007.

17.             J. Cardoso, J. Miller, A. Sheth, and J. Arnold. Modeling quality of service for workflows and cloud service processes. Journal of cloud Semantics, 1:281–308, 2002.

18.             P. P. Chan, M. R. Lyu, and M. Malek. Reliable cloud services: Methodology, experiment and modeling. In Proc. 5th Int'l Conf. cloud Services (ICWS'07), pages 679–686,2007.

19.             P. P.-W. Chan, M. R. Lyu, and M. Malek. Making services fault tolerant. In Proc. 3rd Int'l Service Avail. Symp. (ISAS'06), pages 43–61, 2006.

20.             X. Chen, X. Liu, Z. Huang, and H. Sun. Regionknn: A scalable hybrid collaborative filtering algorithm for personalized cloud service recommendation. In Proc. 8th Int'l Conf. cloud Services (ICWS'10), pages 9–16, 2010.

21.             X. Chen and M. R. Lyu. Message logging and recovery in wireless corba using access bridge. In The 6th Int'l Symp. Autonomous Decentralized Systems, pages 107–114, 2003.

22.             R. C. Cheung. A user-oriented software reliability model. IEEE Trans. Software Engeering, 6(2):118–125, 1980.  B. Chun, D. Culler, T. Roscoe, A. Bavier, L. Peterson, M. Wawrzoniak, and M. Bowman. Planetlab: An overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review, 33(3):3–12, July 2003.


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10.

Authors:

Mrs.J.Nandhini, D.Sharmila

Paper Title:

Energy Efficient Routing Algorithm for Mobile Adhoc Networks – A Survey

Abstract:      Mobile ad hoc networks are infrastructure-less networks used for communication between two or more nodes without a common access point. There are a number of Routing protocols proposed in the recent scenario. In the case of On-Demand routing, Algorithms such as AODV and DSR were considered as one of the effective method for achieving Quality of service parameters compared to Table Driven method. Establishing correct and efficient routes is an important design issue in MANETs along with Energy Efficiency. Energy based papers proposed in the recent years consider the on-demand routing of AODV and DSR and certain modifications have been applied in order to extract a better energy efficient routing algorithm. This paper is a survey of new and improved energy based routing methods in Mobile Adhoc networks.

Keywords:
 MANET, Energy Efficiency, AODV, DSR, Quality of Service.


References:

1.             .Doshi, S.Bhandare, and T.X. Brown. “An On-Demand Minimum Energy Routing Protocol for a Wireless Adhoc Network” ACM Mobile Computing and Comm. Rev., Vol. 6, No. 3, July 2002.
2.             \A.Misra and S.Banerjee. “MRPC: Maximizing Network Lifetime for Reliable Routing in Wireless Environments”, Proc. IEEE Wireless Comm. And Networking Conference, March 2002.

3.             \Changling Liu, Jorg Kaiser. “A Survey of Mobile Ad Hoc  network Routing Protocols”, Global Telecommunications Conference, 2008.

4.             J. Broch, D. Johnson and D. Maltz. “The Dynamic Source Routing Protocol for Mobile Adhoc Networks (DSR)”, IETF Internet Draft, July 2004.

5.             C.E. Perkins and P. Bhagwat. “Highly dynamic Destination sequenced distance vector routing (DSDV) for mobile computers”, Proc. Of ACM SIGCOMM’94, 1994.

6.             Nitiket N Mhala1 and N K Choudhari. “An Implementation possiblites for AODV in real world”,  International Journal of Distributed and Parallel Systems (IJDPS) Vol.1, No.2, November 2010.

7.             Goldsmith AJ, Wicker SB. “Design challenges for energy constrained ad hoc wireless networks”, IEEE Wireless Communications 2002; 9(4): 8–27.

8.             Vinay Rishiwal, S. Verma, and S.K.Bajpai. “QoS Based Power Aware Routing in MANETs”, International Journal of Computer Theory and Engineering, Vol. 1, No. 1, April 2009.

9.             Seyed-Amin Hosseini-Seno, Tat-Chee Wan, Rahmat Budiarto. “Energy Efficient Cluster Based Routing Protocols for MANETs”, International Conference on Computer Engineering and Applications, 2011.

10.          Bulent Tavli and Wendi B. Heinzelman. “Energy Efficient Real Time Multicast Routing in Mobile Adhoc Networks”, IEEE Transactions on Computers, Vol. 60, No. 5, May 2011.

11.          Annapurna P Patil, Dr K Rajani kanth, BatheySharanya, M P Dinesh Kumar, Malavika J. “Design of Energy Efficient Routing Protocol for MANETs based on AODV”, International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.

12.          Mohammad A. Mikki. “Energy Efficient Location Aided Routing Protocol for Wireless MANETs”, International Journal of Computer Science and Information Security Vol. 4, No. 1 & 2, 2009.

13.          Jinhua Zhu and Xin Wang. “Model and Protocol for Energy – Efficient Routing over Mobile Adhoc Networks”, IEEE Transactions on Mobile Computing, Vol. 10, No. 11, November 2011.

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11.

Authors:

Rajesh Singla and Haseena B.A

Paper Title:

BCI Based Wheelchair Control Using Steady State Visual Evoked Potentials and Support Vector Machines

Abstract:    This paper presents a Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface (BCI) system to control a wheelchair in forward, backward, left, right and in stop positions. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM) were designed and compared to yield better accuracy. Ten subjects were participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a predefined movement sequence. One-Against-All (OAA) based multiclass SVM classifier showed better accuracy than the ANN classifiers.

Keywords:
  ANN; Brain Computer Interface; Steady State Visual Evoked Potential; Support Vector Machines


References:

1.              J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G.  Pfurtscheller, and T. M. Vaughan, “Brain Computer Interface for communication and control,” Clin. Neurophysiol. vol. 113, pp. 767-791, 2002.
2.              R. Singla, B. Chambayil, A. Khosla, and J. Santosh “Comparison of SVM and ANN for classification of eye events in EEG,” Journal of Biomedical Sciences and engineering (JBISE), vol 4, No 2, pp. 62-69. Jan. 2011.

3.              T. W. Berger, J. K. Chapin, G. A. Gerhardt, D. J. McFarland et. al., “International assessment of research and development in brain-computer interfaces: report of the world technology evaluation center,” Berlin: Springer, 2007.

4.              M. Cheng, X. R. Gao, S. K. Gao, and D. Xu, “Design and implementation of a brain computer interface with high transfer rates,” IEEE Trans Biomed Eng., vol. 49. No. 10, pp. 1181-1186, 2002.

5.              E. C. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith, R. B. Reilly, and G. McDarby, ‘‘Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment,” EURASIP J. Appl. Signal Process., vol. 2005, no. 19, pp. 3156–3164, 2005.

6.              G. R. Muller-Putz and G. Pfurtscheller, “Control of an electrical prosthesis with an SSVEP-based BCI,” IEEE Trans. Biomed. Eng., vol. 55, no. 1, pp. 361–364, 2008.

7.              H. Cecotti, “A self-paced and calibration-Less SSVEP-based brain–computer interface speller,” IEEE Trans. Neural System and Rehabilitation Engineering, vol.18, no.2, pp.127-133, Apr, 2010.

8.              P. L. Lee, H. C. Chang, T. Y. Hsieh et.al., “A brain wave actuated small robot car using ensemble empirical mode decomposition based approach,” IEEE Trans. Sys. Man and cyber. Part A: Systems and humans. vol. 42, no. 5, pp 1053-1064, Sept. 2012.

9.              D. Zhu, J. Bieger, G. Garcia, and R. M. Aarts., “A survey of stimulation methods used in SSVEP-based BCIs,” Computational Intelligence and Neuroscience, vol. 2010, pp. 12, 2010.

10.           Y. Wang, Y. –T. Wang, and T. –P. Jung, “Visual stimulus design for high-rate SSVEP BCI,” Electron. Lett., vol 46, No. 15, pp. 1057-1058, 2010.

11.           Y. J. Wang, R. P. Wang, X. R. Gao, B. Hong, and S. K. Gao, “A Practical VEP-based brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 234-240, June 2006.

12.           G. R. Muller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, “Steady-State Visual Evoked Potential (SSVEP)-based Communication: impact of harmonic frequency components,” J. Neural Eng., vol. 2, no. 4, pp. 123-130, 2005.

13.           S. Haykin, “Neural Networks: A comprehensive foundation,” Prentice Hall, 1998.

14.           V. Vapnik., “Statistical learning theory,” John Wiley and Sons, Chichester, 1998.

15.           X. Song-yun, W. Peng-wei, Z. Hai-jun, Z. Hai-tao, “Research on the classification of brain function based on SVM,” The 2nd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2008, pp. 1931 – 1934, 2008.

16.           S. M. T. Muller, T. F. Bastos- Filho, and M. Sarcinelli-Filho, “Using a SSVEP BCI to command a robotic wheelchair,” IEEE symposium on Industrial electronics, pp. 957-962, June 2011.

17.           M. Carra and A. Balbinot, “Sensorimotor rhythms to control a wheelchair,” International Journal of Research in Neurology, Vol. 2013, 28 April 2013.


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12.

Authors:

H.B. Kekre, Tanuja Sarode, Prachi Natu

Paper Title:

Image Compression Based on Hybrid Wavelet Transform Generated using Orthogonal Component Transforms of Different Sizes

Abstract:     In this paper, image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is generated using two component transform matrices. One component transform matrix contributes to global properties whereas second one contributes to local properties of an image. Different sizes of component transform matrix can be used to generate hybrid transform matrix so that its size is same as image size.Different colour images of size 256x256 are used for experimentation. Proposed hybrid wavelet transform is applied on red, green and blue planes of image separately. Then in each plane transformed coefficients are sorted in descending order of their energy and lowest energy coefficients are eliminated. Root mean square error between original image and reconstructed image is calculated to check the performance at different compression ratios. By varying the size of pair of component transform matrices, hybrid transform matrix is constructed and results are observed.Also by changing the component matrix which contributes to local properties of image and with size variation, results are observed and compared. It has been observed that if more focus is given on local features, then better results are given by that Hybrid Wavelet Transform. Focusing on local features can be done by selecting larger size of orthogonal component transform that contributes to local properties.

Keywords:
     DCT, DKT, Hybrid Wavelet transform, Real Fourier Transform


References:

1.              Sanjeev Kumar, Varun Sood, “Quality Assessment of    Colour Image Compression using Haar Wavelet Transform”, International Journal of Engineering Trends and Technology- Volume3, Issue3, 2012, pp. 266-269.
2.              V.V. Sunil Kumar, M. IndraSen Reddy, “Image Compression Techniques by using Wavelet Transform”, Journal of InformationEngineering and Applications, Vol 2, No.5, 2012, pp. 235-239.

3.              M. J. Nadenau, J. Reichel, and M. Kunt, “Wavelet Based   Colour Image Compression: Exploiting the Contrast Sensitivity Function,”   IEEE Transactions Image Processing, Vol. 12, no.1, PP. 58.

4.              H.B.kekre, Sudeep D. Thepade, Adib Parkar, “A  Comparison of Haar Wavelets and Kekre’s Wavelets for Storing Colour Informationin a Greyscale Image”, International Journal of Computer Applications (IJCA), Volume 1, Number 11, December 2010, pp. 32-38.

5.              H.B.Kekre, Sudeep D. Thepade, Akshay Maloo, “Face       Recognition using Texture Features Extracted form Walshlet       Pyramid”, ACEEE International Journal on Recent Trends in       Engineering and Technology (IJRTET), Volume 5, Issue 1,       2010.

6.              H.B.Kekre, Sudeep D. Thepade, Akshay Maloo,      “Performance Comparison of Image Retrieval Techniques using Wavelet Pyramids ofWalsh, Haar and Kekre Transforms”, International Journal of Computer Applications (IJCA)Volume 4, Number 10, August 2010, pp. 1-8.

7.              H.B.Kekre, Sudeep D. Thepade, “Image Retrieval using Colour-Texture Features Extracted from Walshlet Pyramid”, ICGST International       Journal on Graphics, Vision and Image Processing (GVIP), Volume 10, Issue I, Feb.2010, pp.9-18.

8.              H.B.Kekre et al. “Kekre transform over Row Mean and Column Mean and Both using Image Tiling for Image Retrieval”,International Journal of Computer and Electrical Engineering, Vol.2, No.6, December, 2010, pp. 964-971.

9.              H.B. Kekre, Tanuja Sarode, Sudeep Thepade, “Inception of Hybrid Wavelet Transform using Two Orthogonal Transforms and It’s use      For Image Compression”, International Journal of Computer Science and Information Security(IJCSIS),Vol. 9, No. 6, 2011, pp. 80-87.

10.           Prabhakar Telagarapu etl. “Image Compression using DCT and Wavelet Transformations”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 4, No. 3, September 2011,  pp. 61- 74.

11.           Yi Zhang and Xing Yuan Wang “Fractal compression coding based on wavelet transform with diamond search” Nonlinear analysis: Real World Applications”, Vol. 13 Issue 1, Feb. 2012, pp. 106-112.

12.           V.V. Sunil Kumar, M. IndraSena Reddy, “Image Compression Technique  by using Wavelet Transform” , Journal of Information Engineering and Applications, Vol. 2, No. 5, 2012, pp.35-39.

13.           G. Boopathi, S.Arokiasamy, “ Image compression: An Approach using Wavelet Transform and Modified  FCM”, International Journal of Computer Applications, Vol.28, no. 2, August 2011, pp. 7-12.

14.           His chin Hsin, Jenn-Jier Lien and Tze Yun Sung, “A Hybrid SPIHT- EBC Image Coder”, IAENG International Journal of Computer Science, Vol. 34, Issue 1, 2007.

15.           Aree Ali Mohammed, Jamal Ali Hussien, “Efficient Hybrid transform Scheme for Medical Image  Compression”, International Journal of Computer Applications (IJCA), Vol. 27,  No. 7, August  2011.

16.           Ashutosh  Dwivedi, et. al., “A novel hybrid image compression technique: Wavelet-MFOCPN”, Proc. of  9th SID’06 Asia chapter, New Delhi, India, pp.492-495,  2006.

17.           V. Singh, N. Rajpal, and K. S. Murthy, “Neuro-Wavelet Based Approach for Image Compression,” Computer Graphics, Imaging and Visualization, CGIV apos’2007, pp. 280-286.

18.           H.B.Kekre, Tanuja Sarode, Prachi Natu, “Image Compression using Real Fourier Transform, It’s Wavelet Transform and Hybrid      Wavelet with DCT”,Accepted in International Journal of Advanced Computer Science and Applications,(IJACSA) Vol. 4, No.5, 2013.

19.           H.B.Kekre, Tanuja Sarode, Prachi Natu, “Efficient Image Compression Technique using Full, Column and Row Transforms on Colour Image”, International Journal of Advances in Engineering and Technology (IJAET), Vol.6 No.1 March 2013, pp. 88-100.


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13.

Authors:

Amarpal Singh, Piyush Saxena, Abhishek Singhal

Paper Title:

Software Testing Estimation using Soft Computing Techniques

Abstract: Software development is an extremely composite plus brainstorming action. In previous days programmers wrote programs by means of machine language in which they exhausted their more time in thinking about an exacting machine's instructions rather than the solution of the problem in their hands. Progressively, program developers switched to advanced stage of programming languages (high-level languages). Software testing is an imperative attribute of software quality. However the prediction of this attribute is a cumbersome process. Therefore various methodologies are proposed so far to estimate the testing time of software. Among them Fuzzy Inference System (FIS) and Adaptive Neuro- Fuzzy Inference System (ANFIS) is one of the sophisticated methods which have immense prediction capability and this paper explores its application to evaluate testing time of the aspect-oriented system. Prediction of testing time is performed by FIS and ANFIS. The results obtained from the current study are compared with adaptive neuro- fuzzy inference system and it is revealed that which model is more useful.

Keywords:
 Module oriented approach (MOA), Aspect oriented software approach (AOSA), Object Oriented Approach(OOA), Fuzzy Inference System (FIS), Adaptive Neuro- Fuzzy Inference System (ANFIS)


References:

1.                 Aggarwal, K.K., Singh, Y., Kaur A, Malhotra R. 2009. Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: A replicated case study. Software Process: Improvement and Practice 2009; (14): pp. 39-62, DOI= http://onlinelibrary.wiley.com/doi/10.1002/spip.389.
2.                 Han, J., Kamber, M. 2001. Data Mining: Concepts and Method. Harchort India Private Limited, 2001.

3.                 Ho, S, Xie, M, Goh, T.N. 2003. A study of the connectionist models for software reliability prediction. Computers and Mathematics with Applications 2003; (46): pp. 1037-1045.

4.                 Hosmer, D., Lemeshow, S. 1989. Applied Logistic regression, John Wiley and Sons 1989.

5.                 Jun, Z. 2007. Predicting software reliability with neural network ensembles. Expert Systems with Applications 2009; 36(2): pp. 216-222. DOI= http://10.1016/j.eswa.2007.12.029.

6.                 Kai, Y.C., Lin, C., Wei, D.W., Zhou, Y.Y., and David, Z. 2001. On the neural network approach in software reliability modeling, Journal of Systems and Software 2001; (58): pp. 47-62.

7.                 Karunanithi, N., Whitley, D., and Malaiya, Y.K. 1992. Prediction of software reliability using connectionist models. IEEE Transactions on Software Engineering, 1992; 18(7): pp. 563-574.

8.                 Lyu, M.R. 1999. Handbook of Software Reliability Engineering. McGraw Hill, India, 1999; 131-151.

9.                 MATLAB TOOLBOX, http://www.mathworks.com “MatLab Toolbox for ANN, FIS, ANFIS”.

10.              Ping, F.P., and Wei, C.H. 2006. Software reliability forecasting by support vector machines with simulated annealing algorithms. The Journal of Systems and Software 2006; 79: pp. 747–755.

11.              Ross, Q. C4.5: 1993. Programs for Machine Learning. Morgan Kaufman Publishers, San Mateo, CA 1993: 231-254.

12.              Rajesh Kumar, P.S. Grover, and Avadhesh Kumar “A Fuzzy Logic Approach to Measure Complexity of Generic Aspect-Oriented Systems”, Journal of Object Technology (JOT), Volume 9, No. 3, pp: 43-57, May/June 2010.

13.              Avadhesh Kumar, Rajesh Kumar, and P.S. Grover, “A Comparative Study of Aspect-Oriented Methodology with Module-Oriented and Object-Oriented Methodologies”, ICFAI Journal of Information Technology, Volume 2, No 4, pp: 7-15, December 2006.

14.              Avadhesh Kumar, Rajesh Kumar, and P.S. Grover, “Towards a Unified Framework for Complexity Measurement in Aspect-Oriented Systems” , 2008 International Conference on Computer Science & Software Engineering (CSSE 2008), Wuhan, China, pp: 98-103, IEEE Computer Society, December 12-14, 2008.


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14.

Authors:

Saideh Naderi, Masoud Jabbarian, Vahid Sattari Naeini

Paper Title:

A Novel Presentation of Graph Coloring Problems Based on Parallel Genetic Algorithm

Abstract:  In  coloring  graph  idealization  the  minimum  number  of  required  colors  for  graphic  coloring  is  determined  in  a way  that  no  contiguous  summit have  the  same  color  this  number  is  called  chromatic  graph number. We  should decide  if  then,  a color for a given  integral  number  M , so  we  use  that  number  with  no  contiguous  summits  of  the  same  color  there  have  been   presented  several  algorithms  for  decision  and  idealization  manners  so  for  such as: reverse  counting  method ,space  mood  counting  method and  etc…that  don’t  follow  multi  statement  time. Here by in  this  paper  we  present  suitable  solutions  for  this  problem  by  genetic  algorithm. In order to evaluate the performance of our new approach, we have conducted several experiments on GCP instances taken from the well known DIMACS Website. The results show that the proposed approach has a high performance in time and quality of the solution returned in solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.

Keywords: 
Graph Coloring Problems (GCPs), Parallel Genetic Algorithms (PGAS), NP-hard, chromosome.


References:

1.                Hertz , and D. de Werra, Using tabu search techniques for graph coloring, Computing, vol. 39, no. 4, pp. 345-351, 1987.
2.                E. Burke and S. Petrovic, Recent research directions in automate timetabling, European Journal of operation research, vol. 140, no. 2,pp. 266-280, 2002.

3.                Glass, C. A.|Prugel-Bennett, Genetic algorithm for graph coloring: Exploration of Galinier and Hao's algorithm, J. Combinatorial Optimization, pp,. 229-236,  2009. 

4.                D. Lim, Y-S. Ong, Y. Jin, B. Sendhoff and B-S. Lee, Efficient hierarchical parallel genetic algorithms using Grid computing, Future Generation Computer Systems, vol. 23, pp. 658-670, 2010.

5.                Z. Konfrst, Parallel genetic algorithms: Advances, Computing trends, Applications and Perspectives, Proc. of the 18th International Parallel and Distributed Processing Symposium (IPDPS’04), 2011.

6.                Ashby, Leif H., and Yampolskiy, Roman V. , Genetic Algorithm and Wisdom of Artificial Crowds Algorithm Applied to Light Up, The 16th International Conference on Computer Games: AI, Animation, Mobile, Interactive, Multimedia, Educational & Serious Games, Louisville, KY, USA: pp. 27-30, 2011.

7.                J. Chen, E. Antipov, B. Lemieux, W. Cedeno, and D. H. Wood, DNA computing implementing genetic algorithms. In L. F. Landweber, E. Winfree, R. Lipton, and S. Freeland, editors, Evolution as Computation, pages 39--49, New York, Springer Verlag, 2008.

8.                Burke, E., Newall, J, A Multi-stage Evolutionary Algorithm for the Timetable Problem. IEEE Trans. Evol. Comput, pp. 63–74, 2007.

9.                Carter, M., Laporte, G., Lee, S.Y, Examination Timetabling: Algorithmic Strategies and Applications. J. Oper. Res. Soc. 47 , pp. 373–383, 2006.

10.             B. B. Mabrouk, H. Hasni, and Z. Mahjoub, On aparallel genetic-tabu search based algorithm forsolving the graph colouring problem. European Journalof Operational Research, 197(3):1192–1201, 2009.


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15.

Authors:

Krivoshapko S. N., Gil-Oulbe MAthieu

Paper Title:

Geometry and Strength of a Shell of Velaroidal Type on Annulus Plan with Two Families of Sinusoids

Abstract:   In this paper, the shells limited by two flat concentric circles are considered.Both families of coordinate lines are sinusoids. Their middle surfaces may be associated to the group of velaroidal type surfaces. Considered surfaces can find application in landscape architecture and also in design of some manufactured details and structures as consist of cyclically repeating identical elements.The stress-strain state of ashell outlined on the considered surface and loaded by the dead weight is defined.

Keywords:
 a velaroidal surface, a thin-walled shell, architecture, the stress-strain state.


References:

1.           Mihailescu M., Horvath I.(1977). Velaroidal shells for covering universal industrial halls, Acta Techn. Acad. Sci. Hung.,85(1-2), pp. 135-145.
2.           Hadid H.A. (1982). Analysis of parabolic velaroidal shells with simply supported boundary conditions,J. Struct. Eng., 8(4), pp. 111-118.

3.           Friaa Ahmed, Zenzri Hatem(1996). On funicular shapes in structural analysis and applications,Eur. J. Mech. A., 15(5), pp. 901-914.

4.           Shtayerman Yu. Ya., Bastatsky B.N.(1960).The Bending of Shallow Shells, M. – L.: Gosenergoizdat, 37 p.

5.           Gogoberidze Ya. A. (1950). The Covering "Darbazi", Tbilisi, «Technica da shroma»,278 p.

6.           Shambina S.L., Neporada V.I. (2012).Velaroidal surfaces and their application in building and architecture, Prazi TDATU, Iss. 4, vol. 53, pp. 168-173.

7.           Bradshaw R., Campbell D., Gargari M., Mirmiran A., and Tripeny P. (2002). Special structures.Past, present, and future, Journal of Structural Engineering, June, 691-701.

8.           Krivoshapko, S.N., Mamieva, I.A. (2012). Outstanding space structures of the last 20 years, Montazhn. ispetsial. raboti v stroitelstve, № 12, 8-14.  


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16.

Authors:

S.P.Victor, M. Antony Sundar Singh

Paper Title:

Design and Development of Abstractness in Graph Mining Technique using Structural Datum

Abstract:    Graphs are everywhere, ranging from social networks and mobile call networks to biological net-works and the World Wide Web. Mining big graphs leads too many interesting applications including cyber security, fraud detection, Web search, recommendation, and many more. In this paper we describe a technique for the conversion of real-time environment to a Graph Mining pattern. We analyze very large, real world graphs with billions of nodes and edges. Our findings include digraph structures in the connected component size distribution. In the future we will extend our research to propose a GraphTemplateConverter for any real-time complex entities.

Keywords:
 Graph mining, Graph pattern, Graph template, Graph network.


References:

1.              J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Ma-honey. Statistical properties of community structure in large social and information networks. In WWW, pages  695-704, 2008.
2.              Liu, F. Guo, and C. Faloutsos. Bbm: Bayesian browsing model from petabyte-scale data. In KDD, pages 537-546, 2009.

3.              Y. Low, J. Gonzalez, A. Kyrola, D. Bick son, C. Guestrin, and J. M. Heller stein. Graph lab: A new framework for parallel machine learning. In UAI, pages 340-349, 2010.

4.              R. Gemulla, E. Nijkamp, P. Haas, and Y. Sisma-nis. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 69-77. ACM, 2011.

5.              Ghoting, R. Krishnamurthy, E. P. D. Pednault,B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian,and S. Vaithyanathan. System: Declarative machine learning on map reduce. In ICDE, pages 231-242, 2011

6.              U. Kang, H. Tong, J. Sun, C.-Y. Lin and C. Faloutsos.Gbase: an ancient analysis platform for large graphs.VLDB J., 21(5):637-650, 2012.


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17.

Authors:

Rashid Hussian, Sandhya Sharma,Vinita Sharma, Sandhya Sharma

Paper Title:

WSN Applications: Automated Intelligent Traffic Control System Using Sensors

Abstract:     In this new Era the growing Vehicle population in all developing and developed country calls for a major improvement and innovation in the existing Traffic Signaling systems. The most widely used automated system uses a simple time based system which working on a time interval basis which is now inefficient for random and non uniform Traffic. Advance automated systems in testing use image processing techniques or advance communication system with an intelligent information gathering systems in vehicles to communicate with signal and ask for routing. This might be implementable in developing countries as they are more complex and expensive also.The Concept Proposed in this paper involves use of Wireless sensor network technology to sense presence of Traffic near any circle or junction and then able to route the Traffic based on Traffic availability or we can say density in desire direction. This system does not require any system in vehicles so can be implemented in any Traffic system quite easily with less time and less expensive also. This system uses Wireless sensor networks Technology to sense vehicles and a microcontroller based routing algorithm programmed for excellent Traffic management.

Keywords:
 Intelligenttrafficsignals,intelligentrouting, smart signals,wireless sensornetworks.


References:

1.              Fernando Losilla 1,*, Antonio-Javier Garcia-Sanchez 1, Felipe Garcia-Sanchez 1,Joan Garcia-Haro 1 and Zygmunt J. Haas 2” A Comprehensive Approach to WSN-Based ITS Applications:A Survey” Sensors 2011, 11, 10220-10265; doi:10.3390/s111110220
2.              Total Numberof Registered motor Vehicles in India 1951-2004, http://morth.nic.in/writereaddata/sublinkimages/table-12458822488.htm

3.              TmoteSky  Datasheet,  http://  www.eecs.  harvard.edu/~  konrad

4.              /projects/shimmer/references/ tmote-sky-datasheet.pdf

5.              ChipconCC2420Datasheet,inst.eecs.berkeley.edu/~cs150/ Documents/CC2420.pdf

6.              Wenjie Chen, Lifeng Chen, Zhanglong Chen, and ShiliangTu, “WITS: A Wireless Sensor Network for Intelligent Transportation System”, 2006 IEEE, IMSCCS'06

7.              John Wiley & Sons, Inc., Hoboken, New Jersey. “WIRELESS SENSOR NETWORKS Technology, Protocols, and Applications”, 2007 in Canada

8.              HemjitSawant, Jindong Tan, “A Sensor Network Approach for Intelligent Transportation Systems”,Electrical and Computer Engineering Michigan Technological Univ. Houghton, Michigan, USA

9.              AmneshGoel,SukanyaRay,NidhiChandra,“Intelligent Traffic Light System to Prioritized Emergency Purpose Vehicles based on Wireless Sensor Network”,International Journal of Computer Applications (0975 – 8887) Volume 40– No.12, February 2012.

10.           KHALIL M. YOUSEF, JAMAL N. AL-KARAKI1 AND ALI M. SHATNAWI, “Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks”, JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, 753-768 (2010).


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18.

Authors:

Ch. Lavanya Susanna

Paper Title:

Interactive Search over XML Data to Obtain Top-K Results

Abstract:      Internet search engines are much popularized keyword search paradigm. However the search engine that uses html based model does not capture more semantics. But the xml model captures more semantics and navigates into document and displays more relevant information. The keyword search is alternative method to search in xml data, which is user friendly, user no need to know about the knowledge of xml data and query languages. This paper focuses on the survey of techniques used to retrieve the top k results from the xml document more efficiently.

Keywords:
 xml, html, keyword search, xml data


References:

1.              L. Guo, F. Shao, C. Botev, and J. Shanmugasundaram, “Xrank: Ranked Keyword Search over Xml Documents,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 16-27, 2003.
2.              S. Ji, G. Li, C. Li, and J. Feng, “Efficient Interactive Fuzzy Keyword Search,”  Proc. Int’l Conf. World Wide Web (WWW), pp. 371-380, 2009.

3.              Y. Xu and Y. Papakonstantinou, “Efficient Keyword Search for Smallest Lcas in XML Databases,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 537-538, 2005.

4.              Y. Xu and Y. Papakonstantinou, “Efficient LCA Based Keyword Search in XML Data,” Proc. Int’l Conf. Extending Database Technology: Advances in Database Technology (EDBT), pp. 535-546, 2008.

5.              Z. Liu and Y. Chen, “Identifying Meaningful Return Information for Xml Keyword Search,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 329-340, 2007.

6.              Jianhua Feng, and Guoliang Li” Efficient Fuzzy Type-Ahead Searchin XML Data” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 5, MAY 2012


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19.

Authors:

Sasikumar Gurumurthy, Valarmozhi

Paper Title:

System Design for Baseline Wander Removal of ECG Signals with Empirical Mode Decomposition Using Matlab

Abstract: The electrocardiogram (ECG) records the cardiac activity and it is extensively used for diagnosis of heart diseases. It is also an essential tool to allow monitoring patient sat home, thereby advancing telemedical applications. Even though these contributions are for different projects, the issue common to each is the use of ECG for remote monitoring and assistance under different telecommunication platforms. The transmission of ECG often introduces noise due to poor channel conditions. In this paper, we propose a new method for removing the baseline wander interferences based on Empirical Mode Decomposition (EMD). EMD is a relatively new, data-driven adaptive technique used to decompose ECG signals into a series of Intrinsic Mode Functions (IMFs).The baseline wander is mainly involved in special lower frequency IMFs. To evaluate the performance of the method, Clinic ECG signals are used. Results indicate that the method is powerful and useful in removing the baseline wander in ECG signal and does not distort the ECG signals.

Keywords:
 Baseline Wander, Empirical mode decomposition, Electro cardio Gram, Intrinsic Mode Functions


References:

1.             K. Hung, Y.-T. Zhang, Implementation of a WAP-based telemedicine system for patient monitoring, IEEE Trans. Inf. Technol. Biomed. 7 (2) (20010) 101–107.
2.             C.H. Salvador, M.P. Carrasco, M.A.G. de Mingo, A.M. Carrero, J.M.Montes, L.S. Martín, M.A. Cavero, I.F. Lozano, J.L. Monteagudo, Airmed-cardio: a GSM and internet services-based system for out-ofhospital follow-up of cardiac patients, IEEE Trans. Inf. Technol. Biomed. 9 (1) (2009) 73–84.

3.             J. Rodríguez, A. Goñi, A. Illarramendi, Real-time classification of ECGs on a PDA, IEEE Trans. Inf. Technol. Biomed. 9 (1) (2008) 23–34.

4.             V.X. Afonso, W.J. Tompkins, T.Q. Nguyen, K. Michler, S. Luo, Comparing stress ECG enhancement algorithms, IEEE Eng. Med. Biol. Mag. 15 (3) (2007) 37–44.

5.             Jacek M. Lęski and Norbert Henzel, “ECG baseline wander and powerline interference reduction using nonlinear filter bank”, signal processing, Vol85, No.4,
pp.781-793, (2005).

6.             Flandrin. P, Rilling. G, Goncalves.P, “Empirical mode decomposition as a filter bank”, IEEE Signal Processing Letters, Vol11, No. 2, pp. 112- 114, 2004.

7.             Gradwohl.J.R, Pottala.E.W., et al, “Comparison of two methods for removing baseline wander in the ECG”, Computers in Cardiology, pp.493-496, 1988.

8.             Huang.N.E, et al, “The empirical mode composition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proceeding of R.Soc.Lond.A, Vol 454,pp. 903-995,1998.

9.             Laguna.P, Jane. R, Caminal. P, “Adaptive Filtering Of ECG Baseline Wander”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp: 508-509,1992.

10.          Jane.R, Laguna. P, et al, “Adaptive baseline wander removal in the ECG: Comparative analysis with cubic spline technique”, Computers in Cardiology, pp.143-146, 1992.


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20.

Authors:

Suryakant Kaiwart

Paper Title:

Algorithm for Person Detection in Adaptive Background Using Matlab Platform

Abstract: Detection of motion is  the first essential process in the extraction of information in moving objects and makes use of stabilization in functional areas, such as tracking, classification, recognition, and so on. In this paper, we propose an approach to motion detection for the automatic visual surveillance system. Our method achieves complete detection of moving objects by involving three significant proposed modules: a background modeling module, a trigger module and an object extraction module. For our proposed first module, a unique two-phase background matching procedure is performed using rapid matching followed by accurate matching in order to produce optimum background pixels for the background model. Next, our proposed trigger module eliminates the unnecessary examination of the entire background region, allowing the subsequent third module to only process blocks containing moving objects. Finally, we get a moving object with subtracted background.

Keywords:
 detection, extraction, surveillance, trigger.


References:

1.                 D. Koller, K. Daniilidis, and H. Nagel, “Model-based object tracking in monocular image sequences of road traffic scenes,” Int. J. Comput. Vis., vol. 10, pp. 257–281, Jun. 1993.
2.                 S. Dockstader and M. Tekalp, “Multiple camera tracking of interacting and occluded human motion,” Proc. IEEE, vol. 89, no. 10, pp. 1441– 1455, Oct. 2001.

3.                 S. Park and J. Aggarwal, “A hierarchical bayesian network for event recognition of human actions and interactions,” Multimedia Syst., vol. 10, no. 2, pp. 164–179, Aug. 2004.

4.                 P. Remagnino, T. Tan, and K. Baker, “Multiagent visual surveillance of dynamic scenes,” Image Vis. Comput., vol. 16, pp. 529–532, Jun. 1998.

5.                 T. Huang and S. Russell, “Object identification: A bayesian analysis with application to traffic surveillance,” Artif. Intell., vol. 103, nos. 1–2, pp. 77–93, Aug. 1998.

6.                 G. L. Foresti, “Real-time system for video surveillance of unattended outdoor environments,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 6, pp. 697–704, Oct. 1998.

7.                 M. Haag and H. H. Nagel, “Incremental recognition of traffic situations from video image sequences,” Image Vis. Comput., vol. 18, pp. 137–153, Jan. 2000.

8.                 T. Darrell, G. G. Gordon, M. Harville, and J. Woodfill, “Integrated person tracking using stereo, color and pattern detection,” Int. J. Comput. Vis., vol. 37, pp. 175–185, Jun. 2000.

9.                 J. M. Ferryman, S. J. Maybank, and A. D. Worrall, “Visual surveillance for moving vehicles,” Int. J. Comput. Vis., vol. 37, no. 2, pp. 187–197, Jun. 2000.

10.              Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 22, no. 8, pp. 809–830, Aug. 2000.

11.              N. M. Oliver, B. Rosario, and A. P. Pentland, “A bayesian computer vision system for modeling human interactions,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 22, no. 8, pp. 831–843, Aug. 2000.

12.              W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 3, pp. 334–352, Aug. 2004.

13.              C. R. Wren, A. Azarbayehani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 19, no. 7, pp. 780–785, Jul. 1997.


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21.

Authors:

Kuldeep Singh, Preeti Abrol, Neelam Rathi

Paper Title:

Review on Digital Stethoscope

Abstract:  the stethoscope is one of the basic diagnostic tools in the medical world. The heart sounds are generated by the beating heart and the resultant flow of blood through it. It can provide the information of patient's cardio respiratory system. The death due to heart diseases has become the second mortality after the stroke in the world .Heart sound stethoscope is primary stage to access. Stethoscope is an acoustic medical device for listening to internal sounds in human body. Some researchers concluded that an abnormal heart-rate profile during exercise and recovery is a predictor of sudden death. Because the incidence of cardiovascular disease increased every year, cardiovascular diseases relating to heart has become worldwide common and high prevalent disease. A digital stethoscope provides revolutionized way of ausculatating the heart sounds.

Keywords:
 auscultation, microcontroller, heart sounds, stethoscope.


References:

1.           Shin, J.Y.; Lim, S.W.; Kim, Y.C.; Kim, S.J.; Cha, E.J.; Lee, T.S. “Portable digital esophageal stethoscope system ”. Annual International Conference of the IEEE.   Publication Year: 2010, Page(s):1844-1847.
2.           Haibin Wang; Jian Chen; Yuliang Hu; Zhongwei Jiang; Choi Samjin, “Heart Sound Measurement and Analysis System with Digital Stethoscope ” Biomedical Engineering and Informatics, 2009. Publication Year: 2009 , Page(s): 1 – 5.

3.           Jatupaiboon, N.; Pan-ngum, S.; Israsena, P. “Electronic stethoscope prototype with adaptive noise cancellation ”, Knowledge Engineering, 2010 8th International Conference on ICT .Publication Year: 2010 , Page(s): 32 – 36.

4.           Udawatta, L.; Abeykoon, A.H.S.; Prasanga, D.K.; Prasad, S.; Perera, W.; Perera, K. “Knowledge on heart patients through stethoscopic cardiac murmur identification for E-healthcare ”. Knowledge Engineering, 2010 8th International Conference on ICT. Publication Year: 2010 , Page(s): 58 – 63.

5.           Jia-Ren Chang Chien; Cheng-Chi Tai. “The implementation of a Bluetooth-based wireless phonocardio-diagnosis system ”, Networking, Sensing and Control, 2004 IEEE International Conference. Volume: 1 Publication Year: 2004, Page(s): 170 - 173 Vol.1

6.           Samuel E. Schmidt; Egon Toft; Claus Holst-Hansen; Johannes J. Struijk “Noise and the detection of coronary artery disease with an electronic stethoscope” 2010 5th Cairo International Biomedical Engineering Conference Cairo, Egypt, December 16-18, 2010 page 54- 56.

7.           Christian McMechan and Poman So “Design and Implementation of a Low Cost Electronic Stethoscope” IEEE714-718.

8.           Ying-Wen Bai and Chao-Lin Lu, “The embedded digital stethoscope uses the adaptive noise cancellation filter and the type I Chebyshev IIR bandpass filter to reduce the noise of the heart sound,” Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 23-25 June 2005, pp. 278-281.


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22.

Authors:

Snekha, Chetna Sachdeva, Rajesh Birok

Paper Title:

Real Time Object Tracking Using Different Mean Shift Techniques–a Review

Abstract:   The many different mean shift techniques for object tracking in real time are discussed in this paper. The mean shift is a non-parametric feature space analysis technique. It is a method for finding local maxima of a density function from given discrete data samples.There are several approaches that use the mean shift techniques for locating target objects. These techniques are taken from the literature dating back to the earliest methods. It is shown that at least 07 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in real time object tracking.

Keywords:
  Mean shift, CAMshift, ABCshift, Path assigned mean shift , SOAMST and  Fuzzy clustering mean shift


References:

1.              Dorin Coamaniciu, Peter Meer ,”Real-time Tracking using Non-rigid Objects using Mean Shift”, Proc. IEEE Conf.Computer Vision and Pattern Recognition, vol. II, pp. 142-149, June 2000.
2.              Dorin Coamaniciu, Peter Meer,” Mean Shift: A Robust Approach towards Feature Space Analysis”,IEEE Trans. Pattern Analysis and Machine Intelligence,vol. 24, no. 5, pp. 603-619, May 2002.

3.              Dorin Coamaniciu, Peter Meer,” Kernel Based Object Tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, pp. 564-577, May 2003.

4.              Gary R Bradski, “Computer Vision Face Tracking For Use in a Perceptual User Interface”, Proc. IEEE Workshop Applications of Computer Vision, pp. 214-219, Oct. 1998.

5.              John G. Allen, Richard Y. D. Xu, Jesse S. Jin,  “Object Tracking Using Camshift Algorithm And Multiple Quantized Feature Spaces”, Proceedings of the Pan-Sydney area workshop on Visual information processing, ser. ACM International Conference Proceeding Series, vol. 100. Darlinghurst, Australia: Australian Computer Society, Inc., 2004, pp. 3–7.

6.              Rustam Stolkin, Ionut Florescu, Morgan Baron, Colin Harrier and Boris Kocherov, “Efficient Visual Servoing With The ABCshift Tracking Algorithm”, 2008 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 2008.

7.              Akash Pooransingh, Cathy-Ann Radix, Anil Kokaram, “The Path Assigned Mean Shift Algorithm: A New Fast Mean Shift Implementation For Colour Image Segmentation”,  2008. ICIP 2008. 15th IEEE International Conference on Image Processing.

8.              Ming-Yi Ju, Chen-Sen Ouyang, Hao-Shiu Chang,”  Mean Shift Tracking Using Fuzzy Color Histogram”, Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, Qingdao, 11-14 July 2010, IEEE.

9.              J. Ning, L. Zhang, D. Zhang, C. Wu, “Scale And Orientation Adaptive Mean Shift Tracking”, IET Computer. Vision, 2012, Vol. 6, Iss. 1, pp. 52–61

10.           Nicole M. Artner, “A Comparison Of Mean Shift Tracking Methods”, in 12th Central European Seminar on Computer Graphics, 2008, pp. 197-204.

11.           Sang Gu Lee, “Image Object Tracking System Using Parallel Mean Shift Algorithm”, The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV’12), 2012.


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23.

Authors:

Apoorvi Sood

Paper Title:

Artificial Neural Networks- Growth & Learn: A Survey

Abstract:    Incremental Learning using Constructive algorithms help us to change the structure of the neural network by adding or removing the links. These algorithms start with a small network which grows dynamically by the addition of hidden layers/units. Thus we need to overcome the problem of over-fitting and get a network with high generalization performance.

Keywords:
   Artificial Neural Networks, Constructive Algorithms, Optimization Algorithms, Genetic Algorithms, non-Evolutionary Algorithms.


References:

1.              Alpaydm, E. (1988) “Grow and Learn” Internal Note, Laμi-EPF Lausanne, Switzerland.
2.              Alpaydm, E. (1990a) Neural models of incremental supervised and unsupervised learning, PhD dissertation, Ecole Polytechnique Fédérale de Lausanne, Switzerland.

3.              Alpaydm, E. (1990b) “Grow and Learn: An Incremental method for category learning” Int. Neural Network Conf., Paris, France.

4.              Alpaydm, E., (1991) “GAL: Networks that grow when they learn and shrink when they forget”, International Computer Science Institute.

5.              Ash, T. (1989) “Dynamic node creation in backpropagation networks,” Connection Science, 1, 365-375.

6.              Bishop, C.M. Neural Networks for Pattern Recognition. London: Oxford University Press, 1995.

7.              Chiang, K-W., Noureldin, A., El-Sheimy, N. (2008) Constructive Neural-Networks-Based MEMS/GPS Integration Scheme IEEE Trans. Vol. 44, NO. 2.

8.              Diederich, J. (1988) “Connectionist recruitment learning” Proc. Of the 8th European conf. On Artificial Intelligence, London, UK.

9.              Fahlman, S.E., Lebiere, C. (1990) “The cascade-correlation architecture,” in Advances in neural information processing systems, D.S. Touretzky (ed.), 2, 524-532, Morgan Kaufman.

10.           Feldman, J., (1982) “Dynamic connections in neural networks,” Biological Cybernetics, 46,27-39.

11.           Frean, M. (1990) “The Upstart Algorithm: A method for constructing and training feedforward neural networks”, Neural Computation,2 198-209.

12.           Haykin, S. Neural Networks: A Comprehensive Foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall, 1999.

13.           Hertz, J., Krogh, A., Palmer, R.G. (1991) Introduction to the theory of neural computation, Addison Wesley.

14.           Hirose, Y., Yamashita, K., Hijiya, S. (1991) “Back-propagation algorithm which varies the number of hidden units,” Neural Networks, 4, 61-66.

15.           Honavar, V., Uhr, L. (1988) “A network of neuron-like units that learns to perceive by generation as well as reweighting of its links,” Proc. of the 1988 Connectionist Summer School, D. Touretzky, G. Hinton, T. Sejnowski (eds.), Morgan Kaufman.

16.           Kitano, H., (1994) Neurogenetic learning: An integrated method of designing and training neural networks using genetic algorithms, Physica D75, pp. 225-238.

17.           Knerr, S., Personnaz, L., Dreyfus, G. (1989) “Single layer learning revisited: A stepwise procedure for building and training a neural network.” In Neurocomputing: Algorithms, architectures, and applications, F. Fogelman-Soulié, J. Hérault (eds.), NATO ASI Series, vol. F68, pp. 41-50. Springer, Heidelberg (1990). 

18.           Ma, L., Khorasani, K., (2002) “Application of Adaptive Constructive Neural Networks to Image Compression,” IEEE Trans. Neural Net., Vol. 13, NO. 5.

19.           Mézard, M., Nadal, J.-P. (1989) “Learning in feedforward layered networks: The tiling algorithm,” Journal of Physics A, 22, 2191-2204.

20.           Md. Islam M., et al (2009) “A New Constructive Algorithm for Architectural and Functional Adaptation of Artificial Neural Networks,” IEEE Trans. Cybernetics, Vol. 39, No. 6.

21.           Müller, B., Reinhardt, J. (1990) Neural Networks: An introduction, Springer Verlag.

22.           Parekh, R., Yang, J., Honavar, V., (2000) “Constructive Neural-Network Algorithms for Pattern Classification,” IEEE Trans. Neural Net., Vol. 11, NO. 2.

23.           Reilly, D.L., Cooper, L.N., Elbaum, C. (1982) “A neural model for category learning,” Biological Cybernetics 45,35-41.

24.           Rizzi, A., Mascioli, F.M.F., Martinelli, G. (2002) “Adaptive Resolution Min-Max Classifiers,” IEEE Trans. Neural Net., Vol. 13, NO. 2.

25.           S,-C. Huang, Y,-F. Huang (1991) “Bounds on the number of hidden neurons in  multilayer perceptrons,” IEEE Trans. Neural Net,. Vol. 2, pp. 47-55.

26.           Schaffer, J.D., Whitely, D., Eshelman, L.J. (1992) “Combinations of genetic algorithms and neural networks,” COGANN-92: International Workshop on
Combinations of Genetic Algorithms and Neural Networks, IEEE Computer Society Press.

27.           Sharma S.K., Chandra P. (2010) “An adaptive slope sigmoidal function cascading neural networks algorithm,” Third International Conference on Emerging Trends in Engineering and Technology, 2010 IEEE.

28.           Sharma S.K., Chandra P. (2012) “Empirical Evaluation of Adaptive Sigmoidal Activation Function on a Constructive Algorithm,” CSI Journal of Computing, Vol. 1 No. 3.

29.           Sharma S.K., Chandra P. (201o) “An Adaptive Slope Basic Dynamic Node Creation Algorithm,” International Conference on Computational Intelligence and Communication Networks.

30.           T.Y. Kwok, D.Y. Yeung (1996) “Constructive algorithms for structure learning in feedforward neural networks for regression problems,” IEEE Trans. Neural Net., Vol. 7, pp. 1168-1183.

31.           T.Y. Kwok, D.Y. Yeung (1997) “Objective Functions for Training new Hidden Units in Constructive Neural Networks,” IEEE Trans. Neural Net., Vol. 8, NO. 5.


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24.

Authors:

Saikat Singha Roy, Joyshri Das, Susovan Mondal

Paper Title:

Effective System Identification Using Fused Network and DE Based Training Scheme

Abstract:     Adaptive direct modeling or system identification finds extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. If the plants or systems are nonlinear, dynamic, single-input single-output (SISO), the identification task becomes more difficult. The dynamic system identification task is basically a model estimation process of capturing the dynamics of the system using the measured data. The Functional Link Artificial Neural Network (FLANN) is a single neuron single layer network first proposed by Pao. The structure of the FLANN is simple as it represents a flat net with no hidden layers. Therefore the computation and learning algorithm used in the architecture is straight forward.In the present investigation the identification problem is performed on three standard benchmark nonlinear dynamic series-parallel models using Differential Evolution (DE) for training the weights of FLANN structure. The performance of the proposed FLANN-DE identification model is compared with FLANN-Genetic Algorithm and FLANN-Back Propagation method.

Keywords:
    Differential Evolution, FLANN, Genetic Algorithm, System Identification.


References:

1.              Y. Xie, B. Guo, L. Xu, J. Li, P. Stoica, Multistatic adaptive microwave imaging for early breast cancer detection, IEEE Trans. Biomed. Eng. 53 (8) (2006) 1647–1657.
2.              S. Chen, S.A. Billings, Representation of non-linear systems: the NARMAX model, Int. J. Control 49 (1989) 1013–1032

3.              M. Adjrad, A. Belouchrani, Estimation of multi component polynomial phase signals impinging on a multisensor array using state-space modeling, IEEE Trans. Signal Process. 55 (1) (2007) 32–45.

4.              H. Hujiberts, H. Nijmeijer, R. Willems, System identification in communication with chaotic systems, IEEE Trans. Circuits Syst. I 47 (6) (2000) 800–808. [5] F. Dinga, T. Chenb, Identification of Hammerstein nonlinear ARMAX systems, Automatica 41 (2005) 1479–1489.

5.              M. Schetzmen, The Voltera and Winner Theories on Nonlinear Systems, Wiley, New York, 1980.

6.              E. Hernandez, Y. Arkun, Control of nonlinear systems using polynomial ARMA models, AICHE J. 39 (3) (1993) 446–460.

7.              T.T. Lee, J.T. Jeng, The Chebyshev polynomial-based unified model neural networks for functional approximation, IEEE Trans. Syst. Man Cybern. B 28 (1998) 925–935.

8.              Narendra, K. S., & Parthasarathy, K. “ Identification and control of dynamical systems using neural networks,”  IEEE Transactions on Neural Networks, vol. 1 no. 1, pp.  4–27, Mar. 1990.

9.              Nguyen, D. H., & Widrow, B. “Neural networks for self-learning control system.” International Journal of Control, vol. 54, no. 6, pp.  1439–1451, 1991.

10.           M. Srinivas, L. M. Patnaik, “Genetic Algorithm: A Survey’’,’ IEEE computer, vol. 27, no. 6, pp. 17-26, 1994.

11.           D. B. Fogel, “An Introduction to Simulated Evolutionary Optimization”, IEEE Trans. On SMC., vol. 24, no. 1, pp.

12.           W. Atmar, “Notes on the Simulation of Evolution”, IEEE Trans. On SMC., vol. 24, no. 1, pp. 130-147, 1994.

13.           R. Storn and K. Price, “Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, vol.11, pp.341-359, 1997.

14.           Vavak, F., Fogarty, T. and Jukes, K “A genetic algorithm with variable range of local search for tracking changing environments”. In Proceedings of the 4th Conference on Parallel Problem Solving from Nature, 1996.

15.           K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series), 1st ed. New York: Springer, 2005, ISBN: 3540209506.

16.           J. Vesterstroem and R. Thomsen, “A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems,” Proc. Congr. Evol. Comput., vol. 2, pp. 1980–1987, 2004.

17.           J. Andre, P. Siarry, and T. Dognon, “An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,” Advance in Engineering Software 32, pp. 49–60, 2001.

18.           O. Hrstka and A. Ku˘cerová, “Improvement of real coded genetic algorithm based on differential operators preventing premature convergence,” Advance in Engineering Software 35, pp. 237–246, 2004.

19.           R.Storn, “Differential evolution design for an IIR-filter with requirements for magnitude and group delay”. Technical Report TR-95-026, International Computer Science Institute, Berkeley, CA 1995.

20.           Qing, A.: Electromagnetic inverse scattering of multiple perfectly conducting cylinders by differential evolution strategy with individuals in groups (GDES). IEEE Trans. Antennas Propagate. 52(5), 1223–1229 (2004).

21.           Lakshminarasimman, L., Subramanian, S.: Hydrothermal optimal power flow using modified hybrid differential evolution. Caledonian J. Engg. 3(1), 8–14 (2007a)

22.           Lakshminarasimman, L., Subramanian, S.: Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution. IEE Proc. Gener. Transm. Distrib. 153(6), 693–700 (2006)

23.           Yang, S., Gan, Y.B., Qing, A, “ Sideband suppression in time-modulated linear arrays by the differential evolution algorithm,” IEEE Antennas Wireless Propagate. Lett. 1, pp. 173–175, 2002.

24.           Y.H. Pao, S. M. Phillips and D. J. Sobajic, “Neural-net computing and intelligent control systems.” Int. J. Conr. , vol. 56, no.2, pp.263-289, 1992.

25.           Patra, J. C., Pal, R. N., Chatterji, B. N., & Panda, G. “Identification of nonlinear dynamic systems using functional link artificial neural networks”, IEEE Transactions in Systems Man and Cybernetics-Part B: Cybernetics, vol.29 no. 2,pp. 254–262, 1999.

26.           D.E Goldbareg, Genetic algorithms in search, optimization, and Machine learning. Reading, M A: Addison-Wesley, 1989.

27.           Gershenfeld, N. A., & Weigend, A. S., “The future of time series: Learning and understanding. Time series prediction: Forecasting the future and past.” Reading, MA: Addison-Wesley, 1993.


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25.

Authors:

Sanmati Jain, Sandeep Agrawal, Asif Iqbal

Paper Title:

Performance Analysis of Parallel Interference Cancellation (PIC) Over Rayleigh Channel In DS- CDMA Systems

Abstract:      In this paper, we present and analyze the performance of a parallel interference cancellation (PIC) scheme for multicarrier (MC) direct-sequence code-division multiple-access (DS-CDMA) systems. In order to mitigate the multi-path interference (MPI) in the DS CDMA system. At each cancellation stage in the proposed PIC scheme, on each subcarrier, a weighted sum of the soft outputs of the other users in the current stage is cancelled from the soft output of the desired user to form the input to the next stage. At the last stage, the interference cancelled outputs from all the subcarriers are maximal ratio combined (MRC) to form the decision statistic. Parallel interference elimination is first proposed in this paper the multi-path interference are evaluated by tentative decision and known user information. Then the performance over Rayleigh fading channel are analyzed and compared to Matched filter, Decorelator, successive interference cancellation (SIC) and conventional parallel interference cancellation (PIC). It is shown that PIC performance can be improved greatly by using this method with simple structure and easy implementation.

Keywords:
  MRC; Multi-path interference Parallel interference cancellation; Rayleigh fading; Serial interference cancellation (SIC);


References:

1.              Analysis of Successive Interference    Cancellation in CDMA systems 2012 Second International Conference on Advanced Computing & Communication Technologies
2.              J.Arun and J.Muralidharan “Highly Efficient BER Performance analysis for interference cancellation in on linear DS CDMA detectors using Dripple algorithm” 2010 IEEE International Conference on Computational Intelligence and Computing Research.

3.              A New Parallel Interference Cancellation Algorithm for RAKE systems 2009 Third International Conference on Genetic and Evolutionary Computing.

4.              Guoxiong Xu and Liangcai Gan. “New Parallel Interference Cancellation for DS/CDMA Systems over Rayleigh Fading Channel”. 1-4244-1312-5/07 2007 IEEE International Conference.

5.              Mohamad Dosaranian Moghadam1, Hamidreza Bakhshi2, and Gholamreza Dadashzadeh “DS-CDMA Cellular Systems Performance with Base Station Assignment, Power Control Error, and Beamforming over Multipath Fading” International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.1, January 2011.

6.              J. M. Holtzman, “Successive interference cancellation for direct sequence code division multiple access”, Military Communications Conference, vol. 3, pp. 997- 1001, 2-5 Oct. 1994.

7.              Peng Hui Tan, and Lars K. Rasmussen “Multiuser Detection in CDMA—A Comparison of Relaxations, Exact, and Heuristic Search Methods”. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 5, SEPTEMBER 2004.

8.              D. Tse and P. Viswanath, Fundamentals of Wireless Communications. Cambridge Univ. Press, 2005..

9.              S. Verd´u, Multiuser Detection. Cambridge Univ. Press, 1998.

10.           Syed S. Rizvi and Khaled M. Elleithy “A Closed-Form Expression for BER to Quantify MAI for Synchronous DS CDMA Multi-user Detector” February 24, 2010.

11.           J. Kazemitabar and H. Jafarkhani, “Performance analysis of multiple antenna multi-user detection,” in Information Theory and Applications Workshop, 2009.

12.           Alexandra Duel-Hallen, Jack Holtzman and Zoran Zvonar "Multiuser Detection for CDMA Systems" IEEE Personal Communications (1995-04). Tan F. Wong: Spread Spectrum & CDMA,” Multi-User Detection.

13.           Mark C. Reed, Paul D. Alexander,” Iterative Multi-User Detection for CDMA with FEC: Near-Single-User Performance”, IEEE transactions on communications, vol. 46, no. 12, December 1998

14.           Nevio Benvenuto, Giambattista Carnevale and Stefano Tomasin,” Energy Optimization of CDMA Transceivers using Successive Interference Cancellation”

15.           Peng Hui Tan†, Lars K. Rasmussen† and Teng Joon Lim,” Iterative Interference Cancellation as Maximum-Likelihood Detection in CDMA”, Centre for Wireless Communications, National University of Singapore.

16.           R. Michael Buehrer, Neiyer S. Correal-Mendoza, and Brian D.Woerner,” A Simulation Comparison of Multiuser Receivers for Cellular CDMA”, IEEE transactions on vehicular technology, vol. 49, no. 4, July 2000.

17.           Lars.K.Rasmussen, Teng.J.Lim, Ann-Louise Johansson,” A Matrix Approach to Successive Interference Cancellation in CDMA”, submitted to IEEE.


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26.

Authors:

Manisha, Partibha Yadav

Paper Title:

Securing MANET Using Artificial Neural Networks

Abstract: In present paper, we are providing enhanced security to Manet using the Back Propagation Method of Artificial Neural Networks. Here we eliminate the use of files for storing the passwords or other encrypted content used in the network , by replacing them with much secure weight matrix of Back Propagation. The neural network system is trained for some data and then file used for storing the data is eliminated, resulting confidentiality of connection. Thus the method makes the system more secure.

Keywords: 
Mobile Adhoc Network, Artificial Neural Network, Pattern Mapping Technique.


References:

1.              Preet Inder Singh, Gaur Sunder Mitra Thakur, “Enhanced Password Based Security System Based on User Behaviour using Neural Network,” International Journal Information Engineering and Electronic Business,vol 2, pp 29-35, 2012.
2.              Dr. B.S. Pradeep, S.Soumya, “Role of ANN in Secured Wireless Multicast Routing during Dynamic Channel Allocation for User Demanded Packet Optimality,” Int. J.Advanced Networking and Applications,vol 3, issue 2, pp 1135-1139, 2011.

3.              Khalil Shihab, “A Cryptographic Scheme Based on Neural Network,” Proceedings of the 10th WSEAS International Conference on COMMUNICATIONS, Vouliagmeni, Athens, Greece, July 10-12, 2006, pp 7-12.

4.              F.Chabaud, A. Joux.,” Differential Collisions in SHA-0,” Advances in Cryptology – Crypto’98, pringer-Verlag, , pp.56-71, August 1998.

5.              National Institute of Standards and Technology, NIST FIPS PUB 186,” Digital Signature Standard,” U.S. Department of Commerce, 1994.

6.              Kohn R., Van Hemmen J. L., “Self-Organizing Maps and Adaptive Filters”, 1991.

7.              Y.F.Yam and T.W.S. Chow, “Determining initial weights of feedforward neural networks based on least square method,” Neural Processing Lett., vol.2, pp. 13-17,1995.

8.              “A new method in determining the Initial weights of feedforward neural networks,”

9.              Bruce Schneier,“Applied Cryptography”, General reference book about crypto algorithms and protocols, aimed at implementers, 2nd edition.

10.           John Cannaddy,” Artificial Neural Networks for Misuse Detection”, School of Computer and Information Sciences Nova Southeastern University Fort Lauderdale, FL 33314


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27.

Authors:

Zaid G. Ali, R. B Ahmad, Abid Yahya

Paper Title:

Burst Fragmentation Model Based on Sequential Burst Allocation Algorithm for Mobile WiMAX

Abstract:  the downlink Bandwidth resources of WiMAX are allocated by the burst allocation algorithm. The algorithm is responsible for calculating the appropriate location of a number of the smallest unit of bandwidth which is called the slot for all users within the downlink subframe in the form of bursts. Resource wastage in the form of unused and unallocated slots is a real common problem accompanies resource management in the burst allocation algorithms. This paper investigates the Sequential Burst Allocation (SBA) that based on sequential slot allocation and burst fragmentation. An analytical model of frame utilization has been derived. Moreover, this paper presents criteria of burst fragmentation and investigates the effect of burst fragmentation to the allocation efficiency. It has been observed from the results that the SBA algorithm outperforms the Standard (ST) algorithm in term of number of users and resource wastage reduction per frame. The research results illustrates that burst fragmentation can enhance the proportion of frame utilization with minor effect to the overhead size. As well as, the results are useful to be a heuristic guide line for MAC layer scheduler to decide the best burst size that can be used.

Keywords:
 Burst allocation, Burst fragmentation, Downlink subframe, Overhead, Mobile WiMAX.


References:

1.                J. Pinola, K. Pentikousis,VTT Technical Research Centre of Finland, " Mobile WiMAX," The Internet Protocol Journal, vol. 11, N0.2, pp. 19-35, June 2008.
2.                S-Y. Tang, P. Muller, H. R. Sharif, WiMAX Security and quality of Service an End-to-End Perspective: John Wiley & Sons Ltd, 2010.

3.                J. G. Andrews, A. Ghosh, R. Muhamed “Fundamentals of WiMAX” prentice hall 2007.

4.                M. H. Shabani, et al., "Survey of Down Link Data Allocation Algorithms in IEEE 802.16 WiMAX," International Journal of Distributed and Parallel Systems, vol. 3, 2012.

5.                R. B. A. Zaid G. Ali, Abid Yahya, L. A. Hassnawi, Zeyid T. Ibraheem, "Improve Downlink Burst Allocation to Achieve High Frame Utilization of Mobile WiMAX (802.16e) " International Journal of Computer Science Issues, vol. Vol.9, November 2012.

6.                J. Z. Xin Jin, Jinlong Hu, Jinglin Shi, Yi Sun, Dutkiewicz E., "An Efficient Downlink Data Mapping Algorithm for IEEE802.16e OFDMA Systems," in Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE, 2008, pp. 1-5.

7.                C.-T. Chiang and K.-P. Shih, "A burst overlapping and scheduling scheme (BOSS) in IEEE 802.16 OFDMA systems," in Ubiquitous and Future Networks (ICUFN), 2012 Fourth International Conference on, 2012, pp. 379-382.

8.                Xin Jin, Jihua Zhou, Jinlong Hu, Jinglin Shi, Yi Sun, Eryk Dutkiewicz, "An Efficient Downlink Data Mapping Algorithm for IEEE802.16e OFDMA Systems," in Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE, 2008, pp. 1-5.

9.                IEEE 802.16 Working Group, "IEEE Draft Standard for local and metropolitan area networks Part 16: Air Interface for Broadband Wireless Access Systems," IEEE P802.16Rev3/D5, March 2012, pp. 1-2614, 2012.

10.             Telesystem Innovations Inc., “Fundamentals of WiMAX: A Technology Primer”, technical report, 2010, Telesystem Innovations, Canada, online on: http://www.tsiwireless.com/docs/whitepapers.

11.             WiMAXForum, "WiMAX™ System Evaluation Methodology V2.1," ed. USA: WiMAX Forum®, 2008.

12.             WiMAX-Forum, "WiMAX Forum® Air Interface Specifications," in Mobile System Profile, ed. US: WiMAX Forum®, 2010.


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28.

Authors:

Tristan Daladier Engouang, Liu Yun

Paper Title:

Africa on the Way to Global Wireless Digital Television

Abstract:   The African governments and the United Nations’ project of enabling full access to information and communication technology (ICTs) to all citizens, is of the most ambitious in Africa. Thanks to advancements in the broadcasting technology, the resulting digital television has led to transform the viewer experience, offering images with far better improved resolution and quality, whereas the sound is of the best quality. From the huge data consumption in mobile telephony causing the scarcity of frequency spectrum, the International telecommunication union (ITU), required from every countries worldwide to migrate from analog to digital signal, which become mandatory because, as of today, only the techniques used in digital broadcasting are spectrum efficient, what means requiring less spectrum for the transmission of a television signal of a very higher quality,  explaining why a huge parts of that spectrum is been freed up for the benefit of multiple other services such as fire, education, emergency, governments, security. The trend as on the buzz in Africa became about switching over, but because, newest television equipments requires investing enormous funds, African countries are expecting foreign companies to operate in the digital television market expanding in the whole continent. In addition to the shortest time left to meet the deadline set on June 17th, 2015 by ITU, of just two years from now, when considering that as of may 17th, 2013, number of African have not started migrating their television system from analog to digital, what explain at this stage the latency and which, is as risky as it could led Africa to rush, avoiding to fulfill proper studies, in term of coverage, and market pricing.  Analyzing properly the African situation, where choosing the wireless television including Satellite and terrestrial, over the cable television appears to be wiser. Moreover, it is worth that the transition started in 2008, in Rwanda, with the opening of the Chinese company Startimes’ subsidiary, after receiving the first terrestrial digital TV operating license for pay television services.

Keywords:
 Digital Television, Africa, Switchover, Satellite, Terrestrial, ICT, ITU.


References:

1.              L. J Hornbeck, "From cathode rays to digital micromirrors: A history of electronic projection display technology," Texas Instruments Technical Journal, vol. 15, no. 3, pp. 7-76, 1998.
2.              Tristan Daladier Engouang and YUN LIU, "AVIS - Applied Visa Information System - Case Study for the Embassy of the Gabonese Republic in China," in ICEIS (1) 13th International Conference on Enterprise Information Systems, vol. 1, Beijing - China, 2011, pp. 197-204.

3.              Xinhua. (2006, November) English.focacsummit.org. [Online]. http://english.focacsummit.org/2006-11/05/content_5166.htm

4.              ITU. (2012, June) FTRA-2012: 13th Edition. [Online]. http://www.itu.int/ITU-D/afr/events/FTRA/2012/index.html

5.              Tristan Daladier Engouang and YUN LIU, "Switchover: A fundamental of the Era of Television Digital Africa Live (E-TDAL)," International Journal of Digital Multimedia Broadcasting, pp. 1-24, Unpublished, June 2013.

6.              Gaboneco. (2012, June) L'Afrique determiné a passé au numérique. [Online]. http://www.gaboneco.com/show_article.php?IDActu=25730

7.              Tristan Daladier Engouang and YUN LIU, "Africa the New Arena of Digital Television," in The 8th International Forum on Strategic Technology (IFOST2013), Ulaanbaatar, Mongolia., 2013, pp. 1-7, In press.

8.              Jean Jaques Massima Landji. (2012, June) 13th Forum on Telecommunication/ICT Regulation and Partnership in Africa (FTRA-2012) Libreville, Gabon. [Online]. http://www.itu.int/ITU-D/afr/events/FTRA/2012/documents/Session1_Massima.pdf

9.              Tristan Daladier Engouang and YUN LIU, "The characteristics of Spread Spectrum CDMA based Systems and China market impacts," in Wireless Communications, Networking and Mobile Computing (WiCOM), 8th International Conference, Shanghai, 21-23 Sept. 2012, pp. 1-8.

10.           Agona Sam and Otim Juliane Sansa, "Readiness of Uganda For Analog to Digital Migration by December,2012," International Journal of Computing and ICT Research, vol. 5, no. Special Issue, pp. 69-79, December 2011.

11.           Overseas Canal+. (2013, March) Canalplus-overseas. [Online]. http://www.canalplus-overseas.com/en/the-offers/africa/sub-saharan-africa

12.           Wikipedia. (2013, January) Wikipedia. [Online]. http://en.wikipedia.org/wiki/DStv

13.           Startimes. (2011) [Online]. http://en.startimes.com.cn

14.           Angulo Jorge, Calzada Joan, and Estruch Alejandro, "Selection of standars for digital television: The battle for Latin America," Telecommunications Policy, vol. 35, no. 8, pp. 773-787, 2011.

15.           Mary Bellis. (2013, May) Inventors.About. [Online]. http://inventors.about.com/od/tstartinventions/a/Television.htm

16.           Wikipedia. (2013, April) Wikipedia. [Online]. http://en.wikipedia.org/wiki/Cable_television

17.           Althos. (2010) Althos. [Online]. http://www.althos.com/tutorial/TV-advertising-tutorial-cable-television-CATV-system.html

18.           Wikipedia. (2013, April) Satellite Television. [Online]. http://en.wikipedia.org/wiki/Satellite_television

19.           Eutelsat. (2013, April) Eutelsat Communication. [Online]. http://www.eutelsat.com/en/satellites/the-fleet/EUTELSAT-10A.html

20.           RascomStar-QAF. (2013, April) RascomSTAR-QAF1R. [Online]. http://www.rascomstar.com/fleet.php

21.           G. Proakis John and G. Manolakis Dimitris, Digital Signal Processing: Principles, Algorithms and Applications, Fourth Edition, 4th ed. Beijing, China: Publishing House of Electronics Industry, 2007.

22.           Herve Benoit, Digital Television Third Edition: Satellite, Cable, Terrestrial, IPTV, Mobile TV in the DVB Framework.: Focal Press, 2008.

23.           Nathan Blaunstein and Christos Christodoulou, Radio Propagation and Adaptive Antennas for Wireless Communication Links: Terrestrial, Atmospheric and Ionospheric, 1st ed. New Jersey, USA: John Willey & Sons, Inc., 2007.

24.           Deer Li and Jianping Pan, "Evaluating MPEG-4/ AVC Video Streaming over IEEE 802.11 Wireless Distribution System," in WCNC 2008, IEEE Wireless Communication & Networking Conference, vol. 9, Las Vegas, Nevada, USA, January 2008, pp. 2147-2152.

25.           Paul Kagame, "Rwanda Welcomes the world," Connect Africa, vol. 1, p. 11, October 2007. [Online].

26.           www.itu.int/ITU-D/afr/ConnectAfrica/HD_ConnectAfrica_Vol1_E.pdf

27.           Ban Ki-moon, "Message to the Connect Africa Summit," Connect Africa, vol. 1, pp. 7-8, October 2007. [Online]. www.itu.int/ITU-D/afr/ConnectAfrica/HD_ConnectAfrica_Vol1_E.pdf

28.           Toure I. Hamadoun, "Africa is Open for business," Connect Africa, vol. 2, p. iv, January 2009. [Online].

29.           www.itu.int/ITU-D/afr/ConnectAfrica/HD_ConnectAfrica_Vol1_E.pdf

30.           Sajda Qureshi, "What is the role of mobile phones in bringing about growth," Information Technology for Development, vol. 19, no. 1, pp. 1-4, 2013.

31.           CDBank. (2012, May) China Develpment Bank. [Online]. http://www.cdb.com.cn/english/NewsInfo.asp?NewsId=4159

32.           Abdelrahim Suleiman, "Afrovision for broadcasting: Extending MENOS services to Africa," Connect Africa, vol. 3, pp. 63-66, January 2010. [Online].

33.           www.itu.int/ITU-D/afr/ConnectAfrica/HD_ConnectAfrica_Vol1_E.pdf

34.           Duncan Wambogo Omole, "Hardnessing information and communication technology (ICTs) to address urban poverty: Emerging open policy lessons for the open knowledge economy," Information Technology for Development, vol. 19, no. 1, pp. 86-96, 2013.


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29.

Authors:

N.Murugendrappa, A.G Ananth

Paper Title:

Interference Cencellation for Ds-Ss Using Rectagular Window Ltv Filter and Sliding Discrete Time Fourier Transforms (Dstft) Techniques

Abstract:    Interference suppression in spread spectrum communication systems is very essential for achieving maximum system performance.  Existing interference suppression methods do not perform well for most types of non stationary signals. First the interference suppression schemes based on orthogonal time-frequency decomposition, wavelets and arbitrary time-frequency signals are considered. These methods often reduce interference substantially; however the minor changes in interference characteristics such as the center frequency may require changes in the mathematical modeling. The rectangular window methods for fractional Fourier transform with accompanying blind interference excision scheme appears very promising for mitigating time-frequency dominated interference. The present work includes simulations with narrowband interference and comparison of the performance and illustration with different methods. The performance of the rectangular window is evaluated with existing Discrete Short time  Fourier Transform algorithm (DSTFT) for filtering with various Jamming-to-Signal Ratios (JSR) starting from 40 dB to 100 dB in steps of 10 dB. Model simulation results with proposed algorithm shows considerable improvement in Signal-to-Noise Ratio (SNR) for the DS-SS signal compared to that of STFT filtering. The results are presented and discussed in the paper.

Keywords:
 (SDTFT), (JSR), (SNR), DS-SS.


References:

1.              H.M. Ozaktas, Z. Zalevsky, M. Alper Kutay, Fractional Fourier Transform with Applications in Optics and Signal Processing, Wiley, New York, 2000 Chapter 4, pp. 117–137,Chapter 6, pp. 210–213, Chapter 10, pp.421–422.
2.              L.B. Almeida, The fractional Fourier transform and time  frequency representation, IEEE Trans. Signal Process. 42 (9) (November 1994) 3084–3091.[3] C. Vijaya, J.S. Bhat, Signal compression using discrete fractional Fourier transform and set partitioning in hierarchical tree, Signal Processing 86 (2006) 1976–1983.

3.              S.-C. Pei, M.H. Yeh, The discrete fractional cosine and sine transforms, IEEE Trans. Signal Process. 49 (6) (June 2001) 1198–1207.

4.              S.-C. Pei, J.-J. Ding, Fractional cosine, sine and Hartley transforms, IEEE Trans. Signal Process. 50 (7) (July 2002) 1661–1680.

5.              G. Cariolaro, T. Erseghe, P. Kraniauskas, The fractional discrete cosine transform, IEEE Trans. Signal Process. 50 (4) (April 2002) 902–911.

6.              E. Jacobsen, R. Lyons, The sliding DFT, IEEE Signal Process. Mag. (March 2003) 74–80.[8] S.V. Narasimhan, S. Veena, Signal Processing Principles and Implementation, Narosa Publishing House, New Delhi,India, 2005 Chapter 5, pp. 122–124.

7.              S.-C. Pei, J.-J. Ding, A closed form discrete fractional and affine Fourier transforms, IEEE Trans. Signal Process. 48 (5) (May 2000) 1338–1353.

8.              Z. Wang, Fast algorithms for the discrete W transform and for the discrete Fourier transform, IEEE Trans. ASSP 32 (8) (August 1984) 803–816.

9.              Luis B.Almeida.The, “The fractional Fourier Transform and time-frequency Repasanations” IEEE Trans.signal Proc.42 (11) (1994)3084-3093.

10.           F.Hlawatch and G.F.Bourdeaux-Bartels. “Liner and quadratic time-frequency signal representations.”IEEE Signal Processing Mag...Vol .no.2 pp.21-67.Apr.1992

11.           L.Cohen. “Time-frequency distributions- A review,” Proce IEEE.vol 77.no.7.app 941-981.july 1989

12.           V.Namias. “The fractional order Fourier transform and its application to quantum mechanics.”J.Inst.Math .appl..Vol.25.pp.241-265.1980

13.           A.C.McBride and F.H.Kerr.“OnNaminas’ fractional Fourier transform s.” IMA J.Appl.Math.vol.39.pp.159-175.1987

14.           V.Ashok Narayanan and K.M.M.Prabhu, “Fractional Fourier Transform: Theory, implementation and error analysis” ELSEVIER Microprocessors and Microsystems 27 (2003) 511-512.

15.           Adhemar Bultheel and Hector E.Martinez Sulbaran “Computation of the fractional Fourier transform” preprint .Feb 1.2004.

16.           Franz Hlawatch, Gerald Matz, Heinrich Kirchauer, and Werner kozek, “time-frequency formulation, design, and implementation of time-varying optimal filter for signal Estimation.” IEEE tran ,.Vol .48.No.5.May 2000.


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30.

Authors:

Prakash S, Shashidhara HR, G.T.Raju

Paper Title:

The Role of an Information Retrieval in the Current Era of Vast Computer Science Stream

Abstract:     The modern era of search technology has changed the way the information searched and retrieved compared to the previous decade of search engines. Today’s search engine has evolved as a way of shifting the locus of control over information dissemination closer to the consumers of that content. Information retrieval being a vast field, has many application related to it. In this paper we analyze various fields in which IR is being used as an application. We divide the application into seven categories; they are Communication, Databases, Natural Language Processing, Multimedia, Document Ranking, Semantic Web and Software Engineering. In this paper it can be observed that the importance of IR in the various fields just by sheer number of categories it supports. The more widely its used, the more it will change the way the mankind is going look at information and world at large.

Keywords:
   Information Retrieval, Web result clustering, Query optimization, Web and Databases, Semantic web, Document ranking, Data Engineering, Natural language processing;


References:

1.              Priebe, Torsten Pernul, Günther, Towards integrative enterprise knowledge portals, Proceedings of the twelfth international conference on Information and knowledge management - CIKM '03,216,2003.
2.              Lane, Nicholas D Lymberopoulos, Dimitrios Zhao, Feng Campbell, Andrew T, Hapori: Context-based Local Search for Mobile Phones using Community Behavioral Modeling and Similarity, 109-118, 2010.

3.              Watson, I B M T J Lu, Jie Zhou, Michelle X, An Interactive, Smart Notepad for Context-Sensitive Information Seeking, 127-136, 2009.

4.              Zealand, New Hinze, Annika M Zealand, New Zealand, New Nichols, David M, Contextual Queries express Mobile Information Needs Categories and Subject Descriptors,327-336.

5.              Kamvar, Maryam, The Role of Context in Query Input: Using contextual signals to complete queries on mobile devices, 405-412, 2004.

6.              Drakatos, Stylianos Pissinou, Niki Douligeris, Christos, A Context-Aware Prefetching Strategy for Mobile Computing Environments, 1109-1116.

7.              Thollot, Raphaël,Text-To-Query: Dynamically building structured analytics to illustrate textual content, 2011.

8.              Bordogna, GloriaPagani, Marco Pasi, Gabriella Psaila, Giuseppe Bergamo, Università Ingegneria, Facoltà Marconi, Viale, Evaluating Uncertain Location-Based Spatial Queries,1095-1100.

9.              Chen, Jidong Guo, Hang, iMecho: a Context-aware Desktop Search System, 2009, 2011.

10.           Li, Yukun Meng, Xiaofeng, Supporting context-based query in personal DataSpace, Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09, 1347, 2009.

11.           Levandoski, Justin J Khalefa, Mohamed E, CareDB: A Context and Preference-Aware Location-Based Database System, 1529-1532, 2010.

12.           Chen, Li Martone, Maryann Fong, Lisa Wong-barnum, Mona, OntoQuest: Exploring Ontological Data Made Easy, 1183-1186, 2006.

13.           Choi, Donjung Kim, Taeyeon Min, Moohong Lee, Jee-Hyong, An approach to use query-related web context on document ranking, Proceedings of the 5th International Confernece on Ubiquitous Information Management and Communication - ICUIMC '11,1.2011.

14.           Dou, Zhicheng Song, Ruihua Wen, Ji-Rong, A large-scale evaluation and analysis of personalized search strategies, Proceedings of the 16th international conference on World Wide Web - WWW '07,581,2007.

15.           Zhu, Yangbo Callan, Jamie Carbonell, Jaime Lazarus, Emma Lazarus, Emma, The Impact of History Length on Personalized Search, 715-716, 2008.

16.           Wei, Xing Peng, Fuchun Tseng, Huihsin Lu, Yumao Dumoulin, Benoit, Context sensitive synonym discovery for web search queries, Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09,1585,2009.

17.           Lioma, Christina Kothari, Alok Schuetze, Hinrich, Sense discrimination for physics retrieval, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11,1101,2011.

18.           Rho, Tobias Appeltauer, Malte Lerche, Stephan Cremers, Armin B. Hirschfeld, Robert, A context management infrastructure with language integration support, Proceedings of the 3rd International Workshop on Context-Oriented Programming - COP '11,1-6,2011.

19.           Varma, Vasudeva, Language-Independent Context Aware Query Translation using Wikipedia Search and Information Extraction Lab Search and Information Extraction Lab,145-150,june-2011.

20.           Khan, Latifur McLeod, Dennis Hovy, Eduard, Retrieval effectiveness of an ontology-based models for information selection, The VLDB Journal the International Journal on Very Large Data Bases, 71-85, 2004.

21.           Vrochidis, Stefanos Patras, Ioannis Kompatsiaris, Ioannis, An eye-tracking-based approach to facilitate interactive video search, Proceedings of the 1st ACM International Conference on Multimedia Retrieval - ICMR '11,1-11,2011.

22.           Jaffe, Alexandar Naaman, Mor Tassa, Tamir Davis, Marc, Generating summaries and visualization for large collections of geo-referenced photographs, Proceedings of the 8th ACM international workshop on Multimedia information retrieval - MIR '06, 89, 2006.

23.           Anderson, Kenneth M. Hansen, Frank Allan Bouvin, Niels Olof, Templates and queries in contextual hypermedia, Proceedings of the seventeenth conference on Hypertext and hypermedia - HYPERTEXT '06, 99,2006.

24.           Yang, Yi-hsuan Wu, Po-tun Lee, Ching-wei Lin, Kuan-hung Hsu, Winston H Chen, Homer, ContextSeer : Context Search and Recommendation at Query Time for Shared Consumer Photos,199-208,2008.

25.           Xing, Xing Zhang, Yi Gong, Bo, Mixture model based contextual image retrieval, Proceedings of the ACM International Conference on Image and Video Retrieval - CIVR '10,251,2010.

26.           Graves, Andrew Lalmas, Mounia, Video retrieval using an MPEG-7 based inference network, Proceedings of the 25th annual international ACM SIGIR
conference on Research and development in information retrieval - SIGIR '02, 339, 2002.

27.           Qi Guo, Eugene Agichtein,Charles L. A.Clarke,Azin Ashkan, In the Mood to Click? Towards Inferring Receptiveness to Search Advertising, International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, 319-324, 2009 IEEE/WIC/ACM.

28.           Emily Hill, Lori Pollock and K. Vijayshanker, Automatically Capturing Source Code Context of NL-Queries for Software Maintenance and Reuse, ICSE’09, May 16-24, 2009, Vancouver, Canada.

29.           El Abbadi, G. Schlageter, K.Y. Whang, MiniCon: A scalable algorithm for answering queries using views, The VLDB Journal (2001) 10: 182–198.

30.           Lau, Raymond Y. K. Bruza, Peter D. Song, Dawei, Towards a belief-revision-based adaptive and context-sensitive information retrieval system, ACM Transactions on Information Systems,1-38,2008.

31.           Dalton, Jeffrey Allan, James Smith, David a, Passage retrieval for incorporating global evidence in sequence labeling, Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11, 355, 2011.

32.           Duarte Torres, Sergio Hiemstra, Djoerd Serdyukov, Pavel, Query log analysis in the context of information retrieval for children, Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10,847,2010.

33.           Parton, Kristen Mckeown, Kathleen, MT Error Detection for Cross-Lingual Question Answering,946-954,august,2010.

34.           Sapino, Maria Luisa Informatica, Dip Torino, Universita Kintigh, Keith W, Integrating and Querying Taxonomies with QUEST,1153-1155,2009.

35.           Chen, Liang Jeff Papakonstantinou, Yannis, Context-sensitive ranking for document retrieval, Proceedings of the 2011 international conference on
Management of data - SIGMOD '11,757, 2011.

36.           Yan, Xiaohui Guo, Jiafeng Cheng, Xueqi, Context-aware query recommendation by learning high-order relation in query logs, Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11,2073,2011.

37.           Daltio, Jaudete Medeiros, Claudia B. Gomes, Luiz Lewinsohn, Thomas Michael, A framework to process complex biodiversity queries, Proceedings of the 2008 ACM symposium on Applied computing - SAC '08,2293,2008.

38.           Thawani, Amit Gopalan, Srividya Sridhar, V, Web-based Context Aware Information Retrieval in Contact Centers Applied Research Group , Satyam Computer Services Ltd , The VLDB Journal,2-5.

39.           Hoon, Gan Keong, Phang Kong, Tang, A Semantic Learning Approach for Mapping Unstructured Query to Web Resources, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06),494-497,2006.

40.           Ozcan, Rifat Altingovde, Ismail Sengor Ulusoy, Ozgur, Cost-Aware Strategies for Query Result Caching in Web Search Engines, ACM Transactions on the Web,1-25,2011.

41.           Bulc, Renato F Sp, Carlos Macedo, Alessandra A Sp, Preto Grac, Maria, Configurable semantic services leveraging applications context-aware.

42.           Sundaresan, Neel Ave, Hamilton Jose, San, Inferring Semantic Query Relations from Collective User Behavior, 349-358, 2000.

43.           Hasan, Mohammad Al Parikh, Nish Singh, Gyanit Sundaresan, Neel, Query suggestion for E-commerce sites, Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11,765,2011.

44.           Li, Cheng-Te Shan, Man-Kwan Lin, Shou-De, Context-based people search in labeled social networks, Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11,1607,2011.

45.           Prakasha S, H R Shashidhar, Dr. G T Raju A Survey on Various Architectures, Models and Methodologies for Information   Retrieval” ,IJCET pp182-194  IAEME, 2013.

46.           Christopher D. Manning “An Introduction to Information Retrieval” Online edition (c) 2009 Cambridge UP ,2009

47.           http://www.webopedia.com/TERM/S/Semantic_Web.html

48.           http://en.wikipedia.org/wiki/Natural_language_processing


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31.

Authors:

Preeti.G.Biradar, Uma reddy.N.V

Paper Title:

Implementation of Area Efficient OFDM Transceiver on FPGA

Abstract:  orthogonal frequency division multiplexing (OFDM) is a modulation technology which is widely adopted in many new and emerging wired and wireless communication systems. OFDM offers superior advantages of high spectral efficiency, robustness against a inter carrier and inter symbol interference. In this paper we are designing a transceiver of an OFDM and it is implemented on FPGA. In order to reduce the circuit complexity and space, pipelined 128 point FFT /IFFT architecture is used and also aims to optimize in terms of area and speed at low frequency.

Keywords:
    FFT/IFFT, OFDM, Pipeline, spectral efficiency


References:

1.                Mounir Arioua et.al, “ VHDL Implementation of an optimized 8-point  FFT/IFFT processor on pipeline architecture for OFDM systems. IEEE , 978-1-61284-732-0/11,2010
2.                Arman Chahardahcherik et.al. “Implementing FFT Algorithms on    FPGA” IJCSNS International Journal of Computer Science and Network Security, VOL No.11, November 2011

3.                Manjunath Lakkannavar et.al, “Design and Implementation of OFDM using VHDL and FPGA” IJEAT ISSN: 2249-8958, volume-1,Issue-6, August 2012.

4.                Asmita Haveliya,  “ Design and Simulation of 32 point fft using radix-2 algorithm for FPGA Implementation”, 2012, second International conference on AC and CT.

5.                Anbarasan et.al, “Design and Implementation of  low power FFT/IFFT processor for wireless communication” International conference on PI and ME”.  March , 2012

6.                Shaminder Kaur et.al.  “FPGA Implementation of OFDM Transceiver using FFT Algorithm” International Journal of Engineering Science and Technology (IJEST) ISSN : 0975-5462 Vol. 4 No.04 April 2012.

7.                Tran-Thong et.al, Fixed Point Fast Fourier Transform Error Analysis”, IEEE Transactions on AS and SP, 24(6): 563573, December 1976.

8.                K. Umapathy et.al.  “Low Power 128-Point Pipeline FFT Processor using Mixed Radix 4/2 for MIMO OFDM Systems” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-5, November 2012 


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32.

Authors:

Muthu, Vinothkumar, Einstein, Subala

Paper Title:

AASLTU: An Advanced System for Location Tracking and Updating

Abstract:   A definition of tracking deals with pursual of something that involves capturing of various events such as location, persons, etc. Now a days mobile phone plays a vital role from communication to store information but when it gets lost we would disconnect. Here we introduce an application called AASLTU (An Advanced System for Location Tracking and Updating). This application will help you locate the street view of the location. This works by using GPS which will track and show the mobile in the map. This application has a web portal which will allocate credentials to every user. These credentials will be stored in the server. So by using this web portal link we can even view the location of our device from any place. This application will also reveal the last updated information of the mobile if it is in the dead state, that is, when it is switched off. When the mobile turns to active state it will start to update the current location of the mobile. We have to just install the application in our android mobile so we can view the location of the mobile from our mobile itself. This is a user friendly application where we can easily locate our mobile by having an authorized access.

Keywords:
 Global Positioning System, Global Positioning System, Location Based Service, Location Proof, Location Privacy, Pseudonym, Colluding Attacks, Delay Lock Loop, Pseudo Random Noise.


References:

1.                Zhichao Zhu and Guohong Cao, “Toward Privacy Preserving and Collusion Resistance in a Location Proof Updating System” IEEE Transactions On Mobile Computing, Vol. 12, No. 1, January 2013.
2.                A.R. Beresford and F. Stajano, “Location Privacy in Pervasive Computing,” IEEE Security and Privacy, 2003.

3.                L. Buttya´n, T. Holczer, and I. Vajda, “On the Effectiveness of Changing Pseudonyms to Provide Location Privacy in VANETs,” Proc. Fourth European Conf. Security and Privacy in Ad-Hoc and Sensor Networks, 2007.

4.                T. Xu and Y. Cai, “Feeling-Based Location Privacy Protection for  Location-Based Services,” Proc. 16th ACM Conf. Computer Comm. Security (CCS), 2009.

5.                J. Freudiger, M.H. Manshaei, J.P. Hubaux, and D.C. Parkes, “On Non-Cooperative Location Privacy: A Game-Theoretic Analysis,” Proc. 16th ACM Conf. Computer and Comm. Security (CCS), 2009.

6.                Gedik and L. Liu, “A Customizable K-Anonymity Model for Protecting Location Privacy,” Proc. IEEE Int’l Conf. Distributed Computing Systems (ICDCS), 2005.

7.                M. Gruteser and D. Grunwald, “Anonymous Usage of Location-Based Services through Spatial and Temporal Cloaking,” Proc. ACM MobiSys, 2003.

8.                T. Jiang, H.J. Wang, and Y.-C. Hu, “Location Privacy in Wireless Networks,” Proc. ACM MobiSys, 2007.

9.                V. Lenders, E. Koukoumidis, P. Zhang, and M. Martonosi, “Location-Based Trust for Mobile User-Generated Content: Applications Challenges and Implementations,” Proc. Ninth Work-shop Mobile Computing Systems and Applications, 2008.

10.             M. Li, R. Poovendran, K. Sampigethaya, and L. Huang, “Caravan: Providing Location Privacy for VANET,” Proc. Embedded Security in Cars (ESCAR) Workshop, 2005.

11.             Z. Zhu and G. Cao, “APPLAUS: A Privacy-Preserving Location Proof Updating System for Location-Based Services,” Proc. IEEE INFOCOM, 2011.

12.             S. Saroiu and A. Wolman, “Enabling New Mobile Applications with Location Proofs,” Proc. ACM 10th Workshop Mobile Computing Systems and Applications (HotMobile ’09), 2009.


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33.

Authors:

Manasee Patil, S.R.N. Reddy

Paper Title:

Comparative Analysis of RFID and Wireless Home/Office Automation

Abstract:    Wireless Sensor Network (WSN) is most widely used wireless technology in different applications. Home automation makes day to day life of people easier.WSN provides flexible management of lighting, heating, cooling and security from anywhere in the home/office [20]. In this project we propose use of both wired and wireless technology for home/ office automation. RFID technology is used for automatic door opening & closing. We also propose use of wireless sensor network for temperature, lighting, smoke detection and automatic door opening & closing.GSM technology is used in this project to monitor and control various devices from outside the home/office.

Keywords:
 Bluetooth, GSM, RFID, Wireless Sensor Network.


References:

1.                 Oke, A.O., O.M. Olaniyi, O.T. Arulogun, and O.M. Olaniyan. 2009. “Development of a Microcontroller-Controlled Security Door System”. Pacific Journal of Science and Technology. 10(2):398-403.
2.                 Verma, G. K.;Tripathi, P., “A Digital Security System with Door Lock System Using RFID Technology”, International Journal of Computer Applications (0975 – 8887), 2010, Vol5, pp 6-8.

3.                 Salim G Shaikh and Shankar D Nawale.,”Secure access of RFID system”, International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012

4.                 Parvety A,Venkata Rohit Raj,Venumadhav Reddy M, Manikanta Chaitanya G “RFID based exam hall maintenance system,” IJCA Special Issue on “Artificial Intelligence Techniques - Novel Approaches & Practical Applications”AIT, 2011

5.                 Muhammad Naveed, Wasim Habib, Usman Masud, Ubaid Ullah, and Gulzar Ahmad,” Reliable and Low Cost RFID Based Authentication System for Large Scale Deployment”,   International Journal of Network Security, Vol.14, No.3, PP. 173{179, May 2012

6.                 T.S.Lim, S.C.Sim, M.M.Mansor “RFID based attendance system,” ISIEA, Kuala Lumpur, Malaysia, October 2009.

7.                 Chen Ying, ZhangFu-Hong, “A System Design for UFH RFID Reader”, IEEE International Conference on Communication Technology Proceedings, ed11th, 2008.

8.                 Xiao, Y., Yu, S., Wu, K., Ni, Q., Janecek., C., Nordstad, J, “ Radio frequency identification: technologies, applications, and research issues”, Wiley Journal of Wireless Communications and Mobile Computing, 2007,Vol 7.

9.                 Farooq, U., Amar, M., Ibrahim, H.R., Khalid, O., Nazir, S., Asad, M.U. “ Cost effective wireless attendance and access control system”,3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010 , Vol 9,pp 475 – 479.

10.              R.Saravanan, A.Vijayaraj, ” Home Security Using Zigbee Technology”, IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 1, No. 2, December 2011

11.              Y.Usha Devi,”  Wireless Home Automation System Using ZigBee”, International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012 1 ISSN 2229-5518

12.              Michal VARCHOLA,” Zigbee Based Home Automation Wireless Sensor Network”, Acta Electrotechnica et Informatica No. 4, Vol. 7, 2007

13.              Dr.S.S.Riaz Ahamed,” The Role Of Zigbee Technology In Future Data Communication System”, Journal of Theoretical and Applied Information Technology 2005 - 2009 JATIT

14.              Tully, A.; Blythe, P.T.” ZigBee for Intelligent Transport System applications”, Road Transport Information and control- RTIC 2008 and ITS United Kingdom Members’ conference, IET May2008.

15.              Yong Tae Park, Sthapit, P.;Jae-Yong Pyun,” Smart digital door lock for the home automation”, TENCON 2009-2009 IEEE Region 10 Conference Jan 2009.

16.              Pik-Yiu Chan,Enderle, J. D.,” Automatic door opener”, Bioengineering conference 2000 Proceedings of the IEEE 26th Annual Northeast 2000

17.              Felix, C. raglend,I.J. “Home Automation using GSM”, Signal Processing communication, Computing and Networking Technologies(ICSCCN), 2011 International conference on July 2011.

18.              Gill, K. Shuang-Hua Yang; Fang Yao; Xin Lu “ ZigBee based Home Automation system”, Consumer Electronics, IEEE Transaction on May 2009.

19.              Ramya, C.M, Shanmugaraj,M.; Prabakaran, R,,” Study on ZigBee technology”, Electronics Computer Technology(ICECT), 2011 3rd International Conference on 8-10 April 2011.

20.              P. N. Narendra Reddy, P. I. Basarkod, S. S. Manvi,” Wireless Sensor Network based Fire   monitoring and Extinguishing System in Real Time Environment”,Int. J. Advanced Networking and  ApplicationsVolume: 03, Issue: 02, Pages:1070-1075 (2011)

21.              Usha Sharma and S.R.N. Reddy” design of home/ office auotomation using wireless sensor network” international journal of computer application, vol. 43, April 2012 pp.53-60

22.              Vini Madan and SRN Reddy” Review of wireless sensor mote platforms”, VSRD international journal of Electrical, Electronic& communication Engineering, vol 2(2),2012,pp 50-55.

23.              I.F. Akyildiz, W.Su, Y.S. Sankarasubramaniam, E.cayirci,” Wireless Sensor networks: a Survey”, computer networks38(2002), 393-422.

24.              Gurpreet Singh, Raghav Bhardwaj, Karamjeet Singh and Sahil Mehla,”ZigBee: A review”, International journal of computer science and technology, vol. 3, Jan- March 2012, pp328-331.

25.              Vini madan, SRN Reddy,” GSM-Bluetooth based remote monitoring and control system with automatic light controller”, International journal of computer applications (0975-8887) vol. 46 no.1, 2012.

26.              Lie Zhang, Zhi Wang,” Integration of RFID into wireless Sensor Network: architecture, opportunities and challenging problems”,Fifth international conference on Grid and cooperative computing workshop (GCCW’06),2006.

27.              R.Jayalakshmamma,P.V.naganjaneyulu,K. Babulu,”Implementation of integrity of voice and face recognition for home security by using GSM and ZigBee”,IJESAT International journal of engineering science & advanced technology,vol.2,Jul-Aug2012,pp.1043-1047.


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34.

Authors:

Ahmed Soufi Abou-Taleb, Ahmed Ahmed Mohamed, Osama Abdo Mohamed, Amr Hassan Abedelhalim

Paper Title:

Hybridizing Filters and Wrapper Approaches for Improving the Classification Accuracy of Microarray Dataset

Abstract:     Feature selection aims at finding the most relevant features of a problem domain. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. This  paper  is  aimed  at  identifying  a  small  set  of  genes,  to  improving computational speed and prediction accuracy; hence we have proposed a three-stage of gene selection  algorithm  for  microarray  data. The proposed approach combines information gain (IG), Significance Analysis for Microarrays (SAM), mRMR (Minimum Redundancy Maximum Relevance) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In the first stage, intersection part of feature sets is identified by applying the (SAM–IG). While, the second minimizes the redundancy with the help of mRMR method, which facilitates the selection of effectual gene subset from intersection part that recommended from the first stage. In the third stage, (SVM-RFE) is applied to choose the most discriminating genes. We evaluated our technique on AML and ALL (leukemia) dataset using Support  Vector  Machines (SVM- RBF) classifier, and  show  the  potentiality  of  the  proposed  method  with  the advantage of improving the classification performance.

Keywords:
 Feature selection, Filters, Wrappers, Support vector machine, Microarray.


References:

1.           E. Bonilla-Huerta, et al., "Hybrid Filter Wrapper with a Specialized Random MultiParent Crossover Operator for Gene Selection and Classification Problems," Bio-Inspired Computing and Applications, pp. 453-461, 2012.
2.           Z. Zhu, Y. S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognition, vol. 40, pp. 3236-3248, 2007.

3.           P. Bermejo, J. A. Gámez, and J. M. Puerta, "A GRASP algorithm for fast hybrid (filter wrapper) feature subset selection in high dimensional datasets," Pattern Recognition Letters, vol. 32, pp. 701-711, 2011.

4.           T. R. Golub, et al., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," science, vol. 286, pp. 531-537, 1999.

5.           El Akadi, et al., "A two-stage gene selection scheme utilizing MRMR filter and GA wrapper," Knowledge and Information Systems, vol. 26, pp. 487-500, 2011.

6.           Liu, H., Dougherty, E. R., Dy, J. G., Torkkola, K., Tuv, E., Peng, H., et al. (2005). Evolving feature selection. Intelligent Systems IEEE, 20(6), 64–76.

7.           Kohavi, R., & John, G. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97, 273–324.

8.           Huang, J., Cai, Y., & Xu, X. (2006). A filter approach to feature selection based on mutual      information. Proceedings of the Fifth IEEE International Conference on Cognitive Informatics. Beijing: China (pp. 84–89).

9.           Deisy, C., Subbulakshmi, B., Baskar, S., & Ramaraj, N. (2007). Efficient dimensionality reduction approaches for feature selection, International Conference on Computational Intelligence and Multimedia Applications. India: Sivakasi (pp.121–127).

10.        Backstrom, L., & Caruana, R. (2006). C2FS: An algorithm for feature selection in cascade neural networks, IEEE International Joint Conference on Neural Networks. Canada: Vancouver, BC, pp. 4748–4753.

11.        Liu, Yue, Yin, Yafeng, Gao, Junjun, & Tan, Chongli (2008). Wrapper feature selection optimized SVM model for demand forecasting. The International Conference on Young Computer Scientists. China: Hunan (pp. 953–958).

12.        Vapnik, V., Guyon, I., Weston, J., & Barnhill, S. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3),389–422.

13.        Cho, S., & Ryu, J. (2002). Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features. Proceedings of the IEEE,  
90(11), 1744–1753.

14.        Zhang, J., Lee, R., & Wang, Y. J. (2003). Support vector machine classifications for microarray expression dataset. IEEE International Conference on Computational Intelligence and Multimedia Applications. Xi’an, China (pp. 67–71).

15.        Fujibuchi, W., & Kato, T. (2007). Classification of heterogeneous microarray data by maximum entropy kernel. BMC Bioinformatics, 8, 267–277.

16.        Cho, S., & Won, H. (2007). Cancer classification using ensemble of neural networks with multiple significant gene subsets. Applied Intelligence, 26(3), 243–250.

17.        H. Peng, F . Long, and C. Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 2005.

18.        M. Chee, R. Y ang, E. Hubbell, A. Berno, X. Huang, D. Stern, J. Winkler ,D. Lockhart, M. Morris, and S. Fodor. Accessing genetic information with high density DNA arrays. Science, 274:6102614, 1996.

19.        Guyon, J. Weston, S. Barhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Mach. Learn., vol. 46, pp. 389–422, 2002.

20.        I. Guyon, J. Weston, S. Barnhill and V. Vapnik, “Gene Selection for Cancer Classification using Support Vector Machines”, Machine Learning,2002, Vol. 46, No. 1-3, pp. 389-422.


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35.

Authors:

S.Ezhil Vannan, S.Paul Vizhian

Paper Title:

Investigation on the Influence of Basalt Short Fiber on Thermo-Physical Properties of Aluminium Metal Matrix Composites

Abstract:      The objective of this research work was to investigate on the influence of basalt fiber on the microstructure and thermo-physical properties of Al /basalt short fiber metal matrix composites (MMCs).  The MMCs were fabricated by liquid metallurgical technique and the basalt short fiber varies from 0 to 10 wt. %. The developed MMCs were characterized for damping, coefficient of thermal expansion, specific heat and electrical resistance using dynamic mechanical analyzer, thermal mechanical analyzer, differential scanning calorimeter and four probe electrometer respectively. The results shows that the specific damping properties and specific heat increase with increasing basalt fibers addition\ but, electrical resistivity and coefficient of thermal expansion decreased.

Keywords:
  Metal Matrix composites (MMCs), Basalt fiber, Dynamic mechanical analyzer (DMA), Thermal mechanical analyzer (TMA), Differential scanning calorimeter (DSC)


References:

1.              Karthikeyan, B, Ramanathan, S, Ramakrishnan, V.Thermo Physical Property Measurement of Metal-matrix Composites Mater & Design 31 2010: pp. S82 – S86.
2.              P. J. Ward, H. V. Atkinson, P. R. G. Anderson, L. G. Elias, B. Garcia, L. Kahlen and J-M. Rodriguez-Ibabe, “Semi-solid processing of novel MMCs based on hypereutectic aluminium-basalt short fiber alloys” Acta Materialia, Vol. 44, No. 5, 1996, pp.1717-1727.

3.              M. Ward-Close, L. Chandrasekaran, J. G. Robertson, S. P. Godfrey and D. P. Murgatroyde, “Advances in the fabrication of titanium metal matrix composite”, Materials Science and Engineering A, Vol. 263, No. 2, 1999, pp. 314-318.

4.              Tjong, S. C, Tam, K. F. Mechanical and Thermal Expansion Behavior of Hipped Aluminum–TiB2 Composites Materials Chemistry and Physics 97 2006: pp. 91 – 97.

5.              Yong Yang, Jie Lan, Xiaochun, Study on bulk aluminum matrix nano-composite fabricated by ultrasonic, dispersion of nano-sized SiC particles in molten aluminum alloy Li Materials Science and Engineering A 380 (2004) 378–383

6.              J.P. Tu, N.Y. Wang, Y.Z. Yang, W.X. Qi, F. Liu, X.B. Zhang, H.M. Lu, M.S. Liu, Preparation and properties of TiB nanoparticle reinforced copper matrix composites by in situ processing, Materials Letters 52 Ž2002. 448–452

7.              Tjong, S. C., Wang, G. S., Mai, Y. W. Low-cycle Fatigue Behavior of Al-based Composites Containing in situ TiB2, Al2O3 and Al3Ti Reinforcements Materials Science and Engineering A 358 2003: pp. 99 – 106.

8.              Eslamian, M., Rak, J., Ashgriz, N. Preparation of Aluminum/Silicon Carbide Metal Matrix Composites Using Centrifugal Atomization Powder Technology 184 2008: pp. 11 – 20.

9.              Huber, T., Degischer, H. P., Lefranc, G., Schmitt, T. Thermal Expansion Studies on Aluminum-matrix Composites with Different Reinforcement Architecture of SiC Particles Composites Science and Technology 66 2006: pp. 2206 – 2217.

10.           Deve, H.E., McCullough, C., 1995. Continuous-fiber reinforced Al composites—a new-generation. JOM Journal of the Minerals Metals and Materials Society 47 (7), 33–37.

11.           Yang, J. B, Lin, C. B, Wang, T. C, Chu, H. Y. The Tribological Characteristics of A356.2Al Alloy/Gr(p) Composites Wear 257 2004: pp. 941 – 952.

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36.

Authors:

Vijay Pal Dhaka, Swati Agrawal

Paper Title:

Optimization of Object-Oriented Metrics Using Hopfield Neural Network

Abstract:  This paper examined the application of Artificial neural network for software quality prediction using object-oriented metrics. Quality estimation include estimating maintainability of software. In this study maintenance effort was chosen as the dependent variable and principal components of object-oriented metrics as the dependent variables. We are prediction the number of lines per changed per class. Two neural network models are used, they are ward neural network and Hopfield neural network. The Artificial neural network prossesses the advantages of predicting software quality accurately and identifies the defects by efficient discovery mechanisms.

Keywords:
   Software quality metrics, maintainability, object-oriented, neural network, principal component analysis


References:

1.              Y. Dash, S.K. Dubey and A. Rana, “Maintainability Measurement in Object Oriented Paradigm”, International Journal of Advanced Research in Computer Science (IJARCS), Vol.3, no.2, , April 2012, pp. 207-213.
2.              H. D. Rombach, “A controlled experiment on the impact of software structure on maintainability”, Software Engineering, IEEE Transactions on, SE-13(3):344–354, March 1987.

3.              D. R Moreau and W. D. Dominick, “Object-Oriented Graphical Information Systems: Research Plan and Evaluation Metrics,” Journal of Systems and Software, vol. 10, 1989, pp. 23-28.

4.              Quah T. S, and M.M.T.Thewin (2003): Application of Neural Networks for Software Quality Prediction using Object-Oriented Metrics, Proceedings of the International Conference on Software Maintenance (ICSM’03), Vol 3.

5.              Kanmani S., V.Sankaranarayanan and P.Thambidurai (2003): A Measurement Model for C++ Program Complexity Analysis, Proceddings of the 9th International Conference EPMESC, Macao, China, pp. 575-580.

6.              Y. Dash, S.K. Dubey and A. Rana, Maintainability Measurement in Object Oriented Paradigm‖, International Journal of Advanced Research in Computer Science (IJARCS), Vol.3, no.2, pp. 207-213, April 2012.

7.              J. T. S. Quah, M. M. T. Thwin, Application of Neural Networks for Software Quality Prediction Using Object-Oriented Metrics, Proceedings of the International Conference on Software Maintenance (ICSM’03), IEEE Computer Society, 2003.

8.              M. M. T. Thwin,T. S. Quah, Application of neural networks for software maintainability prediction using Object-oriented metrics‖, Journal of systems and software, Vol.76, No.2, pp.147-156, 2005.

9.              Tong-Seng Quah, Mie Mie Thwin, Prediction of software development fault in PL/SQL files using Neural network models.

10.           K. K. Aggarwal, Y. Singh,A. Kaur and R. Malhotra, Application of Artificial Neural Network for Predicting Maintainability using Object-Oriented Metrics, World Academy of Science, pp. 140-144, 2006.

11.           Y. Dash and S.K. Dubey, ―Quality Prediction in Object Oriented System by Using ANN: A Brief Survey‖, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 2, no. 2, Feb. 2012.

12.           F. Nielsen, Neural Networks – algorithms and applications. 2001 Available at: http://www.glyn.dk/download/Synopsis.pdf. Accessed on Dec.2011.

13.           K. K. Aggarwal, Y. Singh, A. Kaur and R. Malhotra, Investigating effect of design metrics on fault proneness of object oriented systems, Journal of Object Technology,vol.6, no. 10, pp. 127-141, 2007.

14.           Shaik, N. Satyanarayana, M Huzaifa, N. Shaik, M. Z. Naveed, S. V. A. Rao and C. R. K. Reddy. Invetsigate the result of object oriented design software metrics on fault proneness in object oriented systems: A case study,‖ Journal of emerging trends in computing and emerging sciences, Vol. 2, no. 4, pp. 201-208, 2011.

15.           Zhong, Q. Hu, F. Yang and M. Yin. Software Quality Prediction Method with Hybrid Applying Principal Components Analysis and Wavelet Neural Network and Genetic Algorithm,’ International Journal of Digital Content Technology and its Applications, Vol. 5, no. 3, pp.225-234, 2011.

16.           F. Lanubile, A. Lonigro, G. Visaggio, ―Comparing models for identifying fault-prone software components‖, In: Proc. of the 7th Int’l. Conf. Software Eng. and Knowledge Eng., pp. 312–319, June 1995.

17.           L.C. Briand, J. Wüst, and H. Lounis, ―Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs,‖ Empirical Software Engineering. International Journal (Toronto, Ont.), 6(1), pp.11–58. 2001.

18.           P.V.G.D Prasad Reddy, K.R. Sudha, S. P. Rama and S.N.S.V.S.C Ramesh, ―Software Effort Estimation using Radial Basis and Generalized Regression NeuralNetworks‖, Journal Of Computing, Volume 2, Issue 5, May 2010 


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37.

Authors:

Parvathi R, Shanthi Saravanan D

Paper Title:

Efficient Fingerprint Recognition System using Pseudo 2D Hidden Markov Model

Abstract:   Fingerprint can only uniquely identify a person when compared to other types of biometric features. The existing system used the combination of bayes classifier and Henry classifier to increase the speed of authentication process and to provide accurate classification system respectively. But, the combination of those classifiers in real time systems becomes difficult to implement. This fingerprint recognition system uses the pseudo 2D hidden markov model which considers each types of fingerprint as separate states with different levels of markov chain. During the recognition process, the markov model verifies each super states to identify which types of fingerprint, then it can match the given fingerprint image with the image which are kept in database. The proposed work will improve the speed and recognition rate by using the pseudo 2D hidden markov model.

Keywords:
    fingerprint recognition, hidden markov model, viterbi algorithm, fingerprint classification.


References:

1.             Parvathi R, Sankar M,”Fingerprint Authentication System using Hybrid Classifiers”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012.
2.             K. C. Leung and C. H. Leung, Improvement of Fingerprint Retrieval by a Statistical Classifier, IEEE Transactions on Information Forensics And Security, Vol. 6, No. 1, March 2011 ,pp 59 -69.

3.             Stefan Klein, Asker Bazen, and Raymond VeldhuisChander Kant & Rajender Nath , “Reducing Process-Time for Fingerprint Identification System”, International Journals of Biometric and Bioinformatics, Vol.3, Issue (1).

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

5.             Andrew Senior ,”A Hidden Markov Model Fingerprint Classifier’, proceedings of the 31st Asilomar conference on Signals, Systems and Computers, 1997, pp 306–310.

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

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

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

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

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

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

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

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

14.          Keith Worden, Statistical Pattern Recognition, lecture notes, September 2008.

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

16.          Bazen. Fingerprint Identi_cation - Feature Extraction, Matching, and Database Search. PhD thesis, University of Twente, The Netherlands, 2002.

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

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


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38.

Authors:

Liping Guo, Aleck W. Leedy, Sidney Schaaf, Brian Backs, Mark Gabatino, Nathan James, Mike Pintozzi

Paper Title:

Modeling of AC Contactors to Improve Life

Abstract:    The life expectancy of an AC contactor is adversely affected by electrical arcs and heat rise within the contactor.  Electrical arcing results in erosion in the contact material and also results in failures due to welding.  To find alternative methods of improving contactor life expectancy and reduce the maximum temperature without adding costs to production, a computer model was created for the contactor using MATLAB and Simulink that simulated the dynamics of the contactor at closing.  The model solves equations that use geometries and material properties to estimate contact life and heat generation.  The results from the simulation can be used to run a Design of Experiments analysis to find which combinations improve life and reduce maximum temperature without adding significant costs.

Keywords:
 AC contactors, Design of Experiments (DOE), MATLAB, and Simulink.


References:

1.             M. Braunovic, V. Konchits and N. Myshkin, Electrical Contacts Fundamentals, Applications, and Technology, Boca Raton, FL:  CRC Press, Taylor & Francis Group, 2007.
2.             P. G. Slade, Electrical Contacts Principles and Applications, Boca Raton, FL:  CRC Press, Taylor & Francis Group, 1999.

3.             J. R. Riba Ruiz and A. G. Espinosa, "A computer model for teaching the dynamic behavior of AC contactors," IEEE Transactions on Education, vol. 53, no. 2, pp.
248-256, May 2010.

4.             J. J. Shea, "Modeling contact erosion in three phase vacuum contactors," IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 21, no. 4, pp. 556-564, 1998.

5.             D. M. Burrage, M. A. Goodberlet and H. L. and Malcom, "Simulating Passive Microwave Radiometer Designs Using Simulink," Simulation: Transactions of The Society for Modeling and Simulation International, vol. 78, no. 1, pp. 36-55, 2002.

6.             W. Leedy and R. M. Nelms, "A general method used to conduct a harmonic analysis on carrier-based pulse width modulation inverters," Simulation: Transaction of the Society for Modeling and Simulation International , vol. 87, no. 3, pp. 205-220, 2011.

7.             M. P. Paisios, C. G. Karagiannopoulos and P. D. Bourkas, "Model for temperature estimation of dc-contactors with double-break main contacts," Simulation Modelling Practice and Theory, vol. 15, no. 5, pp. 503-512, 2007.

8.             J. D. Lavers, "Constriction resistance at high signal frequencies," IEEE Transactions on Components and Packaging Technology, vol. 25, no. 3, pp. 446-452, 2002.

9.             H. D. Merchant, G. S. Murty, S. N. Bahadur, L. T. Dwivedi and Y. Mehrotra, "Hardness-temperature relationships in metals," Journal of materials science, pp. 437-442, 1973.

10.          G. Thiagarajan and K. Deshmukh, Mechanics of   materials, 4th Ed., Mission, KS:  SDC Publications,   2010.

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39.

Authors:

S.R.Madkar, R.K.Prasad

Paper Title:

A State of Art on Design of Low Cost Transceiver for Data Acquisition in WSN

Abstract:     This paper explores the potential of WSN in the area of agriculture in India. Aiming at the crop, a multi-parameter monitoring system is designed based on low-power ZigBee wireless communication technology for system automation and monitoring. Real time data is collected by wireless sensor nodes and transmitted to base station using ZigBee. Data is received, saved and displayed at base station to achieve soil temperature, soil moisture and humidity monitoring. The data is continuously monitored at base station and if it exceeds the desired limit, a message is sent to farmer on mobile through GSM network for controlling actions. The implementation of system software and hardware are given, including the design of wireless node and the implementation principle of data transmission and communication modules. This system overcomes the limitations of wired sensor networks and has the advantage of flexible networking for monitoring equipment, convenient installation and  low cost and reliable nodes and high capacity.

Keywords:
 AVR Microcontroller, GSM, remote monitoring, LCD, Sensors, ZigBee.


References:

1.              Yiming Zhou, Xianglong Yang, Wang, L., Yibin Ying Sch. A Wireless Design of Low-Cost Irrigation System Using ZigBee Technology", IEEE 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 1
2.              Shen Jin, Song Jingling, Han Qiuyan, Wang Shengde, Yang Yan, School of Electric and Electronic Engineering, Remote Measurement and Control System for Greenhouse Based on GSM-SMS” IEEE 8th International Conference on Electronic Measurement and Instrument, 2007.

3.              IEEE papers-published in 2010: BYIccae, Galgaliikar, m. m

4.              Daniel K. Fisher and Hirut Kebede “A low-cost microcontroller-based system to monitor crop temperature and water status”, Computers and Electronics in
Agriculture, Elsevier B.V.

5.              “Monitoring the paddy crop field using zigbee network” K. Sriharsha.

6.              For Precision Agriculture using Wireless Sensor Network-A reviewAnjum Awasthi, S.R.N Reddy


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40.

Authors:

Nana Kacharu Zalte, Roshan M. Pandav

Paper Title:

A Novel Approach for Grid Service Reliability Modeling Optimal Task Scheduling Perceiving Fault Recovery

Abstract:      Since last few years, grid technology has come into sight as a significant tool for solving and computing high intensive problems from different area. Grid reliability analysis and modeling are not easy tasks because of the complexity and large scale of the system.  While concerning on large scale system, large subtasks requires time-consuming computation, consequently the reliability of grid service could be rather low.  Our paper tries to focus on this reliability and task scheduling in the grid. In the existing system all researchers focused on the remote node fault recovery where greater waste is consumed on time and resource. Furthermore those systems did not incorporate the fault recovery and the practical constraints of grid resource on optimization. Resultantly our paper considers the Local Node Fault Recovery mechanism into grid systems, and presents a solution to simultaneously maximize the grid service reliability modeling and analysis with this kind of fault recovery thereby minimizing the cost. Our proposed Grid Service Reliability & Node Recovery (GSRNR) mechanism considers some practical, some constraints such as the life times of subtasks, the numbers of recoveries performed in grid nodes, and thus grid service reliability models under these practical constrictions are developed. Presuming the proposed grid service reliability model, a multi-objective task scheduling optimization model is presented, and Min Max scheduling algorithm is developed to solve it effectively.

Keywords:
 Grid Computing, Fault Tolerance, Grid Service Reliability, Local Node Fault Recovery


References:

1.                 Tian-Liang Huang, Tian-An Hsieh, Kuan Chou Lai, Kuan-Ching Li, Ching-Hsien Hsu, and Hsi-Ya Chang.” Fault Tolerance Policy on Dynamic Load Balancing in P2P Grids” 2011 International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11.
2.                 Foster, “The Grid: A new infrastructure for 21st century science,” Physics Today, vol. 55,  no. 2, pp. 42–47, 2002.

3.                 Y. S. Dai, M. Xie, and K. L. Poh, “Reliability of grid service systems,” Computers and Industrial Engineering, vol. 50, no. 1–2, pp. 130–147, 2006.

4.                 S. C. Guo, H.Wan, G. B.Wang, and M. Xie, “Analysis of grid resource compensation in market-oriented environment,” Eksploatacja I Niezawodnooeæ— Maintenance and Reliability, vol. 45, no. 2, pp. 36–42, 2010.

5.                 K. Krauter, R. Buyya, and M. Maheswaran, “A taxonomy and survey of grid resource management systems for distributed computing,” Software— Practice and Experience, vol. 32, no. 2, pp. 135–164, 2002.

6.                 L. Li and L. Y. Li, “Multiple QoS modeling and algorithm in computational grid,” Journal of Systems Engineering and Electronics, vol. 18, no. 2, pp. 412–417, 2007.

7.                 G. Levitin, Y. S. Dai, and B. H. Hanoch, “Reliability and performance of star topology grid service with precedence constraints on subtask execution,” IEEE Trans. Reliability, vol. 55, no. 3, pp. 507–515, 2006.

8.                 G. Levitin and Y. S. Dai, “Grid service reliability and performance in grid system with star topology,” Reliability Engineering and System Safety, vol. 92, no. 1, pp. 40–46, 2007.

9.                 Y. S. Dai, G. Levitin, and X. L. Wang, “Optimal task partition and distribution in grid service system with common cause failures,” Future Generation Computer Systems, vol. 23, no. 2, pp. 209–218, 2007.

10.              Y. S. Dai, Y. Pan, and X. K. Zou, “A hierarchical modeling and analysis for grid service reliability,” IEEE Trans. Computers, vol. 56, no. 5, pp. 681–691, 2007.

11.              T. Paul and X. Jie, “Fault tolerance within a grid environment,” in Proceedings of UK e-Science All Hands Meeting, 2003.

12.              M. Affaan and M. A. Ansari, “Distributed fault management for computational grids,” in Proceedings of the Fifth International Conference on Grid and Cooperative Computing, 2006. [13] L. Jin, W. Q. Tong, J. Q. Tang, and B.Wang, “A fault-tolerance mechanism in grid,” in Proceedings of IEEE International Conference on Industrial Informatics, 2003.

13.              K. Jozsef and K. Peter, “A migration framework for executing parallel programs in the grid,” in Proceedings of European across Grids Conference, 2004.

14.              L. Xing and Y. S. Dai, “A new decision diagram model for efficient analysis on multi-state systems,” IEEE Trans. Dependable and Secure Computing, vol. 6, no. 3, pp. 161–174, 2009.

15.              Y. S. Dai and X. L. Wang, “Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm,” Reliability Engineering and System Safety, vol. 91, no. 9, pp. 1071–1082, 2006.

16.              Y. S. Dai and G. Levitin, “Optimal resource allocation for maximizing performance and reliability in tree-structured grid services,” IEEE Trans. Reliability, vol. 56, no. 3, pp. 444–453, 2007.

17.              Coit and A. Smith, “Reliability optimization of series-parallel systems using genetic algorithm,” IEEE Trans. Reliability, vol. 45, no. 2, pp. 254–260, 1996.

18.              Y. C. Liang and A. E. Smith, “An ant colony optimization algorithm for the redundancy allocation problem (RAP),” IEEE Trans. Reliability, vol. 53, no. 3, pp. 417–423, 2004.

19.              H. Z. Huang, J. Qu, and M. J. Zuo, “Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives,” IIE Transactions, vol. 41, no. 4, pp. 287–298, 2009.

20.              G. D. Caro and M. Dorigo, “AntNet: distributed stigmergetic control for communications networks,” Journal of Artificial Intelligence Research, vol. 9, no. 2, pp. 317–365, 1998.

21.              D. Merkle, M. Middendorf, and H. Schmeck, “Ant colony optimization for resource-constrained project scheduling,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 333–346, 2002.  


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41.

Authors:

Deeksha R Shetty, Savitha Patil

Paper Title:

Improving Accuracy in Mitchell’s Logarithmic Multiplication Using Iterative Multiplier for Image Processing Application

Abstract:       The logarithmic of a binary number may be determined approximately from the number itself by simple shifting and counting. Since the logarithms used are approximate there can be errors in the result. This paper presents a simple and efficient logarithmic multiplier with the possibility to achieve an arbitrary accuracy through an iterative procedure. Digital image processing is used in variety of application .Many algorithm used in image processing include convolution. In this coding is done using VHDL for the FPGA. Synthesis and simulation is done using Xilinx and MATLAB is used to convert input image in to a matrix form which is convolved with the kernel value using proposed multiplier and the result is again converted back into a image form using m.file.

Keywords:
    logarithmic number system , computer arithmetic , digital signal processing, multiplier, convolution.


References:

1.              Chip-Hong Chang and Ravi kumar Satzoda, “A Low Error and High Performance Multiplexer-Based Truncated Multiplier,” IEEE Transactions on Very Large Scale Integration (VLSI) systems, vol. 18, no. 12, pp. 1767-1771, December 2010.
2.              Patriocio Bulic, Zdenka Babic and Aleksej Avramovic, “A Simple Pipelined Logarthmic Multiplier,” IEEE Conference on Computer Design, pp. 230-240, December 2010.

3.              V. Mahalingam, N. Rangantathan, Improving Accuracy in Mitchells Logarithmic Multiplication Using Operand Decomposition, IEEE Transactions on Computers, Vol. 55, No. 2, pp. 1523-1535, December 2006

4.              J. McLaren, “Improved Mitchell-based Logarithmic Multiplier for low-power DSP applications,” Proceedings of IEEE International System On Chip Conference, pp. 53- 56, September 2003.

5.              K.H. Abed, R.E. Sifred, CMOS VLSI Implementation of a Low-Power Logarithmic Converter, IEEE Transactions on Computers, Vol. 52, No. 11, pp. 1421-1433, November 2003.

6.              J.N. Mitchell, Computer multiplication and division using binary logarithms, IRE Transactions on Electronic Computers, vol. EC-11, pp. 512-517, August 1962..

7.              E.L. Hall, D.D. Lynch, S. J. Dwyer III, Generation of Products and Quotients Using Approximate Binary Logarithms for Digital Filtering Applications, IEEE Transactions on Computers, Vol. C-19, No. 2, pp. 97-105. February 1970.

8.              K.H. Abed, R.E. Sifred, VLSI Implementation of a Low- Power Antilogarithmic Converter, IEEE Transactions on Computers, Vol. 52, No. 9, pp. 1221-1228, September 2003.

9.              M.J Duncan,”Improved Mitchell based logarithmic multiplier for low power DSP applications,”IEEE Int’l system on a chip conf pp.17-20,2003.


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42.

Authors:

Antonio Piras, Zhibin Gui, Limin Qiao, Kai Gui, Yongxiang Fan

Paper Title:

Effect of Negative Electrostatic Field Treatment on Germination of Seeds Soaked GA3

Abstract:    Tree seed germination improvement of tree species with shallow dormancy may represent an effective process to enhance restocking of forests and woodlands that have been depleted. Although sometimes conflicting results have been obtained, some studies showed the beneficial effects of applied static electric field (electrostatic field) on seed germination and seedling growth. We think that the different treatment effects reported maybe depended on the dosage, exposition time, process and vigor index of the seeds. We previously showed improved tree seed germination of pine seeds by positive electrostatic field treatment using our apparatus and procedure, and the purpose of the present study was to investigate the effects of the negative electrostatic field treatment on germination, using the same experimental procedure. The results indicated that the negative electrostatic field treatment on pine seeds soaked with 100 ppm of gibberellic acid (GA3) is not so effective as the positive one, with slightly improved germination percentage at the electrical voltage of -500 kV/m 10 min, and increasing the intensity of the negative electrostatic gradient resulted in a reduction of seedling growth.

Keywords:
     Electrostatic Field, GA3, Germination, Seedling Growth, Soaked Seed.


References:

1.             Sidaway GH. Influence of electrostatic field on seed germination. Nature. 1966; 211: 303.
2.             Murr LE. Biophysics of plant growth in an electrostatic field. Nature. 1965; 206: 467-470.

3.             Edwards DK. Influence of electrostatic field on pupation and oviposition in Nepytia Phantasmaria Stkr. (Lepidoptera: Geometridae). Nature. 1961; 191: 976-993.

4.             Murr LE. Plant growth response in a simulated electric field-environment. Nature. 1963; 200: 490-491.

5.             Jorgensen I, and Priestley JH. The distribution of the overhead electrical discharge employed in recent agricultural experiments. J. Agriculture Science. 1914; 6: 337-348.

6.             Shibusawa M and Shibata KJ. Elect. Eng. (Japan).1927; 473:1.

7.             Krueger AP, Kotaka S, and Andriese PC. J. Gen. Physiol. 1962; 45: 879.

8.             Yang Ling, Shen Hai-long. Effect of electrostatic field on seed germination and seedling growth of Sorbus pohuashanesis. Journal of Forestry Research. 2011; 22(1): 27-34.

9.             Pammenter NW, Adamson JH, Berjak P. Viability of stored seed: extension by cathodic protection. Science. 1974; 186: 1123-1124.

10.          Lund EJ. Bio-Electric Fields and Growth. Austin, Texas, 1947.

11.          Gui ZB, Piras A, Qiao LM. Improving tree seed germination by electrostatic field. International Journal of Recent Technology and Engineering. 2013; 1: 87-89.

12.          Gui ZB, Piras A, Qiao LM, Gui K, Wang B. Improving germination of seeds soaked GA3 by electrostatic field treatment. International Journal of Recent Technology and Engineering. 2013; 2: 133-136.

13.          Davière JM, Achard P. Gibberellin signaling in plants. Development. 2013 Mar;140(6):1147-51.

14.          Wang YH, Irving HR. Developing a model of plant hormone interactions. Plant Signal Behav. 2011 Apr;6(4):494-500.

15.          Z. X. Luo and T. C. Jannett, “Optimal threshold for locating targets within a surveillance region using a binary sensor network”, Proc. of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec., 2009.

16.          Z. X. Luo and T. C. Jannett, “Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proc. of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

17.          International Seed Testing Association, International Rules for Seed Testing, Rules 1996.

18.          Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proc. of the 2012 IEEE Radio and Wireless
Symposium, Santa Clara, CA, Jan. 2012. 

19.          Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proc. of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

20.          Murr LE. Mechanism of plant cell damage in an electrostatic field. Nature. 1964; 201: 1305-1306.


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43.

Authors:

Sanjeev Kumar, Ajay Indian, Zubair Khan

Paper Title:

Neural Network Model for Prediction of Ground Water Level in Metropolitan Considering Rainfall-Runoff as a Parameter

Abstract:     In metropolitan area the ground water is the important resource of drinking water. To preserve the ground water level several rain water harvesting techniques are implemented now a days. A neural network model has been developed for ground water level prediction. Various models developed before for ground water level prediction with artificial neural network methodology. Most of these models these models consider rainfall and current ground water level as input parameter. This model considers rainfall-runoff as an important factor which represents the performance of rain water harvesting techniques in urban area. So this model predicts the ground water level with the effect of rain water harvesting techniques.

Keywords:
  Artificial neural network, ground water level, rainfall-runoff, backpropogation feed forward network, Levenberg-marquardt algorithms.


References:

1.             Amutha R. and Porchelvan P. “Seasonal prediction of groundwater levels using anfis and radial basis neural network”, International Journal of Geology, Earth and Environmental Sciences, 2011 Vol. 1 (1) September-December, pp.98-108.
2.             M.KavithaMayilvaganan , K.B.Naidu “Application of Soft computing techniques for Groundwater Level Forecasting”,2012 International Conference on Computer Networks and Communication Systems (CNCS 2012),IPCSIT vol.35(2012) © (2012) IACSIT Press, Singapore.

3.             Sreenivasulu D and Deka P.C “Groundwater Level Forecasting using Radial Basis Function with Limited Data”, International Journal of Earth Sciences and Engineering, ISSN 0974-5904, Volume 04, No 06 SPL, October 2011, pp. 1064-1067.

4.             P. Sujata and G. N. Pradeep “Prediction of groundwater levels using different artificial neural network architectures and algorithms”, ICA2757, 18-May-2011.

5.             Ioannis N. Daliakopoulos, Paulin Coulibaly, Ioannis K. Tsanis “Groundwater Level Forecasting Using Artificial Neural Network”, Journal of Hydrology, 309(2005),
pp 229-240.

6.             P. D. Sreekanth, N. Geethanjali, P. D. Sreedevi, Shakeel Ahmed, N. Ravi Kumar and P. D. Kamala Jayanthi “Forecasting groundwater level using artificial neural networks” , Current science, vol. 96, no. 7, 10 april 2009.

7.             V. Nourani, A. Hosseini Baghanam, F. DaneshvarVousoughi, M.T. Alami “Classification of groundwater level data using som to develop ann-based forecasting model” , International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-2, Issue-1, March 2012.

8.             Heesung Yoon, Seong-Chun Jun, Yunjung Hyun , Gwang-Ok Bae, Kang-Kun Lee, “A comparative study of artificial neural networks and  support vector machines for predicting groundwater levels in a coastal aquifer”, Journal of Hydrology 396 (2011),pp 128–138.

9.             Faridah Othman and Mahdi Naseri “Reservoir inflow forecasting using artificial neural network”, International Journal of the Physical Sciences Vol. 6(3), pp. 434-440, 4 February, 2011.

10.          Purna C. Nayak,Y.R. Satyaji Rao and K. P. Sudheer “Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach”, Water Resources Management (2006) 20: pp 77–90.

11.          Martin T. Hagan and Mohammad B. Menhaj “Training Feedforward Networks with the Marquardt Algorithm”, IEEE Transactions on Neural Networks, Vol. 5, No.6, pp 989-993, November 1994

12.          www.rainwaterharvesting.org, 5 June, 2013

13.          http://www.imd.gov.in/section/hydro/distrainfall/ webrain/delhi/delhi.txt,  3 June, 2013

14.          http://gis2.nic.in/cgwb/ , 9 June, 2013

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44.

Authors:

Nasreen Mev, Brig. R.M. Khaire

Paper Title:

Implementation of OFDM Transmitter and Receiver Using FPGA

Abstract:      Orthogonal Frequency Division Multiplexing (OFDM) is the most promising modulation technique. It has been adopted by most wireless and wired communication standards. The idea is to utilize a number of carriers, spread regularly over a frequency band, in such a way so that the available bandwidth is utilized to maximal efficiency. The objective of this paper is to carry out an efficient implementation of the OFDM system (i.e. transmitter and receiver) using “Field Programmable Gate Array (FPGA)” and find the result by simulating all the blocks used in proposed project by using QuartusII & Modelsim simulation tool.

Keywords:
   OFDM, FPGA.


References:

1.             Ahmed R. S. Bahai and Burton R. Saltzberg, ‘Multi Carrier Digital Communication”. Kluwer   Academic Publishers, 2002.
2.             G.M. Bhat, M. Mustafa, Shabir Ahmad and Javaid Ahmad “modelling and simulation of data scrambler and descrambler for secure data communication”, Indian Journal of Science and Technology 2009.

3.             Dapeng Hao, Pin Yao, and Peter Adam Hoeher “Analysis and Design of Interleaver Sets for Interleave-Division Multiplexing and Related Techniques” 5th International Symposium on Turbo Codes and Related Topics 2008”.

4.             Doelz, M.L., Heald E.T. and Martin D.L. "Binary Data Transmission Techniques for Linear Systems." Proc. I.R.E., 45: 656-661, May 1957.

5.             S. B. Weinstein and P. M. Ebert, “Data transmission by frequency-division multiplexing using the discrete Fourier transform”, IEEE Trans. Communications, COM-19(5): 628-634, Oct. 1971.

6.             Jeffrey G. Andrews, Rias Muhammad, Fundamentals of WIMAX. Prentice Hal Communications Engineering, 2006.

7.             Aseem Pandey, Shyam Ratan Agrawalla & Shrikant Manivannan, “VLSI Implementation of OFDM”, Wipro Technologies, September 2002.

8.             Dusan Matiae “OFDM as a possible modulation technique for multimedia applications in the    range of mm waves”, TUD-TVS, 1998.

9.             J. L. Holsinger, “Digital communication over fixed time-continuous channels with memory, with special application to telephone channels,” PhD thesis, Massachusetts Institute of Technology, 1964.

10.          R. W. Chang, “Synthesis of band-limited orthogonal signals for multichannel data transmission,” Bell Systems Technical Journal, 45:1775–1796, December 1966.

11.          Shahid Abbas, Student Member, IEEE, Waqas Ali Khan, Talha Ali Khan and Saba Ahmed “OFDM Baseband Transmitter Implementation” Compliant IEEE Std 802.16d on FPGA2009

12.          S. Weinstein and P. Ebert “Data transmission by frequency-division multiplexing using the discrete Fourier transform.” IEEE Transactions on Communications, 19(5):628–634, October 1971.

13.          L. J. Cimini “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing.” IEEE Transactions on Communications, 33(7):665–675, July 1985.

14.          Lattice Semiconductor white paper, “Implementing WiMAX OFDM Timing and Frequency Offset Estimation in Lattice FPGAs,” 2005.


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45.

Authors:

H. B. Kekre, Kavita Sonawane

Paper Title:

Performance Evaluation of Bins Approach in YCbCr Color Space with and without Scaling

Abstract:       This paper explores novel idea of feature extraction based on bins approach.  The bins formation process described in this approach is based on the partitioning of the different histograms of the image based on the color components of the image. Basically the feature extracted here deals with the color contents of the image. The use of color contents is explored using two different color spaces namely RGB and YCbCr color space in two forms with and without scaling. Feature extraction phase starts with the separation of the color planes of the image. In this work images in both (RGB and YCbCr) color spaces are separated into R, G, B and Y, Cb, Cr components respectively.  Histogram for each plane is calculated and partitioned into two parts using Centre of Gravity (CG) technique. Three planes, two partitions generate total 23= 8 bins. Data contained by 8 bins is the count of pixels falling in particular range of intensities. This is further processed by computing the first four moments. It generates four types of feature vectors based on four moments namely Mean, Standard Deviation, Skewness and Kurtosis. Feature vector comparison with query is facilitated by means of three similarity measures namely Euclidean distance, Absolute distance and cosine correlation distance. Experimentation is carried out using 2000 images in the database with two color spaces RGB and YCbCr taken into consideration. Result analysis is done by using three performance evaluation parameters Precision Recall Cross over Point, Longest String and Length of String to Retrieve all Relevant images.

Keywords:
    Bins, CG, Histogram, RGB, YCbCr, Mean, Standard deviation, Skewness, Kurtosis, ED, AD, CD, PRCP, LS, LSRR.


References:

1.             Manimala Singha,  K.Hemachandran, “Content Based Image Retrieval using Color and Texture”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012.
2.             Dacheng Tao,  Xiaoou Tang, Xuelong Li, “ Direct Kernel Biased Discriminant Analysis: A New Content-Based Image Retrieval Relevance Feedback Algorithm” IEEE Transactions on Multimedia, Vol. 8, No. 4, August 2006.

3.             Rouhollah Rahmani, Sally A. Goldman, Hui Zhang, Sharath R, “Localized Content Based Image Retrieval”, IEEE Transactions on Pattern Analysis And Machine Intelligence, Special Issue, Nov. 2008.

4.             Ying Liua, Dengsheng Zhanga, Guojun Lua “A survey of content-based image retrieval with high-level semantics”, Pattern Recognition 40 (2007) 262 – 282, www.elsevier.com/locate/patco.

5.             Avi Arampatzis , Konstantinos  Zagoris, Savvas A. Chatzichristo  “Dynamic two-stage image retrieval from large multimedia databases q. Department of Electrical and Computer Engineering, Democritus University of Thrace, University Campus, 67100 Information Processing and Management 49 (2013) 274–285

6.             Aly, M., Welinder, P., Munich, M. E., Perona, P, “Automatic discovery of image families: Global vs. local features”. In ICIP (pp. 777–780). IEEE Explorer

7.             H. K. Lee and Y.-S. Ho, “A region-based image retrieval system using salient point extraction and image segmentation”,  Lecture Notes in Computer Science: Advances in Multimedia Information Processing pages ,209–216, 2002.

8.             E. R. Vimina, K. Poulose Jacob “CBIR Using Local and Global Properties of Image Sub-blocks”, International Journal of Advanced Science and Technology Vol. 48, November, 2012.

9.             Shu-Ching Chen, Stuart H. Rubin, Chengcui Zhang , “A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval”, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications And Reviews, Vol. 36, No. 6, November 2006.

10.          Minakshi Banerjeea, MalayK.Kundua, “Content-based image retrieval using visually significant point features” , Fuzzy Sets and Systems 160 (2009) 3323–3341, www.elsevier.com/locate/fss.

11.          J. Hafner, H.S. Sawhney, W. Esquitz, M. Flickner, W. Niblack, “Efficient color histogram indexing for quadratic form distance functions”, IEEE Trans. PAMI 17 (1995) 729–736.

12.          Wee Kheng Leow, Rui Li “The analysis and applications  of adaptive-binning color histograms”, Computer Vision and Image Understanding 94 (2004) 67–91, www.elsevier.com/locate/cviu

13.          Gauri Deshpande1, Megha Borse, “Image Retrieval with the use of  different color spaces and the texture feature, 2011 International Conference on Software and Computer Applications IPCSIT vol.9 (2011) © (2011) IACSIT Press, Singapore.

14.          Efstathios Hadjidemetriou, Michael D. Grossberg, Shree K. Nayar,  Multiresolution Histograms and Their Use for Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 7, July 2004 831.

15.          Michael Swain, Dana Ballard, “Color indexing”,  International  Journal of Computer Vision, 7(1):11-32, 1991.

16.          Xiang Sean Zhou, Thomas S. Huang, “CBIR: From Low-Level Features to High-Level Semantics”, http://www.ifp.uiuc.edu/xzhou2; Telephone: 217-244-2960; Fax: 217-244-8371.

17.          Greg Pass Ramin Zabih, Histogram Refinement for Content-Based     Image Retrieval, 0-8186-7620-5/96 $5.00 0 1996 IEEE

18.          H. B. Kekre, Kavita Sonawane, “Partitioning of Modified Histograms to Generate 27 Bins Feature Vector to Improve Performance of CBIR”, (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013.

19.          H. B. Kekre, Kavita Sonawane, “Histogram Partitioning for Feature Vector Dimension Reduction in Bins Approach for CBIR”, International Journal of Electronics Communication and Computer Engineering Volume 3, Issue 6, ISSN (Online): 2249–071X,

20.          H. B. Kekre, Kavita Sonawane, “Bins Formation using CG based Partitioning of Histogram Modified Using Proposed Polynomial Transform ‘Y=2X-X2’for CBIR”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 5, 2011.

21.          “Color Tutorial”, www.cirquedigital.com/howto/color_tutorial.html,

22.          Chapter 3: Color Spaces,”

23.          http://compression.ru/download/articles/color_space/ch03.pdf

24.          Dr.H.B.Kekre, Dr.Sudeep D. Thepade, RatneshChaturvedi , “Walsh, Sine, Haar& Cosine Transform With Various Color Spaces for ‘Color to Gray and Back’  CBIR Using Local and Global Properties of Image Sub-blocks”, International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012 349.

25.          Ryszard S. Choras, “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems”, International Journal Of Biology And Biomedical Engineering, Issue 1, Vol. 1, 2007.

26.          Junwei Han, King N. Ngan, “A Memory Learning Framework for Effective Image Retrieval”, IEEE Transactions on Image Processing, Vol. 14, No. 4, April 2005.

27.          Dr. H. B. Kekre, KavitaSonawane, “Effect of Similarity Measures for CBIR Using Bins Approach”, International Journal of Image Processing (IJIP), Volume (6) : Issue (3) : 2012

28.          Lucia Ballerini1, Xiang Li, “A Query-by-Example Content-Based Image Retrieval System of Non-Melanoma Skin Lesions”,homepages. inf.ed.ac.uk/rbf/PAPERS/MCBR09.pdf


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46.

Authors:

Rajesh Mehra, Ginne

Paper Title:

Area Estimation and Design Analysis of Gaussian Pulse Shaping Filter

Abstract:   Gaussian pulse shaping filters plays an important role in digital communications due to its intersymbol interference free property. The pulse shaping filter is a useful means to shape the signal spectrum and avoid Interferences. In this paper a Gaussian filter has been presented for pulse shaping in wireless communication systems. The proposed filter has been designed and simulated using Matlab. The simulated results show that the designed Gaussian filter can be implemented using 11 number of multipliers and 10 number of adders by providing11 multiplications per input sample and 10 additions per input sample. Thus the designed filter provides cost effective solution for mobile and wireless communication systems.

Keywords:
     FIR, GSM, HDTV, MATLAB, WLAN


References:

1.             Wang Wei, Zeng Yifang, Yan Yang, Efficient Wireless Digital Up Converters Design Using System Generator” IEEE 9th International Conference on Signal Processing, pp.443-446, ICSP- 2008.
2.             K. B. Huang, Y. H. Chew, and P. S. Chin “A Novel DS-CDMA Rake Receiver: Architecture and Performance” IEEE International Conference on Communications, pp-2904-2908, ICC-2004.

3.             Rajesh Mehra, Dr. Swapna Devi, “Area Efficient & Cost Effective Pulse Shaping Filter for Software Radios” International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.1, No.3, pp. 85-91, September 2010. 

4.             Yutaka Jitsumatsu, Masato Ogata, and Tohru Kohda, “A Comparison between Prolate Spheroidal and Gaussian FIR Pulse Shaping Filters” International Conference on Signals and Electronic Systems, (ICSES), pp. 14-17, September 2008..

5.             J. Chandran, R. Kaluri, J. Singh, V. Owall and R. Veljanovski “Xilinx Virtex II Pro Implementation of a Reconfigurable UMTS Digital Channel Filter” IEEE Workshop on Electronic Design, Test and Applications, pp.77-82, DELTA-2004.

6.             Rajesh Mehra, Swati Singh, “Design of RRC Filter for ISI removal in Software Defined Radios ” International Journal of VLSI and Signal Processing Applications, (IJVSPA) Vol.1, No.1, pp. 1-5, April 2011.

7.             Mathworks, “Users Guide Filter Design Toolbox 4”, March-2007.


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47.

Authors:

R.Subramanian, K.Thanushkodi

Paper Title:

An Efficient Novel TANAN’s Algorithm for Solving Economic Load Dispatch Problems

Abstract:    The Economic Load Dispatch (ELD) problems in power generation systems is to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Novel TANAN’s Algorithm (NTA), for solving ELD Problem. The main objective is to minimize the total fuel cost of the generating units having quadratic cost characteristics subjected to limits on generator true power output and transmission losses and including valve point loading effects. The NTA is a simple numerical approach based on a parabolic TANAN function. This paper presents an application of NTA to ELD for different IEEE standard test systems. ELD is applied and compared with various optimization techniques. The simulation results show that the proposed algorithm outperforms previous optimization methods.

Keywords:
  Economic Load dispatch, Evolutionary Programming (EP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Taguchi Method(TM). 


References:

1.                D. C. Walters  and  G. B. Sheble, Genetic Algorithm Solution of   Economic    Dispatch with Valve Point Loading,  IEEE Transactions on Power Systems,Vol. 8, No.3,pp.1325–1332, Aug. 1993.
2.                P. H. Chen  and  H.C. Chang,  Large-Scale  Economic  Dispatch  by  Genetic  Algorithm,    IEEE Transactions on Power Systems, Vol. 10, No.4,pp. 1919–1926, Nov. 1995.

3.                D. Simon, “Biogeography-based optimization,” IEEE Trans. Evol. Comput., vol. 12, no. 6, pp.702–713, Dec. 2008.

4.                G. B. Sheble and K. Brittig, “Refined genetic algorithm- economic dispatch example”,    IEEE Trans.      Power Systems,     Vol.10, pp.117-124, Feb.1995.

B.                K. Panigrahi, V. R. Pandi. “Bacterial foraging optimization: Nelder-Mead hybrid algorithm for economic load dispatch.” IET Gener. Transm, Distrib. Vol. 2, No. 4. Pp.556-565, 2008.

5.                Dervis Karaboga and Bahriye Basturk, ‘Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems,’ Springer-Verlag, IFSA 2007, LNAI 4529, pp. 789–798.

6.                R. Storn and  K. Price,  Differential Evolution—A  Simple  and  Efficient  Adaptive Scheme for Global Optimization Over Continuous Spaces, International Computer  Science Institute,, Berkeley, CA, 1995, Tech. Rep. TR-95–012.

7.                I.A Selvakumar and K. Thanushkodi, “A new particle swarm optimization solution to nonconvex economic dispatch problems,” IEEE Trans Power Syst., vol. 22, no. 1, pp. 42-51, Feb 2007.

8.                Mohammad Moradi-Dalvand, Behnam Mohammadi-Ivatloo, Arsalan Najafi, Abbas Rabiee Erratum to “Continuous quick group search optimizer for solving non-convex economic dispatch problems”Electr. Power Syst. Res. 93 (2012) 93–105.

9.                Jong-Bae Park, Member, IEEE, Ki-Song Lee, Joong-Rin Shin, and Kwang Y. Lee, Fellow, IEEE “A Particle Swarm Optimization for Economic Dispatch With Non smooth Cost Functions”, IEEE transactions on power systems, vol. 20, no. 1, February 2005

10.             Derong Liu, Fellow, IEEE, and Ying Cai, Student Member, IEEE “Taguchi Method for Solving the Economic Dispatch Problem With Non smooth Cost Functions”, IEEE transactions on power systems, vol. 20, no. 4, November 2005.

11.             G. Zwe-Lee, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Transactions on Power Systems, vol. 18, pp. 1187-1195, 200.

12.             Nayak, S.K.; Krishnanand,  K.R.;   Panigrahi, B.K.; Rout, P.K. –“Application of Artificial Bee Colony to economic load  dispatch  problem with ramp rate limit and prohibited operating zones”,IEEE word congress on Nature and  Biologically inspired computing (NaBIC)-2009, pp- 1237 – 1242 .

13.             A. Bakirtzis, V. Petridis, and S. Kazarlis,  “Genetic    Algorithm    Solution to the Economic Dispatch Problem”, Proceedings. Inst. Elect. Eng. –Generation, Transmission Distribution, Vol. 141, No. 4, pp. 377–382, July 1994.

14.             K. T. Chaturvedi, M. Pandit, and L. Srivastava, "Self-Organizing Hierarchical Particle Swarm Optimization for Non-convex Economic Dispatch," IEEE Transactions on Power Systems, vol. 23, pp. 1079-1087, 2008.

15.             S. Subramanian, R. Anandhakumar “Dynamic Economic Dispatch Solution Using Composite Cost Function”,International Review of Electrical Engineering,vol 5(issue 4):1664-1669, JULY-AUGUST 2010.

16.             S. Duman, U. Güvenç, N. Yörükeren “Gravitational Search Algorithm for Economic Dispatch with Valve-Point Effects”, International Review of Electrical Engineering,vol 5(issue 6):2890-2895, December2010.

17.             K. Chandrasekaran, Sishaj P Simon” Firefly Algorithm for Reliable/Emission/Economic Dispatch Multi Objective Problem” International Review of Electrical Engineering,vol 7(issue16):3414-3425, February2012.

18.             M. S. Payam, E. Bijami” An Artificial Bee Colony Algorithm to Solve Nonlinear and Nonconvex Economic Dispatch Problem” ” International Review of Electrical Engineering,vol 7(issue4):3414-3425, 5144-5154,February2012.

19.             R. Sharma, P. K. Rout” A Modified Seeker Optimization Based Economic Dispatch of Generators with Valve-Point Effects and Multiple Fuel Options” International Review of Electrical Engineering,vol7(issue6):3414-3425, December 2012

20.             Hosseini, S. H.; Dobakhshari, A. S.; Jalayer, R.” A Novel Mathematical-Heuristic Method for Non-Convex Dynamic Economic Dispatch”, International Review of Electrical Engineering,vol 4(issue1):108-113, JAN-FEB2009.

21.             J. Wood and B. F. Wollenberg, “Power Generation Operation and Control,” 2nd Edition, Wiley, New York, 1996.

22.             Chakrabarti and S. Halder, “Power System Analysis Operation and Control,” 3rd Edition, PHI, New Delhi, 2010 


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48.

Authors:

Tonye K. Jack, Gbanaibolou Jombo

Paper Title:

Experimenting with the Java Computer Language in Engineering Calculations: Application to Statically Indeterminate, Rigid, Multi-Bearing Shaft Analysis

Abstract:     Not many engineering programs written in the Java Computer Language have been applied successfully to complex engineering projects. In this article, the Java computer language is applied to the analysis of statically indeterminate beam models using the Domain and Patch exact Beam analysis method. Java source codes and subroutines for the numerical three-support beam model example presented are listed, to act as reusable productivity tool kit to aid developers to minimize development time and effort.

Keywords:
   Beam Deflection Analysis, Engineering Programs, Java Programming, Shaft Analysis, Shaft Design, Rigid Bearing Analysis, Statically Indeterminate Beam Analysis, Structural Analysis


References:

1.                 R. H. Bannister, “Lecture Notes on Machine Dynamics”,   Parts I & II, Department of Turbo-machinery ands Engineering Mechanics, Cranfield University, England, 1992.
2.                 K. Mischke, (2004). Shafts, ch. 17, pp. 17.1-17.21, Available: www.digitalengineeringlibrary.com

3.                 R. C. Juvinal, K. M. Marshek, Fundamentals of Machine Component Design, 2nd ed., John Wiley, 1991, pp. 790-792

4.                 H. Shames,  Introduction to Solid Mechanics, 2nd ed., Prentice Hall, New Delhi, 1990, ch. 12, pp. 369-395

5.                 J. Den Hartog,, Strength of Materials, Dover, 1961

6.                 E. P. Popov, S. Nagarajan, Z. A. Lu, Mechanics of Materials, 2nd ed., Prentice Hall, New Jersey, 1976.

7.                 D. Deutschman, W. J. Michels, C. E. Wilson, Machine Design, Macmillan, New York, 1975, pp. 236-238

8.                 T. K. Jack, “Lecture Notes on Strength of Materials II,” Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, 2006.

9.                 G. Jombo,  “Computer Program for Multi-bearing Shaft Deflection Calculation and Analysis by Patch and Domain Double Integration Method”, B.Tech Degree, Final Year Project Report, Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, 2008

10.              W. Ker Wilson,  Practical Solutions of Torsional Vibration Problems, 3rd ed., John Wiley, New York, 1956, ch. XI, pp. 562-576

11.              E. Currie, Fundamentals of Programming using Java, Thomson, 2006

12.              http://en.wikipedia.org.wiki/Unified_Modelling_Lamguage

13.              http://en.wikipedia.org.wiki/Class_Diagrams

14.              V. M. Ezeh,  “Computer Assisted Helical Gear Design and Analysis”, B.Tech Degree, Final Year Project Report, Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, 2008

15.              J. Cowell, Essential Java Fast, Springer, 1997

16.              Mcbride, Java Made Simple, Butterworth-Heinemann, 1997

17.              D. Flanagan, JavaScript Pocket Reference, O’reilly, 1998

18.              S. D. Gathman, S. D., “A Text UI for Java AWT, Designing User Interfaces”, Dr. Dobb’s Journal, Sept. 1997.


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49.

Authors:

Ajayi Adebowale, Idowu S.A, Anyaehie Amarachi A.

Paper Title:

Comparative Study of Selected Data Mining Algorithms Used For Intrusion Detection

Abstract:      In the relatively new field of data mining and intrusion detection a lot of techniques have been proposed by various research groups. Researchers continue to find ways of optimizing and enhancing the efficiency of data mining techniques for intrusion attack classification. This paper evaluates the performance of well known classification algorithms for attack classification. The focus is on five of the most popular data mining algorithms that have been applied to intrusion detection research; Decision trees, Naïve bayes, Artificial neural network, K-nearest neighbor algorithm and Support vector machines. We discuss their advantages and disadvantages and finally we induce the NSL-KDD dataset with the respective algorithms to see how they perform.

Keywords:
    Data mining, Intrusion detection, decision trees, Naive bayes, Artificial neural network, k-nearest neighbor, Support vector Machines, NSL-KDD


References:

1.              Axelsson, S. (2000). “The base-rate fallacy and the difficulty of intrusion detection”, ACM Trans. Information and System Security 3 (3), pp. (186-205).
2.              Barbara, D., Wu, N. and Jajodia, S.  [2001]. “Detecting Novel Network Intrusions Using Bayes Estimators”, Proceedings Of the First SIAM Int. Conference on Data Mining, (SDM 2001), Chicago, IL.

3.              Bloedorn et al. (2003). Data Mining for Network Intrusion Detection: How to Get Started. The MITRE Corporation McLean, VA.

4.              Brugger, S. (2004). Data Mining Methods for Network Intrusion Detection, University of California, Davis.

5.              Carbone, P. L. (1997). “Data mining or knowledge discovery in databases: An overview”, In Data Management Handbook, New York: Auerbach Publications.

6.              Didaci, L., Giacinto, A. & Roli, F. (2002). “Ensemble learning for intrusion detection in computer networks”, Proceedings of AI*IA, Workshop on “Apprendimento automatico: metodi e applicazioni”, Siena, Italy.

7.              Eskin, E., Arnold, A., Preraua, M., Portnoy, L. and Stolfo, S. J. (2002). “A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data”, (Barbar & Jajodia Eds.), Data Mining for Security Applications. Boston: Kluwer Academic Publishers.

8.              Frank, J. (1994). ”Artificial intelligence and intrusion detection: Current and future directions”, In Proc. of the 17th National Computer Security Conference, Baltimore, MD. National Institute of Standards and Technology (NIST).

9.              Kesavulu, E., Reddy, V. N. and Rajulu, P. G. (2011). “A Study of Intrusion Detection in Data Mining”. Proceedings of the World Congress on Engineering 2011 Vol IIIWCE 2011, July 6 - 8, 2011, London, U.K.

10.           Lane, T. D. (2000). “Machine Learning Techniques for the computer security domain of anomaly detection”, Ph.D. Thesis, Purdue Univ., West Lafayette, IN.

11.           Lappas, T. and Pelechrinis, K. (2006). Data Mining Techniques for (Network) Intrusion Detection Systems, Department of Computer Science and Engineering Riverside, Riverside CA. [9]. Lee, W. & Stolfo, S.J. (1998). Data mining approaches for intrusion detection, In Proc. of the Seventh USENIX Security Symp., San Antonio, TX.

12.           Lee, W., S. J. Stolfo, & Mok, K. W. (1999). “A data mining framework for building intrusion detection models,”  In Proc. of the 1999 IEEE Symp. On Security and Privacy (pp. 120-132), Oakland, CA: IEEE Computer Society Press.

13.           Lee, W., Stolfo, S.J. & Mok, K.W. (1999). “Mining in a data-flow environment: Experience in network intrusion detection,” (Chaudhuri, S. & Madigan, D. Eds.). Proc. of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99) (pp. 114-124), San Diego, CA: ACM,

14.           Lee, W. & Stolfo, S.J et al. (2000). ”A data mining and CIDF based approach for detecting novel and Distributed intrusions”, In Proc. of Third International Workshop on Recent Advances in Intrusion Detection (RAID 2000), Toulouse, France.

15.           Mukkamala, S. & Sung, A. H. (2008). “Identifying key variables for intrusion detection using neural networks”, Proceedings of 15th International Conference on Computer Communications (pp. 1132-1138).

16.           (SANS: FAQ: Data Mining in Intrusion Detection) http://www.sans.org/securityresources/idfaq/data_mining.php

17.           Tavallaee,M. , Bagheri, E. ,  Lu,W.  & Ghorbani, A. 2009. “A Detailed Analysis of the KDD CUP 99 Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009.


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50.

Authors:

Sucheta Kokate, D. G. Chougule, Manjiri Kokate

Paper Title:

Clustering  of Personalized Documents from the Web by Personal Name Aliases

Abstract:       The web is a huge resource for people who use engines to search documents related to specific person. The traditional approach is to organize search results into groups, one for each meaning of the query. According to the topical similarity of the retrieved documents, these groups are usually constructed but it is impossible for documents to be totally dissimilar and still correspond to the same person. To overcome this problem, in this paper we will implement a rigorous technique to find out all the documents regarding personalized information within short period of time.  In this novel approach we propose a technique in which we cluster personalized documents from the web by personal name aliases.  Given a personal name, the proposed method first extracts a set of candidate aliases and then clusters the documents by these aliases to achieve high accuracy and reduce the complexity.

Keywords:
 Web mining, information retrieval, web text analysis, searching, surfing.


References:

1.             D.V Kalashnikov, s.Mehrotra, R.N,Turen and Z.Chen,”Web People  Search via connction analysis,” IEEE transactions on knowledge and data engineering, vol. 20, no. 11, june 2008.
2.             Bagga and B. Baldwin, “Entity-Based Cross-Document Co-referencing Using the Vector Space Model,” Proc. Int’l Conf. Computational Linguistics (COLING ’98), pp. 79-85, 1998.

3.             Galvez and F. Moya-Anegon, “Approximate Personal Name- Matching through Finite-State Graphs,” J. Am. Soc. for InformationScience and Technology, vol. 58, pp. 1-17, 2007.

4.             T. Hokama and H. Kitagawa, “Extracting Mnemonic Names of People from the Web,” Proc. Ninth Int’l Conf. Asian Digital Libraries (ICADL ’06), pp. 121-130, 2006.

5.             J. Artiles, J. Gonzalo, and F. Verdejo, “A Testbed for People Searching Strategies in the WWW,” Proc. SIGIR ’05, pp. 569-570, 2005.

6.             G. Mann and D. Yarowsky, “Unsupervised Personal Name Disambiguation,” Proc. Conf. Computational Natural Language Learning (CoNLL ’03), pp. 33-40, 2003.

7.             Danushka Bollegala, Yutaka Matsuo, an d Mitsuru Ishizuka” Automatic Discovery of Personal Name Aliases from the Web”, IEEE transactions on knowledge and data engineering, vol. 23, no. 6, june 2011

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51.

Authors:

Fakhreldeen Abbas Saeed

Paper Title:

Comparing and Evaluating Open Source E-learning Platforms

Abstract:   Because of the huge development in E-learning and the spread of its open and close source platforms, and the necessity to have the benefit of it in universities and graduate institutes as basic education or supportive of the traditional education, we have to know how to choose the suitable E-learning Platform from available platforms or to adopt it to suit us. In this paper we explained how to evaluate Open source E-learning Platforms and tried to integrate some of them to produce new platform with great capabilities, more flexible and efficient, Also we used metrics like security, performance, support, interoperability, flexibility, easy of using, management, communication tool, administration tools, course delivery tools and content development to evaluate this E-learning Platform. We obtain that there are differences between E-learning Platforms for each metric so we selected the best four (Moodle 1.9, Claroline 1.8.1, Mambo 4.6.1and Atutor 1.5.4) to integrate it to make a new platform with 97.72 average weights of the metrics while the best Open Source E-learning Platform is 89.4.

Keywords:
  Comparison, Evaluation, E-learning, Open Sources.


References:

1.              Raj Jain,” Art of Computer Systems Performance Analysis Techniques For Experimental Design Measurements Ssimulation And Modeling”,  Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471503363 Pub Date: 05/01/91
2.              The AMA Handbook of E-Learning: Effective Design, Implementation, and Technology Solutions, Piskurich (ed) ,ISBN:0814407218 , AMACOM © 2003.

3.              CMS Matrix, http://www.cmsmatrix.org/matrix/cms-matrix

4.              M. Scriven, Evaluation Thesaurus (4th ed.), Newbury Park,CA: Sage Publications, 1991.

5.              An Evaluation of Open Source E-Learning Platforms Stressing. Adaptation Issues, Sabine Graf and Beate List, Women's Postgraduate College of Internet Technologies, Vienna University of Technology.

6.              Evaluation of e-learning platforms, mSysTech, Stand: 02.03.2009, Version 1.00

7.              Methods to Evaluate Open Source Learning Platforms

8.              Tutor, http://www.atutor.ca

9.              Dokeos, http://www.dokeos.com

10.           dotLRN, http://dotlrn.org

11.           Freestyle Learning, http://www.freestyle-learning.de

12.           ILIAS, http://www.ilias.uni-koeln.de

13.           LON-CAPA, http://www.lon-capa.org

14.           Moodle, http://moodle.org

15.           OpenACS, http://openacs.org

16.           OpenUSS, ttp://openuss.sourceforge.net/openuss

17.           Sakai, http://www.sakaiproject.org

18.           Spaghettilearning, http://www.spaghettilearning.com


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52.

Authors:

Satish K, Anand R, Jitendranath Mungara

Paper Title:

Multicast and Multipath Based on Batch Signature Scheme on Adhoc Networks

Abstract:    Adhoc Networks are becoming a very important tool for many nonconventional civil and military applications. In this adverse environment it is very difficult for the source to send the information to the destination in multicasting style. The main requirement of the network is to provide the security and promising the message integrity. To solve the various problems in the multicasting style for very large adhoc networks a Modified Tiered Authentication Multicast scheme is developed. This scheme has two steps, the first step includes generation of keys and these keys are used to form the Message Authentication Code for every packet sent to the receiver. The second step includes the Message Digest Algorithm 5 is used for message integrity. The movement of nodes in the mobile adhoc network quickly changes the topology resulting in the increase of the overhead message in topology maintenance. There are many clustering schemes are proposed for many adhoc networks. This paper presents on the performance of the Modified TAM protocol in different clustering schemes such as Distance Based Clustering and Weighted Based Clustering. Comparing the generated results from various clustering schemes by using Modified TAM protocol and decide which clustering scheme is better for implementing the Modified TAM protocol.

Keywords:
 Authentication, Clustering, Mobile Adhoc Networks.


References:

1.           Mohamed Younis, Senior Member, IEEE, Osama Farrag, Senior Member, IEEE, and Bryan Althouse, “A Tiered of Multicast Protocol for Ad-Hoc Networks”, 2012.
2.           Ismail Ghazi Shayeb, AbdelRahman Hamza Hussein and Ayman Bassam Nasoura, “A Survey of Clustering Schemes for Mobile Ad-Hoc Network (MANET)”, 2011.

3.           Mohamed Elhawary and Zygmunt J. Haas, Fellow proposed an “Energy-Efficient Protocol for Cooperative Networks”.

4.           Adrian Perrigy, Ran Canetti, Dawn Song, J. D. Tygar, UC Berkeley, Digital Fountain, IBM T.J. Watson, “Efficient and Secure Source Authentication for Multicast”, 2009 IEEE Symposium on Security and Privacy, May 2009, pp. 56-73.

5.           P. B. Velloso, et al., “Trust management in mobile ad hoc networks using a scalable maturity-based model,” IEEE Trans. Network Service Management, vol. 7, no. 3, Sep. 2010.

6.           F. R. Yu, H. Tang, P. Mason, and F. Wang, “A hierarchical identity based key management scheme in tactical mobile ad hoc networks,” IEEE Trans. Netw. Service Management, vol. 7, no. 4, pp. 258–267, Dec. 2010.

7.           M. Hegland, E. Winjum, S. F. Mjolsnes, C. Rong, O. Kure, and P. Spilling, “A survey of key management in ad hoc networks,” IEEE Commun. Surveys & Tutorials, vol. 8, no. 3, pp. 48–66, Dec. 2006.

8.           J. Y. Yu and P. H. J. Chong, “A survey of clustering schemes for mobile ad hoc networks,” IEEE Commun. Surveys & Tutorials, vol. 1, no. 1, pp. 31–48, 2005.

9.           H. Yang, et al., “Security in mobile ad-hoc wireless networks: challenges and solutions,” IEEE Wireless Commun. Mag., vol. 11, no. 1, pp. 1536– 1284, Feb. 2004.

10.        Perrig, R. Canetti, D. Song, and D. Tygar, “Efficient authentication and signing of multicast streams over lossy channels,” in Proc. 2000 IEEE Symposium Security Privacy.

11.        The Network Simulator - ns-2. Available: http://www.isi.edu/nsnam/ns/     

12.        L. Wang and F. Gao, “A secure clustering scheme protocol for MANET,” in Proc. 2010 International Conf. Multimedia Inf. Netw. Security.

13.        L. Junhai, Y. Danxia, X. Liu, and F. Mingyu, “A survey of multicast routing protocols for mobile ad-hoc networks,” IEEE Commun. Surveys & Tutorials, vol. 11, no. 1, pp. 78–91, first quarter 2009.

14.        M. Younis, O. Farrag, and S. Lee, “Cluster mesh based multicast routing in MANET: an analytical study,” in Proc. 2011 IEEE International Conf. Commun..

15.        G. Angione, P. Bellavista, A. Corradi, and E. Magistretti, “A k-hop clustering protocol for dense mobile ad-hoc networks,” in Proc. 2006 IEEE International Conf. Distrib. Computing Systems Workshop.

16.        “A Load-balancing and Energy-aware Clustering Algorithm in Wireless Ad-hoc Networks” Wang Jin, Shu Lei, Jinsung Cho, Young-Koo Lee, Sungyoung Lee, Yonil Zhong.


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53.

Authors:

Deepshikha Bhargava, Madhavi Sinha

Paper Title:

Performance Analysis of Agent based IPSM for Windows based Operating Systems

Abstract:     All processes prevent casual exchange of data. However, occasionally two processes might need to communicate with each other. One method that enables processes to communicate is called Inter Process Synchronization (IPS).  In an Operating System on which several threads run concurrently, it is important to be able to synchronize the activities of various threads. Windows provides several synchronization objects that enable to synchronize a thread's actions with those of another thread. These objects include critical sections, mutexes, events, and semaphores.  The different solutions for Inter Process Synchronization problem are suggested [1] where some of these solutions have their own limitations or performance related issues. The agent based approach used in this paper has suggested a new algorithm for agent IPSM which is an attempt to propose an optimal solution to the problem.  IPSM stands for Inter Process Synchronization Manager which is an agent used for solving the problem of inter process synchronization. In the present paper agent based Inter Process Synchronization Manager (IPSM) is described and its performance is compared with agent based IPSM on different Windows based operating systems.

Keywords:
 Inter Process Synchronization, Inter process Synchronization Manager, agent, IPSM


References:

1.             Jennings N. R., Wooldridge M., “Applications of Intelligent Agents”, Journal of Queen Mary & Westfield College, University of London
2.             Montaner M., “Collaborative Recommender Agents Based On Case-Based Reasoning and Trust”, PhD Thesis in Computer Engineering. Departament of Electronics, Computer Science and Automatic Control. Universitat de Girona. September, 2003.

3.             Hyacinth S. Nwana, “Software Agents: An Overview”, Intelligent Systems Research Advanced Applications & Technology Department ,BT Laboratories, Springer Berlin / Heidelberg, pp 59-78, 1997

4.             Bhargava D., Sinha M., “Agent based design for solving Inter Process Synchronization Problem”, STEPS-INDIA Public Domain (IJAEA), Year 2011, Volume-1, June Issue, print ISSN 0975 – 7783, Online ISSN : 0975 – 7791

5.             Bhargava D., Sinha M., “Performance Analysis of Agent Based IPSM”,  2012 The Ninth International Joint Conference on Computer Science and Software Engineering, Department of Computer and Mul"timedia Engineering University of the Thai Chamber of Commerce, 2012

6.             Braha D., Maimon O. , “The design process: Properties, paradigms, and structure”, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND, Vol. 27, No. 2. (March 1997), pp. 146-166  Key: citeulike:6426312

7.             H. Kitano and M.Asada. RoboCup Humanoid Challenge: That’s One Small Step for A Robot, One Giant Leap for Mankind.In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IRO-98), 1998

8.             K.Kostiadis and H.Hu. A Muti-Threaded Approach to Simulated Soccer Agents for the RoboCup Competition. In M. Veloso, E. Pagello, and H. Kitano, editors, RoboCup-99: Robot Soccer World Cup III. Springer Verlag, 2000

9.             H. Mills. Chief Programmer Teams, Principles and Procedures. Technical Report FCS 71-5108,IBM Federal Systems Division, Gaithersburg, MD, 1971.

10.          L.P. Reis and J. N. Lau. FC Portual Team Description: RoboCup-2000 Simulation League Cham-pion. In P. Stone, T. Balch, and G. Kraetszchmar, editors, RoboCup-200: Robot Soccer World Cup IV. Springer Verlag, Berlin, 2001

11.          M.P. Wand. Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 1994

12.          Brooks, Rodney A.,  Intelligence without Representation, Artificial Intelligence 47:139-160, 1991

13.          U. Ramachandran, M. Solomon, M. Vernon Hardware support for interprocess communication Proceedings of the 14th annual international symposium on Computer architecture. Pittsburgh, Pennsylvania, United States. Pages: 178 - 188. Year of Publication: 1987 ISBN 0-8186-0776-9

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54.

Authors:

J. Bharathi, P. Chandrasekar Reddy

Paper Title:

Segmentation of Touching Conjunct Consonants in Telugu using Minimum Area Bounding Boxes

Abstract:      This paper addresses the problem of segmenting touching characters which are written or printed in the bottom zone. In the segmentation of machine printed Telugu document image, conjunct consonants are more prone to touching due to shape of the characters. It is important to segment them properly to improve the accuracy of the Telugu OCR as otherwise the reconstruction and mapping to editable electronic document is incomplete and often needs lot of tedious manual intervention. It is based on the script level characteristic that the secondary form of consonants are written in smaller size and its bounding box is smaller compared to the primary character. The structural feature of sharp peaks in both left and right side profiles at the touching location of the combined character is used for determining the correct segmentation location. The algorithm is tested on a dataset created from large set of documents. The success rate of 96.39% is achieved.

Keywords:
 Minimum area bounding box, segmentation, side profile peaks, touching conjunct consonants.


References:

1.              Edward Hill, C. “A Primer of Telugu Characters,” Manohar Publications, New Delhi. Can be viewed online at Digital South Asia Library (DSAL), University of Chicago, 1991.
2.              Richard Casey, G., Eric Licolinet, “A Survey of Methods and Strategies in Character Segmentation”, IEEE Trans. In Pattern Analysis and Machine Intelligence, Vol. 18, No. 7, July 1996, pp. 690–706.

3.              Su Liang, Shridhar, M, Ahmadi, M. “Segmentation of Touching Characters in Printed Document Recognition”, Pattern Recognition, Vol. 27, No. 6, 1994,  pp. 825–840.

4.              Min-Chil Jung, Yong-Chul Shin, Srihari, S. N., “Machine Printed Character Segmentation Method using Side Profiles”, IBM Journal of Research and Development, Vol. 26, No. 6, 1999, pp. 647–656.

5.              S. Kahan, T. Pavlidis, and H. S. Baird, “On the recognition of printed characters of any font and size”, IEEE Transactions on PAMI, Vol. 9, No. 2, March 1987,  pp. 274-288.

6.              Utpal Garain and Bidyut B. Chaudhuri, “Segmentation of touching characters in printed Devnagari and Bangla scripts using fuzzy multifactorial analysis”, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 32, No. 4, November 2002,  pp 449-459.

7.              Jindal, M. K., Sharma, R. K., Lehal, G. S.,. “Segmentation of Touching Characters in Upper Zone in Printed Gurmukhi Script”, Compute ’09, Proc. Of 2nd Bangalore Annual Compute  Conference, Article 9, Jan 9-10, 2009, Bangalore.

8.              Neena Madan Davessar, Sunil Madan, Hardeep Singh, "A Hybrid Approach to Character Segmentation of Gurmukhi Script Characters," aipr, pp.169, 32nd Applied Imagery Pattern Recognition Workshop AIPR 2003, 2003, pp 169-173.

9.              Utpal Garain and B. B. Chaudhuri, “Segmentation of Touching Symbols for OCR of Printed Mathematical Expressions: An Approach based on Multifactorial Analysis”, Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR‟05),  IEEE, 2005, pp. 177-181.

10.           Dong-Yu Zhang,  Xue-Dong Tian,  Xin-fu Li, “An Improved method for segmentation of touching symbols in printed mathematical expressions”, International Conference on Advanced Computer Control, ICACC, Vol 2, March, 2010,  pp 251-253.

11.           Pratap Reddy, L., Ranga Babu, T., Venkata Rao, N., Raveendra Babu, B., 2010. “Touching Syllable Segmentation using Split Profile Algorithm”, IJCSI, Vol. 7, Issue 3, No. 9, Nov 2010, pp. 17–26.

12.           Bharathi. J, Chandrasekhar Reddy. P, “Segmentation of Telugu touching conjunct consonant using overlapping bounding boxes”   in International Journal on Computer Science and Engineering (IJCSE), Vol. 5, No. 06, Jun 2013, pp 538-546.

13.           Pavan Kumar. P., Chakravarthi Bhagavathi, Atul Negi, Arun Agarwal, Deekshatulu. B. L. “Towards improving the accuracy of Telugu OCR system”, International Conference on Document Analysis and Recongnition, ICDAR, 2011, pp 910-914.


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55.

Authors:

Kiran S. Darne, Suja S. Panicker

Paper Title:

Use of Fuzzy C-Mean and Fuzzy Min-Max Neural Network in Lung Cancer Detection

Abstract:   Lung cancer is a disease characterized by uncontrolled cell growth in tissues of the lung and is the most common fatal malignancy in both men and women. Early detection and treatment of lung cancer can greatly improve the survival rate of patient. Artificial Neural Network (ANN),  Fuzzy C-Mean (FcM) and Fuzzy Min-Max Neural network (FMNN) are useful in medical diagnosis because of several advantages. Like ANN has fault tolerance, flexibility, non linearity, while FcM gives best result for overlapped data set, data point may belong to more then one cluster center and always converges .and , also, FMNN has advantages like online adaptation, non-linear separability, less training time, soft and hard decision. In this work, we propose to use FcM and FMNN on standard datasets, to detect lung cancer.

Keywords:
 Classification, Clustering, Fuzzy System, FCM, FMNN.


References:

1.              Kiran S. Darne & Suja S. Panicker, “Use of Artificial Neural Networks in Medical Data Classification: A Review”, International Conference on Advanced Computer Sciences, Communication and Information Technologies (ICACSIT), 2012.
2.              Fatma Taher and Rachid Sammouda, “Lung Cancer Detection by Using Artificial Neural Network and Fuzzy Clustering Methods”, IEEE GCC conference   and   exhibition, 2011.

3.              Yongjun WU, Na Wang, Hongsheng ZHANG, Lijuan Qin, Zhen YAN and Yiming WU, “Application of Artificial Neural Networks in the Diagnosis of Lung Cancer by  Computed Tomography”, IEEE conference on Natural Computation, 2010.

4.              Wang Tao, Lv Jianping and Liu Bingxin, “Research of Lung Cancer Screening Algorithm Based On RBF Neural Network”, International Conference on Computer and Management (CAMAN), 2011.

5.              Ankit Agrawal and Alok Choudhary, “Identifying HotSpots in Lung Cancer Data Using Association Rule Mining”, 11th IEEE International Conference on Data Mining Workshops, 2011.

6.              S.Aravind Kumar, Dr.J.Ramesh, Dr.P.T.Vanathi, Dr.K.Gunavathi, “Robust and Automated Lung Nodule Diagnosis from CT Images Based On Fuzzy Systems” International Conference on Process Automation, Control and Computing (PACC),  2011.

7.              Hamada R. H. AI-Abs, Brahim Belhaouari Samir, Khaled Bashir Shaban, and Suziah Sulaiman, “Computer Aided Diagnosis System based on Machine Learning Techniques for Lung Cancer” International Conference on Computer & Information Science (ICCIS), 2012.

8.              Jia Tong, Wei Ying, Wu Cheng Dong,  “A Lung Cancer Lesions Detection Scheme Based on CT Image”, 2nd International Conference on Signal Processing Systems (ICSPS),2010.

9.              Xiaozhou Li, Rong Wang and Ming Lei, “Analysis on data fordetection of Lung cancer using serum auto-fluorescence”, International Symposium on IT in Medicine and Education (ITME),   2011.

10.           Aminmohammad Roozgard, Samuel Cheng, and Hong Liu,   “Malignant Nodule Detection on Lung CT Scan Images with  Kernel RX –algorithm”, Proceedings of
the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2012), 2012.

11.           Jung Hun Oh, Jeffrey Craft, Rawan Al-Lozi, Manushka    Vaidya, “Predicting local failure in lung cancer using Bayesian networks” Ninth International Conference on Machine  Learning and Applications,2010.

12.           Tadashi Kondo, Junji Ueno and Shoichiro Takao, “Medical Image Diagnosis of Lung Cancer by Hybrid Multi-layered GMDH-type Neural Network Using Knowledge Base”, Proceedings of ICME International Conference on Complex Medical Engineering,2012.

13.           Thessa T.J.P. Kockelkorn, Eva M. van Rikxoort, Jan C.Grutters and Bram van Ginneken, “Interactive Lung Segmentation in CT Scans with Severe Abnormalities”2010.

14.           PENG Gang, YANG Xiong, LIU Li , “Parallel Immune Algorithm for Lung Cancer Detection in X-Ray Images Based on Object Shared Space” 12th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2011.

15.           Patrick K. Simpson, “Fuzzy Min-Max                 Neural Networks Parts 1: Classification”,   IEEE Transaction on Neural Network, Vol 3, No.5, 1992.

16.           James C. Bezdek, Robert Ehrlich and William Full , “FCM: The Fuzzy C-Means Clustering Algorithm” IEEE Transactions on Computers and Geosciences, Vol 10, No. 2-3, 1984.

17.           Suja S.Panicker, P.S. Dhabe, M  Dhore “Fault Diagnosis using Fuzzy   Min Max Network Classifier”, International Journal of Artificial Intelligent Systems and Machine Learning, July 2010.

18.           http://www.cancer.gov

19.           https://sites.google.com/site/dataclusteri ngalgorithms/home

20.           http://www.learnartificialneuralnetworks.com/

21.           http://www.topnews.in/health/diseases/lung-cancer?page=2

22.           mldata.org/repository/Data/viewslug/datasets-numeric-veteran/


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56.

Authors:

Nuthan.A.C, Nagaraj.C, Havyas.V.B

Paper Title:

Implementation of Data Encryption Standard Using Reversible Gate Logic

Abstract:    Cryptography is quite an old art where user can encrypt and decrypt the information using the strongest algorithm which is required for the specific task. But, the scope of this unique information hiding technology has been limited only to the field of software application. Data Encryption Standard (DES) is one of widely used standard for data encryption. Implementation of DES has been done in many ways. But in this proposed implementation, DES is implemented using Reversible logic. Till today not many inventions and growth is observed in reversible logic because of the high complexity in designing them. Reversible logic solutions will be explored to achieve increased levels of power efficiency and area efficiency for digital communication based applications. The modular and flexible nature of the design enables easy incorporation of future updates.

Keywords:
 Reversible logic, Cryptography, Data Encryption Standard (DES).


References:

1.             R. Landauer, “Irreversibility and heat generation in the computing process”, IBM J. Research and Development, vol. 5 (3): pp. 183-191, 1961.
2.             Bennett, C.H., “Logical reversibility of computation”, IBM J. Research and Development, vol. 17: pp. 525-532, 1973

3.             Maslov, G. W. Dueck, and D. M. Miller, "Synthesis of Fredkin- Toffoli reversible networks," IEEE Trans. VLSI Systems, vol. 13, no. 6, pp. 765-769, 2005.

4.             R. Feynman, “Quantum mechanical computers”, Optical News, vol. 11, 1985, pp. 11-20.

5.             Milburn, Gerard.j., The Feynman processor perseus books 1998

6.             fredkin, T. Toffoli, “Conservative Logic”, International Journal of Theory of Physics, 21, 1982, pp 219-253

7.             Toffoli T., 1980. Reversible computing, Tech Memo MIT/LCS/TM-151, MIT Lab for Computer Science.

8.             Michael P. Frank Reversible Computing Page.

9.             Carlin Vieri, “Reversible Computing for Energy Efficient and Trustable computation”, April 1998,

10.          P. Picton. Optoelectronic, multivalued, conservative logic. International Journal of Optical Computing, 2:19-29, 1991.

11.          W. D. Pan and M. Nalasani. Reversible logic. IEEE Potentials, pages 38-41, February/March 2005.

12.          U.S. Department of Commerce, William M. Daley, Secretary National Institute of Standards and Technology, Raymond G. Kammer, Director,  FIPS Pub 46-3 Federal Information Processing Standards Publication Reaffirmed 1999 October 25

13.          Cryptography and Network Security Principles and Practices,William Stallings, Fourth Edition.

14.          Feistel, H. "Cryptography and Computer Privacy." Scientific American May 1973.

15.          Shannon, C. "Communication Theory of Secrecy Systems." Bell Systems Technical Journal, No. 4, and 1949.


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57.

Authors:

M. Krupa Swaroopa Rani, P. Jagadamba, G. Kiran Kumar

Paper Title:

Advanced Signal Processing Of Radar Wind Profiler Using Wavelet Transform Techniques           

Abstract:     Atmospheric Signal processing has been one field of signal processing where there is a lot of scope for development of new and efficient tools for cleaning of the spectrum, detection and estimation of the desired parameters.  Atmospheric signal processing deals with the processing of the signals received from the atmosphere when manually stimulated using atmospheric Radar. Removal of clutter and noise in the radar wind profiler is the utmost important consideration in radar. In this paper, we implement wavelet thresholding for removing clutter and noise from radar wind profiler data. By applying the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which identifies the coefficients relevant for clutter and noise and suppresses them and increases the accuracy of wind vector reconstruction.

Keywords:
 Atmospheric Signal Processing, Spectrum, Detection, Clutter, Wind Profiler.


References:

1.              Barth, M., Chadwick, R., and van de Kamp, D., Data    processing algorithmsused by NOAA’s wind profiler demonstration network, Ann. Geophysicae, 12, 518–528, 1994.
2.              Burrus, C. S., Gopinath, R. A., and Guo, H., Introduction to  Wavelets and Wavelet Transforms, Prentice Hall, 1998.

3.              Carter, D., Gage, K. S., Ecklund, W. L., Angevine, W. M., Johnston, P. E., Riddle, A. C., Wilson, J., and Williams, C. R., Developments in UHF lower tropospheric wind profiling at NOAA’s Aeronomy Laboratory, Radio Sci., 30, 977–1001, 1995.

4.              Dahlke, S., Maaß, P., and Teschke, G., Interpolating scaling functions with duals, Tech. Rep. 00-08, Zentrum f¨ur Technomathematik, Universit¨at Bremen, 2000.

5.              Daubechies, I., Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.

6.              Donoho, D. L. and Johnstone, I. M., Minimax estimation via wavelet shrinkage, Tech. Rep. 402, Department of Statistics, Stanford University, 1992.

7.              Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., and Picard, D., Density estimation by wavelet thresholding, Preprint, Dept. of Statistics, Stanford University, 1993.

8.              Farley, D., On-line data processing techniques for MST radars, Radio Sci., 20, 1177–1184, 1985.

9.              Ghebrebrhan, O. and Crochet, M., On full decoding of truncated ranges for ST/MST radar applications, IEEE Trans. Geosci. Electron., 30, 38–45, 1992.

10.           Gossard, E. E., A fresh look at the radar reflectivity of clouds, Radio Sci., 14, 1089–1097, 1979.

11.           Gossard, E. E. and Strauch, R. G., The refractive index spectra within clouds from forward-scatter radar observations, J. Appl. Meteor., 20, 170–183, 1981.

12.           Hildebrand, P. H. and Sekhon, R., Objective determination of the noise level in Doppler spectra, J. Appl. Meteor., 13, 808–811, 1974.

13.           Holschneider, M., Wavelets: An Analysis Tool, Clarendon Press, Oxford, 1995.

14.           Johnstone, I. M. and Silverman, B. W., Wavelet threshold estimators for data with correlated noise, Tech. Rep. Dept. of Statistics, Stanford University, 1995.

15.           Kaiser, G., A Friendly Guide to Wavelets, Birkh¨auser, Basel, 1994.

16.           Keeler, R. J. and Passarelli, R. E., Signal processing for atmospheric radars, in Radar in Meteorology, edited by D. Atlas, chap. 20a, 199–229, American Meteorological Society, Boston, 1990.

17.           Louis, A. K., Maaß, P., and Rieder, A.,Wavelets, Teubner, Stuttgart, 1998.

18.           May, P. T. and Strauch, R. G., An examination of wind profiler signal processing algorithms, J. Atmos. Oceanic Technol., 6, 731–735, 1989.

19.           Meyer, Y., Wavelets: Algorithms and Applications, SIAM, Philadelphia, 1993.

20.           Schmidt, G., R¨uster, R., and Czechowsky, P., omplementary code and digital filtering for detection of weak VHF radar signals from the Mesosphere, IEEE Trans. Geosci. Electron., GE-17, 154–161, 1979.

21.           Spano, E. and Ghebrebrhan, O., Pulse coding techniques for ST/MST radar systems: A general approach based on a matrix formulation, IEEE Trans. Geosci. Remote Sensing, 34, 304–316, 1996.

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23.           Teschke, G., Komplexwertige Wavelets und Phaseninformation, Anwendungen in der Signalverarbeitung, Diplomarbeit, Institut f¨ur Mathematik, Universit¨at Potsdam, 1998.

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25.           V. Sachs, R. and MacGibbon, B., Nonparametric curve estimation by wavelet thresholding with locally stationary errors, preprint, 1998.

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27.           Wilfong, T. L., Merritt, D. A., Lataitis, R. J., Weber, B. L., Wuertz, D. B., and Strauch, R. G., Optimal generation of radar wind profiler spectra, J. Atmos. Oceanic Technol., 16, 723–733, 1999a.


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58.

Authors:

Amandeep Kaur, Garima Saini

Paper Title:

ICI Cancellation Using Zero-Padded Conjugate Transmission with Adaptive Receiver for OFDM Systems

Abstract:      To overcome the effect of Intercarrier Interference (ICI) in OFDM system, the Zero-padded Conjugate scheme with adaptive receiver is proposed.  In the conjugate algorithm first path contains the regular OFDM signal and in second path conjugate of first path is transmitted using Time Division multiplexing (TDM) for Time variant channels. Zero padding is done between two consecutive symbols to mitigate the effect of intercarrier interference to provide better time and frequency synchronization. Adaptive receiver uses block least mean-squared algorithm (BLMS) to adaptively update the frequency offset error. The simulation results are carried out using BPSK, QPSK and 16-QAM modulation techniques. The proposed scheme provides better BER rate than regular OFDM system, Conjugate cancellation (CC) and previous work for AWGN channel.

Keywords:
Orthogonal frequency division multiplexing (OFDM), Adaptive receiver, Block least mean-squared (BLMS) algorithm, Intercarrier Interference (ICI), frequency offset.


References:

1.             R. Parsad, “OFDM for Wireless Multimedia   Communications”, Norwood, MA: Artech House, 2004.
2.             N.A.Kotak, “Simulation & Performance Analysis of    DVB-T System Using Efficient Wireless Channels”, International Conference on Computational Intelligence and Communication Networks (CICN), pp(s): 677 – 681, Dec 9, 2011.

3.             Lu Wei-zhong; Wang Yi-huai; Xi Xue-feng; Zhang Ni, “Research on power line communication and design of PLC modem”, International Conference on Information Science and Engineering (ICISE), pp: 6823-6826, Dec-2010.

4.             B.L Floch, J. Rault, P. Siohan, R. Legouable, C. Gallard, C, “ The birth of Digital Terrestrial Broadcasting in Europe: a brief history of the creation and the standardization phases of Digital Audio Broadcasting(DAB) and Digital Terrestrial TV broadcasting (DVB-T)”, IEEE Conference on the History of Telecommunications Conference (HISTELCON), pp: 1-6, 2010.

5.             W. G. Jeon et al., “An equalization technique for orthogonal frequency-division multiplexing systems in time-variant multipath channels,” IEEE Transaction on Communication, vol. 47, no. 1, pp: 27–32, 2001.

6.             A.N  Husna; S.Y.S Kamilah; B. Ameruddin;  E. Mazlina, “Intercarrier interference (ICI) analysis using correlative coding OFDM system”,  Conference on  Digital object Identifier RF and Microwave Proceedings, pp: 235 – 237, 2004.

7.             C.-L. Wang, Y.-C. Huang and P.-C. Shen, “An intercarrier interference suppression technique using time-domain windowing for OFDM systems," IEEE conference on Vehicular Technology, vol. 5, pp: 2518-2522, May 2006.

8.             H.-G. Ryu, Y.Li, and J.-S. Park, “An improved ICI reduction method in OFDM communication system,” IEEE Transaction on Broadcasting, Vol. 51, no. 3, pp(s): 395–400, 2005.

9.             Y. Zhao and S. Häggman, “Intercarrier interference self-cancellation scheme for OFDM mobile communication systems,” IEEE Transaction on Communication, vol. 49, no. 7, pp: 1185–1191, 2001.

10.          Y. Fu and Chi Chung Ko, “A new ICI self-cancellation scheme for OFDM systems based on a generalized signal mapper,” Proceedings 5thWireless Personal Multimedia Communications, vol. 3, pp: 995–999, 2002.

11.          Y.-H. Peng, “Performance analysis of a new ICI-Self-cancellation- scheme in OFDM systems,” IEEE Transaction on Consumer Electronics, vol. 53, no. 4, pp: 1333–1338, 2007.

12.          V. Kumbasar and O. Kucur, “ICI reduction in OFDM systems by using improved Sinc power pulse”, Digital Signal Processing, Vol.17, Issue 6, pp(s): 997-1006, Nov. 2007.

13.          K. Sathananthan, C. R. N. Athaudage, and B. Qiu, “A novel ICI cancellation scheme to reduce both frequency offset and IQ imbalance effects in OFDM," in Proceedings IEEE 9th International Symposium on Computer Communication, pp. 708-713, July 2004.

14.          H.-G. Yeh, Y.-K. Chang, and B. Hassibi, “A scheme for cancelling intercarrier interference using conjugate transmission in multicarrier communication systems," IEEE Transaction on Wireless Communication, vol. 6, no.1, pp: 3-7, Jan. 2007.

15.          C.-L. Wang and Y.-C. Huang, “Intercarrier interference cancellation using general phase rotated conjugate transmission for OFDM systems,” IEEE Transaction on Communication, vol. 58, no. 3, March 2010.


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59.

Authors:

Rahmat Zolfaghari

Paper Title:

Converting UML Description of Software Architecture to QNM and Performance Evaluation

Abstract:   Converting UML Description of Software Architecture to QNM, provides a comparison between all kinds of designs with respect to performance indicators.Present study suggested a method for converting the UML description designing software to Queuing networks model (QN)model, which provides the application of using the UML in designing software with high performance; in other words it putting the performance in designing software and a high quality software is designed.In order tomodeling the parts of system we use deployment diagram for allocating software components to hardware resources and activity diagrams to model software behavior, and use case diagrams to model workloads with the performance profile and  An algorithm is provided for automatic production of the QN performance model from the XML(Extensible Markup Language) documents .used diagrams with performance profiles(stereotype, label and limitation), using the ExportXMIsoftware rational rose. performace modelload to software tool for performance analysis, so as the designer can test thefulfill of performance goals of his design according to type of different performance parameters and changing in value and chooses the best option in designing

Keywords:  UML(Unified Modeling Language),Queuing networks model (QNA)and Performance evaluation


References:

1.             PEPSY-QNS (Performance Evaluation and Prediction SYstem for Queueing NetworkS), (http://www4.informatik.uni-erlangen.de/Projects/PEPSY/en/), October 2004. 
2.             L.G williums, G.U. smith "performance Evaluation of Software Architectures" proc. Of wosp'98, santa Fe, New Mexico , USA, PP, 164-177 (1998)

3.             Institute of Electrical and Electronic Engineers. "IEEE Standard Glossary of Software Engineering Terminology." IEEE

4.             Standards Collection. New York, NY: Institute of Electrical and Electronics Engineers, 1993.

5.             P. Kahkipuro. “UML-based performance modeling framework for component- based distributed systems“. In R. R.

6.             Dumke, C. Rautenstrauch, A. Schmietendorf, and A. Scholz, editors, Performance Engineering, volume 2047 of LNCS, pages 167–184. Springer-Verlag, 2001.

7.             R.Class, Software Runaway, "Lessons learned form Massive Software project Failures, prentice Hall" 1998smith C.U., Performance Engineering Of Software System, Adison—Wesley(1990)

8.             Cortllessa, V.,Mirandola, R. PRIMA-UML:A Performanc Validation Incremental Methodology on Early UML Diagram 101-129(2002)

9.             Gu, G., Petriu, D.C, XSLT tranformation from UML  models to LQN performance models, In [WOSP02], PP. 227-23

10.          Arief, L.B., Speirs, N.A., A UML Tool for an Automatic Generation of Simulation Program, In [WOSP00] PP. 71-76

11.          Mirco Tribastone and Stephen Gilmore. Automatic extraction of PEPA performance models from UML activity diagrams annotated with the MARTE profile. In Proceedings of the 7th International Workshop on Software and Performance (WOSP2008), pages 67-78, Princeton NJ, USA, 2008.

12.          Mirco Tribastone and Stephen Gilmore. Automatic translation of UML sequence diagrams into PEPA models. In 5th International Conference on the Quantitative Evaluation of SysTems (QEST 2008), pages 205-214, St Malo, France, 2008.

13.          Analysis of a Multimedia Stream Using  SPA, H. Bowman, J.W.Brayans  and a J. Derrick, University of  Kent, 2001

14.          Performance Modelling with UML and  Stochastic Process Algebras, Catherine Canvent, Stephen Glimore, Jane Hiliston, Matthew Prowes and Perdita Stevens,2002G. Gu, Dorina C. Petriu, ”XSLT transformation from UML models to LQN performance  models”, In [ACM02], pp. 227– 234.

15.          http://argouml.tigris.org

16.          http://www.dcs.ed.ac.uk/pepa

17.          http://www.sax.sourceforge.net

18.          UML 1.3 DTD for XMI 1.1: UMLX13-11.dtd. 

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60.

Authors:

K. Al-Mutib, E. Mattar, M. AlSulaiman, H. Ramdane, M. Emaduddin

Paper Title:

Mobile Robot Floor Navigation using RGB+D and Stereo Cameras

Abstract: Real world indoor environments are rich in planar surfaces. Floor detection or ground-plane detection is a crucial requirement for a robotic navigation task. Despite frequent successes in this area, problems with detection of navigable floor with multiple planar and non-planar slopes at multiple heights still exist. For robust and safe navigation, such small variations such as floor joins, carpet deformities, raised steps and floor gradients need to be detected and robot path and kinodynamics plan must be adjusted accordingly. The authors suggest a recursive RANSAC segmentation based algorithm that estimates the dominant and sub-dominant plane models for all the navigable planes within a detected floor or a ground plane. The algorithm also divides the input point clouds intelligently into multiple regions of interest for both efficiency and accuracy enhancement. The recursive estimation approach for determining plane parameters helps to detect multiple planes within each region. Among other benefits of this approach, reduction of search space size for the estimation of plane parameters stands out to be the most striking result of this work. This region wise plane estimation approach also helps to reduce the computational load by selectively dropping less significant floor sections from estimation process. The floor estimation technique coupled with sensor response functions for two different point cloud generators further investigates into the robustness of the method when deployed on two distinct sensors i.e. RGB+D sensor and a stereo vision camera. In our experiments we segment navigable floor planes in real-time for a slowly moving sensor. The location and geometrical parameters of the floor planes are updated in a global coordinate system whenever a change their location is detected. The planes are associated to a grid map which serves as a path-planning reference to a mobile robot used in our experiments. The results of floor detection and the precision of floor anomaly detection are compared sensor-wise and with the ground truth defined by obstacle heights and configuration.

Keywords:
Mobile Robotic System, Stereo Vision, Navigation, Grid-map, 3D terrain Maps.


References:

1.       S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, and others, “KinectFusion: real-time 3D reconstruction and interaction using a mo ing depth camera ” in roceedings of the 24th annual ACM symposium on User interface software and technology, 2011, pp. 559–568.
2.       M eracles B Bolder and C oeric “Fast detection of arbitrar planar surfaces from unreliable 3D data ” in ntelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, 2009, pp. 5717–5724.

3.       K ong and R reen “ round-plane detection using stereo depth alues for heelchair guidance ” in mage and Vision Computing Ne International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-3, July 2013 292 Zealand 2 9 VCNZ’ 9 24th nternational Conference 2 9 pp 97–101.

4.       M Olsson “Obstacle Detection using Stereo Vision using Unmanned round Vehicles ” Lin o ing Uni ersit S eden 2 9

5.       C D antilie S Bota aller and S Nede schi “Real-time obstacle detection using dense stereo ision and dense optical flo ” in Intelligent Computer Communication and Processing (ICCP), 2010 IEEE International Conference on, 2010, pp. 191–196.

6.       E. Fazl-Ersi and J sotsos “Region classification for robust floor detection in indoor en ironments ” mage Anal sis and Recognition pp. 717–726, 2009.

7.       P. Henry et al. RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In Proc. of the Int. Symposium on Experimental Robotics (ISER), 2010.

8.       K Khoshelham and S O Elberin “Accurac and Resolution of Kinect Depth Data for ndoor Mapping Applications ” Sensors ol 12, no. 2, pp. 1437–1454, Feb. 2012.

9.       Point re Research nc “Stereo Accurac and Error Modeling” oint Grey Knowledge Base Article. 19-Apr-2004.

10.    I. Ben- al “Outlier Detection ” in Data mining : a no ledge discovery approach, New York: Springer, 2005


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