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Volume-5 Issue-1: Published on March 05, 2015
13
Volume-5 Issue-1: Published on March 05, 2015
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S. No

Volume-5 Issue-1, March 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Vasudeva G, Cyril Prasanna Raj P

Paper Title:

Study of 8 Bits Fast Multipliers for Low Power Applications

Abstract:   High–speed multiplication has always been a fundamental requirement of high performance processors and systems. With MOS scaling and technological advances there is a need for design and development of high speed data path operators such as adders and multipliers to perform signal processing operations at very high speed supporting higher data rates. In Digital signal Processing applications, multiplication is one of the most utilized arithmetic operations as part of filters, convolves and transforms processors.  It is found in the literature that improving multipliers design directly benefits the high performance embedded processors used in consumer and industrial electronic products. Also significant increase in the bit length increases the critical path affecting the frequency of operations. It is also found that the regular structure required for each processing elements also increases and hence consumes area and power. Hence there is a need for design and development of high-speed architectures for N-bit multipliers supporting high speed and power. In this paper we review the architecture reported in the literature for multipliers and critical issues degrading the speed and power.  Based on the literature review suitable modifications are suggested in the design for high speed and low power multipliers. The multipliers Booth, Wallace tree and Dadda are implemented and the constraints Area, Power and Timing are optimized using software resources NC SIM and VC SIM.

Keywords:
   DSP, microprocessor, NC SIM, VC SIM


References:

1.             Keshab K.Parhi, “VLSI DIGITAL SIGNAL PROCESSING SYSTEMS”, Design and implementation John Wiley and sons (ASIA), 1999.
2.             Neil H.E.Weste, David Harris, Ayan Banerjee, “CMOS VLSI DESIGN”, A circuits and systems perspective. Pearson Education, Third edition 2007, pp345-356.

3.             Robert F.Shaw, “Arithmetic operations in a binary computer”, Review of scientific instruments; vol 21, pp 687-693, 1950.

4.             O.L.Mac Sorley,”High-Speed Arithmetic in Binary Computers”, Proceedings of the IRE, Vol 49, pp.67-91, 1961.

5.             Bruce Gilchrist, J.H. Pomerene and S.Y.Wong, “Fast Carry logic for Digital Computers”, IRE Transactions on Electronic Computers, Vol.EC-4, PP.133-136, 1955.

6.             R. De Mori, “Suggestions for an TC Fast Parallel Multiplier”, Electronics letters, Vol.5, pp 50-51,1965

7.             K’Adrea C.Bickerstaff,Michael J.Schulte,and Earl E. Swartz Lander, Jr., “Reduced Area Multipliers”, Proceedings of the 1993 International Conference on
Application Specific Array Processors, pp.478-489,1993.

8.             Andrew D.Booth, “A Signed Binary Multiplication Technique”, Quarterly Journal of Mechanics and Applied Mathematics, Vol .4, pp.236-240, 1951.

9.             Charles R. Baugh and Bruce .A.Wooley, “A Two’s Complement Parallel Array Multiplication Algorithm”, IEEE Transactions on Computers, Vol. C-22, pp.1045-47,
1973.

10.          Behrooz Parhami, “Computer Arithmetic: Algorithms and Hardware Designs, Newyork: Oxford University press, 2000.

11.          Thomas K.Callaway and Earl E. Swatzlander,JR., “Optimizing Multipliers for WSI”, Proceedings of the 1993 International Conference on Wafer Scale Integration,    pp.85-94,1993.

12.          C.S.Wallace,” A suggestion for a Fast Multiplier”, IEE Transactions on Electronic Computers, Vol.EC-13, pp.14-17, 1964.

13.          Luigi Dadda,” Some Schemes for Parallel Multipliers”, Alta Frequenza, Vol.34, pp.349-356, August 1965.
14.          Advanced Asic Chip Synthesis by Himanshu Bhatnagar Second Edition pp-183-256, 2002.
15.          Wey C.L and Chang T.Y., “Design and analysis of VLSI-based parallel multipliers”, IEEE proceedings computers and Digital Techniques, vol.137, no.4,pp,328-336, July 1990.(Journal paper)


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2.

Authors:

Nandan Makarand Deval

Paper Title:

Secure Steganography Algorithm Based on Cellular Automata using Fibonacci Representation and Reverse Circle Cipher Application for Steganography

Abstract:    Steganography is the act of hiding a message inside another message in such a way that can only be detected by its intended recipient .The process of hiding information inside another media is called steganography .In this technique the basic idea of steganography based on cellular automata using Fibonacci representation. The pixels color component is decompose into Fibonacci domain to extent more available bit-planes which can be used for data hiding for encryption we use reverse circle Cipher .This uses circular substitution and reversal transposition to exploit the benefits of both confusion and diffusion. With the help of these techniques we enhance the capacity of data hiding within image and security.

Keywords:
 Steganography, cellular automata, Fibonacci Representation, encryption, Cipher


References:

1.             . Fridrich, M. Goljan, and R. Du, “Detection of LSB steganography in color and grayscale images,” Magazine of IEEE Multimedia Special Issue on Security, pp. 22-28, October 2001.
2.             S. Dumitrescu, X. Wu and Z. Wang, “Detection of LSB steganography via sample pair analysis”, IEEE Transactions on Signal Processing, vol. 51, no. 7, pp.
1995-2007, July 2003.

3.             A secure steganographic algorithm based on Cellular Automata using Fibonacci representation Tuan Duc Nguyen Department of Computer Science Faculty of Science, Khon Kaen University Khon Kaen, Thailand

4.             Somjit Arch-int Department of Computer ScienceFaculty of Science, Khon Kaen University Khon Kaen, Thailand june 2013

5.             Bruce Schneier, “Applied Cryptography – Protocols, Algorithms, and  Source Code in C”, John Wiley and Sons Inc. Second Edition. pp. 12-30.

6.             Matt Bishop, “Computer Security: Art and Science”, Pearson Education, pp. 270-300, 2005. William Stallings, “Cryptography and Network Security: Principles and Practices” Fourth Edition, Pearson Education, pp. 30-150, April 2006.

7.             Yee Wei Law, Jeroen Doumen, and Pieter Hartel. Survey and Benchmark of Block Ciphers for Wireless Sensor Networks. Transactions on Sensor Networks (TOSN). ACM February 2006

8.             Reverse Circle Cipher for Personal and Network Security Ebenezer R.H.P. Isaac, Joseph H.R. Isaac and J. VisumathiJeppiaar Engineering College Chennai, Tamil Nadu, India

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3.

Authors:

Alyaa Hussein Ali, Shahad Imad Abdulsalam, Ihssan Subhi Nema

Paper Title:

Detection and Segmentation of Hemorrhage Stroke using Textural Analysis on Brain CT Images

Abstract:     The detection of brain strokes from Computed Tomography CT images needs convenient processing techniques starting from image enhancement to qualify the brain image by isolation process, region growing and logical operators (OR and AND). These methods with the help of the simplest segmentation process, which is the thresholding process, are used to extract a stroke region from the CT image of the brain. The median filter is applied to remove the noise from the image. The statistical features calculated using first-order histogram were utilized in the detection of the stroke region. 

Keywords:
 Hemorrhage stroke; CT scan image; Brain segmentation; statistical features.


References:

1.          M. M. Kyaw, ''Computer-Aided Detection system for Hemorrhage contained region", International Journal of Computational Science and Information Technology, Vol. 1, February 2013,  No. 1, PP. 11-16.
2.          T. Gong, "Classification of CT Brain Images of Head Trauma", Springer-Verlag Berlin Heidelberg, 2007, PP. 401-408,.

3.          M. Chawla, "A method for automatic detection and classification of stroke from brain CT images", 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2009.

4.          V. Nagalkar and S. Agrawal, "Ischemic Stroke Detection Using Digital Image Processing by Fuzzy Methods", International Journal of Latest Research in Science and Technology, Vol. 1, November-December 2012, PP. 345-347,

5.          O.E. Ramos and B. Rezaei, "Scene Segmentation and Interpretation Image Segmentation using Region Growing", MSc, Thesis, Computer Vision and Robotics, Universitat de Girona, 2010.

6.          N. Aggarwal and R.K. Agrawal ," First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images", Journal of Signal and Information Processing, Vol. 3, 2012, PP. 146-153.

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4.

Authors:

Chathuri Gunathunga, K. P. Hewagmage

Paper Title:

Implementation of Integrated Virtual Learning Environment Model for Schools with Limited Resources for Online Learning

Abstract:      With the advancement of Information Communication Technology in Sri Lanka teachers should take advantage there to upgrade their teaching technique. Students should be allowed to learn any time anywhere and at their own place. Learning Management System (LMS) plays an important role in ICT enabled learning environment in academic institutes. In K12 education, schoolnet is used to connect all secondary educational institutes and learning communities in a country since they follow the national curriculum. However, a single LMS hosted in the schoolnet network cannot integrate all similar learning communities identified with respect to each school, according to our evaluation of schoolnet LMS in Sri Lanka. After gathering and analyzing different requirements of stakeholders, we propose a suitable hierarchical model to integrate school level LMSs to create a loosely couple distributed learning environment. This model facilitates learners to explore the learning space starting from the classroom based interaction and it promotes the collaborative learning of other teachers and students irrespective of their physical location. In a prototype development, we have implemented suitable software architecture for the proposed model using Moodle LMS. It was designed considering the real world K12 educational administration in Sri Lanka. We also present a methodology to extend a LMS to a Virtual Learning Environment (VLE) which contains learning resources and activities using the model which implemented.

Keywords:
    Learning Management System (LMS), e-Learning, K12 education, blended learning, ICT enabled learning, Moodle, Virtual Learning Environment


References:

1. McCluskey, A 2004 "Schooling: a Sustainable Learning Organisation?," ERNIST project available at http://www.esode.com/downloadable%20files/ERNIST%20Project/schooling.pdf [last accessed 15th May 2011]
2.          SEIR*TEC 2007 "Factors that Affect the Effective Use of Technology for Teaching and Learning Lessons Learned from the SEIR*TEC Intensive Site Schools," SouthEast Initiatives Regional Technology in Education Consortium, 2007.  http://www.seirtec.org/publications/lessons.pdf [last accessed 15th May 2011]

3.          Johnston, J and Barker, LT 2002 "Assessing the Impact of Technology in Teaching and Learning, A Sourcebook for Evaluators," Institute for Social Research, University of Michigan, [Online]. Available: http://www.rcgd.isr.umich.edu/tlt/TechSbk.pdf [Accessed May 15th 2011]

4.          Priyashantha,W.C.P, and Ajith Pasqual , Impact on Learning and Teaching. e-Asia, Digital Learning Conference, December 2009, Colombo, Sri Lanka

5.          K. P. Hewagamage, S. C. Premaratne and K. H. R. A. Peiris. “Design and Development ofBlended Learning through LMS”, 6th International Conference in Web-based Learning (ICWL 2007), August 2007, Edinburgh, United Kingdom

6.          C. Paul Newhouse, “A Framework to Articulate the Impact of ICT on Learning in Schools” in The Impact of ICT on Learning and Teaching, Western Australian Department of Education, 2002, http://www.det.wa.edu.au/education/cmis/eval/downloads/pd/impactframe.pdf [Accessed May 15th 2011]


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5.

Authors:

Satveer Kaur, Jagpal Singh Ubhi

Paper Title:

A Simplified Approach to Analyze Routing Protocols in Dynamic MANET Environment

Abstract:       The fundamental characteristic which differentiates MANETs from other wireless or wired network is mobility and node density. Mobile Wireless Ad Hoc Networks (MANET) is a network without infrastructure, where every node functions as transmitter, router and data sink. Therefore, MANET routing protocols are designed to adaptively cater for dynamic changes in topology while maximizing throughput and packet delivery ratio, and minimizing delay, aggregate good put, average jitter and minimum packet loss. In this paper, the MANET is implemented by using Ad Hoc Demand Vector (AODV), Dynamic Source Routing (DSR), and Dynamic MANET on Demand (DYMO), Optimized Link State Routing (OLSR) and Zone Routing Protocol (ZRP) and simulated on QualNet5.0 simulator. The effect of mobility and density of nodes changing in MANET is investigated and compared a number of reactive, hybrid and proactive routing protocols including AODV, DSR, DYMO, OLSR and ZRP. The simulative study on MANET routing protocols aims to determine the performance of current MANET routing protocols with respect to mobility and node density factors. Results vary when we change the node density. The results of this network are tabulated along with a comprehensive analysis which compares throughput, packet delivery ratio, end to end delay, aggregate good put, average jitter value and packet dropping with node density.

Keywords:
 MANET, QualNet5.0, AODV, DSR, DYMO, OLSR and ZRP.


References:

1.             Fahim Mann and Nauman Mazhar. MANET Routing Protocols vs Mobility Models: A Performance Evaluation. Third International Conference on ICUFN, Pages: 179-184, 2011.
2.             N. Adam, M.Y. Ismail and J. Abdullah. Effect of Node Density on Performances of Three MANET Routing Protocols. International Conference on Electronic Devices, Systems and Applications (ICEDSA2010), Pages: 321-325, 2010.

3.             Liu Tie-yuan, CHANG Liang and Gu Tian-long. Analyzing the Impact of Entity Mobility Models on the Performance of Routing Protocols in the MANET. Third International Conference on Genetic and Evolutionary Computing, Pages: 56-59, 2009.

4.             N. Aschenbruck, E. G. Padilla, M. Gerharz, M. Frank and P. Matrini. Modelling mobility in disaster area scenarios. In Proceedings of MSWiM07, October 2007.

5.             Ashish Shrestha and  Firat Tekiner. Investigation of MANET Routing Protocols for Mobility and Scalability. International Conference on Parallel and Distributed Computing , Applications and Technologies, Pages:451-456, 2010.

6.             Mamoun Hussein Mamon. Important Characteristic of Differences between DSR and AODV Routing Protocol. MCN 2007 Conference.

7.             Bu Sung Lee, Mai Ngoc San, Teck Meng Lim, Chai Kiat Yeo and Boon Chong Seet. Processing Delay as a New Metric for On –Demand Mobile Ad Hoc. International Conference on WiCOM, Pages: 1-4, 2007.

8.             David B. Johson, David A. Maltz and Yih-Chun Hu. The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks. Internet-Draft, draft-ietf-manet-dsr-10.txt.

9.             Samir R. Das, Charles E. Perkins and Elizabeth M. Royer. Performance Comparison of Two On-Demand Routing Protocols for Ad Hoc Networks. IEEE Journal, USA, 2008.

10.          R.E. Thorup. Implementing and evaluating the DYMO routing protocol. Master Thesis at Department of Computer Science, University of Aarhus, Denmark, 2007.

11.          Josh Broach, David A. Maltz, David B. Johson, Yih-Chun Hu & Jorjeta Jetcheva. A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols. Proc. of Mobicom. Texas, 1998.

12.          T. Clausen and P. Jacquet. Optimized Link State Routing Protocol (OLSR), RFC 3626, October, 2003.

13.          G. Lin, G. Noubir and R. Rajaraman. Mobility Models for ad hoc network simulation. International Journal of Computer Science and Network Security, 7(6):160-164, 2007.

14.          N. Aschenbruck, E. G. Padilla and P. Matrini. A survey on mobility models for performance analysis in tactical mobile networks. Journal of Telecommunications and Information Technology, 2: Pages:54-61, 2008.

15.          I. Awan and K. Al-Begain. Performance evaluation  of wireless networks. International Journal of Wireless Information Networks, 13(2): Pages: 95-97, 2006.


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6.

Authors:

B. Sankara Babu, K. Rajasekhar Rao, P. Satheesh

Paper Title:

An Advanced Precision based Approach to String Transformation

Abstract:   Distinct obstacles occur in Natural language processing, Knowledge Engineering, Information Retrieval, Genetics Informatics, Computational molecular biology and Data Mining concerned to String Transformation. Consider an input string, the system automatically produces top k output strings referring to input string. Generally people perform various kinds of spelling errors such as misspell words accidentally while surfing the web. To circumvent such errors, this Paper propounds an advanced Precision based approach to string transformation which is very accurate. The proposed system comprises unique precision value allocated to each alphabet and these are aggregated to give the Total Precision of the particular word. Data sets are trained with the precision based approach by validating them to dictionary called the database. Misspell word precision is compared with the data sets precision and retrieves the top k nearest neighbour output strings relevant to input string. This is one of the best accurate Misspell word and sentence correction approach and experimentally proven on large data sets.

Keywords:
 String Transformation, Precision based Approach, Misspell words, Total Precision.


References:

1.             Wagner, Robert A., and Roy Lowrance. "An extension of the string-to-string correction problem." Journal of the ACM (JACM) 22.2 (1975): 177-183.
2.             Masek, William J., and Michael S. Paterson. "A faster algorithm computing string edit distances." Journal of Computer and System sciences 20.1 (1980): 18-31.

3.             Marzal, Andres, and Enrique Vidal. "Computation of normalized edit distance and applications." Pattern Analysis and Machine Intelligence, IEEE Transactions on 15.9 (1993): 926-932.

4.             Elmi, Mohammad Ali, and Martha Evens. "Spelling correction using context."Proceedings of the 17th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 1998.

5.             Brill, Eric, and Robert C. Moore. "An improved error model for noisy channel spelling correction." Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2000.

6.             Cormode, Graham, and S. Muthukrishnan. "The string edit distance matching problem with moves." ACM Transactions on Algorithms (TALG) 3.1 (2007): 2.

7.             Arasu, Arvind, Surajit Chaudhuri, and Raghav Kaushik. "Transformation-based framework for record matching." Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on. IEEE, 2008.

8.             Okazaki, Naoaki, et al. "A discriminative candidate generator for string transformations." Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2008.

9.             Greenhill, Simon J. "Levenshtein distances fail to identify language relationships accurately." Computational Linguistics 37.4 (2011): 689-698.

10.          Kumar, Akshay. "A Log Linear Probabilistic Model for String Transformation Using Non Dictionary Approach." International Journal of Innovative Research and Development 3.5 (2014).

11.          Jeyalakshmi.S et al. "Improving Efficiency and Accuracy in String Transformation on Large Data Sets."International Journal of Computer Science and Mobile Applications Vol.2 Issue.3, March-2014, pg.55-65 ISSN:2321-8363

12.          Malarvizhi, Mrs P., and Mrs S. Mohana. "A Survey on Various Candidate Generator Methods for Efficient String Transformation."

13.          Yi, Xing, Henry Feild, and James Allan. "Spelling Correction Based on User Search Contextual Analysis and Domain Knowledge."

14.          Angel Wills and D.F.Jenolin Flora. "Approach For Query Reformulation Using Log Linear Model."International Journal of Research in Engineering and Bioscience


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7.

Authors:

D. Y. Patil

Paper Title:

Infrastructure Development in the Border and Non-Border Districts of Punjab                                                    

Abstract:    Infrastructure Development is regarded as a prerequisite for rapid transformation of an economy. Some regions on account of their location disadvantages face some inherent problems regarding development. The present study is an effort to compare the infrastructure development in the border and non-border districts of Punjab. The study compares the infrastructural development in terms of health, education, economic, physical and social sector parameters. The study covers time period from 2002 to 2012. The study revealed that the with the passage of time, the gulf between border and non-border districts with respect to infrastructural development instead of narrowing down, appears to have widened further.

Keywords:
  Infrastructure Development, rapid transformation, border and non-border.


References:

1.             Hirschman, Albert (1958): The Strategy of Economic Development, New Hanes: Yale University Press, p.83.
2.             Nurkse, Ragnar (1980): Problems of Capital Formation in Underdeveloped Countries, (Delhi: Oxford University Press), p.152.

3.             Planning Commission, (2004): Punjab Development Report, Govt of India, New-Delhi, p.596.


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8.

Authors:

Simon Nderitu Watuthu, Michael Kimwele, George Okeyo

Paper Title:

The Key Issues Surrounding Electronic Commerce Information Security Management

Abstract:  The purpose of this study was to identify the key issues surrounding electronic commerce information security management. A descriptive survey research design was conducted to gather primary data.  Information about the current status of ecommerce information security practices and the impediments of these approaches was also collected. A structured questionnaire was used to collect secondary data. Once all the instruments were collected, they were validated edited and then coded.  In the validation process, the collected instruments were checked to determine whether an acceptable sample was obtained in terms of proportion of the issued instrument. Descriptive statistics such as frequency distribution, percentages, means and standard deviations were calculated. This was facilitated by use of the statistical package for social science (S.P.S.S). The observations made from this study are that In Kenya, ecommerce faces numerous information security challenges. Confidentiality and Privacy issues was the top security issue of concern to the respondent’s with 60.7% of the respondents admitting to it. Respondents further considered viruses and malicious software at 46.4%, human errors at 28.6% and also system or software errors at 17.9% as the top three main causes of confidential threat their organizations. Further the study revealed 85.7% of the respondents admitted that their organisation did not use any framework in managing information security.

Keywords:
electronic commerce, information security ecommerce security


References:

1.           Arabjafari, M. (2012). Security in E-commerce. Available at:
2.           http://www.slideshare.net/mohsinq1/security-for-e-commerce? related=1

3.           Ciampo, M. (2012). Security & Guide to Network Security Fundamental, International Edition.Mexico: Cengage Learning

4.           Curtis, G. & Cobham.D. (2005). Business Information System: Analysis, design and practice  Fifth edition. England: Pearson Education Limited Edinburgh hate Harlow. 

5.           Hoodrick, M.T. &Temassia, R. (2011).Introduction to Computer Security. Delhi : Pearson Education

6.           Horak, R. (2002). Communications Systems and Networks, 3rd Edition. New Delhi: Wiley Dreamtech India (P) Ltd

7.           Norman, A. A.&Yasin, N.M. (2011).An Analysis of Information Systems Security Management               (ISSM): The Hierarchical Organizations vs. Emergent Organization. International Journal of Digital Society (IJDS), (2010). 1(3)

8.           Patil, J. (2008). Information Security Framework:  Case Study of a Manufacturing Organization.Seleanu, D. (2013).Cyber security in Canada: Finance industry, government seek ways toshare     Data. Available at: http://blogs.reuters.com/financial-regulatory-forum/2013/07/18/cybersecurity-in-canada-finance-industry-government-seek-ways-to-share-data/

9.           Trepper, C. (2000). E-commerce Strategies.Asoke K Ghosh, Prentice Hall of India Private Limited, Connaught Circus, New Delhi.

10.        Ward T. (2010).  Strategies for Reducing the Risk of eCommerce Fraud.First Data

11.        Whitman M. E. &Mettord H. J. (2012).Principles of Information Security, 4th Edition.Course technology, Cengage learning


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9.

Authors:

José M. Reyes Álamo, Aparicio Carranza

Paper Title:

A Cloud Robotics Architecture with Applications to Smart Homes

Abstract:   Cloud computing is a computational model in which interconnected computers over the Internet work together toward offering greater processing power and storage capabilities than stand-alone solutions. The use of the cloud has found application in a diversity of fields including robotics and mobile computing. This has resulted in the emergence of areas like cloud robotics, a paradigm where robots rely on the cloud to perform their heavy computations and for their storage needs while focusing on simpler computation tasks. The mix of mobile devices and the cloud has created the field of mobile cloud computing (MCC) where mobile devices like smartphones and tablets focus on data gathering and simple processing tasks while using the cloud for complex computations and greater storage. In this paper we review several mobile cloud robotics architectures that combine these concepts. We provide a background of the different technologies used to develop these solutions. We present a prototype implementation of one of the architectural models and also show some practical applications of it using a Smart Home environment as an example

Keywords:  Cloud computing, cloud robotics, mobile cloud, smart home.


References:

1.             Y. Chen, Z. Du, and M. García-Acosta, “Robot as a Service in Cloud Computing,” in 2010 Fifth IEEE International Symposium on Service Oriented System Engineering (SOSE), 2010, pp. 151–158.
2.             G. Hu, W.-P. Tay, and Y. Wen, “Cloud robotics: architecture, challenges and applications,” IEEE Netw., vol. 26, no. 3, pp. 21–28, 2012.

3.             H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches,” Wirel. Commun. Mob. Comput., p. n/a–n/a, 2011.

4.             M. W. Lew, T. B. Horton, and M. S. Sherriff, “Using LEGO MINDSTORMS NXT and LEJOS in an Advanced Software Engineering Course,” in 2010 23rd IEEE Conference on Software Engineering Education and Training (CSEE T), 2010, pp. 121–128.

5.             R. U. Pedersen, J. Nørbjerg, and M. P. Scholz, “Embedded programming education with Lego Mindstorms NXT using Java (leJOS), Eclipse (XPairtise), and Python (PyMite),” in Proceedings of the 2009 Workshop on Embedded Systems Education, New York, NY, USA, 2009, pp. 50–55.

6.             “LeJOS, Java for Lego Mindstorms.” [Online]. Available: http://www.lejos.org/. [Accessed: 22-Oct-2013].

7.             “The Jython Project,” Nov-2013. [Online]. Available: http://www.jython.org/. [Accessed: 11-Nov-2013].

8.             “PyMite: python-on-a-chip,” 2013. [Online]. Available: http://code.google.com/p/python-on-a-chip/. [Accessed: 11-Nov-2013].

9.             “nxt-python - A pure-python driver/interface/wrapper for the Lego Mindstorms NXT robot. - Google Project Hosting.” [Online]. Available: http://code.google.com/p/nxt-python/. [Accessed: 11-Nov-2013].

10.          J. D. Brock and R. F. Bruce, “Sensing the World with a Raspberry Pi,” J Comput Sci Coll, vol. 30, no. 2, pp. 174–175, Dec. 2014.

11.          S. Greenberg and C. Fitchett, “Phidgets: Easy Development of Physical Interfaces Through Physical Widgets,” in Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, New York, NY, USA, 2001, pp. 209–218.

12.          J. Cappos, I. Beschastnikh, A. Krishnamurthy, and T. Anderson, “Seattle: a platform for educational cloud computing,” in Proceedings of the 40th ACM technical symposium on Computer science education, New York, NY, USA, 2009, pp. 111–115.

13.          “Seattle.” [Online]. Available: https://seattle.poly.edu/html/. [Accessed: 23-Oct-2013].

14.          J. M. Reyes Álamo, M. Benito, and A. Carranza, “Towards An Architecture for Mobile Cloud Robotics,” in IHART, Las Vegas, NV, 2013, vol. 31, pp. 391–398.

15.          N. Noury, G. Virone, P. Barralon, J. Ye, V. Rialle, and J. Demongeot, “New trends in health smart homes,” in Enterprise Networking and Computing in Healthcare Industry, 2003. Healthcom 2003. Proceedings. 5th International Workshop on, 2003, pp. 118–127.


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10.

Authors:

Vikal R. Ingle, V. T. Ingole

Paper Title:

Performance Test of Power Transformer Prior to Maintenance Using DGA and Grey Relational Analysis

Abstract:    The insulation of power transformer i.e. oil and paper decomposition recognized by means of dissolved gas-in-oil analysis (DGA). To detect incipient faults in a transformer, standard key gas method of DGA is employed on the basis of quantity of gases released from the oil. This primary information also reflects the overall condition of a transformer. In this paper, condition assessment of power transformer using relative scaling is discussed. Grey relational analysis is identified as best option for relative scaling, wherein the data of fleet connected transformers is compared and accordingly scales them on the strength of score. Grey relational analysis on key gas sample determines the Target Heart Degrees (THD) of a specific transformer. However, THD represent the average estimation of bull’s eye coefficients, calculated by means of attributes with equal weight condition. Subsections linearity relations are utilized to decide seven intervals for ranking purpose. Linear regression demonstrated on subsection linearity relations for different sets of key gas samples. Result shows the dominance of proposed model in deciding the maintenance priorities.

Keywords:
   DGA, Key gas method, Grey Relational Analysis, Target Heart Degree, Rank Approaching Degree, subsections linearity relation.


References:

1.             Heywood R.J., Emsley A.M., and Ali M. “Degradation of cellulosic insulation in power transformers”. Part I: Factors affecting the measurement of the average visco-metric degree of polymerization of new and aged electrical papers‟, IEE Proc., Sci., Meas. Technol., 2000, 147, (2), pp.86-90
2.             Emsley A.M., Xiao, X., Heywood R.J., and Ali, M.: Degradation of cellulosic insulation in power transformers. Part 2: Formation of furan products in insulating oil‟, IEE Proc. Sci., Meas. Technol., 2000, 147, (3), pp. 110-114

3.             Emsley, A.M., Xiao X., Heywood, R.J., and Ali, M. “Degradation of cellulosic insulation in power transformers. Part 3: Effects of oxygen and water on ageing in oil”, IEE Proc., Sci., Meas. Technol., 2000, 147, (3), pp. 115-120

4.             Emsley A.M., Xiao, X., Heywood R.J., and Ali, M.: and Xiao, X., “Degradation of cellulosic insulation in power transformers. Part 4: Effects of ageing on the tensile strength of paper”, IEE Proc., Sci., Meas. Technol., 2000, 147, (6), pp. 285-290

5.             Hossam A. Nabwy, E. A. Rady, A. M. Kozae, A. N. Ebady, “Fault Diagnosis of Power Transformer Based on Fuzzy Logic, Rough Set theory and Inclusion Degree Theory”, The Online Journal on Power and Energy Engineering(OJPEE) Vol.(1)-No.(2),Reference Number: W09-0011

6.             Arjan van Schijndel, “Power Transformer Reliability Modelling” PhD Thesis, 2010 ,A catalogue record is available from the Eindhoven University of Technology Library ISBN: 978-90-386-2282-8

7.             Technical Manual, Department of the Army TM 5-686“Power Transformer Maintenance and Acceptance Testing”, 16 November 1998

8.             Eilert Bjerkan, “High Frequency Modeling Of Power Transformers Stresses And Diagnostics” PhD Thesis, NTNU Norwegian University of Science and Technology

9.             Wang, M., A.J. Vandermaar, and K.D. Srivastava, “Review of Condition Assessment of Power Transformers In Service”, in IEEE Electrical Insulation Magazine. 2002. p. 12-25.

10.          Dr S R Kannan FIET, “Condition Assessment of Power Transformers Status & Challenges”, Third International Conference on Power systems IIT Kharagpur December 27-29, 2009

11.          Jianpo Li, Xiaojuan Chen, Chunming Wu; Information Engineering College Northeast Dianli University Jilin, China “Power Transformer State Assessment Based on Grey Target Theory” 2009 International Conference on Measuring Technology and Mechatronics Automation, IEEE Computer Society, ICMTMA-2009, pp. 664-667.

12.          IEC 60599, “Mineral Oil-Impregnated Electrical Equipment in service-Guide to the Interpretation of Dissolved and free Gases Analysis”, Edition 2, 1999

13.          “IEEE Guide for the interpretation of gases generated in oil immersed transformers”, IEEE Engg.soc., ANSI/IEEE std.C57.104-1991

14.          A.N.Jahromi, R. Peircy, S. Cress,J.R.R.Service and W.Fan, “An Approach to power Transformer Asset Management using Health Index,”IEEE Electrical Insulation Magazine,Vol.25,No.2, 2009,pp.20-34

15.          Naderian, S. Cress and R. Peircy, “An Approach to determine the Health Index of Power Transformers”, Proc. IEEE Int. sump. Electrical Insulation, Vancouver, Cananday, June2008, pp.192-96

16.          Deng, J.L. “The Primary Methods of Grey System Theory,” Huazhong University of Science and Technology Press, Wuhan (2005).

17.          Lu, M.;Wevers, K., “Application of grey relational analysis for evaluating road traffic safety measures: advanced driver assistance systems against infrastructure redesign”, Intelligent Transport Systems, IET Volume 1,Issue: 1,2007,pp. 3-14

18.          Y.Kuo,T.Yang,and G.W.Huang, “The use of grey  relational analysis in solving multiple attributes decision-making problems”, Computer & Industrial Engineering, Vol.55, 2008, pp80-93

19.          Sikun Yang, School of Electrical Engineering, Beijing Jiaotong University, “Application of Grey Target Theory for Handling Multi-criteria Vague Decision Making Problems”,2008 ISECS International Colloquium on Computing, Communication, Control and Management, 3-4 Aug. 2008, pp 467-471.

20.          Jiang Wei, Liang Jiarong, and Jiang Jianbing, “Multi-Objective Vague decision making based on gray connection analysis”, Computer Engineering and Applications, Vol.43,No.18, 2007, pp. 171-173.

21.          S .F. Liu, Y. Lin, Grey Information Theory and Practical Applications, Springer-Verlag, London, 2006


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11.

Authors:

Vipin Kumar, Anupama Sharma, C.B.Gupta

Paper Title:

A Deterministic Inventory Model For Weibull Deteriorating Items with Selling Price Dependent Demand And Parabolic Time Varying Holding Cost

Abstract:     This paper with development of an inventory model when deterioration rate follows Weibull two way parameter distributions. It is assumed that demand rate is function of selling price and holding cost is parabolic in terms of time. In this models both the cases with shortage and without shortage are taken into consideration. Whenever shortage allowed is completely backlogged. To illustrate the result numerical examples are given .the sensitive analysis for the model has been performed to study the effect of changes the value of parameters associated with the model. Mathematics Subject Classification: - 90B05

Keywords:
    EOQ model, deteriorating items, Weibull distribution, shortage, price dependent demand, parabolic holding cost.


References:

1.          Harris FW (1915) Operations and cost. A. W, Shaw Company, Chicago
2.          Wilson RH (1934) A scientific routine for stock control. Harv Bus Rev 13:116–128

3.          Whitin TM (1957) The theory of inventory management, 2nd edition. Princeton University Press, Princeton

4.          Ghare PM, Schrader GF (1963) A model for an exponentially decaying inventory.

5.          Dave U, Patel LK (1981) (T, Si) policy inventory model for deteriorating items with time proportional demand. Journal of Operational Research Society 32:137–142

6.          Chung KJ, Ting PS (1993) A heuristic for replenishment for deteriorating items with a linear trend in demand. Journal of Operational Research Society 44:1235–1241

7.          Wee HM (1995) A deterministic lot-size inventory model for deteriorating items with shortages and a declining market. Computational Operation research
22:345–356

8.          Abad PL (1996) Optimal pricing and lot-sizing under conditions of perishability and partial backordering. Manage Sci 42:1093–1104 Abad PL (2001) optimal price and order-size for a reseller under partial backlogging. Computational Operation research 28:53–65

9.          Chang HJ, Dye CY (1999) An EOQ model for deteriorating items with time varying demand and partial backlogging Journal of Operational Research Society 50:1176–1182

10.       Goyal SK, Giri BC (2001) Recent trends in modeling of deteriorating inventory. European Journal of Operation Research 134:1–16

11.       Ouyang W, Cheng X (2005) An inventory model for deteriorating items with exponential declining demand and partial backlogging. Yugoslav Journal of Operation Research15 (2):277–288

12.       Alamri AA, Balkhi ZT (2007) The effects of learning and forgetting on the optimal production lot size for deteriorating items with time varying demand and deterioration rates. International Journal of Production Economics 107:125–138

13.       Dye CY, Ouyang LY, Hsieh TP (2007) Deterministic inventory model for deteriorating items with capacity constraint and time-proportional backlogging rate. European Journal of Operation Research 178(3):789–807

14.       Roy A (2008) An inventory model for deteriorating items with price dependent  demand and time varying holding cost. Adv Modeling Opt 10:25–37

15.       Liao JJ (2008) An EOQ model with non-instantaneous receipt and exponentialdeteriorating item under two-level trade credit International Journal of Production Economics 113:852–861

16.       Skouri K, Konstantaras I, Papachristos S, Ganas I (2009) Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate.European Journal of Operation Research 192:79–92

17.       Mandal B (2010) An EOQ inventory model for Weibull distributed deteriorating items under ramp type demand and shortages. Opsearch 47(2):158–165

18.       Mishra VK, Singh LS (2010) Deteriorating inventory model with time dependent demand and partial backlogging. Applied Mathematical Sciences 4(72):3611–3619

19.       Tripathy CK, Mishra U[2010] An inventory model for Weibull deteriorating items with price dependent demand and time varying holding cost.

20.       Hung K-C (2011) An inventory model with generalized type demand, deterioration and backorder rates. European Journal of Operation Research 208(3):239–242

21.       Mishra VK, Singh LS (2011a) Inventory model for ramp type demand, timedependent deteriorating items with salvage value and shortages. International Journal of mathematics and statistics 23(D11):84–91


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12.

Authors:

D. J. Evanjaline, P. Rajakumar, N. Kalpana

Paper Title:

Two Tier Security Enhancement for Wireless Protocol WEP (Wired Equivalent Privacy)

Abstract:  A Wireless Local Area Network (WLAN) is a flexible data communication system implemented as an extension to or as an alternative for a wired Local Area Network (LAN). However, anyone can eavesdrop on information so that WLAN has the hidden security trouble such as leaking of electromagnetic wave or eavesdropping of data because WLAN adopts common electromagnetic wave as media to transmit data. Therefore, the security of WLAN is very important and outstanding. In IEEE 802.11, there are three security technologies used to ensure the data security in WLAN—SSID (Service Set Identifier), MAC (Media Access Control), WEP (Wired Equivalent Privacy). The proposed work falls on the third technology namely the WEP protocol. WEP suffered threats of attacks from hackers owing to certain security shortcomings in the WEP protocol. The proposed schemes implemented in two different layers of WLAN network architecture to strengthen the security of WLAN against the key stream reuse attacks and weak IV attacks.

Keywords:
  WLAN, WEP, Initialization Vector.


References:

1.              Mohamed Juwaini, Raed Alsaqour, Maha Abdelhaq , “A Review On WEP Wireless Security Protocol,” Journal of Theoretical and Applied Information Technology, ISSN: 1992-8645, E-ISSN: 1817-3195 Vol. 40 No.1, 15 June 2012.
2.              Dr.R.N. Rajotiya, Pridhi Arora, “Enhancing Security of WI-FI Network”, International Journal of Computer Applications, ISSN: 2250 – 1797, Issue 2, Vol 3, June 2012.

3.              Arash Habibi Lashkari,Farnaz Towhidi,Raheleh Sadat Hosseini.Wired Equivalent Privacy (WEP). International Conference on Future Computer and Communication, IEEE, 2009.

4.              Lashkari, A.H.;   Mansoor, M.;   Danesh, A.S.; Wired Equivalent Privacy (WEP) versus Wi-Fi Protected Access (WPA). International Conference on Signal
Processing System. IEEE, 2009

5.              Lashkari, A.H., A survey on wireless security  Protocols (WEP, WPA and WPA2/802.11i), Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International  Conference on 8-11 Aug 2009, E-ISBN: 978-1-  4244-3878-5

6.              Sandirigama, M, Security weaknesses of WEP  Protocol IEEE 802.11b and enhancing the   Security with dynamic keys, Science and  Technology for Humanity (TIC-STH),  2009, IEEE Toronto International Conference 

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13.

Authors:

Chandan Tamrakar, Chinmay Chandrakar, Monisha Sharma

Paper Title:

Detection of Rpeak Index and Characterization of QRS Complex of the ECG Signal using Virtual Instruments of Lab VIEW

Abstract:       In the ECG signals P, QRS and T waves play an essential role. Various features of these waves provide significant information to diagnose most of the cardiac diseases after preprocessing of the ECG signal. In various features, RR-interval, QRS Duration, and QRS sample Characteristic are the feature, which reveals significant information about the physiological conditions of the patient. In the previous work to find the RR-interval Discrete Wavelet Transform (DWT) technique and by applying a thresholding to peak detection method has been used. The proposed work is totally digital system based to for detection of consecutive Rpeaks in time domain and in the form of sample index finally the RR-interval has been calculated with the help of Waveform Min Max VI and Search Waveform VI of LabVIEW. In the previous work to detect QRS characteristics LabVIEW mathscript tool and simple moving average filter etc. method has been used. This paper deals with a resourceful composite system which has been proposed for detection of Rpeak Index and QRS Duration. In the proposed work QRS characteristics has been extracted from Extract Portion of the Signal VI of LabVIEW for the standard MIT-BIH arrhythmia database. LabVIEW 2013 version provided by National Instruments has been used here to design the feature extractor

Keywords:  Biomedical Signal, Detrending, Denoising, ECG, Feature extraction, LabVIEW, MIT-BIH arrhythmia database, RR-interval, Wavelet Analysis.


References:

1.              Jiapu Pan and willis j. Tompkins, “A Real-Time QRS Detection Algorithm” IEEE transactions on biomedical engineering, vol. BME-32, no. 3, pp. 230-236, march 1985.
2.              Cuiwei Li, Chongxun Zheng, and Changfeng Tai “Detection of ECG Characteristic Points Using Wavelet Transforms” IEEE Transactions on Biomedical
Engineering, Vol. 42, No. 1, January 1995 pp.21-28

3.              S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348,2005

4.              Szi-Wen Chena, Hsiao-Chen Chena, Hsiao-Lung Chanb “A real-time QRS detection method based on moving averaging incorporating with wavelet denoising” computer methods and programs in biomedicine 82 (2006) 187–195.

5.              Natalia M. Arzeno, Zhi-De Deng, and Chi-Sang Poon, Analysis of First-Derivative Based QRS Detection Algorithms IEEE Transactions on Biomedical Engineering, Vol. 55, No. 2, February 2008, pp.478-484

6.              Channappa Bhyri, Kalpana.V, S.T.Hamde, and L.M.Waghmare “Estimation of ECG features using LabVIEW”, TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 2, NO. 1, pp. 320-324, July 2009

7.              Faezipour, Student, Adnan Saeed, Suma Chandrika Bulusu,Mehrdad Nourani, Hlaing Minn, and Lakshman Tamil, “A Patient-Adaptive Profiling Schemefor ECG Beat Classification”, transactions on information technology in biomedicine, vol. 14, no. 5, pp. 1153-1165, September 2010.

8.              M. K. Islam, A. N. M. M. Haque, G. Tangim, T. Ahammad, and M. R. H. Khondokar, (2012). “Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools”, International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, pp. 404-408 June.

9.              Noack, R. Poll, W.-J. Fischer, S. Zaunseder, “QRS Pattern Recognition Using a Simple Clustering Approach for Continuous Data” 2013 IEEE XXXIII International Scientific Conference Electronics and Nanotechnology (ELNANO), pp. 228-232

10.           Swati Banerjee and M. Mitra, (2013). “ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier”, J Med Eng. Technol, 37(4).pp.264–27

11.           http://www.physionet.org/physiotools/wag/wag.pdf

12.           http://www.physionet.org/cgi-bin/atm/ATM?database=mitdb&tool =plot_waveforms 
 

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14.

Authors:

Kushal Dinkar Badgujar, Woldesemayat Muluneh Lemma

Paper Title:

Adaptive Neuro-Fuzzy Controller for High Temperature Gas Cooled Reactor

Abstract:  A Neuro-Fuzzy controller is applied to control the power of a high temperature pebble bed reactor (HTPBR). A simplified model of the reactor and lumped model of heat transfer is developed and used. Xenon feedback with Xenon and Iodine balance equations and feedback with power coefficient of reactivity are included. The inputs to controller are represented using seven fuzzy sets. The output is obtained as linear combinations of the inputs. Simulations were conducted for the case of reducing the reactor power from rated value at 100% to 20% and for the case of raising reactor power from 20% to 100% linearly. In these simulations, the proposed design for controller exhibits faster and more accurate response than conventional controller.

Keywords:
   ANFIS controller, Fuzzy logic, GEN IV reactors, Reactor power control.


References:

1.             D. Ruan. (1995 Aug.). Fuzzy logic in the nuclear research world. Fuzzy Sets and Systems, 74 (1), pp. 5-13. http://www.sciencedirect.com/science/article/pii/016501149500020L
2.             D. Ruan. (1998 Mar.). Intelligent systems in nuclear applications. Int. J. of Intelligent Systems, 13 (2/3), pp. 115-125.  http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1098- 111X(199802/03)13:2/3%3C115::AID-INT2%3E3.0.CO;2-2/abstract

3.             F. Adda, C. Larbes, M. Allek, M. Loudn, 2005. Design of an intelligent fuzzy logic controller for a nuclear research reactor. Progress in Nuclear Energy, 46 (3/4), pp. 328-347.  http://www.sciencedirect.com/science/article/pii/S0149197005000296

4.             C. Liu, J. Peng, F. Zhao, C. Li. (2009, Nov.).Design and optimization of fuzzy-PID controller for the nuclear reactor power control. Nucl. Eng. Des., 239 (11), pp. 2311–2316.  http://www.sciencedirect.com/science/article/pii/S0029549309003215

5.             P. Tiwari, B. Bandyopadhyay, G. Govindarajan, (1996, Aug.).Spatial control of a large pressurized heavy water reactor. IEEE Trans. Nucl. Sci., 43(4), pp. 2440-2453.  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=531794

6.             H.M. Emara, A. Elsadat, A.Bahgat, and M. Sultan, (2002, May), “Power stabilization of nuclear research reactor via fuzzy controllers”, Proceedings of the American Control. [Online]. Available:  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1025469

7.             P.S. Londhe, B.M. Patre, A.P. Tiwari. (2014, Jul.). Fuzzy-like PD controller for spatial control of advanced heavy water reactor. Nucl. Eng. Des., 274, pp. 77–89. ttp://www.sciencedirect.com/science/article/pii/S0029549314002362#

8.             X.K. Wang, X.H. Yang, G. Liu, H. Qian. (2009, Jul.), “Adaptive neuro-fuzzy inference system pid controller for sg water level of nuclear power plant”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics. [Online]. Available:  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5212517

9.             M. N. Khajavi, M. B. Menhaj, A.A. Suratgar, “Fuzzy adaptive robust optimal controller to increase load following capability of nuclear reactors”, International Conference on Power System Technology PowerCon 2000. [Online]. Available:  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=900041

10.          K.D. Badgujar, O.P. Singh, S. Singh, S. T. Revankar. (2012, Jul.), “Power Coefficient of Reactivity Determination for HTPBR and Its Application for Reactivity Initiated Transients”, Proceedings of the 20th ICONE POWER2012, ICONE20POWER2012-55058. [Online]. Available: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1762248

11.           K. D. Badgujar, S. T. Revankar, J. C. Lee, M. H. Kim. (2012, Oct.) ,    “Design of fuzzy-PID controller for high temperature pebble bed reactor”, Trans. of the Korean Nuclear Society,[Autumn Meeting Gyeongju,Gyeongsang,Korea].[Online].Available:  http://www.kns.org/kns_files/kns/file/30Kushal.pdf

12.          K. D. Badgujar, S. T. Revankar, (2013, Jul.), “Design of    fuzzy-pid controller for hydrogen production using HTPBR”, Proceedings of the 21st ICONE POWER2013, ICONE21-15037. [Online].Available: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1829534

13.          K. D. Badgujar, “Studies on Dynamics of High Temperature Pebble Bed Reactor”, M.S. thesis, NETP, IIT Kanpur, Uttar Pradesh, India (April 2009).

14.          Li, H., Gatland, H., (1996, Oct.).Conventional fuzzy control and its enhancements. IEEE Trans. Syst. Man Cybern, B 26 (5), pp.791–797.        http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=537321


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15.

Authors:

Sujatha K, Gunasekaran M

Paper Title:

Qualitative and Quantitative Approaches in Dynamics of Two Different Prey-Predator Systems

Abstract:  This paper describes the dynamical behavior of two different systems consisting of two preys and a predator. It also deals with the stability of   tri-species community in the systems by means of both qualitative and quantitative approaches. The existence and local stability of the equilibrium points of the systems were analyzed. Harvesting activity in both prey and predator populations plays a significant role in controlling the spread of disease.

Keywords:
    Prey-Predator system, Qualitative stability, Iteration matrix, Quantitative stability, Harvesting Activity.


References:

1.          Ashby .W. R.(1952). Design for a Brain. Chapman  & hall, London. Revised   edition 1960.
2.          C.S. Holling, The functional response of predator to prey density and its role in mimicry  and population regulation  Mem Ent Sec  Can., 45, (1965), 1-60.

3.          Dubey B, Chandra P, Sinha P (2003). A model for fishery resource with reserve area. Nonlinear Anal R world Appl 4,  625-637.

4.          Gardner. M.R and W.R.Ashby.1970. Connectance of large dynamical (cybernetic) systems: Critical values for  stability. Nature 228 : 784.

5.          J.D. Murray, Mathematical Biology I – An Introduction, Springer International Edition,2004.

6.          Lotka  AJ. 1925. Elements of mathematical biology. Williams and Wilkins, Baltimore,USA.

7.          Madhusudanan.V,  Gunasekaran.M,  Vijaya.S. Diseased Prey with Harvesting predator in prey-predator system – An  Analytical study   IOSR Journal of Mathematics, ISSN: 2319-765X. volume 9, Issue 6.

8.          Madhusudanan. V, Vijaya.S, Gunasekaran.M .Imapact of Harvesting inThree Species Food web Model with Two Distinct Functional  Responses. IJIRSET, ISSN: 2319-8753, vol.3, Issue 2.

9.          May.R.M ,Leonard W (1975) Nonlinear aspects of competition between three species. SIAM  J Appl Math 29.   243-253.

10.       Md.Sabir Rahman, Santabrata Chakravarty. A predator-prey model with disease in prey. Nonlinear  Analysis:Modelling and control,2013, vol.18,No.2,191-209.

11.       Odum.E.P. 1953. Fundamentals of Ecology. W.B.Saunders, Philadelphia-London.

12.       Port.R.F.and Gelder, T.Van(eds),1995.Mind as Motion: Explorations in the Dynamics of Cognition.MIT  Press,Cambridge,Mass.

13.       Quirk.J.P. and R.Ruppert, 1965. Qualitative economic and the stability of equilibrium. Rev.Econ stud.32 : 311-326.

14.       R.M.May., (1983) Ecology: The structure of food webs, Nature (London) 301, 566-568.

15.       R.M.May.(1973) Qualitative stability in Model Ecosystems, Ecology 54, 638-641.

16.       S.A.Wuhaib, Y.Abu Hasan. A predator-infected prey model with harvesting of infected prey. Science Asia 39S(2013): 37-41.

17.       T.K.Kar and Ashim Batabyal. Persistence and stability of a two prey one predator system. International Journal of  Engineering Science and Technology. Vol.2,No.2,2010, 174-190.

18.       T.K.Kar, H.Matsuda,2007. Global dynamics and controllability of a harvested prey-predator system with Holling type - III functional  response, Non-linear Anal: Hybrid system1, 59-67.

19.       Voltera V. 1962 Opere matematiche mmemorie e note, Vol V. Roma(cremon): Acc.Naz.dei Lincei, Italy.

20.       Zhang X, Chen L, Neumann A (2000). The stage structured predator – prey model and optimal harvesting policy. Math Biosci 168, 201-210.


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16.

Authors:

Ratul Chakraborty

Paper Title:

Web Browser Based Statistical Software - The Next Generation of Statistical Computing

Abstract:     There are essentially two ways to deliver an application on PC/Laptop/tablet/smartphone: as a client-side/native application (developed using the appropriate platform-dependent development kit and installed on user devices) or as a web application (developed using web standards and accessed through a web browser-there's nothing to install on user devices). Traditionally, we are familiar with the native applications. But the recent trend shows that, in near future web applications will become more competitive with native applications due to the ubiquity of web browsers and platform independent programming features. HTML5, Java Scrip and WebGL will bring a new level of computing to the web. At present we have a bunch of native Statistical Computing applications (for PCs and Laptops only) but there is a scarcity of good web application of such type which can run on any computing device (from PC to smartphone) without any hazard.

Keywords:
 Native applications, Web applications, Graphical User Interfaces, Programming Language, Statistical Software.


References:

1.           http://caniuse.com/
2.           http://statcounter.com/

3.           http://clicky.com/

4.           http://w3counter.com/

5.           http://wikimedia.org/

6.           http://statpages.org/

7.           http://statcrunch.com/

8.           http://www.math.montana.edu/Rweb/

9.           http://www.socr.ucla.edu/

10.        http://jstat.github.io/

11.        http://www.openepi.com/

12.        http://www.quantitativeskills.com/sisa/


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17.

Authors:

M. S. Chennakesava Rao, N. V. N. Prabath

Paper Title:

Green Concrete using Agro Industrial Waste(Sugarcane Bagasse ASH)

Abstract:      Today researches all over the world are focusing on ways of utilizing either industrial or agricultural wastes as a source of raw materials for the construction industry. These wastes utilization would not only be economical, but may also help to create a sustainable and pollution free environment. The utilization of industrial and agricultural waste produced by industrial processes has been the focus of waste reduction research for economic, environmental and technical reasons. Sugar-cane bagasse is a fibrous waste-product of the sugar refining industry, along with ethanol vapor. This waste product (Sugar-cane Bagasse Ash) is already causing serious environmental pollution, which calls for urgent ways of handling the waste. Bagasse has mainly contained silica and aluminum ion. In this project, the Bagasse ash has been chemically and physically characterized, and partially replaced in the ratio of 0%, 5%, 10%, 15% and 25% by the weight of cement in concrete. The bagasse ash was then ground until the particles passing the 90 μm sieve size reach about 85% and the specific surface area about 4716 cm2/gm.Ordinary Portland cement was replaced by ground bagasse ash at different percentage ratios. The compressive strengths of different mortars with bagasse ash addition were also investigated. M25 concrete mixes with bagasse ash replacements of 0%, 5%, 10%, 15%, 20% and 25% of the Ordinary Portland cement were prepared with water-cement ratio of 0.42 and cement content of 378 kg/m3 for the control mix. I will test fresh concrete tests like slump cone test where under taken as well as hardened concrete test like compressive strength, split tensile strength, flexural strength at the age of 7days, 28 days and 90 days was obtained. The test results indicated that up to 10% replacement of cement by bagasse ash results in better or similar concrete properties and further environmental and economic advantages can also be exploited by using bagasse ash as a partial cement replacement material ..

Keywords:
 Baggase ash, Fibrous waste product.


References:

1.           , K., Rajagopal, K., &Thangavel K., 2007. “Evaluation of Bagasse Ash as Supplementary Cementitious Material”, Journal of Cement and Concrete Composites
2.           R. Srinivasan, K Sathiya, 2010. “Experimental study on bagasse ash in concrete”, International Journal of Service Learning in Engineering 5(2), p. 60.

3.           Payá,J.,et. al., 2002. Sugarcane bagasse ash (SCBA): “Studies on its properties for reusing in concrete production”, Journal of Chemical technology and Biotechnology 77, p.. 321.

4.           N. B. Singh, V. D. Singh and SaritaRai, 2000. Hydration of Bagasse Ash-Blended Portland cement, Journal of Cement and Concrete Research 30, p. 1485.

5.           SumrerngRukzon, PrinyaChindaprasirt, 2012. Utilization of Bagasse Ash in High Strength Concrete, Journal of Materials and Design 34, p. 45.

6.           V. S. Aigbodion, S. B. Hassan, T. Ause and G.B. Nyior, 2010. Potential Utilization of Solid Waste (Bagasse Ash), Journal of Minerals & Materials Characterization & Engineering 9, p.67-77.

7.           Ganesan, K., Rajagopal, K., &Thangavel, K. 2007. “Evaluation of bagasse ash as supplementary cementitious material”. Cement and Concrete Composites, 29, 515-524.

8.           Committee Board of sugar cane and sugar (2004). Summary of sugar cane and sugar industry in Thailand in 2003/2004, Division of sugar cane and sugar
industry Policy, Ministry of Industry, Vol.2 Bangkok Thailand (in Thai).

9.           Baguant,K., Properties of concrete with bagasse ash as fine aggregate, In Proc 5th CANMET/ACI Intl. conf. on fly ash, silica fume, slag and natural pozzolans in concrete, Ed by Malhotra VM, USA, ACI SP, (1995)153(18), 315-337.

10.        Payá,J.,et. al.,Sugarcane bagasse ash (SCBA): studies on its properties for reusing in concrete production,  Journal of Chemical technology and Biotechnology, (2002)77, 321-325. 6. IS 383 -1970 “Specifications for Coarse and Fine Aggregates from Natural Sources for Concrete”,   Bureau of Indian Standards, New Delhi.

11.        IS 10262 -1981 “IS Method of Mix Design”, Bureau of Indian Standards, New Delhi

12.        IS 516 -1959 “Methods of Tests for strength of concrete”, Bureau of Indian Standards, New Delhi

13.        IS 456 -2000 “Code of Practice for Plain and Reinforced Concrete”, Bureau of Indian Standards, New Delhi.

14.        Ali I (2004) Biomass: An ideal fuel for sugar mills for steam/power generation. Fuel Research Centre, PCSIR, Karachi, XVII(197), Dec

15.        Wang R, Trettin V, Rudert R (2003) Umlauf recrystallization of granulated blast furnace slag and the significance for the hydraulic Reactivity. Institute for Building and Material Chemistry, Siegen University, Wilhelm Dyckerhoff Institute for Building Material Technology. Advances in Cement Research 15:29–33.

16.        Bhatty JI, Gajda J, Miller FM (2001) Use of high-carbon fly ash in cement manufacture. Cement Americas May/June 32–34, 1, 2001.


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Authors:

Narinder Singh Rana, S. N. Panda

Paper Title:

Spectrum of Cyber Threats and Attack Trends in Indian Scenario

Abstract:    With the growth of Internet in the country the dependence of the Indian economy on ICT (Information and Communication Technology) has increased tremendously in last couple of decades, and corresponding has been growth of cyber incidents in the country. In the wake of increasing cyber incidents in India, Indian Computer Emergency Response Team (CERT-In) was constituted by government of India in 2004. In this paper the authors have studied the scope and scale of cyber incidents happening in the country. Website defacement being the most visible part of a cyber incident, have been used to study the trend of cyber attacks in India. Analysis has also been done regarding the various types of domain that have been attacked and the motivation behind these attacks, other common attacks and their growth trends have also been studied with the help of CERT-In data.

Keywords:
  CERT-In, Cyber Incident, Security, Website Defacement.


References:

1.             Kamluk V, “The Botnet Business”, Securelist (May 13, 2008),
2.             https://www.securelist.com/en/analysis/204792003/The_botnet_business?print_mode=1

3.             Srijith K. N, “Analysis of Defacement of Indian Web Sites”, First Monday Journal, 7(12)

4.             Cyber Crime & Security Survey Report 2013, CERT-Australia

5.             Indian Computer Emergency Response Team CERT-In, Annual reports 2006-13

6.             Baker Y S, Bhattacharya S, “Analyzing security threats as reported by the United States Computer Emergency Readiness Team (US-CERT)”, IEEE conference on Intelligence and Security informatics, 2013, pp 10-12

7.             Arce I, “More bang for the bug: An account of 2003's attack trends”, IEEE Security & Privacy, 2004,  2(1), pp 66-68

8.             Stiawan D ,  Idris Y, AbdullahA H, “Attack and Vulnerability Penetration Testing: FreeBSD”, TELKOMNIKA Telecommunication, Computing, Electronics and Control, 11(2)

9.             Ransbotham S, Mitra S, “The Impact of Immediate Disclosure on Attack Diffusion and Volume”, Economics of Information Security and Privacy, 2013, pp 1-12

10.          Common cyber attacks: reducing the impact – CERT UK, Director GCHQ, http://goo.gl/2RaCGD

11.          Eeten M V, Bauer J M,  “The Role of Internet Service Providers in Botnet Mitigation An Empirical Analysis Based on Spam Data”, Social Science Research Network, 2011.


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Authors:

R. R. Nanayakkara, Y. P. R. D. Yapa, P. B. Hevawithana, P. Wijekoon

Paper Title:

Automatic Breast Boundary Segmentation of Mammograms

Abstract:     Accurate breast boundary estimation and segmentation of breast tissue region from the background of the mammogram image is an important pre-processing task in computer-aided diagnosis of breast cancer. This paper presents an automated system to estimate skin-line and breast segmentation. The proposed method is based on automatic seed region selection, modified fast marching algorithm to propagate the seed region and automatic boundary point selection with intensity gradient information to initial boundary estimation and morphological operators to final boundary estimation and breast tissue region segmentation. Performance of the proposed method was tested by using 136 mammogram images with all types of breast tissues taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that the sensitivity of this algorithm is 99.2% of the ground truth breast region and accuracy of the segmentation is 99.0%. By analyzing the results we can conclude that this system is capable of estimate the breast boundary and segment the breast area from background for all three types of breast tissues with high accuracy level.

Keywords:
 Breast Cancer, Mathematical Morphology, Modified Fast Marching Algorithm


References:

1.           J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, F. Bray, GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 11 [Internet].Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr, accessed on 11/2/2015.
2.           R. Bird, Professional quality assurance for mammography screening programs. Radiology. 1990;175(2):587-605. Pub Med PMID: 2217807. DOI:  10.1148/radiology.177.2.2217807.

3.           R.D. Yapa, K. Harada, Breast skin-line estimation and breast segmentation in mammograms using fast-marching method. International Journal of Biological, Biomedical and Medical Sciences. 2008;3(1):54-62.

4.           M.A. Wirth, J. Lyon, D. Nikitenko, A. Stapinski, editors. Removing radiopaque artifacts from mammograms using area morphology. Image Processing Proc SPIE 2004: Proceedings of SPIE Medical Imaging; 2004; San Diego, California, USA: SPIE; 2004. p. 1054–65. DOI: 10.1117/12.535372.

5.           P. Salembier, J. Serra, Flat zones filtering, connected operators, and filters by reconstruction. Image Processing, IEEE transactions on. 1995;4(8):1153-60. DOI: 10.1109/83.403422.    

6.           J. Nagi, S. Abdul Kareem, F. Nagi, S. K. Ahmed, editors. Automated breast profile segmentation for ROI detection using digital mammograms. Biomedical
Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on; 2010. IEEE. DOI: 10.1109/iecbes.2010.5742205.

7.           B. Eli, Eli Billauer's Freelancing Electrical Engineer. Israel: Eli Billauer;  [20 July 2012; cited 2013 03]; Available from: http://billauer.co.il/peakdet.html.

8.           T. Ojala, J. Näppi, O. Nevalainen. Accurate segmentation of the breast region from digitized mammograms. Computerized Medical Imaging and Graphics. 2001;25(1):47-59. DOI: 10.1016/s0895-6111(00)00036-7.

9.           T-K. Lau, W. F. Bischof. Automated detection of breast tumors using the asymmetry approach. Computers and biomedical research. 1991;24(3):273-95. DOI: 10.1016/0010-4809(91)90049-3.

10.        U. Bick, M. L. Giger, R. A. Schmidt, R. M. Nishikawa, D. E. Wolverton, K. Doi. Automated segmentation of digitized mammograms. Academic Radiology. 1995;2(1):1-9. PubMed PMID: 9419517. DOI: 10.1016/s1076-6332(05)80239-9.

11.        J. J. Heine, M. Kallergi, S. M. Chetelat, L. P. Clarke. Multi resolution wavelet approach for separating the breast region from the background in high resolution digital mammography.  Digital Mammography: Springer; 1998. p. 295-8. DOI: 10.1007/978-94-011-5318-8_49.

12.        R. Chandrasekhar, Y. Attikiouzel, editors. Gross segmentation of mammograms using a polynomial model. Engineering in Medicine and Biology Society, 1996 Bridging Disciplines for Biomedicine Proceedings of the 18th Annual International Conference of the IEEE; 1996. IEEE. DOI: 10.1109/iembs.1996.652707.

13.        M. M. Goodsitt, H-P. Chan, B. Liu, S. V. Guru, A. R. Morton, S. Keshavmurthy, et al. Classification of compressed breast shapes for the design of equalization filters in x-ray mammography. Medical Physics. 1998;25(6):937-48. PubMed PMID: 9650184. DOI: 10.1118/1.598272.

14.        C. Zhou, H-P. Chan, N. Petrick, M. A. Helvie, M. M. Goodsitt, B. Sahiner, et al. Computerized image analysis: Estimation of breast density on mammograms. Medical physics. 2001;28(6):1056-69. PubMed PMID: 11439475. DOI: 10.1118/1.1376640.

15.        M. Abdel-Mottaleb, C. S. Carman, C. R. Hill, S. Vafai,Skinline detection in digitized mammograms. Journal of digital imaging. 1997;10:224-5. PubMed Central PMCID: PMC3452812.DOI: 10.1007/BF03168707.            

16.        Morton, H. Chan, M. Goodsitt. Automated model-guided breast segmentation algorithm. Med Phys. 1996;23:1107-8.

17.        N. Karssemeijer, G. Te Brake. Combining single view features and asymmetry for detection of mass lesions.  Digital Mammography: Springer; 1998. p. 95-102. DOI: 10.1007/978-94-011-5318-8_16.

18.        R. Ferrari, R. Rangayyan, J. Desautels, A. Frere, Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc. Proceedings of the 5th international workshop on digital Mammography. 2000:573-9. PubMed PMID: 15125. DOI: 10.1148/radiol.2291032535.

19.        S. Thiruvenkadam, M. Acharyya, N. Neeba, P. Jhunjhunwala, S. Ranjan, editors. A region-based active contour method for extraction of breast skin-line in mammograms. Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 2010. IEEE. DOI: 10.1109/isbi.2010.5490383.

20.        M. A. Wirth, A. Stapinski, editors. Segmentation of the breast region in mammograms using snakes. Computer and Robot Vision, 2004 Proceedings First Canadian Conference on; 2004. IEEE. DOI: 10.1109/cccrv.2004.1301473.

21.        R. Martí, A. Oliver, D. Raba, J. Freixenet, Breast skin-line segmentation using contour growing.  Pattern Recognition and Image Analysis: Springer; 2007. p. 564-71. DOI: 10.1007/978-3-540-72849-8_71.

22.        P. K. Saha, J. K. Udupa, E. F. Conant, D. P. Chakraborty, D.Sullivan, Breast tissue density quantification via digitized mammograms. Medical Imaging, IEEE Transactions on. 2001;20(8):792-803. PubMed PMID: 11513030. DOI: 10.1109/42.938247.

23.        H. E. Rickard, G. D. Tourassi, A. S. Elmaghraby, editors. Self-organizing maps for masking mammography images.Information Technology Applications in Biomedicine, 2003 4th International IEEE EMBS Special Topic Conference on; 2003. IEEE. DOI: 10.1109/itab.2003.1222538.

24.        M. Wirth, J. Lyon, D. Nikitenko, editors. A fuzzy approach to segmenting the breast region in mammograms. Fuzzy Information, 2004 Processing NAFIPS'04 IEEE Annual Meeting of the; 2004. IEEE. DOI: 10.1109/nafips.2004.1336329.

25.        J. M. Gauch. Image segmentation and analysis via multiscale gradient watershed hierarchies. Image Processing, IEEE Transactions on. 1999;8(1):69-79. DOI: 10.1109/83.736688.

26.        W. Wirth, D. Nikitenko, J. Lyon, Segmentation of Breast Region in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm. International Journal on Graphics, Vision and Image Processing. 2005;5(2):45-54.

27.        J.Suckling, The mini-MIAS database of mammograms. In: Society TMIA, editor. Digital Mammography Database ver 1.2.ed.

28.        P. C. Johns, M. J. Yaffe. X-ray characterisation of normal and neoplastic breast tissues.Physics in medicine and biology. 1987;32(6):675. DOI: 10.1088/0031-9155/32/6/002.

29.        J. Méndez, P. G. Tahoces, M. J. Lado, M. Souto, J. Correa, J. J. Vidal. Automatic detection of breast border and nipple in digital mammograms. Computer methods and programs in biomedicine. 1996;49(3):253-62. DOI: 10.1016/0169-2607(96)01724-5.

30.        R. Chandrasekhar, Y. Attikiouzel, editors. Automatic breast border segmentation by background modeling and subtraction. 5th International Workshop on Digital Mammography (IWDM),(Yaffe M ed), Medical Physics Publishing, Madison, USA; 2000.

31.        D. Raba, A. Oliver, J. Martí, M. Peracaula, J.Espunya, Breast segmentation with pectoral muscle suppression on digital mammograms.  Pattern Recognition and Image Analysis: Springer; 2005. p. 471-8. DOI: 10.1007/11492542_58.

32.        S. Dehghani, M.Dezfooli, A method for improve preprocessing images mammography. International Journal of Information and Education Technology. 2011;1(1):90-3. DOI: 10.7763/ijiet.2011.v1.15.


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Authors:

S. M. Rajbhoj, P. B. Mane

Paper Title:

An Approach of Combining Iris and Fingerprint Biometric At Image Level in Multimodal Biometrics System

Abstract: Biometric systems depending on single source of information has many limitations. These are noisy input data, inability to enroll, unacceptable error rates, universality of traits and spoofing. Multimodal biometric system overcomes these limitations by combining information from multiple sensors.  In Image fusion usually images are extracted from single trait using different sensors. This type of fusion is generally used when feature set are homogenous. In this paper a multibiometric system using image level fusion of two most used biometric traits, fingerprint and iris is proposed. The feature set obtained from iris and fingerprint images are incompatible, non-homogenous and relationship between them is not known. Here the pixel information is fused at image or feature level. A unique feature vector is constructed from the textural information of fused image of fingerprint and iris. Feature vector is stored as template and used for matching. Matching is carried using Hamming distance. The proposed framework is evaluated using standard database and database created by us. The system overcomes limitation of unimodal biometric system and equal error rate of 0.4573 has been achieved.

Keywords:
  biometric, fingerprint, iris; wavelet transform, texture, feature level, fusion, hamming distance.


References:

1.              K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, pp. 4–20, Jan 2004.
2.              Ross and A. K. Jain, “Information fusion in biometrics,”Pattern Recognition Letters, vol. 24, pp. 2115–2125, Sep 2003.

3.              K. Jain and A. Ross, Multibiometric Systems. Communications of the ACM, Special Issue on Multimodal Interfaces, 47(1):34–40, January 2004.

4.              Prabhakar, S. and Jain, A. K. “Decision-level Fusion in Fingerprint Verification.”, Pattern Recognition, 35(4):861-874. (2002).

5.              Bhatnagar, J., Kumar, A., Saggar, N.: ‘‘A novel approach to  improve biometric recognition using rank level fusion,’’ Proc.CVPR 2007, Minneapolis, MN, pp. 1–6, (2007).

6.              L. Hong and A. K. Jain, “Integrating faces and fingerprints for personal identification,” IEEE Transactions on PAMI, vol. 20, pp. 1295–1307, Dec 1998. 

7.              Kumar, D. C. M. Wang, H. C. Shen, and A. K. Jain, “Personal verification using palmprint and hand geometry biometric,” in Proc. of 4th Int’l Conf. on Audio and Video-based Biometric Person Authentication (AVBPA), (Guildford, UK), pp. 668–678, Jun 2003.

8.              Fierrez-Aguilar, J., Nanni, L., Lopez-Penalba, J., Ortega-Garcia, J., and Maltoni, D. An On-line Signature Verification System based on Fusion of Local and Global Information. In Fifth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Rye Brook, USA pages 523-532. (2005).

9.              Y. Wang, T. Tan, and A. K. Jain. “Combining Face and Iris Biometrics for Identity Verification.”, In Fourth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Guildford, UK, pages 805-813,  June 2003

10.           Ross, A. K. Jain, and J. Reisman, “A hybrid fingerprint matcher,” Pattern Recognition, vol. 36, pp. 1661–1673, Jul 2003

11.           X. Lu, Y. Wang, and A. K. Jain, “Combining classifiers for face recognition,” in Proc. IEEE Int’l Conf. on Multimedia and Expo (ICME), vol. 3, (Baltimore, MD), pp. 13–16, Jul 2003

12.           G. L. Marcialis and F. Roli, “Fingerprint verification by fusion of optical and capacitive sensors,” Pattern Recognition Letters, vol. 20, pp 1315-1322, Aug 2004.

13.           K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proc. Of Workshop on Multimodal User Authentication, (Santa Barbara, CA), pp. 25–32, Dec 2003

14.           Ross, A. and Govindarajan, R. “Feature Level Fusion Using Hand and Face Biometrics.” In Proceedings of SPIE Conference on Biometric Technology for Human Identification II, volume 5779, pages 196-204, Orlando, USA., (2005)

15.           Asim Baig, Ahmed Bouridane, Fattih K., Gang Qu, “Fingerprint-Iris Fusion based Identification System using a Single Hamming Distance Matcher.”,International Journal of Bio-Science and Bio-Technology, Vol 1, No. 1, Dec 2009.

16.           V. Conte,C. Militello, F Sorbello, “A Frequency based approach for Feature Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems”, IEEE Transactions of System, Man and Cybernetics, vol-40, No.4, July2010. 

17.           Rossani F., Eslava M.T., Ea T., Aml F., Amara A., “Iris Identification and robustness evaluation of wavelet packets based algorithm”, IEEE International Conference on image processing, vol.3, pp. III -257-260

18.           J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1993, 1148–1161  

19.           Libor Masek, "Recognition of Human Iris Patterns for Biometric Identification", Thesis Report School of Computer Science and Software Engineering, Western Australia, 2003

20.           W. W. Boles, & B. Boashah, A Human Identification Technique Using Images of the Iris and Wavelet Transform, IEEE Transaction on Signal Processing, 46(4), 1998, 1185-1188 

21.           Digital Persona homepage, http://www.digitalpersona.com

22.           Center for Biometrics and security Research.   http://www.cbsr.ia.ac.cn/IrisDatabase.htm.


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Authors:

D.V. Biradar, Praful P. Maktedar

Paper Title:

Performance Exploration of QoS parameters in MANET

Abstract:     Nowadays there are large applications expanding for the reliable transport of data packets from source node to the sink node amongst them Mobile Ad hoc network (MANET) has very vast extent of study.  Different mobile sensor nodes are arbitrarily positioned in given network without having much loss of data packets in the network. It encompasses number of sensor nodes having inadequate processing power, communicating over a network. These sensors are scattered in specified network environment so that they gathers data, process that data and send it back to the destination. Various factors are affecting on data transmission process like reporting rate, packet size. Here by changing reporting rate, we calculate packet Delivery Ratio, Packet loss Ratio as well as throughput, control overheads and Energy consumption of a system.  

Keywords:
     Mobile Ad hoc Network; Reliability; Reporting Rate; Packet Delivery ratio; Congestion Control.


References:

1.              Lajos Hanzo, Rahim Tafazolli,”QoS aware Routing and Admission Control in Shadow-Fading Environments for Multirate MANETs” in IEEE journal, vol. 10, No. 5, May 2011.
2.              Mahmoud Al-Shugran, Osman Ghazali, Suhaidi Hassan, Kashif Nisar, and A. Sukhi, M. Arif,”A qualitative Comparison Evaluation of the Greedy forwarding Strategies in Mobile Ad hoc Network”, vol. 36, pp. 887-897, Nov. 2012.

3.              X. Xiang, X. Wang, Y. yang,”Supporting Efficient and Scalable Multicast for Mobile andAd hoc Networks”, vol. 10, No 5, April 2011.

4.              Richard j. La and Eunyoung Seo,” Expected Routing Overheads for location service in MANET under flat geographic routing”, vol. 10, issue 3, March 2011.

5.              Seungjin Park, Seong-Moo Yoo”An Efficient Reliable one- hop broadcast in Mobile Ad hoc  Networks”, vol. 11, pp 19-28, April 2012.

6.              Shengbo Yang, Chai Kiat Yeo, Bu Sung Lee ,“Towards Reliable data                         delivery for highly Dynamic Mobile Ad hoc Networks” Journal IEEE transactions on parallel and Distributed Syatems, vol. 22, issues 12, pp 2100-2107, Dec. 2011.

7.              D.G. Reina, S.L. Toral, P. Johnson and F. Barreiro,”A Reliable Route Selection based on Caution Zone and Arrival Angle”, IEEE communication letters, vol. 15, issues 11, pp. 1252-1255, Nov. 2011.

8.              Xiaoqin Chen, Haley M. Jones and Dhammika Jayalath,” Channel aware Routing in MANET with route Handoff”, vol. 10, issues 1, Jan 2011.

9.              Robert J.Hall,” An Improved Geocast for Mobile Ad hoc Networks”, IEEE communication letters, vol. 10, No. 2, Feb. 2011.

10.           Zhan Bo Su, Yuan Ming Wu,”Prediction based Event to Sink Reliability in Wireless Sensor Network”, vol. 1, pp. 4244-4249, 2009.

11.           Zhenzhen Ye ,AlhussionA.Abouzeid,” Optimal Stochastic Location updates in mobile Ad Hoc Networks”, vol. 10, No. 5, May 2011.


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22.

Authors:

V.S. Malunjkar, M.G. Shinde, R.D. Bansod, A.A. Atre

Paper Title:

Development of a Soft Tool for Estimating Direct Runoff From Watersheds

Abstract: Natural Resources Conservation Services-Curve Number (NRCS-CN) model is the most commonly used hydrological model for runoff estimation.  This paper introduces about the interface developed to estimate curve number and runoff depth for hydrologic evaluations. The programming syntax was developed in Visual Basic 10.0 for its simplicity. The developed tool is easy to handle and can be useful for academicians, scientists and decision makes involved in watershed planning and development.

Keywords:
 Antecedent moisture condition, curve number NRCS-CN method, runoff, watershed.


References:

1.             M. J. Mack, “HER-Hydrologic evaluation of runoff; The Soil Conservation Service Curve Number technique as an interactive computer model”. Comput. Geosci. 1995, 21, p. 929-935.
2.             S. Shadeed and M. Almasri, “Application of GIS based SCS-CN method in West bank catchments, Palestine.” WSE-3, 2010, p. 1-13.

3.             USDA, “Urban hydrology for small watersheds”. United States Department of Agriculture. Natural Resources Conservation Service. Conservation Engineering Division. Tech. Release 55. 2nd ed. Washington, DC. 1986. P. 164.

4.             USDA, “Estimation of direct runoff from storm rainfall”. Part 630. Hydrology National Engineering Handbook. Ch. 10. United States Department of Agriculture. Natural Resources Conservation Service. Washington, DC. 2004. P. 22.

5.             S. K. Mishra and V. P. Singh. “Long-term hydrological simulation based on the Soil Conservation Service curve number”. Hydrol. Process. 2004. 18. p. 1291-1313.

6.             S. K. Mishra and V. P. Singh. “A relook at NEH-4 curve number data and antecedent moisture condition criteria”. Hydrol. Process. 2006. 20. p. 2755-2768.

7.             S. K. Mishra, M. K. Jain, R. P. Pandey and V. P. Singh. “Catchment area-based evaluation of the AMC-dependent SCS-CN-based rainfall-runoff models”. Hydrol. Process. 2005. 19. p. 2701-2718.

8.             N. W. Kim and J. Lee. “Temporally weighted average curve number method for daily runoff simulation” Hydrol. Process. 2008. 22. p. 4936-4948.

9.             R. K. Sahu, S. K. Mishra and T. I. Eldoh. “An improved AMC-coupled runoff curve number model”. Hydrol. Process. 2010. 24. p. 2834-2839.

10.          H. Cao, R. W. Vervoort, and S. M. Dabney. “Variation in curve numbers derived from plot runoff data for New South Wales (Australia)”. Hydrol. Process. 2011. 25. p. 3774-3789.

11.          V. M. Ponce and R. H. Hawkins. “Runoff curve number: has it reached maturity?” J. Hydrol. Eng. 1996. 1. p. 11-19.

12.          J. P. Patil, A. Sarangi, O. P. Singh, A. K. Singh and T. Ahmed. “Development of a GIS interface for estimation of runoff from


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Authors:

Salem S. M. Khalifa, Kamarudin Saadan, Norita Md Norwawi

Paper Title:

Development of Framework for Wireless Intelligent Landmines Tracking System Based on Fuzzy Logic

Abstract:       The losses of developing countries from landmines accidents are very large. Thus, the need for new techniques to improve the efficiency of Landmines tracking systems is evident.  In the recent years, many of research efforts have been directed to develop new and improved landmine detection methods. However, the increased costs of improving these methods led to drive up their prices. Thus they will not be available to the general public. The aim of this paper is to find a cheap and an effective method to help people for protecting and warning them from landmines risk during practiced their daily lives. In this context, this paper presents the design and development of framework for a Wireless Intelligent Landmines Tracking System (IWLTS) using mobile phone based on GPS and fuzzy logic. Proposed framework is really very helpful for the users who living near mine affected areas to track their children and themselves through Smart phones from landmines risk.

Keywords:
  Landmines, Fuzzy logic, Fuzzy set, MATLAB.


References:

1.                BBCNEWS. “South Africa destroys landmine stocks,”1997. http://news.bbc.co.uk/2/hi/19940.stm
2.                V. E. Stephen. “THE ORIGINS OF THE FIRST WORLD WAR,” unpublished.

3.                Handicap International. “Handicap International prepares to launch demining actions in Tripoli,” 2011. http://www.handicap-international.org.uk/resources/latest_news/emergencies/20111021_demining_tripoli

4.                UNICEF. “Children and Landmines: A Deadly Legacy,” 2012. http://www.unicef.org/spanish/protection/files/Landmines_Factsheet_04_LTR_HD.pdf

5.                M, Habib. “Humanitarian demining: The problem, difficulties, priorities, demining technology and the challenge for robotics,” Humanitarian Demining Innovative Solutions and the Challenges of Technology, M. Habib, Ed. I-Tech Education and Publishing, Croatia .2008. pp 1-56.

6.                GAO “Landmine Detection, DoD’s research program need a comprehensive evaluation strategy,” United States General Accounting Office, Apr 2001.

7.                F. Abujarad. "Ground penetrating radar signal processing for landmine detection," PhD diss., Otto-von-Guericke-Universität Magdeburg,
Universitätsbibliothek, 2007.

8.                S. Photiou. “CBRNE-Terrorism Newsletter–2012©.”

9.                J Belezina. “SAPER app turns a smart phone into a mobile bomb sniffer Gizmag,” 2012.  Available on http://www.gizmag.com/saper-explosives-detector-app/22614/

10.             D. Guelle, A. Smith, A., Lewis and T. Bloodworth “Metal detector handbook for humanitarian demining,” European Communities. 2003. ISBN 92-894-6236-1.

11.             K. Park, S. Park, K. Kim, and K. H. Ko, “MULTI-FEATURE BASED DETECTION OF LANDMINES USING GROUND PENETRATING RADAR,” Progress In Electromagnetics Research 134. 2013.

12.             M. K. Habib. “Mechanical Mine Clearance Technologies and Humanitarian Demining: Applicability and Effectiveness,” In Proceedings of the 5th International Symposium on Technology and Mine Problem, California, USA. 2002.

13.             J. MacDonald. Alternatives for landmine detection. No. 1608. RAND Corporation, 2003.

14.             IRIN. “Laying Landmines to Rest? Report, IRIN Web Special on Humanitarian Mine Action, (with special focus on the 2004 Nairobi Summit of a Mine Free World),” 2004.

15.             Weilenmann , and J. Oskar. "Understanding people and animals: the use of a positioning system in ordinary human-canine interaction." InProceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2631-2640. ACM, 2011.

16.             J., G. F. Weetjens, B. K. Mgode, C. C. Davis, and N. W. Beyene. "African giant rats for tuberculosis detection: A novel diagnostic technology." In Global forum update on research for health, vol. 6, pp. 39-42. 2009.

17.             Poling, B. J. Weetjens, C. Cox, N. Beyene, H. Bach, and A. Sully.  "Teaching giant African pouched rats to find landmines: Operant conditioning with real consequences," Behavior analysis in practice 3, no. 2 (2010): 19.

18.             Poling, B. J. Weetjens, C. Cox, N. Beyene A. Durgin and A. Mahoney. "Tuberculosis detection by giant African pouched rats."The Behavior Analyst 34, no. 1. 2011: 47.

19.             Poling, et al. "Two strategies for landmine detection by giant pouched rats."The Journal of ERW and Mine Action 14 .2010.

20.             R. L. Woodfin, ed. Trace chemical sensing of explosives. John Wiley & Sons, 2006. . ISBN-10: 0-471-73839-5.

21.             HCR “HUMANITARIAN DEMINING 2012,” The 9th International Symposium. The Croatian Mine Action Center for testing, development and training. Zagreb. 2012. http://www.ctro.hr/eng/

22.             Anuar, S. Yussof, I, Said  and J. T. T. Chuan.  “The Development Of An Autonomous Personal Mobile Robot System For Land Mines Detection On Uneven Terrain: An Experience,” Advanced Technology Congress, Putrajaya, Malaysia.2003.

23.             H. Najjaran and A. A. Goldenberg. . "Landmine detection using an autonomous terrain-scanning robot." Industrial Robot: An International Journal32, no. 3 .2005.pp 240-247.

24.             S. Havlik. “Land Robotic Vehicles for Demining, Humanitarian Demining,” Maki K. Habib (Ed.), ISBN: 978-3-902613-11-0, InTech ,2008. Available from: http://www.intechopen.com/books/humanitarian_demining/land_robotic_vehicles_for_demining

25.             Khamis. “Minesweepers: towards a landmine-free Egypt,” The Journal of ERW and Mine Action, No. 17.1.2013.

26.             M. Bisgaard, A.  la Cour-Harbo and J. Bendtsen  "Full state estimation for helicopter slung load system." In AIAA Guidance, Navigation and Control Conference and Exhibit. 2007.

27.             M. Bisgaard. Modeling, Estimation, and Control of Helicopter Slung Load System. PhD thesis, Department of Signals and Systems, Aalborg University, Denmark.  Pages256 ISBN 87-90664-34-5.2008. 

28.             T. Lardner. "A Study of Manual Mine Clearance." Geneva International Centre for Humanitarian Demining (GICHD) .2005.

29.             M. K. Habib. "Humanitarian Demining: Reality and the Challenge of Technology-The State of the Arts." International Journal of Advanced Robotic Systems. Vol. 4, No. 2. ISSN 1729-8806, pp. 151-172. 2007.

30.             M. Cain and T. V. Meidinger. "The improved landmine detection system." InThe Detection of Abandoned Land Mines: A Humanitarian Imperative Seeking a Technical Solution, EUREL International Conference on (Conf. Publ. No. 431), pp. 188-192. IET, 1996.

31.             P. Gooneratne, S. C.  Mukhopahyay and G.S. Gupta "A review of sensing technologies for landmine detection: Unmanned vehicle based approach." In second I Conf on Autonomous Robots and Agents (Palmerston North, New Zealand). 2004.

32.             D.W. Gage. "Many-robot MCM search systems." In Proceedings of the Autonomous Vehicles in Mine Countermeasures Symposium, vol. 9, pp. 56-64. 1995.

33.             L. A. Zadeh. "Fuzzy sets." Information and control 8, no. 3 .1965. pp 338-353.

34.             Netto. Lehrbuch der Combinatory. Vol. 7. BG Teubner, 1901.

35.             N. J. Nilsson, "Problem-solving methods in." Artificial Intelligence .1971.

36.             P. Rodjito. “Position tracking and motion prediction using Fuzzy Logic” .Honors Theses, 520.2006.

37.             T. Munakata,. Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science). Springer Publishing Company, Incorporated, 2008.

38.             Lahsasna. "Evaluation of credit risk using evolutionary-fuzzy logic scheme." 2009.

39.             Grosanand and  A.  Abraham.  Intelligent systems: A modern approach. Vol. 17. Springer, 2011.

40.             M, Negnevitsky. Artificial intelligence: a guide to intelligent systems. Pearson Education .2005.

41.             R. Yager and D. Filev. “Generation of Fuzzy Rules by Mountain Clustering,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, 1994. pp. 209-219.


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24.

Authors:

Ram Naresh Mishra, Prabhat Kumar

Paper Title:

Automatic Generation Control of Multi-Area Power Systems with Parallel EHVAC/ HVDC Inter-Ties

Abstract: This paper applies the modern control theory to design optimal AGC regulators using full state vector feedback for multi-area interconnected hydro-thermal power systems and implemented under considerations in the wake of 1% step load perturbation in thermal/hydro area. For the present study, power system model consists of one area with reheat thermal power plant and two area with hydro power plants having identical capacity. The system interconnection is considered namely (I) EHVAC inter-ties only (II) EHVAC in parallel with HVDC inter-ties. The dynamic model of incremental power flow through HVDC transmission link is derived based on frequency deviation at both rectifier and inverter ends. Moreover, the HVDC link is considered to be operating in constant current control mode. The system responses have been simulated in Mat lab. Responses of deviation in frequencies, deviation in tie line powers (EHVAC as well as HVDC) and integral of area control errors have been plotted for 3- area. Thus, on the basis of these responses, the dynamic performance of the system has been studied. Besides this, to study the closed loop system stability, the closed loop system eigen values are computed.

Keywords:
Interconnected power systems; HVDC transmission links; System dynamic performance; EHVAC//HVDC transmission link; Optimal AGC regulator.


References:

1.                FOSHA C.E., and ELGERD 0.I.,”The megawatt- frequency control problem: a new approach via optimal   control theory”, IEEE Trans., PAS-89, 1970, pp.563-577.
2.                IEEE Committee Report,” Dynamic models for steam and hydro turbines in power system Studies”, IEEE Trans., PAS-92, 1973, pp. 1904- 1915.

3.                M Calovic, “linear regulator design for a load and frequency control”, IEEE transactions, PAS (91), Nov/Dec1972, pp. 2271-2285.

4.                M.L. Kothari and J. Nanda, “Application of optimal control Strategy to Automatic generation control of a hydrothermal        system”, IEE proceedings, Vol. 135, Pt. D, No. 4, July, 1988, pp.268-274.

5.                Prabhat Kumar, K E Hole’ and R P Aggarwal, “Design of suboptimal AGC Regulator for Hydro-Thermal Power  System”. IE (I)  Journal. pt EL6, June 1983 ,pp 304-309.

6.                Ibraheem, P Kumar and S Ahmad, “Dynamic Performance Enhancement of Hydro-Power Systems with Asynchronous         Tie-lines”. IE (I) Journal Vol 85, June   2004 pp.  23-34.

7.                O. I. Elgerd and C. Fosha, “Optimum megawatt frequency control of multi-area electric energy systems,” IEEE Trans. Power App. Syst., vol. PAS-89, no. 4, Apr. 1970, pp. 556–563.

8.                CARPENTIER, J.: ‘State of the art review, “To be or not to be modern” that is the question for automatic generation control (point of view of a utility engineer)’,Int. J. Electr.  Power & Energy Syst., 1985,7, pp. 81-91.

9.                D. Das, J. Nanda, M. L. Kothari, and D. P. Kothari, “Automatic generation control of hydrothermal system with new area control error considering generation rate constraint,” Elect. Mach. Power Syst., vol. 18, no. 6, Nov. /Dec. 1990. pp. 461–471.

10.             Ibraheem, Prabhat Kumar, and Dwarka P. Kothari,  “Recent Philosophies of Automatic Generation Control  Strategies in Power Systems”, IEEE transactions on  power systems, vol. 20, no. 1, February 2005, pp.346- 357.

11.             IEEE PES Working Group, Hydraulic turbine and turbine control models for system dynamic,” IEEE Trans. Power Syst., vol. PWRS-7, no. 1, Feb. 1992, pp. 167–174,

12.             IEEE Committee Report, “Standard definitions of terms  for automatic generation control on electric power  systems”, IEEE Trans. Power App. Syst., vol. PAS-89,  Jul./Aug. 1970.pp.7-11.

13.             E.V. Bohn, S.M. Miniesy, “Optimum load-frequency  continuous control with unknown Deterministic power demand”, IEEE committee report, 1971, pp.1910-1915.

14.             Ibraheem & Prabhat Kumar, “Study of Dynamic  Performance of Power Systems with Asynchronous Tie- lines considering Parameter Uncertainties”, IE (I) Journal vol83, June 2004, pp.35-42.

15.             Ibraheem & Prabhat Kumar, “Current Status of the Indian Power Systems and Dynamic Performance Enhancement of Hydro Power Systems with Asynchronous Tie Lines”, Electric Power Components &Systems 2003, pp.605-626.

16.             H. L. Zeynelgil, A. Demiroren, and N. S. Sengor, “The application of ANN technique to automatic generation control for multi-area power system,” Elect. Power Energy Syst., vol. 24, no. 5, Jun.2002,pp. 345–354.

17.             E. V. Bohn and S. M. Miniesy, “Optimum load frequency sample data control with randomly varying system disturbances,” IEEE Trans. Power App. Syst., vol. PAS-91, no. 5, pp. 1916–1923, Sep./Oct. 1972.


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25.

Authors:

Esther Njoki Gacheru, Stephen Onyango Diang’a

Paper Title:

Regulating Building Contractors in Kenya and Challenges of Enforcing the National Construction Authority Mandate

Abstract:   The construction industry in Kenya has not had a regulating body since the disbandment of the National Construction Cooperation in 1988. The National Construction Authority (NCA) was then established in 2012 to regulate the construction sector and was mandated to register and regulate the undertakings of contractors. This research deals with the regulation of building contractors in Kenya and challenges of enforcing the NCA mandates. The main objective of this study is to investigate and document the challenges faced by the NCA in regulating building contractors in Kenya. Data was obtained from building contractors by means of questionnaires. The findings of the research indicated that the major challenges to the effectiveness of the NCA in registering and regulating the practices of building contractors include; corruption, poor sensitization, lack of proper organization of the NCA contractor training programs and centralization of the NCA services.

Keywords:
  Contractors, Kenya, NCA, regulation, registration.


References:

1.              Regulation. (2014, July 29). The American Heritage® Stedman's Medical Dictionary. Retrieved from Dictionary.com website: http://dictionary.reference.com/browse/regulation
2.              Gelder, J. d. (2004). Conceptual modelling of building regulation knowledge. Artificial Intelligence in Engineering, Pages 273–284.

3.              G.O.K. (2012). National Construction Authority Regulation 2012. Nairobi: Government Press.

4.              Nahinja, D. (2014, July 29). Ujenzibora. Retrieved from Ujenzibora: http://www.ujenzibora.com/

5.              Nyaanga, J. K. (2014). The effect of competence of contractors on the. Prime Journal of Social Science (PJSS).

6.              Cattell, K. (1994). Small black builders in South Africa: problems and prospects. Research Paper Series No. 2,. Cape Town: Department of Construction Economics and Management.

7.              CIDB. (2000). Construction Industry Development Board Act no. 38 of 2000. Republic of South Africa.

8.              CIDB. (2004). Construction Industry Development Board Directory. Kuala Lumpur: CIDB.

9.              GOK. (2013, November 26). About Us: The National Construction Authority. Retrieved from The National Construction Authority Web Site: http://www.nca.go.ke/

10.           James E. Fenn, D. M. (2008). The Purposes for State Residential General Contractor Licensing in the United States. International Journal of Construction Education and Research.

11.           Njuguna, H. B. (2002). The construction Industry in Kenya and Tanzania: Understanding the mechanisms that promote growth. The construction industry value chain, 32-41.

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26.

Authors:

H. Mohssine, H. Bouhouch, F. Debbagh

Paper Title:

Calculated and Measured Dark Conductivity in P-Type Polycrystalline CdTe Thin Films

Abstract:    In this study we describe a numerical procedure for modeling the dark conductivity in a p-type polycrystalline Cadmium Telluride (CdTe). We base our approach on the comparison between measured and computed conductivity. For this purpose, the Fermi-Dirac statistic combined with the numerical solution of the charge neutrality equation allows to calculate the exact dark conductivity as function of the temperature. The results are then used to fit the experimental conductivity. Measures have been undertaken on CdTe thin films produced by r-f sputtering on glass substrates at room temperatures. It is shown that the amount of the experimental conductivity can be modeled, Quito precisely, by suitably choosing parameters of localized states, without needing complicated approaches like Mott and seto’s models. However, from a point of view of experimental fitting, it is verified, in accordance with our previous general treatment that the model’s parameters are not unique and cannot be derived from Arrhenius diagram analyses.

Keywords:
   Thins films, CdTe, Sputtering, Conductivity.


References:

1.              Thèse de Doctorat, Spécialité: Matière Condensée, Surface et Interface, Institut National des Sciences Appliquées, Lyon, Mars 2007.
2.              Boudghene Stambouli, ‘Solar Photovoltaic at the Tipping Point: Pathways from Evolutionary to Disruptive and Revolutionary Technologies’, Université des Sciences et de la Technologie d’Oran, USTO.MB, 2009.

3.              R.C. Campbell, ‘A Circuit-Based Photovoltaic Array Model for Power System Studies’, 39th North American Power Symposium, NAPS’07, pp. 97 – 101, Sept. 30 –
Oct. 2, 2007.

4.              Ongaro R. , Pillonnet A. and Garoum M. J.Phys.D: Appl.Phys. 28. 129-37. (1995)

5.              Press.W.H. Flannery.B.P, Teukolsky.S.A and Vetterlihg.W.T Numerical Recipes in Pascal.Cambridge University Press.  (1989)


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27.

Authors:

Usha Yadav, Nilmani Verma

Paper Title:

A Survey on Text Recognition from Natural Scene Images and Videos

Abstract:   Text recognition from any natural scenes images and videos is application of image processing technique. Basically  text recognition is belongs to the pattern recognition which is part of image processing techniques. Now these days text recognition from natural scene images and videos is very difficult task.For make it easy four basic steps must be apply that approaches  are (i) Text image pre processing (ii) character segmentation (iii) character recognition and (iv) Text recognition. In the state of art methods , character segmentation having two major approaches that is Segmentation –based approaches which segment the  text into individual character before recognizing and segmentation-free approaches which recognizes character directly from whole text images without any segmentation.  Character  can also be recognized with two approach that is pattern matching methods in that particular method  character are usually identified by a set of features and machine learning methods in which  the methods are designed that are learn automatically from the image or after extracting feature. Various method has been applied earlier for extracting text from images and videos. These all methods are  trying to provide better result .Various paper use printed text images for recognition their we never required any preprocessing for extracting text . Here is the name of some methods that are used for text recognition that are are Specific directed acyclic graph techniques, scalable feature learning algorithm, k nearest neighbour  technique and back propagation algorithm. All those method which has been applying for text recognition , they all provide accuracy in result or we can say that the recognized text are nearly matched with the original one.

Keywords:
  Neural based OCR, Character segmentation , character recognition, Back propagation neural network model, Unsupervised learning.


References:

1.           Vibhor  Goel,  Anand Mishra, Karteek Alahari, C. V. Jawahar. Whole is Greater than Sum of Parts: Recognizing Scene Text Words. International Conference on Document Analysis and Recognition, Aug 2013, Washington DC, United states.        
2.           Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, Andrew Y. Ng. Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning.International conference on document analysis and recognition 2011.

3.           Saıdane, Z., Garcia, C., Dugelay, J.: The image text  recognition graph (iTRG). In: International Conference on Multimedia and Expo, pp. 266–269 (2009).

4.           kaoula Elagouni, Christophe Garcia ,Pascale Sébillot.A  Comprehensive Neural-Based Approach for Text. rognition in Videos    using Natural Language Processing. ICMR, Trento : Italy (2011).

5.           Khaoula Elagouni, Christophe Garcia, Franck Mamalet, Pascale S  ebillot.Combining Multi-Scale Character Recognition and Linguistic Knowledge for Natural Scene Text OCR .10th IAPR International Workshop on Document Analysis Systems 2012

6.           T. Wang, D. Wu, A. Coates, and A. Ng. End-to-end text recognitionwith convolutional neural networks. In ICPR, 2012.

7.           Mishra, K. Alahari, and C. V. Jawahar. Top-down and bottom-upcues for scene text recognition. In CVPR, 2012.

8.           Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang,

9.           D. J. Wu, and A. Y. Ng. Text detection and character recognition inscene images with unsupervised feature learning. In ICDAR, 2011.

10.        Elagouni, K., Garcia, C., Sebillot, P.: A comprehensive neural-based approach for text recognition in videos using natural language processing. In: International Conference on Multimedia Retrieval (2011).

11.        Khaoula Elagouni, Christophe Garcia, Franck Mamalet,Pascale Sebillot.   Text Recognition in Multimedia Documents: A Study of two   Neural-based OCRs Using and Avoiding Character Segmentation. International Journal on Document Analysis and Recognition, IJDAR, 2014, 17 (1), pp.19-31

12.        Kopf, S., Haenselmann, T., Eelsberg, W.: Robust character       recognition in low-resolution images and videos.Universitat Mannheim/Institut fur Informatik (2005)

13.        Saıdane, Z., Garcia, C.: Automatic scene text recognition using a   convolutional neural network. In: Conference on Computer Vision and Pattern   Recognition, pp. 100–106 (2007).

14.        Kusachi, Y., Suzuki, A., Ito, N., Arakawa, K.: Kanji        recognition in scene images without detection of text  fields robust   against variation of viewpoint, contrast, and  background texture. In: International Conference on Pat tern Recognition, vol. 1, pp. 457–460 (2004).

15.        Chen, D., Odobez, J., Bourlard, H.: Text detection and recognition in   images and video frames. Pattern Recognition 37(3), 595–608 (2004).

16.        Huihuang . Zhao, Dejian. Zhou, Zhaohua. Wu. SMT Product character Recognition Based on BP Neural Network. 2010 Sixth International Conference on Natural Computation (ICNC 2010).

17.        Z. Saidane and C. Garcia. Automatic scene text recognition  using a convolutional neural network. In Proceedings of the Second International Workshop on Camera-Based Document Analysis and Recognition (CBDAR), Sept. 2007.T. M. Rath and R. Manmatha. Word image matching    using dynamic      time warping. In CVPR, 2003.

18.        A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer      networks an unsupervised feature learning,” in International Conference on Artificial Intelligence and Statistics, 2011.

19.        Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Back propagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, pp. 541–551, 1989.

20.        T. Q.  Phan, P. Shivakumara, B. Su, and  C. L. Tan. A gradient vectorFlow based method for video character segmentation. In  ICDAR, 2011.Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang,

21.        D. J. Wu, and A. Y. Ng. Text detection and character recognition inscene images with unsupervised feature learning. In ICDAR,  2011.


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28.

Authors:

Mayank Agrawal, Manuj Mishra, Shiv Pratap Singh Kushwah

Paper Title:

Association Rules Optimization using Particle Swarm Optimization Algorithm with Mutation

Abstract:    In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for solving different NP-complete problems. The basic drawback with PSO algorithm is getting trapped with local optima. So in this work, particle swarm optimization algorithm with mutation operator is used to generate high quality association rules for finding frequent item sets from large data sets. The mutation operator is used after the update phase of PSO algorithm in this work. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using PSO algorithm over these rules the system can predict the rules which contains negative attributes.

Keywords:
     Particle Swarm Optimization (PSO), Mutation, Association rule, Support, Confidence, Frequent item set, Data mining.


References:

1.              U. Fayyad and R. Uthurusamy, “Data Mining and Knowledge Discovery in Databases”, Communications of the ACM, vol. 39, no. 11, 1996, pp.24–34.
2.              J. Han and M.  Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann, 2006.

3.              W. Soto and A. Olaya-Benavides, "A Genetic Algorithm for Discovery of Association Rules." In  Computer Science Society (SCCC), 2011, pp. 289-293. 

4.              X. Yan, C. Zhang and S. Zhang, “Genetic Algorithm- Based Strategy for Identifying Association Rules without Specifying Actual Minimum Support”, Expert Systems with Applications, vol. 36, 2009, pp. 3066–3076.

5.              S. N. Sivanandamand and S. N. Deepa, “Introduction to Genetic Algorithms”, Springer-Verlag Berlin Heidelberg, 2008. 

6.              M. Anandhavalli and S. Kumar Sudhanshu, A. Kumar and M.K. Ghose, “Optimized Association Rule Mining Using Genetic Algorithm”, Advances in Information Mining, vol. 1, issue 2, 2009, pp. 01-04.

7.              P. Wakabi-Waiswa and V. Baryamureeba, "Mining High Quality Association Rules using Genetic Algorithms", In  Proceedings of the twenty second Midwest Artificial Intelligence and Cognitive Science Conference, 2009, pp. 73-78. 

8.              Markus Hegland, “The Apriori Algorithm – a Tutorial”, CMA, Australian National University, WSPC/Lecture Notes Series, 22-27. March 30, 2005.

9.              Pujari A.K., “Data Mining Techniques”, Universities Press, 2001.

10.           S. Ghosh, S. Biswas, D. Sarkar and P.P. Sarkar, “ Mining Frequent item sets using Genetic Algorithm”, international journal of artificial intelligence & applications (IJAIA), Vol. 1, No. 4, October 2010.

11.           Mohit K. Gupta and Geeta Sikka, “ Association Rules Extraction using Multi-objective Feature of Genetic Algorihtm”, Proceedings of the world congresson engineering & computer science 2013 Vol II, WCECS 2013, 23-25 October- 2013, San Francisco, USA.

12.           Amit Singh, Neetesh Gupta and Amit Sinhal, “Artificial Bee Colony Algorithm with Uniform Mutation”, Proceeding of the international conference of soft computing for problem solving (SocPros 2011), Vol.  130, pp 503-511.


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29.

Authors:

Vineet Singh Bhadoriya, Unmukh Dutta              

Paper Title:

Association Rules Optimization using Artificial Bee Colony Algorithm with Crossover

Abstract:     To find the frequent item sets from the large big data sets, association rule mining technique of data mining is used. Computer takes too much time to generate all frequent item sets from large big data sets using association rule mining. This can be enhanced, if the time taken to generate association rules is minimized. So here in this work, artificial bee colony (ABC) algorithm with one additional operator, called crossover operator, is used for optimizing the association rules. Due to the better exploration property, crossover operator is used with artificial bee colony algorithm. Experimental results show that the proposed algorithm, for optimizing association rules from big datasets, efficiency is better than the other previously proposed algorithm like KNN and standard ABC algorithm.

Keywords:
  Artificial bee colony (ABC), Crossover, Association rule, Support, Confidence, Frequent item set, Data mining.


References:

1.                U. Fayyad and R. Uthurusamy, “Data Mining and Knowledge Discovery in Databases”, Communications of the ACM, vol. 39, no. 11, 1996, pp.24–34.
2.                J. Han and M.  Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann, 2006.

3.                W. Soto and A. Olaya-Benavides, "A Genetic Algorithm for Discovery of Association Rules." In  Computer Science Society (SCCC), 2011, pp. 289-293. 

4.                X. Yan, C. Zhang and S. Zhang, “Genetic Algorithm- Based Strategy for Identifying Association Rules without Specifying Actual Minimum Support”, Expert Systems with Applications, vol. 36, 2009, pp. 3066–3076.

5.                S. N. Sivanandamand and S. N. Deepa, “Introduction to Genetic Algorithms”, Springer-Verlag Berlin Heidelberg, 2008. 

6.                M. Anandhavalli and S. Kumar Sudhanshu, A. Kumar and M.K. Ghose, “Optimized Association Rule Mining Using Genetic Algorithm”, Advances in Information
Mining, vol. 1, issue 2, 2009, pp. 01-04.

7.                P. Wakabi-Waiswa and V. Baryamureeba, "Mining High Quality Association Rules using Genetic Algorithms", In  Proceedings of the twenty second Midwest Artificial Intelligence and Cognitive Science Conference, 2009, pp. 73-78. 

8.                Markus Hegland, “The Apriori Algorithm – a Tutorial”, CMA, Australian National University, WSPC/Lecture Notes Series, 22-27. March 30, 2005.

9.                Pujari A.K., “Data Mining Techniques”, Universities Press, 2001.

10.             S. Ghosh, S. Biswas, D. Sarkar and P.P. Sarkar, “ Mining Frequent item sets using Genetic Algorithm”, international journal of artificial intelligence & applications (IJAIA), Vol. 1, No. 4, October 2010.

11.             Mohit K. Gupta and Geeta Sikka, “ Association Rules Extraction using Multi-objective Feature of Genetic Algorihtm”, Proceedings of the world congresson engineering & computer science 2013 Vol II, WCECS 2013, 23-25 October- 2013, San Francisco, USA.

12.             Amit Singh, Neetesh Gupta and Amit Sinhal, “Artificial Bee Colony Algorithm with Uniform Mutation”, Proceeding of the international conference of soft computing for problem solving (SocPros 2011), Vol.  130, pp 503-511.

13.             Xie, Jiahua., Yang, Jie., ”A Novel Crossover  Operator for Particle Swarm Algorithm ”, Machine Vision and Human-Machine Interface (MVHI), 2010 , IEEE Pages 161 – 164


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30.

Authors:

P. Julia Grace, R. Suman Sharma

Paper Title:

An Analytical Study on the Chennai Road Accidents – A Machine Learning Approach

Abstract:      Road traffic accidents are among the top leading causes of deaths and injuries of various levels and indeed one of the major social problems in Chennai. It has a great impact on the socio-economic development of a society. For the occurrence of accidents, both the government and the public have to be blamed. This paper focuses on creating awareness on road safety. The objective is to study the road accidents from different angles and prepare a detailed report after analyzing the various causes of road accidents in this city. The number of accidents occurred in Chennai city during the time period 2009 – 2013 are analyzed using classification, clustering and association algorithm. The ultimate aim of our study is to track few major accidents causes and explicate possible solution to reduce.

Keywords:
   Road traffic accidents, injuries, occurrence of accidents, socio-economic development, classification, clustering and association algorithm.


References:

1.             http://www.tn.gov.in/sta/stat4.html
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31.

Authors:

Ekta Pandey, R.K.Mishra, S.P.Pandey

Paper Title:

Approximation Properties of Some ModifiedSummation-Integral Type Operator

Abstract:       In the present paper, we introduce some Stancu type generalization of Szász-Mirakyan-Baskakov type operators. We estimate the moments for these operators using the hypergeometric series, which can be related to Laguerre polynomials. We estimate point wise convergence, asymptotic expansion and error estimate in terms of higher order modulus of continuity of function in simultaneous approximation for these generalized operators. We use the technique of linear approximating method viz. Steklov mean.

Keywords:
 linear positive operators, simultaneous approximation, Lebesgue function, modulus of continuity, Steklov mean, hypergeometric series, Laguerre polynomial .


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