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Volume-3 Issue-6: Published on January 05, 2014
21
Volume-3 Issue-6: Published on January 05, 2014

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Volume-3 Issue-6, January 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

N. A. Dheringe, B. N. Bansode

Paper Title:

Genetic Algorithm Using Discrete Cosine Transform for Fractal Image Encode

Abstract:    A genetic algorithm using discrete cosine transformation is proposed to speedup the fractal encoder. By using discrete cosine coefficients, the optimal Dihedral transformation between the range block and domain block can be found to save a large number of the redundant MSE computations. Moreover, combining the discrete cosine transformation technique with the genetic algorithm, the length of the chromosome is shortened to smooth the landscape of the search space since the optimal Dihedral index was determined. Hence the encode velocity is accelerated further. Experiments show that the encoding speed of the proposed method is 100 times faster than that of the full search method, while the cost is the 1.1dB loss at the retrieved image quality.

Keywords:
Discrete Cosine Transform, Fractal Image Encode,  Genetic Algorithm.


References:

1.                Ming-Sheng Wu, “Genetic Algorithm using Discrete Cosine Transformation for Fractal Image Encode ”, IEEE, pp. 309-312,  2012.
2.                Yung-Gi, Wu, “Fast Fractal Image Encoder”, International Journal of Information Technology, Vol. 13 No. 1, pp. 15-26,  2007.

3.                Colin Reeves, “ Genetic Algorithms”.

4.                Meenu Bansal, Sukhjeet K. Ranade, “A Review On Fractal Image Compression”, International Journal of Advances in Computing and Information Technology, pp. 265-276, July 2012.

5.                Brendt Wohlberg And Gerhard De Jager, “A Review Of The Fractal Image Coding Literature”, IEEE Transactions On Image Processing, Vol. 8, No. 12, Pp 1716-1729, December 1999.

6.                N.A. Koli & M.S.Koli, “A Survey On Fracytal Image Compression  Key Issues”, Information technology journal, pp. 1085-1095,  2008.

7.                Dr. Fadhil Salman Abed, “ A Proposed Encoding and Hiding Text in an Image by using Fractal Image Compression”, IJCSE, ISSN : 0975-3397 Vol. 4 No. 01 January 2012

8.                Viswanath Sankaranarayanan, “Fractal Image Compression Project Report”, 1998.

9.                Regina K. Ferrell, Shaun S. Gleason, Kenneth W. Tobin, Jr., “ Application of Fractal Encoding Techniques for Image Segmentation”.

10.             S.K. Ghosh J. Mukhopadhyay V.M. Chowdary A. Jeyaram, “Relative Fractal Coding And Its Application In Satellite Image Compression”.


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2.

Authors:

Abdul Hamid M. Ragab, Osama S. Farag Allah, Khalid W. Magld, Amin Y. Noaman

Paper Title:

Security Evaluation of Robust Chaotic Block Cipher

Abstract:     this paper investigates an enhanced robust chaotic block cipher (RCBC) which is used for potential increasing security. It makes heavy use of non-linear Boolean functions including parity and multiplexer functions, in addition to multiplication primitive operation with block size of 256-bits. It greatly guaranteed an increased diffusion achieved per round, allowing for greater security and fewer rounds. Comparative analysis of the cipher with different algorithms such as RC6, and RC5 is investigated; regarding design parameters and speed. The cipher is tested among its several design parameters including word size, number of rounds, and secret key length and their optimal choice values. Security estimation for digital imaging against brute-force, statistical, and differential attacks is explored from strict cryptographic viewpoint. Thorough experimental tests are carried out with detailed analysis, demonstrating the high security of the cipher.

Keywords:
 Block ciphers, Symmetric encryption, Chaos, and Security analysis.


References:

1.             Mariam Babu and K. J. Singh,” Performance Evaluation of Chaotic Encryption Technique”, American Journal of Applied Sciences, 10 (1): 35-41, 2013.
2.             M.Surya B. Rao, V.S. G. Akula,” Chaotic Algorithms used for Encryption and Decryption on Moving Images”, International Journal of Advances in Computer Science and Technology, Volume 2, No.8, August 2013.

3.             Bremnavas1, B.Poorna2 and I. R. Mohamed,” Secured medical image transmission using Chaotic map”, Elixir Comp. Sci. Eng. 54, 2013.

4.             R. K. Purwar,” An Improved Image Encryption Scheme Using Chaotic Logistic Maps”, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 2 Issue 3 May 2013.

5.             O. S. Farag Allah, “An enhanced chaotic keybased RC5 block cipher adapted to image encryption”, International Journal of  Electronics, Volume 99, Issue 7, 2012.

6.             H. H. Ahmed, H. M. Kalash, and O. S. Farag Allah,” Implementation of RC5 Block Cipher Algorithm for Image Cryptosystems”, International Journal of Information Technology, Vol. 3 No. 4, 2007.

7.             H. M. Ragab, and N. A. Ismail, O. S. Farag Allah, “Enhancements and Implementation of  RC6 Block Cipher for Data Security". IEEE Catalog Number: 01CH37239, 2001.

8.             B. Mohamed1, Ghada Zaibi1, A. Kachouri,” Implementation of RC5 and RC6 block ciphers on digital”, The 8th International Multi-Conference on Systems, Signals & Devices,2011.

9.             S. Lian, "A block cipher based on chaotic neural networks," Neuron-computing, doi:10.1016/j.neucom.2008.
10.          S. Lian, "Efficient image or video encryption based on spatiotemporal chaos system," chaos, solutions and fractals, 2007.
11.          S. Behnia, A. Akhshani, A. Akhavan, H. Mahmodi, "Applications of tripled chaotic maps in cryptography," Chaos, Solutions and Fractals, 2007.

12.          O. S. Faragallah,”An Efficient Block Encryption Cipher Based on Chaotic Maps for Secure Multimedia Applications”, Information Security Journal: A Global Perspective, 20:135–147, 2011.

13.          ECB Mode, http://www.cryptopp.com /wiki/ ECB_Mode.

14.          K.T. Huang. , J.H. Chiu. and S. S. Shen,” a novel structure with dynamic operation mode for symmetric-key block ciphers” International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.1,17-36, January 2013.

15.          C.E. Shannon, “Communication Theory of Secrecy Systems,” Bell System Technical Journal, vol. 28, No. 4, pp. 656-715, October 1949.

16.          S. Li, C. Li, G. Chen, K.T. Lo, “Crypto-analysis of the RCES/RSES image encryption scheme,” The journal of systems and software 811130-1143, 2008.

17.          Li , S. Li, G. Alvarez , G. Chen, K.T. Lo, "Cryptanalysis of a chaotic block cipher with external key and its improved version," Chaos, Solutions and Fractals 37,299-307,  2008.

18.          H. H. Ahmed, H. M. Kalash, and O. S. Farag Allah, "An Efficient Chaos-Based Feedback Stream cipher (ECBFSC) for Image Encryption and Decryption”, An International Journal of Computing and Informatics, Vol. 31, No. 1, PP. 121-129, ISSN 0350-5596, 2007.

19.          T. Xiang, K.Wong, X. Liao, "An improved chaotic cryptosystem with external key", Communication in Nonlinear Science and Numerical Simulation 13, 1879-1887, 2008.

20.          M. Amin, O. S. Faragallah, A. A. Abd El-Latif,  "A Chaotic Block Cipher Algorithm for Image Cryptosystems," Journal of Communications in Nonlinear Science and Numerical Simulation, 2010.


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3.

Authors:

 Gwaya Abednego, Wanyona Githae, Masu, Sylvester Munguti

Paper Title:

The Contingencies Allowances In Project Budgeting

Abstract:      Contingency allowances have been used as a tool in project management. However, project sponsors and financiers are not convinced with this type of budget arrangement. The utilization and dissatisfaction with the allowances lies at the discretion of the project team because these reserves are used to pay for changes in a project but at times they are seen as free floating project funds.The study aimed at establishing an empirical approach into the use of contingency allowances focused on substituting it with a more comfortable budget structure that was suitable to financials of projects. Adhering to a budget estimates and managing costs is arguably the most critical measure of a construction project success and as such there should be a more objective method of estimating the contingency funding required not the arbitrary percentage of the basic construction cost.The study undertook an exploratory investigation to establish objective data on use of contingency allowances in project. A questionnaire survey on the experiences and opinions of industry practitioners that is contractors, MOPW (Ministry of Public Works) and consultants on project budgets was circulated. Case interviews on budget checks to form insights, experiences and challenges from projects cost success were carried out and budgets interrogated for exposures and expectations of practitioners.

Keywords:
 Budget allowances contingency allowances, construction funds, project management, models.


References:

1.              Chen, Dong, and Francis T. Hartman.  “A Neural Network Approach to Risk Assessment and Contingency Allocation,” AACE International Transactions: RISK.07.01 RISK.07.06 (2000).
2.              Harbuck, Robert H. “Competitive Bidding for Highway Construction Projects,” AACE International Transactions: EST.09.1-EST.09.4 (2004).

3.              Gichunge, H. (2000). Risk Management in The Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi.

4.              Mbatha, C.M. (1986). Building contract performance “A Case Study of Government Projects in Kenya”.  Unpublished M.A. Thesis. University of Nairobi.

5.              Mbeche, I.M. & Mwandali, D.N. (1996). Management by Projects. A paper presented at the Regional Conference on Construction Management, November, Garden Hotel, Machakos.

6.              Mantel, Samuel J., Jr. and others. Project Management in Practice. New Jersey: John Wiley & Sons, Inc., 2005.

7.              Wanyona G. (2005). Risk management in the cost planning and control of building projects. The case of Quantity Surveying profession in Kenya. Unpublished PhD Thesis. University of Cape Town.


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4.

Authors:

Gwaya Abednego, Wanyona Githae, Masu Sylvester Munguti

Paper Title:

The Need for a Change in the Practice of Project Management in Kenya

Abstract:       Several countries at various levels of socio-economic development have recognized the need and importance of taking measures to improve the performance of their construction industries. One of the means to this end has been to ensure performance efficiency in construction projects execution. As has been widely acknowledged, this requires a deliberate process of continuously monitoring the performance of projects based on relevant indicators. Many project management models have been proposed in literature which measure projects performance under the broad headings of critical success factors and key performance indicators. However, these objectives are faced with several drawbacks. These have to do with the difficulty in developing a realistic and agreed set of indicators due to the very nature of the industry; the number of indicators necessary to give a complete picture and offer relevance and accuracy to the overall result will be very large; the difficulty in collecting and processing the required raw data for estimating the indicators, especially in developing countries; and the need to amend or adapt these criteria and indicators for each country.  At the core of these problems is the fact that most of the existing models emphasize the use of lagging measures instead of leading measures. Worse, they do not emphasize continuous assessment of the project, and finally, these models do not pay attention to needs of the clients as initiators of the project. It also takes into consideration the particular circumstances of the project. In addressing the problems, it is necessary to reconfigure project management in the following regards:
(i)            Moving away from expecting “project autopsy reports” towards “project health reports”

(ii)           Moving away from considering the outcomes of a project in terms of success/failure dichotomy into project performance results in identifiable criteria

(iii)          Acknowledging the uniqueness of every project and the contingency factors which calls for contingency measures of assessment.


Keywords:
  project management, modelling, leading measures and lagging measure


References:

1.              Atkinson, R. (1999), “Project Management: Cost, Time and Quality, Two Best Guesses and A Phenomenon, Its Time to Accept Other Success Criteria”, International Journal of Project Management, 17 (6)337-42
2.              Belassi, W. & Tukel, O.I. (1996). A new framework for determining critical success/failure factors in projects. International Journal of project management, 14, 141 – 151.

3.              Beatham, S., Anumba, C., and Thorpe, T., Hedges, I. (2004), “KPIs: a critical appraisal of their use in construction, Benchmarking”, An International Journal. Vol. 11 No. 1, 2004. pp. 93-117. 

4.              Burke, R. (2007). Introduction to Project Management. Burke Publishing, USA.

5.              Gichunge, H. (2000). Risk Management in The Building Industry in Kenya. Unpublished PhD. Thesis. University of Nairobi.

6.              KPMG international (2010). Project Delivery Strategy: Getting it right.

7.              Masu, S.M. (2006).An Investigation Into The Causes and Impact of Resource Mix Practices in The Performance of Construction Firms in Kenya. Unpublished Phd. Thesis. University of Nairobi.

8.              Mbatha, C.M. (1986). Building contract performance “A Case Study of Government Projects in Kenya”.  Unpublished M.A. Thesis. University of Nairobi.

9.              Mbatha, C.M. (1993). An analysis of Building Procurement Systems, Features and Conception of An Appropriate Project Management Systems for Kenya. PhD Thesis. University of Wuppertal, Germany.

10.           Mbeche, I.M. & Mwandali, D.N. (1996). Management by Projects. A paper presented at the Regional Conference on Construction Management, November, Garden Hotel, Machakos.

11.           Munns, A.K. & Bjeirmi, B.F(1996). The role of project management in achieving project success. International Journal of project management,14, 81 – 87.

12.           PMI 2013. A guide to the Project Management Body of Knowledge

13.           Shenhar, A.J., Levy, O., Dvir, D. (1997), “Mapping the dimensions of project Success”, Project Management Journal 8 (2) 5-13.

14.           Talukhaba, A.A. (1988). Time and Cost Performance of Construction Projects. Unpublished M.A. Thesis. University of Nairobi.

15.           Talukhaba, A.A. (1999). An investigation into The  Factors Causing Construction Project Delays in Kenya. Case Study of High-rise Building Projects in Nairobi. Unpublished PhD. Thesis. University of Nairobi.

16.           Vandevelde, A., Dierdonck, R.V., Debackere, K. (2002), “Practitioners View on Project Performance: A Three-Polar Construct”, Vlerick Leuven Gent Management School Fellows, R., Liu, A (2005), Research Methods for Construction. Blackwell Publishing, pp. 3-34

17.           Wanyona G. (2005). Risk management in the cost planning and control of building projects. The case of Quantity Surveying profession in Kenya. Unpublished PhD Thesis. University of Cape Town.


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5.

Authors:

Gowtham Mamidisetti, G.Tej Varma

Paper Title:

The Need for a Change in the Practice of Project Management in Kenya

Abstract:   In this paper, we develop an analysis method that matches DNS information so that we can compare and contrast performance over protocols for a variety of Internet services. Our initial analyses focus on the basic services that are accessed using protocols, observed client behaviors, and a presentation of performance characteristics of services using both IPv4 and IPv6. Our objective is to detect and expose differences by passive measurement without access to application traffic payloads. To demonstrate our method, Our method uses data collected on the IPv6, including both DNS requests/responses and flow export records for dual-stack hosts operating .Our method expose various performance characteristics of Internet services that support IPv6.

Keywords:
   IPv4, IPv6, DNS.


References:

1.              K. Cho, M. Luckie, and B. Huffaker. Identifying IPv6 Network Problems in the Dual-stack World. In Proceedings of the ACM SIGCOMM Work-shop on Network Troubleshooting: Research, Theory and Operations Practice Meet Malfunctioning Reality, New York, NY, 2004.
2.              C. Labovitz. Six Months, Six Providers and IPv6.       http://ddos. arbornetworks.com/2011/04/six-months-six-providers-and-ipv6/, April 2012.

3.              K. Claffy. Tracking IPv6 Evolution: Data We Have and Data We Need. ACM SIGCOMM Computer Communications Review, 41(3), July 2011.

4.              D. Plonka and P. Barford. Flexible Traffic and Host Profiling via DNS Rendezvous. In Proceedings of  the Securing and Trusting Internet Names Workshop (SATIN 2011), Teddington, UK, April 2011.

5.              CoralReef. http://www.caida.org/tools/measurement/coralreef/, 2008.

6.              L. Colitti, S. Gunderson, E. Kline, and T. Fefice. Evaluating IPv6 Adoption in the Internet. In Proceedings of the Passive and Active Measurement Conference, Zurich, Switzerland, April 2010.

7.              NFDUMP. http://nfdump.sourceforge.net/, 2012

8.              Google.        Protocol     Buffers. https://developers.google.com/ protocol-buffers/, 2012.

9.              L. Colitti, S. Gunderson, E. Kline, and T. Fefice. Evaluating IPv6 Adoption in the Internet. In Proceedings of the Passive and Active Measurement Conference, Zurich, Switzerland, April 2010.


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6.

Authors:

Prabhot Kaur Chahal, Amritpal Singh

Paper Title:

Software Reuse and Reengineering: With A Case Study

Abstract:    Reuse of existing system has been regarded as a feasible solution to solve the problem of software Productivity and Quality. In this paper, the reference paradigms for setting up of reuse reengineering processes, has been explained. Approaches to reengineering and reuse are also discussed.In Product development an important step is to have a clear and correct set of systems requirements. When a product is produced by a variety of models with different set of features, it is desirable to make the requirements Reusable. But this imposes certain restrictions on the Requirements development that are described here.

Keywords:
    System Requirements, Re-engineering, Reuse, Salvaging, Restructuring


References:

1.                 CHIKOFSKY, E. and Cross, J. H. (1990) “Reverse Engineering and Design Recovery: A Taxonomy,” IEEE Software, vol. 7, no. 1, pp. 13-17.
2.                 GUIDE, (1989) Application Reengineering, GUIDE Pub. GPP-208, GUIDE International Corp., Chicago

3.                 ARNOLD, R. S. (1993). “A Road Map Guide to Software Reengineering Technology,” in Software Reengineering, R. S. Arnold (ed.), IEEE Computer Society Press.

4.                 Stan Jarzabek, “Strategic Reengineering of Software: Lifecycle Approach”, 1993, IEEE

5.                 W. Tracz ed. "Tutorial: Software Reuse: Emerging Technology" IEEE Computer Soc. Press. 1988

6.                 P. Freeman ed. "Tutorial: Software Reusability" Computer Soc. Press, 1987.

7.                 Engineering, Portici (Naples), Italy, Dec. 1991. V. R. Basili "Viewing Maintenance as Reuse- Oriented Software Development" IEEE Software.

8.                 Dr. Larisa Melikhova, Albert Elcock, Andrey A. Dovzhikov, Georgii Bulatov, Dr. Dmitry 0. Vavilov, “Reengineering for System Requirements Reuse: Methodology and Use-Case”, 2007, IEEE

9.                 Wayne C. Lim, “article of managed reuse organisational and economics assessment” 1994, IEEE software

10.              Software Engineering Course Given by: Arnon Netzer (ppt)


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7.

Authors:

Prabhjot Kaur Chahal, Jasveen Kaur, Palwinder Sngh

Paper Title:

Digital Watermarking On Bank Note

Abstract:     Watermarks are the marks of authentication or the proof of ownership. In this paper, we  are trying through light on the digital watermarking concepts and their techniques .Elaborating with one of the most commonly observed application of digital watermarking i.e “Banknotes”.  This is the best way to get ride of the illegal currency notes spread all over the world. As the printing company involves the digital platform so the challenging operation is the security of the documents printed so the is doesn’t increase or multiply in number but unwanted sources. Watermarking done on banknotes are of both visible and invisible types. We as common people can detect and check only the visible watermarking.

Keywords:
     Watermark, Watermark Fluid, Dandy Roll Process, Cylinder Roll Process, EURion constellation


References:

1.             http:/www.webopedia.com/TERM/D?digital watermark.html
2.             Manpreet Kaur, Sonika Jindal & Sunny Behal “ A Study of Digital Image Watermarking” Volume 2, Issue 2( ISSN: 2249- 3905) ,February 2012

3.             Evelyn Brannock, Michael Weeks, Robert Harrison, Computer Science Department Georgia State University “Watermarking with Wavelets: Simplicity Leads to Robustness”, Southeastcon, IEEE, pages 587 – 592, 3-6 April 2008

4.             Stefan Katzenbeisser and Fabien  A.P.Petitcolas. “Information hiding Techniques for Steganography and digital watermarking” Artech house. Computer security series, pp.15-23,97-109,200

5.             Charles Way Hun Fung, Antonio Gortan & Walter Godoy Junior “A Review Study on Image Digital Watermarking” 2011.

6.             F. A. P. Petitcolas, R.J. Anderson, R. J. and M. G. Kuhn,“ Information hiding - A survey,” Proceedings of the IEEE, Volume 87, Issue 7, 1999, pages 1062-1078.

7.             Bieemann, Christopher J, “Handbook of  Pulping and Paper marking” San Diego, California, USA, ed-2, 1996

8.             Geoffrey B. Rhoads , “ Digital Watermarking and Banknotes” , 1999

9.             Markus Kuhn: The EURion constellation. Security Group presentation, Computer Laboratory, University of Cambridge, 8 February, 2002.

10.          Ulbrich, Chris, "Currency Detector Easy to Defeat”, 14 January 2004

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8.

Authors:

Tsanaktsidis C.G., Vasiliadis V., Itziou A., Petrakis L.A., Moisiadis S.A.

Paper Title:

Application of Factor Analysis for the Study of Physicochemical Properties in Different Blends of Diesel Fuel with Biodiesel

Abstract:      The present study focused on the investigation of an alternative energy resource, biodiesel. In order to check the appropriateness of biodiesel, its physicochemical properties were analyzed. The main aim of the present study was to investigate the differentiations in the physicochemical properties of several blends of diesel/biodiesel. Thus, the results were integrated through the factorial analysis, and a unit circle was designed in order to study the correlations among the properties (variables) tested. The results of the current study indicated significant correlations among the properties tested. The results of this study can be useful in developing new educational products, with a view to understanding of mathematical concepts through everyday activities such as the use of fuel.

Keywords:
      factor analysis, diesel, biodiesel, physicochemical properties


References:

1.             International Energy Agency, Oil Market Report. A monthly oil market and stocks assessment. http://omrpublic.iea.org/ (2007).
2.             IPCC WGI Fourth Assessment Report, Intergovernmental panel on climate change (2007).

3.             P.T. Vasudevan, M. Briggs, Journal of Industrial Microbiology and Biotechnology, 35, 421 (2008).

4.             M. Canakci, H. Sanli, Journal of Microbiology and Biotechnology, 35, 431 (2008).

5.             M. Mittelbach, B. Pokits, A. Silberholz, Proceedings of an alternative energy conference. ASAE Publication, Nashville, TN, USA, 74 (1992).

6.             C.L., Peterson, D.L. Reece, R. Cruz, J. Thompson, Proceedings of an alternative energy conference. ASAE Publication, Nashville, TN, USA., 99 (1992).

7.             C.L. Peterson, D.L. Reece, B.J. Hammond, J. Thompson, S.M. Beck, ASAE Paper, 94, 6531 (1994).

8.             G. Vellguth, ASAE Paper, 831358 (1983).

9.             D.Y.Z. Chang, J.H. Van Gerpen, I. Lee, L.A. Johnson, E.G. Hammond, S.J. Marley, JAOCS, 73, 1549 (1996).

10.          M. Manzanera, M.L. Molina-Muñoz, González-López, Journal of Recent Patents on Biotechnology, 2, 25 (2008).

11.          C.G. Tsanaktsidis, S.G. Christidis, G.T. Tzilantonis, International Journal of Environmental Science and Development, 1, 206 (2010).

12.          M.S. Graboski, R.L. McCormick, Progress in Energy and Combustion Science, 24, 125 (1998).

13.          J.L. Mauderly, Health issues concerning inhalation of petroleum diesel and biodiesel exhaust. In: Plant Oils as Fuels: Present State of Science and Future Developments (Martini N, Schell J, eds). Berlin: Springer, 92 (1997).

14.          K.J.M. Swanson, C. Madden, A.J. Ghio, Environmental Health Perspectives, 115, 496 (2007).

15.          J.J. Van Gerpen, B. Shanks, R. Pruszko, D. Clements, G. Knothe, Biodiesel Production Technology, Subcontractor Reportperated for the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy by Midwest Research Institute, National Renewable Energy Laboratory NREL/SR-510-36244, Battelle
(2004).

16.          A.L. Petrakis, Lectures Notes in Multivariate Data Analysis, Thessaloniki Greece (2007).


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9.

Authors:

Gwaya Abednego, Wanyona Githae, Sylvester Munguti Masu

Paper Title:

The Need for a Structured Construction Clients’ Performance Assessment in Kenya

Abstract:       In recent years there has been a tremendous increase of construction projects in Kenya. There has also been a growing concern among construction clients on why the industry is not achieving the stated objectives. Clients criticize the industry for not always achieving what they need and the majority of them are not satisfied with the quality of the construction industry.Many of the problems encountered in the design and construction phases originate from poor definition of scope and inadequate pre-project planning. Clients are very instrumental in the early stages of project definition and their input is very essential towards successful project execution. The main problems are frequently attributed to poor planning and poor identification of clients’ needs which act as contributory factors to inadequate project performance.  One approach that could help improve construction project performance is to pay more attention to the role of clients in scope definition particularly at the initial stages of project implementation and also by having a structured client input and performance assessment criteria.The pre-project planning phase presents the best opportunity for clients to achieve their objectives because it is at this stage that they can express their needs properly. Despite their important role in construction projects there has not been much research on a structured assessment of the Clients’ role in construction projects. The main aim of this paper is to develop a framework for improving scope planning and management to enable construction clients overcome the problems they encounter with other project participants. The framework will enable construction clients in Kenya identify and communicate their needs more clearly to the other project participants. In recent years there has been a great concern over the performance of the construction industry in Kenya. For Instance there have been a number of accidents on construction sites. Buildings have been reported to have collapsed in Nairobi and Kiambu among other counties. However, the observed challenges are not unique to the Kenyan Situation. Sherif (2002) has indicated similar challenges in the UK. This has led to many reports being published there criticizing construction, stating that it is characterized by low achievement and low productivity and offering no solutions to overcome some of the stated problems.

Keywords:
 factor analysis, diesel, biodiesel, physicochemical properties


References:

1.             Ahuja,H,N., 1994. Project Management: Techniques on planning and controlling construction projects. John Wiley & Sons Inc.
2.             Burke, R., 2010. Project Management: Planning and Control. John Wiley & Sons Inc. NEWYORK.

3.             CII (1996). The constructability manual, Preparedfor the Construction Industry Institute, Australia. Research Report 8, April.

4.             Egan Report (1998). Rethinking Construction Industry. The Department of Environment Transport and Region, LONDON HMSO.

5.             Gibson, G. and Dumont, P., (1996). Project Rating Index PDRI. A report to the Construction Industry Institute, University of Texas at Austin, Research Report 113-11.

6.             Gibson, G. and Dumont, P., (1997). Team alignment during preproject planning of Capital Facilities. . A report to the Construction Industry Institute, University of Texas at Austin, Research Report 113-12.

7.             Gibson, G., Kaczmarowski, J. and Lore, H., (1993). Modelling preproject planning for the construction of Capital facility” A report to the Construction Industry Institute, University of Texas at Austin.

8.             Hackney, J. , (1992). Control and Management of Capital Projects, 2nd Ed. MacGraw Hill, Inc. NEWYORK.

9.             Latham, M., (1994). Constructing the team. Joint review of procurement and contractual arrangements in the UK construction Industry. HMSO, London.

10.          Oberlender, G.D., (1993). Project Management for Engineering and Construction. McGraw- Hill, Inc. USA.

11.          Sherif, M. (2002). A framework for improving Pre-project planning. PhD thesis. Unpublished.

12.          Turner, J. (1993). Handbook of Project Based Management McGraw- Hill.

13.          Ward, S. C, Curtis, B., Chapman,C.B.(1991). Objectives and Performance in Construction Projects , Construction Manangement and Economics, 9; PP 343-353.


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10.

Authors:

C.M. Niranjana, J.Dhananandhini, K. Rajeswari, A.Dhivya

Paper Title:

A New Approches of Lung Segmentation Using Neuro-Fuzzy Network

Abstract:   The detection of lung cancer in early stage is a difficult problem, because the cancer cell causesmany dangerous effects due to their overlapped structure. Lung cancer is a disease characterized by uncontrolled cell growth in tissues of the lung. The causes of lung cancer is due to smoking, random gas, air pollution, genetics etc. This paper includes two segmentation methods, Neural fuzzy Network (NFN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting the early stage of lung cancer. The manual segmentation of lung cancer consumes more time, inaccurate and it requires well trained people to avoid diagnostic fault. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer which will improve the chances of survival for the patient. However, the gray level and the relative contrast results in inaccurate manner, thus we applied a thresholding technique as a Pre-processing step in all images to extract the nuclei regions, because most of the quantitative procedures are based on its nuclei feature. This thresholding algorithm had succeededin extracting the nuclei regions. Moreover, it succeeded in determining the best range of thresholding values. The NFN and FCM methods are designed to classify the image of N pixels among M classes. This paper includes many color images to test both methods, and NFN has shown a better classification result than FCM, the NFN has succeeded in extracting the nuclei regions.

Keywords:
    Fuzzy C-Mean Clustering, Image Segmentation, Lung cancer, Neural fuzzy network, Thresholding Technique


References:

1.              JiantaoPu, David S. Paik, XinMeng, Justus E. Roos, and Geoffrey D. Rubin (2011) ‗Shape ―Break-and-Repair‖ Strategy and Its Application to Automated Medical Image Segmentation‘, IEEE transactions on visualization and computer graphics, vol. 17, no. 1
2.              S. Saleh, N. Kalyankar, and S. Khamitkar,” Image Segmen-tation by using Edge Detection”, International Journal on Computer Science and Engineering(IJCSE), vol. 2, no. 3, pp. 804-807, 2010

3.              S. Saleh, N. Kalyankar, and S. Khamitkar,” Image Segmen-tation by using Edge Detection”, International Journal on Computer Science and Engineering(IJCSE), vol. 2, no. 3, pp. 804-807, 2010

4.              Dignam JJ, Huang L, Ries L, Reichman M, Mariotto A, Feuer E. “Estimating cancer statistic and other-cause mortality in clinical trial and population-based cancer registry cohorts”, Cancer 10, Aug 2009.

5.              F. Taher and R. Sammouda,” Identification of Lung Cancer based on shape and Color”, Proceeding of the 4th International Conference on Innovation in Information Technology, pp.481-485, Dubai, UAE, Nov. 2007.

6.              JamshidDehmeshki, X. Ye, X. Lin, M. Valdivieso, H. Amin (2007) ‗Automated detection of lung nodules in CT images using shape-based genetic algorithm‘, Computerized Medical Imaging and Graphics 31, 408–417.

7.              F. Taher, and R Sammouda, "Comparison of Hopfield Neural Network and Fuzzy Clustering in Segmenting Sputum Color Images For lung Cancer Diagnosis," ISSPA on Single Processing and its Applications, Feb 2007. 

8.              J. AbuHassam, "A Computer Aided Diagnosis Systems for Early Detection of Lung Cancer Based on the Analysis of Chest Computed Tomography CT Images," M.SC. Thesis, May, 2005.

9.              H. Sun, S. Wang and Q. Jiang, "Fuzzy C-Mean based Model Selection Algorithms for Determining the Number of Clusters," Pattern Recognition, vol. 37, pp.2027-2037, 2004.

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11.

Authors:

Hassan Razouki, Abdellatif Hair

Paper Title:

Towards A New Security Architecture of Mobile Agents

Abstract:    Security presents a crucial point in mobile agent systems and may hinder the expansion and use of this paradigm. The protection of mobile agents is considered as one of the greatest challenges of security, because the platform of execution has access to all the components of the mobile agent. In this paper, we present a new architecture paradigm of mobile agents, which allows the separation of the implementation tasks of the agent and its security mechanisms. Our approach is based on using two strategies of adaptation to adapt the mobile agent security at runtime, depending on the sensitivity of the services required to perform the duties of the agent and the degree of confidence of the visited platforms. The first is a static adaptation performed by the MSAS (the Management System of Agents Security). The second is a reflexive structural dynamic adaptation performed by the mobile agent itself. These two adaptations take into account the dynamic security requirements in systems based on mobile agent.

Keywords:
  Mobile agent, Software components, Static adaptation, Dynamic adaptation, Security, Trusted platform, Cryptography.


References:

1.                J. Baumann, F. Hohl, K. Rothermel and M. Straber, “Mole– Concepts of a mobile agent system,” World Wide Web Journal, vol. 1, no. 6, 1998, pp. 123-137.
2.                D. Kotz, R. Gray, “Mobile Agents and the Future of the Internet,” ACM Operating Systems Review, vol. 33, no. 3, 1999, pp. 7-13.

3.                R.J. Thomas, T.D. Mount, "Using software agents to test electric markets and systems," Power Engineering Society General Meeting, IEEE, Vol. 3, 2005, pp. 2808 – 2812.

4.                N. Karnik, “Security in mobile agents systems,” PhD thesis, Department of Computer Sciences and Engineering, University of Minnesota, Minneapolis, USA, 1998.

5.                E. Bierman, E. Cloete, “Classification of Malicious Host Threats in Mobile Agent Computing,” Proceedings of SACICSIT2002, pp 141-148, 2002.

6.                N. Borselius, “Mobile Agent Security,” Electronics Communication Engineering Journal, IEEE. London, vol. 14, no. 5, 2002, pp 211-218.

7.                P. Bella vista, A. Corradi, C. Frederici, R. Montanari and D. Tibaldi, “Security for Mobile Agents: Issues and Challenges,” Invited Chapter in the Book Handbook of Mobile Computing, I. Mahgoub, M. Ilyas(eds.), CRC Press, 2004.

8.                J. Mir, J. Borrell, “Protecting General Flexible Itineraries of Mobile Agents,” in K. Kim (Ed.): ICICS 2001, LNCS 2288. © Springer-Verlag Berlin Heidelberg 2002, pp 382-396.

9.                H. Yunguick Lee, J. Alves, “The Use of encrypted functions for mobile agent security,” Proceedings of SAICSIT 2002, pp141-148.

10.             Dadon-Elichai, “RDS: Remote Distributed Scheme for Protecting Mobile Agents,” In: The Third International Joint Conference on Autonomous Agents and Mutli-Agent Systems, 2004.

11.             T. Wang, S. Guan and T. Khoon Chan, “Integrity Protection for Code-On-Demand Mobile Agents in E-Commerce,” The Journal of Systems and Software 60, pp 211-221, 2000.

12.             T. Ledoux, N. M.N.Bouraqadi-Saâdani, “Adaptability in mobile agent systems using reflection,” in ECOOP 2000, Workshop on Reflection and Metalevel Architectures, Cannes, France, 2000.

13.             W. Zhi, G. Zhogwen, “A Dynamic Security Adaptation Mechanism For Mobile Agents,” Proceedings of the International Computer Congress, China, 2004, pp 334-339.

14.             N. Amara-hachmi, A. El Fallah-Seghrouchni, “Towards a generic architecture for self-adaptive,” Proceedings of 5th European Workshop on Adaptive Agents and MultiAgent Systems (AAMAS’05), Paris, 2005.

15.             S. Hacini, C. Cheribi and Z. Boufaïda, "Dynamic Adaptability using Reflexivity for Mobile Agent Protection", in Transactions On Engineering, Computing And Technology Enformatika, vol. 17, 2006 ISSN 1305-5313, Cairo, Egypte, pp 222-227,December 2006.

16.             S. Leriche, J. Arcangeli, “Flexible architectures of adaptive agents : the agentφ approach” International journal of grid computing and multi agent systems (IJGCMAS), vol. 1, no. 1, 2010, 55-75.

17.             J. Malenfant, F. Peschanski, L. Seinturier et J-P. Briot, "ALADYN: Architectures logicielles pour l’auto-adaptabilité dynamique", Université Pierre et Marie Curie UFR d’informatique, 2004.

18.             C. Garrigues, N. Migas, W. Buchanan, S. Robles and J. Borrell, “Protecting mobile agents from external replay attacks,” The Journal of Systems and Software, vol. 82, no. 2, 2009, 197–206.

19.             C. Garrigues, S. Robles and J. Borrell, “Securing dynamic itineraries for mobile agent applications,”Journal of Network and Computer Applications, vol.31, no. 4, 2008b, 487–508.

20.             P. Maggi, R. Sisto, “A configurable mobile agent data protection protocol,” In Proceedings of the 2nd International Conference on Autonomous Agents and
Multiagent Systems (AAMAS ’03). ACM Press, 2003, pp. 851–858.

21.             J. Zhou, J.A. Onieva and J. Lopez,”Analysis of a free roaming agent result-truncation defense scheme,” In Proceedings of the IEEE International Confernce on e-Commerce Technology (CEC ’04). IEEE Computer Society, 2004, pp. 221–226.


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12.

Authors:

IKranthi Kiran, A. Jaya Laxmi

Paper Title:

Power Flow Based Contract Path Method for Transmission Pricing

Abstract:     Provision of correct economic signals to the market participants like generation companies, wheeling companies and customers in a deregulated electricity market is necessary. The operation of the wheeling company between generation companies and customers must ensure reliable and secured operation of the overall power system. Proper wheeling cost methodology is needed to allocate the cost of transmission transactions to the customers to achieve it. Accurate transmission pricing scheme still remains a challenging task. This paper gives an overview of different cost components of wheeling party, principles of wheeling pricing and a detailed presentation of a power flow tracing methodology and ‘embedded’ wheeling cost methodology namely ‘Contract path method’. This method is applied to an application example illustrated to calculate the wheeling cost and the results obtained are illustrated..

Keywords:
   Contract path, Embedded cost, Wheeling, Wheeling cost


References:

1.              Z. Yu, D. GachiriNderitu and F. T. Sparrow, “A proposed market power monitoring model for restructuring electricity markets”, IEEE Power Engineering Review, July 2000, pp. 41–42.
2.              R. D. Christie, B. F. Wollenberg and I. Wangensteen, “Transmission management in the deregulated environment”, Proc. of IEEE, Vol. 88, Feb. 2000, pp. 170–195.

3.              Mohammad Shahidehpour, HatimYamin and Zuyi Li, “Market   

4.              Operations in Electric Power Systems: Forecasting,    Scheduling, and Management” , First Edition, John Wiley and Sons Limited, 2002,pp: 1 –20, 57 – 113
and 115– 160.

5.              H.H.Happ,” Selection of methods for evaluating the cost of transmission wheeling services”, Proc. of the American Power Conference, 57th Annual Meeting,
Technology for a Competitive World, Vol. 1, Chicago, IL, Apr. 18–20, 1995, pp. 606–611.

6.              J. W. M. Lima, M. V. F. Pereira and J. L. R. Pereira, “An integrated framework for cost allocation in a multi-owned transmission system”, IEEE Transactions on Power Systems, Vol. 10, May 1995, pp. 971–977.

7.              Sally Hunt and Graham Shuttleworth, “Competition and Choice in Electricity”, First Edition, John Wiley and Sons Limited, 1996,pp: 9 – 52 and 181 – 204.

8.              F. Vojdani, C. F. Imparato, N. K. Saini and H. H. Happ, “Transmission access issues”, IEEE Transactions on Power Systems, Vol. 11, Feb. 1996, pp. 41–51.

9.              Lewis P., "Wheeling and Dealing [Power Transmission]", IEE Review, Vol.42, Issue 5, September 1996, pp.196-198.

10.           F. D. Galiana and M. Phelan, “Allocation of transmission losses to bilateral contracts in a competitive environment”, IEEE Transactions on Power Systems, Vol. 15, Feb. 2000, pp. 143–150.

11.           Loi Lei Lai, “Power System Restructuring and Deregulation: Trading, Performance and Information Technology”, First Edition, John Wiley and Sons Limited, 2002,pp:50 – 75 and 172 – 193.

12.           BaranMesut E., Banunarayanan, Garren Kenneth E., “Equitable Allocation of Congestion Relief Cost to Transactions”, IEEE Transactions on Power Systems, Vol.15, No.2, May 2000, pp.579-585.

13.           Billinton R., Wang P., “Deregulated Power System Planning using a Reliability network Equivalent Technique”, IEE Proc.-Generation Transmission Distribution, Vol.146, No.1, January 1999, pp.25-30.

14.           Fradi, S. Brignone and B. F. Wollenberg, “Calculation of energy transaction factors”, IEEE Transactions on Power Systems, Vol. 16, May 2001,pp. 266–272.

15.           G. Strbac, D. Kirschen and D. Ahmed, “Allocating transmission system usage on the basis of traceable contributions of generators and loads to flow”, IEEE Transactions on Power Systems, Vol. 13, May 1998, pp. 527–534.

16.           J. Pan, Y. Teklu, and S. Rahman, “Review of usage-based transmission cost allocation methods under open access”, IEEE Transactions on Power Systems, Vol. 15, Nov. 2000, pp. 1218–1224.

17.           M. L. Baughman, S. N. Siddiqi and J. W. Zarnikau, “Advanced pricing in electricity systems”, IEEE Transactions on Power Systems, Vol. 12, Feb. 1997, pp. 489–502.

18.           Torre Teofio  De  La ,  Feltes  James  W.,  Roman  Tomas Gomez San , Merrill Hyde M., “Deregulation, Privatization and Competition: Transmission Planning under Uncertainty”, IEEE Transactions on Power Systems, Vol.14, No.2, May 1999, pp.460-465.

19.           Kankar Bhattacharya, Math H.J. Bollen and Jaap E. Daalder, “Operation of Restructured Power Systems”, First  Edition, Kluwer Academic Publishers, 2001, pp: 1 – 72 and 119 – 169.

20.           Allen J. Wood and   Bruce F. Wollenberg, “Power Generation, Operation, and Control”, Second Edition, Wiley India Private      Limited, 2006, pp: 29 – 170 and 363 – 409.  

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13.

Authors:

Sangeeta Srivastava

Paper Title:

Model Transformation Approach for a Goal Oriented Requirements Engineering based WebGRL to Design Models

Abstract:      Web applications have become integral to our lives and there is a lot of emphasis on developing high quality web applications which capture the stakeholder’ goals very closely. Web engineers mostly focus on design aspects only, overlooking the real goals and expectations of the users. Goal oriented Requirement Engineering is a popular approach for Information system development but has not been explored much for Web applications. Goal driven requirements analysis helps in capturing stakeholders’ goals very finely, they enhance the requirements analysis in many ways, as the requirement clarification and the conflicts between requirements can be detected early and design alternatives can be evaluated and selected to suit the requirements. In this paper, we take a step from the requirements phase to the design phase. While adhering to the web based goal oriented requirements engineering in the first phase we move to the A-OOH design models using a model transformation strategy to derive web specific design models supported by a UML profile. This helps in seamlessly generating the web specific design models namely the content, navigation, presentation, business process and adaptivity models. The model transformation approach aims at automatic transformation of the repeatedly refined and resolved alternatives presented by us in the GOREWEB framework as an output to the design models supported by a UML profile. This would lead to a better design and high quality of product development which captures the stakeholders’ goals very closely.

Keywords:
    Goal Oriented Requirements Engineering, Model transformation, UML Profile, Web Engineering.


References:

1.             S. Srivastava., S. Chawla, “Multifaceted Classification of Websites for Goal oriented Requirements Engineering”, S. Ranka et al. (Eds.): IC3 2010, Part I, CCIS 94, pp. 479–485, 2010, Springer-Verlag Berlin Heidelberg 2010
2.             G. Rolland, C., Grosz, G. and Kla, R., “ Experience With Goal-Scenario Coupling In Requirements Engineering”. In Proceedings of the IEEE International Symposium on Requirements Engineering 1998. June 1999. Limerick, Ireland.

3.             Castro, J., Kolp, M., Mylopoulos, J., “Towards Requirements-driven Information Systems Engineering”: the Tropos Project, Information Systems 27 (2002) 365-389.

A.            Anto´, “Goal identification and refinement in the specification of software-based information systems”.1997 Dissertation, Georgia Institute of Technology, Atlanta, USA

4.             D. Bolchini, P. Paolini,  “Goal-Driven Requirements Analysis for Hypermedia-intensive Web Applications”, Requirements Engineering Journal, Springer, RE’03 Special Issue (9) 2004, pp. 85-103.

5.             J. Gordijn, E. Yu, B. V. Raadt,  “ e-Service design using i* and e3 value modeling”,  in IEEE software, vol 23, no.3, 2006.

6.             F. Azam, L. Zhang, R. Ahmad,  “Integrating value based requirements engineering models to WebML using VIP business modeling framework” in International World Wide Web Conference 2007, Canada, pp933-942.

7.             S.Chawla, S.Srivastava, “Goal oriented Requirements Analysis for Web Applications”, in proceedings of International Conference on Computer and Software Modeling 2010,Manila, pp 88-92,  IEEE.

8.             D. Amyot, “ Introduction to the User Requirements Notation: Learning by Example” in  Computer Networks, 42(3), 285–301, 2003.

9.             ITU-T, Recommendation Z.151 (11/08): User Requirements Notation (URN) – Lanuage Definition. http://www.itu.int/rec/T-REC-Z.151/en

10.          D. Schwabe, G. Rossi, “ The Object-Oriented Hypermedia Design Model”. In Communications of the ACM 38(8), pages 45-46, ACM Press, ISSN 0001-0782,
1995 .

11.          D.  Schwabe, G. Rossi, “ An object oriented approach to webbased applications design” .In Theory and Practice of Object Systems, 4(4), pages 207-225, John Wiley & Sons, Inc , ISSN 1074-3227.

12.          D.Schwabe, G. Rossi, “A Conference Review System with OOHDM”. in First Internacional Workshop on Web-Oriented Software

13.          Technology, 05 2001.

14.          D. Schwabe, G. Szundy, S. Moura, F. Lima,  “ Design and implementation of semantic web applications” in WWW 2004 Workshop on Application Design, Development and Implementation Issues in the Semantic Web.

15.          S. Ceri, P. Fraternali, A. Bongio, M. Brambilla, S. Comai, M. Matera, “Designing Data-Intensive Web Applications” in . Morgan Kaufmann Publishers Inc., ISBN 1558608435

16.          S. Ceri, P. Fraternali, A  Bongio, A. Maurino,  “ Modeling data entry and operations in WebML”. In Lecture Notes In Computer Science;Vol. 1997, Selected papers from the Third International Workshop WebDB2000 on The World Wide Web and Databases, pages 201-214, Springer-Verlag, ISBN 3-540-41826-1

17.          N. Koch, “Hypermedia Systems Development based on the UnifiedProcess” in Tech. rept. Ludwig-Maximilians- Universität Munchen.

18.          N. Koch, A. Kraus, R. Hennicker, “The Authoring Process of the UML-based Web Engineering Approach” in Proceedings of the 1st  International Workshop on Web-Oriented Software Technology.

19.          H. Baumeister, N. Koch, L. Mandel, “ Towards a UML Extension for Hypermedia Design” in Proceedings UML’99Conference, France, R., Rumpe, B. (Eds) , LNCS, Vol. 1723, Springer Verlag, 614-629, 1999.

20.          G. J. Houben, F. Frasincar, P. Barna, R. Vdovjak, “ Engineering the presentation layer of adaptable web information systems” in WebEngineering 4th International Conference, ICWE 2004, volume 3140 of Lecture Notes in Computer Science, pages 60-73, Springer, ISBN 3-540-22511-0.

21.          S.Chawla, S. Srivastava, P. Bedi, “GOREWEB Framework for Goal Oriented Requirements Engineering of Web Applications” in  IC3 2011, CCIS 168, pp. 229–241,Springer-Verlag-Berlin2011   http://www.springerlink.com/content/x104307710505727/

22.          Garrig´os, J.  Maz´on,, J. Trujillo., “A Requirement Analysis Approach for Using i*in Web Engineering In ICWE 2009, LNCS 5648, pp. 151–165, 2009. Springer-Verlag Berlin Heidelberg 2009

23.          C. Cachero, J. G´omez, “ Advanced conceptual modeling of web applications: Embedding operation interfaces in navigation design” in: JISBD, pp. 235–248 (2002)

24.          S. Chawla, S. Srivastava , “Implementing Web specific Goal driven Requirements Engineering through WebURN tool”, (UnPublished)

25.          T. Isakowitz, E. Stohr,P. Balasubramanian,” A Methodology for the Design of Structured Hypermedia Applications” in .Communications of the ACM, 38(8),34-44, 1995

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14.

Authors:

Galia V. Tzvetkova

Paper Title:

Extracting Inertial Parameters from Robotic Manipulators Dynamics

Abstract:       The paper presents theoretical bases and simulation results of an identification procedure to estimate inertial parameters of robotic manipulator dynamics. A proximity function is introduced and used as an adjustment element for adaptation of the procedure. The convergence of the estimation process is tested experimentally. The numerical results are shown after computer simulations over numerous robot trajectories. .

Keywords:
     Robot Parameters Identification, Recurssive estimation procedure.


References:

1.              D. Smallwood, L. Whitcomb, Adaptive Identification of Dynamically Positioned Underwater Robotic Vehicles, IEEE Transactions on Control Systems Technology, Vol.11, No. 4, 2003, pp. 505-515
2.              V. Sujan, S. Dubowsky, An Optimal Information Method for Mobile Manipulator Dynamic Parameter Identification, IEEE/ASME Transactions on Mechatronics, Vol.2, No.2, 2003, pp. 215-225.

3.              M. Olsen, J. Swevers, W. Verdonck, Maximum Likelihood Identification of a Dynamic Robot Model: Implementation Issues, The International Journal of Robotics Research, Vol. 21, No.2, 2002, pp.89-96.

4.              Nemes, B. Lantos, Genetic Algorithm-Based Optimization of Fuzzy Logic Systems for Dynamic Modeling of Robots, Periodica Polytechnica Ser. El. Eng. Vol. 43, No. 3, 1999, pp. 177–187.

5.              Y. Maeda,  R. J. P. De Figueiredo, Learning Rules for Neuro-Controller via Simultaneous Perturbation, IEEE Transactions On Neural Networks, Vol.8, No.5, 1997, pp.1119-1130.

6.              K. Yoshida, N. Ikeda, H. Mayeda, Experimental Study of the Identification Methods for the Industrial Robot Manipulator, JRSJ Vol.11, No.4, pp.84-93.

7.              J. C. Spall, Adaptive Stochastic Approximation by the Simultaneous Perturbation Method, IEEE Transactions on Automatic Control, Vol. 45, No. 10, 2000, pp. 1839-1853

8.              W.Khalil, M.Gautier, Calculation of the Identifiable Parameters for Robots Calibration, 9th IFAC/IFORS Symposium on Identification and System Parameter Estimation, Budapest, 1991, pp. 888-892.

9.              Goldenberg, J. Apkarian and H. Smith, An Approach to Adaptive Control of Robot Manipulators Using the Computed Torque Technique, ASME Journal of Dynamic Systems, Measurement, and Control, Vol.111, 1989, pp. 1-8.

10.           G. Tzvetkova, Symbolic Reconstruction of Robot Dynamic Model by Using Matrix Transformations, Proceedings of the IASME International Conference on MECHANICS and MECHATRONICS, UDINE, Italy, March 25-27, CD, 2004, pp.155-159


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15.

Authors:

Y.M.K Priyadarshana, G.D.S.P Wimalaratne

Paper Title:

Sensing Environment through Mobile: A Personalized Wearable Obstacle Detection System for Visually Impaired People

Abstract:  The challenge of improving the mobility of visually impaired   people is still exist in the world even after proposing various types of solutions. This paper presents a wearable obstacle detection approach which can be personalized along with the user through a mobile device. The time of flight characteristic of a sonar wave has been used to identify the obstacles while several methodologies are proposed to classify the identified obstacles. The direction and the distance to an obstacle are informed to the user as a vibration feedback pattern. The approach can be personalized according tocharacteristics of the user such as user’s height, vibration sensitivity, etc. A prototype of this approach has been used to conduct user evaluation experiments which were carried out in an unknown structured environment. Further analysis on familiarization process of this system, obstacle identification and confusions, comparative analysis between white cane and the system and effects of personalization to mobility were executed in order to gauge the effectiveness and the accuracy of the approach. The results of the evaluation have proved that the personalized system facilitate to improve the mobility of visually impaired people while giving an apparent consideration on the 3D environment..

Keywords:
      Echolocation, Obstacle Detection, Personalized, Visually Impaired.


References:

1.              “Visual impairment and blindness,”  Website,  June 2012, Available: http://www.who.int/mediacentre/factsheets/fs282/en/
2.              M. Espinosa, S. Ungar, E. Ocha´ıta, M. Blades, and C. Spencer, “Com- paring methods for introducing blind and visually impaired  people to unfamiliar  urban environments,” Journal of Environmental Psychology, vol. 18, pp. 277–287, 1998.

3.              R.  Schmidt and H.Altner,  Fundamentals of  sensory physiology. Springer, 1986.

4.              “The life of a guide  dog,” Available: http://www.guidedogs.org.uk

5.              G.    C.,    “Blind    People   See     With    Echolocation,”     Available:

6.             
http://kristinasaid.wordpress.com/2011/06/04/blind-people-see-with-echolocation/
7.              Fusiello, A. Panuccio, V. Murino, F. Fontana,  and D. Rocchesso, “A multimodal electronic travel aid device,” in Multimodal Interfaces,2002. Proceedings.Fourth IEEE International Conference on.   IEEE,2002, pp. 39–44.

8.              J. Hesch and S. Roumeliotis, “An indoor localization aid for the visu- ally impaired,”  in Robotics and Automation, 2007 IEEE International Conference on.   IEEE, 2007, pp. 3545–3551.

9.              S. Modi, “Comparison of three obstacle avoidance methods for an au- tonomous guided vehicle,” Ph.D. dissertation, University  of Cincinnati,1998.

10.           V. Kulyukin, C. Gharpure,  J.  Nicholson, and S. Pavithran,  “Rfid in robot-assisted indoor navigation for the visually impaired,” in Intelligent Robots and Systems, 2004.(IROS  2004).  Proceedings.  2004 IEEE/RSJ International  Conference on, vol. 2.   Ieee, 2004, pp. 1979–1984.

11.           L. Russell, “Travel path sounder,” in Proceedings of Rotterdam MobilityRes. Conference,  1965, p. 1965.

12.           V. Singh, R. Paul, D. Mehra, A. Gupta, V. Sharma, S. Jain, C. Agarwal, A. Garg, S. Gujral, M. Balakrishnan et al., “’smartcane for the visually impaired: Design and controlled  field testing of an affordable obstacle detection system,” 2010.

13.           S. Shoval,  J. Borenstein,  and Y. Koren, “The navbelt-a computerized travel aid for the blind  based on mobile  robotics technology,” Biomedical Engineering,  IEEE Transactions  on, vol. 45, no. 11, pp. 1376–1386,1998.

14.           J. Borenstein  and I. Ulrich, “The guidecane-a computerized travel aid for the active guidance of blind pedestrians,” in Robotics and Automation,1997. Proceedings.,  1997 IEEE International Conference  on, vol. 2. IEEE, 1997, pp. 1283–1288.

15.           S. Hashino and R. Ghurchian,  “A blind guidance  system  for street crossings based on ultrasonic sensors,” in Information and Automation (ICIA), 2010 IEEE International  Conference on.  IEEE, 2010, pp. 476–481.

16.           S. Cardin, D. Thalmann,  and F. Vexo,  “Wearable  obstacle detection system for visually impaired people,”  in VR workshop on haptic and tactile perception of deformable objects, 2005, pp. 50–55.

17.           S. Cardin, D. Thalmann,  and F. Vexo, “A wearable system for mobility improvement of visually impaired people,” The Visual Computer, vol. 23, no. 2, pp. 109–118, 2007.


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16.

Authors:

N. Swarna Latha, J. Amarnath

Paper Title:

Determination of Particle Trajectories in a Gas Insulated Busduct (GIB)

Abstract:   SF6 is generally found to be very sensitive to field perturbations such as those caused by conductor surface imperfections and by conducting particle contaminants. A study of CIGRE group suggests that 20% of failure in GIS is due to the existence of various metallic contaminations in the form of loose particles. The presence of contamination can therefore be a problem with gas insulated substations operating at high fields. If the effects of these particles could be eliminated, then this would improve the reliability of compressed gas insulated substation. It would also offer the possibility of operating at higher fields to affect a potential reduction in the GIS size with subsequent savings in the cost of manufacture and installation. The purpose of this paper is to develop techniques, which will formulate the basic equations that will govern the movement of metallic particles like aluminum, copper in a coated with epoxy material as well as uncoated busduct.

Keywords:
       GIB, Busduct, Radial Movement, Axial Movement. 


References:

1.             H Cookson, PC Bolin, HC Doepken, RE Wootton, CM Cooke, JG Trump; “Recent Research in the United States on the Effect of Particle Contamination Reducing the Breakdown Voltage in Compressed Gas Insulated System”; Int. Conf. On Large High Voltage System; Paris, 1976.
2.             H.Anis, KD Srivastava; “Breakdown Characteristics  of Dielectric Coated Electrodes in Sulphur Hexafluoride Gas with Particle Contamination”, Sixth International Symposium on High Voltage Engineering No. 32.06, New Orleans, LA, USA. 1989.

3.             M.M. Morcos, S. Zhang, K.D. Srivastava, and S.M. Gubanski “Dynamics of Metallic particle contamination in GIS with dielectric coating electrodes”, IEEE Transactions on Power Delivery Vol. 15, No. 2, April 2000 p.p. 455-460.

4.             J. Amarnath, B. P. Singh, C. Radhakrishna, S. Kamakshiah “Determination of particle trajectory in a Gas Insulated Busduct predicted by Monte-Carlo technique”, CEIDP, 1999 during 17-21st October 1999, Texas, Austin, USA.

5.             J. Amarnath et al., “Influence of Power Frequency and Switching Impulse Voltage on Particle Movement in GIS – Predicted by Monte-Carlo Technique” International IEEE, High Voltage Workshop, during April 18-21, 2001, Calfornia, USA.

6.             J. Amarnath et al., “Influence of Power Frequency and Impulse Voltage on Motion of Metallic particle in a coated GIS – predicted by Monte-Carlo Technique” 6th International Seminar on Electrical and Electronics Insulating Materials and Systems (IEEMA) during November 23-24, 2000, Hyderabad, INDIA

7.             J. Amarnath et al., “Movement of Metallic Particles in Gas Insulated Substations under the influence of various types of voltages” 11th National Power System Conference (NPSC-2000) during December 20-22, 2000, IISc, Bangalore, INDIA.

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Authors:

Mohannad J Mnati, Saleim Hachem Farhan, Maher Ibraheem Gamaj

Paper Title:

Design and Performance of the 3GPP Long Term Evolution Transceiver Physical Layer in SUI Channels

Abstract:    Third Generation Partnership Project Long Term Evolution (3GPP LTE), emerged with an aim of providing voice, data, video and multimedia services on mobile phones at high speeds and cheap rates. Long Term Evolution (LTE) is one of the 4th generation wireless communications. The major aim of this paper is to analyze the LTE radio frame. We designed and simulated the OFDM system with cyclic prefix. Its Bit Error Rate (BER) is verified by changing the Signal to Noise Ratio (SNR) value. We designed, simulated the QAM digital modulation in SUI channels its BER vs. SNR are verified using simulations on MATLAB.

Keywords:
        Coder, LTE, 3GPP, OFDM, FFT, SUI.


References:

1.              IEEE Communications Magazine, Vol. 51, No.2, February 2013, Enhanced Physical Downlink Control Channel in LTE Advanced Release 11
2.              J. Johansson, W. Hapsari, S.. Kelley, G. Bodog "Minimization of Drive Tests in 3GPP Release 11",  IEEE Communications Magazine, Nov. 2012.

3.              3GPP TS 36.213 V 11.2.0, March 2013; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures, Release 11

4.             V. K. Garg. Wireless Communications and Networking. San Francisco: Morgan Kaufmann, 2007.

5.              IXIA Technologies. “SCFDMA single carrier FDMA in LTE” ixiacom.com, November 2009. [Online]. Available:  http://www.ixiacom.com/pdfs/library/white_papers/SC-FDMA-INDD.pdf [Accessed: June 15, 2010]

6.              Erik Dahlman, Stefan Parkvall, Johan Sköld and Per Beming ,”3G Evolution HSPA and LTE for Mobile Broadband”Second edition,2008.pp 45-55.

7.             E. Dahlman et al., 3G Evolution: HSPA and LTE for Mobile Broadband, 2nd ed., Academic Press, 2008

8.              Agilent Technologies, “3GPP LTE System Overview, Product Development, and Test Challenges,” Application Note, Literature Number 5989-8139EN, May. 2008.

9.              F. Khan, LTE for 4G Mobile Broadband - Air Interface Technologies and Performance, Cambridge University Press, 2009.

10.           C. Berrou, A. Galvieux and P. Thitimajshima, “Near Shannon Limit Error-Correcting Coding and Decoding: Turbo Codes,” Proceedings ICC 93, Geneva Switzerland, May 1993, pp. 1064-1070.

11.           Daniel S. Baum, Stanford University, Simulating the SUI Channel Models, 2001,  IEEE


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18.

Authors:

Gailan Abdul Qadir, Majid S. Naghmash

Paper Title:

Design and Simulation of Programmable AC-DC Converter Using Pulse Width Modulation (PWM) Techniques in MATLAB

Abstract:     This paper presents, a design of Programmable AC-DC Converter Using Pulse Width Modulation (PWM) Techniques in MATLAB with an impression of the well known voltage and current converter topologies used to realize a three-phase PWM AC/DC converter scheme. Preliminary from the voltage source inverter and the current source rectifier, the fundamentals of space vector modulation are summarized. The process of the AC/DC converter in different dynamic states powerfully depends on the modulation method applied. The power of the discussed modulation methods on the line current distortion and the switching frequency has been inspected. This technique depends on off line calculations of the pulses width for the first quarter cycle and stores these into a table. The residual pulses, for total cycle, are generated by using the values of the first quarter since there are conditions of quarter and half – wave regularity. Results show an important saving of microcontroller time and memory.

Keywords:
    AC/DC, MATLAB.


References:

1.             KIM J.-H., SUL S.-K., A Carrier-Based PWM Method for Three-Phase Four-Leg Voltage Source Converters, IEEE Transactions on Industrial Electronics, vol.19, no.1, January 2004.
2.             KWASINSKI A., KREIN P. T., CHAPMAN P. L., Time Domain Comparison of Pulse-Width Modu-lation Schemes, IEEE Power Electronics Letter, vol.1, no.3, September 2003.

3.             ] MONDAL S. K., BOSE B. K., OLESCHUK V., PINTO J. O., Space Vector Pulse Width Modulation of Three-Level Inverter Extending Operation into Overmodulation Region, IEEE Transactions on Power Electronics, vol.18, no.2, March 2003.

4.             ] Michał Knapczykf and F, Krzysztof Pieńkowskif, “ Analysis Of Pulse Width Modulation Techniques For Ac/Dc Line-Side Converters”,  Studia I Materiały Nr 26 2006

5.             ] N. S Ubaida, “ Nonlinear programming approach for the reproduction of the PWM    waveforms” Thesis, university of Baghdad, 1993.

6.             M. Varnovitsky, “A micro computer – based control signal generator for a three phase switching power inverter’ IEEE transaction on industry applications Vol.19, No.2, march 1999.

7.             G. S. Buja , P. Fiorini, “ microcomputer control of PWM inverter “IEEE transaction on industrial Electronics,Vol.29, No.3, August 1997.

8.             S. R. Bowes, M. I. Mech, “ New PWM switching strategy for microprocessor controlled inverter drives “, IEE, proceeding, Vol.133, No.4 July 1988.

9.             V. H. Prasad, “ Analysis and comparison of space vector modulation schemes for three –leg and four –leg voltage source inverter “. Thesis submitted to the Virginia University for the degree of Master of Science, May 1997.

10.          Ph – Krein, “Element of power Electronics”, Oxford university press, 1998.

11.          S. Yeralan, “programming and interfacing the 8051 micro controller” Rigel corporation1998.

12.          Zhihong Ye, “Modeling and control of parallel three – phase PWM converter” Thesis submitted to Virginia University for degree of doctor of philosophy in electrical engineering, September 2000.

13.          T. Juu, R. g. Hoft, “Inverter Harmonic reduction using Walsh function Harmonic elimination Method” IEEE Transactions on power electronics, Vol.12, No.6, November 1997.

14.          Said El-barbare, “Digital control of a three phase 4-wire PWM inverter for pv applications” Chemnitz university of technology, Germany, 2002.


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19.

Authors:

Dipti Singh, Seema Agrawal

Paper Title:

A Novel Hybrid Self Organizing Migrating Algorithm with Mutation for Global Optimization

Abstract:      This paper presents a Novel Hybrid Self Organizing Migrating Algorithm with Mutation for Global Optimization (M-SOMA).  The proposed algorithm includes the hybridization of Self Organizing migrating Algorithm (SOMA) and Non uniform mutation. SOMA is very effective population based algorithm among evolutionary algorithms. Though its convergence is very fast but there are lots of chances to trap in to local optima. As no new points are generated during the search only positions are updated. So to maintain the diversity of the search space and prevent premature convergence it is hybridized with Non Uniform mutation. The proposed algorithm is tested on 15 benchmark unconstrained test problems and its efficiency is compared with SOMA and GA results. On the basis of comparison it is concluded that the presented algorithm shows better performance in terms of function mean best. The graphical results also show that the presented algorithm perform better in terms of efficiency, reliability and accuracy.

Keywords:
Self Organizing Migrating Algorithm, Non-Uniform Mutation, Genetic Algorithm, Global Optimization.


References:

1.             Zelinka,, J. Lampinen,” SOMA- Self Organizing Migrating     Algorithm”, in proceedings of the 6th International Mendel Conference  on Soft Computing, 2000,  pp. 177-187, Brno, Czech, Republic .
2.             G. P. Kasprzyk and M. Jasku, “Application of Hybrid Genetic Algorithms for Deconvulation of Electrochemical Responses in SSLSV Method”, vol. 567, Journal of Electroanalytical chemistry, 2004,  pp. 39 – 66.

3.             K. Deep and Dipti, “A New Hybrid Self Organizing Migrating Genetic    Algorithm for Function Optimization”, IEEE Congress on Evolutionary Computation, 2007, pp. 2796-2803.

4.             Khosravi, A. Lari and J. Addeh , “ A New Hybrid of Evolutionary and Conventional Optimization Algorithm”, vol. 6, Applied Mathematical Sciences, 2012,  pp. 815-825 .

5.             S. Ghatei, et. al., “A New Hybrid Algorithm for Optimization using PSO and GDA”, Journal of Basic and vol. 2, Applied Scientific Research, 2012, pp. 2336-2341.

6.             K. Deep and M. Thakur, “A new mutation operator for real coded genetic algorithms”, vol. 193, Applied Mathematics and computation, 2007, pp. 229-247.

7.             L. N. Xing, Y. W.  Chen and K.W. Yang, “A novel mutation operator base on immunity operation”, vol. 197, European Journal of Operational Research, 2009,  pp. 830-833.

8.             K. Deep and Kedar Nath Das, “ Quadratic approximation based Hybrid Genetic Algorithm Function Optimization”, vol. 203, Applied Mathematics and Computations, 2008 , pp. 86-98, Elsevier.

9.             K. Deep, Shashi and V. K. Katiyar, “A new real coded genetic algorithm operator: Log logistic mutation”, In proceedings of the international conference on soft computing for problem solving, vol. 130, Advances in intelligent and Soft Computing, 2012 , pp. 193-200.

10.          Ahmed Esmin and Stan Matwin ,”A Hybrid Particle Swarm Optimization Algorithm with Genetic Mutation”,  International Journal of Innovative Computing, Information and Control, vol. 9, 2013, pp. 1919-1934.

11.          Zelinka, “SOMA- Self Organizing Migrating Algorithm”,  in New optimization techniques in engineering, G. C. Onwubolu and B.V. Babu, Eds. Berlin, Germany: Springer (2004).


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20.

Authors:

Sanket Sandesh Shahane, Raj B. Kulkarni

Paper Title:

Cloud Auditing: An Approach for Betterment of Data Integrity

Abstract:   Life has been incredibly busy. Therefore the need for data gradually increases over the time. Users require data to be stored somewhere and retrieved easily, whenever needed. Taking these factors into consideration the concept of cloud is now from the fact. It had been the place where one can store the data of different varieties and could be retrieved at any place, at any time. Also, it provides services to its users which include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It substantially becomes effective to a large extent in serving clients, thanks to its properties such as scalability, flexibility, transparency, cost saving ability and eco-friendliness. Each technology had been enforced for making life easier, they’ll continuously persist some problems that had to be delineated with. The technology of cloud also had such issues. The field of cloud computing had been facing the issues associated with data integrity such as replaces, replay and forge attack. The continuous need had emerged out for solving such problems, to keep original user data intact. The proposed system presents a Third Party Auditor (TPA) that could solve these issues by shouldering transparency. The paper discusses methods such as Simple Auditing and Dynamic Auditing for solving above mentioned problems. Both the methods use the Tag Generation technique for doing auditing. TPA neither supports Cloud Service Provider nor did Data Owner hence act as monitoring agent. Auditing is carried through logical operations, without a single copy of an original data, to maintain confidentiality. It takes its auditing decisions based on the generated results providing opportunity to Cloud Service Provider, for improving Cloud’s services. Auditor warns data owner about prohibited activities of Cloud, boosting the quality of the services. The methods of this paper help in improving auditing services to some extent, along with the above mentioned issues related to data integrity.

Keywords:
  Cloud Auditing, Cloud Computing, Cloud Services, Cloud Service Provider, Data Owner, Third Party Auditor.


References:

1.              Mirzaei, Nariman. "Cloud Computing." Pervasive Technology Institute Report, Community Grids Lab, Indiana University (2008): 1
12._Available:http://grids.ucs.indiana.edu/ptliupages/publications/ReportNarimanMirzaeiJan09.pdf.

2.              Huth, Alexa, and James Cebula. "The Basics of Cloud Computing." United_States_Computer, Carnegie-Mellon-University, 2011._Available:https://www.us-cert.gov/sites/default/files/publications/CloudComputingHuthCebula.pdf.  

3.              Cloud Computing in education, UNESCO (United Nations Educational Scientific and Cultural Organization), UNESCO Institute for Information Technologies in Education, Policy brief, September 2010.Available:http://unesdoc.unesco.org/images/0019/001904/190432e.pdf.

4.              Cloud Computing: Benefits, risks and recommendations for information security, ENISA, European Network and Information Security_Agency,_November_2009.Available:http://www.enisa.europa.eu/act/rm/files/deliverables/cloud-computing-risk-assessment/at_download/fullReport.

5.              Hsiao, Hsu-Chun, Yue-Hsun Lin, Ahren Studer, Cassandra Studer, King-Hang Wang, Hiroaki Kikuchi, Adrian Perrig, Hung-Min Sun, and Bo-Yin Yang. "A study of user-friendly hash comparison schemes."In Computer Security Applications Conference, ACSAC'09. Annual, pp. 105-114. IEEE, 2009.

6.              A.R. Yumerefendi and J.S. Chase, “Strong Accountability for  Network Storage,” Proc. Sixth USENIX Conf. File and Storage Technologies (FAST) , pp. 77-92, 2007.

7.              Y. Zhu, H. Wang, Z. Hu, G.-J. Ahn, H. Hu, and S.S. Yau, “Efficient

8.              Provable Data Possession for Hybrid Clouds,” Proc. 17th ACM Conf. Computer and Comm. Security, pp. 756-758, 2010.

9.              G. Ateniese, R.C. Burns, R. Curtmola, J. Herring, L. Kissner, Z.N.J. Peterson, and D.X. Song, “Provable Data Possession at Untrusted Stores,” Proc. 14th ACM Conf. Computer and Comm. Security, pp. 598-609, 2007.

10.           G. Ateniese, R.D. Pietro, L.V. Mancini, and G. Tsudik, “Scalable and Efficient Provable Data Possession,” Proc. Fourth Int’l Conf. Security and Privacy in Comm. Netowrks (SecureComm), pp. 1-10, 2008.

11.           C.C. Erway, A. Kupcu , C. Papamanthou, and R. Tamassia, “Dynamic Provable Data Possession,” Proc. 16th ACM Conference, Computer and Communication Security, pp. 213-222, 2009.

12.           Hovav Shacham, and Brent Waters. "Compact proofs of retrievability." In Advances in Cryptology-ASIACRYPT 2008, pp. 90-107. Springer Berlin Heidelberg, 2008.

13.           Min Xie, Haixun Wang, Jian Yin and Xiaofeng Meng. "Integrity auditing of outsourced data." In Proceedings of the 33rd international conference on Very large data bases, pp. 782-793. VLDB Endowment, 2007.

14.           Philippe Golle, Stanislaw Jarecki, and Ilya Mironov. "Cryptographic primitives enforcing communication and storage complexity." In Financial Cryptography, pp. 120-135. Springer Berlin Heidelberg, 2003.

15.           P. Syam Kumar and R. Subramanian. "An Efficient and Secure Protocol for Ensuring Data Storage Security in Cloud Computing." IJCSI International Journal of Computer Science Issues 8, no. 6, 2011.

16.           Wang Qian, Cong Wang, Kui Ren, Wenjing Lou and Jin Li. "Enabling public auditability and data dynamics for storage security in cloud computing." Parallel and Distributed Systems, IEEE Transactions on 22, no. 5: 847-859. No 5, 2011.

17.           Yan Zhu, Huaixi Wang, Zexing Hu, Gail-Joon Ahn, Hongxin Hu, and Stephen S. Yau. "Efficient provable data possession for hybrid clouds." In Proceedings of the 17th ACM conference on Computer and communications security, pp. 756-758. ACM, 2010.

18.           “The Carbon emissions of server computing for small-to medium sized organizations”, a performance studies of On-premise verses the Cloud, October_2012._Available:https://www.wspgroup.com/Globaln/USA/Environmental/Sustainability/Documents/NRDC-WSP_Cloud_Computing.pdf.

19.           Available: http://www.storagemagazine.co.uk

20.           Sudip Chahal, Krishnamurthy Anandarao, Chris Peters, Shane Healy, Nigel Wayman, Steve Owen, “ Implementing Cloud Storage Metrics to Improve IT Efficiency and Capacity Management”, Intel IT, IT best practices, Cloud Computing and IT Efficiency, July 2011. Available:
http://www.intel.com/content/dam/doc/white-paper/intel-it-implementing-cloud-storage-metrics-paper.pdf.
21.           Saurabh Kumar Garg and Rajkumar Buyya, “Green Cloud computing and Environmental Sustainability”, Cloud computing and Distributed
Systems(CLOUDS)Laboratory.Available:http://www.cloudbus.org/papers/Cloud-EnvSustainability2011.pdf‎

22.           Available: http://www. Investopedia.com

23.           Seny Kamara, Kristin Lauter, “Cryptographic Cloud Storage”, In Financial Cryptography and Data Security, pp. 136-149, Springer Berlin_Heidelberg,_2010,_Available:http://www.informatik.uni-trier.de/~ley/pers/hd/k/Kamara:Seny

24.           Mehul A. Shah, Mary Baker, Jeffrey C. Mogul, Ram Swaminathan ,“Auditing to Keep Online Storage Services Honest.”, In HotOS, 2007. Available:http://www.hpl.hp.com/personal/Mary_Baker/publications/hotos2007.pdf‎pt

25.           “Negotiating the cloud – legal issues in cloud computing agreements”,

26.           Better Practice Guide, Department of Finance and Deregulation, Australian_Government,_November2011._Available:http://agict.gov.au/files/2011/11/Cloud-Legal-Draft-Better-Practice-Guide-November-2011.pdf

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21.

Authors:

Vikas  Kulkarni, Rajesh Nehete

Paper Title:

Simulation and Analysis of Photo-Voltaic (PV) based Solar Inverter System

Abstract:    The energy demand in the world is steadily increasing and new types of energy sources must be found in order to cover the future demands, since the conventional sources are about to be emptied. One type of renewable energy source is the photovoltaic (PV) cell, which converts sunlight to electrical current, without any form for mechanical or thermal interlink.A photovoltaic (PV) based 500W solar inverter system is developed which consists of PV Array, battery bank and solar inverter cum charge controller. The system works on both Solar and AC mains power depending on the energy requirement. The goal of this project is to Simulate, design, develope and analyzes the PV based inverter system. Simulation of solar cell is carried out in MATLAB/SimElectronics which can be used to analyze photovoltaic panel in varying atmospheric conditions. The inverter is MOSFET drive H Bridge PIC controlled single phase SPWM. The system is tested on resistive and inductive load. Voltage/current waveform analysis, power quality and FFT analysis is carried out by using power quality analyzer as well as load sharing between photovoltaic array and battery, battery and mains supply is done.

KeywordS:  PWM-pulse width modulation, SPWM- Sinusoidal Pulse Width Modulation, PV- Photo Voltaic


References:

1.              R. A. Messenger and J. Ventre, Photovoltaic System Engineering, CRC, 2nd ed., 2003.
2.              J. Hu, J. Zhang, and H. Wu, “A novel mppt control algorithm based on numerical calculation for pvgeneration systems,”

3.              F. Luo, P. Xu, Y. Kang, and S. Duan, “A variable step maximum power point tracking method usingdifferential equation solution,” 2007 Second IEEE Conference on Industrial Electronics and Applications,pp. 2259–2263, 2007.

4.              W. Swiegers and J. H. Enslin, “An integrated maximum power point tracker for photovoltaic panels,”Industrial Electronics, 1998. Proceedings. ISIE ’98. IEEE International Symposium on, pp. 40–44, July1998.

5.              S.B. Kjær, J.K. Pedersen, F. Blaabjerg, Power inverter topologies for photovoltaic modules – a review,IEEE proc. of the 37th annual industry application conference (IAS’02), vol. 2, pp. 782-788, 2002.

6.              ‘P929 Recommended Practice for Utility Interface of Photovoltaic (PV) Systems’,IEEE, Sep 1998.

7.              Osorio, C., Recorded Webinar-Model-Based Design for Solar Power Systems.

8.              Zekai, S., Solar Energy Fundamentals and Modelling Techniques, Springer, Ltd., 2008.

9.              Vandana Khanna’s “MATLAB/SIMELECTRONICS Models Based Study of Solar Cells”, InternationalJournal Of Renewable Energy Research Vol.3, No.1, 2013.

10.           Marcel ISTRATE “Modeling And Simulation Of Photovoltaic Arrays”.

11.           Qiu Bin and Ma Yundong “Research on Control Strategies of a Stand-alone Photovoltaic System”.

12.           International Conference on Renewable Energies and Power Quality (ICREPQ’12) Santiago de Compostela (Spain), 28th to 30th March, 2012.

13.           A.Ali Qazalbash, “Design and Implementation of Microcontroller based PWM technique for Sine wave Inverter”.

14.           Messenger, R.A., Ventre, J., Photovoltaic Systems Engineering, CRC Press LLC, 2000.

15.           Power MOSFET Design’, Taylor, B.E., Wiley, 1993, various.


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22.

Authors:

Vahid Nourani

Paper Title:

A Review on Applications of Artificial Intelligence-Based Models to Estimate Suspended Sediment Load

Abstract:     Undeniably application of Artificial Intelligence (AI) has grown increasingly through past years. Hydrology also has its portion of utilization of AI-based models. Among different parts of hydrology, Suspended Sediment Load (SSL) estimation plays an important role since SSL can cause trouble in water resources engineering and environmental procedures. Therefore, employing AI-based models would cause more precise consequences. Recently proposed hybrid models provided more accurate prediction. These models employ AI-based models too, but in comparison, hybrid models forecast phenomena more accurate than sole AI-based models. It is because hybrid models can deal with non-stationary data. In this paper, advantages and disadvantages of both AI-based and hybrid models in the field of SSL modeling are discussed in the details.

Keywords:
  Artificial Intelligence, Hybrid models, Suspended sediment load.


References:

1.              Haykin.  (1999).  Neural networks: a comprehensive foundation. Prentice Hall, University of Michigan, United States.
2.             F.G. Bekele. (2007). Integrated modelling System for multi- objective management of ecosystem services in a watershed. University of Carbondale. United States.

3.             M.L. Minsky, S.A. Papert. (1969).  Perceptrons: An introduction to computional geometry. The MIT Press, Cambridge, MA.

4.             P.J. Werbos. (1975). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.

5.             A.Y. Shamseldin. (1997).  Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199. pp. 272–294.

6.             R.J.  Abrahart,  L.  See,  E. Kneale.(1999). Applying saliency analysis to neural network rainfall-runoff modelling. Proceedings of the 4th International Conference on Geocomputation, Fredericksburg, Virginia. USA, 25-28 July.

7.             A.S.  Tokar,  P.A.  Johnson. (1999). Rainfall-Runoff Modeling using artificial neural networks. J. Hydraul Eng. 4(3). pp. 232–239.

8.             D.P.  Solomatine, K.N.  Dual. (2003). Model trees as an alternative to neural networks in Rainfall-runoff modelling, Hydrol. Sci. J. 48(3). pp. 399–411.

9.             O. Giustolisi,  D. Laucelli.  (2005).  Improving generalization of artificial neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 50(3). pp. 439–457.

10.          H.K. Cigizoglu. (2003). Estimation, forecasting and extrapolation of acceleration data by artificial neural networks. Hydrol. Sci. J. 48(3). pp. 349–361.

11.          H.K. Cigizoglu, O.  Kisi.  (2005). Flow Prediction by three Back Propagation    Techniques Using k-fold Partitioning of Neural Network Training Data. Nord. Hydrol. 36(1). pp. 1–16.

12.          O.  Kisi, H.K. Cigizoglu. (2007). Comparison of different ANN techniques in river flow prediction. Civ. Eng. Environ. Syst. 24(3). pp. 211-231.

13.          S.K.  Jain,  A.  Das, D.K. Srivastava. (1999). Application of ANN for reservoir inflow prediction and opreation. J. Water. Res.  Pl. 125(5). pp. 263–271.

14.          S.K.  Jain. (2001). Development of integrated sediment rating curves using ANNs. J. Hydraul. Eng. 127(1). pp. 30–37.

15.          G. Tayfur. (2002). Artificial neural networks for sheet sediment transport. Hydrol. Sci. J. 47(6). pp. 879–892.

16.          H.K. Cigizoglu. (2004). Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons.  Adv. Water.  Resour. 27: 185-195.

17.          O. Kisi. (2004). Multi-layer perceptrons with Levenberg – Marquardt optimization algorithm for suspended sediment concentration prediction and estimation. Hydrol. Sci. J. 49(6). pp. 1025–1040.

18.          O.  Kisi. (2005). Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J. 50(4). pp. 683–696.

19.          H.K. Cigizoglu, O. Kisi. (2006). Methods to improve the neural network performance in suspended sediment estimation.  J. Hydrol. 317. pp. 221–238.

20.          V.  Nourani. (2009). Using artificial neural networks (ANNs) for sediment load forecasting of Talkherood River mouth. J. U. E. E. 3(1). pp. 1–6.

21.          V. Nourani,  O. Kalantari,  A. Baghanam. (2012). Two Semidistributed ANN-Based Models for Estimation of Suspended Sediment Load. J. Hydraul. Eng. 17(12). pp. 1368–1380.

22.          J.S.R.  Jang.  (1993). Adaptive-network-based fuzzy inference system. IEEE, Man, and Cybernetics. 23(3). pp. 665–685.

23.          J.S.R. Jang,  C.T. Sun,  E. Mizutani. (1997). Neuro-Fuzzy and Soft Computing. Prentice Hall. ISBN 0-13-261066-3. pp.  335–368.

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26.          V.  Nourani,  O. Kisi,  M. Komasi. (2011).  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process.  J. Hydrol. 402. pp. 41–59.

27.          O. Kisi. (2009). Evolutionary fuzzy models for river suspended sediment concentration estimation. J. Hydrol. 372. pp. 68–79.

28.          M. Cobaner, B. Unal, O. Kisi. (2009). Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydrometeorological data. J. Hydrol. 367. pp.  52–61.

29.          T.  Rajaee,  S.A.  Mirbagheri, V.  Nourani, A.  Alikhani.  (2010). Prediction of daily suspended sediment load using wavelet and neuro-fuzzy combined model. Int. J. Environ. Sci. Technol. 7(1). pp. 93–110.

30.          O.  Mohamad Rezapour,  T.  Shui,  A.A.  Dehgani.  (2010).  Review of genetic programming in water resource engineering. AIBAS. 4(11). pp. 5663–5667.

31.          D.E. Goldberg. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Mass.

32.          N. Cousin, D.A. Savic. (1997). A rainfall-runoff model using genetic programming. Centre for Systems and Control Engineering. Report No. 97/03, School of Engineering, University of Exeter, Exeter, United Kingdom. p. 70.

33.          J.P. Drecourt. (1999). Application of neural networks and genetic programming to rainfall-runoff modeling. D2K Technical Report 0699-1-1, Danish Hydraulic Institute. Denmark.

34.          V. Babovic,  M. Keijzer,  D.R. Aguilera, J.  Harrington. (2001). Automatic Discovery of Settling Velocity Equations. D2K Technical Report. D2K-0201-1.

35.          E.L.  Harris, V.  Babovic,  R.A.  Falconer, (2003). Velocity predictions in compound channels with vegetated floodplains using genetic programming. JRBM. 1(2). pp. 117–123.

36.          J.  Dorado, J.R.  Rabunal,  A.  Pazos,  A.  Rivero,  J. Santos.  (2003). Prediction and modelling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Eng. Appl. Artif. Intel. 17. pp. 329–343.

37.          O. Giustolisi. (2004). Using genetic programming to determine Chezy resistance coefficient in corrugated channels. J. Hydro. Inform. 6. pp. 157–173.

38.          J.R.  Rabunal,  J.  Puertas,  J.  Suarez,  D.  Rivero. (2007). Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol. Process. 21. pp. 476–485.

39.          V. Nourani, M. Komasi, M. Alami. (2012). Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modeling of Rainfall–Runoff Process. J. Hydraul. Eng. 17(6). pp. 724–741.

40.          H. Hakimzadeh, V. Nourani, A. Amini. (2013). Genetic Programming Simulation of Dam Breach Hydrograph and Peak Outflow Discharge. J. Hydraul. Eng. 10.1061/(ASCE)HE.1943-5584.0000849.

41.          V. Babovic. (2000). Data mining and knowledge discovery in sediment transport. Compute -Aided. Civ. Inf. 15(5). pp. 383–389.

42.          J.A. Zyserman, J. Fredsoe. (1994). Data analysis of bed concentration of suspended sediment. J. Hydraul. Eng. 120(9). pp. 1021-1042.

43.          A.S. Kizhisseri, D. Simmonds, Y. Rafiq, M. Borthwick. (2005). An Evolutionary computation approach to sediment transport modeling. In: Fifth International Conference on Coastal Dynamics. Barcelona. Spain.

44.          Aytek,  O.  Kisi.  (2008).  A genetic programming approach to suspended sediment modelling. J. Hydrol. 351. pp. 288–298.

45.          O.  Kisi,  A. Guven. (2010). A machine code-based genetic programming for suspended sediment concentration estimation,  Adv. Eng. Softw. 41. pp. 939–945.

46.          V.  Vapnik,  C.  Cortes.  (1995).  Support Vector Networks, Machine Learning. 20. pp. 1–25.

47.          E.  Kakaei, A.  Moghddam Nia,  A. Ahmadi. (2013). Daily suspended sediment load prediction using artificial neural networks and support vector machines. J. Hydrol. 478. pp. 50–62.

48.          V. Vapnik. 1998. Statistical Learning Theory. Wiley, New York.

49.          T. Rajaee, V. Nourani, M. Zounemat-Kermani, O. Kisi. (2011). River suspended sediment load prediction: Application of ANN and wavelet conjunction model. J. Hydraul. Eng. 16(8). pp. 613–627.

50.          T. Partal, H.K. Cigizoglu. (2008). Estimation and forecasting of daily suspended sediment data using wavelet-neural networks.  J.  Hydrol. 358. pp. 317–331.

51.          O.  Kisi,  I.  Yuksel,  E. Dogan. (2008). Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques.  Hydrolog.  Sci.  J. 53 (6). pp. 1270–1285.

52.          Y.M.  Zhu,  X.X.  Lu, Y.  Zhou. (2007). Suspended sediment flux modeling with artificialneural network: an example of the Longchuanjiang River in the  UpperYangtzeCatchment, China, Geomorphology 84. pp. 111–125.

53.          B. Bhattacharya, R.K. Price, D.P. Solomatine. (2007). Machine learning approach to modelling sediment transport. J. Hydraul. Eng. 133(4). pp. 440–450.

54.          H. M.D.  Azamathulla,  A. A.B.  Ghani,  C.K. Chang, Z.  Abu Hasan, N.A.  Zakaria.  (2010).  Machine learning approach to predict sediment load - a case study. Clean-SoilAir Water. 38 (10). pp. 969–976.

55.          L.C.  Jie,  S.T.  Yu. (2011). Suspended sediment load estimate using support vector machines in Kaoping River basin. IEEE Trans. Consum. Electron. 459. pp. 1750–1753. 

56.          M. Cimen.  (2008). Estimation of daily suspended sediments using support  vector  machines.  Hydrol. Sci. J. 53. pp. 656–666.

57.          O.  Kisi.  (2012). Modeling discharge-suspended sediment relationship using least square support vector machine. J. Hydrol. 456. pp. 110–120.

58.          H.C.  Zhou, Y.  Peng,  G.H.  Liang. (2008). The research of monthly discharge predictor corrector model based on wavelet decomposition. Water. Resour. Manag. 22. pp. 217–227.

59.          O.  Kisi. (2010). Daily suspended sediment estimation using neuro-wavelet models. Int. J. Earth. Sci.  99. pp. 1471–1482.

60.          V.  Nourani,  A.  Yahyavi Rahimi, F.H.  Nejad. (2013). Conjuction of ANN and threshold based wavelet de-noising approach for forecasting suspended sediment load. Int. J. Manag. Inf. Tech. 3(1). pp. 9–26.


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23.

Authors:

Haysam A. Ali Alamin, Eltyeb E.Abed Elgabar

Paper Title:

Success Factors for Adopting E-learning Application in Sudan

Abstract:      This paper aims to highlight the main factors that support the use of E-learning technologies, and the benefit of such applications in developing countries as general and in Sudan as a particular situation, toward the achievement of E-learning goals such as reaching the maximum number of customers “Students”, Improving the quality of the services "Education", and better interaction with full and part-time students. All these goals lead finally to students' satisfaction. The factors that this paper discusses will be mainly divided into two main categories, 1- The technical factors, or the infrastructure of the Information & Communication Technology (ICT) in Sudan. 2- Social factors that cause the success and popularity of the applications of E-learning in Sudan.

Keywords:
   E-learning, Sudan, ICT infrastructure, Popularity, Social factors, Technical factors


References:

1.              Thomas ülsmann ,Low Cost Distance Education Strategies: The use of appropriate information and communication technologies, ,IRRODL,(2004)
2.              Grönlund, Å, & Islam, Y. M. , A mobile e-learning environment for developing countries: the Bangladesh virtual interactive classroom. Information Technology for Development,2010, 16(4), 244–259.

3.              African Virtual University (http://www.avu.org/Press-Releases/african-virtual-university-oer-voted-best-emerging-initiative-by-the-global-community.html)

4.              Larry Attree, China and conflict- affected states Between principle and pragmatism, case study, Saferworld,(2012).

5.              Shanmugaratnam, Nadarajah. Post-war development and the land question in South Sudan. Norwegian University of life sciences (UMB). Noragric, (2008).

6.              Jon Lunn, Sudan: Peace or war, unity or secession, House of Commons Libarary,2010. 

7.              Horton, William. E-learning by design. Wiley. com, (2011).‏

8.              Zhu, Chang. "Student satisfaction, performance, and knowledge construction in online collaborative learning." Journal of Educational Technology & Society 15.1 (2012): 127-136.‏

9.              Bhuasiri, Wannasiri, et al. "Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty." Computers & Education 58.2 (2012): 843-855.

10.           Wei, June, J. Zhuo, and Hongmei Zhang. "Development of a mobile learning model with usability features for online education." International Journal of Mobile Learning and Organisation 2.1 (2008)

11.           Gedaref Digital City Organization, http://gedaref.com/ADBCP/recov/newone/index.php,Sudan

12.           The National Telecommunication Corporation, First Telecommunication Book, Sudan,NTC,(2009)

13.           The National Telecommunication Corporation,  http://www.ntc.gov.sd/images/stories/ docs/arabic/ mobileindicators.pdf, Sudan,NTC,(2013)

14.           The Central Intelligence Agency, https://www.cia.gov/library/publications/the-world-factbook /geos /su.html,2013.

15.           Awad Haj Ali Ahmed and Abu Bakr Baize, Sudan Sudanese experience in the trend towards e-government, the Faculty of Computer Science, Neelain University,  2002.


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24.

Authors:

Ritu Ahlawat

Paper Title:

Hydrological Data Network Modelling Using Artificial Neural Network in Betwa Catchment

Abstract:  Design of hydrological data network depends not only on physical parameters but also uncertainty in volume and flow of rainfall and runoff. Bias in uncertainty level can’t be removed fully, hence use of artificial neural network (ANN) based on training of past dataset can provide useful insight in determination of optimal network. In this paper, an attempt has been made to use the power of soft computing in terms of ANN based analysis of rainfall data of Betwa river catchment. Genetic feed forward algorithm and sensitivity analysis of mean data was done in EXCEL based version of NeuroSolution Software. Minimum spatial error in rainfall values of catchment provided clues about location of stations.  

Keywords:
    Spatial error, sensitivity, hydrological data network, uncertainty in rainfall 


References:

1.             R. M. Holger, R.M. and C. D. Graeme, "The use of artificial neural network for the prediction of water quality parameters", Water Resource Research, Vol. 32(4), 1966, pp.1013-1022.
2.             S. E., Cieniawski, J.W., Eheart, J. W. and S. Ranjithan "Using genetic algorithms to solve a multiobjective groundwater monitoring problem". Water Resource Research, Vol. 31(2), 1995, pp. 399-409.

3.             D. C. Dandy, R. Simpson, and L. J. Murphy, "An improved genetic algorithm for pipe network optimisation", Water Resource Research, Vol. 32(2), 1996, pp. 499-558.

4.             R. Ahlawat, Water Resource Potential and Utilisation in the Bundelkhand Region. Unpublished M. Phil Dissertation, University of Delhi, Department of Geography, 1999.

5.             P. Saxena, "Bundelkhand - A case study in hydrography and water resources". Transactions of the Indian Council of Geographers, Vol. 5, 1968, pp. 133-139.

6.             J. C. Principe, Euliano, N.R. and Lefebvre, Neural and Adaptive Systems: Fundamentals through Simulations. New York: John Wiley & Sons, 2000.

7.             Kuo-Lin Hsll, , H. V. Gupta, and S. Sorooshian, “Artificial neural network modeling of the rainfall runoff process”, Water Resource Research, Vol. 31(10), 1995, pp. 2517-2530.

8.             V. M. Johnson, and L. L. Rogers, “Accuracy of neural network approximators in simulation optimization”. Journal of Water Resources Planning and Management, Vol. 126 (2), 2000, pp. 48-56.

9.             Moody, and D. B. Katz, “Artificial intelligence in the study of mountain landscapes”, in Geographic Information Science and Mountain Geomorphology,  M.P. Bishop and J. F. Shroder Jr, Eds. UK: Praxis Pub, 2004, pp. 219-247.

10.          M. Nieda, “Use of network algorithms in spatially distributed models for the study of river basin response”, in Application of Geographic Information Systems in Hydrology and Water Resource Management, IAHS Pub No. 235, K. Kocar and H.P. Nachtnebel, Eds. 1996, pp. 207-214.

11.          S. Openshaw, and C. Openshaw,  Artificial Intelligence in Geography. Chichester, England: John Wiley & Sons, 1997.

12.          S. Sanga, and A. Jain, “Rainfall-runoff pattern mapping using ANN”, in Proceedings of the International Conference on Hydrology and Watershed Management, Vol. I & II,  B.Venkateswara Rao, K.R. Reddy, C. Sarada and K. Raju, Eds. Hyderabad: BSP Pub, 2003, pp. 220-226 


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25.

Authors:

Mohamed Abdelhadi, Tiruveedula Gopi Krishna, Ghassan Kanann

Paper Title:

Developed Taxonomy for Information Retrieval Systems Based on Arabic Language

Abstract:  This research has introduced a fully developed Taxonomy for Information Retrieval System based on Arabic Language; and also studied the rationale behind Information Retrieval from Arabic Linguistics with respect to new prospective for Arabic-IRs in such well-done Data organization. It has indeed led to an improved computational framework as well as provided excellent solutions for Arabic-IR system by means of emerging both Arabic Linguistics and Information Retrieval Systems.  

Keywords:
     Arabic Language, Information Retrieval Systems, Knowledge Management Systems.


References:

1.              S. E. Robertson, S. Walker, and M. Beaulieu, Okapi at TREC–7: automatic ad hoc, filtering, VLC and filtering tracks. In Proceedings of 7th Text Retrieval Conference (TREC-7), pages 253–264. NIST special publication, 1999.
2.              Khoja, S. and Garside, R. "Stemming Arabic Text", Computing Department Lancaster University, Lancaster, 1999.

3.              Singhal, C. Buckley, and M. Mitra,” Pivoted document length normalization”, In Proceedings of 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 21–29, Zurich, Switzerland, 1996, ACM Press.

4.              Hani Safadi. Dr. Oumayma Dakkak, Dr. Nada Ghneim, Computational Methods to Vocalize Arabic Texts, 2006.

5.              Jacques Savoy, Yves Rasolofo, and Report on the TREC-11 Experiment: “Arabic, Named Page and Topic Distillation Searches”, Jacques Savoy, Yves Rasolofo, 2002.

6.              Kareem Darwish and Douglas W. Oard, “CLIR Experiments at Maryland for TREC-2002: Evidence combination for Arabic-English retrieval”, 2002.J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.

7.              Aitao Chen and Fredric Gey,” Building an Arabic Stemmer for Information Retrieval”, 2002.

8.              Abdelali, A,” Localization in modern standard Arabic, Journal of the American Society for Information Science and Technology, 55, 1, 23-28,2004.

9.              Suleiman, Y,” The Arabic Language and National Identity”, Washington, D.C. Georgetown University Press, 2003.

10.           Elkhafaifi, H.M,” Arabic language planning in the age of globalization, Language Problems and Language Planning”, Vol. 26, No. 3, 253-269,2002.J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com

11.           Beesley, Kenneth R. “Arabic Finite-State Morphological Analysis and Generation”, In COLING the 16th International Conference on Computational Linguistics, Copenhagen, Denmark, pp. 89-94, 1996


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26.

Authors:

Mohamed A. Ramadan, Mohamed S. Al-Luhaibi

Paper Title:

Application of Sumudu Decomposition Method for Solving Linear and Nonlinear Klein-Gordon Equations

Abstract:   In this paper, Sumudu decomposition method is applied to solve various forms of linear and nonlinear Klein-Gordon equations. The technique is a combined form of the Sumudu transform method and the Adomian decomposition method.  The nonlinear term can easily be handled with the help of Adomian polynomials which is considered to be a clear advantage of this technique. We illustrate this technique with the help of four examples. The results reveal that the proposed algorithm is very efficient, simple and can be applied to other nonlinear problems. 

Keywords:
 Sumudu decomposition method; Sumudu transform; Adomian polynomials; Linear and nonlinear Klein-Gordon equations.


References:

1.             S. T. Mohyud-Din, M. A. Noor and K. I. Noor,  "Some relatively new techniques for nonlinear         problems",       Mathematical Problems in    Engineering, Vo. 2009, Article ID   234849, 25pages. 2009.
2.             A. M. Wazwaz, "The modified decomposition  method for analytic treatment of differential   equations", Applied Mathematics and Computation, Vol. 173, pp. 165-176,      2006.

3.             H. He, "Variational iteration method-akind of nonlinear analytical technique: some examples",International Journal of Nonlinear Mechanics,  Vol. 34, pp. 699-708, 1999.

4.             .H. He, "Homotopy perturbation technique," Computer Methods in Applied Mechanics  and Engineering, Vol. 178, pp. 257-262, 1999.

5.             V. Daftardar-Gejji and H. Jafari, "An iterative method for solving nonlinear functional  equations", Journal of   Mathematical Analysis  and Applications, Vol. 316, No. 2, pp. 753-763,         2006.

6.             G. Adomian, "Solving frontier problems of physics: The decomposition method",   Kluwer Acad. Publ,        1994.

7.             E. Alizadeh, M. Farhadi, K. Sedighi, H. R.  Ebrahimi-Kebria, and A. Ghafourian,   "Solution  of the Falkner-Skan  equation for wedge by       Adomian Decomposition method",Communications        in Nonlinear Science and     Numerical       Simulation, Vol. 14, No. 3, pp.  724-    733, 2009.

8.             A. M. Wazwaz, "The modified decomposition        method and Pâté approximants for a boundary         layer equation in    unbounded domain", Applied        Mathematics and Computation, Vol. 177, No. 2, pp.         737-744, 2006.

9.             N. Bellomo and R. A. Monaco, "A comparison between Adomian's decomposition methods and perturbation    techniques for nonlinear random   differential equations", Journal of Mathematical   Analysis and Applications, Vol. 110, No. 2, pp.  495-502, 1985.

10.          R. Race, "On the Adomian decomposition method  and comparison with Picard’s method",Journal of Mathematical Analysis and Applications, Vol. 128, pp. 480-483, 1987.

11.          G. Adomian and R. Rach, "Noise terms in  decomposition solution series", Computers and Mathematics with Applications, Vol. 24, No. 11, pp. 61--64, 1992.

12.          A. M. Wazwaz, "Necessary conditions for the   appearance of noise terms in decomposition  solution series", Applied   Mathematics and Computation, Vol. 81, pp. 1718-1740, 1987.

13.          G. K.Watugala, "Sumudu transform-a new integral transform to solve differential   equations and control  engineering problems", Mathematical Engineering in Industry, Vol. 6, No. 4, pp. 319-329, 1998.

14.          S. Weerakoon,"Application of Sumudu transform  to partial differential equations", International Journal of Mathematical Education in Science and   Technology, Vol. 25, No. 2, pp. 277-283, 1994.

15.          S.Weerakoon, "Complex inversion formula for Sumudu transform", International Journal of  Mathematical   Education in Science and Technology, Vol. 29, No. 4, pp. 618-621, 1998.

16.          M. A. Asiru, "Further properties of the Sumudu transform and its applications", International Journal of Mathematical Education in Science and Technology, Vol. 33,  No. 3, pp. 441-449, 2002.

17.          Kadem, "Solving the one-dimensional neutron transport equation using Chebyshev polynomials and the Sumudu transform", Analele Universitatii dinOradea, Vol. 12,pp.153-171, 2005.

18.          A. Kilicman, H. Eltayeb, and K. A. M. Atan, "A note on the comparison between Laplace and Sumudu transforms",         Iranian Mathematical Society, Vol. 37, No. 1, pp.131-141, 2011.

19.          A. Kilicman and H. E. Gadain, "On the applications of Laplace and Sumudu  transforms" Journal of the Franklin          Institute, Vol. 347, No. 5, pp. 848-862, 2010.

20.          H. Eltayeb, A. Kilicman, and B. Fisher, "A new integral transform and associated distributions", Integral  Transforms and Special Functions, Vol. 21, No. 5-6, pp. 367- 379, 2010.

21.          A. Kilicman and H. Eltayeb, "A note on integral transforms and partial differential equations", Applied         Mathematical Sciences, Vol. 4, No. 1-4, pp. 109-118, 2010.

22.          Kilicman, H. Eltayeb, and R. P. Agarwal, "On Sumudu transform and system of  differential equations", Abstract  and Applied Analysis, Article ID598702, 11 pages, 2010.

23.          J. Zhang, "A Sumudu based algorithm for solving differential equations", Academy of Sciences of Moldova, Vol.   15, No. 3, pp. 303-313, 2007.

24.          [H. Eltayeb and A. Kilicman, "Application of Sumudu Decomposition Method to Solve  Nonlinear System of Partial Differential Equations", Journal of Abstract
and Applied nalysis, Vol. 2012, Article ID 412948, 13 pages, 2012.

25.          D. Kumar, J. Singh and S. Rathore, "Sumudu Decomposition Method for Nonlinear Equations", International Mathematical Forum, Vol. 7, No. 11, pp. 515 - 521, 2012.

26.          M. A. Rana, "A new computational technique for Sumudu transforms based on Adomian decomposition method",    Life Science Journal, Vol. 10, No. 9s, 2013

27.          A.M. Wazwaz, "new algorithm for calculating Adomian polynomials for nonlinear operators", Applied Mathematics and Computation, Vol. 111, pp. 53-69, 2000.

28.          M. Yaseen and M. Samraiz, "The modified new iterative method for solving linear and nonlinear Klein-Gordon       equations", Applied Mathematical Sciences, Vol. 6, No. 60, pp. 2979 - 2987, 2012.

29.          J. Singh, D. Kumar and S. Rathore, "Application of homotopy perturbation transform  method for solving linear and  nonlinear Klein-Gordon equations," Journal of Information and Computing Science, Vol. 7, No. 2, pp. 131-139,           2012.

30.          S.T. mohyud-din, A. yildirlm, "Variational iteration method for solving Klein-Gordon equations", Journal of Applied Mathematics, Statistics and Informatics, Vol. 6, No. 1, 2010.


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27.

Authors:

V.Prasath, R.Buvanesvari, P.Nithin, S.Banu, K.Rajeswari

Paper Title:

Graphical Password Authentication Using Persuasive Cued Click-Points Mechanism

Abstract:    This paper presents an integrated evaluation of the Persuasive Cued Click-Points graphical password scheme, including usability and security evaluations, and implementation considerations. An important usability goal for knowledge-based authentication systems is to support users in selecting passwords of higher security, in the sense of being from an expanded effective security space. We use persuasion to influence user choice in click-based graphical passwords, encouraging users to select more random, and hence more difficult to guess, click-points. 

Keywords:
  Persuasive Cued Click-Points


References:

1.              Tara H R, Usha and Ganeshayya I Shidaganti, “Knowledge Based Authentication Mechanism Using Persuasive Cued Click Points”, June – 2013
2.              Sonia Chiasson ,Elizabeth Stobert, Alain Forget, Robert Biddle and Paul C. Van Oorschot, “Persuasive Cued Click-Points: Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism”, March 2012

3.              Wazir Za ada Khan, M Y Aalsalem and Yang Xiang, “A Graphical Password Based System for Small Mobile Devices”, September 2011.

4.              S.Chiasson, R. Biddle, and P. van Oorschot, “A Second Look at the Usability of Click-Based Graphical Passwords,” Proc. ACM Symp. Usable Privacy and Security (SOUPS), July 2007.

5.              S.Chiasson, A. Forget, R. Biddle, and P. van Oorschot, “Influencing Users towards Better Passwords: Persuasive Cued Click- Points,” Proc. British HCI Group Ann. Conf. People and Computers: Culture, Creativity, Interaction, Sept. 2008.

6.              S.Chiasson, A. Forget, E. Stobert, P. van Oorschot, and R. Biddle, “Multiple Password Interference in Text and Click-Based Graphical Passwords,” Proc. ACM Conf. Computer and Comm. Security (CCS), Nov. 2009.

7.              E. Stobert, A. Forget, S. Chiasson, P. vanOorschot, and R. Biddle, “Exploring Usability Effects of Increasing Security in Click-Based Graphical Passwords,” Proc. Ann. Computer Security Applications Conf. (ACSAC), 2010.

8.              J. Yan, A. Blackwell, R. Anderson, and A. Grant, “The Memorability and Security of Passwords,” Security and Usability: Designing Secure Systems That People Can Use, L. Cranor and S. Garfinkel, eds., ch. 7, pp. 129-142, O’Reilly Media, 2005.

9.              L. Jones, A. Anton, and J. Earp, “Towards Understanding User Perceptions of Authentication Technologies,” Proc. ACM Workshop Privacy in Electronic Soc., 2007.

10.           D. Florencio and C. Herley, “A Large-Scale Study of WWWPassword Habits,” Proc. 16th ACM Int’l World Wide Web Conf.(WWW), May 2007.

11.           M. Weir, S. Aggarwal, M. Collins, and H. Stern, “Testing Metrics for Password Creation Policies by Attacking Large Sets of Revealed Passwords,” Proc. ACM Conf. Computer and Comm. Security (CCS), 2010.

12.           D. Florencio and C. Herley, “Where Do Security Policies Come from” Proc. Symp. Usable Privacy and Security, 2010.


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28.

Authors:

Ajay Kumar

Paper Title:

Power Budget for Single Mode Optical Fiber

Abstract:     The allocation of power losses between optical source and detector is referred to as the power budget.  To ensure that the fiber system has sufficient power for correct operation, we need to calculate the span's power budget, which is the maximum amount of power it can transmit.  Transmission distance depends on transmitter output power, receiver’s sensitivity,  fiber quality, splice loss, connector loss, safety margin and signal losses caused by other factors.  The reliability or performance of a fiber optic communication system can be enhanced  through careful selection and matching of all the subparts (transmitter, fiber optic, receiver, connectors).The  optic fiber should be matched and verified according to its applications. It should be stresses that the optimization of communication system based on fiber optics has quite a number of parameters. The only way to enhance on such system is to use a thorough designing approach and tuning it with the feed-back received from an economical analysis.

Keywords:
   Power Budget, transmitter power, receiver sensitivity, connector loss & splice loss.


References:

1.             R. AGGARWAL, P. MOORE, Digital communication for protection. III: Fiber optics, IEE Power Engineering Journal, vol. 8, no. 5, pp. 241-246, Oct. 1994.
2.             John M. Senior, Optical Fiber Communication, Principals And Practice, 2nd edition, Prentice Hall, 1992.

3.             Ivan P. Kaminow and Thomas L.Loch, Optical Fiber Telecommunications IIIA, Academic Press, 1997.

4.             G. Agrawal, “Fiber-Optic Communication Systems”, New York, N.Y.: John Wiley & Sons, pp. 172, 1997.

5.             Edward A. Lee and David G. Messerchmitt, Digital Communication, Kluwer Academic Publishers, Boston, 1988.

6.             Theodore S. Rappaport, Wireless Communication: Principals and Practice, N.J., Prentice Hall PTR, 1996.

7.             B. Mukherjee, “Optical Communication Networks”, McGraw-Hill, New York, 2003.

8.             D. Banerjee and B. Mukherjee, “A Practical Approach for routing and Wavelength Assignment in Large Wavelength Routed Optics Networks”, IEEE Journal on Selected Areas in Communications, vol.14, no.5, pp.903-908, June 1996.

9.             C. YEH, Handbook of fiber optics. Theory and applications, Academic Press Inc., San Diego, CA, USA, pp.187-211, 1990

10.          G. SNELL, An introduction of fiber optics and broadcasting, SMPTE Journal, vol. 105, no. 4, pp. 1-7,1996.


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29.

Authors:

K.Venkatraman, Sudhir.P.J.Raj, R. AntoRose, K.Pasupathi, S.Manikandan

Paper Title:

Asymptotic Power Limits for Ad-Hoc Networks Reconsider the Smart Antenna Cases

Abstract:  Smart antennas can be useful in significantly increasing the capacity of wireless ad hoc networks. A number of media access and routing protocols have been recently proposed for the use with such antennas, and have shown significant performance improvements over the omnidirectional case. However, none of these works explores if and how different directional and smart antenna designs affect the asymptotic capacity bounds, derived by Kumar and Gupta[12]. These bounds are inherent to specific ad-hoc network characteristics, like the shared wireless media and multi-hop connectivity, and pose major scalability limitations for such networks.  In this work, we present how smart antennas can affect the asymptotic behavior of an ad-hoc network’s capacity. Specifically, we perform a capacity analysis for an ideal flat-topped antenna, a linear phased-array antenna, and an adaptive array antenna model. Finally, we explain how an ad-hoc network designer can manipulate different antenna parameters to improve the scalability of an ad-hoc network.  

Keywords:
    capacity; ad-hoc; directional antennas; smart antennas;


References:

1.             W. F. Gabriel, ”Adaptive Processing Array Systems,” in Proceedings of IEEE, Vol. 80, Issue 1, Jan 1992.
2.             Asympotic power limits for Ad-hoc Networks reconsider the e  Directional and Smart Antenna Cases

3.             Bellofiore, J. Foutz, R. Govindarajula, I. Bahceci, C.A. Balanis,  A.S. Spanias, J.M. Capone, and T.M. Duman, “Smart antenna system analysis, integration and performance for mobile ad-hoc networks (MANETs),” IEEE Transactions on Antennas and Propagation, Volume: 50 Issue: 5, May 2002 Page(s): 571 –581

4.             R. Radhakrishnan, D. Lai, J. Caffery, and D.P Agrawal, “Performance comparison of smart antenna techniques for spatial multiplexing in wireless ad hoc networks,” The 5th International Symposium on Wireless Personal Multimedia Communications, 2002, Volume: 1, 2002. Page(s):168-171.

5.             T. Ohira, “Analog smart antennas: an overview,” The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2002, Volume: 4, 2002, Page(s): 1502 –1506.

6.             T. Ohira, “Blind adaptive beamforming electronically-steerable parasitic array radiator antenna based on maximum moment criterion,” in IEEE Antennas and Propagation Society International Symposium, 2002, Vol. 2, 2002, Page(s): 652 –655.

7.             T. Ohira, and K. Gyoda, “Electronically steerable passive array radiator antennas for low-cost analog adaptive beamforming,” in Proceedings of IEEE International Conference on Phased Array Systems and Technology 2000, Page(s): 101 –104.

8.             Nasipuri, S. Ye, J. You, and R. E. Hiromoto, “A MAC protocol for mobile ad hoc networks using directional antennas,” IEEE Wireless Communications and Networking Conference (WCNC’2000), 2000.

9.             M. Takai, J. Martin, R. Bagrodia, and A. Ren, “Directional Virtual Carrier Sensing for Directional Antennas in Mobile Ad Hoc Networks,” Proc. ACM MobiHoc ‘2002, June 2002

10.          R. Roychoudhury, X. Yang, R. Ramanathan, and N. Vaidya, “Medium Access Control in Ad Hoc Networks Using Directional Antennas,” in Proc. of  MOBICOM ‘2002, Semptember 2002.

11.          L. Bao, and J.J. Garcia-Luna-Aceves, “Transmission Scheduling in Ad Hoc Networks with Directional Antennas,” in Proc. of ACM/IEEE MOBICOM ‘2002, Semptember 2002.

12.          P. Gupta, and P. R. Kumar, “The capacity of wireless networks," IEEE Transactions on Information Theory, vol. 46, pp. 388-404, March 2000.

13.          Spyropoulos, and C. S. Raghavendra, “Capacity Bounds for Ad-hoc Networks using Directional Antennas,” to appear in Proc. IEEE ICC ‘2003, May 2003.

14.          J. Li, C. Blake, D. S. J. Decouto, H. I. Lee, and R. Morris,”Capacity of Wireless Ad Hoc Networks,” in Proc. MOBICOM ‘2001, July 2001.

15.          S. Toumpis, and A.J. Goldsmith,”Capacity Regions for Wireless Ad Hoc Networks,” in Proc. IEEE ICC ‘2002, May 2002.

16.          Ram Ramanathan, "On the Performance of Beamforming Antennas in Ad Hoc Networks", Proc. of the ACM/SIGMOBILE MobiHoc 2001.

17.          Spyropoulos, and C. S. Raghavendra, “Energy Efficient Communication in Ad Hoc Networks Using Directional Antennas,” in Proc. IEEE INFOCOM ‘2002, June 2002.

18.          Nasipuri, K. Li, and U. R. Sappidi, “Power Consumption and Throughput in Mobile Ad hoc Networks using Directional Antennas,” in Proc. of 11th Conference on Computer Communications and Networks (ICCCN ’02), October 2002.

19.          Balanis, Antenna Theory: Analysis and Design, 2nd ed. New York: Wiley, 1997.

20.          IEEE Local and Metropolitan Area Network Standards Committee, Wireless LAN medium access control (MAC) and physical layer (PHY) specifications, IEEE standard 802.11-1999, 1999.

21.          S. Bandyopadhyay, K. Hasuike, S. Horisawa, and S. Tawara,”An Adaptive MAC and Directional Routing Protocol for Ad Hoc Wireless Network Using ESPAR Antenna,” in Proc. ACM MobiHoc ‘2001.

22.          http://www.wolfram.com/products/mathematica/

23.          T. S. Rappaport, Wireless Communications: Principles and Practice, Prentice Hall, 1996

24.          L. L. Xie, and P. R. Kumar, “A Network Information Theory for Wireless Communication: Scaling Laws and Optimal Operation,” submitted to IEEE Transactions on Information Theory, April 2002.

25.          Network connectivity paper


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30.

Authors:

Vani Maheshwari, Unmukh Dutta

Paper Title:

Comparative Study of Different Modified Artificial Bee Colony Algorithm with Proposed ABC Algorithm

Abstract:   Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the standard artificial bee colony algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm and simulated results are compared with different modified version of artificial bee colony algorithms, like ABC with uniform mutation, ABC with crossover and mutation and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm.  

Keywords:
     Artificial Bee Colony, ABC, crossover, Mutation, Genetic Algorithm, GA.

References:

1.             Dervis Karaboga • Bahriye Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm” J Glob Optim (2007), pp 459-471
2.             D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey, Technical Report-TR06, 2005

3.             Basturk, B., Karaboga, D. “An artificial bee colony (ABC) algorithm for numeric function optimization”. In: IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA (May 2006).

4.             Xiaohu Shi, Yanwen Li, Haijun Li, Renchu Guan, Liupu Wang and Yanchun Liang: “A Hybrid Swarm Intelligent Method Based on Genetic Algorithm and Artificial Bee Colony,” ICSI 2010, Part I, LNCS 6145, pp. 558–565, 2010. Springer-Verlag Berlin Heidelberg 2010.

5.             Zhi-Feng Hao, Zhi-Gang Wang and Han Huang, "A Particle swarm optimization algorithm with crossover operator", in Proc. of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007.

6.             Dongyong Yang, Jinyin Chen and Matsumoto Naofumi, “Self-adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization”, ICNC 2007.

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

8.             J.H. Holland, “ Adaptation in Natural and Artificial System,” The D.E. Goldberg, “Genetic Algorithms in  Search, Optimization   & Machine  Learning.   Reading,”MA: Addison-Wesley, 1989.

9.             J.H. Holland, “Adaptation in Natural and Artificial System, The University of Michigan Press”, Ann Arbor,1975.

10.          G-. G. Jin, S-. R. Joo, “A Study on a Real-Coded Genetic Algorithm,” Journal of Control, Automation, and Systems Engineering, vol. 6, no. 4, pp. 268-274, April 2000.

11.          Singh, N. Gupta and A. Singhal,” Artificial bee colony algorithm with uniform mutation”,  Proceedings of the  International Conference on Soft Computing for Problem  Solving (SocProS 2011), Volume 130, 2012, pp 503-511, December 20-22, 2011.

12.          M. Gupta and G. Sharma,” An Efficient Modified Artificial Bee  Colony  Algorithm  for  Job  scheduling  problem”,  International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume -1, Issue -6, January2012.  


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31.

Authors:

Shivakumar L N, Kiran Kumar G R, Marulasiddappa H B

Paper Title:

Implementation of Network Reconfiguration Technique for Loss Minimization on a Standard 16 Bus Distribution System 

Abstract:    Due to deregulation and restructuring it is expected that a large number of distributed generators will be connected to the distribution network. This will result in power distribution systems having locally looped networks and bi-directional power flows affecting existing operational schemes.  The distribution system reconfiguration is one of the important control schemes which can be affected by the presence of distributed generators.This paper presents a simple approach for distribution reconfiguration with distributed generators.  The distributed generators are considered as constant power sinks and the loss minimization algorithm has usual constraints along with line capacity constraint which limits the reverse power flow of distributed generators.  Standard switching indices are used for network reconfiguration and the algorithm is tested on a on a standard 16 bus system. 

Keywords:
      Distributed generation (DG), Network reconfiguration, Loss reduction


References:

1.             Pathomthat Chiradeja, R.Ramakumar, “An Approach to Quantify the Technical Benefits of Distributed Generation”, IEEE Transactions on energy conversion, Vol.19,No.4, December 2004.
2.             Joon-Ho Choi, Jae-Chul Kim “Network Reconfiguration at the power distribution system with dispersed generations for loss reduction”, IEEE 2000, pp 2363-2367.

3.             Whei-Min Lin, Hong-Chan Chin, “ A new approach for distribution feeder reconfiguration for loss reduction and service restoration, IEEE transactions on power delivery, Vol. 13, No. 3, July 1998.

4.             Hong-Chan Chin, Kun-Yuan Huang, “ A simple distribution reconfiguration algorithm for loss minimization”, IEEE 2000, pp607-611

5.             Joon-Ho Choi,  Jae-Chul Kim, Seung-II Moon, “Integrating strategy of dispersed generation to automation distribution     centre for distribution network reconfiguration”, international Conference on Electrical Engineering, 2002 . 


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32.

Authors:

Rajesh Singla, Neha Sharma

Paper Title:

Function Classification of EEG Signals Based on ANN

Abstract:     The motor imagery is limited in pattern variety, so in our work, six motor imageries including wrist, elbow, wrist rotation clockwise/anticlockwise and ankle backward/forward moment were used in this system. This paper described the auditory paradigm for recording of  motor imagery signals and the relevant coefficient was used for signal analysis and recognition. EEG signals were decomposed into wavelet coefficients by discrete wavelet transform on which SVD technique is applied to get singular value used as feature vectors, presenting them into ANN classifier.

Keywords:
BCI, EEG, Wavelet Transform, SVD,ANN


References:

1.           Wolpaw J R, Birbaumer N, McFarland D J, Pfurtscheller G and Vaughan T M 2002 Brain–computer interfaces for communication and control Clin. Neurophysiol.
2.           Vaughan T M et al 2003 Brain–computer interface technology: a review of the second international meeting IEEE Trans. Neural Syst. Rehabil. Eng.

3.           H. H. Ehrsson, S. Geyer, and E. Naito, "Imagery of Voluntary Movement of Fingers, Toes, and Tongue Activate Corresponding Body-Part-Specific Motor
Representations," J Neurophysiol, vol. 90, pp. 3304-3316, November 1, 2003 2003.

4.           Baoguo Xu, Aiguo Song ,Juan Wu, “Algorithm of Imagined Left-right Hand Movement Classification Based on Wavelet Transform and AR - Parameter Model,” , Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on 6-8 July 2007, pp. 539–542, .

5.           G. Gage, K. Ludwig, K. Otto, E. Ionides, and D. Kipke. Naive coadaptive cortical control. J. Neural Eng., vol. 2, 2005, 52–63..

6.           Mousavi, E.A.; Maller, J.J.; Fitzgerald, P.B.; Lithgow, B.J. Wavelet common spatial pattern in asynchronous offline brain computer interfaces. Biomed. Signal Process. Control 2011, 6, 121–128

7.           Jiang, W.; Guizhi, X.; Lei, W.; Huiyuan, Z. Feature Extraction of Brain-Computer Interface Based on Improved Multivariate Adaptive Autoregressive Models. In Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics (BMEI’10), Yantai, China, October 2010; pp. 895–898.

8.           T. Wang, J. Denga, B. He, “Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns”, in Clinical Neurophysiology, Vol. 115, 2004, pp.2744–2753. 

9.           “The Wavelet Tutorial by Robi Polkar" http://users.rowan.edu/~polikar/WAVELETS/WTpar14.html

10.        B. Scholkopf, A.J. Smola, Learning with kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press. London, England, 2002.

11.        Blankertz B, Curio G and M¨uller K R 2002 Classifying single trial EEG: towards brain computer interfacing Adv. Neural Inf. Process. Syst. (NIPS 01) 14 157–64

12.        Fukunaga K 1990 Statistical Pattern Recognition 2nd edn (New York: Academic)

13.        S. O. Haykin. Neural Networks and Learning Machines. Prentice Hall, 2008.

14.        Ientilucci, E.J., (2003). \Using the Singular Value Decomposition". http://www.cis.rit.edu/~ejipci/research.htm

15.        L. Qin and B. He. A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications, J. Neural Eng., vol. 2, no. 4, 2005, 65–72.

16.        Kübler, A., Furdea, A., Halder, S., Hammer, E. M., Nijboer, F., and Kotchoubey, B. (2009). brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients.Ann. NY Acad. Sci. 1157, 90–100.

17.        Krauledat M, Tangermann M, Blankertz B, Müller K-R, Towards Zero Training for Brain Computer Interfacing. 2008PLoS ONE 3(8): e2967.

18.        S. Lemm, B. Blankertz, G. Curio, and K.-R. Müller. Spatio-spectral filters for improved classification of single trial EEG.. IEEE Trans. Biomed. Eng., vol. 52, no. 9, Sep. 2005, 1541–1548.

19.        L. Qin, L. Ding, and B. He. Motor imagery classification by means of source analysis for brain computer interface applications. J. Neural Eng., vol. 1, 2004, 135–141.

20.        Lemm S, Schäfer C, Curio G, “BCI Competition 2003--Data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements,” IEEE Trans. Biomed Eng., vol. 51, no. 6pp. 1077-1080, June 2004. 

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33.

Authors:

Raghvendra Omprakash Singh, Yuvraj B. Hembade, Rohit S. Kulkarni

Paper Title:

Software Testing Optimization from Defect Management Models

Abstract: Quality of software is dependent on various attributes such as testing, metric and prediction of bugs before deployment which will lead to effective maintenance. Software complexity and bugs again are interrelated. In this paper we are making a comparative study of defect prediction mechanisms. We propose few design ideas for empirical prediction of defect decay. Our research direction will be triggered by the design ideas we are going to propose. We intend to propose a holistic model for correct prediction of defect decay. We also want to perform empirical validation of our model and fine tune it so that its estimates are better than state-of-the-art.

Keywords:
Defect Prediction, defect decay, quality, testing, metrics.


References:

1.             Allen, J. F. Using Entropy for Evaluating and Comparing Probability Distributions, available at: http://www.cs.rochester.edu/u/james/CSC248/Lec6.pdf
2.             Mockus, S. G. Eick, T. L. Graves, and A. F. Karr, "New roles for change management data in software engineering", Technical Report, National Institute of Statistical Sciences, 1999.

3.             Basili, V. R., and Perricone, B. Software errors and complexity: An empirical investigation. Communications of the ACM, 27(1):42 – 52, 1984.

4.             Eick, S. G., Graves, T. L., Karr, A. F., Marron, J.S., and Mockus, A. Does Code Decay? Assessing the Evidence from Change Management Data. IEEE Trans. on Software Engineering, 27(1):1–12, 2001.

5.             Eick, S.G., Graves, T.L., Karr, A.F., Mockus, A., Schuster, P. Visualizing Software Changes, IEEE Trans. on Software Engineering, vol. 28,  no. 4,  pp. 396-412,
April,  2002.

6.             F. Padberg, “Maximum Likelihood Estimates for the Hypergeometric Software Reliability Model,” Int’l J. Reliability, Quality, and Safety Eng., July 2002.

7.             Graves, T. L., Karr, A. F., Marron, J. S. and Siy, H. P. Predicting fault incidence using software change history. IEEE Trans. on Software Engineering, 26(7):653–661, 2000.

8.             J.D. Musa, A. Iannino, and K. Okumoto, Software ReliabilityMeasurement, Prediction, Application. McGraw-Hill, 1987.Models" ,Centre for Software Reliability.

9.             Ostrand, T. J., Weyuker, E. J., Bell, R. M. Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. Software Eng. 31(4): 340-355 (2005)

10.          Pareto Law: http://www.it-cortex.com/Pareto_law.htm

11.          Perry, D. E.  and Evangelist, W. M. An Empirical Study of Software Interface Faults — An Update. In Proceedings of the 20th Annual Hawaii International Conference on Systems Sciences, pages 113–136, Hawaii, USA, January 1987.

12.          Perry, D. E.  and Steig, C.S. Software Faults in Evolving a Large, Real-Time System: a Case Study’. In Proceedings of the 4th European Software Engineering Conference, Garmisch, Germany, September 1993.

13.          Rajesh Kulkarni, P. Padmanabham & M. S. Namose, "Improving Software Quality Attributes of PS using Stylecop",Global Journal of Computer Science and Technology Volume XIII Issue VIII Version I,2013

14.          Syed Waseem Haider, Joa˜o W. Cangussu, Kendra M.L. Cooper and Ram Dantu,"Estimation of Defects Based on Defect Decay Model: ED3M",IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 34, NO. 3, MAY/JUNE 2008

15.          http://www.issco.unige.ch/en/research/projects/ewg95//node1.html


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34.

Authors:

Majid S. Naghmash, Faeza A. Abid

Paper Title:

Design and Implementation of Dual-mode Programmable Decimation Filter for WCDM and GSM Systems

Abstract:  In this paper, the dual mode receiver for wide and narrow band wireless system is investigated. In the current wireless system like GSM and WCDMA, the receiver base station should extract the individual radio channels from digitized wideband signal at extremely high sampling rate. However, the base station receiver must be capable at the same time isolate multiple channels from different bandwidths equivalent to channel bandwidths of different communication principles. The key requirements of current wireless systems is the reconfigurability and low complexity. The reconfigure digital filter design based on decimation is proposed to support current systems and accelerate the transition to new wireless generation using SIMULINK block set environment in MATLAB program. To extract the pass band widths for different standards, an cascaded digital filter need to be used in this case. The experiments and simulation results shows an important improvement in the complexity reduction and power consumption over the conventional approach.

Keywords:
    SIMULIN, K MATLAB    


References:

1.             Rajesh Mehra, Swapna Devi’ “Efficient Hardware Co-Simulation of Down Convertor for Wireless Communication Systems” International Journal of VLSI Design & Communication Systems (VLSICS), pp. 13- 21, Vol.1, No.2, June 2010.
2.             Majid Salal Naghmash and Mohd Fadzil Ain “Design of minimum error digital down-converter (DDC) for GSM mask requirements”, Journal of Engineering and Technology Research Vol.1 (5), pp.091-101, August, 2009.

3.             Kyung-ho Hwang and Dong-ho Cho, “Software Defined Radio Technology,” Telecommunications Review, vol . 10, no. 1, pp. 130-143,  Jan/Feb. 2000,

4.             Rajesh Mehra, Dr. Swapna Devi, “FPGA Based Design of an Optimized Digital Down Converter for Software Radio Wireless Applications” Innovative Conference on Embedded Systems, Mobile Communication and Computing, pp. 43-51, ICEMC2-2010.

5.             Young-bum Chang, “A Block FIR Filtering Architecture for IF Digital Down Converter,” Journal of IEEK, vol. 37, no. 9, , pp. 465-473. Sept. 2000

6.             LIN Fei-yu1, QIAO Wei-ming, WANG Yan-yu, LIU Tai-lian1, FAN Jin, HANG Jian-chuan, “Efficient WCDMA Digital Down Converter Design Using System
Generator”. Proceeding of International Conference on Space Science and Communication, pp.89-92, IEEE-2009.

7.             S. Im, W. Lee, C. Kim, Y. Shin, S.H. Lee, and J. Chung, “Implement of SDR-Based Digital IF Channelizer / De channelizer for Multiple CDMA Signals,” IEICE Trans. Communications, vol . 83, no. 6,  pp. 1282-1289. June 2000

8.             Alan Y. Kwentus, “Application of Filter Sharpenning to Cascaded Integrator-Comb Decimation Filters,” IEEE, vol . 45, no. 2, pp. 457-467. Feb. 1997

9.             Emad S Malik, Khaled A. Shehata, Ahmad H. Madian, “ Design of Triple Mode Digital Down Coverter for WCDMA, CDMA 2000 and GSM of Software Defined
Radios” International Conference on Microelectronics, pp. 272-275, IEEE-2009.

10.          Hyuk J. Oh, Sunbin Kim, and Ginkyu Choi, “On the Use of Interpolated Second-Order Polynomials for Efficient Filter Design in Programmable Downconversion,” IEEE J. Select. Areas Commun., vol. 17, no. 4, , pp. 551-560., Apr. 1999

11.          G. Mazzini, G. Setti, and R. Rovatti, “Chip pulse shaping in asynchronous chaos-based DS-CDMA,” IEEE Trans. Circuits Syst. I, vol. 54, no. 10, pp. 2299–2314, Oct. 2007.

12.          Amir Beygi, Ali Mohammadi, Adib Abrishamifar. “AN FPGA-BASED IRRATIONAL DECIMATOR FOR DIGITAL RECEIVERS” in 9th IEEE International Symposium on Signal Processing and its Applications, pp. 1-4, ISSPA-2007.

13.          Ming Jian, Weng Ho Yung, and Bai Songrong, “An Efficient IF Architecture for Dual-Mode GSM/W-CDMA Receiver of a Software Radio,” IEEE Int’l Workshop on Mobile Multimedia Communications, vol. 87932, , pp. 21-24.  Nov. 1999

14.          M. Cummings, S. Haruyama, “FPGA in the Software Radio”. IEEE Communications    Magazine, v37, pp.108-112. Feb. 1999.

15.          Rabiner, Crochiere, Optimum FIR Digital Filter Implementations for Decimation,Interpolation, and Narrow-Band Filtering, IEEE  Transactions on Acoustics, Speech, andSignal Processing, October 1975.

16.          Rajesh Mehra, Dr. Swapna Devi, “FPGA Based Design of an Optimized Digital Down Converter for Software Radio Wireless Applications” Innovative Conference on Embedded Systems, Mobile Communication and Computing, pp. 43-51, ICEMC2-2010.

17.          Xilinx Corp., Virtex-4 XtremeDSP Design Considerations User Guide,2008              

18.          Xilinx Corp., DDS Compiler v2.1, 2008

19.          Xilinx Corp., System Generator User Guide,2008

20.          Xilinx Corp., Xilinx ISE 10.1 Software Manuals, 2008.


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35.

Authors:

Anjali Barmade, Madhu M.Nashipudinath

Paper Title:

An Efficient Strategy to Detect Outlier Transactions

Abstract:   Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection.Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. .Outlier detection methods are divided into transaction specific and non transaction specific outlier detection methods,In these paper  we are going to focus mainly on transaction specific methods and detect outlier transactions from transactional databases e.g. purchase of the data at the store, customer dataset at a company.Here we are going to compare two transaction specific methods and find efficient method from them.

Keywords:
     outlierdetection,transactional databases,association rule,frequent pattern


References:

1.             Li-Jen Kao, Yo-Ping Huang*,  “An Efficient Strategy to Detect Outlier Transactions for Knowledge Mining.”IEEE 2011
2.             K. Narita and H. Kitagawa, “Outlier detection for transaction Databases using association rules,” in Proc. of the 9th Int. Conf. on Web-Age Information Management, Zhangjiajie, Hunan, pp.373-380, July 2008

3.             Z. He, X. Xu and S. Deng, “Fp-outlier: Frequent pattern based outlier detection,” Computer Science and Information System, vol. 2, no. 1, pp.103-118, June 2005.

4.             Koufakou1 E.G. Ortiz1 M. Georgiopoulos1 G.C. Anagnostopoulos2 K.M. Reynolds, “A Scalable and Efficient Outlier Detection Strategy for Categorical Data” IEEE 2007

5.             Li-Jen Kao, Yo-Ping Huang*,  “Association Rules Based Algorithm for Identifying Outlier Transactions in Data Stream”, IEEE 2012

6.             J. Han, J. Pei and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Dallas, Texas, USA, pp.1-12, May 2000.

7.             VARUN CHANDOLA, Outlier Detection : A Survey

8.             Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, Outlier Detection Techniques

9.             S. Ramaswamy, R. Rastogi and K. Shim, “Efficient algorithms for mining outliers from large data sets,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Dallas, Texas, USA, pp.427-438, May 2000.

10.          K. Das and J.G. Schneider, “Detecting anomalous records in categorical datasets,” in Proc. of the Second Int. Conf. on Knowledge Discovery and Data Mining, San Jose, California, USA, pp.220-229, August 2007.

11.          D. Burdick, M. Calimlim and J. Gehrke, “Mafia: A maximal frequent itemset algorithm for transactional databases,” in Proc. of the 17th Int. Conf. on Data Engineering, Heidelberg, Germany, pp.443-452, April 2001.

12.          R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, pages 487–499, 1994.

13.          Arning, R. Agrawal, and P. Raghavan. A linear method for deviation detection in large databases. In KDD, pages 164–169, 1996.

14.          G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In FIMI, 2003.

15.          K. Narita and H. Kitagawa. Detecting outliers in categorical record databases based on attribute associations. In APWeb, 2008.

16.          Dr. Shuchita Upadhyaya, Karanjit Singh, “Classification based outlier detection techniques”

17.          Knorr E.M., Ng R.T., Tucakov V., “Distance based method: algorithm and applications”

18.          Sridhar Ramaswamy, “Efficient algorithms for mining outliers from large datasets
19.      Rajendra Pamula,”Outlier detection method based on clustering”

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36.

Authors:

Satish Kamble, Sachin Meshram, Rahul Thokal, Roshan Gakre

Paper Title:

Developing a Multitasking Shopping Trolley Based On RFID Technology

Abstract:    RFID (radiofrequency identification) technology offers the ability to provide many new services and conveniences in the retail environment. An innovative product with societal acceptance is the one that aids the comfort, convenience and efficiency in everyday life. Purchasing and shopping at big malls is becoming daily activity in various cities. We can see big rush at these malls on holidays and weekends. People purchase different items and put them in trolley. After completion of purchases, one needs to go to billing counter for payments. At billing counter the cashier prepare the bill using bar code reader which is very time consuming process and results in long queue at billing counter. In this paper, we discuss a product “Developing a Multitasking Shopping Trolley Based On RFID Technology” being developed to assist a person in everyday shopping in terms of reduced time spent while purchasing. The main objective of proposed system is to provide a technology oriented, low-cost, easily scalable, and rugged system for assisting shopping in person. The developed system consists of 3 key components/modules (a) Server Communication component (SCC) (b) User Interface and display component (UIDC), and (c) Automatic billing component (ABC). SCC establishes and maintains the connection of the shopping cart with the main server. UIDC provides the user interface and ABC handles the billing in association with the SCC. These 3 modules are integrated into an embedded system and are tested to satisfy the functionality. Smart shopping carts with electronic displays, in communication with a computer system, can display an overall description with cost details associated with a Shopping list Databases. Smart cart, also equipped with RFID tags, can also verify the Purchase of the items as they are placed in the cart and, if desired, communicate with a billing system to automatically bill the shopper for the purchases.

Keywords:
RFID Reader, RFID Tag, Shopping Trolley, VB.Net, Embedded ‘C’, Workstation.


References:

1.             C. Buragohain, D. Agrawal, and S. Suri. Distributed navigation algorithms for sensor networks. In IEEE INFOCOM, 2006.
2.             J.Suryaprasad, B.O.P. Kumar, D. Roopa and A.K. Arjun, "A Novel Low-Cost Intelligent Shopping Cart”, IEEE 2nd International Conference on Networked Embedded Systems for Enterprise Applications, pp.1-4, 2011.

3.             Parvathy A, Venkat Rohit Raj “ rfid based examination hall system”, a paper on IEEE paper.

4.             Kamran Ahasan,Paul Kingston  IEEE paper on “rfid applications:an  introductory and exploratory study”.

5.             Mingyan Li, Radha  Poovendran, Rainer Falk paper on “multi-domain access control using asymmetric key based tag reader mutual authentication

6.             T. Dimitriou. A lightweight RFID protocol to protect against traceability  and cloning attacks. In Conference on Security and Privacy for Emerging Areas in  Communication Networks – SecureComm, Athens, Greece, September 2005. IEEE.

7.             A. Juels. RFID security and privacy: A research survey. IEEE Journal on Selected Areas in Computing, 24(2):381–394, February 2006.


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37.

Authors:

Rajesh F. Kale, N.G.Gore, P.J.Salunke

Paper Title:

Cost Optimization of R.C.C. T-Beam Girder

Abstract:     In this present study, cost optimization approach of R.C.C. T-beam girder is presented. The main objective function is to minimize the total cost in the design process of the bridge system considering the cost of materials. The cost of each structural element covers that of material and labor cost for reinforcement, concrete and formwork. For a particular girder span and bridge width, the design variables considered for the cost minimization of the bridge system, are deck slab depth, width of web of girder and, girder depth, (i.e. X1, X2, X3 resp.) Design constraints for the optimization are considered according to IRC-21:2000 (Indian road congress) Standard Specifications. The optimization process is done for different grade of concrete and steel. The comparative results for different grade of concrete and steel is presented in tabulated form. The optimization problem is characterized by having a combination of continuous, discrete and integer sets of design variables. The structure is modeled and analyzed using the direct design method. Optimization problem is formulated is in nonlinear programming problem (NLPP) by SUMT. The model is analyzed and designed for an optimization purpose by using Matlab Software with SUMT (Sequential Unconstrained Minimization Technique), and it is capable of locating directly with high probability the minimum design variables. Optimization for reinforced concrete R.C.C. T-beam girder system is illustrated and the results of the optimum and conventional design procedures are compared.

Keywords:
 Deck slab, R.C.C T-Beam girder, Reinforced Concrete, Structural optimization.


References:

1.           IRC: 21-2000-“Standard Specifications and Code of Practice for Road Bridge Section III – Cement Concrete (Plain and Reinforced - Second Revision)”, Indian Road Congress.
2.           IRC: 06-2010-“Standard Specifications and Code of Practice for Road Bridges, Section II - Cement Concrete (Loads and stresses – 5th Revision)”, Indian Road Congress.

3.           IS: 456-2000 – “Code of Practice for Plain and Reinforced Concrete”, Indian Standards Institution.

4.           Ministry of Shipping and Transport (Road Wings), “Standard Plans for Highway Bridges, Volume III”, Concrete T-beam Bridges.

5.           Krishna Raju N., “Advanced Reinforced Concrete Design”, 2nd edition.

6.           Ramamrutham, S.,“Design of Reinforced Concrete Structures”, Dhanpat Rai & sons, New Delhi, 1982.

7.           Victor D.J. – “Essentials of Bridge Engineering” – Oxford & IBH Publishing Co., Calcutta.

8.           Rakshit K.S. – “Load Distribution in Bridge Decks: A comparative Study” Construction Engineers of India, 1966 State Engineers Association, Calcutta.

9.           Dr. V. K. Raina – “Raina’s Concrete Bridge Practice Analysis, Design & Economics, Third edition”, Shroff Publication New Delhi 2009.

10.        “Engineering Optimization” by S.S. Rao.

11.        M. Z. Colin and A. J. Mac Rae “Optimization Of Structural Concrete Beams”, Journal Of Structural Engineering - American Society of Civil Engineering, pp.1573-1588 (1984).

12.       Iqbal motiwala “Design of simple span R.C.C. T-beam bridge by working stress design method and ultimate strength design method- an economical comparison of both methods”, Brigham young university 1969.


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Authors:

Abhishek Sengupta, Sarika Saxena

Paper Title:

A Computational Model of Mitochondrial Beta- Oxidation Highlighting the Implications on Uremia Disease in Human

Abstract:      Enzyme deficiencies can segment the metabolic reactions in the mitochondrion and may spark to augmentation of specific substrates causing severe clinical manifestations. During the state of starvation mitochondrial oxidation of long-chain fatty acids procures a vital source of energy for the heart as well as for skeletal muscle. A computational kinetic network of reactions, compounds and parameters was constructed to correlate the mitochondrial fatty acid beta-oxidation to disease conditions. Carnitine deficiency limits the availability of the long chain acyl-CoAs inside the mitochondrial matrix. Majorly, carnitine is necessary for fatty acid transport to sites of beta-oxidation in the mitochondria. An increased ratio of long-chain acyl-carnitine (LCAC) to free carnitine, was observed when carnitine level was declined. This verifies that uremic patients have altered carnitine metabolism. Subjecting the constructed model of the biochemical reactions involved in fatty acid catabolism to further simulations at varying concentrations will provide predictive models to identify the disease targets.

Keywords:
 Mitochondrial Beta-Oxidation, Carnitine Deficiency, Computational Model, Uremia.


References:

1.                Pollitt RJ, Leonard JV. (1998): Prospective surveillance study of medium chain acyl-CoA dehydrogenase defi ciency in the UK. Arch. Dis. Child., 79: 116–119
2.                Guarnieri G,  Biolo G, Vinci P, Massolino B,  Barazzoni R . (2007) Advances in Carnitine in Chronic Uremia. Journal of Renal Nutrition, 17(1): 23-29.

3.                Smogorzewski M, Perna AF, Borum PR, Massry SG. (1988) Fatty acid oxidation in the myocardium: effects of parathyroid hormone and CRF. Kidney Int., 34(6):797-803

4.                De los Reyes B, Navarro JA, Pérez-García R, Liras A, Campos Y, Bornstein B, Arenas J. (1998) Effects of L-carnitine on erythrocyte acyl-CoA, free CoA, and glycerophospholipid acyltransferase in uremia. American Journal Of Clinical Nutrition, 67(3):  386-390.               

5.                Rinaldo P, Matern D, Bennett MJ. (2002) Fatty acid oxidation disorders. Annu Rev Physiol, 64:477–502.

6.                Pollitt R.J. (1999) Defects in mitochondrial fatty acid oxidation: clinical presentations and their role in sudden infant death. Padiatr. Padol., 28: 13–17.

7.                Marcovina SM, Sirtori C,  Peracino A,  Gheorghiade M, Borum P,Remuzzi G, and Ardehali H. (2013) Translating the basic knowledge of mitochondrial functions to metabolic therapy: role of L-carnitine. Transl Res, 161(2): 73–84

8.                Bonnefont JP, Djouadi F, Prip-Buus C, Gobin S, Munnich A, Bastin J. (2004)  Carnitine palmitoyltransferases 1 and 2: biochemical, molecular and medical aspects. Mol. Aspects Med, 25: 495–520.

9.                Wolfe L, Jethva R, Oglesbee D, et al. (2011) Short-Chain Acyl-CoA Dehydrogenase Deficiency. GeneReviews (Pagon RA, Bird TD, Dolan CR, et al.)

10.             Moczulski D, Majak I, Mamczur D. (2009) An overview of beta-oxidation disorders. Postepy Hig Med Dosw (Online), 63: 266-77

11.             Vockley J. (1994)  The changing face of disorders of fatty acid oxidation.  Mayo Clin. Proc, 69: 249–257.

12.            Fontaine M, Briand G, Largilliere C, Degand P, Divry P, Vianey- Saban C, Mousson B, Vamecq J. (1998) Metabolic studies in a patient with severe carnitine palmitoyltransferase type II defi ciency. Clin. Chim. Acta, 273: 161–170.


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39.

Authors:

Raunak Jangid, Kapil Parkh, Pradeep Anjana

Paper Title:

Reducing the Voltage Sag and Swell Problem in Distribution System Using Dynamic Voltage Restorer with PI Controller

Abstract:       One of the major problems observed in distribution system in recent days is Power Quality. Today most of the people are using the sophisticated electrical equipment based on semiconductor device, these equipment pollute the power quality. The sag and swell problem not only occur by the disturbed power quality but also due to high system tapping at the point of common coupling in the system. The non linear load is also creating the same problem at the load end. The Dynamic Voltage Restorer is recognized as the best solution for mitigation of voltage sag and swell associated problems in the highly taped distribution system. This work presents the simulation modeling and analysis of advanced DVR system for solving these problems. The PI control scheme is used for generating the gate pulse for IGBT bridge converter. The reference DC voltages are taken from the battery. The three phase fault is creating in the system and for analyzing the result. The role of DVR is to compensate the load current and voltage is investigated during the fault condition. Over all the DVR is improving the voltage quality as well as the reactive power demand during the uncharacteristic condition

Keywords:
Voltage sag, swell, DVR, PI controller, LC filter


References:

1.                 F. B. Ajaei, S. Afsharnia, A. Kahrobaeian, and S. Farhangi, “ A Fast and Effective Control Scheme for the Dynamic Voltage Restorer,” IEEE Transactions On Power Delivery, Vol. 26, no. 4, pp. 2398-2404, Oct. 2011.
2.                 Ding Ning,  Cai Wei,  Suo Juan, Wang Jianwei, and Xu Yonghai, “ Voltage Sag Disturbance Detection Based On RMS Voltage Method,” IEEE power And Energy Engineering Conference, pp.1-4, March 2009.

3.                 O. Al-Mathnani, M. A.  Hannan, M. Al-Dabbagh, M. A. Mohd Ali, and A. Mohamed, “Development of New Control Strategy for Voltage Sag Mitigation,” 2nd IEEE International Power and Energy Conference, pp. 318-323, Dec.2008.

4.                 H. K. Al-Hadidi, A. M. Gole, and D. A.  Jacobson, “Minimum Power Operation of Cascade Inverter-Based Dynamic Voltage Restorer,” IEEE Transactions on Power Delivery, Vol. 23, no. 2, pp. 889-898 , April 2008

5.                 B. Singh, P. Jayaprakash, D. P. Kothari, A. Chandra, and  Kamal-Al-Haddad, “ Indirect Control of Capacitor Supported DVR for Power Quality Improvement in Distribution System,” 21st Century IEEE Power And Energy Society General Meeting- Conversion And Delivery Of Electrical Energy, pp. 1-7 , July 2008.

6.                 Chi-Seng Lam, Man-Chung Wong, and Ying-Duo Han, “Voltage Swell And Overvoltage Compensation With Unidirectional Power Flow Controlled Dynamic Voltage Restorer,” IEEE Transactions On Power Delivery, Vol. 23, no. 4, pp. 2513-2521, Oct. 2008

7.                 N.  G. Jayanti, M. Basu, I. Axente, K. Gaughan, and M. F. Conlon,  “ Sequence analysis based DSP controller for Dynamic Voltage Restorer (DVR),” IEEE Power Electronics Specialists Conference, pp. 3986-3991, June 2008.

8.                 M. I. Marei, E. F. El-Saadany, and M. M. A. Salama, “A New Approach to Control DVR Based on Symmetrical Components Estimation,” IEEE Transactions On Power Delivery, Vol. 22, no. 4, pp. 2017-2024, Oct. 2007.

9.                 J. G. Nielsen, and F. Blaabjerg, “A Detailed Comparison of System Topologies for Dynamic Voltage Restorers,” IEEE Transactions On Industry Applications, Vol. 41, no. 5, pp. 1272-1280, Sep./Oct. 2005.

10.              S. U. Ahn, J. A.  Jardini, M. Masuda, F.A.T. SiIva, S. Copeliovitch, L. Matakas, W. Komatsu, M. G. F. Ortiz, J. Camargo, and E. R. Zanetti,  “ Mini-DVR - Dynamic Voltage Restorer with Functions of Reactive Compensation and Active Harmonic Filter,” IEEE/PES Transmission & Distribution Conference & Exposition: Latin
America, pp. 845-852, Nov. 2004.

11.              Y. H. Chrmg, G. H. Kwon, T. B. Park, and K. Y.  Lim, “ Voltage Sag and Swell Generator for the Evaluation of Custom Power Devices,” IEEE Power Engineering Society General meeting, Vol. 4, pp.2503-2507, July 2003.

12.              S. S. Choi, B. H.  Li, and D. M. Vilathgamuwa, “ A Comparative Study Of Inverter- And Line-Side Filtering Schemes In The Dynamic Voltage Restorer,” IEEE Power Engineering Society Winter meeting, Vol.4, pp. 2967-2972, Jan. 2000.

13.              D. Hongfa, J. Gao, and Duan Xianzhong, “New Concepts of Dynamic Voltage Restoration for Three-phase Distribution Systems,” IEEE Power Engineering Society Summer meeting, Vol. 3 , pp.1427-1432, July 2000.

14.             S. A. Mohammed, A. G.  Cerrada, M. A. Abdel-Moamen, and B. Hasanin, “Dynamic Voltage Restorer (DVR) System for Compensation of Voltage Sags, State-of-the-Art Review,” International Journal of Computational Engineering Research, Vol. 3, Issue. 1, pp.177-183, Jan. 2013..

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40.

Authors:

Imranullah Khan, Tan Chong Eng

Paper Title:

The Performance Improvement of Inter-Relay Cooperative Wireless Communication using Three Time Slots TDMA based Protocol over Rician Fading

Abstract:  Time Division Multiple Access (TDMA) amplify and forward based protocols have been investigated previously by various researchers. However, the gap is still there to investigate the performance of these protocols using inter-relay communication in order to improve the diversity order at destination over Rician fading channel. Therefore, the aim of this paper is to propose TDMA based amplify and forward three time slot protocol with inter-relay communication over Rican fading channel. The proposed protocol is also investigated for various relay locations in order to optimize the best relay locations in terms of less bit error rate (BER). It is concluded that the proposed amplify and forward three time slot protocol (PAFP) perform better in terms of low BER values as compared to previously proposed amplify and forward (PPAF) two time slots and three time slots protocols.  Moreover, PAFP shows better results as compared to PPAF three time slots protocol in terms of less BER values when the inter-relay distance is minimum

Keywords:
 Cooperative inter-relay wireless communication, AF Protocol, TDMA, BER.


References:

1.                Goldsmith., “Wireless communications”, Cambridge University Press, New York, 2005.
2.                Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity-Part I: System description,” IEEE Trans. Commun., vol. 51, no. 11, pp.1927–1938, Nov. 2003.

3.                J. Laneman, D. Tse, and G. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans. Inf. Theory, vol. 50, no. 12, pp. 3062–3080, 2004.

4.                J. Laneman and G. Wornell, “ Energy-efficient antenna sharing and relaying for wireless networks,” in Proc. Wireless Commun. Networking Conf. 2000, vol. 1, pp. 3062-3080.

5.                J. Laneman and G. Wornell, “Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks,” IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2415–2425, 2003.

6.                Kwasinski and K. Liu, “Source-channel-cooperation tradeoffs for adaptive coded communications,” IEEE Trans.Wireless Commun., vol. 7, no. 9, pp. 3347–3358, Sep. 2008.

7.                Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative Communication in Wireless Networks,” in IEEE Communications Magazine, pp. 74-80, 2004.

8.                P. A. Anghel and M. Kaveh, “ Exact symbol error probability of a co-operative network in a Rayleigh fading environment,” IEEE Tracsac. On Wireless Commum., vol. 3, no. X, pp. 1416-1421, Sep. 2004.

9.                R. Pabst et al., “Relay-based deployment concepts for wireless and mobile broadband radio,” IEEE Communications Magazine, vol. 42,no. 5, pp. 80-89, Sep. 2004.

10.             J. W. Mark and W. Zhuang, Wireless Communications and Networking. Upper Saddle River, NJ: Prentice-Hall, 2003.

11.             H. Jiang and W. Zhuang, “Quality-of-service provisioning in future 4G CDMA cellular networks,” IEEE Wireless Commun., vol. 11, no. 2,pp. 48–54, Apr. 2004.

12.             H. Jiang,W. Zhuang, X. Shen, and Q. Bi, “Quality-of-service provisioning and efficient resource utilization in CDMA cellular communications,”IEEE J. Sel. Areas Commun., vol. 24, no. 1, pp. 4–15, Jan. 2006.

13.             R. U. Nabar, H. Bolcskei, and F. W. Kneubuhler, “Fading relay channels: Performance limits and space–time signal design,” IEEE J. Sel. Areas Commun., vol. 22, no. 6, pp. 1099–1109, Aug. 2004.

14.             S. A Fares., F Adachi., and E Kudoh., (2008) Novel Cooperative Relaying Network Scheme with exchange communication and distributed transmit beam forming, THE 5TH IEEE VTS ASIA PACIFIC WIRELESS COMMUNICATIONS SYMPOSIUMS (APWSC 2008), Sendai, Japan, August.

15.             U.R. Tanoli, R. Abbasi, Q.J. Utmani, M. Usman, I, Khan, S. Jan, “Hybrid TDMA-FDMA based inter-relay communication in cooperative networks overNakagamim fading channel” Emerging Technologies (ICET) International conference, pp. 1-5, 2012.

16.             U. R. Tanoli, R. Abbasi, Q. J. Utmani, I. Khan and S. Jan, “Performance Analysis of Cooperative Networks with Inter-Relay Communication over Nakagami-m and Rician Fading Channels”, International Journal of Multi Disciplinary Sciences and Engineering , vol. 3, no. 4, 2012

17.             U. Khan, T. Chong, “The Performance Enhancement of inter-relay cooperative network using amplify and forward time division multiple access (TDMA) protocol”, 2nd International Conference on Interactive Digital Media ICIDM 2013, submitted.

18.            S. Atapattu, N. Rajatheva, and C. Tellambura, “Performance Analysis of TDMA Relay Protocols Over Nakagami-mFading”IEEE Transactions on Vehicular Technology, vol. 59, no. 1, 2010.


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41.

Authors:

Kamlesh Borkar, Ashish Damke, Bhakti Sawarkar, Prashnnaki Gedam, Akash Wankhede

Paper Title:

Id Wisdom through Click Based Graphical Password Authentication

Abstract:   ID Wisdom through Click Based Graphical password Authentication” is a click-based graphical password scheme, a cued-recall graphical password technique. Users Click on one point per image for a sequence of images. The next image is based on the previous click-point. Performance was very good in terms of speed, accuracy, and number of errors. Users preferred CCP to PassPoint, saying that selecting and remembering only one point per image was easier, and that seeing each image triggered their memory of where the corresponding point was located.  Secure Web accessibility through Click Based Graphical password Authentication also provides greater security than PassPoints because the number of images increases the workload for attackers

Keywords:
Graphical Passwords, Computer Security, Authentication, Web Access through Graphical Password, Secure Web Access.


References:

1.                 R.Dhamija and A. Perrig. “Déjà Vu: A User Study Using Images for Authentication”. In 9th USENIX Security Symposium, 2000.
2.                 Real User Corporation: Passfaces. www.passfaces.com

3.                 Jermyn, I., Mayer A., Monrose, F., Reiter, M., and Rubin.“The design and analysis of graphical passwords” in Proceedings of USENIX Security Symposium, August 1999.

4.                 HaichangGao, ZhongjieRen, Xiuling Chang, Xiyang Liu UweAickelin, "A New Graphical Password Scheme Resistant to Shoulder-Surfing"

5.                 Pinkas, B. and T. Sander. Securing Passwords Against Dictionary Attacks. ACM CCS, 2002

6.                 Suo, X, Y. Zhu, and G.S. Owen. Graphical Passwords: A Survey. Annual Computer Security Applications Conference, 2005.

7.                 Thorpe, J. and P.C. van Oorschot. Human-Seeded Attacks and Exploiting Hot-Spots in Graphical Passwords.16th USENIX Security Symposium, 2007.

8.                 van Oorschot, P.C., S. Stubblebine. On Countering Online Dictionary Attacks with Login Histories and Humans-in-the-Loop.ACM Trans. Information and System Security 9(3), 235-258, 2006.

9.                Weinshall, D. Cognitive Authentication Schemes Safe Against Spyware (Short Paper). IEEE Symposium on Security and Privacy, 2006.


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42.

Authors:

Puja S.Prasad, Hitesh R.Yerekar, Parag G.Satpute, Gaurav P.Borkar, Ajinkya S. Shendre

Paper Title:

ERP Sales and Inventory Management System

Abstract:    The aim of this project to developing an ERP Sales and Inventory Management System (SIMS) for a departmental store. This system can be used to store the details of the inventory, stock maintenance, update the inventory based on the sale details, generate sales and inventory reports periodically etc.This project is to categorize individual aspects for sales and inventory managements system, in this system we are solving various problem affecting to direct sales managed by RSM (Regional Sales Manager), ASM (Area Sales Manager) and SO (Sales Officer) who those are monitoring our team response in term of target on various industrial products

Keywords:
 Regional Sales Manager, Area Sales Manager, Sales Officer, Distributor


References:

1.             ASM - Ruchi India Ltd, Pune. (Used the billing calculation sheet for understanding how calculations are maintained.)
2.             www.oracle.com

3.             Dinshaw’s Products Distributer, Pune. (Used the billing calculation sheet for understanding how calculations are maintained.)

4.             “Database Management System”- Elmasri and Navathe

5.             www.inventory.com

6.             Www.Academic.edu.Inventory management system report by-Babasab Patil.

7.             Www.Academic.edu.Inventory management system report by-Babasab Patil.

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

9.             http://www.scribd.com/doc/61612900/Sales-and-Inventory -Management-System

10.         http://www.eresourceerp.com/ERP_for_Sales_Management.html


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43.

Authors:

Suresh Subramanian , Sivaprakasam

Paper Title:

Efficient Algorithm for Removing Duplicate Documents

Abstract:     Internet or Web world has a large amount of information, which may be html documents, word, pdf files, audio and video files, images etc. Huge challenges are being faced by the researches to provide the required and related documents to the users according to the user query. Additional overheads are available for researchers pertaining to identify the duplicate and near duplicate web documents.  This paper addresses these issues through Genetic Algorithm and Duplicate Web Documents Identification Function is used to improve relevance of retrieved documents by removing the duplicate records from the dataset.

Keywords:
  Redundancy; Duplicate Web-pages; Inverted Index; Genetic Algorithm; Web Content Mining.


References:

1.              Suresh S, Sivaprakasam, Genetic Algorithm with a ranking based objective function and inverse index representation for web data mining, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, September – October (2013), pp. 84-90.
2.              Broder A Z,Glassman S C,Manasse M S,Syntactic clustering of the Web. The Sixth International Conference On World Wide Web 1997.

3.              Min-yan Wang, Dong-Sheng Liu(2009): the Research of web page De-duplication  based on web pages Re-shipment Statement, First International Workshop on Database Technology and Applications, pp 271-274.

4.              Charika M S, Similarity estimation techniques from rounding algorithms, in proceedings of 34th Annual ACM symposium on Theory of Computing, (Montreal, Quebec, Canada, 2002) pp. 380-388.

5.              G. Poonkuzhali et al./ International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4026-4032.

6.              Li Zhiyi, Liyang Shijin, National Research on Deleting Duplicated Web Pages: Status and Summary, Library and Information Service,2011, 55(7):  pp.118-121.

7.              Gaudence Uwamahoro, Zhang Zuping, Efficient Algorithm for Near Duplicate Documents Detection, International Journal Of Computer Science Issues, Vol 10, issue 2, March 2013.

8.              Metzler, D., Bernstein, Y., Croft, W.B., Moffat, A., Zobel,J., Similarity Measures for Tracking Information Flow, In: The 14th ACM Conference on Information and Knowledge Management (CIKM 2005), 2005, pp.517–524

9.              Zobel J, Alistair Moffat, Inverted files for Text Search Engines, ACM Computing Surveys, Vol . 38, No. 2, article 2006, pp. 1-55.

10.           Ajik Kumar Mahapatra, Sitanath Biswas, Inverted Index Techniques, International Journal of Computer Science Issues, Vol. 8, Issue 4, No. 1, 2011.

11.           Yerra, R., and Yiu Kai, NG., A sentence-Based Copy Detection Approach for Web Documents, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 3613, 2005, pp.557-570.

12.           Ammar A., Rasha S., Genetic Algorithm in Web Search Using Inverted Index Representation, 5th IEEE GCC Conference, 2009.

13.           T. H. Haveliwala, A. Gionis, D. Klein, and P. Indyk.Evaluating strategies for similarity search on the Web. In Proc. 11th International World Wide Web Conference, pages 432-442, May 2002.

14.           Junxi An, Pengsen Chen,  The Chinese duplicate web pages detection algorithm based on Edit Distance.Journal of Software, Vol.8, No.7, July 2013

15.          Al-Dallal, A., & Shaker, R. (2009b). Genetic algorithm based mining for HTML document. Retrieved from http://wwwis.win.tue.nl/bnaic2009/papers/junk/bnaic2009_submission_87.pdf


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44.

Authors:

C. Bhadane, H. A. Mody, D. U. Shah, P. R. Sheth

Paper Title:

Use of Elastic Search for Intelligent Algorithms to Ease the Healthcare Industry

Abstract:      Our project is optimizing and adding an intelligent algorithm to a cross-platform software that tabulates the medicines and provides for easy retrieval of a list of similar medicines based upon a keyword. Relational databases and schematic databases had several drawbacks. This lead to the use of Elastic Search as the indexing technique for the project. Elastic Search being an unconventional Database (in the sense it is schema less), has certain drawbacks while dealing with data. Inspite of its real-time efficiency while handling terabytes of data and auto cluster balancing, it could use some improvement while deploying on the client side. We plan to use elastic search’s analytics tool to help improve the queried data. And also preprocess the query to make it faster before sending it over to the server so as to reduce the time latency while being used over slower data connections

Keywords:
   Relational databases and schematic databases had several drawbacks. .


References:

1.           All the documentation provided by www.elasticsearch.com
2.           Blogs by the owner of ElasticSearch, Mr. Shay Banon.

3.           http://proceedings.spiedigitallibrary.org/proceed

4.           http://www.elasticsearch.org/tutorials/javascript-web-applications-and-elasticsearch/

5.           http://euphonious-intuition.com/2012/07/an-introduction-to-mapping-in-elasticsearch/

6.          https://engineering.aweber.com/using-elasticsearchs-aliases/


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45.

Authors:

AF. Elgamal*, N.A. Mosa, N.A. Amasha

Paper Title:

Application of Framework for Data Cleaning to Handle Noisy Data in Data Warehouse

Abstract:       Data cleaning is a complex process which makes use of several technology specializations to solve the contradictions taken from different data sources. In fact, it represents a real challenge for most organizations which need to improve the quality of their data. Data quality needs to be improved in data stores when there is an error in input data, abbreviations or differences in the archives derived from several data bases in one source. Therefore, data cleaning is one of the most challenging stages to clear repeated archives, because it deals with the detection and removal of errors, filling in missing values, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies to improve the quality of the data gathered from distributed sources. It is particularly crucial to extract a correct conclusion from data in decision support systems (DSS). This paper presents an application of general framework for the data cleaning process, which consists of six steps, namely selection of attributes,  formation of tokens,  selection of the clustering algorithm,  similarity computation for the selected attributes,  selection of the elimination function,  and finally merge. A proposed software is developed with SQL Server 2010 and C# 2010. 

Keywords:
    Data cleaning, Data quality, Data warehouse, Duplicate elimination. .


References:

1.                Magdi Kamel, "Data Warehousing and Mining", IGI Global, 2009.(URL:http://www.igi-global.com/chapter/data-preparation-data-mining/10872)
2.                Israr Ahmed and Abdul Aziz," Dynamic Approach for Data Scrubbing Process", International Journal on Computer Science and Engineering Vol. 02, No. 02, pp 416-423, 2010.

3.                Jason D. Van Hulse, TaghiM. Khoshgoftaar, Haiying Huang, "The pairwise attribute noise detection algorithm", Knowl Inf Syst, Springer, 2006.

4.                Enrico Fagiuoli, Sara Omerino and Fabio Stella, "Mathematical Methods for Knowledge Discovery and Data Mining ", IGI Global,2008.  (URL:http://www.igi-global.com/chapter/bayesian-belief-networks-data-cle-aning/26141)

5.                Hamid Haidarian Shahri and Ahmad Abdollahzadeh Barforush, "A Flexible Fuzzy Expert System for Fuzzy Duplicate Elimination in Data Cleaning", Springer, DEXA 2004, LNCS 3180, pp. 161 - 170, 2004.

6.                Galhardas, H. Florescu, D. Shasha and D. Simon, "An extensible framework for data Cleaning", In Proceedings of 18th international conference on data engineering, IEEE Computer Society, San Jose, 2000.

7.                Judice, Lie Yongkoh," Correlation-Based Methods for Biological Data Cleaning", DOCTOR OF PHILOSOPHY, National University of Singapore, 2007.

8.                Rohit Anantha krishna, Surajit Chaudhuri and Venkatesh Ganti, "Eliminating Fuzzy Duplicates in Data Warehouses", Proceedings of the 28th VLDB Conference, Hong Kong, China, 2002.

9.                Mikhail Bilenko and Raymond J. Mooney, "Adaptive Duplicate Detection Using Learnable String Similarity Measures", Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC,pp.39-48,August 2003.

10.             Arvind Arasu, Surajit Chaudhuri, Zhimin Chen, Kris Ganjam, Raghav Kaushik and Vivek Narasayya," Towards a Domain Independent Platform for Data Cleaning", Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2011. 

11.             Rand Siran Gu," Data Cleaning Framework: an Extensible Approach to Data Cleaning ", degree of Master of Science in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2010.

12.             J. Jebamalar Tamilselvi and V. Saravanan," A Unified Framework and Sequential Data Cleaning Approach for a Data Warehouse", IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.5, May 2008.

13.             D.Lavanya and K.Usha Rani, "Analysis of Feature Selection with Classification: Breast Cancer Datasets", Indian Journal of Computer Science and Engineering, Vol. 2 No. 5, Oct-Nov 2011.

14.             Lifang GU, Rohan Baxter, Deanne Vickers and Chris Rainsford," Record Linkage: Current Practice and Future Directions", "Canberra, ACT 2601, Australia.

15.             (URL: http://datamining.csiro.au)

16.             Christie I. Ezeife AND Timothy E. Ohanekwu, " Use of Smart Tokens in Cleaning Integrated Warehouse Data", International Journal of Data Warehousing & Mining, 1(2), 1-22, April-June 2005.

17.             K. M. Bataineh, M. Naji, M. Saqer, " A Comparison Study between Various Fuzzy Clustering Algorithms", Jordan Journal of Mechanical and Industrial Engineering, Volume 5, Number 4, Aug. 2011.

18.             Jebamalar Tamilselvi J. and Saravanan V., "Token-based method of blocking records for large data warehouse", Advances in Information Mining, ISSN: 0975–3265, Volume 2, pp-05-10, 2010.

19.             Rohan Baxter, Peter Christen and Tim Churches, "A Comparison of Fast Blocking Methods for Record Linkage", CMIS Technical Report, 2003.

20.             S. Chaudhuri, V. Ganti, and R. Kauskik," A Primitive Operator for Similarity Joins in Data Cleaning ", (URL:http://www.yumpu.com/en/document/view/11885816/apr-imitive-operator-for-similarity-joins-microsoft-research)

21.             S. Chaudhuri, K. Ganjam, V. Ganti, and R. Motwani, "Robust and efficient fuzzy match for online data cleaning. In Proceedings of the ACM SIGMOD, June 2003.

22.             Wai Lup Low, Mong Li Lee and Tok Wang Ling, "A knowledge-based approach for duplicate elimination in data cleaning ", Information Systems 26, pages 585–606, 2001.

23.            J. Jebamalar Tamilselvi and V. Saravanan, " Detection and Elimination of Duplicate Data Using Token-Based Method for a Data Warehouse: A Clustering Based Approach ", International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 5, 2009.


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46.

Authors:

Megha K. Mehta, Nehal G. Chitaliya, Paru Thakkar, Madhukar B. Potdar

Paper Title:

Comparison of Performance of Various Image Fusion Techniques using IKONOS-2 Data

Abstract: Image fusion or spectral merging techniques combine low spatial resolution multispectral data with high spatial resolution PAN data to extract maximum advantage in terms of both spatial and spectral resolutions. Many methods have been suggested in literature for image fusion depending on color and fusion models. In this paper, the process of Image fusion/pan-sharpening following 10 approaches based on different color models and spatial transformations have been compared using IKONOS-2 pan and multispectral data. Also, a concept of controlled injection of intensity component is introduced to study its impact on the variation of spectral correlation. The performances of various methods are evaluated through various statistical parameters.

Keywords:
 Color Models, Image Fusion, Spectral Merging,  PAN-sharpening,  IKONOS.


References:

1.             Jose A. Malpica,” Hue Adjustment to IHS Pan-Sharpened IKONOS Imagery for Vegetation Enhancement”, IEEE Geoscience And Remote Sensing Letters, Vol. 4, No. 1, January 2007, pp. 27-31.
2.             Jaewan Choi, Kiyun Yu, and Yongil Kim ,”A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 49, No. 1, January 2011, pp. 295-309.

3.             Firouz Abdullah Al-Wassai, N.V. Kalyankar, Ali A. Al-Zuky ,”The IHS Transformations Based Image Fusion”, Computer Vision and Pattern Recognition (cs.CV),July 2011

4.             Miloud Chikr El-Mezouar, Nasreddine Taleb, Kidiyo Kpalma, and Joseph Ronsin ,”An IHS-Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 49, No. 5, May 2011, pp. 1590-1602.

5.             Kazi A. Kalpoma and Jun-ichi Kudoh ,”Image Fusion Processing for IKONOS 1-m Color Imagery”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 45, No. 10, October 2007, pp. 3075-3086.

6.             Yun Zhang , Gang Hong, “ An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images”, science direct information fusion, August 2004, pp. 225-234.

7.             Zhijun Wang, Djemel Ziou, Costas Armenakis, Deren Li, and Qingquan Li, “ A Comparative Analysis of Image Fusion Methods”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 43, No. 6, June 2005, pp. 1391-1402.

8.             Myungjin Choi,  “A New Intensity-Hue-Saturation Fusion Approach to Image Fusion With a Tradeoff Parameter”, IEEE Transactions On Geoscience And Remote Sensing, Vol. 44, No. 6, June 2006, pp. 1672-1682.

9.             Li S., Kwok J. T., Wang Y., “Using the Discrete Wavelet Frame Transform To Merge Landsat TM And SPOT Panchromatic Images”,Information Fusion 3, 2002, pp.17–23.

10.          Hsu S. L., Gau P.W., Wu I L., and Jeng J.H., “Region-Based Image Fusion with Artificial Neural Network”, World Academy of Science, Engineering and Technology, 53, 2009, pp 156 -159.

11.         T. Tu, S. Su, H. Shyu, and P. S. Huang, “A new look at his-like image fusion methods”, Inf. Fusion, vol. 2, April 2001,  pp. 177-186.


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47.

Authors:

Pranjali Raturi, Vishal Gupta, Samreen Eram

Paper Title:

Proposed Propagation Model for Dehradun Region

Abstract:    This paper presents a review of the outdoor propagation prediction models for GSM 1800 MHz in which propagation model was developed after a measurement drive was undertaken for the purpose of data collection. The appearance of the foliage medium in the path of communication link has found to play a significant role on the quality of service (QoS) for wireless communication over many years. Four GSM base stations operating at 1800 MHz band were used for the purpose of measurement in sub-urban region of Patel Nagar and kargi, Dehradun. The measured values that were first fitted using fitting tool were compared with Free Space Path Loss model, COST 231 Hata model, Hata model and COST 231 Walfish-Ikegami (W-I) model. These outdoor propagation models discussed helps to measure accurate path loss prediction within a particular scenario also a good outdoor propagation model facilitates optimized network planning, design and implementation process of a wireless network .

Keywords:
propagation model, X model, QoS


References:

1.             V. Erceg et al. “Channel Models for Fixed Wireless Applications”, IEEE 802.16 Broadband Wireless Access Working Group, 2001.
2.             M. Hata, “Empirical Formula for Propagation Loss in Land Mobile Radio Services”, IEEE Transaction on Vehicular Technology, Vol. 29, pp. 317-325, September 1981.

3.             Y. Okumura, “Field Strength and its Variability in VHF and UHF Land-Mobile Radio-Services”, Review of the Electrical Communication Laboratory, Vol. 16, September-October 1968.

4.             COST Action 231, “Digital Mobile Radio Toward Future Generation Systems”, final report, European Communities, EUR 18957,1999.

5.             Theodore S. R. 2006, “Wireless Communication Principles and Practice”, Prentice-Hall, India.

6.             Kwak, D. Y., Lee, C. H., Kim, S. C., Lim, J. W., Lee, S.S. Enhanced  urban path loss prediction model with new correction factors. IEICE Trans. Commun. E89-B, 4(2006).

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48.

Authors:

O.A. Mohamed Jafar, R. Sivakumar

Paper Title:

Hybrid Fuzzy Data Clustering Algorithm Using Different Distance Metrics: A Comparative Study

Abstract:     Clustering is the process of grouping a set of objects into a number of clusters.  K-means and  Fuzzy c-means (FCM) algorithm have been extensively used in cluster analysis.  However, they are sensitive to noise and do not include any information about spatial context.  A Penalized Fuzzy c-means algorithm (PFCM) was developed to overcome the drawbacks of FCM algorithm.  Euclidean distance measure is commonly used by many researchers in traditional clustering algorithms.  In this paper, a comparative study on hybrid fuzzy data clustering algorithm using different distance metrics such as Euclidean, City Block and Chessboard is proposed. The K-means, FCM and hybrid K-PFCM algorithms are experimented and tested on five real-world benchmark data sets from UCI machine learning repository. The experimental results show that FCM and hybrid K-PFCM algorithms report good performance for Chessboard distance. The hybrid K-PFCM algorithm shows best objective function value than K-means algorithm.  The performance of the algorithms is also evaluated through standard cluster validity measures. The Hybrid K-PFCM algorithm is effective under the criteria of PC, PE and intra-cluster distance.

Keywords:
 Data clustering, K-means, Fuzzy c-means, Penalized Fuzzy c-means, Hybrid K-PFCM, Distance metrics, Cluster validity measures.


References:

1.             J. Han and M. Kamber, “Data mining: concepts and techniques,”  Morgan Kaufmann, San Francisco, 2001.
2.             A.K.  Jain, M.N. Murty, P.J. Flynn, “Data clustering: a review,”  ACM Computing Surveys, vol. 31, pp. 264-323, 1999.

3.             C. Fraley and A.E. Raftery, “How many clusters?  Which clustering method?  Answers via Model-Based Cluster Analysis,” Department of Statistics, University of Washington, Tech. Rep. No. 329, 1998. 

4.             R. Xu and D II Wunsch, “Survey of clustering algorithms,”  IEEE Trans Neural Netw, vol. 16, Issue 3, pp. 645-678, 2005.  

5.             P. Berkhin, “Survey clustering data mining techniques,” Accrue Software, San Jose, California,  Tech. Rep., 2002.

6.             J. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281-297, 1967.

7.             L. Kaufman and P. Rousseeuw, “Finding groups in data: an introduction to cluster analysis,” Wiley, New York, 1990.

8.             J.C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,”  J. Cybernet,  vol 3,           pp. 32-57, 1973.

9.             J.C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” Plenum Press, New York, 1981.

10.          LiXiang Jun, Wu You Xin, Qiu Ji Guang and Wan Li Hui, “The applications of fuzzy c-means clustering in macro-economic forecast,” Second International Symposium on Electronic Commerce and Security, vol. I, pp. 609-611, 2009.

11.          Singh Yadav, Ramjeet, Pradap Singh and Vijendra, “Modeling academic performance evaluation using fuzzy c-means clustering techniques,” International Journal of Computer Applications, vol. 60, pp. 15-23, 2012.

12.          Miin-Shen Yang, “On a class of fuzzy classification maximum likelihood procedure,”  Fuzzy Sets and Systems, vol. 57, pp. 365-375, 1993.

13.          Miin-Shen Yang and Chen-Feng Su, “On parameter estimation for normal mixtures based on clustering algorithms,” Fuzzy Sets and Systems, vol. 68, pp. 13-28, 1994.

14.          Yong Yang and Shuying Huang, “Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term,” Computing and Informatics, vol. 26, pp. 17-31, 2007.

15.          K.V. Mardia, J.T. Kent and J.M. Bibby, “Multivariate analysis,” Academic Press, 1979.

16.          G.A.F. Seber, “Multivariate observations,” Wiley, 1984.

17.          M. Mimack, Gillian, J. Mason, Simon, S. Galpin, and Jacquelin, “Choice of distance matrices in cluster analysis: Defining regions,”  Journal of climate, vol. 4, Issue 12, pp. 2790-2797, 2001.

18.          R.K. Bock and W. Krisher, “The data analysis brief book,” Springer-Verlag, New York, 1998.

19.         Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis, “On Clustering Validation Techniques,” Journal of Intelligent Information Systems, vol. 17:2/3, pp. 107-145, 2001.


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