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Volume-4 Issue-5: Published on November 05, 2014
15
Volume-4 Issue-5: Published on November 05, 2014

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S. No

Volume-4 Issue-5, November 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

G. Vijaya Lakshmi, C. Shoba Bindhu

Paper Title:

A Queuing Model To Improve Quality of Service by Reducing Waiting Time in Cloud Computing

Abstract:     Cloud computing is an emerging technology to provide cost effective  and to deliver the business applications, services in an adaptable way. In cloud computing, multi resources such as processing, bandwidth and storage, need to be allocated simultaneously to multiple users.  The When cloud computing users(CCU’S) requests for the service to the cloud computing service  providers (CCSP)  at the same time  but while at a  moment, if  cloud computing server is busy CCU’s needs to enter into the waiting line until CCSP completes its service to the previous CCU . So this may leads to  bottleneck in the network. . Therefore cloud computing users neither utilize the resources nor waits in the queue. Cloud Computing service providers use multiple servers to reduce the waiting time .Therefore, it is necessary to consider a measure for congestion control in cloud computing environment. This paper proposes a   (M/M/C): (∞/FIFO)  Queuing model  which is applied  at  multiple servers inorder to  reduce  waiting time , queue  length also   improving  the  network performance and  QOS effectively in cloud computing environment.

Keywords:
Cloud Computing, waiting time, Queuing Theory,   QOS.


References:

1.             Suneeta Mohanty, Prasant Kumar Pattnaik and Ganga Bishnu Mund  “A Comparative Approach to Reduce the Waiting Time Using Queuing Theory in Cloud Computing Environment”  International Journal of Information and Computation Technology. ISSN 0974-2239  Volume 4,  Number 5 (2014),  pp. 469-474.
2.             N.Ani Brown Mary  and K.Saravanan “  Performance factors of Cloud Computing Data Centers  using [(m/g/1) : (_/gdmodel)] Queuing systems”  International journal of grid computing & applications (ijgca) vol.4, no.1, march 2013.

3.             Kusaka, T, Okuda, T, Ideguchi, T, Xuejun,Tian “ Queuing theoretic approach to server allocation problem in time-delay cloud computing systems “ Teletraffic
congress (ITC), 2011, 23rd International publications , 2011 pp:310-311.

4.             C. Knessl, B. Matkowsky, Z. Schuss and C. Tier, “Asymptotic analysis of a state-dependent M/G/1queueing system,” SIAM J. Appl. Math. 46 (1986) 483–505.

5.             Souvik Pal and P. K. Pattnaik, “Efficient architectural Framework of Cloud Computing”, in  “International Journal of Cloud Computing and Services Science (IJ-CLOSER)”, Vol.1, No.2, June    2012, pp. 66-73.

6.             P.Mell and T. Grance,  “Definition of Cloud Computing” v15, National Institute of Standards and Technology (NIST), 2009.   

7.             T. sai Sowjanya et al, “The Queuing Theory in cloud Computing to Reduce the Waiting Time”, International Journal of Computer Science and Engineering Technology, April 2011, Vol. 1, Issue 3, pp. 110-112.

8.             K. Xiong and H. Perros, “Service performance and analysis in cloud computing,” in Proceedings of the 5th World Congress on Services (SERVICES ’09), Los Angeles, Calif, USA, July 2009, pp. 693–700.

9.             B. Yang, F. Tan, Y.-S. Dai, and S. Guo, “Performance evaluation of cloud service considering fault recovery,” in   Proceedings of the 1st International Conference on Cloud Computing (CloudCom '09), Beijing, China, December 2009, pp. 571–576.

10.          W. Ellens, M. Zivkovic, J. Akkerboom, R. Litijens, and H. Berg, “Performance of cloud computing  centers with multiple  priority classes,” in Proceedings of the 5th IEEE International Conferenceon Cloud Computing,Honolulu, Hawaii, USA, June 2012, pp. 245–252.

11.          X. M. Nan, Y. F. He, L. Guan. “Optimal Resource Allocation for Multimedia Cloud Based on Queuing Model”, Multimedia Signal Processing (MMSP), 2011 IEEE 13th International Workshop on, 2011, pp.1-6 .


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2.

Authors:

Chinmay Swami, Prasad Tarte, Sagar Rakshe, Sumit Raut, Nuzhat F. Shaikh

Paper Title:

Detecting the Age of a Person Through Web Browsing Patterns: A Review

Abstract:      As the use of internet is growing day by day, the basic attributes of the user such as his age, his location, his preferences are of a great value to various business corporations. We are aware of the fact that there is some connection between the browsing behavior and the basic characteristics of the user. In this paper we have made an effort to summarize the various aspects related to detecting the age of the person through his browsing patterns.

Keywords:
  Age Prediction, Back-Propagation Neural Networks, Connectionism and neural nets, Concept learning.


References:

1.                 Misha Kakkar, DivyaUpadhyay, “Web Browsing Behaviors based on age detection”,  ISSN: 2231-2307, Volume-3, Issue-1, March 2013, International Journal of Soft Computing and Engineering (IJSCE)
2.                 Santosh Kabbu, Eui-Hong Han, George Karypis, “Content-Based Methods for Predicting Web-Site Demographic Attributes”. NSF (IIS-0905220, OCI-1048018,IOS-0820730), NIH (RLM008713A), and the Digital Technology Centreat the University of Minnesota

3.                 Claudia Peersman, Walter Daelemans, Leona Van Vaerenbergh,, “Predicting Age and Gender in Online Social Networks”, Conference’10, Month 1–2, 2010, City, State, Country, Copyright 2010 ACM 1-58113-000-0/00/0010.

4.                 Jeff Heaton, Heaton Research, Inc. (25 November 2005),Introduction to neural networks with java,ISBN-10: 097732060X, ISBN-13:978-0977320608.

5.                 Lenhart, S. Fox. Bloggers: A portrait of the internet’s new storytellers. http://www.pewinternet.org/pdfs/PIP%20Bloggers%20Report%20July%2019%202006.pdf

6.                 E.B. Baum and D. Haussler,

7.                 ``What size net gives valid generalization?,''

8.                 Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.

9.                 M.H Hassoun “Fundamentals of Artificial Neural Networks”,

10.              IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 42, NO. 4, JULY 1996.

11.              Jonathan Schler, Moshe Koppel, Shlomo Argamon,James Pennebaker “Effects of Age and Gender on Blogging” 

12.              Copyright © 2005, American Association for Artificial Intelligence(www.aaai.org)

13.              Reyhaneh Tamimi, Prof. Dr. Mohammad Ebrahim

14.              Mohammad pourzarandi, “ The Application of Web Usage Mining In E-commerce Security”,  978-1-4799-0393-1/13/$31.00 ©2013 IEEE


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3.

Authors:

Akshay Sanjay Borse, Ammen Sanjay Siingh, Arshad Harun Attar, Nuzhat F Shaikh

Paper Title:

EAR Recognition using Artificial Neural Networks

Abstract:       Authentication of a person is to ascertain his/her identity is an important problem in the society. Among various physiological traits(biometrics) , ear has gained much popularity in recent years as it has been formed to be the reliable biometrics for human recognition .Ear recognition consists of the two important steps:-
I.             Ear Detection

II.            Ear Recognition

Ear Detection causes out the segmentation of the ear from the profile face. In this project we have decided to work on an available database and implement the neural network for the classification of the person from the specific image.


Keywords:
   Ear recognition, SURF matching, Neural Networks, Concept Learning.


References:

1.                 Burge, M., Burger, W., “Ear biometrics in computer vision,”In Proceedings of 15th International Conference on Pattern Recognition, 2000: 822-826W.
2.                 E.B. Baum and D. Haussler, ``What size net gives valid generalization?,'' Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.

3.                 Jeff Heaton, Heaton Research, Inc. (25 November 2005)”,Introduction to neural networks with java,”ISBN-10: 097732060X, ISBN-13:978-0977320608.

4.                 Mark Burge and Wilhelm Burger. “Ear biometrics in computer vision”. In Proceedings of International Conference on Pattern Recognition (ICPR' 00), volume 2, pages 822-826, 2000.

5.                 David J. Hurley, Mark S. Nixon, and John N. Carter. “Force Field feature extraction for ear biometrics”. Computer Vision and Image Understanding, 98(3):491-512, 2005.

6.                 Ping Yan and KevinW. Bowyer. “Empirical evaluation of advanced ear biometrics”. In Proceedings of International Conference on Computer Vision and Pattern Recognition-Workshop, volume 3, pages 41-48, 2005.

7.                 L. Alvarez, E. Gonzalez, and L. Mazorra. “Fitting ear contour using an ovoid model”. In Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST' 05), pages 145-148, 2005.

8.                 Ping Yan and K.W. Bowyer. “Biometric recognition using 3D ear shape”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8):1297-1308, 2007.

9.                 Saeeduddin Ansari and Phalguni Gupta. “Localization of ear using outer helix curve of the ear”. In Proceedings of the International Conference on Computing: Theory and Applications (ICCTA' 07), pages 688-692, 2007.

10.              Li Yuan and Zhi-Chun Mu. “Ear detection based on skin-color and contour information”. In Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC' 07), volume 4, pages 2213{2217, 2007.

11.              M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models.International Journal of Computer Vision, 1(4):321-331, January 1988. 
 

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4.

Authors:

Jyothi Bandi, K. Rakesh

Paper Title:

Explicit Pulse Triggered Flip Flop Design based on a Signal Feed-Through Scheme

Abstract:        In this paper, a new explicit pulse triggered flip-flop (P-FF) design is implemented and simulated in GENERIC-TDK 130-nm technology. This explicit pulse triggered flip flop consist of a pulse generator and a true single phase clock latch based on a signal feed through scheme. The pulse generator is built with two CMOS inverters along with transmission gate logic which reduces the complexity of the circuit.  The Pulse generation logic used in the explicit mode by a single pulse generator is shared for many number of flip flop at a time result in reduction of power  not only this overall transistor count and delay can also been reduced. The transistor count has been reduced from 24 transistors to 16 transistors and power dissipated is 21.2133u watts. And this flip flop can achieve better D-Q delay and by using this explicit pulse triggered flip flop a synchronous counter is constructed and power dissipated is very less i.e.  19.928  nwatts.

Keywords:
    flipflop , to reduce no:of transistor , delay, power


References:

1.             Xia W.Q.shui,X.Y and Yao, W.L “Dual-vth based double-edge explicit-pulseed level-converting flip-flop” in  IEEE-International Conference on Electronics ,communications and control (ICECC),(2011)
2.             Bhargavaram,D.and Pillai,M.,”low power dual edge triggered flip-flop”.in Advance in Engineering,science and Management (ICAESM),International Conference on IEEE,(2012),63-67.

3.             B. Kong, S. Kim, and Y. Jun, “Conditional-capture flip-flop for statistical power reduction,” IEEE J. Solid-State Circuits, vol. 36, no. 8, pp. 1263–1271, Aug. 2001.

4.             N. Nedovic, M. Aleksic, and V. G. Oklobdzija, “Conditional pre charge techniques for power-efficient dual-edge clocking,” in Proc. Int. Symp. Low-Power Electron. Design, Aug. 2002, pp. 56–59.

5.             P. Zhao, T. Darwish, and M. Bayoumi, High-performance and low power conditional discharge flip-flop,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 12, no. 5, pp. 477–484, May 2004.

6.             M.-W. Phyu, W.-L. Goh, and K.-S. Yeo, “A low-power static dual edge triggered flip-flop using an output-controlled discharge configuration,” in Proc. IEEE Int. Symp. Circuits Syst., May 2005, pp. 2429–2432.

7.             Y.-T. Hwang, J.-F. Lin, and M.-H. Sheu, “Low power pulse triggered flip-flop design with conditional pulse enhancement scheme,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 20, no. 2, pp. 361–366, Feb. 2012.

8.             Low-Power Pulse-Triggered Flip-Flop Design Based on a Signal Feed-Through Scheme IEEE transactions on very large scale integration (VLSI) systems, vol. 22, no. 1, january 2014


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5.

Authors:

Abdulhassan Nasayif Aldujaili

Paper Title:

Design of Programmable Linear Phase Equiripple FIR Filter based Chebyshev and Remez Algorithms 

Abstract:         In this paper, a novel algorithm of programmable linear phase Equiripple finite impulse response (PFIR) filter is developed and improved. The proposed algorithm is incorporated with Remez algorithm to calculate the minimum filter order and Chebyshev algorithm to minimize the error by optimize the filter order.  A new algorithm and technique has been used to reduce the ripple in the pass-band filter response by insert different weights used in the different band. Additionally, the weights at the pass-band region are set to 30 times more than the stop-band weights to improve the adjacent band rejection and blocker response. Results show an development of passband ripple with improvement in the adjacent band rejection of 18% and 11% in blocker requirements more than conventional filter. These results confirm the validity of the proposed algorithms and the techniques used are promising to support the new generation requirements of wireless communication system.

Keywords:
     Linear phase, Equiripple, FIR, Chebyshev, Remez


References:

1.             IEEE DSP Committee, editor. Selected Papers In Digital Signal Processing,II. IEEE Press,1976.
2.             N. L. Carothers, A Short Course on Approximation Theoryhttp://personal.bgsu.edu/carother/Approx.html, 1998.

3.             Rudi, “Design of High-Order Chebyshev FIR Filters in the Complex Domain Under Magnitude Constraints”, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 46, NO. 6, 1998, pp. 1676- 1681

4.             Xiaoping, and Ruijie, “On Chebyshev Design of Linear-Phase FIR FiltersWith Frequency Inequality Constraints”,  IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS, VOL. 53, NO. 2, PP. 120-124/2006

5.             McCLellan,  T.W. FIR digital filter design techniques using weighted Chebyshev approximation, Proc. IEEE, 63, 1975, 1975,  pp.595-610.

6.             Chen, “Design of Equiripple Linear-Phase FIR Filters Using MATLAB”, IEEE transaction, 2011:

7.             Kaiser, “Handbook for Digital Signal Processing”, John Wiley & Sons, Table 4.84, 1993

8.             Sven, “Applied Signal Processing ETB006 FIR Filter Design” Chapter-1 pp.1-5, 2004 available: http://www.bth.se/tek/asb.nsf/attachments/Assignment_grade4_pdf/$file/Assignment_grade4.pdf

9.             Kurt, “Acceleration and implementation of DSP Phase-Based frequency estimation algorithm: Matlab/Simulink to FPGA Via Xilinx system generator” Master thesis in electrical engineering, University of Binghamton, State University of New York /2004 , pp.44-45.


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6.

Authors:

Nisha Sharma, P. N. Barwal

Paper Title:

Electronic Project Proposal Management System for Research Projects Based on Integrated Framework of Spring and Hibernate

Abstract:    A lightweight e-Project proposal Management system based on Open sources spring and Hibernate has been designed and developed in this paper. The system is developed to overcome the lengthy and time consuming process of obtaining a research project proposal, getting them scrutinized, deciding on the reviewers, obtaining   progress reports and required  certificates , monitoring etc. Automating these processes using the web application will streamline all these activities. The Object Relation mapping of hibernate and the Inversion of Control management, Model-View-Controller design pattern of spring have been used in the architecture. Spring Provide best code reuse along with legible code structure. ORM characteristic of hibernate make it easy to implement the transplant and manipulation of databases. The developed system is a multitier system including presentation layer, Business layer, data persistence layer and database layer which can separate presentation logic from business logic and improves reusability, reliability, maintainability of the system along with low coupling.

Keywords:
 Spring, Hibernate, Object Relation Mapping, Multitier System, MVC Architecture, and Inversion of Control.


References:

1.              Zhang Shengwen, Wang Xiangbing “An E-commerce System Structure Research Based on WSH(Webwork, Spring, Hibernate)”, II International Conference on Computer Science and Network Technolog.IEEE
2.              Jia qiaojie, Li juanli, Wang yuanyuan “Design and Implementation of Remote Online Examination System Based on Integration Framework”,IEEE.

3.              Dawei LIU, “Design and Implementation of High-quality Course Scoring System Based on Struts and Spring and Hibernate Architecture”, International Conference of Information Technology, IEEE 2011.

4.              Ren Yongchang, “Application Research for Integrated SSH Combination Framework to Achieve MVC Mode”, IEEE.

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7.

Authors:

Absalom H. V. Lamka, Sylvester Munguti Masu, Githae Wanyona, Stephen Dianga, Abednego Oswald Gwaya

Paper Title:

Factors Influencing Effective Productivity on Construction Sites in Nairobi County

Abstract:     The construction industry has been cited to have a muiltiplier effect in the performance of any economy. It is necessary to make the industry more efficient and effective in terms of better utilization of resources. Part of the most important resources include labor, materials and money. The lack of data on labor productivity in Kenya has made planning and estimation of activities on construction sites unpredictable. This paper is based on a study of labor productivity in Nairobi with the aim of providing up to date data on what can assist building consultants, contractors and developers in the planning and management of construction processes. The prioritization of the factors which affect productivity in labor intensive construction will enable the project team to leverage the limited resources at their disposal to improve the onsite labor management, in order to improve labor productivity efficiently. The research design used in this study was the survey methodology where project managers, contractors and developers were engaged on their experiences in Labor productivity in the construction industry. During the study, the participants were asked to rank and assess the factors that affect productivity on labor intensive construction The paper has further compared its findings on labor outputs for selected operations (e.g. masonry and painting) with theoretical propositions from previous studies (Wachira,1999) and the practices in Kenya. The data obtained from the field was quantitatively analyzed using Statistical Package for Social Sciences (SPSS) and Microsoft Excel software. The study established that delivery of materials, adequacy of supervision and motivation of workers, are the most important factors affecting labor productivity. These factors can be improved through training in skills like planning, scheduling and motivation of workers. The results of this research can be used to excite academic research in this area. Furthermore, the findings are useful towards the necessary training for the construction industry to be more efficient and effective in Kenya.

Keywords:
       construction industry, labor productivity, intensive construction, output, productivity measurement, productivity improvement.


References:

1.              Armstrong, M. (2006). A hand book of resource management Practice. London: Kogan page.
2.              Ashworth, Allan (1988).Cost studies of Buildings, Harlow: Published by Longman Group

3.              Enshassi, e. a. (2007). Factors affecting  productivity in building projects. Journal of Civil Engineering and management , 245-254.

4.              Forster, G. (1989). Construction site studies production, administration and personnel. London and New York: Published by Longman.

5.              Ghemawat, P. (1985, March–April). Building Strategy on the Experience Curve . Harvard Business Review

6.              Heap, A. (1987). Improving site productivity in the construction industry. GENEVA: ILO.

7.              Heizer, e. a. (1990). Production and operations management "strategic and tactical decisions". New Jersey: Prentice hall.

8.              Hillebrandt, P. M. (2000). Economic Theory and the Construction Industry (3rd Edition ed.).

9.              Horner, R. a. (2001). More for less:Contractors guide for improving productivity in construction. Westminster, London: CIRIA Publications.

10.           ILO. (1996-2013). Introduction to Work Study. GENEVA: Publications of international labor Office.

11.           KNBS, R. o. (2012). Kenya National Bureau of statistics. Nairobi: Government printer.

12.           Kwakye, A. A. ( 2000). Construction Project Administration in practice. Edinburgh Gate: Addison Wesley Longman Lt.

13.           Mbiti, T. K. (2008). A System Dynamics Model of Construction Output in Kenya, PhD Thesis, School of Property and Construction Project Management, RMIT University, Melbourne, Australia. Melbourne: RMIT University.

14.           Thomas, e. A. (2004). Demotivating Factors Influencing The Productivity In The Construction Industry, International Journal Of Project Managers, Vol. 22 Issue 2. International Journal Of Project Managers , Vol. 22 Issue 2.

15.           Tromp, K. a. (2009). Proposal and thesis writing an introduction. Nairobi: Pauline publications africa.

16.           Wachira, I. N. (1999). Labor Productivity in the Construction industry in Kenya . ntInternational Symposium on Customer Satisfaction - A Focus for Research and Practice, (pp. 1 -9). Publisherin-house publishing.

17.           Wilcox, e. a. (2000). Management and productivity. Washington: Transportation research board.


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8.

Authors:

Abednego Oswald Gwaya, Sylvester Munguti Masu, Walter Odhiambo Oyawa

Paper Title:

Development of a Benchmarking Model for Construction Projects in Kenya

Abstract:      The construction industry is a crucial sector both for developed and developing economies. It contributes 10% towards GDP for developed economies and more than 4% for developing economies. The industry has often faced many challenges in form of cost and time overruns and quality issues. Project management was introduced as a solution to the perennial problems of cost, time and quality in execution of construction projects. But the much touted benefits are not always achieved leaving clients with a lot of disappointments. It can be argued that the traditional project management variables have been inadequate in the assessment and control of construction projects. This paper set out to develop the most appropriate project management variables for Kenya to enable achieve an efficient and effective construction industry. The purpose of this paper is to develop a project monitoring model for construction projects to fulfill two main objectives: to provide a project success index for every finished project in order to compare them with each other and to establish a benchmark for future improvement in success of construction project execution. The methodology adopted in this paper was, first, to undertake a literature review on existing methodologies. Then a research instrument in form of a questionnaire was developed and a survey approach was used. Based on a sample size of 580 members with a response rate of 344 members and or 59.4%, descriptive statistics and principal component analysis were employed for processing data to come up with project success criteria. The model’s output is a project success index which is calculated based on seven project success criteria. The findings can be of valuable use both for academia in form of more research discourse in the field of project management and for industry participants in form of model application.

Keywords:
  Construction project, Project success criteria, Project monitoring, project success index, success factor. .


References:

1.                A.P.C. Chan, A.P.L. Chan. Key performance indicators for measuring construction success. Benchmarking: An International Journal. 2004, 11(2), 203-221. 
2.                Sadeh, D.Dvir, A. Shenhar. The role of contract type in the success of R&D defense projects under increasing uncertainty. Project Management Journal. 2000, 31(3), 14-21.

3.                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

4.                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. 

5.                Brundtland, G (ed) (1987), Our Common Future: The World Commission on Environmental and Development, Oxford University Press.

6.                CIB (1999), Managing Construction Industry Development in Developing Countries: Report on the First Meeting of the CIB Task Group 29. Arusha, Tanzania, 21-23 September. Rotterdam, as quoted in Ofori (2001), “indicators for measuring construction industry development”, Building Research & Information, Vol. 29, No. 1, pp 40-50

7.                C.S. Lim, M.Z. Mohamed. Criteria of project success: an exploratory re-examination. International Journal of Project Management. 1999, 17(4), 243-48.

8.                De wit, A. (1988), “Measurement of project Success”, International of Project Management, 6 (3),164-170, Butterworth & co (Publishers) Ltd.

9.                Du Plessis, C. D. (2002), Agenda 21 for Sustainable Construction in Developing Countries –A discussion document.

10.             H. Kerzner. In Search of Excellence in Project Management. Van Nostrand Reinhold. 1998, New York, NY

11.             J.F.Y. Yeung, A.P.C. Chan, D.W.M. Chan, L.K. Li. Development of a Partnering Performance Index (PPI) for construction projects in Hong Kong: a Delphi study. Construction Management and Economics. 2007, 25(12), 1219–1237.

12.             J.K. Pinto, D.P. Slevin. Project success: definitions and measurement techniques. Project Management Journal. 1988, 19(3), 67-73.

13.             Muchungu, P. K.(2012). The contribution of human factors in the performance of construction projects in Kenya. Unpublished Phd. Thesis. University of Nairobi

14.             J. Klagegg, K. Samset, O.M. Magnussen. Improving Success in Public Investment Projects: Lessons from Government Initiative in Norway to Improve Quality
at Entry. Paper presented at the 19th IPMA World Congress. 2005.

15.             Ofori, G. (2000), “Challenges of Construction Industries in Developing Countries: Lessons from Various Countries”, Conference Paper, Challenges Facing Construction Industries in Developing Countries, 2nd International Conference on Construction in Developing Countries: Challenges facing the construction industry in developing countries 15-17 November 2000, Gabarone, Botswana 

16.             Patanakul , P. and Milosevic, D. (2009), “The Effectiveness in Managing a group of Multiple Projects: Factors of influence and Measurement Criteria”, International Journal of Project Management Vol.27,  pp 216-233.

17.             Project Management Institute (PMI, 2010). Project Management Body of Knowledge ; A guide to project Management Handbook.

18.             Shenhar, A.J., Levy, O., Dvir, D. (1996), “Towards a typological theory of Project Management”, Research Policy 25(4), 607-632.

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

20.             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

21.             World Bank (1994), World Development Report 1994: Infrastructure for Development, World Bank, Washinton, D.C

22.             Zawdie, G., Langford, D. (2000), “The state of construction and infrastructure in sub-Saharan Africa and strategies for a sustainable way forward”, paper presented at 2nd International Conference on construction in Developing Countries: Challenges facing the construction industry in developing countries, Gabarone, 15-17 November, available at: www.odsf.co.za/cdproc/2nd_proceedings.html (accessed 31 March 2014),  


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9.

Authors:

Abednego Oswald Gwaya, Sylvester Munguti Masu, Walter Odhiambo Oyawa

Paper Title:

The Role of Servant Leadership in Project Management in Kenya

Abstract:   Leadership is believed to be important to project success despite a limited number of studies on the topic. Servant leadership, for example, has never been studied in the context of the project environment or project success. Servant leadership does, however, include a number of skills that have been found to be important to the management of projects such as: Listening, empathy, healing, awareness, persuasion, conceptualization, foresight, stewardship, commitment to the growth of people and community building.  For that reason, the research herein will contribute new knowledge to the study of leadership in project management. The study investigated the relationship between servant leadership and project outcomes. The project management profession is undergoing tremendous growth worldwide as officials of corporations, governments, academia and other organizations recognize the value of common approaches and educated employees for the execution of projects (Ives, 2005). Ives (2005) acknowledged that implementation of strategic change has been a business problem for decades and still is a problem. The discipline of project management is a key strategy to manage change in organizations (Kloppenborg & Opfer, 2002). Project management techniques may be a partial solution to the problem of implementing of strategic change. Construction projects globally have often failed to achieve expected results. In Kenya, for instance we have been experiencing cost and time overruns on projects which are further compounded with quality issues. This even when professors are involved in projects execution (Muchungu, 2012). Even when teams are disassembled and reassembled with a different team leader and or project manager results have varied. Since the latter years of the 1980s, the links between the implementation of change and project management has been strengthened (Ives, 2005). Organizational systems are open, complex, and political, creating a greater level of uncertainty and contributing to an unstable and changing project environment (Ives, 2005; Thomas & Bendoly, 2009). The high level of uncertainty and change challenges traditional systematic approaches to project management. The emphasis of the traditional approach was more on project processes, tools and techniques and less on the leadership of projects. This study determines to what extent servant leadership can contribute to project success. The outcome of this study indicates that servant- leadership is present in a majority of successful projects.  The results from this study could benefit project management practitioners by providing specific constructs that can be applied towards improving the current approaches to project management leadership. The study will add to the body of knowledge on leadership in project management.

Keywords:
   Servant leadership, Project Management, Project Success, Project Leaders, Project execution, Project Human Resources.


References:
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2.              Belassi, W., & Tukel, O. I. (1996). A new framework for determining critical success/failure factors in projects. International Journal of Project Management, 14
(3), 141-151.

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4.              Berg, M. E., & Karlsen, J. T. (2007). Mental models in project management coaching. Engineering Management Journal, 19(3), 3-14.

5.              Blackburn, S. (2002). The project manager and the project-network. International Journal of Project Management, 20(3), 199-204.

6.              Blanchard, K. (1998). Servant-leadership revisited. In L. C. Spears (Ed.), Insights on leadership: Service, stewardship, spirit, and servant-leadership (pp. 21-28). New York. Wiley.

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9.              Chabursky, L. (2005). Dramatically increase project success with radical QA: A guide to managing expectations and results. Journal of Quality Assurance Institute, 19(3), 29-36.

10.           Cleland, D. I. (2004). The evolution of project management. IEEE transactions on engineering Management, 51(4), 396-397.

11.           Cooper, D. R., & Schindler, P. S. (2003) Business research methods (8th ed.). Boston: McGraw Hill Irwin.

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13.           Dainty, A., Cheng, M., & Moore, D. (2005). Competency-based model for predicting construction project managers’ performance. Journal of Management in Engineering, 21(1), 2-9. 

14.           Dvir, T., Edin, D., Avolio, B. J., & Shamir, B. (2002). Impact of transformational leadership on follower development and performance; a field experiment. Academy of Management Journal, 45(4), 735-744.

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

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33.           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.

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35.           Muchungu, P. K.(2012). The contribution of human factors in the performance of construction projects in Kenya. Unpublished Phd. Thesis. University of Nairobi.

36.           Neuhauser, C. (2007). Project manager leadership behaviors and frequency of use by female project managers. Project Management Journal, 38(1), 21-31.

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Authors:

Niharika Purbey, Khyati Pawde, Shreya Gangan, Ruhina Karani

Paper Title:

Using Self-Organizing Maps for Recommender Systems

Abstract:    In this paper, we present an approach called Self-Organizing Map and its application in recommendation systems. Self-Organizing map is a popular unsupervised artificial neural network algorithm. We discuss the SOM algorithm in detail and evaluate its performance. The SOM technique has various advantages over general mining algorithms and hence we choose to discuss this technique. Traditionally, with recommendation systems, collaborative filtering or hybrid systems are used. However, if these techniques are used with artificial neural networks like SOM, the system becomes more efficient.

Keywords:
    Self-Organizing Map (SOM), Recommender Systems, Neural Network, Feature Map, Unsupervised Learning.


References:

1.             Dickerson, K.; Ventura, D., "Music recommendation and query by-content using Self-Organizing Maps", Neural Networks (IJCNN), The 2009 International Joint Conference on, pages: 705 - 710
2.             Sam Gabrielsson, Stefan Gabrielsson, “The use of Self-Organizing Maps in Recommender Systems”, A survey of the Recommender Systems field, 2006, pages 1-184

3.             O’Donovan, J. and Smyth, B. (2005). Trust in Recommender Systems. IUI ’05: Proceedings of the 10th international conference on Intelligent user interfaces, pages 167–174, New York, NY, USA. ACM Press.

4.             Vembu, S. and Baumann, S. (2004). A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems. In Proceedings of the 2nd International Symposium on Computer Music Modeling and Retrieval, pages 119–129

5.             Ziegler, C.-N., McNee, S., Konstan, J., and Lausen, G. Improving recommendation lists through topic diversification. In Proceedings of the 14th International World Wide Web Conference (Chiba, Japan, May 2005), ACM Press.

6.             Damminda Alahakoon, Saman L.Halgamuge, Bala Srinivasan, Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery, IEEE Transactions on Neural Networks, VOL. 11, NO. 3, May 2000, pages 601-613

7.             T. Kohonen, Self-Organizing Maps. Berlin, Germany: Springer-Verlag, 1995.

8.             L. D. Alahakoon and S. K. Halgamuge, “Knowledge discovery with supervised and unsupervised self evolving neural networks,” in Proc. Int.Conf. Information-Intelligent Systems, 1998, pp. 907–910.

9.             Kohonen T,Samuel K, Krista L, Jarkko S, Jukka H, Vesa P,Antti S, “Self Organization of a Massive Document Collection”, IEEE Transactions on Neural Networks, VOL 11, NO.3, May 2000, pp. 574-585

10.          S. K. Halgamuge, “Self evolving neural networks for rule based data processing,” IEEE Trans. Signal Processing, vol. 44, no. 11, 1997.

11.          T. Kohonen, “Comparison of SOM point densities based on different criteria,” Neural Comput., vol. 11, no. 8, pp. 2171–2185, 1999.

12.          P. Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi, Discovering Data Mining —From Concept to Implementation. Englewood Cliffs, NJ: Prentice-Hall, 1998.

13.          Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., and Riedl, J. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM 1994 Conference on Computer-Supported Cooperative Work (Chapel Hill,NC, USA), 1994, ACM, pp. 175–186.

14.          P. T. Quinlan, “Structural change and development in real and artificial neural networks,” Neural Networks, vol. 11, pp. 577–599, 1998.


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11.

Authors:

Dipali Bhatt, Harish Narula

Paper Title:

G-Netadmin-A Network Management System

Abstract:     Network management is a hard task. G-NETADMIN – A network Management System (NMS) is a set of software and hardware tools for the monitoring and management of networks. Technological development in the mobile communication area has been growing substantially in recent years. Currently, two technologies have appeared as big players in the wireless arena. One is the result of the development and migration of LAN technologies, standardized by the IEEE as 802.11 or wireless fidelity (Wi-Fi). The other is General Packet Radio System. This paper, the G-NETADMIN, envisages to remotely control and monitor the network terminals that are connected through a LAN while the administrator is far away from the network. This also increases the scope of the administrator, by accessing the network from his mobile phone and also helps him to control the network using the same.

Keywords:
     Remote network administration, Authentication, Net view, Process management, Remote handling, Message sending, AT commands, GSM Modem


References:

1.              Vieira Junior and Anido, “The Architecture of a Novel Tool for Network Management Using GSM/GPRS Mobile  Devices”, Consumer Communications and Networking Conference, 2004. CCNC 2004. First IEEE.
2.              Amol Poman et al, “GSM Based LAN Monitoring System”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3) , 2012.

3.              E. Wong, “A Phone-Based Remote Controller  For Home And Office Automation”, IEEE Trans.Consumer Electron., vol. 40, no.1, pp.  28-33, February 1999.

4.              Nitin D. Shelokar and Dr. S.A.Ladhake, “Network Handle by mobile” in International Journal of Computer Trends and Technology, May to June Issue 2011.

5.              SIMCom – A company of SIM Tech, AT Commands Set, SIM300_ATC_V1.06, 2006.

6.              GSM SYSTEM SURVEY, Student text, EN/LZT 1233321, R5B revised upgrade edition. p 192.

7.              Lauri Pesonen , “GSM Interception”, Helsinki University of Technology, 2009.


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12.

Authors:

Mahmoudreza Ahmadi, Hamidreza Ghaffari

Paper Title:

Reducing the Size of Very Large Training Set for Support Vector Machine Classification

Abstract:      Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. In this paper, we introduce a method based on edge recognition technique to find low-value data, where to keep input data distribution, we use clustering algorithm like k-means to compute clusters centers. Data is selected through edge recognition algorithm and cluster centers, are used to build a training data set. Reconstructed data set with small size, increase the speed of training process procedure without decreasing classification precision. But, as we used k-means algorithm, it is required to initially specify the number of classes. We try to get a proper procedure by improving edge recognition algorithm to reduce data, also using hierarchical clustering algorithm and similarity percent to compute number of clusters instead of using k-means algorithm, and compare results of these two algorithms.

Keywords:
Support vector machine, k-means, optimization, edge recognition, cluster, hierarchical, similarity percent.


References:

1.           Chih-Wei Hsu, and Chih-Jen Lin,”A Comparison of methods for multiclass support vector machines”, IEEE Transactions on neural netwrks, 2002,  Vol. 13, No. 2.
2.           Smola, and B. Scholkopf, "Sparse greedy matrix approximation for machine learning", In Proceedings of the Seventeenth International Conference onMachine Learning, 2000, Stanford, USA, 911-918.

3.           S. Vishwanathan, and M. N. Murty, "SSVM: a simple SVM algorithm". Proceedings of the 2002 International Joint Conference, 2002, Vol.3, 2393-2398.

4.           W. Tsang, and J. T. Kwok, and P-M. Cheung, "Core Vector Machines: Fast SVM Training on Very Large Data Sets". In Journal of Machine Learning Research, 2005, 6:363-392.

5.           C. Yang, and R. Duraiswami, and L. Davis, "Efficient kernel machines using the improved fast Gauss transform". In Advances in Neural Information Processing Systems, 2005,  Vol.17.

6.           D. Pavlov, and 1. Mao, and B. Dom, "Scaling-up support vector machines using boosting algorithm". In Proceedings of the International Conference on Pattern Recognition, 2000, vol.2, 2219-2222.

7.           R. Collobert, and S. Bengio, and Y. Bengio, "A parallel mixture of SVMs for very large scale problems", In Neural Computation, 2002.

8.           B. LI, and Q.WANG, and J. HU,”A Fast SVM Training Method for Very Large Datasets”, Proceedings of International Joint Conference on Neural Networks, 2009, Atlanta, Georgia, USA.

9.           R. Koggalage, and S. Halgamuge, "Reducing the Number of Training Samples for Fast Support Vector Machine Classification", In Neural Information Processing, 2004, Vol.2, No.3, 57-65.

10.        D. Ziou, and S. Tabbone, "Edge detection techniques - an overview". In International Journal of Pattern Recognition and Image Analysis, 1997.

11.        S. Wei, ”Building Boundary Extraction Based On LIDAR Point Clouds Data”, Ocean College of Shanghai Fisheries University, Shanghai2009.

12.        E. Reddy, and J. Bellary, ”Multi-Class Support Vector Machines – A Comparative Approach” , International Journal of Applied Physics and Mathematics, Vol. 2,No.4, 2012.

13.        G. Chen, J. Xu, X. Xiang,” Neighborhood Preprocessing SVM for Large-scale Data Sets Classification”, Fifth International Conference on Fuzzy Systems and
Knowledge Discovery.

14.        M. Lozano, J. S´anchez, F. Pla, “Reducing Training Sets by NCN-based Exploratory Procedures”,Dept. Lenguajes y Sistemas Inform´aticos, Universitat Jaume I Campus Riu Sec, 12071 Castell´on, Spain.

15.        J. GOU, L. DU, T. XIONG, “Weighted K-nearest Centroid Neighbor Classi_cation”, Journal of Computational Information Systems 8: 2 (2012) 851–860. 


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13.

Authors:

Abdul Malik S. Al-Salman, Ali  El-Zaart, Yousef Al-Suhaibani, Khaled Al-Hokail, Abdu Gumaei

Paper Title:

Designing Braille Copier Based on Image Processing Techniques

Abstract:       Braille is a very important communication code for low vision and blind people. Recently, there has been an increasing trend to use computers for entering, editing and printing Braille documents using special purpose software and printers. Also, there is a large number of old Braille documents that have started to wear out and they need to be reproduced so that they can be preserved and copies made available to many people. Hence, the motivation of this research is the need to duplicate many of Braille documents automatically in very easy manner (like traditional photocopying machine) to be preserved and copies made available to many people. Implications of this research include building a Braille Copier machine that produces copies of Braille documents in exact format regardless of the language used. In addition, this machine is able to work as two-in-one (Coping and Printing). The method used requires optical recognition and image processing techniques so that Braille papers can be copied in similar way to copying ordinary printed text. The results obtained were excellent as we were able to copy Braille documents successfully for both single and double sided papers.

Keywords:
 Braille Image Segmentation, Braille Cells, Verso dots, Recto dots, Grid formation and Cell Detection


References:

1.             Dubus, J.P., Benjelloun, M., Devlaminck, V., Wauquier, F., Altmayer, P., "Image processing techniques to perform an autonomous system to translate relief braille into black-ink, called: Lectobraille", Proceedings of the Annual International Conference of the IEEE (Engineering in Medicine and Biology Society), Nov. 4-7, New Orleans, LA, USA, pp.1584–1585. DOI:10.1109/IEMBS.1988.94726, 1988.
2.             Mennens, J., Tichelen L. V., Francois G., and Engelen J., "Optical Recognition of Braille Writing Using Standard Equipment", IEEE Transactions on Rehabilitation Engineering, 2(4):207–212, DOI:10.1109/ 86.340878, 1994.

3.             Ritchings, R. T., Antonacopoulos, A., Drakopoulos, D., "Analysis of Scanned Braille Documents", In: Dengel, A., Spitz, A.L. (eds.), Document Analysis Systems, World Scientific Publishing Company, pp:413–421, 1995.

4.             Blenkhorn, P., "A System for Converting Braille into Print. IEEE Transactions on Rehabilitation Engineering", 3(2): 215–221, 1995.

5.             Hentzschel, T. W., and Blenkhorn, P., "An Optical Reading Systems for Embossed Braille Characters using a Twin Shadows Approach", Journal of MicroComputer Applications, 18(4):341-354. DOI: 10.1016/S0745-7138(05)80034-X, 1995.

6.             Oyama, Y., Tajima, T., and Koga, H., "Character Recognition of Mixed Convex-Concave Braille Points and Legibility of Deteriorated Braille Points", System and Computer in Japan, 28(2): 44–53. DOI: 10.1002/(SICI)1520-684X(199702)28:2<44::AID-SCJ5>3.0.CO;2-R, 1997.

7.             Ng, C., Ng, V., and Lau, Y., "Regular Feature Extraction for Recognition of Braille", Proceedings in Third International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 99, pp.302–306. DOI:10.1109/ICCIMA.1999.798547, 1999.

8.             Murray, I. and Dias, T., "A portable device for optically recognizing Braille", PART I: Hardware Development, Proceedings in the Seventh Australian and New Zealand Intelligent Information Systems Conference, pp.129–134. DOI: 10.1109/ ANZIIS.2001.974063, 2001.

9.             Wong, L., Abdulla, W., and Hussmann, S., "A Software Algorithm Prototype for Optical Recognition of Embossed Braille", Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 23-26,  IEEE Xplore Press, pp:586-589.DOI:10.1109/ICPR.2004.1334316, 2004.

10.          Antonacopoulos, A. and Bridson D., 2004. A robust Braille recognition system, In: Document Analysis Systems VI, Lecture Notes in Computer Science (3163), Springer Berlin/Heidelberg, pp.533-545. DOI: 10.1007/978-3-540-28640-0_50. ISSN 0302-9743.

11.          Falcón, N., Travieso, C. M., Alonso, J. B., and Ferrer M. A., "Image Processing Techniques for Braille Writing Recognition", Lectures Notes in Computer Science, Ed. Springer, pp.379-385. ISSN: 0302-9743, 2005.

12.          Namba, M., and Zhang, Z., "Cellular Neural Network for Associative Memory and Its Application to Braille Image Recognition", Proc. of IJCNN’06, pp. 4716-4721. DOI:10.1109/IJCNN.2006.247066, 2006.

13.          Al-Salman, A., Al-Ohali, Y., Al-Kanhal, M., and Al-Rajih, A., "An Arabic Optical Braille Recognition System", ICTA'07, Hammamet, Tunisia, pp.81-87, 2007.

14.          Tanaka, M., Miyata, K., and Chonan, S., "A Wearable Braille Sensor System With a Post Processing", IEEE/ASME Transactions on Mechatronics, 12(4):430 438. DOI:10.1109/TMECH.2007.901923, 2007.

15.          AL-Saleh, A., El-Zaart, A., and Al-Salman, A., "Dot Detection of Braille Images Using A Mixture of Beta Distributions", Journal of Computer Science (Science Publications) 7 (11): 1749–1759, ISSN 1549-3636, 2011.

16.          Al-Salman, A., El-Zaart, A., Al-Salman, S., Gumaei, A., "A Novel Approach for Braille Images Segmentation, International Conference on Multimedia Computing and Systems (ICMCS 2012), Tangier, May 10-12, IEEE Xplore Press, pp.190-195. DOI:. DOI:10.1109/ICMCS.2012.6320146, 2012.

17.          Al-Shamma, S. D. and Fathi, S., "Arabic Braille Recognition and Transcription into Text and Voice", 5th Cairo International Biomedical Engineering Conference 2010, Cairo, December 16-18, pp.227-231. DOI: 10.1109/CIBEC.2010.5716095, 2010.

18.          Padmavathi, S., Manojna, K. S. S., Reddy, S. S., Meenakshy, D., "Conversion of Braille to Text in English, Hindi and Tamil Languages", International Journal of Computer Science, Engineering and Applications, 3(3):19:32. DOI:10.5121/ijcsea.2013.3303, 2013.

19.          Shreekanth, T., and Udayashankara, V., "A Review on Software Algorithms for Optical Recognition of Embossed Braille Characters", International Journal of Computer Applications, Published by Foundation of Computer Science, New York, USA. 81(3):25-35. DOI: 10.5120/13993-2015, 2013.

20.          Tetsuya, W., and Susumu, O., "A study on legible braille patterns on capsule paper: Diameters of braille dots and their interspaces on the original ink-printed paper", The Bulletin of the National Institute of Special Education, 30:1-8, 2003.

21.          El-Zaart, A., and DjemelZiou, "Statistical Modeling of multimodal SAR Images", International Journal of Remote Sensing, 28(10):2277–2294. DOI: 10.1080/ 01431160600933997, 2007.


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14.

Authors:

Manpreet Kaur

Paper Title:

Comparative Study of Parallel Odd Even Transposition and Rank Sort Algorithm

Abstract:  In this paper, the execution behaviours of different parallel sorting algorithms like odd-even transposition sort and parallel rank sort have been invested with multithreading. Multithreading in JAVA programming language provides a mechanism where parallel algorithms are implemented by far. The performance of implemented algorithms is evaluated on the basis of execution time. It has been found that parallel odd even Transposition algorithm is giving better performance as compared with rank sort algorithm.

Keywords:
   Parallel sorting algorithms, performance analysis, Multithreading.


References:

1.           Kalim Qureshi and Haroon Rashid,“A     Practical Performance Comparison of Parallel Matrix Multiplication Algorithms on Network of Workstations.”, IEEE Transaction Japan, Vol. 125, No. 3,2005.
2.           www.encyclopedia.com/doc/1O11-oddeventranspositionsort.html

3.           D. Bitton, D. DeWitt, D.K. Hsiao, J. Menon, A Taxonomy of Parallel Sorting, ACM Computing Surveys, 16,3,pp. 287-318.

4.           Song, Y.D., Shirasi, B. A Parallel Exchange Sort Algorithm. South Methodist University, IEEE.

5.           www.economyinformatics.ase.ro/content/EN5/alecu.pdf

6.           Parallel Rank Sort Assist Lecturer Felician ALECU Economy Informatics Department, A.S.E Bucharest, Economy Informatics.

7.           S.Lakshmivardan and S.K Dhall “ Analysis and Design of Parallel Algorithms “ McGraw-Hill 1990

8.           “Parallel Programming techniques and applications using networked workstations and parallel computer” Barry Wilkison and Michael Allen.


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15.

Authors:

Jaison John, C. Sathish Kumar

Paper Title:

A Comparative Study of Genetic Algorithm and Particle Swarm Optimization based Optimizations of PID Controller Parameters

Abstract:   Proportional-Integral-Derivative (PID) control is the most commonly used algorithm for industrial control. The process of computing and setting the optimal gains for P, I and D to get an ideal response from a control system, called as tuning, is a very difficult task. In this paper, two types of nature inspired algorithms genetic algorithm (GA) and particle swarm optimization (PSO) techniques are used for optimizing the PID parameters. These techniques have been observed to be capable of locating high performance areas in complex domains without experiencing the difficulties associated with high dimensionality or false optima. Hard disk drive read/write head servo control system and DC motor control are used in the simulation study for depicting the efficacy of the proposed methods. PID controllers optimized using GA and PSO are observed to provide better time domain performance in comparison with conventionally used tuning method of Ziegler-Nichols. 

Keywords:
    Genetic Algorithm, Particle Swarm Optimization, Tuning of PID Controller, Ziegler-Nichols.


References:

1.             D. B. Ender, “Process control performance not as good as you think”, control engineering, 40 (10), 1993, pp. 173-186.
2.             D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, MA: Addison-Wesley, 1989.

3.             R. C. Eberhart, Y. Shi, “Particle swarm optimization: developments, applications and resources,” Proc. 2001 Congress on Evolutionary Computation, 2001, 81-86.

4.             R. C. Dorf  and R. H. Bishop, Modern Control Systems, 10th ed., 2004, Upper Saddle River, NJ: Pearson Education.

5.             K. J. Astrom and T. Hagglund, “Automatic Tuning of PID Controllers”, Instrument Society of America, North Carolina, USA, 1988.

6.             N. Thomas and  P. Poongodi, “Position  Control  of  DC  Motor   using  Genetic  Algorithm  Based  PID  Controller,” Proc. World Congress on Eng., vol II, WCE 2009, London, U.K.

7.             J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization”, IEEE International Conf. on Neural Networks, 1995, pp.1942-1948.

8.             Z. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE trans. energy conversion,  Nov. 2002

9.             GAOT - A Genetic algorithm Optimization Toolbox for use with MATLAB, http://www.daimi.au.dk/~pmn/Matlab/dochome/toolbox/ GAOT/gaotindex.html

10.          PSOT - a Particle Swarm Optimization Toolbox for use with MATLAB, http://www.mathworks.com/matlabcentral/fileexchange/7506-particle-swarm-optimization-toolbox


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16.

Authors:

Jayshree Ghorpade, Shamika Mukane, Devika Patil, Dhanashree Poal, Ritesh Prasad

Paper Title:

Novel Method for Graphical Passwords using CAPTCHA

Abstract:    Cyber security is an important issue to tackle. Various user authentication methods are used for this purpose. It helps to avoid misuse or illegal use of highly sensitive data. Text and graphical passwords are mainly used for authentication purpose. But due to various flaws, they are not reliable for data security. Text passwords are insecure for reasons and graphical are more secured in comparison but are vulnerable to shoulder surfing attacks. Hence by using graphical password system and CAPTCHA technology a new security primitive is proposed. We call it as CAPTCHA as gRaphical Password (CaRP). CaRP is a combination of both a CAPTCHA and a graphical password scheme. In this paper we conduct a comprehensive survey of existing CaRP techniques namely ClickText, ClickAnimal and AnimalGrid. We discuss the strengths and limitations of each method and point out research direction in this area. We also try to answer “Are CaRP as secured as graphical passwords and text based passwords?” and “Is CARP protective to relay attack?"

Keywords:
     CAPTCHA, CaRP, passwords, graphical, techniques.


References:

1.             Bin B. Zhu, Jeff Yan, Guanbo Bao, Maowei Yang, and Ning Xu, “CAPTCHA as Graphical Passwords—A New Security Primitive Based on Hard AI Problems”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO. 6, JUNE 2014
2.             Matthew Dailey, Chanathip Namprempre,“A Text-Graphics Character CAPTCHA for Password Authentication”

3.             T. S. Ravi Kiran, Y. Rama Krishna, “Combining CAPTCHA and graphical passwords for user authentication” , International Journal of Research in IT & Management, Volume 2, Issue 4 (April 2012) (ISSN 2231-4334)

4.             Liming Wang, Xiuling Chang, Zhongjie Ren, Haichang Gao, Xiyang Liu, Uwe Aickelin, “Against Spyware Using CAPTCHA in Graphical Password Scheme”

5.             Luis von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford, “CAPTCHA: Using Hard AI Problems For Security”

6.             Xiaoyuan Suo, Ying Zhu, G. Scott. Owen, “Graphical Passwords: A Survey”, Department of Computer Science Georgia State University


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17.

Authors:

M. A. Ramadan, Talaat S. EL-Danaf, Hanem Galal

Paper Title:

Chebyshev-Sinc Collocation Schemes for Solving a Class of Convection Diffusion Equations

Abstract:     Tthis paper, is concerned with obtaining numerical solutions for a class of convection-diffusion equations (CDEs) with variable coefficients. Our approaches are based on collocation methods. These approaches implementing all four kinds of shifted Chebyshev polynomials in combination with Sinc functions to introduce an approximate solution for CDEs . This approximate solution can be expressed as a finite double summation from the product of Sinc functions and shifted Chebyshev polynomials. The time derivatives for all four kinds of shifted Chebyshev polynomials are expressed here as linear combinations from Chebyshev polynomials themselves. New formulas for the integer derivatives with respect to time t and space x, respectively of the unknown function with two variables is expressed in terms of the product of Sinc functions and shifted Chebyshev polynomials themselves also. Special attention is given to the numerical results obtained by the proposed approaches in order to demonstrate the accuracy and efficiency of the newly proposed approaches.

Keywords:
 Chebyshev polynomials; Sinc functions - accuracy and efficiency- shifted Chebyshev polynomials


References:

1.                M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions, Dover, New York, (1964).
2.                G. Ben-Yu and X. Cheng-Long, Hermite pseudo spectral method for nonlinear partial differential equations, J. Comput. Math., 34, p.(859-872), 2000.

3.                C. Canuto, M. Y. Hnssalni, A. Quarteroni and T. A. Zang, Spectral Methods in Fluid Dynamics, Springer-Verlag, New York, (1988).

4.                X. Cheng-Long and G. Ben-Yu, Laguerre pseudo pectral method for non-linear partial differential equations, J. Comput. Math., 20, p.(413-428), 2002.

5.                S. Das, Functional Fractional Calculus for System Identification and Controls, Springer, New York, (2008).

6.                E. H. Doha, A. H. Bahrawy and S. S. Ezz-Eldien, Efficient Chebyshev spectral methods for solving multi-term fractional orders differential equations, Appl. Math. Modeling, 35, p.(5662- 5672), 2011
7.                M. Dehghan, A. Saadatmandi, The numerical solution of a nonlinear system of second-order boundary value problems using the Sinc-collocation method, Math Comput Model, 37, p.(1434- 1441), 2007.

8.                M. Inc, The approximate and exact solutions of the space-and time-fractional Burger's equations with initial conditions by VIM, J. Math. Anal. Appl., 345, p.(476-484), 2008.

9.                H. Jafari and V. Daftardar-Gejji, Solving linear and non-linear fractional diffusion and wave equations by Adomian decomposition method, Appl. Math. And Comput., 180, p.(488-497), 2006.

10.             M. M. Khader, Talaat S. El Danaf, A. S. Hendy, A computational matrix method for solving systems of high order fractional differential equations, Applied Mathematical Modelling, 37, p.(4035-4050), 2013.

11.             AA. Kilbas, HM. Srivastava, Trujillo JJ, Theory and applications of fractional differential equations, San Diego, Elsevier, 2006.

12.             Luchko Y, Goreno R, An operational method for solving fractional differential equations with the Caputo derivatives, Appl Math Comput, 24, p.(207-233), 1999.

13.             Y. Lin , C. Xu, Finite difference/spectral approximations for the time-fractional diffusion equation, J Comput Phys, 255, p.(1533-1552), 2007.

14.             J. Lund, K. Bowers, J.A. Tenreiro Machado (Eds.), Sinc methods for quadrature and differential equations, Philadelphia: SIAM, 1992.

15.             Su Lijuan, Wenqia Wang, Hong Wang, A characteristic difference method for the transient fractional convection diffusion equations, Applied Numerical Mathematics, 61, p.(946-960), 2011.

16.             M. M. Meerschaert and C. Tadjeran, Finite difference approximations for two-sided space fractional partial differential equations, Appl. Numer. Math., 56, p.(80-90), 2006.

17.             K. S. Miller and B. Ross, An Introduction to the Fractional Calculus and Fractional Differential Equations, John Wily Sons, Inc. New York, (1993).

18.             Hong-Kui Pang, Hai-Wei Sun, Multigrid method for fractional diffusion equations, Journal of Computational Physics, 231, p.(693-703), 2012.

19.             Podlubny, Fractional Di_erential Equations, Academic Press, New York, 1999.

20.             K. Parand, M. Dehghan, Pirkhedri, A. Sinc-collocation method for solving the Blasius equation, PhysLett A, 37, p.(4060-4065), 2009.

21.             Abbas Saadatmandi, Mehdi Dehghan and Mohammad-Reza Azizi, The Sinc Legendre collocation method for a class of fractional convection diffusion equations with variable coefficients, Commun Nonlinear Sci Numer Simulat, 17, p.(4125-4136), 2012.

22.             Saadatmandi, M. Dehghan, A tau approach for solution of the space fractional diffusion equation, Comput Math Appl, 62, p.(1135-1142), 2011.

23.             Saadatmandi, M. Razzaghi, A. The numerical solution of third-order boundary value problems using Sinc-collocation methodCommun Numer Meth Eng,23, p.(681-690), 2007.

24.             J. Sabatier, O.P. Agrawal, J.A. Tenreiro Machado (Eds.), Advances in Fractional Calculus Theoretical Developments and Applications in Physics and Engineering, Springer, 2007.

25.             J. Stenger F, Numerical methods based on Sinc and analytic functions, New York, Springer- Verlag, 1993.

26.             Su L, Wang W, Xu Q, Finite difference methods for fractional dispersion equations, Appl Math Comput, 216, p.(3329-3334), 2010.

27.             Hong Wang, Kaixin Wang, Treena Sircar, A direct O(Nlog2N) finite difference method for fractional diffusion equations, Journal of Computational Physics, 229, p.(8095-8104), 2010.

28.             Tadjeran, MM. Meerschaert, Scheffer HP, A second-order accurate numerical approximation for the fractional diffusion equation, Comput Math Appl, 213, p.(205-213), 2006.

29.             Tadjeran and M. M. Meerschaert, A second-order accurate numerical method for the two dimensional fractional diffusion equation, J. Comput. Phys., 220, p.(813-823), 2007.

30.             MujeeburRehman and Rahmat Ali Khan, Numerical solutions to initial and boundary value problems for linear fractional partial differential equations, Applied mathematical modeling, 37, p.(5233-5244), 2013.

31.             S. B. Yuste, Weighted average finite difference methods for fractional diffusion equations, Journal of Computational Physics, 216, p.(264-274), 2006.


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18.

Authors:

M. S. Mythili, A. R. Mohamed Shanavas

Paper Title:

A Study on Autism Spectrum Disorders using Classification Techniques

Abstract:      In recent years, the academic establishments  has undertaken variety of initiatives to support school boards, college authorities, special schools and colleges in teaching students with Autism Spectrum disorders(ASD). Autism Spectrum Disorders (ASD) are complex neurological disorders that have a lifelong effect on the event of assorted talents and skills. The foremost vital goal of the paper is to review the autism problem, to detect the levels of autism with the help of data mining classification algorithms. The data mining has been typically accepted as a decision making process to facilitate higher resource utilization in terms of autism students’ performance.

Keywords:
 Autism Spectrum Disorders, data mining


References:

1.                 Guidelines for Educating Students with Autism Spectrum Disorders”, October 2010 Virginia Department of Education, Office of Special Education and Student Services.
2.                 “Guidelines for Identification and Education of Children and Youth with Autism “, July 2005, CONNECTICUT STATE DEPARTMENT OF EDUCATION Division of Teaching and Learning Programs and Services Bureau of Special Education

3.                 Felix D. C. C. Beacher et al., “Sex Differences and Autism: Brain Function during Verbal Fluency and Mental Rotation”, June 2012 | Volume 7 | Issue 6 | e38355

4.                 Volkmar FR, Klin A (2000) Pervasive Developmental Disorders. In: Kaplan and Sadock’s Comprehensive Textbook of Psychiatry CDROM 7th edition. Publisher: Lippincott Williams and Wilkins, Philadelphia, PA.

5.                 “Autism Spectrum Disorders” Centre for Developmental Disability Health Victoria Building 1, 270 Ferntree Gully Road, NOTTING HILL, VIC 3168 Telephone: (03) 9902 4467 Facsimile: (03) 8575 2270 E-mail: cddh@monash.edu.

6.                 “Autism Spectrum Disorders: Information Pack” Autism Victoria Inc. ABN 15 600 724 949 A14601C 24 Drummond St, Carlton, 3053, Postal Address: P.O. Box 374, Carlton South, 3053 E: info@autismvictoria.org.au T: 03 9657 1600 W: www.amaze.org.au.

7.                 “A Parent’s Guide to Evidence-Based Practice Autism”, Copyright © 2011 National Autism Center. [8] Cosgrove KP, Mazure CM, Staley JK (2007) Evolving knowledge of sex differences in brain structure, function, and chemistry. Biological psychiatry. Oct 15;62(8): 847–55.

8.                 Baron-Cohen S (2009) Autism: the empathizing-systemizing (E-S) theory. Annals of the New York Academy of Sciences. Mar;1156: 68–80.

9.                 Eric Zander et al., “An introduction to autism”, AUTISMFORUM Handikapp & Habilitering, Box 17519, 118 91 Stockholm 08-690 60 52, www.autismforum.se, autismforum@sll.se Zander, E. An introduction to autism ¼ 1/9 2004.

10.              Kathleen T Quach et al., “Application of neural networks in classification of autism diagnosis based on gene expression signatures”

11.              Demuth et al. “Neural network toolbox for use with MATLAB.”,1993. Rachna Ahuja et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, 2166-2170 www.ijcsit.com 2169

12.              Nagesh Adluru et al., “Characterizing brain connectivity using - radial nodes: application to autism classification”. [14] Hagmann, P., Kurant, M., Gigandet, X., et. al.: Mapping human whole-brain structural networks with Diffusion MRI. PLoS one 2(7) (2007).

13.              Prud’hommeaux et al., “Classification of atypical language in autism”, in Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics, pp: 88-96, 2011.

14.              Kathleen T Quach et al., “Application of Artificial Neural Networks in Classification of Autism Diagnosis Based on Gene Expression Signatures”.

15.              Alexander Genkin et al., “Large-scale Bayesian logistic regression for text categorization”, Technometrics, pp: 291-304, 2007. Rachna Ahuja et al, / (IJCSIT) International Journal of Computer Science and Information Technologies


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19.

Authors:

M. M. Mohie El-Din, M. S. Kotb , W. S. Emam

Paper Title:

Bayesian Estimates based on Combined Hybrid Censored Data from the Modified Weibull Lifetime Model

Abstract:       In this article, we derive Bayesian stimations of three parameters and some survival time parameters e.g. reliability and hazard functions in the modified Weibull distribution based on combined hybrid censored data. Finally, a real life data set and simulation data are used to illustrate the discussed methodology.

Keywords:
  Bayesian estimation; modified Weibull distribution; order statistics; combined hybrid censored data.


References:

1.             AL-Hussaini, E. K., Al-Dayian, G. R. and Adham, S. A., (2000). On finite mixture of two-component Gompertz lifetime model. J. Statist. Comput. Simul., 67,1-20.
2.             Basu, A. P. and Ebrahimi, N.,(1991). Bayesian approach to life testing and reliability estimation using asymmetric loss function. J. Stat. Plan. Infer., 29,21-31.

3.             Chen, S. and Bhattacharya, G. K.,(1988). Exact confidence bounds for an exponential parameter under hybrid censoring. Commun. Stat. Theory and Methods, 17, 1857-1870.

4.             Childs, A., Chandrasekhar, B., Balakrishnan, N. and Kundu, D. (2003). Exact likelihood inference based on type-I and type-II hybrid censored samples from the exponential distribution. Ann. Inst. Stat. Math., 55,  .

5.             Epstein, B.,  . Truncated life tests in the exponential case. Ann. Math. Stat., 25,  .

6.             Draper, N. and Guttman, I., (1987). Bayesian analysis of hybrid life tests with exponential failure times. Ann. Inst. Stat. Math., 39, 219-225.

7.             Fairbanks, K., Madson, R. and Dykstra, R., (1982). A confidence interval for an exponential parameter from a hybrid life test. J. American Stat. Assoc., 77, 137-140.

8.             Gupta, R. D. and Kundu, D. (2006). On the comparison of Fisher information matrices of the Weibull and generalized exponential distributions. J. Stat. Plann. Infer., 136, 

9.             Huang, W. T. and Yang, K. C., (2010). A new hybrid censoring scheme and some of its properties. Tamsui Oxford J. Math. Sci., 23(4), 355-367.

10.          Jeong, H. S., Park, J. I. and Yum, B. J.,(1996). Development of (r,T) hybrid sampling plans for exponential lifetime distributions. J. Appl. Stat., 23, 601-607.

11.          Lawless, J. F., (1982). Statistical model & methods for lifetime data, (NewYork: Wiley).

12.          Martz, H. F. and Waller, R. A., (1982). Bayesian reliability analysis, (New York: Wiley).

13.          Nelson, W. B., (1982). Applied life data analysis, (New York: Wiley).

14.          Sarhan, A. M. and Zaindin, M., (2009). Modified Weibull distribution, Appl. Sci., 11, 123-136

15.          Soland, R.M.,(1969). Bayesian analysis of the Weibull process with unknown scale and shape parameters IEEE Trans. Reliab. R18, 181-184

 

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20.

Authors:

A. A. Shaikh, P. S. Shinde, S. R. Singh, S. Chandra, R. A. Khan

Paper Title:

A Review on Virtual Dressing Room for E-Shopping using Augmented Reality

Abstract:  Augmented Reality combined with new algorithms and social media technologies have started a revolutionary shift away from the classic desktop paradigm and into the direction of intuitive, “natural interaction” where people interface with the technological world through hand gestures, speech and body language. The virtual dressing room will make use of Human Computer Interface and Augmented Reality and it will be used for online shopping. This will facilitate the shopping experience by letting customers to try-on apparel and/or mix and match accessories without being physically present in the retail shop. These platforms are not only powerful decision tools for the on-line shopper, but also contribute to the fun factor of in-store shopping. The system gets the data of custom body sizes to construct virtual fitting model through photos already uploaded. The model then tries on different costumes and the system shows the fitting effect. Augmented Reality Virtual Dressing room works by superimposing the model or picture of a garment or accessory within the live video feed of the customer. The super-imposed model or picture of the garment or accessory will then track the movements of the customer so it appears as if the customer is wearing the virtual item in the video-view. In addition, omnipresent social networking features allow sending photos or videos of the shopper wearing the apparel for quick feedback. The proposed project can achieve real-time, high-fidelity cloth simulation and provide encouraging online virtual fitting experiences.

Keywords:
  Augmented Reality, Edge detection, Gesture recognition, Human-Computer Interaction, Information Kiosk, Motion tracking.


References:

1.                Ronald Azuma, “A Survey of Augmented Reality,” In Presence: Teleoperators and Virtual Environments 6, 4 (August 1997), 355-385.
2.                Welch, Robert B, “Perceptual Modification: Adapting to Altered Sensory Environments,” Academic Press (1978). ISBN 0-12-741850-4.  
3.                Deering, Michael, “High Resolution Virtual Reality,” Proceedings of SIGGRAPH '92 (Chicago, IL, 26-31 July 1992). In Computer Graphics 26, 2 (July 1992), 195-202.
4.                Foley, James D., Andries van Dam, Steven K. Feiner, and John F.   Hughes, Computer Graphics: Principles and Practice (2nd edition). Addison-Wesley (1990).

5.                HIPR2 homepage at The University of Edinburgh School of Informatics,http://en.wikipedia.org/wiki/Image_subtraction#cite_note-1

6.                R. Gonzales and R. Woods, Digital Image Processing,

7.                Addison Wesley, 1992, pp 47 - 51, 185 - 187.

8.                V. I. Pavlovic, R. Sharma, and T. S. Huang,  “Visual interpretation of hand gestures for human computer interaction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 677–695, Jul. 1997.

9.                L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257–285,Feb. 1989.

10.             S. Mitra and T. Acharya,  Data Mining: Multimedia, Soft Computing, and Bioinformatics. New York: Wiley, 2003.

11.             S. Mitra and T. Acharya, “Gesture Recognition: A Survey,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 37, NO. 3, MAY 2007.


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21.

Authors:

L. Berriche, N. Al-Ghamdi, A. Al-Qahtani, A. Al-Garni, M. Al-Azmi

Paper Title:

MIMO Systems Performance Simulator

Abstract:     Multi Input Multi Output (MIMO) has emerged as a hot topic in wireless communications. This is due to possible dramatic increase in reliability and capacity as compared to single antenna solution. In this project we are developing a MIMO System Performance Simulation (MSPS) tool by using MATLAB for making more realistic studies of MIMO systems. By using this simulator one can see the Bit Error Rate (BER) performance of the system and Minimum Mean Square Error of  LMMSE estimator according to given Pilot to Data Power Ratio (PDPR), pilot schemes and based on two types of channel model correlated and non-correlated.

Keywords:
  MIMO, Channel estimation, Symbol detection, simulator..


References:

1.                C. Smith and D. Collins, 3G Wireless Networks, Mc Graw Hill, 2006.
2.                B. Hassibi and B. M. Hochwald, "How Much Training is Needed in Multiple Antennae Wireless Links?," IEEE Transaction on Information Theory, vol. 49, no. 4, pp. 2515-2528, 2003.

3.                J. G. Andrews, "MIMO-OFDM Design using LabVIEW," University of Texas, 2006. [Online]. Available: http://users.ece.utexas.edu/~jandrews/molabview.html. [Accessed 10 2014].

4.                M. Bazdresch, "Simulation Platform for MIMO Systems," in IEEE 16th Conference on Electronics, Communications and Computers, 2006.

5.                G. J. Miao, Signal Processing for Communications, Norwood: Artech House, 2007.

6.                Mertins, Signal Analysis, Wiley, 1999.

7.                M. Biguesh and A. B. Gershman, "Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals," IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 884-893, 2006.

8.                T. Kim and G. J. G. Andrews, "MIMO OFDM with Variable (PDPR) Simulator Tutorial".


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