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Volume-5 Issue-3, July 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

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Issa Khalil ALHasanat, Ayman A. Rahim A. Rahman

Paper Title:

The Fact of use Mobile Learners at the Arab Open University in Learn Arabic language

Abstract: This study aims to investigate the fact of the use of mobile learning at Arab Open University students in order to help them for learn Arabic, and study sample consisted of (245) students, who study decision Arabic for primary school teachers, was applied to identify prepared by the researcher on students to identify these uses, and contain this questionnaire four axes, namely: mobile phones used by students, and mobile services that benefit students to learn Arabic, and the purposes for which Students use mobile phones, and the obstacles faced by the students' learning through mobile phones (m-phones) to learn Arabic. The study concluded with a set of recommendations to contribute to improvie the use of the Arabic language learning via mobile devices, based on what resulted from the results of the study.[1]

Smartphones, technology, instructional aide


1. Heyasat Ahmad (2009), "Showing the third generation cellular companies"alray newspaper 3 July P 24
2. (Recycling in the Heart of Dixie: eCycle Best? s Top 5 Recyclers in Alabamaby Andrew Del Prado On Jan. 7, 2015)

3. Goh,Kinsguk, (2006)”Getting Ready for Mobile Learning-Adaptation Perspective”,JI of Educational Multimedia and Hypermadia,Vol.15.No.(2),pp.175-198

4. Yu-Shun Wang, Ming-Cheng Wu and Hisu-YuanWang. (2009).Investigating the determinants and age and gender differences in the acceptance of mobile learning .British Journal of Educational Technology,Vol (40)No(1),pp.92-118.

5. Ria (2014).” THE USE OF SMARTPHONES AMONG STUDENTS IN RELATION TO THEIR EDUCATION AND SOCIAL LIFE “Nicoletti Morphitou University of Nicosia, Greece ,icicte2014 pp73-81

6. Jessica L. Buck, Elizabeth McInnis, Casey Randolph (2013),” The New Frontier of Education: The Impact of Smartphone Technology in the Classroom”. 2013 ASEE Southeast Section Conference.

7. Attewell.J(2005) Mobile Technologies and Learning. Technology Enhanced Learning Research Center. Published By the Learning and Skills Development Agency,UK.

8. Cavus,N and Dogan ,I.(2009).”M-Learning:An experiment in using SMS to support learning new English language words”.British Journal of Educational Technology.Vol 40.No 1.pp.78-91.

9. Corbett,S.(2008).”Can the cell phone help and global poverty?”.The New York Times. April 13, Retrieved from http://www.nytimes.com/2008/04/13/magazine/13 anthropologyt.html.

10. Mohamed,A(2009).Mobile Learning Transforming the Delivery of Education and Training,Issues in Distance Education Series,Published by AU Press,Athabasca University.

11. Prensky,M.(2005).”In What Can You Learn from a Cell Phone? Almost Anything”.Journal of online education,Vol 5,No(1),pp.34-41.

12. Quinn C (2000). “mLearning:mobile ,wireless,in your-pocket learning LineZine.Fom:www.linezine.com/2.1/features /cqmmwipy.htm,

13. Shuler, C.(2009).Pockets of Potential:Using Mobile Technologies to Promote Children’s Learning,The Joan Ganz Coony Center at Sesame workshop .New York.

14. Sharples M(2000). “The design of personal mobile technologies for lifelong learning”. Computers and Education, Vol34, No(2),pp.177-193.

15. Siau,K,Lim,E.-P.&Shen,Z.(2001).”Mobile commerce: promises, challenges,and research agenda “.Journal of Database Management,Vol12,No(3),pp.4-13.

16. Rene, J. E. (2011). Handheld education: Applied mobile technology. Choice, 48(9), 1605-1608,

17. Martin, F., Pastore, R., & Snider, J. (2012). Developing mobile based instruction. TechTrends,56(5), 46-51. doi: http://dx.doi.org/10.1007/s11528-012-0598-9

18. Miangah, T.M. & Nezarat, A. (2012). Mobile-assisted language learning. International Journal of Distributed and Parallel Systems, 3(1), 309-319.

19. Begum, R. (2011). Prospect for cell phones as instructional tools in the EFL classroom: A case

20. Study of Jahangirnagar University, Bangladesh. English Language Teaching, 4(1), 105-115.

21. Gikas, J. Grant, M. M. (2013, October). Mobile computing devices in higher

22. Education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18-26.

23. Benson, V., & Morgan, S, (2013). Student experience and ubiquitous learning

24. Wankel, L. A., & Blessinger, P. (2013). Increasing student engagement and

25. Saylor. M. (2012). The mobile wave: how mobile intelligence will change everything.

26. Freeman, K. (2012). Low income students’ test scores leap 30% with smartphone use.

27. Marshable US & World. Retrieved from

28. http://mashable.com/2012/10/15/wireless-reach-






Ashwini B. M, Y. P. Gowramma

Paper Title:

Implementation of Encrypted Visual Cryptographic Shares using RSA Algorithm on FPGA

Abstract: The project presents an approach for encrypting visual cryptographically generated image shares using RSA algorithm. The Visual Cryptography Scheme is a secure method that encrypts a secret document or image by breaking it into shares. A distinctive property of Visual Cryptography Scheme is that one can visually decode the secret image by superimposing shares without computation. By taking the advantage of this property, third person can easily retrieve the secret image if shares are passing in sequence over the network. RSA algorithm is used for providing the double security of secret document. The RSA is a new method to encrypt the data by using private and public keys. Thus secret share are not available in their actual form for any alteration by the adversaries who try to create fake shares. The scheme provides more secure secret shares that are robust against a number of attacks & the system provides a strong security for the handwritten text, images and printed documents over the public network. Field Programmable Gate Arrays (FPGAs) are widely used to implement special purpose processors. FPGAs are economically cheaper for low quantity production because its function can be directly reprogrammed by end users. The aim of this project is to design a hardware on which we can encrypt/decrypt a confidential data using visual cryptography and RSA algorithm. in order to reduce the hardware consumption, here I have designed the FPGA such that, we can encrypt the part of the image at a time and we are going to repeat the process until all pixels are encrypted/decrypted.

Visual Cryptography; Encryption; Information Security; VCshares


1. Parakhand S. kak“A Recursive Threshold Visual Cryptography Scheme” .Department of Computer Science, Oklahoma State University Stillwater,OK74078.
2. Padhmavati, P. Nirmal Kumar, M. A. Dorai Rangaswamy“A Novel Scheme for Mutual Authentication and Cheating Prevention in Visual Cryptography Using Image Processing”. Department of Computer Science &Engineering, Easwari Engineering College, Chennai, DOI: 02, ACS.2010.01.264, 2010 ACEEE.

3. Chandramathi S, Ramesh Kumar R., Suresh R. and HarishS. “An overview of visual cryptography” International Journal of Computational intelligenceTechniques, ISSN: 0976–0466&E-ISSN: 0976–0474Volume 1, Issue 1, 2010, PP-32-37

4. Jenaand S. Jena “A Novel Visual Cryptography Scheme”.

5. Néelima. Guntupallieta “An Introduction to Different Types of Visual Cryptography Schemes” ,International Journal of Science and Advanced Technology (ISSN2221-8386),Volume 1No7September 2011,PP198-205.

6. M. Naorand A. Shamir “Visual Cryptography”. Advances in Cryptology EUROCRYPT ’94. Lecture Notesin Computer Science, (950):1–1, 1995.

7. M. Nakajima and Y. Yamaguchi “Extended Visual Cryptography for Natural Images” .Department of Graphic and Computer Sciences, Graduate School of Arts and sciences, the University of Tokyo 153-8902, Japan.

8. P.S.Revenkar, Anisa Anjum, W.Z.Gandhare “Survey of Visual Cryptographic Schemes”. International Journal of Security and Its ApplicationsVol.4, No.2, April, 2010.

9. Shyamalendu Kandar & Arnab Maiti “K-Secret Sharing Visual Cryptography Scheme for Color Image Using Random Number”. International Journal of Engineering Science and Technology (IJEST), ISSN0975-5462, Vol.3 No.3Mar201, PP1851-1857

10. Ujjwal Chakrabortyet al, “Design and Implementation of a (2,2)and a(2,3) Visual Cryptographic Scheme” International Conference [ACCTA-2010],Vol.1Issue2,3,4, PP128-134

11. Vaibhav Choudhary “An Improved Pixel Sieve Method for Visual Cryptography” International Journal of Computer Applications,(0975–8887)Volume12–No.9,January2011.

12. Wei-QiYan, DuoJin, Mohan S Kankanhalli “Visual Cryptography for print and scan applications “School of Computing ,National University of Singapore,Singapore117543

13. Y. Bani, Dr. B. Majhiand R.S. Mangrulkar,2008. A Novel Approach for Visual Cryptography Using a Watermarking Technique. In Proceedings of 2nd National Conference ,India Com2008.

14. Behrouz A. Forouzon, “Cryptography & Network security”4th Edition.





Alhamzah Taher Mohammed

Paper Title:

Design and Enhancement of Space-Time Block-Code for MC-CDMA OFDM by Phase Matrix in Flat and Selective Fading Channels

Abstract: In this paper, we combine a space-time block code (STBC) with a multi-carrier code division multiple access (MC-CDMA) system. MC-CDMA is probable to be one of the most promising access methods for future wireless communication schemes. In fact, MC-CDMA achievements the benefits of both the orthogonal frequency division multiplex (OFDM) multi-carrier modulation and of the code division multiple access (CDMA) technique. A development of space-time, block-coded (STBC) multicarrier code-division multiple-access (MC-CDMA) system using phase matrix in multipath fading channel is proposed, and the performance of the system is analyzed. The bit error rates BER numerical results show that the better performance of the STBC-MC-CDMA system with phase matrix can be achieved when comparing with system without using phase matrix. As a result, it can be seen from the proposed technique that a high performance improvement was obtained over the conventional MC-CDMA, where the Bit Error Rate (BER) is mainly reduced under different channel characteristics for frequency selective fading and the AWGN channel.



1. S. Hara and R. Prasad, "Design and performance of multicarrier CDMA system in frequency selective Rayleigh fading channels," IEEE Transactions on Vehicular Technology, vol. 48, no.5, pp.1584-1595, Sep. 1999., 1999.
2. H. H. Ajra, Md. Zahid; Islam, Md. Shohidul,, "BER Analysis of Various Channel Equalization Schemes of a QO-STBC Encoded OFDM based MIMO CDMA System," International Journal of Computer Network & Information Security;Feb2014, Vol. 6 Issue 3, p30, 2014.

3. S.Hara and R. Prasad, "Overview of multi carrier CDMA," IEEE Communication Magazine, vol. 35, no.12, pp. 126- 133, Dec. 1997., 1997.

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5. C. S. Leandro D'Orazio, Massimo Donelli, Jérôme Louveaux and Luc Vandendorpe,, "A Near-Optimum Multiuser Receiver for STBC MC-CDMA Systems Based on Minimum Conditional BER Criterion and Genetic Algorithm-Assisted Channel Estimation," EURASIP Journal on Wireless Communications and Networking, 2011.

6. G. Foschini and M. Gans, "On the limits of wireless communications in a fading environment when using multiple antenna, ," Wireless Personal Commuication, vol. 6, no. 3, pp. 311-335, Mar. 1998., 1998.

7. S.M. Alamouti, "A simple transmit diversity technique for wireless communications," IEEE Journal on Selected Areas in Communication, vol.16, no.8, pp. 1451-1458, Oct. 1998., 1998.

8. F. T. Inoue M, Nakagawa M,, "Space time transmit site diversity for OFDM multi- base station system. ," International Workshop on Mobile and Wireless Communications Network, Sweden, 30-34, 2002.

9. S. K. P. Sadananda Behera, "Performance Analysis of Low-Complexity Multiuser STBC MC-CDMA System," Intelligent Computing, Communication and Devices Advances in Intelligent Systems and Computing vol. Volume 309, , pp 223-228, 2014.

10. N. Kumaratharan, "STTC based STBC site diversity technique for MC-CDMA system," Computing Communication and Networking Technologies (ICCCNT), 2010 International Conference on, 2010.

11. Zhi Zliang and Li Guoqing, "A Novel Decoding Algorithm of STBC for CDMA Receiver in Multipath Fading Environments,” " IEEE Trans. on Comm., vol. 49, pp. 1956-1959, April 2001., 2001.

12. Y. J. H. Salih Mohammed Salih, Talib Mahmoud Ali,, "A Proposed Improvement Model for MC-CDMA in Selective Fading Channel," Anbar Journal of Engineering Sciences (AJES-2009), vol. AJES, Vol. 2, No. 1, 2009.






Alhamzah Taher Mohammed

Paper Title:

Zvezdan Stojanović, Dušan Savić

Abstract: new services, like IPTV, VoD, broadband access to Internet have very high demand for the bandwidth. xDSL technologies are mainly used as solution for this damand by the greatest operators in Bosnia and Herzegovina (BH Telecom, M:TEL). Thattechnologies have restriction regarding from the distance between central office (CO) where is operator's equipment and subsribers. Solution for this problem is some form of the next generation access (NGA) technology which is used in European Union (EU). In this paper is made comparison between situation with broadband technologies in European Union and BiH with possible direction of development. It is described why broadbandtechnologies in access network is so important.

triple play, quadruple play, NGA, FTTx


1. European Commission, Broadband markets, Digital Agenda Scoreboard for 2014.
2. European Commission, State of the telecoms services in Europe, e-Scoreboard 2013.

3. ITU-T G.984.1, Gigabit capable passive optical networks (GPON), General characteristics, 2008.

4. H.Widiger, A.Strzeletz, D. Timmermann, Evaluation of Dynamic Bandwidth Allocation Algorithms for G-PON Systems using a reconfigurable Hardware Testbed, Institute of Applied Microelectronics and Computer Engineering University of Rostock, 18051 Rostock, Germany, 2008.

5. V.A Raspopovic, G. Markovic, V.Radonjic, Pasivne optičke mreže za pristup, 25th PosTel, Beograd, pp 291-302.

6. S.Han, W. Yue and S. Smith, FTTx and xDSL: A Business Case Study of GPON versus Copper for Broadban Access Networks, Fujitsu, technical documentations, 2006.

7. CRA, The Annual Report of the Communications Regulatory Agency, Sarajevo, 2013.

8. J. Prience, “The Dynamic Effects of Triple Play Bundling in Telecommunications”, Research Program of Digital Communications, by Time Warner Cable, 2012.

9. http://www.bhtelecom.ba/

10. http://www.mtel.ba/

11. http://www.hteronet.ba/






Ali Mirshahi, Hashem Mirzaei Najafi, Mohammad-R. Akbarzadeh-T, Maryam Ebrahimi Nik

Paper Title:

Automatic Quality Enhancement of Radiographic Images by Fuzzy Logic

Abstract: Although much progress has been made in X-ray imaging, conventional radiography is still used in many developing countries as well as less developed countries due to its lower cost and availability. These conventional approaches are however significantly influenced by multiple factors such as sensor and environmental noises, age of developer and fixing materials, exposure factors and the experience of the operator. The goal of this study is to apply a novel post processing technique to get digital image advantages with conventional radiographic images. Specifically, we propose a novel fuzzy system to create a standard gray scale level image. As a result, image details are clearer and can be better enhanced by morphological edge operations. This image enhancement can lead to faster and more accurate interpretation by medical professionals. A number of experiments on rats, rabbits, and birds confirm utility of the proposed approach..

Computer-Assisted, Fuzzy Logic, Radiography, Image Enhancement.


1. L. Jian, P. Bao-Chang, H. Yong-Hui, F. Xiao-Yan, and T. Jian-Hui, “Image segmentation of bone in X-ray pictures of feet”, In: IEEE 2009 Wavelet Analysis and Pattern Recognition, 12-15 July 2009, Baoding, China. New York, NY, USA: IEEE, pp. 57-61.
2. P. Peloschek, S. Nemec, P. Widhalm, R. Donner, Birngruber E, Thodberg HH, Kainberger F, Langs G. “Computational radiology in skeletal radiography,”. Eur J. Radiol 2009, pp. 252-257.

3. T-R. Song, and W. Zheng, “The research of X-ray bone fracture image enhancement algorithms,” In: IEEE 2010 Computer, Mechatronics, Control and Electronic Engineering (CMCE), 24-26 August 2010, Changchun, China. New York, NY, USA: IEEE, pp. 384-387.

4. Frosio, M. Lucchese, F. Lissandrello, and NA. Borghese, “Automatic contrast enhancement in digital radiography,” In: IEEE 2008 Nuclear Science Symposium Conference Record, 2008 NSS '08 IEEE; 19-25 October 2008, Dresden, Germany. New York, NY, USA: IEEE, pp. 4331-4334.

5. BS. Sharif, SA. Zaroug, EG. Chester, JP. Owen, and EJ. Lee, “Bone edge detection in hand radiographic images,” In: IEEE 1994 Engineering in Medicine and Biology Society, 1994 Engineering Advances: New Opportunities for Biomedical Engineers Proceedings of the 16th Annual International Conference of the IEEE, 3-6 November 1994, Baltimore, MD, USA. New York, NY, USA: IEEE, pp. 514-515.

6. MS. Dinesh, Bhanuprakash, and R. Ashok, “Vision system for bone measurement from digital hand radiograph,” In: IEEE 1995 Engineering in Medicine and Biology Society, 1995 and 14th Conference of the Biomedical Engineering Society of India An International Meeting, Proceedings of the First Regional Conference, IEEE, 15-18 February 1995, New Delhi, India. New York, NY, USA: IEEE, pp. SPC9-SP10.

7. RS. Gaborski, and BK. Jang, “Enhancement for computed radiographic images,” In: IEEE 1995 Computer-Based Medical Systems, 1995, Proceedings of the Eighth IEEE Symposium on, 9-10 June 1995, Lubbock, TX, USA. New York, NY, USA: IEEE, pp. 27-34.

8. Y. Lujun, J.K Chang, and A. Basu, “Synthesis-based scalable image enhancement for digital radiography,” In: IEEE 2002 Image Processing 2002 Proceedings 2002 International Conference on, 22-25 September 2002, Rochester, NY, USA. New York, NY, USA: IEEE, pp. II-973-II-976.

9. H. Mirzaei, M. Jafari, and A. Mirshahi, “Considering the effect of using JPEG images on accuracy results of radiology images and application programs,” IJSCE 2013, pp. 489-492.

10. S. Soroori, F. Hosseini, A. Zamani-Moghaddam, M. Hosseininejad, I. Karimi, M. Masoudifard, and MM. Dehghan, “Assessment of avian osteoporosis by a quantitative radiographic method,” IRAN J VET RES 2012, pp. 317-322.

11. WN. Brown, “Bone density computing machine,” Proceedings of the National Electronics Conference, 26-28 September 1949, Chicago, USA. pp. 64-71.

12. GC. Henny, “Roentgenographic estimation of the mineral content of bone,” Radiology 1950, pp. 202-210.

13. HE. Meema, CK. Harris, and RE. Porrett, “A method for determination of bone-salt content of cortical bone,” Radiology 1964, pp. 986-997.

14. AH. Mark, YS. Hazel, Y S, and AF. John, “Computerized methods for X-ray-based small bone densitometry,” Comput Meth Prog Bio 2004, 35-42.

15. RC. Gonzalez, and RE. Woods, Digital image processing. Upper Saddle River, NJ, USA: Prentice Hall, 2008.

16. J-SR. Jang, C-T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Upper Saddle River, NJ, USA: Prentice Hall, 1997.






Fraol Bekana, J. Mehedi

Paper Title:

A Review of Clustering Schemes for Mobile Ad Hoc Networks

Abstract: Due to its vast application, maintaining the connectivity and forwarding the information in mobile ad hoc network (MANET) is very crucial to increase the efficiency as well as the performance of the system. One way of guarantying this performance to a large and dynamic network is through clustering. A number of researchers came up with a variety of approaches and performance metrics for ad hoc clustering. In this paper, we have presented a comprehensive review of various proposed clustering schemes for MANET. The classification and analysis of these schemes are done depending on their cluster formation. Descriptions of their approaches, evaluations of their performance, discussions of their advantages and disadvantages of each clustering schemes are presented. We believe that this paper will enable readers to get more understanding of ad hoc clustering and indicate research trends in the area.

Clustering, CDS, Mobile ad hoc Networks


1. Sze-Yao Ni, Yu-Chee Tseng, Y.S. Chen, and J.P. Sheu, The broadcast storm problem in a mobile ad hoc network, In Proc. of the 5th ACM/IEEE Int. Conference on Mobile Computing and Networking (MobiCom ’99), pages 151–162, New York, NY, USA, 1999.
2. C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks,” IEEE JSAC, vol. 15, Sept. 1997, pp. 1265–75.

3. C.R. Lin and M. Gerla, “A Distributed Control Scheme in Multi-hop Packet Radio Networks for Voice/Data Traffic Support,” Proc. IEEE ICC’95, pp. 1238–42, June 1995.

4. T.C. Hou and T.J. Tsai, “An Access-Based Clustering Protocol for Multihop Wireless Ad Hoc Networks,” IEEE JSAC, vol. 19, no. 7, pp. 1201–10, July 2001.

5. Yu .J.P and Chong P.H.J, A Survey of Clustering Schemes for Mobile Ad Hoc Networks, IEEE Communications Surveys and Tutorials, Vol. 7, No. 1, pp. 32-48, 2005.

6. J. Wu, Ming Gaol, and Ivan Stojmenovic, “On Calculating Power-Aware Connected Dominating Sets for Efficient Routing in Ad Hoc Wireless Networks,” J. Commun. and Networks, vol. 4, no. 1, pp. 59–70, Mar. 2002.

7. J.H. Ryu, S. Song, and D.H. Cho, “New Clustering Schemes for Energy Conservation in Two-Tiered Mobile Ad-Hoc Networks,” in Proc. IEEE ICC’01, vo1. 3, pp. 862–66, June 2001.

8. M. Chatterjee, S.K. Das, and D. Turgut, “An On-Demand Weighted Clustering Algorithm (WCA) for Ad hoc Networks,” in Proc. IEEE Globecom’00, pp. 1697–701, 2000.

9. A.B. McDonald and T.F. Znati, “Design and Performance of a Distributed Dynamic Clustering Algorithm for Ad-Hoc Networks,” in Proc. 34th Annual Simulation Symp., pp. 27–35, Apr. 2001.

10. T. Ohta, S. Inoue, and Y. Kakuda, “An Adaptive Multihop Clustering Scheme for Highly Mobile Ad Hoc Networks,” in Proc. 6th ISADS’03, Apr. 2003.

11. D.J. Baker and A. Ephremides, A distributed algorithm for organizing mobile radio telecommunication networks, in: Proceedings of the 2nd International Conference on Distributed Computer Systems, pp. 476–483, April 1981.

12. Ephremides, J.E. Wieselthier and D.J. Baker, A design concept for reliable mobile radio networks with frequency hopping signaling, in: Proceedings of IEEE, Vol. 75(1) (1987) 56–73.

13. M. Gerla and J.T. Tsai, “Multiuser, Mobile, Multimedia Radio Network,” Wireless Networks, vol. 1, pp. 255–65, Oct. 1995.

14. D. Gavalas, G. Pantziou, C. Konstantopoulos, Basilis Mamalis, Stable and Energy Efficient Clustering of Wireless Ad-Hoc Networks with LIDAR Algorithm, P. Cuenca and L. Orozco-Barbosa (Eds.): PWC 2006, LNCS 4217, pp. 100–110, 2006.

15. CH.V. Raghavendran, G. Naga Satish, P. Suresh Varma, I.R. Krishnam Raju , Enhancing the Performance of Routing in Mobile Ad Hoc Networks using Connected Dominating Sets, Special Issue of International Journal of Computer Applications, pp. 0975 – 8887, 2012.

16. D.J. Baker and A. Ephremides, The architectural organization of a mobile radio network via a distributed algorithm, IEEE Transactions on Communications COM-29 11 (1981) 1694–1701.

17. C.S. Victor and G. Amalanathan, Construction of Strategic Connected Dominating Set For Mobile Ad Hoc Networks, Journal of Computer Science 10 (2): 285-295, 2014.

18. Yongsheng Fu, Xinyu Wang, Shanping Li, Construction K-Dominating Set with Multiple Relaying technique in Wireless Mobile Ad Hoc Networks, IEEE Computer Society; International Conference on Communications and Mobile Computing, 2009.

19. Ling Ding et al, Distributed Construction of Connected Dominating Sets with Minimum Routing Cost in Wireless Networks, IEEE Computer Society;International Conference on Distributed Computing Systems, 2010.

20. Kazuya Sakai, Scott C.H. Huang, Wei-Shinn Ku, Min-Te Sun, and Xiuzhen Cheng, Timer-Based CDS Construction in Wireless Ad Hoc Networks, IEEE Transactions on Mobile Computing, Vol. 10, No. 10, October 2011.

21. R. Zheng and R. Kravets, “On-demand Power Management for Ad Hoc Networks,” in Proc. IEEE Infocom’03, vol. 1, pp. 481–91, Mar.–Apr. 2003.

22. J. Gomez et al., “PARO: Supporting Dynamic Power Controlled Routing in Wireless Ad Hoc Networks,” Wireless Networks, vol. 9, pp. 443–60, 2003.

23. J. Wu and H. Li. On calculating connected dominating set for efficient routing in ad hoc wireless networks. Proc. of the 3rd Int ’1 Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, pages 7-14, 1999.

24. Naixue Xiong, Xingbo Huang, Hongju Cheng, and Zheng Wan, “Energy-Efficient Algorithm for Broadcasting in Ad Hoc Wireless Sensor Networks” Sensors 2013, 13, 4922-4946; doi: 10.3390/s130404922.

25. Mritunjay Rai, Shekhar Verma, Shashikala Tapaswi, A Power Aware Minimum Connected Dominating Set for Wireless Sensor Networks, Journal of Networks, Vol. 4, No. 6, August 2009.

26. P. Basu, N. Khan, and T.D.C. Little, “A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks,” in Proc. IEEE ICDC-SW’01, pp. 413–18, Apr. 2001.

27. C.C. Chiang, H.K. Wu, W. Liu, M. Gerla, “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel,” Proceedings of SICON 1997.

28. Yan Zhang, Jim Mee Ng, Chor Ping Low, A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks, Elsevier, Computer Communications, Vol. 32, pp. 189–202, 2009.

29. M. Chatterjee, S. Dasand D. Turgut, “WCA: a weighted clustering algorithm for mobile ad hoc networks,” Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), 5, pp.193-204, 2002.

30. Mohamed Aissa, Abdelfettah Belghithb and Khalil Drira, New strategies and extensions in weighted clustering algorithms for mobile Ad Hoc networks, Procedia Computer Science 19 ( 2013 ) 297 – 304.

31. M. Amine Abid, Abdelfettah Belghith, Stability routing with constrained path length for improved routability in dynamic MANETs, The International Journal of Personnal and Ubiquitous Computing, Springer; Volume 15, Issue 8 (2011).

32. S.K.B. Rathika and J. Bhavithra, An Efficient Fault Tolerance Quality of Service in Wireless Networks Using Weighted Clustering Algorithm; Bonfring International Journal of Research in Communication Engineering; Vol. 2, Special Issue 1, Part 4; February 2012.

33. W. Jin,et al. A load-balancing and energy-aware clustering algorithm in wireless ad-hoc networks; Embedded and Ubiquitous Computing EUC 2005 Workshops; Lecture Notes in Computer Science Volume 3823; pp 1108-1117; 2005.

34. Xi'an Jiaotong, et al. WACHM: Weight Based Adaptive Clustering for Large Scale Heterogeneous MANET; Communications and Information Technologies; ISCIT '07; 2007.

35. Hui Cheng, Jiannong Cao, Xingwei Wang, Sajal K. Das, Shengxiang Yang, Stability-aware Multi-Metric Clustering In Mobile Ad Hoc Networks With Group Mobility, Wireless Communications and Mobile Computing; 9:759 771, Published online 21 April 2008 in Wiley InterScience.

36. Christian Bettstetter, The Cluster Density of a Distributed Clustering Algorithm in Ad Hoc Networks, IEEE International Conference Communications; Page(s): 4336 - 4340 Vol.7; 2004.

37. Wei-dong Yang, Guang-zhao Zhang, A Weight-Based Clustering Algorithm for mobile Ad Hoc network, IEEE Computer Society, 2007.

38. Yang Wei-dong, Weight-Based Clustering Algorithm for Mobile Ad Hoc Network, IEEE: Cross Strait Quad-Regional Radio Science and Wireless Technology Conference; July 26-30, 2011.

39. Seiven Leu, Ruay-Shiung Chang, A weight-value Algorithm for Finding Connected Dominating Sets in a MANET, Journal of Network and Computer Applications, Vol. 35, pages 1615-19, March 2012.






Majzoob Kamal Aldein Omer, Mohmed Sirelkhtem Adrees, Osama E. Sheta

Paper Title:

Alternative Central Mobile Application Strategy to Deaf and Dumb Education in Third World Countries

Abstract: The study aims to apply the strategy to help deaf students and dumb in academic achievement by using mobile learning technology application , This sample of the students have a high potential for the use of mobile applications and has a capacity of great learning via mobile. Smart mobile phones have the ability to create a good educational content of images, shapes, graphics and illustrations appropriate signs to the Deaf and Dumb students and the production of educational content suitable for individual differences in education between them and meets their needs mental and their interests that are different from ordinary students in Education. The paper focuses on the educational content of the component images, graphs, and illustrations appropriate signs to the Deaf and Dumb students because it is not easy to understand by a normal listener on the opposite and to make things worse. In fact the technology is used to achieve the interaction between deaf and dump children with others.

Educational Content, Deaf and Dumb Student, Scalability, Integration, home user, institute user, provider user


1. N.Hema , Ms. P. Thamarai , Dr. T.V.U. KiranKumar ( 2013 ), Handheld Deaf and Dumb Communication Device based on Gesture to Voice and Speech to Image/Word Translation with SMS Sending and Language Teaching Ability .
2. R. E. Mitchell and M. A. Karchmer. Chasing the mythical ten percent: Parental hearing status of deaf and hard of hearing students in the United States. Sign Language Studies, 4(2):138{163, 2004.

3. Morford, Jill Patterson, and MacFarlane, James, Winter 2003,”Frequency Characteristics of AmericanSign Language”.Sign Language Studies, Volume 3, Number 2, pp. 213-225

4. S. Zhao, M. Wang, Z. Wei, 2013 “ A New Type of Deaf-Mute Sign Language Recognition System Based on the Mobile Communication Platform and Terminal Equipment”

5. Dalia Nashat, Abeer Shoker, Fowzyah Al-Swatand Reem Al-Ebailan , (2014) , AN ANDROID APPLICATION TO AIDUNEDUCATED DEAF-DUMB PEOPLE

6. Kimberly A. Weaver, Kimberly A. (2012), Mobile Sign Language Learning Outside the Classroom.

7. ITU release 2014 ICT – Geneva, 5 May 2014






Abhishek M. Kinhekar, Parmalik Kumar

Paper Title:

Router Node Placement in Distributed Sensor Networks: A Review of Optimized Methods

Abstract: designing a distributed wireless sensor network can be an arduous task if not done with simulation tools. Simulation with a tool should provide a robust and efficient solution of the problem with quick response time but available simulation tools for designing these wireless sensor networks are very limited. Pugelli, Mozumdar, avagno and Sangiovanni-Vicentelli[1] proposed an interactive design tool that can assist rapid design of sensor network. This tool synthesizes networks using Dijkstra’s algorithm but its execution time is very high when the network size is relatively large (n>=50). Moreover, it produces sub-optimal solution with large number of router nodes. In this paper, we present efficient and robust synthesis algorithm that exclusively reduce running time.

wireless sensor network; router placement; synthesis algorithm; Simulation Tools


1. Mohammad Mozumdar, Arun Ganesan, Alireza Ameri Daragheh, "Optimizing router node placement for desining Distributed Sensor networks" 2014 IEEE International Conference on Distributed Computing in Sensor Systems
2. Puggelli, M. Mozumdar, L. Lavagno, A. L. Sangiovanni-Vincentelli, “Routing-Aware Design of Indoor Wireless Sensor Network Using an Interactive Tool”, IEEE Systems Journal, Volume:PP, Issue:99, 2013, pp. 1-14.
3. Chih-Yung Chang, Jang-Ping Sheu, Senior Member, IEEE, Yu-Chieh Chen, and Sheng-Wen Chang, "An Obstacle-Free and Power-Efficient Deployment algorithm for wireless sensor networks", IEEE Transactions on Systems, Man, and Cybernetics—part a: Systems and Humans, Vol. 39, No. 4, July 2009, 795
4. M. Gibney, M. Klepal, J. T. ODonnell, “Design of Underlying Network Infrastructure of Smart Building, Intelligent Environments”, 2008 IET 4th International Conference.
5. Y. Wang, C. Hu, Y. Tseng, “Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks”, in Proceedings of First International Conference on Wireless Internet
6. M. Mozumdar, F. Gregoretti, L. Lavagno, L. Vanzago, andS. Olivieri, “A Framework for Modeling, Simulation and Automatic Code Generation of Sensor Network Application”, Proc. of SECON, 2008, pp. 515–522.
7. M. Mozumdar, L. Lavagno, L. Vanzago, and Alberto L.Sangiovanni-Vincentelli. HILAC: A framework for Hardware In the Loop simulation and multi-platform Automatic Code Generation of WSN Applications”, In Proc. of SIES, pages 88-97, Italy, 2010.
8. Pavlos Papageorgiou, "Literature Survey on Wireless Sensor Networks", pavlos@eng.umd.edu, July 16, 2003
9. Xuesong Liu, Burcu Akinci, and James H. Garrett, Ömer Akin, "Requirements for a computerized approach to plan sensor placement in the HVAC systems", Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor)
10. Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit, "Harmony Search-based K-Coverage Enhancement in Wireless Sensor Networks", World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering Vol:9, No:1, 2015
11. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli, “Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.
12. M. Gibney, M. Klepal, and J. T. O’Donnell, “Design of underlying networkinfrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.
13. Y. Wang, C. Hu, and Y. Tseng, "Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks," in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.
14. A. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .






Abhishek M. Kinhekar, Parmalik Kumar

Paper Title:

Router Nodes Placement Optimization for Designing a Distributed Sensor Network

Abstract: The recent advancements in Distributed Wireless Sensor Network has stimulated the need for the newer and enhanced version of algorithms, which will not only reduce the delay in the processing but also consumes much less power. Distributed Sensor networks are most employed and have much scope for their optimization in working. In this paper we explore to find and compare about wireless sensor network, router placement, synthesis algorithm and simulation tools of DWSN.

wireless sensor network; router placement; synthesis algorithm; Simulation Tools


1. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli. “Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.
2. M. Gibney, M. Klepal, and J. T. O’Donnell. “Design of underlying network infrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.

3. Y. Wang, C. Hu, and Y. Tseng, "Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks," in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.

4. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .

5. J. Chang, P. Hsiu, and T. Kuo. “Search-oriented deployment strategies for wireless sensor networks,” in 10th IEEE Int. Symp. on Object and Component-Oriented Real- Time Distributed Computing, 2007,(ISORC ‘07), 2007, PP. 164-171.

6. N. Akshay, M. P. Kumar, and B. Harish. “An efficient approach for sensor deployments in wireless sensor networks,” in Int. Conf. on Emerging Trends in Robotics and Communication Technologies, 2010, PP. 350-355.

7. X. Bai, S. Kumar, D. Xuan, Z. Yun, and T. H. Lai. “Deploying wireless sensors to achieve both coverage and connectivity,” in Proc. 7th ACM Int. Symp. On Mobile ad hoc Networking and Computing, 2006, PP. 131-142.

8. Y. Wang, C. Hu, and Y. Tseng. “Efficient deployment algorithms for ensuring coverage and connectivity of wireless Sensor networks,” in Int. Conf. on Emerging Trends in Robotics and Communication Technologies (INTERACT), 2010, PP. 114- 121.

9. T. Clouqueur, V. Phipatanasuphom, P. Ramanathan, and K. K. Saluja. “Sensor deployment strategy for target detection,” in Proc. 1st ACM Int. Workshop on Wireless Sensor Networks and Applications, 2002, PP. 42-48.

10. Elysium Ltd. “JPEG”. Internet: http://www.ipeg.org/ 2007[0ct. 2, 2013].

11. Mohammad Mozumdar, Arun Ganesan, Alireza Ameri Daragheh, "Optimizing router node placement for desining Distributed Sensor networks" 2014 IEEE International Conference on Distributed Computing in Sensor Systems
12. Puggelli, M. Mozumdar, L. Lavagno, A. L. Sangiovanni-Vincentelli: Routing-Aware Design of Indoor Wireless Sensor Network Using an Interactive Tool, IEEE Systems Journal, 2013 (Volume:PP, Issue:99), pp. 1-14.
13. Chih-Yung Chang, Jang-Ping Sheu, Senior Member, IEEE, Yu-Chieh Chen, and Sheng-Wen Chang, "An Obstacle-Free and Power-Efficient Deployment algorithm for wireless sensor networks", ieee transactions on systems, man, and cybernetics—part a: systems and humans, vol. 39, no. 4, july 2009 795

14. M. Gibney, M. Klepal, J. T. ODonnell: Design of Underlying Network Infrastructure of Smart Building, Intelligent Environments,2008 IET 4th International Conference.

15. Y. Wang, C. Hu, Y. Tseng: Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks,in Proceedings. First International Conference on Wireless Internet

16. M. Mozumdar, F. Gregoretti, L. Lavagno, L. Vanzago, andS. Olivieri, A Framework for Modeling, Simulation and Automatic Code Generation of Sensor Network Application, Proc. of SECON ’08, pp. 515–522.

17. M. Mozumdar, L. Lavagno, L. Vanzago, and Alberto L.Sangiovanni-Vincentelli. HILAC: A framework for Hardware In the Loop simulation and multi-platform Automatic Code Generation of WSN Applications. In Proc. of SIES, pages 88-97, Italy, 2010.

18. Pavlos Papageorgiou, "Literature Survey on Wireless Sensor Networks", pavlos@eng.umd.edu, July 16, 2003

19. Xuesong Liu, Burcu Akinci, and James H. Garrett, Ömer Akin, "Requirements for a computerized approach to plan sensor placement in the HVAC systems" © Nottingham University Press Proceedings of the International Conference on Computing in Civil and Building Engineering W Tizani (Editor)

20. Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit, "Harmony Search-based K-Coverage Enhancement in Wireless Sensor Networks" World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering Vol:9, No:1, 2015

21. Puggelli, M. M. R. Mozumdar, L. Lavagno, and A. L. Sangiovanni-Vincentelli.“Routing-aware design of indoor wireless sensor networks using an interactive tool.” IEEE Systems Journal vol. PP, Issue: 99, 03 Dec 2013.

22. M. Gibney, M. Klepal, and J. T. O’Donnell. “Design of underlying networkinfrastructure of smart building,” in Proc. 4th Int. Conf. on Intelligent Environments, 2008, PP. 1-4.

23. Y. Wang, C. Hu, and Y. Tseng, "Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks," in Proc. 1st Int. Conf. on Wireless Internet, 2005, PP. 114-121.

24. A. Pinto, M. D’Angelo, C. Fishione, E. Scholte, A. Sangiovanni-Vicentelli. “Synthesis of embedded networks for building automation and control,” in Proc. American Control Conference, 2008, PP. 920-925 .






Hafiz Jabr Younis, Alaa Al Halees, Mohammed Radi

Paper Title:

Hybrid Load Balancing Algorithm in Heterogeneous Cloud Environment

Abstract: Cloud computing is a heterogeneous environment offers a rapidly and on-demand wide range of services to the end users.It’s a new solution and strategy for high performance computing where, it achieve high availability, flexibility, cost reduced and on demand scalability. The need to efficient and powerful load balancing algorithms is one of the most important issues in cloud computing to improve the performance. This paper proposed a hybrid load balancing algorithm to improve the performance and efficiency in heterogeneous cloud environment. The algorithm considers the current resource information and the CPU capacity factor and takes advantages of both random and greedy algorithms. The hybrid algorithm has been evaluated and compared with other algorithms using cloud Analyst simulator. The experiment results show that the proposed algorithm improves the average response time and average processing time compared with other algorithms.

Cloud Computing, Cloud Analyst, Scheduling algorithm, Virtual Machine Load Balancing.


1. Florence, A.P. and V. Shanthi, Intelligent Dynamic Load Balancing Approach for Computational Cloud. International Journal of Computer Applications, 2013: p. 15-18.
2. Sharma, T. and V.K. Banga, Efficient and Enhanced Algorithm in Cloud Computing. International Journal of Soft Computing and Engineering (IJSCE), March 2013. 3 (1).

3. Zhang, Q., L. Cheng, and R. Boutaba, Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 2010. 1(1): p. 7-18.

4. Khatib, V. and E. Khatibi, Issues on Cloud Computing : A Systematic Review, in International Conference on Computational Techniques and Mobile Computing. 2012: Singapore.

5. Sareen, P., Cloud Computing: Types, Architecture, Applications, Concerns, Virtualization and Role of IT Governance in Cloud. International Journal of Advanced Research in Computer Science and Software Engineering, 2013. 3(3): p. 533-538.

6. O., K.S., I. F., and A. O., Cloud Computing Security Issues and Challenges. International Journal of Computer Networks (IJCN), 2011. 3(5): p. 247-255.

7. Sajid, M. and Z. Raza, Cloud Computing: Issues & Challenges, in International Conference on Cloud. 2013. p. 35-41.

8. Mohapatra, S., K.S. Rekha, and S. Mohanty, A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing. International
Journal of Computer Applications, 2013. 68.

9. Ray, S. and A. De Sarkar, Execution Analysis Of Load Balancing Algorithms In Cloud Computing Environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 2012. 2(5): p. 1-13.

10. Yao, J.H., Ju-hou, Load Balancing Strategy Of Cloud Computing Based On Artificial Bee Algorithm in Computing Technology and Information Management (ICCM). 2012, IEEE: Seoul. p. 185 - 189.

11. Shameem, P.M. and R.S. Shaji, A Methodological Survey on Load Balancing Techniques in Cloud Computing. International Journal of Engineering and Technology (IJET), 2013. 4(5): p. 3801-3812.

12. Behal, V. and A. Kumar, Cloud Computing: Performance Analysis Of Load Balancing Algorithms In Cloud Heterogeneous Environment, in Confluence The Next
Generation Information Technology Summit (Confluence). 2014, IEEE: Noida. p. 200 - 205.

13. Kaushik, V.K., H.K. Sharma, and D. Gopalani, Load Balancing In Cloud Computing Using High Level Fragmentation Of Dataset, in International Conference on Cloud, Big Data and Trust. 2013. p. 118-126.

14. Mehta, R., P. Yask, and T. Harshal, Architecture For Distributing Load Dynamically In Cloud Using Server Performance Analysis Under Bursty Workloads. 2012. 1(9).

15. Tiwari, M., K. Gautam, and K. Katare, Analysis of Public Cloud Load Balancing using Partitioning Method and Game Theory. International Journal of Advanced Research in Computer Science and Software Engineering, 2014. 4(2): p. 807-812.

16. Deepika, D. Wadhwa, and N. Kumar, Performance Analysis of Load Balancing Algorithms in Distributed System. Advance in Electronic and Electric Engineering, 2014. 4(1): p. 59-66.

17. Ratan, M. and J. Anant, Ant colony Optimization: A Solution of Load Balancing in Cloud. International Journal of Web & Semantic Technology (IJWesT), 2012. III.

18. Sethi, S., S. Anupama, and K. Jena, S, Efficient load Balancing in Cloud Computing using Fuzzy Logic. IOSR Journal of Engineering (IOSRJEN), 2012. 2(7): p. PP 65-71.

19. Hu, J., et al., A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment, in 3rd International Symposium on Parallel Architectures, Algorithms and Programming. 2010, IEEE. p. 89-96.

20. Sharma, T. and V.K. Banga, Proposed Efficient and Enhanced Algorithm in Cloud Computing. International Journal of Engineering Research & Technology (IJERT), 2013. 2(2).

21. Singh, A., R. Bedi, and S. Gupta, Design and implementation of an Efficient Scheduling algorithm for load balancing in Cloud Computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2014. 3(1).

22. cloudsim. cloudbus; Available from: http://www.cloudbus.org/cloudsim/.

23. Pakize, S.R., S.M. Khademi, and A. Gandomi, Comparison Of CloudSim, CloudAnalyst And CloudReports Simulator in Cloud Computing. International Journal of Computer Science And Network Solutions, 2014. 2: p. 19-27.






Sanket Panda, Shaurya Nigam, Rohit Kumar, Mamatha HR

Paper Title:

A Performance Study of SIFT, SIFT-PCA and SIFT-LDA for Face Recognition

Abstract: Humans have the ability to identify faces instantly with minimum effort and inspired by this, Face Recognition (FR) tries to imitate this ability by using numerous effective algorithms and has been extensively developed in the last decade. FR has received a lot of attention because of its wide range of its applications. Since Humans store and retrieve images instantly when needed, FR imitates this procedure by holding images in a database and trains them to recognize faces. Although many impactful algorithms have been developed, they are not entirely effective in unconstrained settings. Hence, we thoroughly compare the SIFT method and its two variations SIFT-PCA and SIFT-LDA to prove that the variations are better alternatives to regular SIFT.

Face Recognition; SIFT; PCA; LDA.


1. D.G. Lowe. Distinctive image features from scale-invariant key-points. International Journal of Computer Vision, 60(2):91–110, 2004.
2. AT&TDataset:http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

3. GrimaceDataset: http://cmp.felk.cvut.cz/~spacelib/faces/grimace.html

4. Isra’a Abdul, Ameer Abdul Jabbar, Jieang Tan “Adaptive PCA-SIFT matching approach for Face recognition application”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol I, pp 1-5, IMECS 2014.

5. Face 95: http://cmp.felk.cvut.cz/~spacelib/faces/faces95.html

6. Y. Ke and R. Sukthankar. Pca-sift: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, pages 506–513, 2004.

7. Alsaqre, Falah E., and Saja Al-Rawi. "Symmetry Based 2D Singular Value Decomposition for Face Recognition." In Digital Information Processing and Communications, pp. 486-495. Springer Berlin Heidelberg, 2011


9. L-F Chen, H-Y Mark Liao, M-T Ko, J-C Lin, and G-J Yu, “A new LDA-based face recognition system which can solve the small sample size problem”, Pattern Recognition, vol. 33, pp. 1713–1726, 2000.

10. De Carrera, Proyecto Fin. "Face recognition algorithms." Master's thesis in Computer Science, Universidad Euskal Herriko (2010).1

11. Wold, Svante, Kim Esbensen, and Paul Geladi. "Principal component analysis." Chemometrics and intelligent laboratory systems 2, no. 1 (1987): 37-52.

12. Zhao, Wenyi, et al. "Face recognition: A literature survey." Acm Computing Surveys (CSUR) 35.4 (2003): 399-458.

13. Bicego, Manuele, Andrea Lagorio, Enrico Grosso, and Massimo Tistarelli. "On the use of SIFT features for face authentication." In Computer Vision and Pattern Recognition Workshop, 2006. CVPRW'06. Conference on, pp. 35-35. IEEE, 2006.

14. Križaj, Janez, Vitomir Štruc, and Nikola Pavešić. "Adaptation of SIFT features for robust face recognition." In Image Analysis and Recognition, pp. 394-404. Springer Berlin Heidelberg, 2010.

15. M. Martinez, A. C. Kak, “PCA versus LDA”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, 2001, pp.228-233

16. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification. Wiley, New York (2001)

17. Face 96: http://cmp.felk.cvut.cz/~spacelib/faces/faces96.html

18. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proceedings of ComputerVision and Pattern Recognition, June 2003.






Shalini Dutta, Sudhakar S. Jadhav

Paper Title:

Image Fusion Conspectus

Abstract: Technological advancements have brought extensive research in the field of Image Fusion. Image fusion is the process of amalgamation of relevant information from a set of input images into a single image which in turn is better informative, complete and accurate. This paper presents an overview of Image Fusion. The silhouette of the paper is anticipated to cover Image fusion right from its inception till the future research prospects. This covers the various fusion systems and techniques of image fusion such as Spatial Domain methods like Weighted Pixel Averaging, Select Maximum/Minimum, Principal Component Analysis (PCA), Frequency/Transform Domain methods like Pyramid Decomposition (Laplacian, FSD, Ratio, Gradient, Morphological), Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) based image fusion. A comparative study of various image fusion techniques and their analyzed results are enlisted. Vivacious applications of image fusion also are highlighted as well. The compendium is concluded with the analysis of better approach as a result of the comparative study and the future scope of research perseveres.

Image Fusion, Discrete Wavelet Transform (DWT), Weighted Pixel Averaging, Select Maximum/Minimum, Principal Component Analysis (PCA), Pyramid Methods, Artificial Neural Network (ANN)


1. Dr. S.S.Bedi and Rati Khandelwal, “Comprehensive and comparative study of image fusion techniques”, International Journal of Soft Computing and Engineering (IJSCE), IISN: 2231-2307,Volume-3,Issue-1,March 2013
2. Deepak Kumar Sahu and M.P.Parsai, “Different image fusion techniques-A critical review”, International Journal of Modern Engineering Research (IJMER), Vol. 2, Issue. 5, Sep.-Oct. 2012, pp-4298-4301

3. Qiguang Miao, "New Advances in Image Fusion", ISBN 978-953-51-1206-8, Published: November 20, 2013 under CC BY 3.0 license. © The Author(s).

4. http://www.ece.lehigh.edu/SPCRL/IF/image_fusion.htm.

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7. Toet, “Image fusion by a ratio of low-pass pyramid, Pattern Recognition Letters” (1989) 245–253

8. R.D. Lillquist, “Composite visible/thermal-infrared imaging apparatus. United States” Patent 4,751,571 (1988).

9. P. Ajjimarangsee, T.L. Huntsberger, “Neural network model for fusion of visible and infrared sensor outputs, in: P.S. Schenker (Ed.), Sensor Fusion, Spatial Reasoning and Scene Interpretation”, The International Society for Optical Engineering, 1003, SPIE, Bellingham, USA, 1988, pp. 153–160

10. N. Nandhakumar, J.K. Aggarwal, “Integrated analysis of thermal and visual images for scene interpretation”, IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (4) (1988) 469–481

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13. Koren, I., Laine, A., Taylor, F,”Image fusion using steerable dyadic wavelet”. In: Proceedings of the International Conference on Image Processing, Washington, USA, 1995. pp. 232–235

14. Deepali A.Godse, Dattatraya S. Bormane (2011) “Wavelet based image fusion using pixel based maximum selection rule” International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 7 July 2011, ISSN : 0975-5462

15. Susmitha Vekkot, and Pancham Shukla “A Novel Architecture for Wavelet based Image Fusion”. World Academy of Science, Engineering and Technology 57 2009

16. Shih-Gu Huang, “Wavelet for Image Fusion”

17. Yufeng Zheng, Edward A. Essock and Bruce C. Hansen, “An Advanced Image Fusion Algorithm Based on Wavelet Transform – Incorporation with PCA and Morphological Processing”

18. Gonzalo Pajares, Jesus Manuel de la Cruz “A wavelet-based image fusion tutorial” 2004 Pattern Recognition Society.

19. Chetan K. Solanki Narendra M. Patel, “Pixel based and Wavelet based Image fusion Methods with their Comparative Study”. National Conference on Recent Trends in Engineering & Technology. 13-14 May 2011.

20. M .Chandana,S. Amutha, and Naveen Kumar, “A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform”. International Journal of Research and Reviews in Computer Science (IJRRCS) Vol. 2, No. 4, August 2011, ISSN: 2079-2557

21. V.P.S. Naidu and J.R. Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis”. Defence Science Journal, Vol. 58, No. 3, May 2008, pp. 338-352 Ó 2008, DESIDOC






Adnan Hussein Ali

Paper Title:

Performance Evaluation of Wi-Fi Physical Layer Based QoS Systems on Fiber Using OPNET Modeler

Abstract: Wireless Fidelity (WiFi) network is based on the IEEE 802.11 standard. WiFi units are used to provide a connection of local devices within homes or businesses. In this paper, OPNET Modeler is used to module and simulate a WiFi networks in fixed local area networks to estimate their performance based on End to End Delay and WiFi voice-packet delay for both WiFi base line and WiFi base fiber. Simulation results indicate that base line has delay larger than base fiber.

Wireless LAN, Wi-Fi, End to End delay, OPNET.


1. Zainab T. Alisa, “Evaluating the Performance of Wireless Network using OPNET Modeler”, International Journal of Computer Applications, Volume 62– No.13, January 2013.
2. P.Trimintzios1 and G. Georgiou, “WiFi and WiMAX Secure Deployments” Journal of Computer Systems, Networks, and Communications Volume 2010, Article ID

3. L.M.S.C. of the IEEE Computer Society, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Higher-Speed Physical ayer Extension in the 2.4 GHz Band,” ANSI/IEEE Standard 802.11-1999TM.

4. Kritika, N., Namarta. “Performance Evaluation of 802.11 WLAN Scenarios in OPNET Modeler” International Journal of Computer Applications, May 2011.

5. H. Zhu, M. Li, I. Chlamtac, B. Prabhakaran, “A survey of quality of service in IEEE 802.11 networks”, IEEE Wireless Communications, 2004.

6. Garima Malik *, Ajit Singh, “Performance Evaluation of WiFi and WiMax Using Opnet “, International Journal of Advanced Research in Computer Science and Software, Volume 3, Issue 6, June 2013.

7. L. Das Dhomeja, Shazia Abbasi, Asad Ali Shaikh1, Y. A. Malkani, “PERFORMANCE ANALYSIS OF WLAN STANDARDS FOR VIDEO CONFERENCING APPLICATIONS”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 3, No. 6, December 2011

8. S. Banerji, R. Singha Chowdhury, “On IEEE 802.11: Wireless LAN Technology “International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol. 3, Issue. 4, 2013.

9. Eldad Perahia, Michelle X. Gong, “Gigabit Wireless LANs: an overview of IEEE 802.11ac and 802.11ad “, Intel Corporation

10. OPNET Technologies, OPNET WORK 2007 proceedings (online). Available: http://www.opnet.com/opnetwork2007.






Aktham Hasan Ali

Paper Title:

Design and Performance of Code Division Multiple Access Physical Layer Transceivers in Flat and Selective Fading Channels

Abstract: Code Division Multiple Access (CDMA) is the technology used in all third generation cellular communications networks, and it is a promising candidate for the definition of fourth generation standards. The wireless mobile channel is typically frequency-selective causing interference among the users in one CDMA cell. In this work, CDMA Transceivers block has been studied widely, and an analysis of proposed model based on Orthogonal frequency-division multiplexing OFDM based Fourier transform on in Flat and Selective Fading Channels

CDMA, OFDM, IFFT, FFT, Flat Fading, Selective Fading, Channels .


1. D. V. Ageev, "Bases of the Theory of Linear Selection. Code Demultiplexing," Proceedings of the Leningrad Experimental Institute of Communication:, pp. 3–35, 1935.
2. P. C. Lajos L. Hanzo, Jurgen Streit,, "Wireless Video communcation " ISBN: 978-0-7803-6032-7 1092 pages February 2001, Wiley-IEEE Press, 2001.

3. L. K. Haohong Wang, Ajay Luthra, Song Ci,, 4G Wireless Video Communications, 2009.

4. Hui Liu and Huiun Yin, "Receiver Design in Multi -carrier Direct Sequence CDMA Communications " IEEE Trans. on Comm, vol. 49, 2001.

5. V. Khairnar, Bhopal, M. P., India ; Mathur, Jitendra ; Singh, Hema,, "Design and performance analysis of DS-CDMA rake receivier for wireless communication," Electronics and Communication Systems (ICECS), 2014 International Conference on, 2014.

6. K.Wirisal. (2002). OFDM Air Interface Design for Multimedia Communication.

7. S. H. a. S. Prasad, "Overview of Multi-carrier CDMA " IEEE Communication Magazine vol. 35, pp. 126-133, 1997.

8. A.Persson et al, "A Unified Analysis for Direct-Sequence CDMA in the Down Link of Systems " IEEE Transactions on Communication , 2003.

9. e. a. Sadayuki Abeta, "Performance of Coherent Multi-carrier /DS-CDMA and MC-CDMA for broadband Packet Wireless Access," IEICE, Trans. on Comm, , vol. E84-B, 2001.

10. e. a. M. J. Juntti, "Genetic algoriths for multi-user detectioin in syncronous CDMA," IEEE Int .Sym on information theory ISIT97, p. 492, 1997.

11. K. Y. a. L.Hanzo, "Hybrid Genetic Algorith based Multi-user Detection Schemes for Synchronous CDMA systems " pp. 1400-1404, 2000.

12. Hany Farid and Eero P. Simoncelli, "Differentiation of Discrete Multidimensional Signals," IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 4, APRIL 2004, vol. 13, 2004.

13. X. W. a. W. Yang, "Performance of Space-Time Block-Coded Multicarrier DS-CDMA System in aMultipath Fading Channel," internatioal Symposium on Mcrowave Anteena propagation and EMC Technology for Wireless Communication Proceedings, pp. 1551-1555, 2005.





Fatima Faydhe AL-Azzawi, Saleim Hachem Farhan, Maher Ibraheem Gamaj

Paper Title:

M-FSK in Multi Coding and Channel Environments

Abstract: Frequency-shift keying (FSK) is a frequency modulation scheme in which digital information is transmitted through discrete frequency changes of a carrier wave currently used by manufacturers of low power low data rate data transmission equipment. The power efficiency of this modulation increases as the signal alphabet increases at the expense of increased complexity and reduced bandwidth efficiency. Most early telephone-line modems used frequency-shift keying (FSK) to send and receive data at rates up to about 1200 bits per second. In this paper M-FSK have been tested under multi-channel environments AWGN, Rayliegh fading and Risian fading channels in term of BER with coherent and non- coherent demodulation and deferent Size of modulation constellation, Improving techniques used to enhanced the performance of the system under AWGN where convolutional code with hard and soft decision, extended Golay code and Reed-Solomon code, the ratio of energy in the specular component to the energy in the diffuse component (linear scale) and diversity used to improve the performance under Rayliegh and Risian fading channels.

M-FSK, FSK with matlab, M-FSK coding, multi-channel.


1. Proakis, J. G., Digital Communications, 5th Ed., McGraw-Hill, 2008.
2. Simon, M. K., and Alouini, M. S., Digital Communication over Fading Channels –A Unified Approach to Performance Analysis, 1st Ed., Wiley,2000.

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5. Sklar, B., Digital Communications, 2nd Ed., Prentice-Hall, 2001.

6. Gulliver, T. A., "Matching Q-ary Reed-Solomon codes with M -ary modulation," IEEE Trans. Commun., vol. 45, no. 11, Nov. 1997, pp. 1349-1353.

7. Dimpal joshi, Kapil gupta," SEP performance of MFSK in Rician fading channel based on MGF method'', IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 897-899.







Kanwaljeet Singh, Avtar Singh Buttar

Paper Title:

Study of Spectrum Sensing Techniques in Cognitive Radio: A Survey

Abstract: the wireless traffic is increasing in an unparalleled way, which causes radio spectrum shortage. The fixed spectrum assignment policy makes this problem more critical. Cognitive radio is one answer to spectrum scarcity problem. In Cognitive radio, the licensed bands are opportunistically accessed when primary user is absent. The first step to a cognitive radio network is the spectrum sensing. An efficient and fast spectrum sensing can make cognitive radio more useful practically. In this paper we discuss several spectrum sensing techniques used in cognitive radio. The vacant frequency spectrum is first sensed by the cognitive radio users, for this purpose several spectrum sensing techniques are used. Spectrum sensing is one of the features of cognitive radio which tells us the availability of vacant bands (also called spectrum holes). In this survey, we analyze the non-cooperative, cooperative and interference based spectrum sensing techniques in cognitive radio. Also in the last, an introduction of some miscellaneous techniques has been given.

Cognitive Radio (CR), spectrum sensing, Primary User (PU), fusion center, multi-taper spectrum estimation, Power Spectral Density (PSD).


1. J. mitola III, ”Cognitive radio: an integrated agent architecture for software defined radio,” Ph.D Thesis, KTH Royal Institute of Technology, Sweden, 2000.
2. FCC, ET Docket No 02-135 Spectrum Policy Task Force (SPTF) Report, Nov. 2002.

3. Ian F. Akyildiz, Brandon F. Lo, Ravikumar (2011), ”Cooperative Spectrum Sensing in Cognitive Radio networks: A Survey, Physical Communication”, pp:40-62.

4. Shahzad A. et. Al. (2010), “Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems,” Australian
Journal of Basic and Applied Sciences.

5. Zi- Long Jiang, “Wavelet Packet Entropy Based Spectrum Sensing in Cognitive Radio” IEEE international conference Xi’an, 2011.

6. Ekram Hossain, Dusit Niyato, Zhu Han (2009), “Dynamic Spectrum Access and Management in Cognitive Radio Networks”, Cambridge University Press.

7. Z. Tian and G. B. Giannakis, “Compressed sensing for wideband cognitive radios”.

8. Jun Ma, Y.L. Geoffrey,B.H. Juang, 2009, “Signal Processing in Cognitive Radio”, Proceeding of the IEEE, vol.97, No.5

9. B. Farhang-Boroujeny, “Filter Banks Spectrum Sensing for Cognitive Radios”, IEEE Transaction on Signal Processing, Vol. 56, pp. 1801-1811, May 2008.

10. Qing Zhao, Brian M. Sadler,” A Survey of Dynamic Spectrum Access”, IEEE Signal Processing magazine, May, 2007, pp.79-89.

11. C. Clancy, -Formalising the interference temperature model, J. Wireless Communication Mobile Computing, 2007.






Milad Ghanbari, Abozar Godarzi Mehr, Hamid Nehzat

Paper Title:

Introducing an Intelligent Transportation System Decision Support Model for the Highways in Iran Based on Fuzzy Logic

Abstract: The significance of inner and inter-city highways in terms of security, environmental pollution, and the capacity and density of the lanes has led to implementation of intelligent transportation infrastructure. The use of Intelligent Transportation Systems (ITS) economizes on costs and time. ITS enjoying high technology in information processing, communications, electronic controll establish a proper and safe relationship between man, vehicles, and roads. This paper aimed to introduce a Decision Support System (DSS) in order to select the kind of intelligent transportation system for the highways in Iran. The research taking advantage of the ideas of some experts in the field of traffic and transportation performed fuzzy logic (FL) model in MATLAB software. The validity of the model was studied and confirmed in a case study of two highways.

fuzzy logic, traffic engineering, intelligent transportation system, highway capacity, decision support system.


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2. Van der Kroon, P., Camolino, R., & Jandrisits, M. (2009). The E-Safety Forum Intelligent Infrastructure Working Group–Identifying the Expectations Towards the Road Infrastructure Side of Cooperative Systems. In 16th ITS World Congress and Exhibition on Intelligent Transport Systems and Services.

3. Dabahde, V. V., & Kshirsagar, R. V. FPGA-Based Intelligent Traffic Light Controller System Design.

4. Farzaneh, K. (2009). A Comprehensive Survey to Identify System Concepts & ICT Requirements of IRAN Intelligent Transportation System (IRAN ITS).

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7. Yoon, S. W., Velasquez, J. D., Partridge, B. K., & Nof, S. Y. (2008). Transportation security decision support system for emergency response: A training prototype. Decision Support Systems, 46(1), 139-148.

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10. Ülengin, F., Önsel, Ş., Ilker Topçu, Y., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.‏

11. Nwagboso, C., Georgakis, P., & Dyke, D. (2004). Time compression design with decision support for intelligent transport systems deployment. Computers in Industry, 54(3), 291-306.‏

12. Crainic, T. G., Gendreau, M., & Potvin, J. Y. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies, 17(6), 541-557.

13. Ying, X., Mengxin, L., & YangXue, Z. J. Traffic Flow Forecasting Algorithm based on Spatio-temporal Relationship.

14. Borne, P., Fayech, B., Hammadi, S., & Maouche, S. (2003). Decision support system for urban transportation networks. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 33(1), 67-77.

15. Vannieuwenhuyse, B., Gelders, L., & Pintelon, L. (2003). An online decision support system for transportation mode choice. Logistics Information Management, 16(2), 125-133.

16. Ülengin, F., Önsel, Ş., Topçu, Y. I., Aktaş, E., & Kabak, Ö. (2007). An integrated transportation decision support system for transportation policy decisions: The case of Turkey. Transportation Research Part A: Policy and Practice, 41(1), 80-97.

17. Arampatzis, G., Kiranoudis, C. T., Scaloubacas, P., & Assimacopoulos, D. (2004). A GIS-based decision support system for planning urban transportation policies. European Journal of Operational Research, 152(2), 465-475.

18. Painho, M., Oliveira, T., & Henriques, R. (2011). A Decision Support System for the Public Transportation Sector: The SIGGESC Project. Proceedings of the 11st CAPSI, October 19th–21st Lisbon, Portugal.

19. Alipour, B. (2011). Intelligent transportation systems: Past, present and look to future by using Grid technology. In 5th Symposium on Advances in Science and Technology May.

20. Hegyi, A., De Schutter, B., Hoogendoorn, S., Babuska, R., van Zuylen, H., & Schuurman, H. (2001). A fuzzy decision support system for traffic control centers. In Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE(pp. 358-363). IEEE.

21. Almejalli, K., Dahal, K., & Hossain, M. A. (2007). Intelligent traffic control decision support system. In Applications of Evolutionary Computing (pp. 688-701). Springer Berlin Heidelberg.

22. Dos Santos Soares, M., Vrancken, J., & Wang, Y. (2010, April). Architecture-based development of road traffic management systems. In Networking, Sensing and Control (ICNSC), 2010 International Conference on (pp. 26-31). IEEE.

23. Liu, H., Zhang, K., Wang, X., Qi, T., & Wang, C. (2005). Effective and sustainable development of chinese national intelligent transportation system architecture. Transportation Research Record: Journal of the Transportation Research Board, (1910), 46-56.

24. Mihyeon Jeon, C., & Amekudzi, A. (2005). Addressing sustainability in transportation systems: definitions, indicators, and metrics. Journal of infrastructure systems, 11(1), 31-50.






Uwa C. U, Nwafor J. C

Paper Title:

Relevance of Science and Technology on Environmental Commons: The Nigerian Experience

Abstract: A journey through Nigeria, either by road, air, or rail shows a scintillating environment fully endowed with abundant of resources, from a rich ecosystem through rich mangrove and rain forests to plateau, mountain vegetation interspersed with rivers, lakes in different climatic regions and in different stages of utilization and management. All these influence man’s existence but the extent of their influence on him depends on his capabilities to transform the applicable environment. Man applies science and technology in his quest to satisfy his day to day needs. Man will necessarily succumb to the dictates of environmental fallouts, if man is ill-equipped. With the necessary skills and knowledge and right application of the tools. It is on this premise that this study examines man and his physical environment, his application of science and technology to transform this environment to meet his immediate needs, the impact on it’s environment and it’s influence on man setting useful environmental laws and strategies for the way forward are then discussed. This study also deals with the laws, which helps to manage the environment for better wage and improve the living condition of man and nature via the waste management methods.

scintillating environment fully endowed utilization and management, laws and strategies.


1. Agwu, E. I. C; Ezedike, C. E., and Egbu, A. U. Eds (2000). The Concept and Procedure of Environmental Impact Assessment (EIA), Crystal Publishers, Okigwe, Nigeria.
2. Baba, J. M. (2004): Sustainable Development and the Nigerian Environment. The Nigerian Geographical Journal, New Series Vol. 1:1-4.

3. Castells, M. (2000): European Cities. The Information and Society and the Global Economy. Oxford, Blackwell.

4. Castells, M. (2004): The Information Age. Economy, Society and Culture. Oxford Blackwell.

5. Ezenwa, E. (1993): Man and His Environment: An Introduction, Enugu: New Generation Books.

6. Fajuyigbe, M. O. (2007): Art and Aesthetics of the Built and Natural Environment. An Appraisal of Obafemi Awolowo University Community Proceeding on towards a Sustainable Built and Natural Environment Awolowo – University Ile-Ife, 223-224.

7. FEPA (1998): Guidelines and Standards for Environmental Pollution Control in Nigeria.

8. Folorunsho, D. O. (2005): The Challenges of Environment, Energy and Safety to Manufacturing. African Journal of Arts and Ideas 4:207-212.

9. Harvey, S. A. (2005): Reformation of Geographical Landscape. Journal of Environmental Sciences Vol. 10:4-12.

10. Mabogunje, A. I. (1978): Towards an Urban Policy in Urbanization Process and Problems in Nigeria. Soda P. S. et al. (eds) Ibadan University Press 7-20pp.

11. NEST (2004): Nigeria’s Threatened Environment: A National Profit Ibadan: NEST.

12. Oruwari, T. (2000): Open Space Design and Health Implications in Nigeria in A. O. Bayo et al. (eds) Effective Housing in the 21st Century Environ. Forum, Federal University of Technology, Akure, 89 – 95pp.

13. The Nigerian Environment: A Quarterly Newsletter of the Federal Environmental Protection Agency (FEPA), Vol. 8 No. 1 & 2.

14. Udoessien, E. I. (2003): Basic Principles of Environmental Science. Etiliew International Publishers, Uyo, 25 – 66pp.

15. Udoh, F. D., Mahadev, S. and Varma, S. M. (2002): Developing an Emission Inventory for Akwa-Ibom State, Nigeria Air Quality proposal prepared by Fadeux International to the Ministry of Environment Uyo, Akwa-Ibom State, Nigeria.

16. United Nations Environment Programme (1972): Geo, (2000) New York.






Uwa Clementina Ukamaka, Nwafor J. C

Paper Title:

Climate Change Effects on Environmental Flora in the Nigerian Terrain: Health Implications on Mankind

Abstract: Environmental conditions play a key role in defining the function and distribution of flora, in combination with other factors. Changes in long term environmental conditions that can be collectively coined climate change are known to have had enormous impacts on flora diversity pattern in the past and are seen as having significant current impacts. Researchers predict that climate change will remain one of the biodiversity patterns in the future. Adopting the survey method of research, this study investigates the importance of Juglans regia (walnuts) commonly known as walnut, in the areas of food and medicine in Nigeria. Some factors that are responsible for biodiversity depletion in environmental flora forms a major focus of this work. The concept of ecosystem or biosphere as a circle of life receives highlight. This work also details the purposes, significance, educational implication as well as policy implication of the concept of biodiversity loss.

Climate change, environmental flora, biodiversity, ecosystem and Juglans regia


1. Barrat, S. C. H. and Joshna, R. K (1991): Genetic and Conservation or Rare Plants. New York; Oxford University Press.
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3. Carthew, S.M (1993): Patterns of Flowering and Fruit Production in a Natural Production of Banksia Spinulosa. Australia Journal of Botany 41: 468-480.

4. Daniel, B.B and Edward, A.K. (1998): Environment Science Earth as a Living Plant. New York: John Willey and Sons: 102-104Pp.

5. Davidson, O. Halsnaes, K. Hug, S, Kok, M., Metz, B. S. Okona, Y. and J. Verhagen (2003): The Development and Climate Nexus: the Case of Sub-saharal Africa, Climate Policy, 351, 597-5113.

6. “Ecosystems”. Think Quest http://library.thinkquest.org/11353//Ecosystems. hta (Accessed 25th July 2012).

7. Environmental Science, Earth as a Living Planet. New York John Willey and Sons 102 – 104pp.

8. IPCC (2001): Climate Change 2001: Impacts Adaptation and Vulnerability: IPCC Working Group II Third Assessment Report. Mc Carthy, J. J., O. J. Canizian, N. A. Leary, D. J. Pokten, and K. S. White (Eds) Cambridge University Press.

9. Lynch, E. and Lande, I. (1993): Effects of Climate Change on Biodiversity. New York, Oxford University Press, 2pp.

10. Margaret, B. (1982): Major Requirements Environmental Education Journal on Environmental Conservation No. 2 (9): 136.

11. Midegley, G. F. L. Hainah, D. Millar, W. Tiller, Aid A. Booth, (2003): “Developing Regional Aid Species – Level Assessment of Climate Change Impacts on Biodiversity in the Cape Floristic Region”. Biological Conservation 112 91 – 2); 87 – 97.

12. Okoro, O. (1986): Biology aspects of Seed Production by Pinus caribaea Morelet variety Handurensis Barret and Golfari in Nigeria. A Ph.D Thesis of the Department of Forest Resources Management, University of Ibadan, Ibadan.

13. Oni, O. (1989): Fruit Abortion in a West African Hardwood, Terminalia Ivorensis Journal of Tropical Forest Science 2(4): 280 – 285.

14. Petanidou, T. Ellis, W. N.; Margaris, N. S. and Vokou, D. (1997): Constraints on Flowering Phenology in Phyganic (East Mediterranean Shrub) Community. American Journal of Botany 82 (5): 607 -620.

15. Peter, J. B. (2002): Biodiversity and Conservation. www.workbank.org/htm/ schools/glossarythm; 44-48pp.

16. Pickering, C. M. (1995): Variation in Flowering Parameters within and among Five Species of Australian Ranunculus. Aust, J. Bot 43: 103 – 112.

17. Reid, H., Pisupati, B. and H. Baulch (2004): How Biodiversity and Climate Change Interact Scider. Net. Biodiversity Dossier Policy Brief.

18. Rosakar, R. (2003): Phonological Pattern of Terrestrial Plants. Ann Rev. Ecol. Syst. 16: 1769 – 214.

19. Thomas, C. D.; Jones, B. A. and Adams, E. C. (2004): Extinction Risk from Climate Change. Nature Vol. 427: 145 – 148.






Abdullah M. Alnajim

Paper Title:

An Automated Analyzer for Users' Anti-Phishing Behaviour within a LAN

Abstract: Phishing is a security attack that seeks to trick people into revealing sensitive information about themselves and their Internet accounts. This paper proposes a novel anti-phishing approach that is deployed within a Local Area Network (LAN). The approach is a model that automatically perform ongoing analysis for users behaviours against phishing attacks and then based on the results it decides whether to train them or not against phishing. The aim is to enhance the phishing countermeasures applied on a LAN by making users aware of phishing attacks. A prototype proof of concept implementation is presented in this paper in order to test the approach’s applicability. The prototype of the new model shows that the approach model runs and performs the concept.

Modeling, Analyzer, Blacklists, LAN, e-Commerce Security, Network, Proxy, Online Banking Security, Phishing, Pharming.


1. The National Consumers League Projects (2015). Phishing. Available: http://www.fraud.org/scams/internet-fraud/phishing, last access on 15/5/2015.
2. G. K. Tak, N. Badge, P. Manwatkar, A. Ranganathan, S. Tapaswi, “Asynchronous Anti Phishing Image Captcha approach towards Phishing”. Proc. the 2nd International Future Computer and Communication (ICFCC), Wuhan, IEEE Press, pp. V3-694 - V3-698.

3. Alnajim, and M. Munro “An Approach to the Implementation of the Anti-Phishing Tool for Phishing Websites Detection”. Proc. International Conference on Intelligent Networking and Collaborative Systems (INCoS 2009). Barcelona, Spain, IEEE Press, 2009, pp. 105 - 112.

4. J. S. Downs, M. B. Holbrook and L. F. Cranor, “Decision strategies and susceptibility to phishing”. Proc. the 2nd symposium on usable privacy and security. New York, USA, ACM Press, 2006, pp. 79 – 90.

5. Anti-Phishing Working Group APWG. (2015). Phishing Activity Trends Report, 4th Quarter 2014. Available: http://docs.apwg.org/reports/apwg_trends_report_q4_2014.pdf, last access on 26 June 2015.

6. S. A. Robila and J. W. Ragucci, “Don't be a Phish: Steps in User Education”. Proc. 11th annual SIGCSE conference on innovation and technology in computer science education. New York, ACM Press, 2006, pp. 237 – 241.

7. Symantec. (2004). Mitigating Online Fraud: Customer Confidence, Brand Protection, and Loss Minimization. Available: http://www.antiphishing.org/sponsors_technical_papers/symantec_online_fraud.pdf, last access on 21/3/2007.

8. Alnajim and M. Munro, “Effects of Technical Abilities and Phishing Knowledge on Phishing Websites Detection”. Proc. the IASTED International Conference on Software Engineering (SE 2009), Innsbruck, Austria, ACTA Press, 2009, pp. 120-125.

9. Y. Zhang, J. I. Hong and L. F. Cranor, “Cantina: a content-based approach to detecting phishing web sites”. Proc. 16th international conference on WWW. New York, ACM Press, 2007, pp. 639 – 648.

10. G. Xiang, J. Hong, C. P. Rose, L. Cranor, “CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites”. ACM Transactions on Information and System Security (TISSEC), 2011 , Volume 14 Issue 2, New York, ACM Press, Article No. 21 .

11. H. Bo, W. Wei, W. Liming, G. Guanggang, X. Yali, L. Xiaodong, M. Wei, “A Hybrid System to Find&Fight Phishing Attacks Actively”. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Lyon, IEEE Computer Society, 2011, pp. 506-509

12. Alnajim and M. Munro, “An Evaluation of Users' Tips Effectiveness for Phishing Websites Detection”. Proc. 3rd IEEE International Conference on Digital Information Management ICDIM, London, IEEE Press, 2008, pp. 63-68.

13. Microsoft Corporation. (2007). Microsoft Security for Home Computer Users Newsletter. Available: http://www.microsoft.com/protect/secnews/default.mspx, last access on 16 March 2007.

14. S. Sheng, B. Magnien, P. Kumaraguru, A. Acquisti, L. F. Cranor, J. Hong and E. Nunge, “Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phish”. Proc. 3rd symposium on usable privacy and security SOUPS. New York, ACM Press, 2007, pp. 88 – 99.

15. P. Kumaraguru, Y. Rhee, A. Acquisti, L. F. Cranor, J. Hong and E. Nunge,”Protecting people from phishing: the design and evaluation of an embedded training email system”. Proc. the SIGCHI conference on Human factors in computing systems. New York, USA, ACM Press, 2007, 905 – 914.

16. Alnajim and M. Munro, “An Anti-Phishing Approach that Uses Training Intervention for Phishing Websites Detection”. Proc. 6th IEEE International Conference on Information Technology - New Generations (ITNG). Las Vegas, IEEE Computer Society, 2009, pp. 405-410.

17. Alnajim, “High Level Anti-Phishing Countermeasure: A Case Study”. Proc. The The World Congress on Internet Security (WorldCIS-2011), London, UK, IEEE Press, 2011, pp. 139 – 144.

18. Alnajim, 2015. “A Country Based Model Towards Phishing Detection Enhancement”. International Journal of Innovative Technology and Exploring Engineering
(IJITEE), 2015, Volume 5 Issue 1, pp. 52 - 57.

19. W. Jia, and W. Zhou, “Distributed Network Systems: From Concepts to Implementations”. New York: Springer, 2004.

20. M. P. Singh, “the Practical Handbook of Internet Computing”. USA: Chapman & Hall/CRC Publisher, 2005.

21. Y. Xiao and H. Chen, “Mobile Telemedicine: A Computing and Networking Perspective”. USA: Auerbach Publications, 2008.






Adhvik Shetty, Subham Chatterjee, Parimala R

Paper Title:

Predicting Behaviors of Stock Market

Abstract: Prices of stock depend on a variety of factors. Predicting and building a model is a daunting task to any analyst. To predict the behavior of stock market, one goes through the company news, economic and political news and global sentiments. Considering the large number of news articles, there are some which can be missed out. Also it is impossible to focus on each and every news article as soon as it is published on the internet. In this paper, we analyze the sentiment generated by news articles and correlate the sentiment with the actual change in stock market prices. This gives a deeper insight into the correlation and tells us how much news articles influence the stock market. After extensive research we have decided to use a hybrid technique involving machine learning and natural language processing concepts. We have used n–gram as the feature creation, chi square as the feature selection and support vector machines as the classification technique. Improving the accuracy of predicting stock market trends, we hope to aid investors in better decision making based on real time sentiment of news articles.



1. An extensive empirical study of feature selection metrics for text classification by George Gorman, Journal of Machine Learning Research 3(2003)
2. Preprocessing the Informal Text for efficient Sentiment Analysis by I Hemalatha, Dr. GP Saradhi Verma, and Dr A Govardhan, Internal Journal of Emerging Trends and Technology in Computer Science

3. Xiaodong Li, Haoran Xie, Li Chen, Jianping Wang and Xiaotie Deng. “News impact on stock price return via sentiment analysis.” Knowledge-Based Systems(2014).

4. Gonçalves, Pollyanna, Matheus Araújo, Fabrício Benevenuto, and Meeyoung Cha. "Comparing and Combining Sentiment Analysis Methods." ACM(2013).

5. Butler, M., Keselj, V. 2009. “Financial Forecasting using Character N-Gram Analysis and Readability Scores of Annual Reports”, Advances in AI

6. Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: a case study. In Proceeding of the intl. conference on recent advances in natural language processing. Borovets, BG.

7. S. Das and M. Chen. Yahoo! for anazon: Extracting market sentiment from stock message boards. In Proc. of the 8th APFA, 2001.

8. M. Thelwall. Heart and soul: Sentiment strength detection in the social web with sentistrength. http://sentistrength.wlv.ac.uk/documentation/ SentiStrengthChapter.pdf

9. B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In ACL Conference on Empirical Methods in Natural Language Processing, pages 79–86, 2002.

10. Antweiler, W., Frank, M.Z. 2004. "Is all that talk just noise? The information content of internet stock message boards”, The Journal of Finance, Volume 59, Number 3, June 2004, pp. 1259-1294

11. Gidofalvi, G. & Elkan, C. 2003. “Using News Articles to Predict Stock Price Movements. Technical Report”, Department of Computer Science and Engineering, University of California, San Diego

12. Joachims, T., 1998. “Text categorization with support vector machines: Learning with many relevant features”, Proceedings of the European Conference on Machine Learning, Springer-Verlag.

13. Schumaker, R.P., Chen, H. “Textual analysis of stock market prediction using breaking financial news: the AZFin Text System”, ACM Transactions on Information Systems 27 (2) (2009).






Ghazy Al- Hamed

Paper Title:

Effect of Turnover on Jordanian Health Care Organizations

Abstract: Productivity is very important issue for any Health Care organization. There are several factors on which productivity of an organization mostly depends upon. Employee’s turnover is one of them which is considered to be one of the challenging issues in business nowadays. The impact of turnover has received considerable attention by senior management, human resources professionals and industrial psychologists. It has proven to be one of the most costly and seemingly intractable human resource challenges confronting by several organizations globally. The purpose of this research is therefore, to find out the actual reasons behind turnover and its damaging effects on the performance of different Jordanian Health Care Organizations.The objectives of the study is to ascertain the cause of Employees turnover , To determine the effect of employee turnover , To measure the satisfaction level of employees in the health organizations, and finally to build model to reduce turnover in health organizations. This study focused on the effect of employee turnover on Health Care Organizations with reference to the Jordan Health Care organizations (JHCOS). High employee turnover rates affect efforts to attain organizational objectives. In addition, when the Health Care Organizations loses a critical employee, the effects on innovation, consistency in providing service to patients and timely delivery of services to patients may be negatively affected. The research design used in this study was the quantitative approach, which allowed the researcher to use structured questionnaires in collecting data. The simple random sampling technique was used to select four hundred respondents from all levels of management in the Jordan Health Care organizations. The total number of population that the questionnaires were administered was four hundred (400), of which three hundred and seventy four (374) was retrieved shaped, (, .93% of total population. Analytical statistics was used to analyze and test hypothesis ,(SPSS) was used for that. The study found positive turnover and negative turnover effect the performance of Jordanian Health Care Organizations, The study show also that Gender and Age not affect the Health Care Organizations Turnover causes, but Educational background, Status of respondents, and Work experience have an effect on Health Care Organizations Turnover causes. The study illustrate that adopted mode suggested effect on reducing turnover in Jordanian Health Care Organizations , the Model include ( improve work environment ,build trust ,recognize good performance, develop of employee, adopt good benefits and incentives system) on reducing Health Care organization Turnover, Turnover, however, had dual effects on the health organization; positive and negative effects. Whiles employee turnover introduced new ideas and skill into the health organization, it’s also lead to difficulties in attracting new staff. To reduce the rate of turnover, management should be assure that the environmental condition of employees is convenient.

Whiles employee turnover introduced new ideas and skill into the health organization, it’s also lead to difficulties in attracting new staff. To reduce the rate of turnover, management should be assure


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8. Meyer, J.P., 2001. Organizational commitment: Personnel psychology and HRM. London, England: Wiley.

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Battu Deepa, M. Hemalatha

Paper Title:

Solar Energy Tracking System Using At89s52 Microcontroller and L293d Motor Driver Circuit

With the increasing demand of energy and the diminution of the fossil fuels, with increase in pollution level and depletion of the ozone layer the demand for the natural and renewable sources of energy is the need of the hour and this has been the point of discussion all over the world with many organizations working for the utilization of these resources and its promotion and United Nations allocating huge amount of funds for its promotion we have also made an effort to contribute a bit in the same direction. It is a known fact that the most unutilized source of energy is solar energy. This paper deals with a microcontroller based solar panel tracking system. Solar tracking enables more energy to be generated because the solar panel is always able to maintain a perpendicular profile to the sun’s rays. Development of solar panel tracking systems has been ongoing for several years now. As the sun moves across the sky during the day, it is advantageous to have the solar panels track the location of the sun, such that the panels are always perpendicular to the solar energy radiated by the sun A solar energy tracker is a device used for orienting a solar photovoltaic panel or lens towards the sun. Hence the sun tracking system can collect more energy.

Solar system, solar panel, microcontroller AT89S52, LCD HITACHI 44780, L293D MOTOR DRIVER CIRCUIT.


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