iosr-JCE   Volume-2 ~ Issue-4

Abstract: This paper discusses an efficient and effective approach for identifying and tracking of moving object from a video. A video is captured by stationary camera. Moving object tracking and detection from video sequences has applications in several areas such as automatic video surveillance, motion-based recognition, video indexing, human-computer interaction, traffic monitoring, and vehicle navigation. In this work, we present a computer vision-based approach for object tracking and detection. A method is proposed to detect and track moving object through video even if background is changed at any instant and capable of plotting a 3D graph mesh based on the moving object in between any number of frames per second. We use consecutive frame analysis technique to detect background changing criteria and use morphological filtering for image enhancement. Finally, we will get the co-ordinates for the moving object and these co-ordinates are imported to any other 3D software's like MAYA etc to analyze or edit the results calculated by the algorithm.
Keywords- Object tracking, Object detection, Motion estimation, Computer vision.

[1] Pengwei LIU, Huiyuan WANG et al. "Motion Compensation Based Detecting and Tracking Targets in Dynamic Scene", IEEE, pp.703-706, 2010.
[2] Sajjad Torkan, Alireza Behrad "A New Contour Based Tracking Algorithm Using Improved Greedy Snake", IEEE, 2010.
[3] Baiyang Liu, Lin Yang et al. "An Adaptive Tracking Algorithm Of Lung Tumors In Fluoroscopy Using Online Learned Collaborative Trackers", IEEE, pp.209-212, 2010.
[4] Alexander Toshev, Ameesh Makadia, Kostas Daniilidis "Shape-based Object Recognition in Videos Using 3D Synthetic Object Models", IEEE, pp.288-295, 2009.
[5] Ming-Yu Shih, Yao-Jen Chang, Bwo-Chau Fu, and Ching-Chun Huang "Motion-based Background Modeling for Moving Object Detection on Moving Platforms", IEEE, pp.1178-1182, 2007.
[6] Mark Ritch, Nishan Canagarajah "Motion-Based Video Object Tracking In The Compressed Domain", IEEE, pp-301-304, 2007.
[7] Minglun Gong "A GPU-based Algorithm for Estimating 3D Geometry and Motion in Near Real-time", IEEE, 2006.
[8] Huiqiong Chen, Derek Rivait and Qigang Gao "Real-Time License Plate Identification by Perceptual Shape Grouping and Tracking", IEEE, pp.1352-1357, 2006.
[9] Mohammed Sayed and Wael Badawy "A novel motion estimation method for mesh- based video motion tracking", IEEE, pp.337-340, 2004.
[10] Li-Qun Xu "Simultaneous Tracking And Segmentation Of Two Free Moving Hands In A Video Conferencing Scenario", IEEE, pp.49-52, 2003...................

Abstract: Wireless Sensor Networks (WSN) consists of distributed sensor devices. Healthcare application domain is one of the emerging domains in the current world. Nowadays WSN is more popular in healthcare applications and it produces enormous amount of data in a periodic interval. The data should be effectively stored and later it should be processed and analyzed by the doctors to understand the health conditions of the patients. But the main drawback of WSN is it could not able to store large amount of data. Hence there is a need for the scalable environments like Grid to effectively store the data and use later for processing and analyzing the data. To accomplish the objective, in this research paper we have investigated to integrate WSN with Grid environments using the resource broker based approach. The proposed work is integrated with WSN using the Proxy Connector to collect the healthcare data. The collected data is parsed and allocated to the Grid resources using Genetic Algorithm (GA) based scheduling mechanism. The proposed work is aimed to decrease the data transfer time and increase the success rate of data job requests and throughput.
Keywords: Grid Computing, Wireless Sensor Networks (WSN), Genetic Algorithm, Healthcare applications, Scheduling.

[1] I. Foster et al., The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration, Grid Forum white paper, 2003
[2] J. Carretero, F. Xhafa, and A. Abraham, "Genetic algorithm based schedulers for grid computing systems," International Journal of Innovative Computing, Information and Control (ICIC), vol. 3, pp 1349-4198, 2007.
[3] M. Aggarwal, R. D. Kent and A. Ngom, "Genetic Algorithm Based Scheduler for Computational Grids," in Proc. of 19th IEEE International Symposium on High Performance Computing Systems and Applications, 2005.
[4] Y. Gao, H. Rong, and J. Z. Huang, "Adaptive grid job scheduling with genetic algorithms," Future Generation Computer Systems, vol. 21, pp.151-161, 2005.
[5] Discovery Net Project, http://www.discovery-on-the.net.
[6] S. Jin, M. Zhou, and A. S. Wu, "Sensor network optimization using a genetic algorithm," in Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics, 2003.
[7] Souto E., et al, Mires: A publish/subscribe middleware for sensor networks. In ACM Personal and Ubiquitous Computing, 10(1): 37-44.
[8] E. Jovanov, "Patient Monitoring using Personal Area Networks of Wireless Intelligent Sensors," Biomedical Sciences Instrumentation, vol. 37, 2001, pp. 373–378.
[9] Y. Yao and J. Gehrke, "The Cougar Approach to In-Network Query Processing in Sensor Networks." ACM SIGMOD Record, vol. 31, no. 3, ACM Press, 2002, pp. 9–18.
[10] M. Gaynor, S. L. Moulton, M. Welsh, E. LaCombe, A. Rowan, and J. Wynne. Integrating wireless sensor networks with the Grid. IEEE Internet Computing, 8(4):32–39, August 2004.
[11] B. Krishnamachari, D. Estrin, and S. Wicker. The impact of data aggregation in wireless sensor networks. In Proceedings of thr IEEE 22nd International Conference on Distributed Computing Systems Workshop, pages 575–578, July 2002.
[12] Lim H. B., Teo Y.M., Mukherjee P., Lam V.T. et al. 2005. Sensor Grid: Integration of Wireless Sensor Networks and the Grid, In Proc. of the IEEE Conf. on Local Computer Networks.

Abstract: Software development effort and cost prediction is one of the important activities in software project management. Accuracy in prediction is a challenge for software developers. There are many models exists that defines a relationship between size and effort. Cost of developing a project increases with complexity of project accuracy predictions are strongly required during the early stages of project development. Because data and info available at the starting phases of project is not complete, not consistent and not even certain. An objective of the software engineering community is to develop a useful model that define the development life-cycle and accurately predict the cost of developing a software product. In this paper we discuss Neuro-Fuzzy model deals with this situation. Neuro-Fuzzy models are the combination of Artificial Neural Network and Fuzzy Logic. Artificial Neural Network has the ability to learn from previous data. It model complex relationships between both independent variables (cost drivers) and dependent variables (effort). Fuzzy logic simulates the human behavior and reasoning. Fussy logic is basically used in situation where decision making is very difficult and conditions are not clearly defined. Facts that may be dismissed are focused in this technique.
Keywords: Neural Network, Fuzzy Logic, Artificial Neural Network.

[1] Boehm. "Software Engineering Economics", Prentice Hall, 1981.
[2] Idri, A., S. Mbarki, et al. "Validating and understanding software cost estimation models based on neural networks". Information and CommunicationTechnologies: From Theory to Applications, 2004. Proceedings. 2004 International Conference on, 2004.
[3] M. Jorgensen, B. Boehm, et al. "Software Development Effort Estimation: Formal Models or Expert Judgement?" Software, IEEE 26(2): 14-19, 2009.
[4] Vahid Khatibi, Dayang N. A. Jawawi "Software cost Estimation Methods: A Review", 2010-2011. Journal of Emerging Trends in Computing and Information Sciences on 2010-2011.
[5] Mr. Ihtiram Raza Khan, Prof Afshar Alam, Ms. HumaAnwar. "Efficient Software Cost Estimation using Neuro- Fuzzy Technique", National Conference onRecent Developments in Computing and its application, August 2009.
[6] Huang X, Ho D, Ren J, Capretz L, " A Soft Computing Framework for Software Effort Estimation"Soft Computing Journal, Springer, available at www.springeronline.com, 2005.
[7] Ali Idri, Taghi M. Khoshgoftaar and Alain Abran. "Can Neural Networks be easily Interpreted in Software Cost Cost Estimation?", 2002 World Congress on Computational Intelligence, Honolulu, Hawaii, May 12-17, 2002.
[8] Parvinder S. Sandhu, Porush Bassi and Amanpreet Singh Brar. "Software Effort Estimation Using Soft Computing Techniques", World Academy of Science, Engineering and Technology, 46, 2008.

Abstract : The concept of cloud computing is gaining large exposure now, due to which, every industry aims at leasing resources and complete their jobs by the services provided by the cloud computing. The important service it provides is the Infrastructure as Service (IaaS) which helps company to lease computer resources in terms of memory or processing capacity. The major factor which determines the efficiency of resource utilization lies in scheduling of resources present at that point of time in a cloud. In this paper we will show how to effectively allocate the resource using one of the nature inspired algorithm, Bee algorithm.
Keywords: Cloud Computing; Resource Scheduling; Bee algorithm; Waggle; Scout job

[1] Gunho Leey, Byung-Gon Chunz, Randy H. Katz, "Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud", University of California, Berkeley, Yahoo! Research
[2] Amazon ec2. http://aws.amazon.com/ec2.
[3] Marco A. S. Netto and Rajkumar Buyya, "Offer-based Scheduling of Deadline-Constrained Bag-of-Tasks Applications for Utility Computing Systems", IEEE International Symposium on Parallel&Distributed Processing, 2009.
[4] Suraj Pandey, LinlinWu, Siddeswara Mayura Guru, Rajkumar Buyya, A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, IEEE International Conference on Advanced Information Networking and Applications (AINA), 2010.
[5] B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica. Mesos, "A platform for fine-grained resource sharing in the data center", ACM Workshop on Scientific Cloud Computing, 2011.
[6] A. Ghodsi, M. Zaharia, B. Hindman, A. Konwinski, S. Shenker, and I. Stoica, "Dominant resource fairness: Fair allocation of multiple resource types", in Proc. Proceedings of the 8th USENIX conference on Networked systems design and implementation, 2011.

Abstract: The Vulnerability problem is discovered by presenting an attack injection methodology in software components. The Attack injection methodology, implemented follows an approach similar to hackers and security analysts to discover vulnerabilities in network-connected servers. AJECT uses a specification of the server's communication protocol and predefined test case generation algorithms to automatically create a large number of attacks. The attack injection methodology is used for vulnerability detection and removal. It mimics the behavior of an adversary by injecting attacks against a target system while inspecting its execution to determine if any of the attacks has caused a failure. The observation of some abnormal behavior indicates that an attack was successful in triggering an existing flaw. After the identification of the problem, traditional debugging techniques can be employed, for instance, by examining the application's control flow while processing the offending attacks, to locate the origin of the vulnerability and to proceed with its elimination. The methodology was implemented in a tool called AJECT. The tool was designed to look for vulnerabilities in network server applications, although it can also be utilized with local daemons. We chose servers because, from a security perspective, they are probably the most relevant components that need protection because they constitute the primary contact points of a network facility. AJECT does not need the source code of the server to perform the attacks, i.e., it treats the server as a black box. However, in order to be able to generate intelligent attacks, AJECT has to obtain a specification of the protocol utilized in the communication with the server.
Keywords: Attack Injection, Fault Injection, Software Vulnerabilities, Software Engineering, Testing.

[1]. P. Verissimo, N. Neves, C. Cachin, J. Poritz, D. Powell, Y.Deswarte, R. Stroud, I. Welch, "Intrusion-Tolerant Middleware:The Road to Automatic Security," IEEE Security and Privacy,vol. 4, no. 4, pp. 54-62, July/Aug. 1996.
[2]. N. Neves, J. Antunes, M. Correia, P. Verissimo, R. Neves,"Using Attack Injection to Discover New Vulnerabilities," Proc.Int'l Conf. Dependable Systems and Networks, June 2006.
[3]. M. Crispin, "Internet Message Access Protocol—Version 4rev1,"Internet Eng. Task Force, RFC 3501, Mar,2003.
[4]. J. Arlat, A. Costes, Y. Crouzet, J.-C. Laprie, D. Powell, "Fault Injection and Dependability Evaluation of Fault-Tolerant Systems,"IEEE Trans. Computers, vol. 42, no. 8, pp. 913- 923, Aug. 1993.
[5]. M.-C. Hsueh, T.K. Tsai, "Fault Injection Techniques and Tools," Computer, vol. 30, no. 4, pp. 75-82, Apr. 1997.
[6]. J. Myers and M. Rose, "Post Office Protocol—Version 3," RFC 1939 (Standard), updated by RFCs 1957, 2449, [Online] Available : http://www.ietf.org/rfc/rfc1939.txt,May 1996.
[7]. J. Carreira, H. Madeira, J.G. Silva, "Xception: Software Fault Injection and Monitoring in Processor Functional Units," Proc. Int'l Working Conf. Dependable Computing for Critical Applications,pp. 135-149, [Online] Available : http://citeseer.ist.psu.edu/54044.html; http://dsg.dei. uc.pt/Papers/dcca95.ps.Z, Jan. 1995.

Abstract : There are many instances of road accidents round the world due to driver's lack of attention in driving. One of the prime reasons can be drowsiness. In this paper, a drowsiness detection system using Haar like feature technique is implemented for eye detection and a simulator for testing the system was built. An audible alert is connected with the system to acknowledge the drowsiness of the driver. A simulator is made by SDL (Simple Direct Media Layer) which is a cross platform multimedia development API library to provide low level access to joystick, mouse, etc. Whenever the driver is outside the lane in the simulator then also it generates an audible warning signal.
Keywords – Eye Detection, Face Detection, Haar like features, USART.

[1] Paul Stephen Rau, "Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Data Analyses and Progress".
[2] Nikhil R Pal, Chien-Yao Chuang, Li-Wei Ko, Chih-Feng Chao, Tzyy- Ping Jung, Sheng-Fu Lieng, Chin-Teng Lin, "EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach".
[3] Gabriela Dorfman Furman, Armanda Baharav, "Investigation of Drowsiness while Driving Utilizing Analysis of Heart Rate Fluctuations".
[4] Paul Viola, Michael J. Jones, "Robust Real-Time Face Detection".
[5] QNX, "Simple Direct Media Layer", [http://www.libsdl.org/].
[6] Datasheets of ATmega8, MAX232.

Abstract : Context – Offshore software development outsourcing (OSDO) is a modern business strategy for developing high quality software at low-wage countries at reduced cost. Outsourcing is a contract based association between vendor and client organization, where a client organization contracts-out with vendor organization for software development work to be provided by vendor organization in return for remuneration. Appropriate management of the OSDO contract plays an important role in the success of outsourced projects.
Objective - The objective of this protocol is to identify different critical success factors (CSFs) and critical barriers (CBs) that are considered critical for successful management and execution of the outsourcing contract in different stages of the contract i.e. pre-contract, during the contract and post-contract.
Method – Systematic Literature Review (SLR) will be used for the identification of the aforementioned factors. SLR is based on a structured protocol, and is therefore, different from ordinary review. SLR give more prescribed and less biased result from the ordinary literature review.
Expected Outcome – We have developed the SLR protocol and are currently in process of its implementation. The expected outcomes of this review will be the identification of critical success factors (CSFs) and critical barriers (CBs) to be addressed by vendor organizations for appropriate management of OSDO contract. The ultimate outcome of the research is to develop Outsourcing Contract Management Model (OCMM).
Keywords - Systematic Literature Review, Outsourcing Contract Management, Software Development Outsourcing; Client-Vendor Relationships

[1]. Khan, S.U., Software Outsourcing Vendors' Readiness Model ,PhD thesis, in School of Computing and Maths. 2011, Keele Graduate School, UK. p. 381.
[2]. Lago, P., H. Muccini, and M. Ali-Babar. Developing a Course on Designing Software in Globally Distributed Teams. in IEEE International Conference on Global Software Engineering, ICGSE08. 2008.
[3]. Ali-Babar, M., J. Verner, and P. Nguyen, Establishing and maintaining trust in software outsourcing relationships: An empirical investigation. The Journal of Systems and Software, 2007. 80(9): p. 1438–1449.
[4]. Palvia, P.C., A dialectic view of information systems outsourcing - pros and cons. Information and Management, 1995. 29(5): p. 265-275.
[5]. Hongxun, J., et al. Research on IT outsourcing based on IT systems management. in ACM International Conference Proceeding Series, Vol. 156. 2006.
[6]. United-Nations, World Investment Report. The shift towards services, New York and Geneva. 2004.
[7]. Lacity, M.C. and L.P. Willcocks, Global Information Technology Outsourcing. Wiley, Chichester, 2001.
[8]. McCarthy, J., 3.3 Million U.S. Services Jobs to Go Offshore. Forrester Research, 2002.
[9]. Palvia, S.C.J., Global Outsourcing of IT and IT Enabled Services: Impact on US and Global Economy. Journal of Information Technology Case and Applications, 2003. 5(3): p. 1-8.
[10]. Oza, N.V., Hall, Tracy, Rainer, Austen and Grey, Susan Grey., Trust in software outsourcing relationships: An empirical investigation of Indian software companies,. Information & Software Technology, 2006. 48(5): p. 345-354.

Abstract: This research is intended to develop a concept based information retrieval system for text documents in two phases: Therefore, this idea motivated us to develop a concept based information retrieval system for text documents. This system tries to provide additional semantics as conceptually related words with the help of glosses to the query words and keywords in the documents by disambiguating their meanings. Here, various senses provided by WSD algorithm have been used as semantics for indexing the documents to aid the information retrieval system. Later, this research has also been motivated to do ontology based information retrieval from Tamil text documents which improve the retrieval performance in a better way due to the incorporation of domain semantics. Here, the performance of IR has been improved by including more indexing information about the documents such as associated meaning with the words. The Word Sense Disambiguation is the process of finding correct senses of a word, among other senses associated with the words. The introduction of semantics in word level to improve Word Senses Disambiguation has been considered in this thesis specifically to improve the accuracy of WSD and thus in turn to improve the IR performance. In this work, the glosses of the indexed words in WordNet are utilized as conceptual information, which acts as an additional semantics for WSD. This concept based WSD, has been used in semantic chaining to cluster documents, which is used for IR performance.
Keywords: Word Senses Disambiguation (WSD), Information Retrieval (IR), Ontology.

[1]. Pablo Castells, Miriam Ferna´ndez, and David Vallet, An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval, "IEEE Transactions On Knowledge And Data Engineering, Vol. 19, No. 2, February 2007".
[2]. Tao Jiang,Ah-Hwee Tan, Senior Member, IEEE, and KeWang, Mining Generalized Association of Semantic Relation from Textual Web Content. "IEEE Transactions On Knowledge And Data Engineering, Vol. 19, No. 2, pp.32-35, February 2007".
[3]. V. Christophides, G. Karvounarakis, D. Plexousakis, and S.Tourtounis, "Optimizing Taxonomic Semantic Web Queries Using Labeling Schemes," J. Web Semantics, vol. 1, no. 2, pp. 207-228,2003.
[4]. M. Cristani and R. Cuel, "A Survey on Ontology Creation Methodologies," Int‟l J. Semantic Web and Information Systems, vol. 1, no. 2, pp. 49-69, 2005.
[5]. W.B. Croft, "Combining Approaches to Information Retrieval," Advances in Information Retrieval, pp. 1-36, Kluwer Academic, 2000.
[6]. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R.Harshman, "Indexing by Latent Semantic Analysis," J. Am. Soc.Information Science, vol. 41, no. 6, pp. 391-407, 1990.
[7]. S. Dill, N. Eiron, D. Gibson, D. Gruhl, R. Guha, A. Jhingran, T.Kanungo, K.S. McCurley, S. Rajagopalan, A. Tomkins, J.A.Tomlin, and J.Y. Zien, "A Case for Automated Large Scale Semantic Annotation," J. Web Semantics, vol.1, no.1, pp.115-132, 2003.
[8]. S. Gauch, J. Chaffee, and A. Pretschner, "Ontology-Based Personalized Search and Browsing," Web Intelligence and Agent Systems, vol. 1, nos. 3-4, pp. 219-234, 2003.
[9]. A. Go´mez-Pe´rez, M. Ferna´ndez-Lo´pez, and O. Corcho, Ontological Engineering. Springer-Verlag, 2003.
[10]. N. Guarino, C.Masolo, and G.Vetere, "OntoSeek: Content-Based Access to the Web," IEEE Intelligent Systems, vol. 14, no. 3, pp. 70- 80, 1990.

Abstract: Cloud computing is a latest new computing paradigm where applications, data and IT services are provided over the Internet. The Job Scheduling is the key role in cloud computing systems. One technique is to use gang scheduling where a set of tasks is scheduled to execute simultaneously on a set of processors. Usually tasks are scheduled by user requirements. So, taking existing model new scheduling strategy proposed to overcome the problems of the performance unpredictability with the help of scheduling technique between user and resources.
Keywords: Cloud Computing, Gang Scheduling, Virtual Machine, Cloud models

[1] Yutaka Ishikawa and Shinpei Kato (2009), "EDF Scheduling with gang approach" 30th IEEE Real-Time Systems Symposium
[2] Helen d. karatza, (2006) "Scheduling gangs in a distributed system" I.J. of SIMULATION, Vol. 7, No. 1
[3] Helen D. Karatza, Ioannis A. Moschakis, (2011) "Performance and Cost evaluation of Gang Scheduling in a Cloud Computing System with Job Migrations and Starvation Handling" 978-1-4577-0681-3/11/2011 IEEE
[4] Zafeirios C. Papazachos and Helen D. Karatza, (2007) "Gang Scheduling with Precedence Constraints"
[5] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia (2009) "Above the Clouds: A Berkeley View of Cloud Computing" February 10, 2009
[7] Y. Wiseman and D. G. Feitelson, "Paired gang scheduling," IEEE Trans. Parallel Distrib. Syst., vol. 14, no. 6, pp. 581–592, 2003
[8] L. Nie and Z. Xu, "An adaptive scheduling mechanism for elastic grid computing," in Proc. of the 2009 Fifth Int. Conf. on Semant, Knowl. and Grid, SKG '09. Washington, DC, USA: IEEE Computer Society, 2009, pp. 184–191.
[9] http://www.technopulse.com/2011/06/cloud-service-models-saas-paas-iaas.html
[10] Amazon elastic compute cloud (amazon ec2). [Online]. Available: http://aws.amazon.com/ec2/
[11] Hori A, Tezuka H, Ishikawa Y (1998) Overhead analysis of preemptive gang scheduling. In: Proc of the workshop on job sched strateg for parallel process. Lect notes in comp sci, vol 1459. Springer, Berlin, pp 217–230
[12] Karatza H (2009) Performance of gang scheduling strategies in a parallel system. Simul Model Pract Theory17:430–441. doi:10.1016/j.simpat.2008.10.001
[13] Papazachos Z, Karatza H (2009) Performance evaluation of gang scheduling in a two-cluster system with migrations. In: 8th int workshop perform model, evaluation optim of ubiquitous comp and netwsyst, Rome, Italy 2009. doi:10.1109/IPDPS.2009.5161172
[14] Z. C. Papazachos and H. D. Karatza, "The impact of task service time variability on gang scheduling performance in a two-cluster system," Simul. Modell. Pract. Theory, vol. 17, no. 7, pp. 1276–1289, 2009.
[15] http://www.cloudtweaks.com/2012/04/cloud-computing-cloud-service-models-part-3/
[16] http://thecloudtutorial.com/cloudtypes.html