iosr-JCE   Second International Conference on Emerging Trends in Engineering' 2013

Abstract:Steganography is an important area of research in recent years involving a number of application. it is the science of embedding information into the cover images viz. text,video, and images. this article reviews stegnography based on digital image. Concept and priniciple of steganography are illustrated. Different embedding techniques that are LSB, Spatial domain ,DCT, Huffman encoding,DWT embedding method are generalized . then the performance specification of image steganography is disscussed . An image based steganography that combines LSB, DCT, and compression techniques on raw image to enhance the security of the payload.

Keywords-Least significant bit (LSB), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)

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[2]. K.B.Raja, C.R.Chowdary,"A Secure Image Steganography using LSB,DCT and compression Techniques on Raw Images", IEEE -2005.

[3]. Mamta Juneja & Parvinder Singh Sandhu,"Designing of Roboust Image Steganography Technique Based on LSB Insertion and Encryption", 2009 ICARTCC

[4]. Piyush Marwaha & Paresh Marwaha,"Visual Cryptographic Steganography in Images", 2010 Second international conference on computing, communication and networking technologies.

[5]. Amitava Nag, Sushanta Biswas,"A Novel Techniques for image steganography based on DWT and Huffman Encoading", IJCSS, Vol(4): Issue (6)

[6]. Hniels Provos & Peter Honeyman,"Hide & Seek : An Introduction to Steganography" IEEE Computer Society Pub-2003.

[7]. Feng Pan, & Jun Li,"Image Steganography Method Based on PVD and Modules Function",IEEE-2011.

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[10]. Hassan mathkour,Batool Ai,sadoon, "A New Image Steganography Technology" ,IEEE-2008ntly connected mobile networks," in Proceedings of ACMSIGCOMM workshop on Delay Tolerant Networking (WDTN), 2005.

Abstract: To implement Data mining Software using SRDT model that improves performance in time, space, accuracy and anti-noise capability in comparison with other models like VFDT, VFDTc for classifying data streams. The model we have created is used for classification purpose that gives the accuracy above 90% while classifying the streaming data. Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both .This is core a model. We can use this model in various applications like Online Shares Trading, Human Resource Management System, Phone Call Records, Project Management System etc

[1.] Xue-Gang Hu, Pei-Pei Li, Xin-Dong Wu, and Gong-Qing Wu. "A Semi-random Decision-Tree for Mining data streams". JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY. Sept. 2007
[2.] Pedro Domingos & Geoff Hulten."Mining High Speed Data Stream".
[3.] http://dms.irb.hr/tutorial/tut_dtrees.php.
[4.] http://ftp.utcluj.ro/pub/users/nedevschr/Av/5_EvaluareClassification/entropy.pdf
[5.] http://en.wikipedia.org/wiki/Entropy_(information_theory).
[6.] Software Engineering A Practitioner's Approach- Rojar S. Pressman.
[7.] The Unified Modeling Language User Guide – Grady Booch, James Rumbaugh

Abstract:With the advent of Web 2.0, end users generate and share more and more content. One such service in this context is the collaborative edition of online documents. This service is commonly provided through Cloud Computing as Software as a Service. However, the Cloud paradigm still requires users to place their trust in Cloud providers with regard to privacy. This is the case of Google Docs; a very popular service without privacy support for the documents stored on its servers so here we discuss the issues and proposes the add-on utility to guarantee privacy of shared documents in Google Docs.

Keywords-Authentication, cloud, collaboration, GDocs, Time key

[1] De Capitani di Vimercati, S. DTI,Univ. degli Studi di Milano, Crema, Italy Foresti, S.; Jajodia, S.; Paraboschi, S. Pelosi, G. ; Samarati, P. "Encryption based Policy Enforcement for Cloud Storage" in Distributed Computing Systems Workshops (ICDCSW), 2010 IEEE 30th International Conference on 21-25 June 2010, PP: 42 – 51.

[2] Tien-Dung Nguyen Dept. of Comput. Eng., Internet Comput. & Security Lab., Suwon, South Korea Eui- Nam Huh"An Efficient key MANAGEMENT FOR SECURE MULTICAST IN SENSOR CLOUD "Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on 23-25 May 2011 PP: 3 – 9.

[3] Lilian Adkinson-Orellana1, Daniel A. Rodríguez-Silva1, Felipe Gil-Castiñeira2, Juan C. Burguillo- Rial2. "Privacy for Google Docs: Implementing a Transparent Encryption Layer" www- gti.det.uvigo.es/~darguez/pub/2010_CloudViews_GoogleDocs.

[4] Lilian Adkinson-Orellana, Daniel A. Rodriguez-Silva, Francisco J. "Sharing Secure Documents in the Cloud. A Secure Layer For GoogleDocs" Proceedings of 1st International Conference on Cloud Computing and Services ... www-gti.det.uvigo.es/~darguez/publications1.html.

[5] Gabriele D‟Angelo Fabio Vitali University Bologna Italy, "content cloaking: preserving privacy with Google Docs and other web applications" proceedings of the 2010 ACM symposium on Applied Computing .PP: 826-830, ACM New York.

Abstract: Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints. It is used in computer vision, medical imaging, automatic target recognition and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements. Automatic image registration is still an actual challenge in several fields. Several methodologies have been proposed to tackle this problem, however, two approaches are widely used in this context: edge-based and region-based. In edge-based methods, the local discontinuities are detected first and then connected to form longer, hopefully complete, boundaries. In region-based methods, areas of an image with homogeneous properties are found, which in turn give the boundaries. The two methods are complementary, and one may be preferred to the other for some specific applications like document image analysis. Although several methods for automatic image registration have been proposed in the last few years, it is still far from a broad use in several applications, such as in remote sensing. In this paper, a method for automatic image registration through recursive threshold-based image segmentation is proposed.

Index Terms— Binary image , Image Registration, Image Wrapping, Segmentation

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[6]. J. Inglada and A. Giros, "On the possibility of automatic multisensory image registration," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10,pp. 2104–2120, Oct. 2004.
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Abstract: Mobile operating system consists of different types of embedded system. Generally embedded systems require optimized compilers to produce high quality codes as they have limited general purpose register set. Normally memory or registers can be used to store the results of computation of a program. But accessing a register requires less machine cycles as compared to memory access, but due to limited number, registers have to be utilized very efficiently. The main objective of this paper is to propose an efficient way to hold as many live variables as possible in registers in order to avoid expensive memory accesses. Normally register allocation problem is based on graph colouring problem. An evolutionary algorithm for graph colouring register allocation problem is used for efficient register allocation.

Index Terms: compilers, compiler optimization, register allocation, evolutionary algorithm, embedded systems

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[3] P. Briggs. "Register Allocation via Graph Coloring". Ph.d Thesis, Rice University, April 1992.
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Abstract: Cloud computing is a highly scalable and cost-effective infrastructure for running HPC, enterprise and Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. Data centers hosting cloud computing applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. With energy shortages and global climate change leading our concerns these days, the power consumption of data centers has become a key issue. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud computing on the environment. In order to design such solutions, deep analysis of Cloud is required with respect to their power efficiency. We need to address various elements of Clouds which contribute to the total energy consumption and how it is addressed in the literature. We also discuss the implication of these solutions for future research directions to enable green Cloud computing. This paper also explains the role of Cloud users in achieving this goal. Keywords: Green cloud, dynamic provisioning, multi-tenancy, Datacenter Efficiency

[1] "Energy efficient management of data centre resources for cloud computing:A vision,architectural elements and Open Challenges" Rajkumar Buyya,Anton Beloglazov,Jemal Abawajy Proc. of 9th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2009), Rio De Janeiro, Brazil, May 2009.
[2] " Green Cloud computing and Environmental Sustainability" Saurabh Kumar Garg and Rajkumar Buyya IEEE Xplore,
[3] "Performance Evaluation of a green Scheduling algorithm for energy savings in cloud computing" Troung Vinh Troung Duy,Yukinori Sato,Yashushi Inoguchi IEEE Xplore, March 2010.
[4] Buyya, R., Yeo, C.S. and Venugopal, S. 2008. Market-oriented Cloud computing: Vision, hype, and reality for delivering it services as computing utilities. Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications, Los Alamitos, CA, USA.

Abstract: In last decade, there has been explosive growth in vehicular sector and the number of vehicles flying on the road in all the parts of the country. For the better management of vehicular traffic, it is necessary to keep track of vehicles on the basis of their number plates. The proposed project suggests an automated way of parking toll collection based on the number plates of the vehicle and the time for which the vehicle is parked in the parking lot. This work deals with the problem from field of artificial intelligence, computer vision (image processing) and neural networks in the construction of an Automatic Number Plate Recognition System (ANPR).This problem includes mathematical principles and algorithms, which ensures a various processes to carry out the steps for the product. The acquisition of the image takes place with any camera with capability of capturing image with good quality. The emphasis of this paper is on the localization of number plate using contours tracing technique along with edge detection and sharpening of edge using Canny's edge detection algorithm. Moreover, we focus on this paper a new algorithm based on artificial neural network (ANN) is used for recognition of number plate characters. This paper makes use of various algorithms in each category from number plate detection to actual recognition of characters which enhances the performance of the system up to the maximum extent possible with less efforts and use of computational resources.

Keywords: Artificial Neural Networks, Canny Edge Detection, Character segmentation, Contours, Image Processing Region of interest (ROI)

[1] M.I.Khalil,Car Plate Recognition Using the Template Matching Method, International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 20101793-8201

[2] Kaushik Deba, Md. Ibrahim Khana, Anik Sahaa, and Kang-Hyun Job, An Efficient Method of Vehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network, Science Direct, CSIT-2012

[3] Kumar Parasuraman, SVM Based License Plate Recognition System

[4] Daggu Venkateshwar Rao*, Shruti Patil, Naveen Anne Babu and V Muthukumar, Implementation and Evaluation of Image Processing Algorithms on Reconfigurable Architecture using C-based Hardware Descriptive Languages International Journal of Theoretical and Applied Computer Sciences Volume 1 Number 1 (2006) pp. 9–34 (c) GBS Publishers and Distributors (India) Books: [5] Rafael C Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using Matlab(Pearson Education Inc. 2009)

[6] Ondrej Martinsky, ,Algorithmic and Mathematical Principles of Automatic Number Plate Recognition Systems, B.SC. BRNO 2007 [7]www.Imageprocessingplace.com/downloads_V3/root_downloads/tutorials/contours_tracing_Abeer_george_Ghuneim/alj.html

Abstract: Cloud computing is one of the emerging fields in the computer world these days. Cloud computing is attracting everyone with its benefits. Now companies are shifting their focus onto cloud computing. But to be a part of cloud computing environment and to take advantages of cloud computing, legacy applications need to be migrated to cloud. Cloud migration is the process of transitioning all or part of a company's data, applications and services from onsite computers behind the firewall to the cloud or moving them from one cloud environment to another. After migrating to the cloud, the information will be available on the internet so that more people can have access to it as needed.

Keywords-Cloud Computing, Cloud Migration, Challenges issu).

[1] Anthony T. Velte, Toby J. Velte, & Robert Elsenpeter.(2010), Cloud Computing: A Practical Approach. United States, TATA McGraw-Hill.

[2] Tim Mather, Subra Kumaraswamy, & Shahed Latif. (2009), Cloud Security and Privacy, Sebastopol, USA. O'Reilly Media, Inc.

[3] Tom Laszewski & Prakash Nauduri. (2012), Migrating to cloud. Waltham, USA, Elsevier.

[4] Yashpalsinh Jadeja & Kirit Modi. (2012), Cloud Computing - Concepts, Architecture and Challenges. 2012 International Conference on Computing, Electronics and Electrical Technologies [ICCEET].

[5] Richard Holland (2011), Ten Steps to Successful Cloud Migration. Eagle Genomics Ltd. White Paper.

[6] Salvatore D'Agostino, Miha Ahronovitz, & Joe Armstrong (2011), Moving to the Cloud (Version 1.0). A white paper produced by the Cloud Computing Use Cases Discussion Group

[7] Srinivasa Rao V, Nageswara Rao N K, & E Kusuma Kumari (2005 - 2009), Cloud Computing: An Overview. Journal of Theoretical and Applied Information Technology
[8] IBM.com, Grace Walker, (Dec 2010), Cloud computing fundamentals. http://www ibm.com/developerWorks.
[9] Webopedia.com, Cloud Migration, www.webopedia.com/TERM/C/cloud_migration.html
[10] Ramcoblog.com, (2010) Migrating to the cloud: A 5 Ste Structured Approach, http://www.ramcoblog.com/migrating-to- the-cloud-a-5-step-structured-approach.

Abstract: Fast retrieval of the relevant information from the databases has always been a significant issue. Different techniques have been developed for this purpose, one of them is Data Clustering. Clustering implies filtering results obtained from search engine and provide more flexible result In this paper Data Clustering is discussed along with various approaches and their analysis.

Keywords - clustering ,stemming ,stopword, filtering ,query .

1[1] D.V. Kalashnikov, S.Mehrotra, R.N.Turen and Z.Chen, "Web People Search via Connection Analysis" IEEE
a. Transactions on Knowledge and data engg.Vol 20, No11, November 2008.0.
[2] D.V. Kalashnikov, S. Mehrotra, Z. Chen, R. Nuray-Turan, and N.Ashish, "Disambiguation Algorithm for
a. People Search on the Web," Proc. IEEE Int'l Conf. Data Eng. (ICDE '07), Apr. 2007.
[3] D.V. Kalashnikov, R. Nuray-Turan, and S. Mehrotra, "Towards Breaking the Quality Curse. A Web-Querying
a. Approach to Web People Search," Proc. SIGIR, July 2008.
[4] R. Bekkerman, S. Zilberstein, and J. Allan, "Web Page Clustering Using Heuristic Search in the Web Graph," Proc. Int'l Joint Conf. Artificial Intelligence (IJCAI), 2007.
[5] 5) M. F. Porter. An algorithm for suffix stripping. Program Vol. 14, no. 3, pp 130-137.
[6] Data Clustering and Its ApplicationsRaza Ali (425), Usman Ghani (462), Aasim Saeed (464) .

Abstract: The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval (IR) community. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) community. It is the clustering of results returned by the search engines into meaningful, thematic groups. This paper gives issues that must be addressed in the development of a Web clustering engine and categorizes various techniques that have been used in clustering of web search results. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure.

Keywords – Web search, clustering, information retrieval

[1] D.A.Nikam,A.B.Rajmane, Cluster Based Web Search, International Journal of Advanced Research in Computer Science &
Second International Conference on Emerging Trends in Engineering (SICETE) 57 | Page
Dr.J.J.Magdum College of Engineering, Jaysingpur
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[4] Wang Y and Kitsuregawa M (2001) Link Based Clustering of Web Search Results. In Proceedings of The Second InternationalConference on Web-Age Information Management (WAIM2001), XiAn, P.R.China, Springer -Verlag LNCS.

[5] M. F. Porter. An algorithm for suffix stripping. Program Vol. 14, no. 3, pp 130 -137.

[6] YANG, Y. AND PEDERSEN, J. O.(1997) A comparative study on feature selection in text categorization.. In Proceedings of the14th International Conference on Machine Learning (ICMC). Morgan Kaufmann, San Francisco, 412 –420.

[7] LIU, T., LIU, S., CHEN, Z., AND MA, W.-Y. 2003. An evaluation on feature selection for text clustering. In Proceedings of the20th International Conference on Machine Learning, August 21–24, T. Fawcett and N. Mishra, Eds. AAAI Press, 488–495.

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[9] KATIFORI, A., HALATSIS, C., LEPOURAS, G., VASSILAKIS, C., AND GIANNOPOULOU, E. 2007. Ontology visualization methods a survey. ACM Comput. Surv. 39, 4, 1–43.

Abstract: File Transfer Protocol (FTP) is widely used protocol for transferring files over a network. However, there exists some secure vulnerability in the protocol. For example, both passwords and files are transmitted in plaintext. Although some new FTPs such as FTPS have been proposed and applied to overcome these vulnerabilities, there are many drawbacks such as lack of flexibility in use, failing to meet specific security requirements, etc. Given these facts, the FTP and its requirements are studied deeply and a new secure FTP system is designed in this paper. In the new system, a dynamic password mechanism is combined with face recognition technology to achieve mutual authentication, key distribution and secure information transmission. The security level selection mechanism is adopted to meet individual security requirements. The resource access control mechanism is used to keep the server from unauthorized access attacks. Analysis shows that compared with existing FTP systems, the new system makes not only data transmission securer but also system in use easier, more flexible and efficient.

Keywords- Dynamic password, Interrupted Resume, Hash value, XOR function, Face recognition value

1] RFC-959 J. Postel, J. Reynolds, l SI, "File Transfer Protocol (FTP)," Oct 1985. Available: http://www.ietforg/rfc/
[2] RFC 4217: P. Ford-Hutchinson, IBM UK Ltd, "Securing FTP with TLS," Oct 2005. Available: http://www.ietforg/rfc/
[3] RFC 4251: T.Ylonen, T. and C. Lonvick, Ed. Cisco Systems, Inc, "The Secure Shell (SSH) Protocol Architecture," Jan 2006. Available: http://www.ietforg/rfc/

[4] Y Ma, H. T. Liu, B. Y Cai, "Design and implementation of a secure FTP system," Applications and Software; 2007.

[5] Liu Xia, Feng Chao-sheng, "A FTP Mutual Authentication Scheme Based on Dynamic Password", Communications Technology; 2010.

[6] Liu Xia, Feng Chao-sheng, Yuan Ding, Wang Can, "A Design of Secure FTP System", Communications, circuits and systems; 2010. [7] Liu Cuilin, Shen Yongjun, Zang Guidong, Wen Fang, "E-mail System based on Dynamic password and Face Recognition"; 2010

Abstract: This paper give an introduction of Artificial and computational intelligence. The artificial intelligence (AI) is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment. Computational Intelligence is the study of design of intelligent agents. The goal of computational intelligence (CI) is to understand both natural and artificial systems including bioinformatics, linguistics, robotics, games, neural networks, fuzzy systems and evolutionary computation.

Keywords – Artificial Intelligence, software architecture, Reusability, Software engineering.

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