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Volume-1 Issue-1: Published on March 05, 2011
Volume-1 Issue-1: Published on March 05, 2011

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Volume-1 Issue-1, March 2011, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Stuti Asthana, Farha Haneef, Rakesh K Bhujade

Paper Title:

Handwritten Multiscript Numeral Recognition using Artificial Neural Networks

Abstract:   Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like MIndia where multiple languages and scripts are used .Because of intermixing of these languages; it is very difficult to understand the script in which the pin code is written. Objective of this paper is to resolve this problem through Multilayer feed-forward back-propagation algorithm using two hidden layer. This work has been tested on five different popular Indian scripts namely Devnagri, English, Urdu, Tamil and Telugu. Network was trained to learn its behavior by adjusting the connection strengths on every iteration. The resultant of each presented training pattern was calculated to identify the minima on the error surface for each training pattern. Experiments were performed on samples by using two hidden layers and the results revealed that as the number of hidden layers is increased, more accuracy is achieved in large number of epochs.

 Numeral Recognition, Artificial Neural Network, Supervised learning, Back Propagation Algorithm.


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3.        G.G. Rajaput and Mallikarjun Hangarge, ''Recognition of isolated handwritten Kannada numerals based on image fusion method: '', PReMI07, LNCS.4815, pp.153-160, 2007.

4.        U. Pal, K. Roy and F. Kimura, "A Lexicon driven method for unconstrained Bangla handwritten word recognition", In Proc. 10th IWFHR, pp. 601-606, 2006.
5.        K. Roy, “On the development of an optical character recognition system for Indian postal automation”, Ph.D. Thesis, Jadavpur University, 2008.
6.        Muller et al. An Introduction to Backpropagation Learning Algorithms, IEEE Transactions on Neural Networks, Vol. 12, No. 2, March 2009

7.        Dong Xiao Ni,Proceedings of Students/Faculty Research Day,CSIS , Pace University,.Application of Neural Network to character recognition, May 2007.

8.        Reza Gharoie Ahangar ,Farajpur Ahangar,” Farsi Character Recognition using Artificial Neural Network” IJCSIS International Journal of Computer Science and Information Security,Vol.4,no.1&2,2009

9.        Le Cun, Y., Bottou, L., and Ha_ner, P. (2008). Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278-2324.

10.     Altun, H., Curtis,KM,1997An improved neural network learning. Proceeding of the Fourth IEEE International Conference on Electronics, Circuits and Systems, VOL.1,Page29-33.




D R . Sanjay Sharma, Pushpinder Singh Patheja, Akhilesh A. Waoo

Paper Title:

Challenges for Mobile Wireless Devices for Next Generation in Pervasive Computing

Abstract:   Network computing and mobile computing are fast becoming a part of everyday life. We expect devices like PDAs, mobile phones, offices PCs and even home entertainment systems to access information and work together in one integrated system and the challenge is to combine these technologies into a seamless whole and on the Internet. The aim of Pervasive Computing is for computing available wherever it's needed. It spreads intelligence and connectivity to more or less everything. So conceptually, ships, aircrafts, cars, bridges, tunnels, machines, refrigerators, door handles, lighting fixtures, shoes, hats, packaging clothing, tools, appliances, homes and even things like our coffee mugs and even the human body and will embedded with chips to connect to an infinite network of other devices and to create an environment where the connectivity of devices is embedded in such a way that it is unobtrusive and always available. Pervasive computing, therefore, refers to the emerging trend toward numerous, easily accessible computing devices connected to an increasingly ubiquitous network infrastructure. What is really different about mobile wireless device? The devices are smaller and bits travel by wireless rather than Ethernet. How can this possibly make any difference? Isn’t a mobile system merely a special case of a distributed system? Are there any new and deep issues to be investigated, or is pervasive computing just the latest fad? This paper is my attempt to answer these questions.

  AMPS, CDMA, GSM, IMT-2000, Pervasive Computing, Ubiquitous Networks.


1.        Howard, J. H., Kazar, M. L., Menees, S.G., Nichols, D. A., Satyanarayanan, M., Sidebotham, R. N., West, M.J. Scale and Performance in a Distributed File System. A(34 Transactions on Computer Systems 6( 1), February. 1988.
2.        Kistler, J.J., Satyanarayanan, M. Disconnected Operation in the Coda File System. ACM Transactions on Computer Systems 10(1), February, 1992.

3.        Kistler, J.J., Disconnected Operation in a Distributed File System. PhD thesis, Department of Computer Science, Carnegie Mellon University, May, 1993.

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8.        Satyanarayanan, M. The Influence of Scale on Distributed File System Design. IEEE Transactions on Software Engineering 18(l), hnuafy, 1992.

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12.     G. Lawton, “Vendors battle over mobile-OS Market,” IEEE Computer, February 1999, pp. 13- 15. D. N. Knisely, S.Kumar, S. Laha, and S. Nanda, “Evolution of wireless data services: IS-95 to cdma2000,” IEEE Communications, vol.36, pg.140-149, Oct. 1998

13.     Wideband CDMA, Special issue of IEEE Communications, September 1998. Third Generation of Mobile Systems in Europe, Special issue of the IEEE Personal Communications, April 1998.

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15.     N. Sollenberger, N. Seshadri, and R. Cox, “The Evolution of IS-136 TDMA for Third Generation Wireless Services”, IEEE Personal Communications, Vol. 6, No. 3, June 1999, pp. 8-18.

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17.     S. Kumar and S. Nanda, “High Data-Rate Packet Communications for Cellular Networks Using CDMA: Algorithms and Performance,” IEEE JSAC, Vol. 17, No, 3, March 1999..

18.     S. Kishore, J-C. Chen, K. M. Sivalingam, and P. Agrawal, “Adaptive Power Control and Scheduling Algorithms Based on Battery Power Level for CDMA Wireless Networks,” in Proc. IEEE International Conference on Universal Personal Communications (ICUPC), (Florence, Italy), pp. 967-971, Oct. 1998.

19.     J-C. Chen, K. M. Sivalingam, P. Agrawal, and S. Kishore, “A Comparison of MAC Protocols for Wireless Local Networks Based on Battery Power Consumption,“, Proc. IEEE INFOCOM , (San Francisco, CA), pp. 150-157, March 1998.

20.     P. Ramanathan, K. M. Sivalingam, P. Agrawal and S. Kishore, “Resource allocation during handoff through dynamic schemes for mobile multimedia wireless networks”, Proc. IEEE INFOCOM, (New York, NY), Mar. 1999.

21.     Debashis Saha, Amitava Mukherjee, “Pervasive Computing: A Paradigm for the 21st Century,” Published by the IEEE Computer Society, March 2003.

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23.     M. Satyanarayanan, “Pervasive Computing: Vision and Challenges,” IEEE Personal Communication, Aug. 2001, pp.10-17.




Ghanshyam Prasad Dubey, Neetesh Gupta, Rakesh K Bhujade

Paper Title:

A Novel Approach to Intrusion Detection System using Rough Set Theory and Incremental SVM

Abstract:   Intrusion Detection System (IDS) is software and/or hardware designed to detect unwanted attempts at accessing, manipulating, and/or disabling of computer systems, mainly through a network, such as the Internet. These attempts may take the form of attacks, as examples, by crackers, malware and/or disgruntled employees. An IDS cannot directly detect attacks within properly encrypted trafficOn detection of such sign triggers of IDS to report them generate the alerts. These alerts are presented to a human analyst who evaluates them and initiates an adequate response. In Practice, IDSs have been observed to trigger thousands of alerts per day, most of which are mistakenly triggered by begin events such as false positive. This makes it extremely difficult for the analyst to correctly identify alerts related to attack such as a true positive. Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection. Intrusion detection systems aim to identify attacks with a high detection rate and a low false positive. We use RST (Rough Set Theory) and Incremental SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features were selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. Using this method, it can overcome the shortages of SVM time-consuming of training and massive dataset storage. The simulation experiments with KDD Cup 1999 data demonstrate that our proposed method achieves the increasing performance for intrusion detection.

  Intrusion Detection, Support Vector Machine, Rough Set Theory, Data Mining


1.        KDD Cup 1999 Data,,August 2003.
2.        M.A. Aydın, A.H. Zaim, K.G. Ceylan, A hybrid intrusion detection system design for computer network security. Computers & Electrical Engineering, 2009. 35(3): 517-526.

3.        Mukkanmala S,Sung,” A Feature ranking and detection for intrusion” Proceedings of the International Conference on Information and Knowledge Engineering-IKE 20022002:503-509.

4.        MIT Lincoln Labs, 1999 DARPA intrusion detection evaluation,available at:

5.        W.T. Yue, Y.U. Ryu, The management of intrusion detection: Configuration, inspection, and investment. European Journal of Operational Research, 2009. 195(1): 186-204.

6.        B. Morin, M. Ludovic, H. Debar, M. Ducass, A logic-based model to support alert corre lation in intrusion detection. Information Fusion,2009. 10(4): 285-299

7.        P.García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges”. Computers & Security, 2009. 28(1-2): 18-28.

8.        J. Yang, X. Liu, T. Li, G. Liang, S. Liu, Distributed agents model for intrusion detection based on AIS. Knowledge-Based Systems, 2009. 22(2): 115-119.

9.        R. Beghdad, Critical study of neural networks in detecting intrusions. Computers & Security, 2008. 27(5-6): 168-175.

10.     S.-J. Horng, P. Fan, Y.-P. Chou, Y.-C. Chang, Y. Pan, A feasible intrusion detector for recognizing IIS attacks based on neural networks.Computers & Security,
2008. 27(3-4): 84-100.




Simmy Hirkaney, Sandip Nemade, Vikash Gupta

Paper Title:

Power Efficient Design Of Counter On .12 Micron Technology

Abstract:   As chip manufacturing technology is suddenly on the threshold of major evaluation, which shrinks chip in size and performance is implemented in layout level which develops thelow power consumption chip, using recent CMOS, micron layout tools. This paper compares 2 architectures in terms of the hardware implementation, power consumption and CMOS layout usingMicrowind CMOS layout tool. Thus it provides solution to a low power architecture implementation of Counter in CMOS VLSI. The Microwind program allows the designer to design andsimulate an integrated circuit at physical description level.

  microwind, micron Technology, layout,asynchronous counter.


1.        M. Quirk and J. Serda, “Semiconductor manufacturing Technology,” New Jersey: Prentice Hall, pp.388-434, 2001, IEEE.
2.        K. Agarwal, H. Deogun, D. Sylvester, and K. Nowka. Power gating with multiple sleep modes. In Proceedings of the 7th ACM/IEEE
3. International Symposium on Quality Electronic Design, January 2006
4.        John Faricelli, “Layout-Dependent Proximity Effectsin Deep Nanoscale CMOS”, April, 16, 2009.

5.        James R. Sheets, Bruce W. Smith; “Microlithography Science and Technology,” New York: Marcel Dekker, pp. 317-365, 2000.

6.        William Gerard Hurley, Senior Member, IEEE, and Chi Kwan Lee “Development, Implementation, and Assessment of a Web-Based Power Electronics Laboratory in IEEE TRANSACTIONS ON EDUCATION, VOL. 48, NO. 4, NOVEMBER 2005.

7.        Behrooz Vahidi, Senior Member, IEEE, and Jamal Beiza “Using Spice in Teaching Impulse Voltage Testing of Power Transformers to Senior Undergraduate Students” in TRANSACTIONS ON EDUCATION, VOL. 48, NO. 2, MAY 2005

8.        A. Rantala, S. Franssila, K. Kaski, J. Lampinen, M. Aberg and P. Kuivalainen “Improved neuron MOS-transistor structures for integrated neural network circuits”, IEE Proceedings- Circuits, Devices and Systems, vol. 148, pp 25-34,Feb. 2001.

9.        Prof. Yusuf Leblebici, CMOS Digital Integrated Circuits, TMH, 2003.

10.     W. Wolf, Modern VLSI Design- Systems on Silicon, Prentice Hall, 1998.

11.     Neil H. E. Weste, Principal of CMOS VLSI Design, Pearson Education, 2003

12.     L. Shang, L. Peh, and N. Jha. Dynamic voltage scaling with links for power optimization of interconnection networks. In Proceedings of International Symposium on High- Performance Computer Architecture, pages 91–102, 2003, IEEE

13.     A.M. Shams and M.A. Bayoumi, “A novel high-performance CMOS 1-bit full-adder cell”, IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, vol. 47, pp. 478 –481, May 2000.

14.     Y. Tsividis, Operation and Modeling of The MOS Transistor, Mc Graw-Hill, 1999.




Badri Patel, Vijay K Chaudhari, Rajneesh K Karan, YK Rana

Paper Title:

Optimization of Association Rule Mining Apriori Algorithm Using ACO

Abstract:   Association rule mining is an important topic in data mining field. In a given large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. Apriori algorithm that generates all significant association rules between items in the database. On the basis of the association rule mining and Apriori algorithm, this paper proposes an improved algorithm based on the Ant Colony Optimization algorithm. We can optimize the result generated by Apriori algorithm using Ant colony optimization algorithm. The algorithm improved result produces by Apriori algorithm. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years.

  Association rule mining, Apriori algorithm, Ant Colony Optimization (ACO) algorithm, data mining


1.        Yan-hua,Wang, Xia Feng,’The optimization of Apriori algorithm based on directed network. 3rd international Symposium on intelligent information technology application, 2009.
2.        J Han, M Kamber,’ Data mining: Concepts and techniques’, Morgan Kaufman Publishes, 1992.

3.        Gao, Shao-jun Li,’ A method of improvement and optimization on association rules appriori algorithm’, proceeding of the 6th cogress on intelligent control and automation,2006 pp5901-5905

4.        Estefan G.M de L Manoel atl.,’ Minimum number of switching operations via ant colony optimization’, 19th internation conference on electricity distribution Vienna, 21-24 May2007.

5.        R. Agrawal,T. Imielinski, A. Swami,’ Mining Association rules between sets of items in large database,’ proceeding of 1993 ACM SIGMOD conference Washington DC, USA.

6.        AK Jain, MN Murthy, PJ Flynn,’ Data clustering- A review’, ACM Computing Surveys, Oct. 2001

7.        M Dorigo , T Stutzle, ‘Ant colony optimization’, The MIT press Cambridge, MA.

8.        Karla Taboada, S Mabu, E Gonzales,’ Genetic Network programming for fuzzy association rule based classification, 2009.

9.        Karla Taboada,Shingo Mabu, Eloy Gonzales,” Genetic Network Programming for Fuzzy Association Rule-Based Classification”, 2009




Gurpreet Singh Chhabra, Dipesh Sharma

Paper Title:

Cluster-Tree based Data Gathering in Wireless Sensor Network

Abstract:   WSN consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. As sensor nodes are deployed in sensing field, they can help people to monitor and aggregate data. Researchers also try to find more efficient ways of utilizing limited energy of sensor node in order to give longer life time of WSNs. Network lifetime, scalability, and load balancing are important requirements for many data gathering sensor network applications. Therefore, many protocols are introduced for better performance. In the available literature, multi-hop routingprotocol is well known for power saving in data gathering [5]. Researchers have used such types of the cluster-based (e.g., LEACH, EERP), the chain-based (e.g. PEGASIS) and the tree-based (e.g. TREEPSI) to establish their energy-efficient routing protocols. In this paper, we propose an improved version which uses both cluster and tree based protocols. The proposed protocol improves the power consumption by improving FND.

  Wireless Sensor Networks (WSNs), First Node Death (FND), Energy efficient, Multi-hop routing protocol.


1.        R. Min, M, Bhardwaj, S. Cho, A. Sinha, E. Shih, A. Wang, and A. P.Chandrakasan,‖ Low Power Wireless Sensor Networks‖,Proceedings of International Conference, Bangalore, India,January 2001.
2.        J. M.Rabaey, M. J.Ammer, J. L. da Silva Jr., D. Patel, S.Roundy, ―Pico Radio supports ad hoc ultra low power wireless networking,‖ IEEE Computer, Vol. 33, pp. 42-48, July 2000

3.        K. Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie., ―Protocols for selforganization of a wireless sensor network,‖ IEEE Personal Communications, Vol. 7, No. 5, pp. 16-27, October 2000.

4.        S.S. Satapathy and N. Sarma ,‖ TREEPSI: tree based energy efficient protocol for sensor information ,‖Wireless and Optical Communications Networks 2006, IFIP International Conference 11-13 April 2006 .

5.        W. Heinzelman, A. Chandrakasan, and H. Balakrishnan,―Energy-Efficient Communication Protocol for Wireless Microsensor Networks,‖ Proc. Hawaii Conf. System Sciences, Jan. 2000.

6.        S. Lindsey, C. Raghavendra and K. M. Sivalingam, ―Data Gathering Algorithms in Sensor Networks Using Energy Metrics,‖ IEEE Transactions On Parallel and Distributed Systems, Vol . 13, No. 9, September 2002.PEGASIS

7.        S. Lindsey, C. Raghavendra,‖Pegasis: Power-Efficient gathering in sensor information systems,‖ In: Proc. of the IEEE Aerospace Conference, 2002. Pp.1-6.

8.        Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, ―A survey on Sensor Netw orks,‖ IEEE Communications Magazine, vol. 40, Issue: 8, pp. 102-114, August 2002.

9.        Q. Jiang and D. Manivannan , ―Routing Protocols for Sensor Networks,‖ IEEE Consumer Communications and Networking Conference, 2004.




 Tejpal Singh, Vinod Kumar, Khushboo Saxena, Akanksha Saxena 

Paper Title:

 Evaluation of Security Conditions of Protocols for Data Routing in Wireless Sensors Networks

Abstract:   A wireless sensor networks have recently emerged as successful technologies in a number of application domains. WSN design is influenced by many factors such as transmission errors, network topology and power consumption. Security in wireless sensor network is a challenging task; the need to build security services into them remains however a considerable challenge as the hardware used often shows serious processing and energy limitations. This work evaluates the impact of a security conditions and robustness criteria of routing protocols for WSN. Some of ITS features are not found in existing WSN simulation systems. It will provide the opportunity to implement and evaluate routing algorithms are designed to be that secure but for which there are in the experimental studies on the robustness and real impact of designed security mechanisms. This evaluation will focus primarily on examining the effectiveness of the provided security mechanisms. Additionally, it will also assess the impact of these mechanisms in relation to energy consumption, reliability, latency and resistance of the protocol, regarding the coverage and the scale of the network. 

security system, wireless sensor network, routing algorithms, attacks 


1. WIRELESS SENSOR NETWORK SECURITY ANALYSIS, Hemant Kumar Kalita1 and Avijit Kar[19], International Journal of Next-Generation Networks (IJNGN),Vol.1, No.1, December 2009
2. Wireless Networks. Security Vulnerabilities In Wireless Sensor Networks: A Survey. Journal of Information Assurance and Security, 5([19]010):031–044, 2009.
3. Security Issues in Wireless Sensor Networks Zoran S. Bojkovic, Bojan M. Bakmaz, and Miodrag R. Bakmaz, INTERNATIONAL JOURNAL OF COMMUNICATIONS Issue 1, Volume 2, 2008
4. Mark Luk, Adrian Perrig, Ghita Mezzour, and Virgil Gligor. MiniSec: a secure sensor network communication architecture. pages 479–488, Cambridge, Massachusetts, USA, 2007. ACM.
5. Paolo Baronti, Prashant Pillai, Vince W.C. Chook, Stefano Chessa, Alberto Gotta, andY. Fun Hu. Wireless sensor networks: A survey on the state of the art and the 80[19].15.4 and ZigBee standards. Computer Communications, 30(7):1655–1695, May 2007.
6. M. Blum, Tian He, Sang Son, and John A Stankovic. IGF: a State-Free robust communication protocol for wireless sensor networks.
7. IEEE Standard for Information Technology - Telecommunications and information exchange between systems - Local and metropolitan area networks - specific requirement Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 2007.
8. Jing Deng, Richard Han, and Shivakant Mishra. Insens: Intrusion-tolerant routing for wireless sensor networks. Comput. Commun., [292:216–230, 2006.
9. Fei Hu and Neeraj K. Sharma. Security considerations in ad hoc sensor networks. Ad Hoc Networks, 3(1):69–89, January 2005.
10. Kemal Akkaya and Mohamed Younis. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3):325–349, May 2005.
11. William Stallings. Cryptography and Network Security (4th Edition). 2005
12. Anthony D. Wood, Lei Fang, John A. Stankovic, and Tian He. SIGF: a family of configurable, secure routing protocols for wireless sensor networks. pages 35–48, Alexandria, Virginia, USA, 2006. ACM.
13. Bryan Parno, Mark Luk, Evan Gaustad, and Adrian Perrig. Secure sensor network routing: a clean-slate approach. In CoNEXT ’06: Proceedings of the 2006 ACM CoNEXT conference, pages 1–13, New York, NY, USA, 2006. ACM.
14. B. Parno, A. Perrig, and V. Gligor. Distributed detection of node replication attacks in sensor networks. pages 49–63, 2005.
15. Wireless Sensor Systems and Networks: Technologies, Applications, Implications and Impacts David J. Nagel Professor of Engineering and Applied Science The George Washington University
16. Wireless Sensor Networks: Principles and Applications Chris Townsend, Steven Arms MicroStrain, Inc.
17. Adrian Perrig and Haowen Chan. Security and Privacy in Sensor Networks
18. Chris Karlof and David Wagner. Secure routing in wireless sensor networks: attacks and 
19. The contiki operating system - home. _ HYPERLINK _
20. Chris Karlof, DavidWagner, and Naveen Sastry. TinySec: a link layer security architecture for wireless sensor networks. pages 162–175, Baltimore, MD, USA, 2004. ACM.
21. D. Dolev and A. Yao. On the security of public key protocols.  Evaluation of Security Conditions of Protocols for Data Routing in Wireless Sensors Networks Information Theory, IEEETransactions on, 29(2):198–208, 1983
22. TinyOS community forum || an open-source OS for the networked sensor regime.
23. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks:a survey. Computer Networks, March 2009.
24. Adrian Perrig, Robert Szewczyk, Victor Wen, David Culler, and J. D. Tygar. Spins: Security protocols for sensor networks.
25. Yih-Chun Hu, Adrian Perrig, and David B. Johnson. Rushing attacks and defense in wireless ad hoc network routing protocols. In WiSe ’03: Proceedings of the 2nd ACM workshop on Wireless security, pages 30–40, New York, NY, USA, 2003. ACM.
26. Adrian Perrig, Robert Szewczyk, Victor Wen, David Culler, and J. D. Tygar. Spins: Security protocols for sensor networks. In Wireless Networks, pages [23]9–199, 2001.




Wasim Khan, Shiv Kumar, Neetesh Gupta, Nilofar Khan 

Paper Title:

Signature Based Approach For Image Retrieval Using Color Histogram And Wavelet Transform

Abstract:   In  this  article,  a  method  is  proposed  for  image mining based on analysis of color and texture properties of an image. Color and texture are the primitive image descriptors in content  based  image  retrieval  systems.  We  first  resize  all  the database  images  to  obtained  similar  space.    For  color  based Image  Retrieval,  HSV  color  model  is  used  to  obtain  color histogram  of  image  and  for  texture  extraction  Haar  wavelet transform is used. Then the proposed method evaluates different number of signatures   for HSV and wavelet coefficients. Similar process  is  applied  on  the  submitted  query  image.  Finally Euclidian distance between query image and database images is calculated and the images  having  minimum  distances  are extracted from the database as results. 

  Color  histogram,  Content-based  image  retrieval, Euclidian distance, Haar wavelet transform.  HSV model


1.        Color Texture Moments For Content  Based Image Retrieval, Hui    Yu, Mingjing Li, Hong-Jiang Zhang, Jufu  Feng1. 
2.        Entropy-Based Indexing On Color And Texture In Image Retrieval  Anirban Das.

3.        A survey of methods for colour image    indexing and retrieval in image database.Raimondo Schettini , Gianluigi ciocca, Silvia Zuffi.

4.        Texture Based Image Indexing and Retrieval . N Ganeshwara rao, Dr. V vijaya Kumar, V Venkata Krishna.

5.        Histogram Re nement for Content-Based Image Retrieval, Greg Pass Ramin Zabih

6.        Entropy-Based Indexing On Color And Texture In Image Retrieval, Anirban Das.

7.        Spiral Bit-string Representation of Color for Image Retrieval. Abdel hamid Abdesselam1, Hui Hui Wang2, and arayanan Kulathuramaiyer.
8.        Content Based Image Retrieval Techniques  ,  Shikha Nirmal, Proceedings of the 3rd National Conference; INDIA Co m-2009 Computing For Nation Development, February26 –  27, 2009 Bharti Vidhyapeet ’s  Institute  o f Computer  Applications  and management,  New  Delhi
9.        Texture Based Image Indexing and Retrieval,    Dr. V Vijaya Kumar N  Gnaneswara Rao,  V Venkata Krishna   IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.5, May 2009.




Deepshikha Patel, Monika Bhatnagar

Paper Title:

Mobile SMS Classification An Application of Text Classification

Abstract:   Text Classification is the process of classifying documents into predefined classes based on its content. Text classification is important in many web applications like document indexing, document organization, spam filtering etc. In this paper we analyze the concept of a new classification model which will classify Mobile SMS into predefined classes such as jokes, shayri, festival etc. All sms are converted into text documents. After preprocessing vector space model is prepared  d weight is assigned to each term. In the proposed model we have used entropy term weighting scheme and then PCA is used for reparameterization. Artificial Neural Network is used for classification.

  Text Classification, Short messaging service (sms), feature selection, Principal Component Analysis, Neural Network.


1.        Selamat, 2003. Studies on Mobile Agents for Query Retrieval and Web Page Categorization Using Neural Networks, in Division of Computer and Systems Sciences, Gradute School of Engineering, vol. Doctoral. Osaka: Osaka Prefecture University , pp. 94.
2.        R. A. Calvo, M. Partridge, and M. A. Jabri, 1998. A Comparative Study of Principal Component Analysis Techniques, presented at In Proc. Ninth Australian Conf. on Neural Networks, Brisbane

3.        P. Frasconi, G. Soda and A. Vullo. “Text categorization for multi page document: a hybrid naive Bayes HMM approach”, In proceeding of 1st ACM/IEEE-CS joint conference on Digital libraries; ACM Press New York, NY, USA, pages 11-20. 2001

4.        A.M. Kibriya, E. Frank, B. Pfahringer and G. Holmes. “Multinomial naive bayes for Text categorization” revisited. AI 2004: Advances in Artificial Intelligence, 3339, pp. 488–499, 2004.

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S. Praveen Kumar, K. Anusha, R.Venkata Ramana

Paper Title:

A Novel Approach to Enhance Robustness in Steganography Using Multiple Watermark Embedding Algorithm

Abstract:   This paper deals with an enhanced technique that improves robustness in steganography. A multiple watermark embedding algorithm has been proposed to embed multiple text messages as watermarks simultaneously in the same watermark space while minimizing the watermark (distortion) energy. The watermark is embedded in the DCT coefficients of the green channel of the color image. The algorithm takes into account the perceptual capacity of each coefficient inside the DCT blocks before embedding the watermark information. Therefore, the first 16 low frequency coefficients (excluding the DC value) in the 8×8 DCT block was screened and the eight coefficients with the maximum magnitudes were selected for embedding. The algorithm used is blind and does not require the original image for extracting the watermark. The watermarking method is robust against JPEG compression, additive noise, cropping, scaling, low-pass and median filtering.

  Steganography, DCT, MWE, pseudo random bit sequence, pseudo random positive real numbers, zero –mean Gaussian with variance.


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5.        Pseudo  random  bit  sequence:

6.        Zero  mean  Gaussian  with  variance:




Kavita Deshmukh, Deepshikha Patel, Nitesh Gupta, Shiv Kumar

Paper Title:

Efficient Coding Mechanism for Low Power Consumption in Wireless Programmable Devices

Abstract:   Due to the continuous advancement in technologyembedded devices are playing important role in everyone’s day to day life. Everyone is moving towards wireless embeddedsystems, but there is important concern about power consumption in such devices. While designing a lot of care has to be taken especially in power optimization because there is no regular power supply in this kind of devices. Power optimization can be done either by changes in hardware components or changes in software programs developed for various applications. Changes in hardware, is vendor dependent and only software level changes can be done after the manufacturing of the device. This paper represents software level methods for optimizing the power in ireless embedded devices. Previously loop optimization methods where used and tested to reduce the power consumption by some degree, similarly loop unrolling and loop alignment were also used in the application to improve the performance in terms of power. In this paper we are combining some software level methods like nested switches, no of parameters , no of local variables , size of constructor, data type etc. with the loop unrolling. Our coding methodology will improve the program for consumption of the power while executing various instructions under the embedded systems.

  embedded systems, power optimization, software level methods, loop optimization, coding mechanism.


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