Narsimha Raju

From Msplacements

Url: http://students.iiit.ac.in/~narsimha_raju
E-mail: narsimha_raju@research.iiit.ac.in
Contact No: +91 9985739857

Career objective

   To procure a Research and Development position in broad areas of image and video processing. 



Education

  • Master of Science by Research (Computer Science and Engineering) International Institute of Information Technology, Hyderabad, A.P. (CGPA: 7.16/10) Expected. October 2009
  • Bachelor of Technology (Computer Science and Engineering) Vignan Institute of Technology and Science, JNTU, Hyderabad, A.P. (Aggregate: 71%) May 2005.
  • Board Of Intermediate Education, A.P. Sri Chaitanya Junior College, Vijayawada, A.P. (Aggregate: 90%) May 2001.
  • Board Of Secondary Education, A.P. Ravindra Model High School, Sadasivapet, A.P. (Aggregate: 68%) May 1999

Academic & Research Experience

  • Working as a research assistant at Center for Visual Information Technology since August 2006, IIIT Hyderabad.
  • Worked in National Informatics Centre (NIC), Hyderabad as an intern from
  • Worked as a mentor for Distributed Multimedia Retrieval in Networked Systems (IDA Implementation) in Spring 2007.

Primary Research & MS Thesis

  • Fast Encryptions for the Video Data

Publications

  • A Novel Video Encryption Technique Based on Secret Sharing. In proc. of International Conference on Image Processing (ICIP-2008), San Diego, USA.
  • A Real-Time Video Encryption Algorithm Exploiting the Distribution of the DCT Coefficients. In proc. of IEEE Region 10 Conference (TENCON-2008), HCU-Hyderabad, India.
  • Fast and Secure Real-time Video Encryption. In proc. of Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP-2008), IIT-Kharagpur, India.

    Academic Achievements & Professional Memberships
    • Sun Certified Programmer (SCJP) for Java 2 Platform 1.4.
    • Course work

        Computer Vision, Cryptography and Network Security, Data Warehousing and Data Mining, Digital Image Proce- 
      ssing, Pattern Recognition, Topics in Information Security.



      Other Interests
      Swimming, Cricket, Carroms, Chess,Sudoku, Comics.



      Computer Skills

      • Programming Languages Used  : - C, C++, JAVA
      • Operating Systems:  : - UNIX (Linux), .Windows operating platforms
      • Internet Technologies Used  : - HTML, WML
      • Database Technologies Used  :- MySQL
      • Tools/Libraries Used  : - OpenCV , FFMPEG, DALI etc.

      Projects

      • A Novel Scrambling Scheme for Digital Video Encryption
        It is a scrambling scheme employing novel grouping of the DCTs to encrypt the compressed video data. DCT coefficients are divided into 64 groups according to their positions in 8 × 8 size blocks, and scrambled inside each group.
      • The Global K-means Clustering Algorithm
        In this project, we find the number of cluster using incrementally approach of clustering the data points. The basic idea of this algorithm is that an optimal solution for M clusters can be obtained using a series of local searchs.
      • 3D Photo Browser
        In this work, our objective is to browse the collection of photographs in 3D instead of general 2D view. It consists of three modules namely 2D Browser, Multi-View Reconstruction and 3D Browser. We did finding the 3D points from the 2D correspondences.
      • Mining on the Chess Dataset
        In this work, we try to extract useful knowledge for chess players from a huge dataset of previously played game using data mining technique. We used Aprori technique for mining, set of pieces a player need to protect till the end of the play in order to win the game
      • Implementation of Various Cryptographic Encryption Algorithms
        In this work, we implemented some of encryption algorithms like Diffie-Hellman key exchange, Shamir secret sharing scheme, RC5 and DES. Apart from this we prepared lecture notes on Secret Sharing Scheme.
      • Term Paper
        In this work, we analyzed the paper “Dynamic itemset counting for market basket data” and proposed some methods from which itemset calculation needs fewer passes over the data than the classic approach.

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