Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Radical Data Compression Algorithm Using Factorization
Peter Zirra, Gregory Wajiga
Pages - 221 - 226     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Data Compression, Lossy Compression, Lossless Compression
This work deals with encoding algorithm that conveys a message that generates a “compressed” form with fewer characters only understood through decoding the encoded data which reconstructs the original message. The proposed factorization techniques in conjunction with lossless method were adopted in compression and decompression of data for exploiting the size of memory, thereby decreasing the cost of the communications. The proposed algorithms shade the data from the eyes of the cryptanalysts during the data storage or transmission.
CITED BY (3)  
1 Bai, J., & Pu, T. C. (2014, May). Online Data Compression Technique for Real Time Data of Energy Management System in the Industrial Production. In Applied Mechanics and Materials (Vol. 519, pp. 70-73).
2 Pu, T., & Bai, J. (2014, May). An auto regression compression method for industrial real time data. In Control and Decision Conference (2014 CCDC), The 26th Chinese (pp. 5129-5132). IEEE.
3 Garba, A. M., & Zirra, P. B. Analysing Forward Difference Scheme on Huffman to Encode and Decode Data Losslessly.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Libsearch 
7 Bielefeld Academic Search Engine (BASE) 
8 Scribd 
9 SlideShare 
10 PdfSR 
1 W.S. Steven.The Scientist and Engineer’s Guide To Digital Signal Processing. Califonia: Technical Publishing, 2007.
2 E. B. Guy. “Introduction to Data Compression”. Internet: www. eecs.harvard. edu/~michaelm /CS222/ compression.pdf, 2010 [Jan. 24, 2011].
3 A.G. Fraser. “Data Compression and Automatic Programming”. Internet: www.comjnl.oxfordjournals.org, 2010 [Oct. 16, 2010].
4 N. Ziviani, E. Moura, G. Navarro, and R. Baeza-Yates. “Compression: A key for next generation text retrieval systems”. IEEE Computer Society, 2000, 33(11), pp. 37- 44. 2000
5 A. Kattan. “Universal lossless compression technique with built in encryption”. M. Sc. Thesis, University of Essex, UK., 2006.
6 S. Khalid. Introduction to Data Compression (2nd ed.) New York : Morgan Kaufmann Publishers Inc., 2000, pp 151-218.
7 E.J.D. Garba and S.E Adewumi. “A Cryptosystems Algorithm Using Systems of Nonlinear Equations”. Iranian Journal of Information Science And Technology, 2003, 1(1), pp. 43- 55.
8 M. Milenkovic. Operating System: Concepts and Design, New York: McGrew-Hill, Inc., 1992.
9 V. Singla, R. Singla, and S. Gupta. “Data Compression Modelling: Huffman and Arithmetic” International Journal of The Computer, the Internet and Management, 2008, 16(3), pp 64- 68
10 Cryptography FAQ (03/10: Basic Cryptology), "3.5. What are some properties satisfied by every strong cryptosystem?" [cited 23 August 2006]; Available: http://www.faqs.org/faqs/cryptographyfaq/part03/index.html.
11 A. Hauter, M.V.C., R. Ramanathan. Compression and Encryption. CSI 801 Project Fall 1995. December 7, 1995 [cited 10 March2006];
12 Available: http://www.science.gmu.edu/~mchacko/csi801/proj-ckv.html.
13 How does cryptography work. 12 March 2006 [cited 12 March 2006 ]; Available: http://www.pgpi.org/doc/pgpintro.
Mr. Peter Zirra
- Nigeria
Professor Gregory Wajiga
- Nigeria