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RNS Based on Shannon Fano Coding for Data Encoding and Decoding Using {2n-1, 2n, 2n+1} Moduli Sets

Received: 23 January 2018     Accepted: 1 February 2018     Published: 16 March 2018
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Abstract

The main objective of any communication system is to transmit data with minimum error rate in data communication. This paper presents information encryption and decryption in data communication with Shannon fano compression techniques using Residue Number System (RNS). The current network communication system involves exchange of information with highly secured data and reduction in both the space requirement and speed for data storage and transmission. For this purpose error detection and correction techniques are used, Our proposed scheme uses the Chinese Remainder Theorem (CRT) which are smaller and needs to be performed in parallel, therefore from the first decoding we can easily identify if error is in a channel. The algorithm applies CRT to detect, locate and correct error by eliminating look up table, therefore the scheme provides a memory less based scheme. It uses a pipelining approach to breakdown the problem with a level of complexity O(n) after decoding and performing consistent checks on all the residue, therefore the overall delay will be lesser and efficient.

Published in Communications (Volume 6, Issue 1)
DOI 10.11648/j.com.20180601.15
Page(s) 25-29
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Shannon Fano, Residue Number System, Forward Conversion, Information Encryption and Decryption, Mixed Radix Conversion

References
[1] Diffie, W., & Hellman, M. (1976). New directions in cryptography. IEEE transactions on Information Theory, 22(6), 644-654.
[2] Stallings, W. (2006). Cryptography and network security: principles and practices. Pearson Education India.
[3] Alvarez, G., & Li, S. (2006). Some basic cryptographic requirements for chaos-based cryptosystems. International Journal of Bifurcation and Chaos, 16(08), 2129-215.
[4] Zissis, D., &Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation computer systems, 28(3), 583-592.
[5] Reghbati, H. K. (1981). Special feature an overview of data compression techniques. Computer, 14(4), 71-75.
[6] Lelewer, D. A., & Hirschberg, D. S. (1987). Data compression. ACM Computing Surveys (CSUR), 19(3), 261-296.
[7] Parhami, B. (1999). Computer arithmetic (Vol. 20, No. 00). Oxford university press.
[8] Gbolagade, K. A., Chaves, R., Sousa, L., & Cotofana, S. D. (2010, May). An improved RNS reverse converter for the {2 2n+1−1, 2n, 2n−1} moduli set. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on (pp. 2103-2106). IEEE.
[9] Alhassan, A., Saeed, I., & Agbedemnab, P. A. (2015). The Huffman’s Method of Secured Data Encoding and Error Correction using Residue Number System (RNS). Communication on Applied Electronics (CAE) Journal, Foundation of Computer Science (FCS), New York, USA.
[10] Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3-55.8.
[11] Chanhemo, W., Mgombelo, H. R., Hamad, O. F., &Marwala, T. (2011). Design of Encoding Calculator Software for Huffman and Shannon-Fano Algorithms. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 5(3), 267-273.
[12] Omondi, A. R., & Premkumar, B. (2007). Residue number systems: theory and implementation (Vol. 2). World Scientific. Huffman, D. A., 1952. A method for the Construction of Minimum-redundancy Code. Proceedings of the Institute of Radio Engineers, 40(9): 1098-1101.
Cite This Article
  • APA Style

    Idris Abiodun Aremu, Kazeem Alagbe Gbolagade. (2018). RNS Based on Shannon Fano Coding for Data Encoding and Decoding Using {2n-1, 2n, 2n+1} Moduli Sets. Communications, 6(1), 25-29. https://doi.org/10.11648/j.com.20180601.15

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    ACS Style

    Idris Abiodun Aremu; Kazeem Alagbe Gbolagade. RNS Based on Shannon Fano Coding for Data Encoding and Decoding Using {2n-1, 2n, 2n+1} Moduli Sets. Communications. 2018, 6(1), 25-29. doi: 10.11648/j.com.20180601.15

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    AMA Style

    Idris Abiodun Aremu, Kazeem Alagbe Gbolagade. RNS Based on Shannon Fano Coding for Data Encoding and Decoding Using {2n-1, 2n, 2n+1} Moduli Sets. Communications. 2018;6(1):25-29. doi: 10.11648/j.com.20180601.15

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  • @article{10.11648/j.com.20180601.15,
      author = {Idris Abiodun Aremu and Kazeem Alagbe Gbolagade},
      title = {RNS Based on Shannon Fano Coding for Data Encoding and Decoding Using {2n-1, 2n, 2n+1} Moduli Sets},
      journal = {Communications},
      volume = {6},
      number = {1},
      pages = {25-29},
      doi = {10.11648/j.com.20180601.15},
      url = {https://doi.org/10.11648/j.com.20180601.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20180601.15},
      abstract = {The main objective of any communication system is to transmit data with minimum error rate in data communication. This paper presents information encryption and decryption in data communication with Shannon fano compression techniques using Residue Number System (RNS). The current network communication system involves exchange of information with highly secured data and reduction in both the space requirement and speed for data storage and transmission. For this purpose error detection and correction techniques are used, Our proposed scheme uses the Chinese Remainder Theorem (CRT) which are smaller and needs to be performed in parallel, therefore from the first decoding we can easily identify if error is in a channel. The algorithm applies CRT to detect, locate and correct error by eliminating look up table, therefore the scheme provides a memory less based scheme. It uses a pipelining approach to breakdown the problem with a level of complexity O(n) after decoding and performing consistent checks on all the residue, therefore the overall delay will be lesser and efficient.},
     year = {2018}
    }
    

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    AB  - The main objective of any communication system is to transmit data with minimum error rate in data communication. This paper presents information encryption and decryption in data communication with Shannon fano compression techniques using Residue Number System (RNS). The current network communication system involves exchange of information with highly secured data and reduction in both the space requirement and speed for data storage and transmission. For this purpose error detection and correction techniques are used, Our proposed scheme uses the Chinese Remainder Theorem (CRT) which are smaller and needs to be performed in parallel, therefore from the first decoding we can easily identify if error is in a channel. The algorithm applies CRT to detect, locate and correct error by eliminating look up table, therefore the scheme provides a memory less based scheme. It uses a pipelining approach to breakdown the problem with a level of complexity O(n) after decoding and performing consistent checks on all the residue, therefore the overall delay will be lesser and efficient.
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Author Information
  • Computer Science Department, Lagos State Polytechnics, Lagos, Nigeria

  • Department of Computer Science, Kwara State University, Kwara, Nigeria

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