Two Dimensional Cellular Automata for Pseudo Random Number Generation
Vivekananda M1, R.P. Das2, K.V. Ramana Rao3
1M. Vivekanada, Department of ECE, JNTUK University/Pydah College of Engineering and Technology/ Visakhapatnam, India.
2R.P. Das, Department of ECE, JNTUK University/Pydah College of Engineering and Technology / Visakhapatnam, India.
Manuscript received on 15 November 2012 | Revised Manuscript received on 25 November 2012 | Manuscript Published on 30 November 2012 | PP: 94-96 | Volume-1 Issue-6, November 2012 | Retrieval Number: E0340101612/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cellular automata (CA) were initiated in the early 1950s to develop complex structures of capable self-reproduction and self-repair. Since then many researchers have taken attend in the study of CA. Initially, CA concept was first introduced by von Neumann von Neumann (1966) for the proposal of modeling biological self-reproduction. His primary interest was to derive a computationally universal cellular space with self-reproduction configurations. Afterward, a new phase of activities was started by Wolfram Wolfram (1983; 1984), who pioneered the investigation of CA as a mathematical model for self-organizing statistical systems. Wolfram was proved that the randomness of the patterns generated by maximum-length CA is significantly better than other widely used methods, such as linear feedback shift registers. The intensive interest in this field can be attributed to the phenomenal growth of the VLSI technology that permits cost-effective realization of the simple structure of local-neighborhood CA Wolfram (1986). In this paper, an efficient PRNG based on hybrid between one-dimension (1-D) and two-dimension (2-D) CA is proposed. In the phase of the evolution of 2-D CA cells, the proposed CA PRNG is based on von Neumann neighborhood, in that this method refers to the five cells and new control values (rule decision input value & linear control input value) to decide a rule. In the phase of the evolution of 1-D CA cells, on the other hand,< 90, 150 > rule combination is used because this rule combination is better than the others Hortensius et al. (1989). In the meantime, the proposed CA PRNG is compared with previous works Guan et al. (2004); Tomassini et al. (2000) to check the quality of randomness. The proposed CA PRNG could generate a good quality of randomness because the proposed CA PRNG is better than the previous works and passed by the ENT Walker. In this project we have tried to verify the basic characterization of 2-D CA using a Xilinx Spartan 3E fpga.
Keywords: About four key words or phrases in alphabetical order, separated by commas.
Scope of the Article: Artificial Neural Network