Three-rules set of one dimensional cellular automata with two states and three neighbors improves description ability

Abstract

Abstract:In the present paper, we investigate the description ability of digital sound data by the rule set of one dimensional cellular automata with two state and three neighbors referred to as 1-2-3 CA hereafter. It has been shown that the two-rules set of (#90, #180) has the highest description ability of all the possible two-rules sets. For several sound data, however, the data amount of the resultant codes becomes larger than original data, originating into the limitations of the two-rules set of (#90, #180). In order to overcome the limitations, we try to improve the description ability of digital sound data by adding another rule to (#90, #180). Therefore, we evaluate the description ability for the all the possible sets, where we add another rule to (#90, #180). From computer experiments, for the three-rules set of (#45, #90, #180), the averaged length of the rule sequences of the resultant codes becomes shorter, and a ratio of the reduced search number takes larger for all the data. Thus, we succeed to improve the description ability.

Dynamical features of rule sequences of one dimensional cellular automata with two states and three neighbors which realize the description of digital sound data

“…Our approach has been inspired by "rule dynamics" proposed by Aizawa and Nagai [3][4][5][6][7], which is related to several challenging works. Some pioneer workers have been investigated that chaotic dynamics can be applied to realize complex information processing and complex control via certain simple and deterministic rules.…”

Analysis of cellular automata governed by simple time-variant rules

“…Referring to the rules, the spatiotemporal patterns have been analyzed. Real/potential engineering applications are many, including sound data description, image processing, feature extraction, and block cipher [4][5][6][7][8]. Analysis of the CAs is important from both viewpoints of nonlinear dynamics and engineering applications.…”

“…Using the two feature quantities, we investigate TCAs of 16 cells. This size of TCA corresponds to a mapping on a set of 16-bit binary vectors and is applicable to sound data description [4,5]. Based on the sound data, we have prepared target spatiotemporal patterns.…”