• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.6
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• pp.673-679
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• 2014

The important requirements of designing a pattern classifier are high throughput and low memory requirements, and low cost hardware implementation. A pattern classifier by using Multiple Attractor Cellular Automata(MACA) proposed by Maji et al. reduced the complexity of the classification algorithm from $O(n^3)$ to O(n) by using Dependency Vector(DV) and Dependency String(DS). In this paper, we generate a pattern classifier using LFSR to improve efficiently the space and time complexity and we propose a method for finding DV by using the 0-basic path. Also we investigate DV and the attractor of the generated pattern classifier. We can divide an n-bit DS by m number of $DV_i$ s and generate various pattern classifiers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1577-1584
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• 2015

In this paper, we propose a method for generating pattern classifier based on LFSR. The proposed pattern classifier bosed on LFSR is easy to see non-reachable state, and we can obtain dependency vector by using the 0-basic path. Also, we propose a method for generating pattern classifiers based on LFSR which correspond to given dependency vector.