Random sequence generation by cellular automata
Advances in Applied Mathematics
Neural network learning and expert systems
Neural network learning and expert systems
Evolving Cellular Automata Based Associative Memory for Pattern Recognition
HiPC '01 Proceedings of the 8th International Conference on High Performance Computing
Theory and application of cellular automata for pattern classification
Fundamenta Informaticae - Special issue on cellular automata
Design of Nonlinear CA Based TPG Without Prohibited Pattern Set In Linear Time
Journal of Electronic Testing: Theory and Applications
Cellular Automata
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Classification of CA rules targeting synthesis of reversible cellular automata
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
Directory based cache coherence verification logic in CMPs cache system
Proceedings of the First International Workshop on Many-core Embedded Systems
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The special class of irreversible cellular automaton (CA) with multiple attractors is of immense interest to the CA researchers. Characterization of such a CA is the necessity to devise CA based solutions for diverse applications. This work explores the essential properties of CA attractors towards characterization of the 1-dimensional cellular automata with point states (single length cycle attractors). The concept of Reachability Tree is introduced for such characterization. It enables identification of the pseudo-exhaustive bits (PE bits) of a CA defining its point states. A theoretical framework has been developed to devise schemes for synthesizing a single length cycle multiple attractor CA with the specific set of PE bits. It also results in a linear time solution while synthesizing a CA for the given set of attractors and its PE bits. The experimentation establishes that the proposed CA synthesis scheme is most effective in designing the efficient pattern classifiers for wide range of applications.