Signature analysers based on additive cellular automata

A novel scheme for signature analysis based on cellular automata (CA) is proposed. The state transition behavior of such signature analyzers has been modeled by Markov chain. It has been shown that a special class of such CAs achieves a steady-state aliasing probability lower than 1/2/sup n/ (for an n-cell CA) for specific ranges of input probabilities of the incoming error pattern. The dynamic behavior of linear feedback shift registers (LFSRs) has also been compared with CAs with the same characteristic polynomials. This work establishes the fact that CA-based signature analyzers outperform those based on LFSRs as regards both steady-state and dynamic behavior.<>