Aliasing probability for multiple input signature analyzers with dependent inputs

The authors consider the aliasing probability in multiple-input data compressors used in self-testing networks. It is shown that a far more general class of linear machines, linear-feedback shift registers can be used for data-compression purposes. The steady-state value of the aliasing probability is independent of the correlation of the data streams at the inputs of the data compressor. The function of these machines is modeled by a Markov process. The aliasing probability is the same as for the well-understood signature analysis registers with a single input. An easy-to-check criterion is given to decide whether a given linear machine falls into this class of multiple-input data compressors. Two special kinds of circuits are analyzed in more detail with respect to their aliasing properties: linear-feedback shift registers with multiple inputs and linear cellular automata. Simulation results show the effect of the next state function on the steady-state value of the aliasing probability and the effect of correlation on the transient