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<p>A computationally efficient Markov state space model is developed for determining the aliasing probability of a linear feedback shift register when used for test data reduction. The model studied can be used to test data errors which have a constant of probability of error, correlated or repeated use errors, or time varying error probability. Based on a number of simulations of various error models and feedback polynomials it appears that a primitive polynomial, with about half its terms nonzero, has the best dynamic performance in most cases.</p>
feedback signature compression; Markov state space model; aliasing probability; linear feedback shift register; data reduction; error models; feedback polynomials; logic testing; Markov processes; shift registers.

J. Robinson, "Aliasing Probabilities for Feedback Signature Compression of Test Data," in IEEE Transactions on Computers, vol. 40, no. , pp. 867-873, 1991.
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