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Showing papers on "Hidden Markov model published in 1981"


DOI
P.H. Todd1
01 Jun 1981
TL;DR: An algorithmic derivation is described for a minimal-order Markov model for the problem of obtaining sliding-window detection probabilities and the advantage of this model over previous ones is the ease with which it may be run on a computer.
Abstract: An algorithmic derivation is described for a minimal-order Markov model for the problem of obtaining sliding-window detection probabilities. The advantage of this model over previous ones is the ease with which it may be run on a computer. A computer program for determining detection probabilities is described.

5 citations


Journal ArticleDOI
TL;DR: A conditional incremental-runlength coding algorithm based on the Markov source model is investigated and is found to be one of the best codes, especially for the documents of high complexity.
Abstract: The conditional runlength code is known to be optimum for the Markov source model, but it is complicated in implementation and not efficient for practical line-based transmission. To avoid these disadvantages, a conditional incremental-runlength coding algorithm based on the same model is investigated. The entropy of the incremental runs is shown to be less than that of the runs, but additional information is required for the state identification. Yet, for a practical case with two composite states, the required state information is reduced so much that the new coding is shown to be better than the conditional runlength coding in line-based transmission. The coding efficiency is compared with some other well-known codes for the standard CCITT test documents. It is found to be one of the best codes, especially for the documents of high complexity.

4 citations