While any digital computer can be designed using the tools of sequential network theory, it becomes practically impossible to handle a state table description and the appropriate synthesis methods for even modestly sized systems. Therefore, the most common approach to logic design considers subunits of the computer as individual sequential networks, i.e., sequential machines. They are designed separately and assembled into the overall system. Proper partitioning and an adequate system description are very essential in such a design effort. Functional microprogramming can greatly facilitate these tasks.

Instead of estimating correlation functions by conventional means, a specific polarity scheme may be used on bounded processes. The method is based on a rather simple relationship between the correlation functions before and after infinite clipping, provided that stochastic reference signals of uniform distribution are added to the process. This correlation technique has been known for some time. Because of the apparent computational advantages, its application to the estimation of correlation functions from discrete or sampled data is being examined. A general derivation of the appropriate moment relationship is given and a complete mean-square error analysis of estimates is provided under the assumption of white-noise-type reference signals. It is shown that correlation function estimates obtained by this polarity method possess a mean-square error that differs from the error of conventional estimates only by a term proportional to1/N, whereNis the sample size. This term may be made arbitrarily small. Thus, only small degradations in the accuracy of estimates have to be expected when using the polarity approach.