In this paper, we examine the joint signal sampling and detection problem when noisy samples of a signal are collected in the sequential fashion. In such a scheme, at the each observation time point we wish to make a decision that the observed data record represents a signal of the assumed target form. Moreover, we are able simultaneously to recover a signal when it departs from the target clas...

Sampling rate conversion (SRC) is one of important issues in modern sampling theory. It can be realized by up-sampling, filtering, and down-sampling operations, which need large complexity. Although some efficient algorithms have been presented to do the sampling rate conversion, they all need to compute the N-point original signal to obtain the up-sampling or the down-sampling signal in the tim...

This paper describes the use of stochastic simulation techniques to reconstruct biomedical signals not directly measurable. In particular, a deconvolution problem with an uncertain clearance parameter is considered. The problem is addressed using a Monte Carlo Markov Chain method, called the Gibbs Sampling, in which the joint posterior probability distribution of the stochastic parameters is es...