We review the limited progress which has been made to date on the problem of identiication of independent sourcess from a linear mixture of such sources when the sources are non-stationary. In particular, we investigate the model of Matsuoka et al and show that their claim to have a linear model is in fact not correct. We demonstrate a hierarchical feedforward model which is more linear than th...

This study evaluates the potential of the entropy rate contour to identify stationary and non-stationary segments of speech signals. The segmentation produced by an entropy rate-based method is compared to the manual phoneme segmentations of the TIMIT and the KIEL corpora. Characteristic points, i.e. steepest rises and falls of the entropy rate curve and its maxima and minima are investigated t...

Low diversity in a genetic algorithm (GA) can cause the search to become stagnant upon reaching a local optimum. To some extent, non-stationary tasks avoid this problem, which would be a desirable feature of GA for stationary tasks as well. With this in mind, we show that several methods of introducing artificial non-stationary elements help to promote diversity in a GA while working on an inhe...

A crucial goal in many experimental fields and applications is achieving sparse signal approximations for the unknown signals or functions under investigation. This fact allows to deal with few significant structures for reconstructing signals from noisy measurements or recovering functions from indirect observations. We describe and implement approximation and smoothing procedures for volatili...

Recursive inverse filtering with non-stationary filters is becoming a useful tool in a range of applications, from multi-dimensional inverse problems to wave extrapolation. I formulate causal non-stationary convolution and combination and their adjoints in such a way that it is apparent that the corresponding non-stationary recursive filters are true inverse processes. Stationary recursive inve...

Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assumes that the data are generated by a stationary process. However, there are interesting and important circumstances where that assumption will not hold and potential non-stationarity cannot be ignored. Here we introduce a new cl...

This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationar...