Diversity combining techniques play an important role in combating the destructive effects of channel fading in wireless communication systems. In this work we present a blind diversity combining technique for Rayleigh flat fading channels based on Hammerstein type filters. We show that the performance of this technique is very close to ideal MRC system which is accepted as an optimum receiver ...

We consider the source extraction problem for stereo instantaneous musical mixtures with more than two sources. We prove that usual separation methods based only on spatial diversity have performance limitations when the sources overlap in the time-frequency plane. We propose a new separation scheme combining spatial diversity and structured source priors. We present possible priors based on no...

There is a problem of applying of combining rules to evidence which were got from different information sources in framework of Dempster-Shafer theory. In this work the conflict measure and index of decreasing of ignorance in frame of Dempster-Shafer theory are introduced for characterization of quality of applied combining rules. Those functionals are analyzed on the bodies of evidences of spe...

Diversity exploits the random nature of radio propagation by finding multiple independent (or at least uncorrelated) signal paths for comunication. Here we examine the microscopic diversity techniques to deal with small-scale fading characterized by deep and rapid amplitude fluctuations which occurs as the mobile moves over distances of just a few wavelengthes. There are four major diversity co...

In this paper, we continue the theoretical and experimental analysis of two widely used combining rules, namely, the simple and weighted average of classifier outputs, that we started in previous works. We analyse and compare the conditions which affect the performance improvement achievable by weighted average over simple average, and over individual classifiers, under the assumption of unbias...

A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown that there is no overall winning combining rule and that bad classifiers as well as bad feature sets may contain val...