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Experimental Comparison of Sparse Signal Recovery Algorithms
Hoskovec, J. ; Tichavský, Petr
This report presents an experimental comparison of some of the newest and/or most common algorithms that are used for solving the sparse recovery problem: matching pursuit, orthogonal matching pursuit (OMP), A*OMP, basis pursuit, re-weighted least square (also known as FOCUSS), re-weighted L1 optimization (RL1). The comparison is done on synthetic (random) data set (dictionary) of the size 50x250 and 500x2500.
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Automatic Removal of Sparse Artifacts in Electroencephalogram
Zima, Miroslav ; Tichavský, Petr ; Krajča, V.
This report presents an algorithm for removing artifacts from EEG signal, which is based on the method of independent component analysis utilizing the signal nonstationarity or sparsity of the artifacts. The algorithm is computationally very fast, enables online processing of long data records with excellent separation accuracy. The algorithm also incorporates using wavelet denoising of the artifact components, recently proposed by Castellanos and Makarov, which reduces distortion of the cleaned data.
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Artifact removal from EEG recordings III
Zima, Miroslav ; Tichavský, Petr ; Krajča, V.
Electroencephalogram (EEG) recordings are often corrupted by presence of unwanted artifact signals. This work is focused on removal of artifact that have a relatively short duration and a large amplitude - such as eye blinks, and patient movement artifacts. It presents a method of removal of these artifacts using methods of independent component analysis in short windows. The method is tested on neonatal (8 channel) EEG recordings. The recordings may have an arbitrary length.
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O odhadu vzájemné informace
Marek, Tomáš ; Tichavský, Petr
The mutual information is useful measure of a random vector component dependence. It is important in many technical applications. The estimation methods are often based on the well known relation between the mutual information and the appropriate entropies. In 1999 Darbellay and Vajda proposed a direct estimation methods. In this paper we compare some available estimation methods using different 2-D random distributions.
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