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Segmentation, feature extraction and classification of signal components belong to very common problems in various engineering, economical and biomedical applications. The paper is devoted to the use of discrete wavelet transform (DWT) both for signal preprocessing and signal segments feature extraction as an alternative to the commonly used discrete(More)
A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and(More)
Age-related changes in the EEG energy and spectral composition were examined in 17,722 healthy subjects (truck drivers), 20 to 70 years old. Linear correlations between age vs global EEG energy and spectral powers (SPs) of EEG frequency ranges were estimated by linear regression analysis. Significant dependences of the global EEG energy and SPs of all EEG(More)
Multi-spectral fusion focused on the task of image enhancement by processing raw data collected at various electromagnetic bands via passive sensors. Passive spectral sensors collect information about the scene based on the inherently reflected or emitted energy of the scene and is represented by spectral distributions and intensities. These systems can be(More)
At present vision prosthesis proposes transmission of only a limited amount of visual information. Cutaneous receptor field may serve as a information channel. It has similar information-processing ability as retina. Lower information capacity of the skin may be compensated by wavelet transform image compression. Advances in microtechnology have facilitated(More)
Evolutionary meta-heuristics are designed for optimization using population with selection and mutation operators. Novelty of our approach is based on competition of various operators from mutation portfolio. Resulting meta-heuristic is successfully tested on the feature selection task: searching for a sparse sub-model having the best possible value by(More)
A three-layer perceptron ANN is designed to avoid difficulties during learning process. The resulting V-shaped Artificial Neural Network has universal approximation property and its learning is based on the minimization of least squares sum. The main advantage of this approach is in the absence of flat domains with a small norm of objective function(More)
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