Sound propagation from the vehicles moving on the city roundabout, with taking into account the wind is investigated. Solution of the problem for one moving sound source is found by means of the integral Fourier transforms extended over space variables and time. Inverse transforms are calculated approximately, using a stationary phase method and iterative technique. The solution for a general problem is obtained as a superposition of many partial solutions. The numerical analysis of noise characteristics is performed for the three-way Korfanty roundabout case in Łódź.
EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.