The object of the research was to investigate the spectral properties of Rayleigh-type surface waves, generated by shot-hole explosions during seismic refraction experiments which were carried out in the area of the Bohemian Massif and West Carpathians. The records of displacement amplitudes were spectrally analyzed and prevailing frequency fp, relative Δfr and absolute widths of the spectra Δfa were chosen as essential parameters. Whilst the prevailing frequencies were recorded within the interval f ÷ 0.80 - 3.70 Hz at the site of the observations, situated on the territory of the Bohemian Massif, the respecti ve frequency range f ÷ 0.80 - 2.6 Hz was found in the West Carpathians. Some functional dependences of the spectral amplitude parameters on epicentral distance were observed and regularities of their decrease were defined. Moreover, the influence of local seismogeological conditions at the shot point as well as at the site of observation occurred., Karel Holub., and Obsahuje bibliografické odkazy
This paper provides a method for indexing and retrieving Arabic texts, based on natural language processing. Our approach exploits the notion of template in word stemming and replaces the words by their stems. This technique has proven to be effective since it has returned significant relevant retrieval results by decreasing silence during the retrieval phase. Series of experiments have been conducted to test the performance of the proposed algorithm ESAIR (Enhanced Stemmer for Arabic Information Retrieval). The results obtained indicate that the algorithm extracts the exact root with an accuracy rate up to 96% and hence, improving information retrieval.
Automatic detection and classification of cardiac arrhythmias with high accuracy and by using as little information as possible is highly useful in Holter monitoring of the high risk patients and in telemedicine applications where the amount of information which must be transmitted is an important issue. To this end, we have used an adaptive-learning-rate neural network for automatic classification of four types of cardiac arrhythmia. In doing so, we have employed a mix of linear, nonlinear, and chaotic features of the R-R interval signal to significantly reduce the required information needed for analysis, and substantially improve the accuracy, as compared to existing systems (both ECG-based and R-R interval-based). For normal sinus rhythm (NSR), premature ventricular contraction (PVC), ventricular fibrillation (VF), and atrial fibrillation (AF), the discrimination accuracies of 99.59%, 99.32%, 99.73%, and 98.69% were obtained, respectively on the MIT-BIH database, which are superior to all existing classifiers.