2003/3

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    Exploitation of Higher Order Moments Increase the Tracking Aircraft by the Extended Alpha-Beta Filter
    (Společnost pro radioelektronické inženýrství, 2003-09) Dostal, Zdenek; Mokris, Igor
    The paper analyzes the possibility of exploitation of higher order moments for increasing the precision of tracking of a flying aircraft by the α-β filter. For tracking of a flying aircraft by the α-β filter the 3rd and 4th order moments in 3D space are used.
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    W-CDMA Uplink Capacity and Interference Statistics of a LongGroove-Shaped Road Microcells Using A Hybrid Propagation Model
    (Společnost pro radioelektronické inženýrství, 2003-09) Taha-Ahmed, Bazil; Calvo-Ramon, Miguel; de Haro-Ariet, Leandro
    The uplink capacity and the interference statistics of the sectors of a long groove-shaped road W-CDMA microcell are studied. A model of 9 microcells in a groove-shaped road is used to analyze the uplink. A hybrid model for the propagation is used in the analysis. The capacity and the interference statistics of the cell are studied for different sector ranges, different specific attenuation factors, different antenna side lobe levels and different bend losses.
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    EEG Signal Classification: Introduction to the Problem
    (Společnost pro radioelektronické inženýrství, 2003-09) Stastny, Jakub; Sovka, Pavel; Stancak, Andrej
    The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic shapes of human brain activity (EEG signal). The great variability of EEG potentials requires using of context information and hence the classifier based on Hidden Markov Models (HMM). The suitable parameterization, model structure as well as training and classification process are suggested on the base of spectral analysis results and experience with the speech recognition. The training and the classification are performed with the disjoint sets of EEG realizations. Classification experiments are performed with 10 randomly chosen sets of EEG realizations. The final average score of the distal/proximal movement classification is 80%; the standard deviation of classification results is 9%. The classification of the extension / flexion gives comparable results.
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    Preemphasis Influence on Harmonic Speech Model with Autoregressive Parameterization
    (Společnost pro radioelektronické inženýrství, 2003-09) Pribilova, Anna
    Autoregressive speech parameterization with and without preemphasis is discussed for the source-filter model and the harmonic model. Quality of synthetic speech is compared for the harmonic speech model using autoregressive parameterization without preemphasis, with constant and adaptive preemphasis. Experimental results are evaluated by the RMS log spectral measure between the smoothed spectra of original and synthesized male, female, and childish speech sampled at 8 kHz and 16 kHz. Although the harmonic model is used, the benefit of the adaptive preemphasis could be valid for the source-filter model, as well.
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    Using the Variable-Length Arithmetic for an Accurate Poles-Zeros Analysis
    (Společnost pro radioelektronické inženýrství, 2003-09) Dobes, Josef; Michal, Jan
    In the paper, a reduction algorithm for transforming the general eigenvalue problem to the standard one is presented for both classical full-matrix methods and a sparse-matrix technique appropriate for large-scale circuits. An optimal pivoting strategy for the two methods is proposed to increase the precision of the computations. The accuracy of the algorithms is furthermore increased using longer numerical data. First, a ORQJ.GRXEOH precision sparse algorithm is compared with the GRXEOH precision sparse and full-matrix ones. Finally, the application of a suitable multiple-precision arithmetic library is evaluated.