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    Wyświetlanie 1-4 z 4
    Tytuł:
    Combining Multiple Sound Sources Localization Hybrid Algorithm and Fuzzy Rule Based Classification for Real-time Speaker Tracking Application
    Autorzy:
    Ibala, C
    Astapov, S
    Bettens, F
    Escobar, F
    Chang, X
    Valderrama, C
    Riid, A
    Powiązania:
    https://bibliotekanauki.pl/articles/398033.pdf
    Data publikacji:
    2013
    Wydawca:
    Politechnika Łódzka. Wydział Mikroelektroniki i Informatyki
    Tematy:
    DSB
    GCC
    lokalizacja
    śledzenie
    MVDR
    logika rozmyta
    klasyfikacja
    rozpoznawanie mowy
    biometryka głosu
    FPGA
    localization
    tracking
    fuzzy logic
    classification
    speaker recognition
    Opis:
    This work present a novel approach to track a specific speaker among multiple using the Minimum Variance Distortionless Response (MVDR) beamforming and fuzzy logic ruled based classification for speaker recognition. The Sound sources localization is performed with an improve delay and sum beamforming (DSB) computation methodology. Our proposed hybrid algorithm computes first the Generalized Cross Correlation (GCC) to create a reduced search spectrum for the DSB algorithm. This methodology reduces by more than 70% the DSB localization computation burden. Moreover for high frequencies Sound sources beamforming, the DSB will be preferred to the MVDR for logic and power consumption reduction.
    Źródło:
    International Journal of Microelectronics and Computer Science; 2013, 4, 1; 12-25
    2080-8755
    2353-9607
    Pojawia się w:
    International Journal of Microelectronics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new approach for the clustering using pairs of prototypes
    Autorzy:
    Jezewski, M.
    Czabanski, R.
    Leski, J.
    Horoba, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/333693.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    fuzzy clustering
    pairs of prototypes
    fuzzy rule-based classification
    grupowanie rozmyte
    pary prototypów
    rozmyta klasyfikacja oparta na regułach
    Opis:
    In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 113-121
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Design of fuzzy rule-based classifiers through granulation and consolidation
    Autorzy:
    Riid, A.
    Preden, J.-S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91638.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    pattern recognition
    fuzzy classification
    complexity reduction
    Opis:
    This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 137-147
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On wavelet based enhancing possibilities of fuzzy classification methods
    Autorzy:
    Lilik, Ferenc
    Solecki, Levente
    Sziová, Brigita
    Kóczy, László T.
    Nagy, Szilvia
    Powiązania:
    https://bibliotekanauki.pl/articles/384751.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    fuzzy classification
    wavelet analysis
    fuzzy rule interpolation
    structural entropy
    Opis:
    If the antecedents of a fuzzy classification method are derived from pictures or measured data, it might have too many dimensions to handle. A classification scheme based on such data has to apply a careful selection or processing of the measured results: either a sampling, re‐ sampling is necessary. or the usage of functions, transfor‐ mations that reduce the long, high dimensional observed data vector or matrix into a single point or to a low num‐ ber of points. Wavelet analysis can be useful in such cases in two ways. As the number of resulting points of the wavelet ana‐ lysis is approximately half at each filters, a consecutive application of wavelet transform can compress the me‐ asurement data, thus reducing the dimensionality of the signal, i.e., the antecedent. An SHDSL telecommunication line evaluation is used to demonstrate this type of appli‐ cability, wavelets help in this case to overcome the pro‐ blem of a one dimensional signal sampling. In the case of using statistical functions, like mean, variance, gradient, edge density, Shannon or Rényi en‐ tropies for the extraction of the information from a pic‐ ture or a measured data set, and they don not produce enough information for performing the classification well enough, one or two consecutive steps of wavelet analy‐ sis and applying the same functions for the thus resulting data can extend the number of antecedents, and can dis‐ till such parameters that were invisible for these functi‐ ons in the original data set. We give two examples, two fuzzy classification schemes to show the improvement caused by wavelet analysis: a measured surface of a com‐ bustion engine cylinder and a colonoscopy picture. In the case of the first example the wear degree is to be deter‐ mine, in the case of the second one, the roundish polyp content of the picture. In the first case the applied statisti‐ cal functions are Rényi entropy differences, the structural entropies, in the second case mean, standard deviation, Canny filtered edge density, gradients and the entropies. In all the examples stabilized KH rule interpolation was used to treat sparse rulebases.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 2; 32-41
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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