Temat: non-stationary signals - Katalog OPAC zbiorów

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Tytuł:: Adaptacyjna analiza czasowo-częstotliwościowa niestacjonarnego sygnału pomiarowego
Adaptive time - frequency analysis of a non-stationary measuring signal
Autorzy:: Pałczyńska, B.
Powiązania:: https://bibliotekanauki.pl/articles/151732.pdf
Data publikacji:: 2012
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: sygnały niestacjonarne
spektrogram adaptacyjny
algorytm pogoni za dopasowaniem
non-stationary signals
adaptive spectrogram
matching pursuit algorithm
Opis:: W artykule zaprezentowano wirtualny analizator czas-częstotliwość zaprojektowany w środowisku programowania LabVIEW, w którym zaimplementowano metodę adaptacyjną analizy czasowo-częstotliwościowej opartą na algorytmie pogoni za dopasowaniem (ang. matching pursuit MP). Przedstawiono wyniki analiz sygnału pomiarowego, reprezentującego poziom natężenia wolnozmiennego pola magnetycznego, zarejestrowanego w otoczeniu okrętowych odbiorników dużej mocy.
In the paper there is presented a virtual time-frequency analyzer designed with use of LabVIEW programming environment, in which there is implemented an adaptive method of time-frequency analysis based on the matching pursuit algorithm (Fig. 1). Matching pursuit (MP) is an iterative algorithm using a redundant dictionary of functions in order to select the functions, which best match the signal components. Thanks to the varying window size and modulation frequency, MP enables an adaptive (i.e. fitting local structures) signal representation. Linear Gaussian chirplets were used as the elementary functions. Measuring signals are the sum of time-limited waveforms occurring at different time instants and having different bandwidths (Fig. 2). The application of the time-frequency adaptive method certainly provides a significantly better joint time-frequency resolution in comparison with other quadratic joint time-frequency distributions. The analysis of the measuring signal of low-varying magnetic field recorded in the surroundings of high power ship receivers is presented (Figs. 3, 4, 5). Estimation of the usefulness of the implemented method for measurements of the non-stationary magnetic field intensity of ship electromagnetic environment is performed. It is shown that the adaptive time-frequency-domain representation of the magnetic field intensity was successfully applied to determine, with some time resolution, the moments when the analyzed signal components of different frequencies occurred.
Źródło:: Pomiary Automatyka Kontrola; 2012, R. 58, nr 11, 11; 934-936
0032-4140
Pojawia się w:: Pomiary Automatyka Kontrola
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution
Autorzy:: Świercz, E.
Powiązania:: https://bibliotekanauki.pl/articles/907771.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sygnał niestacjonarny
klasyfikacja sygnału
rozpoznawanie obrazów
transformator czas-częstotliwość
non-stationary signals
signal classification
pattern recognition
time-frequency transforms
Opis:: A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 135-147
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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