Zwektoryzowane algorytmy obliczania Transformaty S dla środowiska wieloprocesorowego

• Autorzy: Tariov A. , Gliszczyński M.

Warianty tytułu

EN

Vectorized S Transform algorithms for multi-processor platform

• Języki publikacji: PL

Abstrakty

PL

W pracy został zaprezentowany wektoryzowany algorytm obliczania transformaty S w dwóch wariantach - w postaci sekwencyjno-równoległej pozwalającej na oszczędzenie zasobów sprzętowych oraz w postaci równoległej pozwalającej wykorzystać, nowoczesne wielordzeniowe platformy obliczeniowe. W drugim przypadku możliwa jest znaczna redukcja czasu trwania algorytmu. Obie metody mogą znaleźć zastosowanie praktyczne zależnie od oczekiwanej dokładności (rozdzielczości) i szybkości działania jak też możliwości platformy obliczeniowej.

EN

In the paper the algorithm for calculating N by N-point S Transform is presented. In a sequential, recursive option hardware resources saving is available, while on the other hand, a parallel version of the algorithm allows increasing the accuracy and reducing the time when using multi-core platforms. Two of these approaches can be implemented in practical use depending on the expected accuracy, speed and power of the hardware platform. At the beginning of the paper uses of S Transform with other similar solutions are described. Advantages and disadvantages of S Transform, which are good properties of the time-frequency analysis of non-stationary signals thanks to a movable, different sized Gaussian window, but at the same time a long computation time of the standard, sequential method, are considered. Next, the theoretical, continuous form of the transform and the discrete form with the sequential algorithm are presented. Later The main part of the work deals with synthesis of the sequential and parallel version of the algorithm in the matrix-vector form. The data flow in the algorithms in space and time is shown in Figs. 1 and 2 (for sequential and parallel approach). Finally, the computation times of two versions are compared. The advantage of the two presented approaches is simple and understandable tensor product representation which makes the implementation easy. The sequential algorithm can be used for slower platforms, where the real time analysis is not necessary, while the parallel version offers quick computation on multi-core processors.

Słowa kluczowe

PL

transformata S; algorytmy równoległe

EN

parallel algorithms; S-transform; Kronecker product

• Wydawca: Wydawnictwo PAK
• Czasopismo: Pomiary Automatyka Kontrola
• Rocznik: 2011
• Tom: R. 57, nr 11
• Strony: 1401--1403
• Opis fizyczny: Bibliogr. 17 poz., rys., wzory

Twórcy

autor

Tariov A.

autor

Gliszczyński M.

• Zachodniopomorski Uniwersytet Technologiczny, Wydział Informatyki, ul. Żołnierska 49, Szczecin,

Bibliografia

• [1] Stockwell R. G., Mansinha L. and Lowe R. P.: Localization of complex spectrum: the S transform, IEEE Transactions on Signal Processing, 144, 998-1001 (April 1996).
• [2] Dash P., Panigrahi B. and Panda G.: Power quality analysis using S-transform, IEEE Transactions on Power Dilivery, 18 (April 2003).
• [3] Eramian M., Schincariol R. A., Stockwell R. G., Lowe R. P. and Mansinha L.: Review of applications of 1D and 2D S-transforms, Proc. SPIE 3078, 558–568 (April 1997).
• [4] Chu P. C.: The S-transform for obtaining localized spectra, Mar. Technol. Soc. J. 29 (1996) 28-38.
• [5] Mohammad Javad Dehghani: Comparison of S-transform and Wavelet Transform in Power Quality Analysis, World Academy of Science, Engineering and Technology, 50, 2009, pp. 395-398.
• [6] Stockwell R. G.: Why use the S-Transform?, AMS Pseudo-differential operators: partial differential equations and time–frequency analysis, Vol. 52, (2007), pp: 279-309.
• [7] Robert Pinnegar C. and Lalu Mansinha: The S–transform with windows of arbitrary and varying shape, Geophysics, Vol. 68, No. 1, (2003), pp: 381-385.
• [8] Mansinha L., Stockwell R. G. and Lowe R. P.: Pattern analysis with two-dimensional spectral localisation: Applications of two-dimensional S transforms, Physica A, 239, (1997), pp. 286-295.
• [9] Pinnegar C. R.: Time-frequency and time-time filtering with the S-transform and TT-transform, Digital Signal Processing 15, (2005), pp: 604-620.
• [10] Schimmel M. and Gallart J.: The Inverse S-Transform in filters with Time-Frequency Localization, IEEE Trans. Signal Processing 55 (11), (2005), pp: 4417-4422.
• [11] Lee I. W. C. and Dash P. K. (2003): S-transform-based intelligent system for classification of power quality disturbance signals, IEEE Transactions on Industrial Electronics, Vol. 50 No. 4, pp. 800-5.
• [12] Pinnegar C. R. and Mansinha L.: Time-local spectral analysis for non-stationary time series: the S-transform for noisy signals, Fluctuation and Noise Letters, 2003, Vol. 3 No. 3, pp. L357-L364.
• [13] Carine Simon, Sergi Ventosa, Martin Schimmel, Alexander Heldring, Juan Jo Danobeitia, Josep Gallart and Antoni Manuel: The S-Transform and Its Inverses: Side Effects of Discretizing and Filtering. IEEE Transactions on Signal Processing, 2007, vol. 55, no. 10, pp. 4928-4937.
• [14] Leonowicz Z., Lobos T., Wozniak K.: Zastosowanie transformaty S do analizy sygnałów niestacjonarnych w elektrotechnice. XXVIII Międzynarodowa Konferencja z Podstaw Elektrotechniki i Teorii Obwodów: IC-SPETO 2005, Gliwice-Ustroń, 11-14.05.2005. T. 2. pp. 379-382.
• [15] Dagman E. E., Kukharev G. A.: Szybkie dyskretne transformaty ortogonalne, Wydawnictwo Nauka, 1983.
• [16] Horn Roger A., Johnson Charles R.: Topics in Matrix Analysis, Cambridge University Press, 1991.
• [17] Ţariov A.: Modele algorytmiczne i struktury wysokowydajnych procesorów cyfrowej obróbki sygnałów, Szczecin, Informa, 2001.