Keywords
multi-iterative methods of signals filtering
criteria necessary and sufficient number of iterations нестаціонарні неперіодичні сигнали
багатоітераційні методи фільтрації сигналів
критерії необхідної та достатньої кількості ітерацій
How to Cite
Abstract
An analysis of efficiency of procedure-oriented criteria for determining the required number of filtration iterations of non-stationary non-periodic signals by the multi-iterative method of the moving average with an increasing width of the filtering window on an instance of pulses of voltage on the plasma-erosive load and of current in it had fulfilled. Two main groups of criteria are considered which are based on a comparison of the signal at the current iteration of its filtering either with the signal at the previous iteration or with a reference signal. Also criterion which has properties of criteria of both these groups is considered. The low effectiveness and nonuniversality of the known criteria is shown. New objectively-oriented criteria for the necessary and sufficient number of iterations of filtering non-stationary nonperiodic signals, adaptive to the requirements for further signal processing, are proposed and an analysis of their effectiveness had fulfilled. References 13, figures 2, tables 4.
References
Baskakov S.I. Radio engineering circuits and signals. – Moskva: Vysshaia shkola, 1988. – 448 p. (Rus)
Goronovsky I.S. Radio engineering circuits and signals. – Moskva: Radio i sviaz, 1986. – 512 p. (Rus)
Davydov A.V. Signals and Systems. Lectures and practical work on the PC. Introduction to Signals and Systems Theory. – Available at: http://geoin.org/signals/index.html. (Accessed 06.02.2017). (Rus)
Zagretdinov A.R., Busarov A.V., Busarov V.V. Comparison of methods for stopping sifting in the empirical mode decomposition of signals // Inzhenernyi vestnik Dona. – 2015. – No 3. – Available at: www.ivdon.ru/ru/magazine/archive/n3y2015/3238. (Accessed 06.02.2017). (Rus)
Matveyev Yu.N., Simonchik K.K., Tropchenko A.Yu., Khitrov M.V. Digital processing of signals. – Sankt-Petersburg: S-PbNIU ITMO, 2013. – 166 p. (Rus)
Tsidelko V.D., Yaremchuk N.A. Measurement uncertainty. Data processing and presenting the measurement result. – Kyiv: Polítekhníka, 2002. – 176 p. (Ukr)
Shydlovska N.A., Zakharchenko S.M., Cherkaskyi O.P. Comparison of the smoothing efficiency of signals of voltage on the plasma-erosive load and its current by multi-iterative filtration methods // Tekhnichna Elektrodynamika. – 2017. – No 4. – Pp. 3–13. (Ukr)
Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N.-Ch., Tung C.C, Liu H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis // Proc. R. Soc. London A, Math. Phys. Sci. – 1998. – Vol. 454. – Issue 1971. – Pp. 903–995.
Kabir M.A., Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains // Biomedical Signal Processing and Control. – 2012. – No 7. – Pp. 481–489.
Karthikeyan P., Murugappan M., Yaacob S. ECG signal denoising using wavelet thresholding techniques in human stress assessment // International Journal on Electrical Engineering and Informatics. – 2012. – Vol. 4. – No 2. – Pp. 306–319.
Komaty A., Boudraa A.O., Augier B., Daré-Emzivat D. EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs // IEEE Transactions on Instrumentation and Measurement. – 2014. – Vol. 63. – No 1. – Pp. 27–34.
Sadooghi M.S., Khadem S.E. A new performance evaluation scheme for jet engine vibration signal denoising // Mechanical Systems and Signal Processing. – 2016. – Vol. 76–77. – Pp. 201–212.
Yang G., Liu Y., Wang Y., Zhu Z. EMD interval thresholding denoising based on similarity measure to select relevant modes // Signal Processing. – 2015. – Vol. 109. – Pp. 95–109.
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