Advertisement

Automation and Remote Control

, Volume 80, Issue 7, pp 1279–1287 | Cite as

A Search Method for Unknown High-Frequency Oscillators in Noisy Signals Based on the Continuous Wavelet Transform

  • I. V. ShcherbanEmail author
  • N. E. KirilenkoEmail author
  • S. O. KrasnikovEmail author
Intellectual Control Systems, Data Analysis

Abstract

We propose a method for finding a priori undefined structures of unknown temporal fluctuations for frequency oscillators of various intensities as part of the output signals of synchronized dynamical systems. Unlike traditional approaches, the developed method is based on the continuous wavelet transform of the observed signal and is efficient in cases when frequency characteristics of the desired pattern are close to the noise characteristics of the output signal.

Keywords

continuous wavelet transform wavelet entropy pattern 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sedov, A.S. and Racheva, S.N., Applying Wavelet Analysis to Study Impulse Activity of Neurons in the Human Brain, Proc. 9th Conf. “Neiroinformatika-2007,” 2007, vol. 2, no. 1, pp. 77–87.Google Scholar
  2. 2.
    Basar, E., Schurmann, M., Demiralp, T., Basar-Eroglu, C., and Ademoglu, A., Event-related Oscillations are ‘Real Brain Responses’—Wavelet-Analysis and New Strategies, Int. J. Psychophysiol., 2001, vol. 39, pp. 91–127.CrossRefGoogle Scholar
  3. 3.
    Misrikhanov, A.M., Wavelet Transform Methods: Application in Electroenergetics, Autom. Remote Control, 2006, vol. 67, no. 5, pp. 682–697.MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Blanco, S., Figliola, A., Quiroga, R.Q., Rosso, O.A., and Serrano, E., Time-Frequency Analysis of Electroencephalogram Series. III. Wavelet Packets and Information Cost Function, Phys. Rev. E, 1998, vol. 57, pp. 932–940.CrossRefGoogle Scholar
  5. 5.
    Rosso, O.A., Blanco, S., Yordanova, J., Kolev, V., Schurmann, M., Figliola, A., and Basar, E., Wavelet Entropy: A New Tool for Analysis of Short Duration Brain Electrical Signals, J. Neurosci. Meth., 2001, vol. 105, pp. 65–75.CrossRefGoogle Scholar
  6. 6.
    Yordanova, J., Kolev, V., Rosso, O.A., Schurmann, M., Sakowitz, O.W., Ozgoren, M., and Basar, E., Wavelet Entropy Analysis of Event-Related Potentials Indicates Modality-Independent Theta Dominance, J. Neurosci. Meth., 2002, vol. 117, pp. 99–109.CrossRefGoogle Scholar
  7. 7.
    Smolentsev, N.K., Osnovy teorii veivletov. Veivlety v MATLAB (Fundamentals of Wavelet Theory. Wavelets in MATLAB), Moscow: DMK Press, 2005.Google Scholar
  8. 8.
    Rangaiyan, R.M., Analiz biomeditsinskikh signalov. Prakticheskii podkhod (Analysis of Biomedical Signals. A Practical Approach), Moscow: Fizmatlit, 2007.Google Scholar
  9. 9.
    Cek, M.E., Ozgoren, M., and Savaci, F.A., Continuous Time Wavelet Entropy of Auditory Evoked Potentials, Computers Biol. Medici., 2010, vol. 40, pp. 90–96.CrossRefGoogle Scholar
  10. 10.
    Astaf’eva, N.M., Wavelet Analysis: Theoretical Foundations and Sample Applications, Uspekhi Fiz. Nauk, 1996, vol. 166, no. 11, pp. 1145–1170.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Southern Federal UniversityRostov-on-DonRussia

Personalised recommendations