A.V. Voloshko, R. Almabrok

Èlektron. model. 2020, 42(5):97-110 


Based on the analysis of the application of the methods of reducing the negative impact of noise in the presence of white noise, white Gaussian noise and other types of noise caused by distortions in the electrical network, a modified, adaptive to harmonic composition and type of distortion of the information form is proposed for the accuracy and speed of information signal processing signal compression / recovery method using orthogonal wavelet transformations. It is shown that the quality of noise component removal and compression of information signals is greatly influenced by the choice of threshold type and wavelet basis.


wavelet analysis, type of threshold value, information signal with noise.


  1. Malla, (2005), Veyvlety v obrabotke signalov [Wavelets in signal processing], Mir, Moscow, Russia.
  2. Guo, Q. and Zhang, C. (2012), “A noise reduction approach based on Stein’s unbiased risk estimate”, Science Asia, Vol. 38, no. 2, pp. 207-
  3. Donoho, D.L. (1995), “De-noising by soft thresholding”, IEEE Transactions on Information Theory, Vol. 41, Iss. 3, pp. 613-
  4. Lu, J., Hong, L., Dong, Y. and Zhang, Y. (2016), “A New Wavelet Threshold function and Denoising Application”, Mathematical Problems in Engineering, Vol. 49, рр. 1-9.
  5. Luo, G. and Chang, D. “Wavelet Denoising”, Advanced in Wavelet Theory and Their Applications in Engineering. Physics and Technology, available at: (accessed September 15, 2020).
  6. Bykova, V. and Cherepashchuk, G.A. (2009), “Noise reduction method for correction of results dynamic measurements using orthogonal wavelets”, Aviatsionno-kosmicheskaya tekhnika i tekhnologiya, no. 5 (62), pp. 80-84.
  7. Katsyv, S.SH. and Mokin, B.I. (2005), Matematychni modeli determinizatsiyi protsesiv v systemakh elektropostachannya [Mathematical models of deterministic processes in electrical systems], UNIVERSUM, Vіnnitsya, Ukraine.
  8. Walczak, and Massart, D.L. (1997), “Noise suppression and signal compression using the wavelet packet transform”, Chemometr. Intell. Lab. Syst, Vol. 36, pp. 81-94.
  9. Lang, M. (1996), “Noise redaction using an undecimated discrete wavelet transform”, IEEE Signal Processing Letters, 3, pp. 10-12.
  10. Donoho, D. and Johnstone, J. (1994), “Ideal spatial adaption wavelet shrinkage”, Biometrika, Vol. 81, pp. 425-455.
  11. Santoso, S., Powers, F.J. and Grady, W.M. (1997), “Power quality disturbance data compression using wavelet transform methods”, IEEE Transaction on Power Delivery, Vol. 12, no. 3, pp. 1250-1257.
  12. Bykova, T.V. (2009), “Synthesis of the correction operator for dynamic measurement results in the basis of orthogonal wavelets”, Aviatsionno-kosmicheskaya tekhnika i techno­logiya, no. 2 (59), pp. 103-108.
  13. Voloshko, A.V. (2007), “Wavelet Analysis for Compression and Recovery of an Electrical Load Schedule”, Energetics, economics, technology, ecology, no. 2, pp. 60-65.
  14. Lee, J.H. and Yang, S.J. (1986), “Perfect Reconstruction Filter Banks Having Linear Phase”, IEEE Transactions on Acoustics, Sound and Signal Processing, Vol. 34, no. 6, pp. 1401-1408.

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