A Hybrid Model of 1-D Signal Adaptive Filter Based on the Complex Use of Huang Transform and Wavelet Analysis

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Sergii Babichev 1,* Oleksandr Mikhalyov 2

1. Jan Evangelista Purkyně University in Ustí nad Labem, Ustí nad Labem, Czech Republic

2. National Metallurgical Academy of Ukraine, Dnipro, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2019.02.01

Received: 6 Aug. 2018 / Revised: 5 Oct. 2018 / Accepted: 25 Nov. 2018 / Published: 8 Feb. 2019

Index Terms

Denoising, Empirical mode decomposition, Huang transform, Wavelet analysis, Thresholding, Shannon entropy


The paper presents the results of the research concerning the development of the hybrid model of 1-D signal adaptive filter based on the complex use of both the empirical mode decomposition and the wavelet analysis. Implementation of the proposed model involves three stages. Firstly, the initial signal is decomposed to the empirical modes by the Huang transform with allocation the components, which contain the noise. Then the wavelet filtering is performed to remove the noise component. The optimal parameters of the wavelet filter are determined based on the minimal value of ratio of Shannon entropy for the filtered data and the allocated noise component and these parameters are determined depending on type of the studied component of the signal. Finally, the signal is reconstructed with the use of the processed modes. The results of the simulation with the use of the test data have shown higher effectiveness of the proposed method in comparison with standard method of the signal denoising based on wavelet analysis.

Cite This Paper

Sergii Babichev, Oleksandr Mikhalyov, "A Hybrid Model of 1-D Signal Adaptive Filter Based on the Complex Use of Huang Transform and Wavelet Analysis", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.2, pp.1-8, 2019. DOI:10.5815/ijisa.2019.02.01


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