A hybrid model of 1-D signal adaptive filter based on the complex use of Huang transform and wavelet analysis

A hybrid model of 1-D signal adaptive filter based on the complex use of Huang transform and wavelet analysis

Автор: Sergii Babichev, Oleksandr Mikhalyov

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 2 vol.11, 2019 года.

Бесплатный доступ

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.

Еще

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

Короткий адрес: https://sciup.org/15016567

IDR: 15016567   |   DOI: 10.5815/ijisa.2019.02.01

Список литературы A hybrid model of 1-D signal adaptive filter based on the complex use of Huang transform and wavelet analysis

  • Kalman, R.E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1): pp. 35–45, 1960.
  • Wiener, N. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York: Wiley, 1949.
  • Staub, S., Andrä, H., Kabel, M. Fast FFT based solver for rate-dependent deformations of composites and nonwovens. International Journal of Solids and Structures, 154, pp. 33-42, 2018.
  • Cui, L., Ma, F., Gu, Q., Cai, T. Time-Frequency Analysis of Pressure Pulsation Signal in the Chamber of Self-Resonating Jet Nozzle. International Journal of Pattern Recognition and Artificial Intelligence, 32(11), art.no. 1858006, 2018.
  • Daubechies, I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, vol. 36(5), pp. 961–1005, 1990.
  • Coifman, R., Meyer, Y., and Wickerhauser, M. Wavelet analysis and signal processing. Wavelets and their applications, pp. 153–178, 1992.
  • Bodyanskiy, Y., Perova, I., Vynokurova, O., Izonin, I. Adaptive wavelet diagnostic neuro-fuzzy network for biomedical tasks. 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2018 – Proceedings, pp. 711–715, 2018.
  • Rouhollah Pour, H., Asgari Marnani, J., Tabatabei, A.A. A Novel Method for Crack Detection in Steel Cantilever Beam Using Wavelet Analysis by Combination Mode Shapes. International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.4, pp. 1-12, 2018.
  • Venkata Ramana, M., Sreenivasa Reddy, E., Satayanarayana, C.H. Curvelet Transform for Efficient Static Texture Classification and Image Fusion. International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.5, pp. 64-71, 2018.
  • Cevik, N., Cevik, T. Discrete Wavelet Transform based High Performance Face Recognition Using a Novel Statistical Approach. International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.6, pp. 1-9, 2018.
  • Ameen, M.M., Eleyan, A. Score Fusion of SIFT & SURF Descriptors for Face Recognition Using Wavelet Transforms, International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.10, pp. 22-28, 2017.
  • Torse, D., Desai, V., Khanai, R. Classification of EEG Signals in a Seizure Detection System Using Dual Tree Complex Wavelet Transform and Least Squares Support Vector Machine, International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.1, pp. 56-64, 2018.
  • Hegde, G., Reddy, K.S., Shetty Ramesh, T.K. A new approach for 1-D and 2-D DWT architectures using LUT based lifting and flipping cell. AEU - International Journal of Electronics and Communications, 97, pp. 165-177, 2018.
  • Too, J., Abdullah, A.R., Mohd Saad, N., Mohd Ali, N., Musa, H. A detail study of wavelet families for EMG pattern recognition. International Journal of Electrical and Computer Engineering, 8(6), pp. 4221-4229, 2018.
  • Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, N., Yen, N., Tung, C., Liu, H. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Math. Phys. Eng. Sci., 454 , pp. 903–995, 1998.
  • Huang, N.E. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev. Geophys. No.46, pp. 1–23, 2008.
  • Li, W., Kuang, G., Xiong, B. Decomposition of multicomponent micro-Doppler signals based on HHT-AMD. Applied Sciences (Switzerland), 8(10), art. no. 1801, 2018.
  • Soualhi, A., Medjaher, K., Zerhouni, N. Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1), art. no. 6847199, pp. 52-62, 2015.
  • Susanto, A., Liu, C.-H., Yamada, K., Hwang, Y.-R., Tanaka, R., Sekiya, K. Application of Hilbert–Huang transform for vibration signal analysis in end-milling. Precision Engineering, 53, pp. 263-277, 2018.
  • Susanto, A., Liu, C.-H., Yamada, K., Hwang, Y.-R., Tanaka, R., Sekiya, K. Milling process monitoring based on vibration analysis using Hilbert-Huang transform. International Journal of Automation Technology, 12(5), pp. 688-698, 2018.
  • Trusiak, M., Styk, A., Patorski, K. Hilbert–Huang transform based advanced Bessel fringe generation and demodulation for full-field vibration studies of specular reflection micro-objects. Optics and Lasers in Engineering, 110, pp. 100-112, 2018.
  • Oweis, R.J., Abdulhay, E.W. Seizure classification in EEG signals utilizing Hilbert-Huang transform. BioMedical Engineering Online, 10, art. no. 38, 2011.
  • Huang, N.E., Wu, M.-L., Qu, W., Long, S.R., Shen, S.S.P. Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Applied Stochastic Models in Business and Industry, 19 (3), pp. 245-268, 2003.
  • Yuan, H., Liu, X., Liu, Y., Bian, H., Chen, W., Wang, Y. Analysis of Acoustic Wave Frequency Spectrum Characters of Rock Mass under Blasting Damage Based on the HHT Method. Advances in Civil Engineering, art. no. 9207476, 2018.
  • Hu, Zh., Bodyanskiy, Y., Tyshchenko, O., Boiko, O. A Neuro-Fuzzy Kohonen Network for Data Stream Possibilistic Clustering and Its Online Self-Learning Procedure. Applied Soft Computing, Vol.68, pp.710-718, 2018.
  • Hu, Zh., Bodyanskiy, Y., Tyshchenko, O. A Deep Cascade Neural Network Based on Extended Neo-Fuzzy Neurons and its Adaptive Learning Algorithm. Proc. of 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), May 29 – June 2, 2017, Kyiv, Ukraine, pp.801-805.
  • Wang, J., Wang, Y., Wang, W., Ren, S. Discrete linear canonical wavelet transform and its applications. Eurasip Journal on Advances in Signal Processing, 2018(1), art. no. 29, 2018.
  • Babichev, S., Lytvynenko, V., Gozhyj, A., Korobchynskyi, M., Voronenko, M. A fuzzy model for gene expression profiles reducing based on the complex use of statistical criteria and shannon entropy. Proceedings of 1st International Conference on Computer Science, Engineering and Education Applications, ICCSEEA2018, Kiev, Ukraine, Vol.754, pp. 545-554, 2019.
  • Babichev, S., Lytvynenko, V., Skvor, J., Korobchynskyi, M., Voronenko, M. Information Technology of Gene Expression Profiles Processing for Purpose of Gene Regulatory Networks Reconstruction. Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, art. no. 8478452, pp. 336-341, 2018.
  • Babichev, S., Korobchynskyi, M., Lahodynskyi, O., Korchomnyi, O., Basanets, V., Borynskyi, V. Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles. EasternEuropean Journal of Enterprise Technologies, No. 1 (4-91), pp. 19-32, 2018.
  • Bowman, D.C., Wilcock, W.S.D. Unusual signals recorded by ocean bottom seismometers in the flooded caldera of Deception Island volcano: Volcanic gases or biological activity. Antarctic Science, 26(3), pp. 267-275, 2014.
  • Babichev S. Technology of wavelet-filtration of the gene expression profiles in order to remove the background noise. Control Systems and Computers, No. 5, pp. 25-42, 2017.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp