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A Nascent Approach for Noise Reduction via EMD Thresholding

  • Rashi KohliEmail author
  • Shubhi Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

This paper presents and highlights the analysis of data using a novel approach and method called empirical mode decomposition for noise reduction in nonstationary and nonlinear signals. Here, noise reduction is done via thresholding process using this fully data-driven technique. To begin with the process of EMD, the first step is to break down the incoming signal (generally consists of so many frequency components) into a number of monotone signals called intrinsic mode functions (IMFs) and then thresholding is used to reduce the noise from decomposed IMFs. The research paper objective is to suppress the noise signals using the appropriate threshold level in the process of creating and proposing the reduction approach in empirical mode decomposition. Summation of filtered IMFs gives the original signal. All the simulations are done by the MATLAB to verify the expected results.

Keywords

EMD IMFs Soft thresholding Hard thresholding AWGN 

References

  1. 1.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and Hilbert spectrum for non-linear and nonstationary time series analysis. Proc. R. Soc. London A 454, 903–995 (1998) http://rspa.royalsocietypublishing.org/content/454/1971/903.full.pdf+html
  2. 2.
    Flandrin, P., Goncalves, P., Rilling, G.: Detrending and denoising with empirical mode decomposition. In: Proceedings of the 12th European Signal Processing Conference (EUSIPCO’04), pp. 1581–1584, Vienna, Austria (September 2004)Google Scholar
  3. 3.
    She, L., Xu, Z., Zhang, S., Song, Y.: De-noisng of ECG based on EMD improved-thresholding and mathematical morphology operation, vol. 2, pp. 838–842 (2010)  https://doi.org/10.1109/bmei.2010.5639920
  4. 4.
    Kim, D., Oh, H.-S.: EMD: a package for empirical mode decomposition and hilbert spectrum. Contributed Res. Art. R. J. 1(1), (2009) ISSN2073-4859 [URL: http://journal.r-project.org/2009-1/RJournal_2009-1_Kim+Oh.pdf]
  5. 5.
    Donohue, D.L.: De-noising by soft-thresholding. IEEE Trans. Inform. Theory, 41(3), 613–627 (1995)Google Scholar
  6. 6.
    Kopsinis, Y., McLauglin, S.: Improved EMD using doubly-iterative sifting and high order spline interpolation. J. Adv. Sig. Process. (JASP), vol. 2008, Article ID 128293, 8 pages, 2008.  https://doi.org/10.1155/2008/128293
  7. 7.
    Boudraaand, A.O., Cexus, J.C.: Denoising via empirical mode decomposition. In: Proceedings of the IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP ’06), p. 4, Marrakech, Morocco (March 2006)Google Scholar
  8. 8.
    Srivastava, D., Kohli, R., Gupta, S.: Implementation and statistical comparison of different edge detection techniques. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds.) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol. 553. Springer, Singapore (2017)Google Scholar
  9. 9.
    Arora, G., Bibhu, V., Kohli, R., Pavani, P.: Multimodal biometrics for improvised security. In: 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), pp. 1–5 (2016)Google Scholar
  10. 10.
    Mohguen, W., Bekka, R.E.: Empirical mode decomposition based denoising by customized thresholding. World Acad. Sci. Eng. Technol. Int. Electron. Commun. Eng. 11(5) (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Amity UniversityGreater NoidaIndia

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