Skip to main content
SpringerLink
Log in

An improved empirical mode decomposition by using dyadic masking signals

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The masking signal technique provides a way to improve the empirical mode decomposition (EMD) method, which can decompose signals adaptively. In this paper, dyadic masking signals whose frequency is only determined by the first masking signal are first designed. Then, the algorithm of EMD improved by dyadic masking signals termed as EMD–DMS is proposed. The experimental results obtained by using white Gaussian noise and real-world signals are presented to demonstrate the effectiveness of the EMD–DMS algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454, 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  2. Yan, R.Q., Gao, R.X.: Hilbert–Huang transform-based vibration signal analysis for machine health monitoring. IEEE Trans. Instrum. Meas. 55(6), 2320–2329 (2006)

    Article  Google Scholar 

  3. Boudraa, A.O., Cexus, J.C.: EMD-based signal filtering. IEEE Trans. Instrum. Meas. 56(6), 2196–2202 (2007)

    Article  Google Scholar 

  4. Senroy, N., Suryanarayanan, S.: Two techniques to enhance empirical mode decomposition for power quality applications. In: IEEE Power Engineering Society General Meeting, pp. 1–6 (2007)

  5. Senroy, N., Suryanarayanan, S., Ribeiro, P.F.: An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans. Power Syst. 22(4), 1843–1850 (2007)

    Article  Google Scholar 

  6. Laila, D.S., Messina, A.R., Pal, B.C.: A refined Hilbert–Huang transform with applications to interarea oscillation monitoring. IEEE Trans. Power Syst. 24(2), 610–620 (2009)

    Article  Google Scholar 

  7. Deering, R., Kaiser, J.F.: The use of a masking signal to improve empirical mode decomposition. In: Proceedings IEEE International Conference Acoustics, Speech, and Signal Processing, pp. 485–488 (2005)

  8. Flandrin, P., Rilling, G., Goncalvés, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)

    Article  Google Scholar 

  9. Wu, Z.H., Huang, N.E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A 460, 1597–1611 (2004)

    Article  MATH  Google Scholar 

  10. Yang, Y.L., Deng, J.H.: Empirical mode decomposition as a tree-structured filter: a tutorial view. In: Proceedings of the International Conference on Biomedical Engineering and Computer Science, pp. 314–317 (2010)

  11. Yang, Y.L., Deng, J.H., Wu, C.P.: Analysis of mode mixing phenomenon in the empirical mode decomposition method. In: Proceedings of the 2nd International Symposium on Information Science and Engineering, pp. 553–556 (2009)

  12. Yang, Y.L., Deng, J.H., Tang, W.C., et al.: Nonuniform extrema resampling and empirical mode decomposition. Chin. J. Electron. 18(4), 759–762 (2009)

    Google Scholar 

  13. Yang, Y.L., Miao, C.Y., Deng, J.H.: An analytical expression of empirical mode decomposition based on B-spline interpolation. In: Circuits, Systems & Signal Processing (2013). doi:10.1007/s00034-013-9592-5

  14. Jerri, A.J.: The Shannon sampling theorem—its various extensions and applications: a tutorial review. Proc. IEEE 65(11), 1565–1596 (1977)

    Google Scholar 

  15. Unser, M.: Sampling—50 years after Shannon. Proc. IEEE 88(4), 569–587 (2000)

    Article  Google Scholar 

  16. Yang, Y.L., Miao, C.Y., Deng, J.H.: Research on the mechanism of masking signals technique to improve empirical mode decomposition. J. Inf. Comput. Sci. 9(12), 3509–3516 (2012)

    Google Scholar 

Download references

Acknowledgments

This research is partially supported by the key technologies R & D program of Tianjin, China, No. 13ZCZDGX01000, Natural Science Foundation of Tianjin, China, No. 12JCZDJC27800, and Natural Science Foundation for Youth of Tianjin, China, No. 13JCQNJC00900. The authors would like to thank Prof. Ran Tao for helpful discussions. The authors also acknowledge the anonymous reviewers for their helpful comments on the manuscript.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tianjin Polytechnic University, No. 399 Binshuixi Road, Xiqing District, 300387,  Tianjin, China

    Yanli Yang

  2. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China

    Yanli Yang & Jiahao Deng

  3. Dalian ShengLiLai Monitoring Technology Co., Ltd., Dalian, China

    Dali Kang

Authors
  1. Yanli Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiahao Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dali Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanli Yang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, Y., Deng, J. & Kang, D. An improved empirical mode decomposition by using dyadic masking signals. SIViP 9, 1259–1263 (2015). https://doi.org/10.1007/s11760-013-0566-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0566-7

Keywords

Search

Navigation