Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT

Digital Signal Processing: A Review Journal

Volumn 24, Issue , 2014, Pages 170-186

  Shen, Wen Chung ^a   Chen, Yu Hao ^a  

^a Andy)   (Taiwan)

Author keywords

EMD; HHT; Mode mixing; SAEMD

Indexed keywords

COMPUTATIONAL COMPLEXITY; COST EFFECTIVENESS; DATA HANDLING; FREQUENCY ESTIMATION; MATHEMATICAL TRANSFORMATIONS; MIXING; PROBLEM SOLVING; REAL TIME SYSTEMS;

CORRELATION COEFFICIENT; EMPIRICAL MODE DECOMPOSITION; INTRINSIC MODE FUNCTIONS; LOW COMPUTATIONAL COMPLEXITY; MODE MIXING; NONLINEAR AND NON-STATIONARY SIGNALS; REAL-TIME DATA PROCESSING; SAEMD;

SIGNAL PROCESSING;

EID: 84883682792     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2013.08.004     Document Type: Article

References (20)

1. [1] Huang, N.E., Shen, Z., Long, S.R., Wu, M.L., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 454:1971 (1998), 903–995.
2. [2] Wu, Z., Huang, N.E., Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal. 1:1 (2009), 1–41.
3. [3] Delechelle, E., Lemoine, J., Oumar, N., Empirical mode decomposition: An analytical approach for sifting process. IEEE Trans. Signal Process. Lett. 12:11 (2005), 764–767.
4. [4] Zarraga, F. Lezama, Rios, A.L., Esquivel, P., Messina, A.R., A Hilbert–Huang based approach for on-line extraction of modal behavior from PMU data. North American Power Symposium (NAPS), 2009, 1–6.
5. [5] Deering, R., Kaiser, J.F., The use of a masking signal to improve empirical mode decomposition., 18–23 March IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSPʼ05), vol. 4, 2005, 485–488.
6. [6] 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 (2007), 1843–1850.
7. [7] Liao, A.H., Shen, C.C., Li, P.C., Potential contrast improvement in ultrasound pulse inversion imaging using EMD and EEMD. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57:2 (2010), 317–326.
8. [8] Hilbert, D., Grundzüge einer allgemeinen Theorie der linearen Integralgleichungen. 1953, Chelsea Pub. Co.
9. [9] Gabor, D., Theory of communication. IEE Proc. 93 (1946), 429–457.
10. [10] Bedrosian, E., A product theorem for Hilbert transform. IEEE Proc. 51 (1986), 868–869.
11. [11] Boashash, B., Estimating and interpreting the instantaneous frequency of a signal. IEEE Proc. Fund. 80 (1992), 520–538.
12. [12] Titchmarsh, E.C., Introduction to the Theory of Fourier Integrals. 1948, Oxford University Press/Clarendon Press.
13. [13] Copson, E.T., Asymptotic Expansions. 1967, Cambridge University Press.
14. [14] Rilling, G., Flandrin, P., One or two frequencies? The empirical mode decomposition answers. IEEE Trans. Signal Process. 56 (2008), 85–95.
15. [15] Wu, Z., Huang, N.E., A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A 460 (2004), 1597–1611.
16. [16] Wu, F., Qu, L., An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery. J. Sound Vib. 314:3 (2008), 586–602.
17. [17] Rilling, G., Flandrin, P., Goncalves, P., On empirical mode decomposition and its algorithms. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 2003, 1–5.
18. [18] Dätig, M., Schlurmann, T., Performance and limitations of the Hilbert–Huang transformation (HHT) with an application to irregular water waves. Ocean Eng. 31 (2004), 1783–1834.
19. [19] Xu, S., Common Algorithm of Computers. 1995, Qinghua University, Beiking, 191–196.
20. [20] Wang, L., Vai, M.I., Mak, P.U., Ieong, C.I., Hardware-accelerated implementation of EMD. International Conference on Biomedical Engineering and Informatics (BMEI), vol. 2, 2010, 912–915.