Extending the scope of empirical mode decomposition by smoothing

Eurasip Journal on Advances in Signal Processing

Volumn 2012, Issue 1, 2012, Pages

  Kim, Donghoh ^a   Kim, Kyungmee O ^b   Oh, Hee Seok ^c  

^a Sejong University   (South Korea)

^b Konkuk University   (South Korea)

^c Seoul National University   (South Korea)

Author keywords

Empirical mode decomposition; Intrinsic mode function; Irregularly spaced data; Noisy signals; Sifting; Smoothing

Indexed keywords

EMPIRICAL MODE DECOMPOSITION; INTRINSIC MODE FUNCTIONS; IRREGULARLY SPACED DATA; NOISY SIGNALS; SIFTING; SMOOTHING;

ELECTRICAL ENGINEERING; TECHNOLOGY;

SIGNAL PROCESSING;

EID: 84887053112     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1186/1687-6180-2012-168     Document Type: Article

References (20)

1. Priestley MB: Spectral Analysis and Time Series,. vols. 1 and 2 (Academic Press, New York, 1981)
2. Mallat S: A Wavelet Tour of Signal Processing. (Academic Press, New York, 2009)
3. Daubechies I: Ten Lectures on Wavelets. (SIAM, Philadelphia, 1992)
4. Vidakovic B: Statistical Modeling by Wavelets. (John Wiley & Sons, New York, 1999)
5. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH: The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc. Lond. A 1998, 454:903-995. (Pubitemid 128733183)
6. Huang NE, Shen SSP: Hilbert-Huang Transform and Its Applications. (World Scientic, Singapore, 2005)
7. Boudraa AO, Cexus JC: EMD-based signal filtering. IEEE Trans. Instrum. Meas 2007, 56:2196-2202. (Pubitemid 350268449)
8. Wu Z, Huang NE: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adapt. Data Anal 2009, 1:1-49.
9. Xu Z, Huang B, Zhang F: Improvement of empirical mode decomposition under low sampling rate. Signal Process 2009, 89:2296-2303.
10. Diop EHS, Alexandre R, Boudraa AO: Analysis of intrinsic mode functions: a pde approach. IEEE Signal Process. Lett 2010, 17:398-401.
11. Hastie T, Tibshirani R: Generalized additive models. (Chapman and Hall, London, 1990)
12. Rilling G, Flandrin P: One or two frequencies? the empirical mode decomposition answers. IEEE Trans. Signal Process 2008, 56:85-95.
13. Park M, Kim D, Oh HS: A reinterpretation of EMD by cubic spline interpolation. Adv. Adapt. Data Anal 2011, 3:527-540.
14. Lindberg T: Scale-Space Theory in Computer Vision. (Kluwer, Boston, 1994)
15. Silverman BW: Using Kernel density estimates to investigate multimodality. J. Roy. Stat. Soc. B 1981, 43:97-99.
16. Donoho DL, Johnstone IM: Adapting to unknown smoothing via wavelet shrinkage. J. Am. Stat. Assoc 1995, 90:1200-1224. (Pubitemid 126093135)
17. Donoho DL, Johnstone IM: Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994, 81:425-455.
18. Fan J, Gijbels I: Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation. J. Roy. Stat. Soc. B 1995, 57:371-394.
19. Marron JS, Adak S, Johnstone IM, Neumann MH, Patil P: Exact risk analysis of wavelet regression. J. Comput. Graph. Stat 1998, 7:278-309.
20. Cai TT: Adaptive wavelet estimation: a block thresholding and oracle inequality approach. Ann. Stat 1999, 27:898-924.