The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding
Abstract
One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.
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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. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
A Wavelet Tour of Signal Processing
(1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,227
Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,760
(2009) · Encyclopedia of Neuroscience · doi:10.1007/978-3-540-29678-2_6014 · Cited by 2,850
(1996) · Adapted Wavelet Analysis · doi:10.1201/9781439863619-15 · Cited by 2,703
Empirical Mode Decomposition as a Filter Bank
Flandrin, P., Rilling, G., & Goncalves, P. (2004) · IEEE Signal Processing Letters · doi:10.1109/lsp.2003.821662 · Cited by 2,583
A study of the characteristics of white noise using the empirical mode decomposition method
Wu, Z., & Huang, N. E. (2004) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2003.1221 · Cited by 1,701
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Mallat, S. (2008) · Academic Press eBooks · Cited by 947
One or Two Frequencies? The Empirical Mode Decomposition Answers
Rilling, G., & Flandrin, P. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.906771 · Cited by 611
Pattern Recognition, Third Edition
Theodoridis, S., & Koutroumbas, K. (2006) · Academic Press, Inc. eBooks · Cited by 582
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A review on empirical mode decomposition in fault diagnosis of rotating machinery
Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.09.015 · Cited by 1,886
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & Garcia-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 963
Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains
Kabir, M. A., & Shahnaz, C. (2012) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.11.003 · Cited by 444
EMD interval thresholding denoising based on similarity measure to select relevant modes
Yang, G., Liu, Y., Wang, Y., & Zhu, Z. (2015) · Signal Processing · doi:10.1016/j.sigpro.2014.10.038 · Cited by 149
Mariyappa, N., Sengottuvel, S., Parasakthi, C., Gireesan, K., Janawadkar, M. P., Radhakrishnan, T. S., & Sundar, C. S. (2014) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2014.06.023 · Cited by 38
Metadata
- Authors
- Kopsinis, Y., & McLaughlin, S.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2009-04
- Volume
- 57
- Issue
- 4
- Pages
- 1351-1362
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2109896502
- Cited by
- 676
- References
- 17
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- closed
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