Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80310
Title: Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition
Authors: Wang, Chenxing
Kemao, Qian
Da, Feipeng
Keywords: Empirical mode decomposition
Games
Indexes
Signal processing algorithms
Time-frequency analysis
White noise
Technological innovation
Issue Date: 2016
Source: Wang, C., Kemao, Q., & Da, F. (2016). Regenerated Phase-Shifted Sinusoid-Assisted Empirical Mode Decomposition. IEEE Signal Processing Letters, 23(4), 556-560.
Series/Report no.: IEEE Signal Processing Letters
Abstract: The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary large size of the ensemble and the inevitable manual intervention limits the application of EEMD. In this letter, a novel regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) is proposed. Sinusoids with different scales are iteratively generated and added to cope with all possible MMPs in different intrinsic modes (IMs), where each sinusoid is designed adaptively and automatically. Furthermore, the sinusoids are shifted for better retaining the details of each IM and eliminating the added sinusoids. In the comparison experiments, the RPSEMD provides more reasonable results with less computation time.
URI: https://hdl.handle.net/10356/80310
http://hdl.handle.net/10220/40536
ISSN: 1070-9908
DOI: 10.1109/LSP.2016.2537376
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2016.2537376].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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