An improved bidimensional empirical mode decomposition: A mean approach for fast decomposition

Chin Yu Chen, Shu Mei Guo, Wei Sheng Chang, Jason Sheng Hong Tsai, Kuo Sheng Cheng

研究成果: Article

12 引文 斯高帕斯(Scopus)

摘要

In this paper, a mean approach is proposed to accelerate bidimensional empirical mode decomposition (BEMD). In the envelope generation process, the proposed method uses a modified mean filter to approximate the interpolated envelope of the conventional BEMD, and utilizes a convolution algorithm based on singular value decomposition (SVD) to further reduce the computation time. Order statistics filter width determination, originally used in fast and adaptive bidimensional empirical mode decomposition (FABEMD), is applied to adaptively formulate an envelope. Considering the computation efficiency, the proposed method improves the algorithm for calculating distances among extrema by using Delaunay triangulation (DT). The experimental results show that the mean approach can produce intrinsic mode functions faster than FABEMD, while retaining acceptable quality.

原文English
頁(從 - 到)344-358
頁數15
期刊Signal Processing
98
DOIs
出版狀態Published - 2014 五月 1

    指紋

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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