Parallel implementation of empirical mode decomposition for nearly bandlimited signals via polyphase representation

被引:0
|
作者
Qiuliang Ye
Bingo Wing-Kuen Ling
Daniel P. K. Lun
Weichao Kuang
机构
[1] Guangdong University of Technology,School of Information Engineering
[2] The Hong Kong Polytechnic University,Department of Electronic and Information Engineering
来源
关键词
Empirical mode decomposition; Polyphase representation; Parallel implementation; Bandlimited signals;
D O I
暂无
中图分类号
学科分类号
摘要
Nearly bandlimited signals play an important role in the biomedical signal processing community. The common method to analyze these signals is via the empirical mode decomposition approach which decomposes the non-stationary signals into the sums of the intrinsic mode functions. However, this method is computational demanding. A natural idea to reduce the computational cost is via the block processing. However, the severe boundary effect would happen due to the discontinuities between two consecutive blocks. In order to solve this problem, this paper proposes to realize the parallel implementation via polyphase representation. That is, the empirical mode decomposition is implemented on each polyphase component of the original signal. Then each sub-signals are combined after upsampling. The simulation results show that our proposed method achieves the approximate intrinsic mode functions both qualitatively and quantitatively very close to the true intrinsic mode functions. Besides, compared with the conventional block processing method which significantly suffered from the boundary effect problem, our proposed method does not have this issue.
引用
收藏
页码:225 / 232
页数:7
相关论文
共 50 条
  • [1] Parallel implementation of empirical mode decomposition for nearly bandlimited signals via polyphase representation
    Ye, Qiuliang
    Ling, Bingo Wing-Kuen
    Lun, Daniel P. K.
    Kuang, Weichao
    SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (02) : 225 - 232
  • [2] PARALLEL IMPLEMENTATION OF MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION
    Chang, Li-Wen
    Lo, Men-Tzung
    Anssari, Nasser
    Hsu, Ke-Hsin
    Huang, Norden E.
    Hwu, Wen-mei W.
    2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, : 1621 - 1624
  • [3] Empirical Mode Decomposition for Trivariate Signals
    ur Rehman, Naveed
    Mandic, Danilo P.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (03) : 1059 - 1068
  • [4] Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition
    Y. Mao
    P. Que
    Russian Journal of Nondestructive Testing, 2007, 43 : 196 - 203
  • [5] Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition
    Mao, Y.
    Que, P.
    RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING, 2007, 43 (03) : 196 - 203
  • [6] Empirical mode decomposition of some nonstationary signals
    Hughes, DH
    INDEPENDENT COMPONENT ANALYSES, WAVELETS, AND NEURAL NETWORKS, 2003, 5102 : 290 - 301
  • [7] Parallel Ensemble Empirical Mode Decomposition and Its Application in Feature Extraction of Partial Discharge Signals
    Zhu Y.
    Wang L.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2018, 33 (11): : 2508 - 2519
  • [8] Hardware Design and Implementation for Empirical Mode Decomposition
    Chen, Pei-Yin
    Lai, Yen-Chen
    Zheng, Ju-Yang
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2016, 63 (06) : 3686 - 3694
  • [9] Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition
    Shen, Jian
    Zhang, Yanan
    Liang, Huajian
    Zhao, Zeguang
    Dong, Qunxi
    Qian, Kun
    Zhang, Xiaowei
    Hu, Bin
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 : 356 - 365
  • [10] Parallel Implementation of the Ensemble Empirical Mode Decomposition and Its Application for Earth Science Data Analysis
    Shen, Bo-Wen
    Cheung, Samson
    Wu, Yu-Ling
    Li, Jui-Lin F.
    Kao, David
    COMPUTING IN SCIENCE & ENGINEERING, 2017, 19 (05) : 49 - 57