Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

2015 ◽  
Vol 45 ◽  
pp. 36-54 ◽  
Author(s):  
Weichao Kuang ◽  
Zhijing Yang ◽  
Bingo Wing-Kuen Ling ◽  
Charlotte Yuk-Fan Ho ◽  
Qingyun Dai
Keyword(s):  
Empirical Mode Decomposition ◽  
Discrete Time ◽  
Multiresolution Analysis ◽  
Decomposition Approach ◽  
Mode Decomposition ◽  
Time Signals

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Speech Enhancement: A Multivariate Empirical Mode Decomposition Approach

2013 ◽  
pp. 192-199 ◽  
Author(s):  
Jordi Solé-Casals ◽  
Esteve Gallego-Jutglà ◽  
Pere Martí-Puig ◽  
Carlos M. Travieso ◽  
Jesús B. Alonso
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Enhancement ◽  
Decomposition Approach ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

Empirical Mode Decomposition-Based Hierarchical Multiresolution Analysis for Suppressing Noise

2020 ◽  
Vol 69 (4) ◽  
pp. 1833-1845 ◽  
Author(s):  
Yang Zhou ◽  
Bingo Wing-Kuen Ling ◽  
Xiaozhu Mo ◽  
Yitong Guo ◽  
Zikang Tian
Keyword(s):  
Empirical Mode Decomposition ◽  
Multiresolution Analysis ◽  
Mode Decomposition
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