ECG noise reduction using empirical mode decomposition based on combination of instantaneous half period and soft-thresholding

2014 ◽  
Author(s):  
Shamim Samadi ◽  
Mohammad B. Shamsollahi
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Half Period ◽  
Soft Thresholding ◽  
Mode Decomposition

Noise reduction based on empirical mode decomposition for full waveforms data of GaoFen-7 laser altimetry

2020 ◽  
Vol 49 (11) ◽  
pp. 20200261-20200261
Author(s):  
刘向锋 Xiangfeng Liu ◽  
黄庚华 Genghua Huang ◽  
张志杰 Zhijie Zhang ◽  
王凤香 Fengxiang Wang ◽  
舒嵘 Rong Shu
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Laser Altimetry ◽  
Mode Decomposition ◽  
Full Waveforms

scholarly journals NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 06 (02) ◽  
pp. 1350009 ◽  
Author(s):  
OLEG O. MYAKININ ◽  
DMITRY V. KORNILIN ◽  
IVAN A. BRATCHENKO ◽  
VALERIY P. ZAKHAROV ◽  
ALEXANDER G. KHRAMOV
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Reduction Method ◽  
Tissue Structure ◽  
Diagnostic Process ◽  
Important Process ◽  
Post Processing ◽  
Mode Decomposition ◽  
Mesh Implant

In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

scholarly journals Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71374-71386 ◽  
Author(s):  
Wen-Long Hou ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang ◽  
Meng-Di Deng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Random Noise ◽  
Shearlet Transform ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

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