A Denoising Method Based on EEMD and Interval-Thresholding Strategy

2014 ◽  
Vol 902 ◽  
pp. 336-340 ◽  
Author(s):  
Zhi Zhou ◽  
Xing Man Yang ◽  
Gang Chen
Keyword(s):  
Simulation Experiment ◽  
Adaptive Signal Processing ◽  
Basis Functions ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Signal Processing Method ◽  
Adaptive Signal ◽  
Conventional Signal ◽  
Selection Of

As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

scholarly journals A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Can He ◽  
Jianchun Xing ◽  
Juelong Li ◽  
Qiliang Yang ◽  
Ronghao Wang
Keyword(s):  
Simulation Experiment ◽  
Simple Calculation ◽  
Signal Denoising ◽  
Determination Method ◽  
Propagation Characteristics ◽  
Correlation Index ◽  
Denoising Method ◽  
Threshold Determination ◽  
New Correlation ◽  
Simulation Results

Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions.

A New Denoising Method via Empirical Mode Decomposition

2014 ◽  
Vol 651-653 ◽  
pp. 2090-2093 ◽  
Author(s):  
Shou Cheng Zhang ◽  
Li Li Sui
Keyword(s):  
Empirical Mode Decomposition ◽  
Basic Principle ◽  
Random Noise ◽  
Doppler Signal ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Mode Decomposition

In non-parametric signal denoising area, empirical mode decomposition is potentially useful. In this paper, the wavelet thresholding principle is directly used in EMD-based denoising. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded. A novel threshold function is proposed to improve denoising effect by exploiting the special characteristics of the hard and soft thresholding method. The denoising method is validated through experiments on the “Doppler” signal and a real ECG signal from MIT-BIH databases corrupted by additive white Gaussian random noise. The simulations show that the proposed EMD-based method provides very good results for denoising.

Research on Application of Wavelet Denoising Method Based on Signal to Noise Ratio in the Bench Test

2013 ◽  
Vol 457-458 ◽  
pp. 1156-1162 ◽  
Author(s):  
Jian Jun Zhong ◽  
Sheng Nan Fang ◽  
Chang Ying Linghu
Keyword(s):  
Signal To Noise Ratio ◽  
Basis Functions ◽  
Bench Test ◽  
Signal Denoising ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Denoising Method ◽  
Acceleration Signal ◽  
Noise Ratio ◽  
Filtering Effect

During the tests of the vehicle automatic transmission bench, the acceleration signal is needed to be denoised. As a means of denoising, wavelet threshold denoising method has small amount of calculation and better filtering effect. However, adopting different wavelet basis functions as well as different threshold rules might have a direct effect on the signal denoising. In this paper, we firstly construct the simulated noisy signal approximated to the observed signal, and then do the signal denoising experiment of parameter matching. Secondly, seven Symlets wavelet basis functions and four classical wavelet threshold rules are selected and tested one by one. Signal to noise ratio (SNR) and root mean square error (RMSE) of the denoised signal, the evaluation indicators, are calculated and carried out in accordance with the merits of denoising effect. Thus the optimal combination of the fixed threshold rule and sym8 wavelet basis function is obtained. Finally, this combination is used in the bench test to denoise the angular acceleration signal, and good filtering effect is achieved.

SAVMD: An adaptive signal processing method for identifying protein coding regions

2021 ◽  
Vol 70 ◽  
pp. 102998
Author(s):  
Qian Zheng ◽  
Tao Chen ◽  
Wenxiang Zhou ◽  
Sajid A. Marhon ◽  
Lei Xie ◽  
...  
Keyword(s):  
Signal Processing ◽  
Adaptive Signal Processing ◽  
Processing Method ◽  
Protein Coding ◽  
Coding Regions ◽  
Signal Processing Method ◽  
Adaptive Signal

Filters Parametrized by Orthonormal Basis Functions for Active Noise Control

2005 ◽  
Author(s):  
Jie Zeng ◽  
Raymond de Callafon
Keyword(s):  
Orthonormal Basis ◽  
Noise Control ◽  
Active Noise Control ◽  
Adaptive Signal Processing ◽  
Basis Functions ◽  
Signal Process ◽  
Identification Process ◽  
Construction Methods ◽  
Active Noise ◽  
Adaptive Signal

Parametrization of filters on the basis of orthonormal basis functions have been widely used in system identification and adaptive signal processing. The main advantage of using orthonormal basis functions for a filter parametrization lies in the possibility of incorporating prior knowledge of the system dynamics into the identification process and adaptive signal process. As a result, a more accurate and simplified filter with less parameters can be obtained. In this paper, several construction methods of orthonormal basis function are discussed and analyzed. An application of active noise control based on these orthonormal basis constructions is presented.

Adaptive Signal Processing Method for Speech Organ Diagnostics

2016 ◽  
Vol 59 (5) ◽  
pp. 485-490 ◽  
Author(s):  
A. Yu. Tychkov ◽  
A. K. Alimuradov ◽  
P. P. Churakov
Keyword(s):  
Signal Processing ◽  
Adaptive Signal Processing ◽  
Processing Method ◽  
Signal Processing Method ◽  
Adaptive Signal
Sign in / Sign up

Export Citation Format

Share Document