A New Signal Denoising Algorithm from Wavelet Modulus Maxima

2007 ◽  
Author(s):  
Yi Liu ◽  
Xu Cheng
Keyword(s):  
Signal Denoising ◽  
Modulus Maxima

Research on Ring Fire Diagnosis of DC Motor Based on Discrete Wavelet Lipschitz Index

2015 ◽  
Vol 9 (1) ◽  
pp. 33-37 ◽  
Author(s):  
Tang Zhao-ping ◽  
Yang Qing-ping ◽  
Tang Shuai ◽  
Zhang Wen-sheng ◽  
Sun Jian-ping
Keyword(s):  
Dc Motor ◽  
Wavelet Function ◽  
New Method ◽  
Discrete Wavelet ◽  
Modulus Maxima ◽  
The Way

The favorable localization features of discrete wavelet provide a new method for detecting the mutational points of electric spark signal. In this paper, by means of discrete wavelet function called db5, using the way of 6 scales wavelet, analyzing the gathered electric spark signal and by extracting the modulus maxima of the 6 layers detailed signal coefficient, the signal’s mutational points were located exactly and successfully. In addition, via the modulus maxima to calculate Lipschitz index, measuring signal’s singularity, the signal’s mutational time was confirmed. The result of the simulation shows that this method can detect not only the time and size effectively if the ring fire happens but also the failure of the locomotive traction dc motor, timely and precisely. In this way, the operation safety of the train is ensured.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

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