scholarly journals Graph signal denoising using t-shrinkage priors

2022 ◽  
Author(s):  
Sayantan Banerjee ◽  
Weining Shen
Keyword(s):  
Signal Denoising ◽  
Shrinkage Priors

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).

scholarly journals A novel underwater acoustic signal denoising algorithm for Gaussian/non-Gaussian impulsive noise

2020 ◽  
pp. 1-1
Author(s):  
Jingjing Wang ◽  
Jiaheng Li ◽  
Shefeng Yan ◽  
Wei Shi ◽  
Xinghai Yang ◽  
...  
Keyword(s):  
Acoustic Signal ◽  
Impulsive Noise ◽  
Signal Denoising ◽  
Underwater Acoustic ◽  
Non Gaussian ◽  
Underwater Acoustic Signal

ECG signal denoising based on similar segments cooperative filtering

2021 ◽  
Vol 68 ◽  
pp. 102751
Author(s):  
Baoying Liu ◽  
Yuanlu Li
Keyword(s):  
Signal Denoising ◽  
Ecg Signal

scholarly journals Bayesian cumulative shrinkage for infinite factorizations

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 745-752 ◽  
Author(s):  
Sirio Legramanti ◽  
Daniele Durante ◽  
David B Dunson
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Monte Carlo Algorithm ◽  
Model Complexity ◽  
Latent Factors ◽  
Shrinkage Process ◽  
Shrinkage Prior ◽  
Performance Gains ◽  
Shrinkage Priors ◽  
Model Dimension

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

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