scholarly journals Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 19880-19911 ◽  
Author(s):  
Liantao Wu ◽  
Kai Yu ◽  
Dongyu Cao ◽  
Yuhen Hu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Signal Transmission ◽  
Sparse Signal

scholarly journals Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 517 ◽  
Author(s):  
Yunfei Ma ◽  
Xisheng Jia ◽  
Qiwei Hu ◽  
Daoming Xu ◽  
Chiming Guo ◽  
...  
Keyword(s):  
Fault Detection ◽  
Compressive Sensing ◽  
Health Management ◽  
Signal Transmission ◽  
Vibration Signal ◽  
Experimental Tests ◽  
Reconstructed Signal ◽  
Signal Simulation ◽  
Value Decomposition ◽  
Laplace Priors

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests.

scholarly journals A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5327-5342 ◽  
Author(s):  
Sanjeev Sharma ◽  
Anubha Gupta ◽  
Vimal Bhatia
Keyword(s):  
Compressive Sensing ◽  
Sparse Signal ◽  
Measurement Matrix ◽  
Uwb Communication

Heterogeneous Bayesian compressive sensing for sparse signal recovery

2014 ◽  
Vol 8 (9) ◽  
pp. 1009-1017 ◽  
Author(s):  
Kaide Huang ◽  
Yao Guo ◽  
Xuemei Guo ◽  
Guoli Wang
Keyword(s):  
Compressive Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery

Sparse Signal Aloha: A Compressive Sensing-Based Method for Uncoordinated Multiple Access

2017 ◽  
Vol 21 (6) ◽  
pp. 1301-1304 ◽  
Author(s):  
Liantao Wu ◽  
Peng Sun ◽  
Ming Xiao ◽  
Yuhen Hu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Multiple Access ◽  
Sparse Signal

NonConvex Iteratively Reweighted Least Square Optimization in Compressive Sensing

2011 ◽  
Vol 341-342 ◽  
pp. 629-633
Author(s):  
Madhuparna Chakraborty ◽  
Alaka Barik ◽  
Ravinder Nath ◽  
Victor Dutta
Keyword(s):  
Computer Simulation ◽  
Compressive Sensing ◽  
Least Square ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Least Square Approach ◽  
Simulation Results ◽  
Regularization Strategy

In this paper, we study a method for sparse signal recovery with the help of iteratively reweighted least square approach, which in many situations outperforms other reconstruction method mentioned in literature in a way that comparatively fewer measurements are needed for exact recovery. The algorithm given involves solving a sequence of weighted minimization for nonconvex problems where the weights for the next iteration are determined from the value of current solution. We present a number of experiments demonstrating the performance of the algorithm. The performance of the algorithm is studied via computer simulation for different number of measurements, and degree of sparsity. Also the simulation results show that improvement is achieved by incorporating regularization strategy.

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