scholarly journals The Critical Study of Mutual Coherence Properties on Compressive Sensing Framework for Sparse Reconstruction Performance: Compression vs Measurement System

2019 ◽  
Vol 1196 ◽  
pp. 012074 ◽  
Author(s):  
Nur Afny C. Andryani ◽  
Kadek Dwi Pradnyana ◽  
Dadang Gunawan
Keyword(s):  
Compressive Sensing ◽  
Measurement System ◽  
Sparse Reconstruction ◽  
Critical Study ◽  
Mutual Coherence ◽  
Reconstruction Performance

Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System

2020 ◽  
Author(s):  
Jie Tian ◽  
Xiaopu Zhang ◽  
Yong Chen ◽  
Peter Russhard ◽  
Hua Ouyang
Keyword(s):  
Compressive Sensing ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Uniform Sampling ◽  
Blade Vibrations ◽  
Timing Data ◽  
Blade Tip ◽  
Simultaneous Identification ◽  
Multi Mode

Abstract Based on the blade vibration theory of turbomachinery and the basic principle of blade timing systems, a sparse reconstruction model is derived for the tip timing signal under an arbitrary sensor circumferential placement distribution. The proposed approach uses the sparsity of the tip timing signal in the frequency domain. The application of compressive sensing in reconstructing the blade tip timing signal and monitoring multi-mode blade vibrations is explored. To improve the reconstruction effect, a number of numerical experiments are conducted to examine the effects of various factors on synchronous and non-synchronous signals. This enables the specific steps involved in the compressive sensing reconstruction of tip timing signals to be determined. The proposed method is then applied to the tip timing data of a 27-blade rotor. The results show that the method accurately identifies the multi-mode blade vibrations at different rotation speeds. The proposed method has the advantages of low dependence on prior information, insensitivity to environmental noise, and simultaneous identification of synchronous and non-synchronous signals. The experimental results validate the effectiveness of the proposed approach in engineering applications.

Compressive sensing of phased array ultrasonic signal in defect detection: Simulation study and experimental verification

2017 ◽  
Vol 17 (3) ◽  
pp. 434-449 ◽  
Author(s):  
Zhiliang Bai ◽  
Shili Chen ◽  
Qiyang Xiao ◽  
Lecheng Jia ◽  
Yanbo Zhao ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Defect Detection ◽  
Phased Array ◽  
Electrical Discharge Machining ◽  
Optimal Algorithm ◽  
Greedy Algorithms ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Reconstruction Performance ◽  
Ultrasonic Phased Array

Ultrasonic phased array techniques are widely used for defect detection in structural health monitoring field. The increase in the element number, however, leads to larger amounts of data acquired and processed. Recently developed compressive sensing states that sparse signals may be accurately recovered from far fewer measurements, suggesting the possibility of breaking through the sampling limit of the Nyquist theorem. In light of this significant advantage, the novel use of the compressive sensing methodology for ultrasonic phased array in defect detection is proposed in this work. Based on CIVA software, we first present a simulated study on the effectiveness of the compressive sensing applied in ultrasonic phased array in defect detection through the average mean percent residual difference at varying compression rates. The results particularly show that the compressive sensing yields a breakthrough of the sampling limitation. We then experimentally demonstrate comparative analyses on the signals extracted from three types of artificial flaws (through-hole, flat-bottom hole, and electrical discharge machining notches) on two different specimens (made of aluminum and 20# steel). To find the optimal algorithm combination, the best sparse representation basis is chosen among fast Fourier transform, discrete cosine transform, and 34 wavelet kernels; the reconstruction performance is compared between five greedy algorithms; and the recovery accuracy is further improved via four sensing matrices selection. We also evaluate the influence of the sampling rate, and our results are comparable with the gold standard of signal compression, namely, the discrete wavelet transform.

scholarly journals CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING MATRIX

2021 ◽  
Vol 1 (1) ◽  
pp. 134-145
Author(s):  
Hadeel S. Abed ◽  
Hikmat N. Abdullah
Keyword(s):  
Compressive Sensing ◽  
Probability Of Detection ◽  
Main Step ◽  
Mutual Coherence ◽  
Sensing Matrix ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Chebyshev Map ◽  
Recovery Error ◽  
Three Stages

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing  is the main step of CR.  Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening  in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconstruct a sparse signal. İn this paper a new chaotic matrix is proposed based on Chebyshev map and modified gram Schmidt (MGS). The CS based proposed matrix is applied for sensing  real TV signal as a PU. The proposed system is tested under two types of recovery algorithms. The performance of CS based proposed matrix is measured using recovery error (Re), mean square error (MSE), and probability of detection (Pd) and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the proposed system has low Re and high Pd under low SNR values and has low MSE with high compression.

scholarly journals Performance Evaluation of Group Sparse Reconstruction and Total Variation Minimization for Target Imaging in Stratified Subsurface Media

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1245 ◽  
Author(s):  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Wenji Zhang
Keyword(s):  
Total Variation ◽  
Sparse Reconstruction ◽  
L1 Norm ◽  
Total Variation Minimization ◽  
Electromagnetic Data ◽  
Norm Minimization ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Target Imaging ◽  
Quantitative Performance

Sparse reconstruction methods have been successfully applied for efficient radar imaging of targets embedded in stratified dielectric subsurface media. Recently, a total variation minimization (TVM) based approach was shown to provide superior image reconstruction performance over standard L1-norm minimization-based method, especially in case of non-point-like targets. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for embedded target extent. In this paper, we provide qualitative and quantitative performance evaluations of TVM and GSR schemes for efficient and reliable target imaging in stratified subsurface media. Using numerical electromagnetic data of targets buried in the ground, we demonstrate that GSR and TVM provide comparable reconstruction performance qualitatively, with GSR exhibiting a slight superiority over TVM quantitatively, albeit at the expense of less flexibility in regularization parameters.

Fast Sparse Reconstruction of Sound Field Via Bayesian Compressive Sensing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ding-Yu Hu ◽  
Xin-Yue Liu ◽  
Yue Xiao ◽  
Yu Fang
Keyword(s):  
Compressive Sensing ◽  
Sound Field ◽  
Sparse Reconstruction ◽  
Limited Attention ◽  
Reconstruction Method ◽  
Sound Sources ◽  
Computation Efficiency ◽  
Reconstruction Methods ◽  
Spatially Extended ◽  
Value Decomposition

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed one has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, as it is developed in the framework of multitask compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

scholarly journals Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

2018 ◽  
Vol 159 ◽  
pp. 01061
Author(s):  
Endra Oey ◽  
Dadang Gunawan ◽  
Dodi Sudiana
Keyword(s):  
Compressive Sensing ◽  
Projection Matrix ◽  
Signal Quality ◽  
Tight Frames ◽  
Analysis Model ◽  
Mutual Coherence ◽  
Model Based ◽  
Equiangular Tight Frames ◽  
Simulation Results ◽  
Matrix Design

Co-sparse analysis model based-compressive sensing (CAMBCS) has gained attention in recent years as alternative to conventional sparse synthesis model based (SSMB)-CS. The equivalent operator as counterpart of the equivalent dictionary in the SSMB-CS is introduced in the CAMB-CS as the product of projection matrix and transpose of the analysis dictionary. This paper proposes an algorithm for designing suitable projection matrix for CAMB-CS by minimizing the mutual coherence of the equivalent operator based on equiangular tight frames design. The simulation results show that the CAMB-CS with the proposed projection matrix outperforms the SSMB-CS in terms of the signal quality reconstruction.

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