Support-Set-Assured Parallel Outsourcing of Sparse Reconstruction Service for Compressive Sensing in Multi-clouds

2015 ◽  
Author(s):  
Yushu Zhang ◽  
Jiantao Zhou ◽  
Leo Yu Zhang ◽  
Fei Chen ◽  
Xinyu Lei
Keyword(s):  
Compressive Sensing ◽  
Sparse Reconstruction ◽  
Support Set

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.

Review of Sparsity Known and Blind Sparsity Greedy Algorithms for Compressed Sensing

2013 ◽  
pp. 125-138
Author(s):  
Chun-Yan Zeng ◽  
Li-Hong Ma ◽  
Ming-Hui Du ◽  
Jing Tian
Keyword(s):  
Compressed Sensing ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Diagrammatic Representation ◽  
Support Set ◽  
Exact Reconstruction ◽  
Signal Characteristics ◽  
Sparsity Level

Sparsity level is crucial to Compressive Sensing (CS) reconstruction, but in practice it is often unknown. Recently, several blind sparsity greedy algorithms have emerged to recover signals by exploiting the underlying signal characteristics. Sparsity Adaptive Matching Pursuit (SAMP) estimates the sparsity level and the true support set stage by stage, while Backtracking-Based Adaptive OMP (BAOMP) selects atoms by thresholds related to the maximal residual projection. This chapter reviews typical sparsity known greedy algorithms including OMP, StOMP, and CoSaMP, as well as those emerging blind sparsity greedy algorithms. Furthermore, the algorithms are analysed in structured diagrammatic representation and compared by exact reconstruction probabilities for Gaussian and binary signals distributed sparsely.

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 Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements

2020 ◽  
Vol 9 (3) ◽  
pp. 2569-2576
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Hyperspectral Image ◽  
Satellite Image ◽  
Research Work ◽  
Reconstruction Algorithm ◽  
Hyperspectral Data ◽  
Sparse Reconstruction ◽  
Total Variation Regularization ◽  
Reconstruction Process

Hyperspectral image compression using compressive sensing technique is very much important in the area of satellite image compression because it can greatly en hance the compression rate. The research work proposes a novel data adaptable sparse reconstruction algorithm for the reconstruction of hyperspectral images from compressive sensing measurements. In the proposed algorithm, compressive sensing technique is used for the compression of HSIs, where Gaussian i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem containing total variation regularization and data adaptable parameter terms. The regularization terms are added to provide hyperspectral data characteristics as priors into the objective function. The addition of priors helps in convergence of the algorithm into the desired solution. The algorithm alternatively reconstructs the end member matrix and abundance matrix instead of reconstructing the entire HSI at once, thereby reducing the computational complexity at the reconstruction process. To nullify the effect of modelling errors, debiasing is performed.

scholarly journals 2-D Joint Sparse Reconstruction and Micro-Motion Parameter Estimation for Ballistic Target Based on Compressive Sensing

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4382
Author(s):  
Jiaqi Wei ◽  
Shuai Shao ◽  
Lei Zhang ◽  
Hongwei Liu
Keyword(s):  
Parameter Estimation ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Motion Parameter ◽  
Model Complexity ◽  
Sparse Reconstruction ◽  
Echo Signal ◽  
Joint Processing ◽  
Motion Parameter Estimation ◽  
Micro Motion

The sparse frequency band (SFB) signal presents a serious challenge to traditional wideband micro-motion curve extraction algorithms. This paper proposes a novel two-dimension (2-D) joint sparse reconstruction and micro-motion parameter estimation (2D-JSR-MPE) algorithm based on compressive sensing (CS) theory. In this technique, the 2D-JSR signal model and the micro-motion parameter dictionary are established based on the segmented SFB echo signal, in which the idea of piecewise effectively reduces the model complexity of ballistic target. With the accommodation of the CS theory, the 2D-JSR-MPE of the echo signal is transformed into solving a sparsity-driven optimization problem. Via an improved orthogonal matching pursuit (OMP) algorithm, the high-resolution range profiles (HRRP) can be reconstructed accurately, and the precise micro-motion curves can be simultaneously extracted on phase accuracy. The employment of 2-D joint processing can effectively avoid the interference of the sparse reconstruction error caused by cascaded operation in the subsequent micro-motion parameter estimation. The proposed algorithm benefits from the anti-jamming characteristic of the SFB signal and 2-D joint processing, thus remarkably enhancing its accuracy, robustness and practicality. Extensive experimental results are provided to verify the effectiveness and robustness of the proposed algorithm.

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