The Hardware Design and Implementation of a Signal Reconstruction Algorithm Based on Compressed Sensing

2012 ◽  
Author(s):  
Guoyan Li ◽  
Junhua Gu ◽  
Qingzeng Song ◽  
Yicai Lu ◽  
Bojun Zhou
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Hardware Design ◽  
Reconstruction Algorithm ◽  
Design And Implementation

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. G83-G92
Author(s):  
Ya Xu ◽  
Fangzhou Nan ◽  
Weiping Cao ◽  
Song Huang ◽  
Tianyao Hao
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Gravity Data ◽  
Signal Reconstruction ◽  
Gaussian Function ◽  
Reconstruction Algorithm ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Grid Data ◽  
2D Data

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

scholarly journals WSNs Compressed Sensing Signal Reconstruction Based on Improved Kernel Fuzzy Clustering and Discrete Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Zhou-zhou Liu ◽  
Shi-ning Li
Keyword(s):  
Compressed Sensing ◽  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Signal Reconstruction ◽  
Differential Evolution Algorithm ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Evolution Algorithm ◽  
Discrete Differential Evolution

To reconstruct compressed sensing (CS) signal fast and accurately, this paper proposes an improved discrete differential evolution (IDDE) algorithm based on fuzzy clustering for CS reconstruction. Aiming to overcome the shortcomings of traditional CS reconstruction algorithm, such as heavy dependence on sparsity and low precision of reconstruction, a discrete differential evolution (DDE) algorithm based on improved kernel fuzzy clustering is designed. In this algorithm, fuzzy clustering algorithm is used to analyze the evolutionary population, which improves the pertinence and scientificity of population learning evolution while realizing effective clustering. The differential evolutionary particle coding method and evolutionary mechanism are redefined. And the improved fuzzy clustering discrete differential evolution algorithm is applied to CS reconstruction algorithm, in which signal with unknown sparsity is considered as particle coding. Then the wireless sensor networks (WSNs) sparse signal is accurately reconstructed through the iterative evolution of population. Finally, simulations are carried out in the WSNs data acquisition environment. Results show that compared with traditional reconstruction algorithms such as StOMP, the reconstruction accuracy of the algorithm proposed in this paper is improved by 36.4-51.9%, and the reconstruction time is reduced by 15.1-31.3%.

Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950031
Author(s):  
Ashok Naganath Shinde ◽  
Sanjay L. Lalbalwar ◽  
Anil B. Nandgaonkar
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Haar Wavelet ◽  
Evaluation Procedure ◽  
Grey Wolf Optimizer ◽  
Reconstruction Algorithms ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

Industrial Ultrasonic Imaging Based on Compressed Sensing

2012 ◽  
Vol 256-259 ◽  
pp. 2328-2332
Author(s):  
Guang Zhi Dai ◽  
Wei Yi Lin ◽  
Guo Qiang Han
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Signal Reconstruction ◽  
Ultrasonic Imaging ◽  
Reconstruction Algorithm ◽  
Physical Structure ◽  
Finite Rate ◽  
Array Technology ◽  
Ultrasonic Phased Array ◽  
Finite Rate Of Innovation

Industrial ultrasonic imaging system based on compressed sensing(IUICS),is still lack of available implementation, due to its difficulty in hardware realization.However,thanks to the recent finite rate of innovation and ultrasonic phased array technology,it is possible to apply Compressive Sensing framework to industrial ultrasonic imaging system.In this paper,we propose an available scheme of industrial ultrasonic imaging,which includes the sampling of signal,reconstruction algorithm and its physical structure, based on Compressed Sensing.

An Optimised Signal Reconstruction Algorithm for Compressed Sensing

2013 ◽  
Vol 333-335 ◽  
pp. 567-571
Author(s):  
Zhao Shan Wang ◽  
Shan Xiang Lv ◽  
Jiu Chao Feng ◽  
Yan Sheng ◽  
Zhong Liang Wu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Small Proportion ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Orthogonal Matching Pursuit ◽  
Signal Recovery ◽  
Successful Recovery ◽  
Stagewise Orthogonal Matching Pursuit ◽  
Convergence Of Algorithm

Signal recovery is a key issue in compressed sensing field. A new greedy reconstruction algorithm termed Optimised Stagewise Orthogonal Matching Pursuit (OSOMP) is proposed, which is an improved version for Stagewise Orthogonal Matching Pursuit (StOMP). In preselection step, OSOMP chooses several coordinates with a calculated threshold to accelerate the convergence of algorithm. In following pruning step, a small proportion of selected coordinates are discarded according to the amplitude of estimated signal, thus most false discovered coordinates can be swept away. Experimental results show that in OSOMP, the scale of estimated support can be controlled very well, and the successful recovery rate is also much higher than that in StOMP.

De-Noising of Underwater Acoustic Weak Signals Based on Compressed Sensing Technique

2014 ◽  
Vol 989-994 ◽  
pp. 3718-3721 ◽  
Author(s):  
Li Li Zhao ◽  
Xiao Wei Dai
Keyword(s):  
Compressed Sensing ◽  
Wavelet Transformation ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
New Method ◽  
Sparse Signal ◽  
Weak Signals ◽  
Underwater Environment ◽  
Good Potential ◽  
Simulation Results

To remove the noise of weak signals detected in the underwater environment, the existing de-noising algorithms cannot perform satisfactorily. This paper aims to developing a new method to solve the problem, where the compressed sensing theory, wavelet transformation based filtering techniques and sparse signal reconstruction algorithm are employed. The simulation results show that the proposed algorithm performs favorably and has good potential to be used in some engineering applications.

Research on Image Compressed Technology Based on Compressed Sensing

2014 ◽  
Vol 687-691 ◽  
pp. 3632-3635
Author(s):  
Tong Huang ◽  
Si Fei Shao
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Processing Theory ◽  
Sample Data ◽  
Perception Theory ◽  
And Storage ◽  
Matrix Design

Compressed sensing theory is a new signal processing theory in recent years, which is the birth of the signal processing field. Compared to the traditional Nyquist sampling rate, with little sample data quantity, compressed sensing theory saves subsequent processing time and storage space, making it a broad application prospect in the signal processing field. This paper first discuss the three key problems of the application of the compressed perception theory: signal sparse representation, machine measurement matrix design and signal reconstruction algorithm, preliminarily study the application of the compression perception theory in image compression technology, and giving the reconstructed image under different compression rate and PSNR. Computer simulation results show the feasibility of theory.

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