A Construction Algorithm of Measurement Matrix with Low Coherence for Compressed Sensing

2016 ◽  
pp. 160-166
Author(s):  
Shengqin Bian ◽  
Zhengguang Xu ◽  
Shuang Zhang
Keyword(s):  
Compressed Sensing ◽  
Measurement Matrix ◽  
Low Coherence ◽  
Construction Algorithm

Research on Compressed Sensing Matrix Based on Data Compressing of Insulator Leakage Current

2013 ◽  
Vol 475-476 ◽  
pp. 451-454
Author(s):  
Xue Ming Zhai ◽  
Xiao Bo You ◽  
Ruo Chen Li ◽  
Yu Jia Zhai ◽  
De Wen Wang
Keyword(s):  
Compressed Sensing ◽  
Data Compression ◽  
Leakage Current ◽  
Sparse Matrix ◽  
Circulant Matrix ◽  
Measurement Matrix ◽  
Sensing Matrix ◽  
Significant Step ◽  
On Line

Insulator fault may lead to the accident of power network,thus the on-line monitoring of insulator is very significant. Low rates wireless network is used for data transmission of leakage current. Making data compression and reconstruction of leakage current with the compressed sensing theory can achieve pretty good results. Determination of measurement matrix is the significant step for realizing the compressed sensing theory. This paper compares multiple measurement matrix of their effect via experiments, putting forward to make data compression and reconstruction of leakage current using Toeplitz matrix, circulant matrix and sparse matrix as measurement matrix, of which the reconstitution effect is almost the same as classical measurement matrix and depletes computational complexity and workload.

An improved measurement matrix of compressed sensing for synthetic aperture ultrasound imaging

2022 ◽  
Vol 188 ◽  
pp. 108592
Author(s):  
Ping Wang ◽  
Xuegong Liu ◽  
Xitao Li ◽  
Dawod Al-Qadasi ◽  
Linhong Wang
Keyword(s):  
Compressed Sensing ◽  
Ultrasound Imaging ◽  
Synthetic Aperture ◽  
Measurement Matrix

scholarly journals Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 436
Author(s):  
Yunfei Cheng ◽  
Ying Hu ◽  
Mengshu Hou ◽  
Tongjie Pan ◽  
Wenwen He ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Prior Information ◽  
Classification Performance ◽  
Experimental Results ◽  
Measurement Matrix ◽  
Ecg Signals ◽  
Proposed Model ◽  
Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

scholarly journals A Method of Constructing Measurement Matrix for Compressed Sensing by Chebyshev Chaotic Sequence

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1085
Author(s):  
Renjie Yi ◽  
Chen Cui ◽  
Yingjie Miao ◽  
Biao Wu
Keyword(s):  
Compressed Sensing ◽  
High Probability ◽  
Hardware Implementation ◽  
Electric Circuit ◽  
Chaotic Sequence ◽  
Measurement Matrix ◽  
Chaotic Sequences ◽  
Reconstruction Performance ◽  
Simulation Results ◽  
Sample Distance

In this paper, the problem of constructing the measurement matrix in compressed sensing is addressed. In compressed sensing, constructing a measurement matrix of good performance and easy hardware implementation is of interest. It has been recently shown that the measurement matrices constructed by Logistic or Tent chaotic sequences satisfy the restricted isometric property (RIP) with a certain probability and are easy to be implemented in the physical electric circuit. However, a large sample distance that means large resources consumption is required to obtain uncorrelated samples from these sequences in the construction. To solve this problem, we propose a method of constructing the measurement matrix by the Chebyshev chaotic sequence. The method effectively reduces the sample distance and the proposed measurement matrix is proved to satisfy the RIP with high probability on the assumption that the sampled elements are statistically independent. Simulation results show that the proposed measurement matrix has comparable reconstruction performance to that of the existing chaotic matrices for compressed sensing.

scholarly journals Image Encryption Scheme with Compressed Sensing Based on New Three-Dimensional Chaotic System

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 819 ◽  
Author(s):  
Yaqin Xie ◽  
Jiayin Yu ◽  
Shiyu Guo ◽  
Qun Ding ◽  
Erfu Wang
Keyword(s):  
Compressed Sensing ◽  
Image Encryption ◽  
Chaotic System ◽  
Three Dimensional ◽  
Encryption Algorithm ◽  
Encryption Scheme ◽  
High Complexity ◽  
Measurement Matrix ◽  
The Core ◽  
New Chaotic System

In this paper, a new three-dimensional chaotic system is proposed for image encryption. The core of the encryption algorithm is the combination of chaotic system and compressed sensing, which can complete image encryption and compression at the same time. The Lyapunov exponent, bifurcation diagram and complexity of the new three-dimensional chaotic system are analyzed. The performance analysis shows that the chaotic system has two positive Lyapunov exponents and high complexity. In the encryption scheme, a new chaotic system is used as the measurement matrix for compressed sensing, and Arnold is used to scrambling the image further. The proposed method has better reconfiguration ability in the compressible range of the algorithm compared with other methods. The experimental results show that the proposed encryption scheme has good encryption effect and image compression capability.

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