Measurement Matrix Construction Based on Differential Evolution Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1007-1010
Author(s):  
Hua Xu
Keyword(s):  
Compressed Sensing ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimization Procedure ◽  
Ldpc Code ◽  
Measurement Matrix ◽  
Reconstruction Performance ◽  
The Matrix ◽  
Novel Method ◽  
Matrix Construction

Measurement matrix construction is important to compressed sensing. A novel method, MMC-DE (Measurement Matrix Construction based on Differential Evolution), is proposed in this paper. The matrix is based on the quasi-cyclic Low-Density Parity-Check (LDPC) code. This proposed method aims at constructing the quasi-cyclic matrix with the best girth during the optimization procedure. It can consequently result in improving the reconstruction performance of the measurement matrix for compressed sensing. Simulation results demonstrate that the proposed measurement matrix is better than the matrix of Tanner code and array code. It is also easy to implement and hardware friendly.

A novel method of global optimisation for wavefront shaping based on the differential evolution algorithm

2021 ◽  
Vol 481 ◽  
pp. 126541
Author(s):  
Yingzi Hua ◽  
Xiubao Sui ◽  
Shenghang Zhou ◽  
Qian Chen ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Global Optimisation ◽  
Novel Method ◽  
Wavefront Shaping ◽  
Evolution Algorithm

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%.

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.

Compressive sensing measurement matrix construction based on improved size compatible array LDPC code

2015 ◽  
Vol 9 (11) ◽  
pp. 993-1001 ◽  
Author(s):  
Haiying Yuan ◽  
Hongying Song ◽  
Xun Sun ◽  
Kun Guo ◽  
Zijian Ju
Keyword(s):  
Compressive Sensing ◽  
Ldpc Code ◽  
Measurement Matrix ◽  
Matrix Construction

A Randomly Permuted Fourier Measurement Matrix for UWB-PPM Signals

2014 ◽  
Vol 556-562 ◽  
pp. 2646-2649 ◽  
Author(s):  
Hai Bo Yin ◽  
Jun An Yang ◽  
Wei Dong Wang
Keyword(s):  
Compressed Sensing ◽  
Structural Features ◽  
Measurement Matrix ◽  
Reconstruction Accuracy ◽  
High Sampling ◽  
Reconstruction Performance ◽  
Simulation Results ◽  
Hardware Costs ◽  
Fourier Matrix

Compressed Sensing is likely to provide an effective way for lowering the extremely high sampling speed of UWB signal while the design of CS measurement matrix is of great significance for reducing the number of observations and hardware costs as long as improving the reconstruction accuracy. In this paper, with the combination of the structural features of the Fourier matrix and the idea of entry permutation of determined matrices, we propose a new measurement matrix of which the Fourier transformed entries are randomly permuted. Simulation results show that the same algorithm has a better reconstruction performance with the proposed measurement matrix rather than Gaussian/ Bernoulli matrix.

scholarly journals Photosynthetic pigment-protein complexes optical response modeling optimized by Differential evolution: algorithm convergence study

2021 ◽  
Vol 2090 (1) ◽  
pp. 012028
Author(s):  
Denis D Chesalin ◽  
Roman Y Pishchalnikov
Keyword(s):  
Differential Evolution ◽  
Higher Plants ◽  
Protein Complexes ◽  
Incident Light ◽  
Photosynthetic Pigment ◽  
Differential Evolution Algorithm ◽  
Optimization Procedure ◽  
Optical Response ◽  
Algorithm Convergence ◽  
Pigment Protein Complexes

Abstract Photosynthetic pigment-protein complexes are the essential parts of thylakoid membranes of higher plants and cyanobacteria. Besides many organic and inorganic molecules they contain pigments like chlorophyll, bacteriochlorophyll, and carotenoids, which absorb the incident light and transform it into the energy of the excited electronic states. The semiclassical theories such as molecular exciton theory and the multimode Brownian oscillator model allows us to simulate the linear and nonlinear optical response of any pigment-protein complex, however, the main disadvantage of those approaches is a significant amount of effective parameters needed to be found in order to reproduce the experimental data. To overcome these difficulties we used the Differential evolution method (DE) that belongs to the family of evolutionary optimization algorithms. Based on our preliminary studies of the linear optical properties of monomeric photosynthetic pigments using DE, we proceed to more complex systems like the reaction center of photosystem II isolated from higher plants (PSIIRC). PSIIRC contains only eight chlorophyll pigments, and therefore it is potentially a very promising subject to test DE as a powerful optimization procedure for simulation of the optical response of a system of interacting pigments. Using the theoretically simulated linear spectra of PSIIRC (absorption, circular dichroism, linear dichroism, and fluorescence), we investigated the dependence of the algorithm convergence on DE settings: strategies, crossover, weighting factor; eventually finding the optimal mode of operation of the optimization procedure.

scholarly journals Image Encryption Scheme with Compressed Sensing Based on a New Six-Dimensional Non-Degenerate Discrete Hyperchaotic System and Plaintext-Related Scrambling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 291
Author(s):  
Chunyang Sun ◽  
Erfu Wang ◽  
Bing Zhao
Keyword(s):  
Compressed Sensing ◽  
Image Compression ◽  
Image Encryption ◽  
Chaotic System ◽  
Chaotic Systems ◽  
Chaotic Map ◽  
Hyperchaotic System ◽  
Encryption Scheme ◽  
Measurement Matrix ◽  
Reconstruction Performance

Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.

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