Sparse-matrix-based compressed sensing for spectrum sensing in Flexible Wireless System

2012 ◽  
Author(s):  
Doohwan Lee ◽  
Yoshiyuki Kabashima ◽  
Koujin Takeda ◽  
Takayuki Yamada ◽  
Kazunori Akabane ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spectrum Sensing ◽  
Sparse Matrix ◽  
Wireless System

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.

scholarly journals Eigenvalue-Based Spectrum Sensing for Multiple Received Signals Under the Non-Reconstruction Framework of Compressed Sensing

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 4891-4901 ◽  
Author(s):  
Yulong Gao ◽  
Yanping Chen ◽  
Yongkui Ma ◽  
Chenguang He ◽  
Linxiao Su
Keyword(s):  
Compressed Sensing ◽  
Spectrum Sensing

A blind spectrum sensing based on low-rank and sparse matrix decomposition

2018 ◽  
Vol 15 (8) ◽  
pp. 118-125
Author(s):  
Junsheng Mu ◽  
Xiaojun Jing ◽  
Hai Huang ◽  
Ning Gao
Keyword(s):  
Spectrum Sensing ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank

scholarly journals Compressed Sensing Reconstruction based on Adaptive Scale Parameter using Texture Feature

2019 ◽  
Vol 8 (12) ◽  
pp. 5455-5461
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Scale Parameter ◽  
Sparse Matrix ◽  
Texture Feature ◽  
Projection Matrix ◽  
Original Image ◽  
Scaling Factors ◽  
Mri Scan ◽  
The Mean

While taking an MRI scan, the patients cannot static for a long time during the motions; the image formation process can create artifacts that may reduce the image quality. The Compressed Sensing (CS) mechanism is employed to reconstruct the original image from the limited data given as the sparse matrix. Hence, CS can be utilized to reduce the acceleration time for an MRI scan considering the patient's health. So the sensing method is implemented by a suitable projection matrix for reconstructing the sparse signals from a few numbers of measurements using Compressed Sensing. The CS guarantees the recovery of the original image with high probability based on random Gaussian projection matrices. However, sparse ternarius projections are more apt for the implementation of hardware. In this article, the proposed deep learning method is employed to obtain a very sparse ternary projection in Compressed Sensing. Compressed Sensing Reconstruction using an adaptive scale parameter based on the texture feature is used to improve the image quality. The two scaling factors αx and αy are assigned to specify the fixed scale for changing the improvement of the image quality. In the parameter using texture feature, the αx and αy are assigned to α as an adaptive scale based on texture feature. In the TACS-SDANN architecture, there are two layers namely the sensing layer which trains the projection matrix and a reconstruction layer which trains for non-linear sparse matrix continuously using Auto-encoder. Experimentally, the scaling factors are calculated on the training data to get the mean PeakSignal-to-Noise Ratio (PSNR) for improving the image quality. Hence a new deep network layer is employed to improve the image quality in this proposed method. Hence the consequence of the proposed method is compared with the SDANN method based on the mean Peak-Signal-to-Noise Ratio (PSNR) to check the image quality. From that comparisons, the TACS-SDANN architecture is proposed to yield a better performance.

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