Neural signal compression using a minimum Euclidean or Manhattan distance cluster-based deterministic compressed sensing matrix

Compressed Sensing Based Neural Signal Processing and Performance Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1595 ◽

2014 ◽

Vol 513-517 ◽

pp. 1595-1599

Author(s):

Yu Xi Zhang ◽

Wen Gui Fan ◽

Jin Ping Sun

Keyword(s):

Signal Processing ◽

Compressed Sensing ◽

Power Consumption ◽

Digital Signal ◽

Data Rate ◽

Acquisition System ◽

Signal Acquisition ◽

Signal Compression ◽

Sensing Matrix ◽

Neural Signal

Measurement of neural signal provides important value for study of brain function and the pathogenesis of neurological. With emerging extensive research of electrical activity, more and more neural signal need to be collected, transmitted and stored, making the compression processing of neural signal become important part of digital signal processing. In recent years, ASIC-based wireless neural signal acquisition system has been developed rapidly, encountered strict restrictions on power consumption which is dominant determined by the data rate and complexity of algorithm. In order to reduce power consumption, lower data rate and algorithm with lower complexity needed to be selected when design a neural acquisition system. This paper focus on neural signal compression method based on compressed sensing and its performance and compare it with conventional compression algorithm. We compare complexity of various algorithms in the view of circuit complement, show that the complexity of neural signal compression can be dramatically reduced by using specially designed compressed sensing matrix, thereby reducing the system power consumption.

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A Method of Reweighting the Sensing Matrix for Compressed Sensing

IEEE Access ◽

10.1109/access.2021.3054807 ◽

2021 ◽

Vol 9 ◽

pp. 21425-21432

Author(s):

Lei Shi ◽

Gangrong Qu ◽

Qian Wang

Keyword(s):

Compressed Sensing ◽

Sensing Matrix

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.451 ◽

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.

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A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation

Journal of Applied Mathematics ◽

10.1155/2013/548979 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Jing Liu ◽

ChongZhao Han ◽

XiangHua Yao ◽

Feng Lian

Keyword(s):

Compressed Sensing ◽

Phased Array ◽

Reduction Method ◽

Doa Estimation ◽

Estimation Methods ◽

Sensing Matrix ◽

Replacement Method ◽

Precise Estimation ◽

Novel Method ◽

Measurement Vector

A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.

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