Hardware Implementation of Iterative Method With Adaptive Thresholding for Random Sampling Recovery of Sparse Signals

2018 ◽  
Vol 26 (5) ◽  
pp. 867-877 ◽  
Author(s):  
Mohammad Fardad ◽  
Sayed Masoud Sayedi ◽  
Ehsan Yazdian
Keyword(s):  
Iterative Method ◽  
Random Sampling ◽  
Hardware Implementation ◽  
Adaptive Thresholding ◽  
Sparse Signals

Adaptive thresholding algorithm and its hardware implementation

1994 ◽  
Vol 15 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Jeng-Daw Yang ◽  
Yung-Sheng Chen ◽  
Wen-Hsing Hsu
Keyword(s):  
Hardware Implementation ◽  
Adaptive Thresholding ◽  
Thresholding Algorithm

scholarly journals Microwave Medical Imaging Based on Sparsity and an Iterative Method With Adaptive Thresholding

2015 ◽  
Vol 34 (2) ◽  
pp. 357-365 ◽  
Author(s):  
Masoumeh Azghani ◽  
Panagiotis Kosmas ◽  
Farokh Marvasti
Keyword(s):  
Medical Imaging ◽  
Iterative Method ◽  
Adaptive Thresholding

A Solution Method for Truncated Normalized Max Product Fuzzy Set of Equations

2003 ◽  
Vol 11 (03) ◽  
pp. 361-372
Author(s):  
Roelof K. Brouwer
Keyword(s):  
Iterative Method ◽  
Finite Number ◽  
Fuzzy Set ◽  
State Vector ◽  
Hardware Implementation ◽  
Solution Method ◽  
Recurrent Network ◽  
The State ◽  
Linear Threshold ◽  
Fully Connected

An iterative method of solving a set of equations based on the truncated normalized max product is described. The operation may serve as the transformation for the set of fully connected units in a fully recurrent network that might otherwise consist of linear threshold units. Because of truncation and normalization the network acting under this transformation has a finite number of states and components of the state vector are bounded. Component values however are not restricted to binary values as would be the case if the network consisted of linear threshold units but can now take on the values in the set {0, 0.1,..0.9, 1}. This means that each unit although still having discrete output can provide finer granularity compared to the case where a linear threshold unit is used. Truncation is natural in hardware implementation where only a finite number of places behind the decimal are retained.

Hardware-efficient random sampling of fourier-sparse signals

2012 ◽  
Author(s):  
Patrick Maechler ◽  
Norbert Felber ◽  
Hubert Kaeslin ◽  
Andreas Burg
Keyword(s):  
Random Sampling ◽  
Sparse Signals

scholarly journals Noises in randomly sampled sparse signals

2014 ◽  
Vol 27 (3) ◽  
pp. 359-373 ◽  
Author(s):  
Ljubisa Stankovic
Keyword(s):  
Compressive Sensing ◽  
Random Sampling ◽  
Domain Analysis ◽  
External Noise ◽  
Sampling Theorem ◽  
Sparse Signals ◽  
Gradient Based

Sparse signals can be recovered from a reduced set of randomly positioned samples by using compressive sensing algorithms. Two main reconstruction directions are in the sparse transformation domain analysis of signals and the gradient based algorithms. In the transformation domain analysis, that will be considered here, the estimation of nonzero signal coefficients is based on the signal transform calculated using available samples only. The missing samples manifest themselves as a noise. This kind of noise is analyzed in the case of random sampling, when the sampling instants do not coincide with the sampling theorem instants. Analysis of the external noise influence to the results, with randomly sampled sparse signals, is done as well. Theory is illustrated and checked on statistical examples.

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