Results 41 to 50 of about 141,696 (278)

Constraint removal for sparse signal recovery [PDF]

Signal Processing, 2012
This paper presents a new iterative algorithm called constraint removal (CR) for the recovery of a sparse signal x from an incomplete number of linear measurements y such that ym× 1= Am× nxn× 1 and ...
Şahin, Ahmet, Özen, Serdar
openaire   +2 more sources

Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging [PDF]

Radioengineering, 2018
In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline.
P. Huang, X. Li, H. Wang
doaj  

Sparse signal recovery based on adaptive algorithms for debris detector

AIP Advances, 2021
Inductive debris sensors are generally used in online debris detection and perform well in monitoring the wear condition of rotating facilities. The detection accuracy is restricted by the superposition and noise of the impedance signal.
Bingsen Xue, Xingming Zhang, Yunzhe Xu, Yehui Li, Hongpeng Zhang, Changli Yu   +5 more
doaj   +1 more source

Performance Bounds For Co-/Sparse Box Constrained Signal Recovery

Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2019
The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity.
Kuske Jan, Petra Stefania
doaj   +1 more source

Sparse Signal Recovery via Rescaled Matching Pursuit

Axioms
We propose the Rescaled Matching Pursuit (RMP) algorithm to recover sparse signals in high-dimensional Euclidean spaces. The RMP algorithm has less computational complexity than other greedy-type algorithms, such as Orthogonal Matching Pursuit (OMP).
Wan Li, Peixin Ye
doaj   +1 more source

Methods for Distributed Compressed Sensing

Journal of Sensor and Actuator Networks, 2013
Compressed sensing is a thriving research field covering a class of problems where a large sparse signal is reconstructed from a few random measurements.
Dennis Sundman, Saikat Chatterjee, Mikael Skoglund   +2 more
doaj   +1 more source

Trainable ISTA for Sparse Signal Recovery [PDF]

2018 IEEE International Conference on Communications Workshops (ICC Workshops), 2018
In this paper, we propose a novel sparse signal recovery algorithm called Trainable ISTA (TISTA). The proposed algorithm consists of two estimation units such as a linear estimation unit and a minimum mean squared error (MMSE) estimator-based shrinkage unit.
Daisuke Ito, Satoshi Takabe, Tadashi Wadayama   +2 more
openaire   +3 more sources

Glymphatic Dysfunction Reflects Post‐Concussion Symptoms: Changes Within 1 Month and After 3 Months

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mild traumatic brain injury (mTBI) may alter glymphatic function; however, its progression and variability remain obscure. This study examined glymphatic function following mTBI within 1 month and after 3 months post‐injury to determine whether variations in glymphatic function are associated with post‐traumatic symptom severity ...
Eunkyung Kim, Han Gil Seo, Roh‐Eul Yoo, Byung‐Mo Oh   +3 more
wiley   +1 more source

A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

IEEE Access, 2018
In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu   +3 more
doaj   +1 more source

Bayesian Hypothesis Testing for Block Sparse Signal Recovery

, 2015
This letter presents a novel Block Bayesian Hypothesis Testing Algorithm (Block-BHTA) for reconstructing block sparse signals with unknown block structures.
Korki, Mehdi, Zayyani, Hadi, Zhang, Jingxin   +2 more
core   +1 more source