Results 71 to 80 of about 279,028 (324)
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 +3 more
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Sampling and Super-resolution of Sparse Signals Beyond the Fourier Domain
, 2018 Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super-resolution. In many cases, however, Fourier domain may not be the natural choice.
Bhandari, Ayush, Eldar, Yonina C.
core +1 more source
Sparse Recovery Using Sparse Random Matrices [PDF]
, 2010 Over the recent years, a new linear method for compressing high-dimensional data (e.g., images) has been discovered. For any high-dimensional vector x, its sketch is equal to Ax, where A is an m×n matrix (possibly chosen at random). Although typically the sketch length m is much smaller than the number of dimensions n, the sketch contains enough ...
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Performance Analysis for Sparse Support Recovery [PDF]
IEEE Transactions on Information Theory, 2010 Submitted to IEEE Trans.
Tang, Gongguo, Nehorai, Arye
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Shuffled multi-channel sparse signal recovery
Signal Processing, 2023 Submitted to ...
Koka, Taulant +3 more
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Advanced Engineering Materials, EarlyView.This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo +3 more
wiley +1 more source
Theoretical Linear Convergence of Deep Unfolding Network for Block-Sparse Signal Recovery [PDF]
, 2021 Rong Fu +3 more
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Adaptive algorithm for sparse signal recovery [PDF]
Digital Signal Processing, 2019 Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Most of the existing algorithms to solve the optimization problems involve either simplifying assumptions, relaxations or high computational expenses.
Fekadu L. Bayisa +3 more
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Elinvar Materials: Recent Progress and Challenges
Advanced Engineering Materials, EarlyView.Elinvar materials, exhibiting temperature‐invariant elastic modulus, are critical for precision instruments and emerging technologies. This article reviews recent progress in the field, with a focus on the anomalous thermoelastic behavior observed in key material systems.
Wenjie Li, Yang Ren
wiley +1 more source
Sparse Recovery with Partial Support Knowledge [PDF]
, 2011 The goal of sparse recovery is to recover the (approximately) best k-sparse approximation x of an n-dimensional vector x from linear measurements Ax of x. We consider a variant of the problem which takes into account partial knowledge about the signal. In particular, we focus on the scenario where, after the measurements are taken, we are given a set S
Do Ba, Khanh, Indyk, Piotr
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