Results 71 to 80 of about 277,175 (273)
Shuffled multi-channel sparse signal recovery
Signal Processing, 2023 Submitted to ...
Koka, Taulant +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
Support Recovery of Sparse Signals [PDF]
, 2010 We consider the problem of exact support recovery of sparse signals via noisy measurements. The main focus is the sufficient and necessary conditions on the number of measurements for support recovery to be reliable.
Jin, Yuzhe +2 more
core
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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Advanced Engineering Materials, EarlyView.This study reports lightweight polyetherimide triply periodic minimal surfaces lattices coated with carbon nanotube‐reinforced epoxy that combine mechanical robustness with self‐sensing. The conformal coating enhances stiffness, strength and energy absorption while enabling reliable strain monitoring.
A. Triay +3 more
wiley +1 more source
Verifiable conditions of $\ell_1$-recovery of sparse signals with sign restrictions
, 2010 We propose necessary and sufficient conditions for a sensing matrix to be "s-semigood" -- to allow for exact $\ell_1$-recovery of sparse signals with at most $s$ nonzero entries under sign restrictions on part of the entries.
A. Bruckstein +12 more
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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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Gradient-Based Methods for Sparse Recovery [PDF]
SIAM Journal on Imaging Sciences, 2011 16 pages, submitted to SIAM Journal on Imaging ...
Hager, William +2 more
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Distributed Multi-View Sparse Vector Recovery
IEEE Transactions on Signal Processing, 2023 In this paper, we consider a multi-view compressed sensing problem, where each sensor can only obtain a partial view of the global sparse vector. Here the partial view means that some arbitrary and unknown indices of the global vector are unobservable to that sensor and do not contribute to the measurement outputs.
Zhuojun Tian +2 more
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Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source