Results 81 to 90 of about 141,696 (278)
Blind Recovery of Sparse Signals From Subsampled Convolution [PDF]
IEEE Transactions on Information Theory, 2017 Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors tailored to specific application have been developed in practical applications. In particular, sparsity models have provided promising priors.
Kiryung Lee +3 more
openaire +2 more sources
Imaging of Biphoton States: Fundamentals and Applications
Advanced Functional Materials, EarlyView.Quantum states of two photons exhibit a rich polarization and spatial structure, which provides a fundamental resource of strongly correlated and entangled states. This review analyzes the physics of these intriguing properties and explores the various techniques and technologies available to measure them, including the state of the art of their ...
Alessio D'Errico, Ebrahim Karimi
wiley +1 more source
Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing
International Journal of Distributed Sensor Networks, 2013 In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate ...
ZheTao Li +3 more
doaj +1 more source
Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement
Algorithms, 2013 This paper designs and evaluates a variant of CoSaMP algorithm, for recovering the sparse signal s from the compressive measurement given a fixed low-rank subspace spanned by U.
Jun He, Ming-Wei Gao, Lei Zhang, Hao Wu
doaj +1 more source
Mapping Nanoscale Buckling in Atomically Thin Cr2Ge2Te6
Advanced Functional Materials, EarlyView.Atomic‐resolution STEM is used to resolve nanoscale buckling in monolayer Cr2Ge2Te₆. A noise‐robust image analysis reconstructs three‐dimensional lattice distortions from single plan‐view images, revealing pronounced defect‐driven nm‐scale out‐of‐plane buckling.
Amy Carl +20 more
wiley +1 more source
Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery
IEEE Access, 2019 Compressive sensing is a desirable technique to acquire and reconstruct signals at sub-Nyquist rates. Recently, several deep learning-based studies on solving the compressive sensing problem have been carried out, which dramatically reduce the intensive ...
Cong Zou, Fang Yang
doaj +1 more source
Sparse Signal Recovery With Side Information
, 2009 Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland ...
Cheng, Samuel +2 more
openaire +1 more source
Unveiling Multidimensional Physicochemical Design Principles for Tissue Processing Hydrogels
Advanced Functional Materials, EarlyView.This study establishes a materials‐based design framework for polymer hydrogels in tissue clearing, linking physicochemical properties to performance in tissue processing, labeling, and imaging. By analyzing rheology, swelling, porosity, antibody diffusion, mechanical performance, and thermochemical stability across platforms, this work provides a ...
Sangjae Kim +8 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A dual‐layer living hydrogel, ProΦGel, integrates bacteriophages and probiotics for synergistic wound infection therapy. The outer gelatin‐based matrix releases phages on demand in response to P. aeruginosa infections, while inner alginate beads sustain probiotic delivery.
Siyuan Tao +6 more
wiley +1 more source
Sparse Recovery for DOA Estimation With a Reflection Path
IEEE Access, 2018 In a low-angle tracking scenario, it is difficult to get an accurate estimation of the direction-of-arrival (DOA) due to the presence of reflection path.
Huayang Chen +3 more
doaj +1 more source