Results 71 to 80 of about 66,822 (262)
IET Signal Processing, 2023 Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications.
Zonglong Bai +3 more
doaj +1 more source
Sparse signal recovery in Hilbert spaces [PDF]
2012 IEEE International Symposium on Information Theory Proceedings, 2012 This paper reports an effort to consolidate numerous coherence-based sparse signal recovery results available in the literature. We present a single theory that applies to general Hilbert spaces with the sparsity of a signal defined as the number of (possibly infinite-dimensional) subspaces participating in the signal's representation.
Graeme Pope, Helmut Bölcskei
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New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Blind Phase Calibration In Sparse Recovery
EUSIPCO - 21st European Signal Processing Conference - 2013, 2013 Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco ...
Bilen, Cagdas +3 more
openaire +4 more sources
Gradient-Based Methods for Sparse Recovery [PDF]
SIAM Journal on Imaging Sciences, 2011 16 pages, submitted to SIAM Journal on Imaging ...
William W. Hager +2 more
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Fusogenic RNA Nanomodules for Fusion‐Mediated and Multiplexed siRNA Delivery
Advanced Functional Materials, EarlyView.A fusogenic lipid‐layered RNA nanomodules (L‐CRAMs) enable high‐capacity and long‐lasting siRNA delivery through membrane fusion. These nanomodules carry exceptionally large siRNA payloads, avoid conventional endosomal uptake, and release multiple functional siRNAs through Dicer‐mediated processing.
Sunghyun Moon +5 more
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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Advanced Healthcare Materials, EarlyView.Catechol‐functionalized cellulose hydrogels are developed as injectable, bioadhesive platforms for retinal neuroprotection. The hydrogels exhibit tunable rheological and mechanical properties, strong tissue adhesion, and sustained antioxidative activity.
Kai‐Hsiang Chang +4 more
wiley +1 more source
Sparse ECG Denoising with Generalized Minimax Concave Penalty
Sensors, 2019 The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis.
Zhongyi Jin +3 more
doaj +1 more source
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source