Results 51 to 60 of about 72,358 (316)
Distributed Compressed Sensing for Static and Time-Varying Networks [PDF]
IEEE Transactions on Signal Processing, 2014 We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only communication with neighbors in the network.
Patterson, Stacy +2 more
openaire +2 more sources
Beyond Inflammation: Why Understanding the Brain Matters in Inflammatory Arthritis
Arthritis Care &Research, EarlyView.Persistent pain remains a major challenge in inflammatory arthritis, even when joint inflammation is well controlled. Pain and associated symptoms such as fatigue cannot be explained by peripheral inflammation alone but reflect altered central pain processing. These changes may arise through “top‐down” mechanisms, reflecting pre‐existing dysfunction in
Eoin M. Kelleher +2 more
wiley +1 more source
Dianxin kexue, 2016 Wideband distributed cooperative spectrum sensing based on compressed sensing can not only reduce high sampling rate,but also improve the spectrum sensing performance in low signal to noise ratio environment.In order to further enhance the spectrum ...
Yuzhi YAN +3 more
doaj +2 more sources
Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform
IEEE Access, 2022 The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes.
Bo Xu +6 more
doaj +1 more source
Sample Distortion for Compressed Imaging
, 2013 We propose the notion of a sample distortion (SD) function for independent and identically distributed (i.i.d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder
Davies, Mike E., Guo, Chunli
core +1 more source
On U-Statistics and Compressed Sensing II: Non-Asymptotic Worst-Case Analysis [PDF]
, 2012 In another related work, U-statistics were used for non-asymptotic "average-case" analysis of random compressed sensing matrices. In this companion paper the same analytical tool is adopted differently - here we perform non-asymptotic "worst-case ...
Lim, Fabian, Stojanovic, Vladimir
core +1 more source
Arthritis Care &Research, Accepted Article.Objectives Sjögren's disease is an autoimmune disorder that can impact multiple organ systems, including the peripheral nervous system (PNS). PNS manifestations, which can exist concurrently, include mononeuropathies, polyneuropathies, and autonomic nervous system neuropathies. To help patients and providers in the decision‐making process, we developed
Anahita Deboo +19 more
wiley +1 more source
Multi Terminal Probabilistic Compressed Sensing [PDF]
, 2014 In this paper, the `Approximate Message Passing' (AMP) algorithm, initially developed for compressed sensing of signals under i.i.d. Gaussian measurement matrices, has been extended to a multi-terminal setting (MAMP algorithm).
Haghighatshoar, Saeid
core +2 more sources
The Immobilization of Hyaluronic Acid in 3D Hydrogel Scaffolds Modulates Macrophage Polarization
Advanced Biology, EarlyView.This study explores the use of collagen‐hyaluronic acid (HA) hydrogels for the 3D culture of macrophages, providing a useful tool for modelling macrophage behavior in tissues and diseases. It highlights how hydrogel composition, mechanical properties, and preparation methods influence macrophage behavior, revealing for the first time that HA's ...
Tiah CL Oates +7 more
wiley +1 more source
Digital Communications and Networks, 2023 Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model.
Han Wang +5 more
doaj +1 more source