Results 101 to 110 of about 487,780 (314)
Advanced Engineering Materials, EarlyView.This article examines the effect of postprocessing heat treatment on 316L stainless steel (SS316L) produced via laser powder bed fusion (LPBF). Heat treatment slightly affects property anisotropy, reduces strength, and enhances elongation. However, it increases corrosion susceptibility due to nanoscale precipitations, though overall corrosion ...
Baibhav Karan +5 more
wiley +1 more source
Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate
Sensors, 2015 Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs).
Davide Brunelli, Carlo Caione
doaj +1 more source
A Compressive Method for Centralized PSD Map Construction [PDF]
arXiv, 2016 Spectrum resources are facing huge demands and cognitive radio (CR) can improve the spectrum utilization. Recently, power spectral density (PSD) map is defined to enable the CR to reuse the frequency resources regarding to the area. For this reason, the sensed PSDs are fused by a Fusion Center (FC) which the sensed PSDs are collected by the distributed
arxiv
Advanced Engineering Materials, EarlyView.This article introduces the Dataspace Management System (DSMS), a methodological framework realized in software, designed as a technology stack to power dataspaces with a focus on advanced knowledge management in materials science and manufacturing. DSMS leverages heterogeneous data through semantic integration, linkage, and visualization, aligned with
Yoav Nahshon +7 more
wiley +1 more source
Distributed Sparse Signal Recovery For Sensor Networks
, 2013 We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective ...
Eldar, Yonina C. +2 more
core +1 more source
Ontologies for FAIR Data in Additive Manufacturing: A Use Case‐Based Evaluation
Advanced Engineering Materials, EarlyView.An ontology‐based approach for generating findable, accessible, interoperable, reusable data in additive manufacturing is explored, focusing on powder bed fusion. The article highlights the benefits of enhanced data findability and digital twin enablement, while addressing challenges like data integration complexity and the need for specialized ...
Thomas Bjarsch +2 more
wiley +1 more source
A Sparsity Adaptive Algorithm for Wideband Compressive Spectrum Sensing
Dianxin kexue, 2014 Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, in fact,it is unknown and time-varying. To solve the problem, a sparsity adaptive algorithm for wideband spectrum sensing was proposed.
Zhijin Zhao, Junwei Hu
doaj +2 more sources
Current Trends and Future Perspective for Cold Spray Metal‐Ceramic Composites
Advanced Engineering Materials, EarlyView.The use of cold spray (CS) technology for creating cermet deposits, combining ceramic and metallic components, is advancing rapidly. Cermets offer excellent temperature handling and erosion resistance for diverse applications. This review outlines the current state of the art, focusing on the deposition dynamics and strategies to optimize the ...
Romario A. Wicaksono +2 more
wiley +1 more source
Probability-based pilot allocation for MIMO relay Distributed compressed sensing channel estimation
EURASIP Journal on Advances in Signal Processing, 2018 Multiple-Input Multiple-Output (MIMO) relay communication systems are used as an efficient system in spectral efficiency and power allocation view point. In these systems, some of the facilities need channel state information (CSI).
Abbas Akbarpour-Kasgari +1 more
doaj +1 more source
Compressive neural representation of sparse, high-dimensional probabilities [PDF]
arXiv, 2012 This paper shows how sparse, high-dimensional probability distributions could be represented by neurons with exponential compression. The representation is a novel application of compressive sensing to sparse probability distributions rather than to the usual sparse signals.
arxiv