Results 101 to 110 of about 924,853 (350)

Graph Spectral Image Processing

, 2018
Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks).
Cheung, Gene, Magli, Enrico, Ng, Michael, Tanaka, Yuichi   +3 more
core   +1 more source

Programmable rate modem utilizing digital signal processing techniques [PDF]

The need for a Programmable Rate Digital Satellite Modem capable of supporting both burst and continuous transmission modes with either Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK) modulation is discussed.
Naveh, Arad
core   +4 more sources

Bioinformatics procedure for investigating senolytic (anti-aging) agents: A digital signal processing technique. [PDF]

Aging Med (Milton), 2023
Nwankwo N, Okafor I.
europepmc   +1 more source

Modular architectures for two-dimensional digital signal processing [PDF]

, 1988
Alexander C. Loui, A.N. Venetsanopolous, C.L. Nikias   +2 more
openalex   +1 more source

Robocasting of a Water‐Based Biopolymer/WO3 Nanopowder Paste as a Precursor to Tungsten Carbide Lattices

Advanced Engineering Materials, EarlyView.
This study demonstrates a novel, additive manufacturing approach to produce complex, porous tungsten carbide structures using water‐based direct ink writing/robocasting. Leveraging a modified commercial printer and heat treatment, the process yields lightweight, electrically conductive 3D architectures capable of supporting a mechanical load.
James Bentley Bevis, Manasi Shah, Sanketh Gombi, Rodrigo Martinez‐Duarte   +3 more
wiley   +1 more source

Features of digital signal processing algorithms using Galois fields GF(2n+1). [PDF]

PLoS One, 2023
Suleimenov IE, Vitulyova YS, Matrassulova DK.   +2 more
europepmc   +1 more source

History and some applications of digital signal processing [PDF]

, 1983
Bernard Gold
openalex   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Digital Signal Processing [PDF]

, 1984
Contains introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)National Science Foundation (Grant ECS80-07102)Bell ...
Baggeroer, Arthur B., Bordley, Thomas E., Chan, Philip, Curtis, Susan R., Davis, Randall, Deadrick, Douglas S., Dove, Webster P., Dowla, Farid U., Frisk, George V., Griffin, Daniel W., Harrison, William A., Izraelevitz, David, Lim, Jae S., Martinez, Dennis M., Milios, Evangelos E., Musicus, Bruce R., Myers, Cory, Oppenheim, Alan V., Pappas, Thrasyvoulos N., Richard, Michael D., Sekiguchi, Hiroshi, Sundaram, Ramakrishnan, Wengrovitz, Michael S.   +22 more
core  

Graph Signal Processing: Overview, Challenges and Applications

, 2018
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing.
Frossard, Pascal, Kovačević, Jelena, Moura, José M. F., Ortega, Antonio, Vandergheynst, Pierre   +4 more
core   +1 more source