Results 31 to 40 of about 622,624 (287)
Dynamic Graph Learning: A Structure-Driven Approach
Mathematics, 2021 The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the ...
Bo Jiang +5 more
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Filtering Random Graph Processes Over Random Time-Varying Graphs [PDF]
, 2017 Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochastic ...
Isufi, Elvin +3 more
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Tropical graph signal processing [PDF]
2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017 For the past few years, the domain of graph signal processing has extended classical Fourier analysis to domains described by graphs. Most of the results were obtained by analogy with the study of heat propagation. We propose to perform a similar analysis in the context of tropical algebra, widely used in theoretical computer science to monitor ...
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Node-Adaptive Regularization for Graph Signal Reconstruction
IEEE Open Journal of Signal Processing, 2021 A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang +3 more
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Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid [PDF]
IEEE Transactions on Signal Processing, 2021 The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for the spatio-temporal properties of voltage phasor measurements, by showing how the well-known power systems modeling ...
Raksha Ramakrishna, Anna Scaglione
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Sampling of graph signals via randomized local aggregations [PDF]
, 2019 Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing. While in classical signal processing sampling is a well defined operation, when we consider a graph signal many new challenges arise and ...
Fracastoro, Giulia +2 more
core +2 more sources
Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network
Sensors, 2023 In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang +4 more
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Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing
IEEE Access, 2018 Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand.
Bochao Zhao +3 more
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Graph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs
, 2019 The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and ...
Stankovic, Ljubisa +5 more
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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 +3 more
core +1 more source