Results 21 to 30 of about 4,335,273 (293)
On the Fractionalization of the Shift Operator on Graphs
IEEE Access, 2022 The theory of graph signal processing has been established with the purpose of generalizing tools from classical digital signal processing to the cases where the signal domain can be modeled by an arbitrary graph.
Guilherme B. Ribeiro +2 more
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Pavement Distress Estimation via Signal on Graph Processing
Sensors, 2022 A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road
Salvatore Bruno +4 more
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Discrete Signal Processing on Graphs [PDF]
IEEE Transactions on Signal Processing, 2013 In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph.
Aliaksei Sandryhaila, José M. F. Moura
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Wide-Sense Stationarity in Generalized Graph Signal Processing [PDF]
, 2022 We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space.
Tay, Wee Peng, Jian, Xingchao
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Harmonic state estimation based on graph signal processing
IET Generation, Transmission & Distribution, 2022 This paper presents a novel harmonic state estimation (HSE) methodology based on graph signal processing. The proposed approach can perform HSE without calculating harmonic impedance. The proposed methodology involves graph signal theory for constructing
Ying Wang +5 more
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Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model [PDF]
, 2022 Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data.
Wang, Xinjue +2 more
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Gene selection for cancer detection using graph signal processing
Informatics in Medicine Unlocked, 2021 Background:: Gene databases are usually large in volume and contain information on thousands of genes. The data are numerical and represent the expression levels of the genes.
Wei Wang +3 more
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Modelling and studying the effect of graph errors in graph signal processing
, 2021 Publisher Copyright: © 2021The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix.
Miettinen, Jari +2 more
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Spatio‐temporal signal recovery under diffusion‐induced smoothness and temporal correlation priors
IET Signal Processing, 2022 In this work, the signal recovery problem regarding incomplete and noisy spatio‐temporal signals is studied. A spatio‐temporal signal is considered as a time‐varying graph signal and a diffusion‐induced first‐order Markov signal model is developed to ...
Shiyu Zhai +3 more
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Random-Walk Laplacian for Frequency Analysis in Periodic Graphs
Sensors, 2021 This paper presents the benefits of using the random-walk normalized Laplacian matrix as a graph-shift operator and defines the frequencies of a graph by the eigenvalues of this matrix.
Rachid Boukrab, Alba Pagès-Zamora
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