Time-Varying Graph Signal Denoising via Median Filters
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2020.3017800
Abstract: Graph Signal Processing (GSP) leverages pair-wise relationship between nodes of a graph to formulate operators on signals defined over the nodes. Most existing graph signal operators in the literature are linear, and can be described… read more here.
Keywords: graph signal; time; varying graph; median filters ... See more keywords
Statistical Graph Signal Recovery Using Variational Bayes
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2020.3045217
Abstract: This brief investigates the problem of Graph Signal Recovery (GSR) when the topology of the graph is not known in advance. In this brief, the elements of the weighted adjacency matrix is statistically related to… read more here.
Keywords: graph signal; statistical graph; recovery using; signal recovery ... See more keywords
Joint Topology Learning and Graph Signal Recovery Using Variational Bayes in Non-Gaussian Noise
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2021.3109339
Abstract: This brief proposes a joint graph signal recovery and topology learning algorithm using a Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is assumed that the graph signal is Gaussian Markov… read more here.
Keywords: topology; noise; non gaussian; graph signal ... See more keywords
Non-Intrusive Load Disaggregation Using Graph Signal Processing
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DOI: 10.1109/tsg.2016.2598872
Abstract: With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a graph signal processing (GSP)-based… read more here.
Keywords: non intrusive; signal processing; gsp based; approach ... See more keywords
A Graph Signal Processing Framework for Detecting and Locating Cyber and Physical Stresses in Smart Grids
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Smart Grid"
DOI: 10.1109/tsg.2022.3177154
Abstract: Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, a graph signal processing (GSP) framework is used to… read more here.
Keywords: signal processing; detecting locating; graph signal; cyber physical ... See more keywords
Multimodal Dynamic Brain Connectivity Analysis Based on Graph Signal Processing for Former Athletes With History of Multiple Concussions
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2020.2982765
Abstract: The study of structure-function relationships in the brain has been an active area of research in neuroscience. The availability of brain imaging data that captures the structural connectivity and functional co-activation of the brain regions… read more here.
Keywords: graph signal; former athletes; signal processing; connectivity ... See more keywords
Joint Graph Learning and Blind Separation of Smooth Graph Signals Using Minimization of Mutual Information and Laplacian Quadratic Forms
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2023.3240893
Abstract: The smoothness of graph signals has found desirable real applications for processing irregular (graph-based) signals. When the latent sources of the mixtures provided to us as observations are smooth graph signals, it is more efficient… read more here.
Keywords: information; graph signals; smooth graph; graph signal ... See more keywords
Proportionate Adaptive Graph Signal Recovery
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DOI: 10.1109/tsipn.2023.3277591
Abstract: This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence than least mean… read more here.
Keywords: proportionate type; graph signal; graph; adaptive graph ... See more keywords
On Local Distributions in Graph Signal Processing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2022.3223217
Abstract: Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the… read more here.
Keywords: local distributions; processing local; graph signal; signal processing ... See more keywords
Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing
Sign Up to like & getrecommendations! Published in 2021 at "eLife"
DOI: 10.7554/elife.59811
Abstract: Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical… read more here.
Keywords: graph signal; characterization; clinical pathways; signal processing ... See more keywords