Results 21 to 30 of about 4,180,610 (156)
Applied Network Science, 2021 Complex networks lend themselves for the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting ...
Stefan Bloemheuvel +2 more
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GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
, 2023 The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP. The companion model shows that GSP can be made equivalent to DSP 'plus' appropriate boundary conditions (bc) - this is shown under broad conditions and holds for arbitrary undirected or directed ...
Shi, John, Moura, Jose M. F.
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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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Correlation-based Graph Smoothness Measures In Graph Signal Processing
, 2023 Publisher Copyright: © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.Graph smoothness is an important prior used for designing sampling strategies for graph signals as well as for regularizing the problem of graph learning ...
Miettinen, Jari +3 more
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EURASIP Journal on Audio, Speech, and Music Processing, 2023 The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit from the learned multiple graphs’ property
Tingting Wang +4 more
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Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a
Xiaoying Song, Li Chai
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Short-Time Prediction Model for Urban Traffic Flow Based on Joint Spatio-Temporal Learning [PDF]
Jisuanji gongcheng, 2023 A joint spatio-temporal analysis can reflect the changing pattern of a studied object in the spatio-temporal dimension, which is significant for revealing the spatio-temporal interactions and mechanisms of regional processes.With a focus on joint spatio ...
GE Yuran, FU Qiang
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A Graph Signal Processing Framework for the Classification of Temporal Brain Data
, 2021 Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes.
Dorina Thanou +3 more
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Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences
Sensors, 2023 As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors.
Bochao Zhao +3 more
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Graph signal processing over a probability space of shift operators
, 2023 Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology.
Ji, Feng, Tay, Wee Peng, Ortega, Antonio
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