Results 11 to 20 of about 593 (116)
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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A graph-based narrowband matched-field source localization method [PDF]
JASA Express Letters, 2021 Matched field processing (MFP) has been regarded as one of the most successful acoustical methods for positioning underwater sources. In this paper, the narrowband MFP method is combined with a recently developed framework—the graph signal processing ...
Peng Xiao, Jianmin Yang
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Signal Processing on Simplicial Complexes With Vertex Signals
IEEE Access, 2022 In classical graph signal processing (GSP), the underlying topological structures are restricted in terms of dimensionality. A graph or a 1-complex is a combinatorial object that models binary relations, which do not directly capture complex high arity ...
Feng Ji, Giacomo Kahn, Wee Peng Tay
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Remote Sensing, 2022 Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction
Kefei Liao +3 more
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Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
IEEE Transactions on Signal Processing, 2022 V2: Added missing space in arXiv title, V3: Revised paper following journal ...
John Shi, Jose M. F. Moura
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A Regularized Graph Neural Network Based on Approximate Fractional Order Gradients
Mathematics, 2022 Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP.
Zijian Liu +3 more
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Multilayer graph spectral analysis for hyperspectral images
EURASIP Journal on Advances in Signal Processing, 2022 Hyperspectral imaging has broad applications and impacts in areas including environmental science, weather, and geo/space exploration. The intrinsic spectral–spatial structures and potential multi-level features in different frequency bands make ...
Songyang Zhang, Qinwen Deng, Zhi Ding
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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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Neuroscience Informatics, 2022 Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) applications. Electroencephalography (EEG) signals, which record brain
Aref Einizade +4 more
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