Results 241 to 250 of about 1,721,122 (260)

Fourier transform for signals on dynamic graphs

2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
Signal processing on graphs offers a new way of analyzing multivariate signals. The different relationships among the sources generating the multivariate signals can be captured by weighted graphs where the nodes are the signal sources and the edges correspond to the relationships between these signals.
Arash Golibagh Mahyari, Selin Aviyente
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A graph Fourier transform and proportional graphs

Random Structures & Algorithms, 1995
AbstractA Fourier transform for (real valued) functions of graphs is denned. This is used to study and characterize some classes of graphs that arise as exceptional cases in limit theorems for subgraph counts in random graphs.
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A new windowed graph Fourier transform

2017 4th NAFOSTED Conference on Information and Computer Science, 2017
Many practical networks can be mathematically modeled as graphs. Graph signal processing (GSP), intersecting graph theory and computational harmonic analysis, can be used to analyze graph signals. Just as short-time Fourier transform (STFT) for time-frequency analysis in classical signal processing, we have windowed graph Fourier transform (WGFT) for ...
Le Trung Thanh, Nguyen Linh-Trung, Nguyen Viet Dung, Karim Abed-Meraim   +3 more
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Fourier Transform vs. Graph Fourier Transform for EEG-Based Emotion Recognition

2020
Electroencephalogram (EEG)-based feature extraction for emotion recognition is a very challenging task. The vast majority of the feature extraction approaches are based on the frequency characteristics of the EEG signals which are extracted using, e.g., the traditional Fourier Transform approach.
Panagiotis C. Petrantonakis, Anastasia Karatzia, Ioannis Kompatsiaris   +2 more
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Graph Fourier Transform for directed graphs based on Lovász extension of min-cut

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
A key tool to analyze signals defined over a graph is the so called Graph Fourier Transform (GFT). Alternative definitions of GFT have been proposed, based on the eigen-decomposition of either the graph Laplacian or adjacency matrix. In this paper, we introduce an alternative approach, valid for the general case of directed graphs, that builds the ...
Sardellitti, Stefania, Barbarossa, Sergio, Di Lorenzo, Paolo   +2 more
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Slides: Graph Fourier Transform for Directed Graphs

Graph Fourier transform (GFT) is one of the fundamental tools in graph signal processing to decompose graph signals into different frequency components and effectively represent graph signals with strong correlations using various modes of variation.
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Graph Fourier Transform Based on ℓ1 Norm Variation Minimization

Applied and Computational Harmonic Analysis, 2021
Chao Huang, Jianfeng Huang, Lihua Yang
exaly  

Windowed Fractional Fourier Transform on Graphs: Properties and Fast Algorithm

Digital Signal Processing, 2021
Bing-Zhao Li
exaly  

Graph Fourier Transforms on Signed Graphs

Proceedings of the 2024 12th International Conference on Communications and Broadband Networking
Yang Chen 0047, Menglin Wang, Changhao Li, Cheng Cheng 0003   +3 more
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Fourier transformations of hypergraphs and graphs

Cybernetics, 1981
Bandman, O. L., Markova, V. P.
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