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A new windowed graph Fourier transform
2017 4th NAFOSTED Conference on Information and Computer Science, 2017Many 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 +2 more
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On factor graphs and the Fourier transform
Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252), 2002We introduce the concept of convolutional factor graphs, which represent convolutional factorizations of multivariate functions, just as conventional (multiplicative) factor graphs represent multiplicative factorizations. Convolutional and multiplicative factor graphs arise as natural Fourier transform duals.
Yongyi Mao, Frank R. Kschischang
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The fractional Fourier transform on graphs
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017The emerging field of signal processing on graphs merges algebraic or spectral graph theory with discrete signal processing techniques to process signals on graphs. In this paper, a definition of the fractional Fourier transform on graphs (GFRFT) is proposed and consolidated, which extends the discrete fractional Fourier transform (DFRFT) in the same ...
Yi-Qian Wang +2 more
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Fourier transform for signals on dynamic graphs
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014Signal 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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Discrete signal processing on graphs: Graph fourier transform
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for the representation and analysis of datasets with complex structure. Such datasets arise in many social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to structured datasets by viewing them as signals represented by graphs, so ...
Aliaksei Sandryhaila, José M. F. Moura
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A graph Fourier transform and proportional graphs
Random Structures & Algorithms, 1995AbstractA 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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The windowed two-dimensional graph fractional Fourier transform
Digital Signal ProcessingBing-Zhao Li, Jian-Yi Chen
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A parallel algorithm for computing Fourier transforms on the star graph
IEEE Transactions on Parallel and Distributed Systems, 1994The n-star graph, denoted by S/sub n/, is one of the graph networks that have been recently proposed as attractive alternatives to the n-cube topology for interconnecting processors in parallel computers. We present a parallel algorithm for the computation of the Fourier transform on the star graph. The algorithm requires O(n/sup 2/) multiply-add steps
Paraskevi Fragopoulou, Selim G. Akl
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Fourier Transform vs. Graph Fourier Transform for EEG-Based Emotion Recognition
2020Electroencephalogram (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 +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), 2017A 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 +2 more
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