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CV-EEGNet: A Compact Complex-Valued Convolutional Network for End-to-End EEG-Based Emotion Recognition. [PDF]

Sensors (Basel)
Wang W, Yang D, Yang Y, Xie Y, Liu X, Yu Y, Shi K.   +6 more
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Redundant graph Fourier transform

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015
Signal processing on graphs is a new emerging field that processing high-dimensional data by spreading samples on networks or graphs. The new introduced definition of graph Fourier transform shows its importance in establishing the theory of frequency analysis or computational harmonic analysis on graph signal processing. We introduce the definition of
Xianwei Zheng, Yuanyan Tang, Jiantao Zhou   +2 more
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Graph Fractional Fourier Transform: A Unified Theory

IEEE Transactions on Signal Processing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tuna Alikasifoglu, Bunyamin Kartal, Aykut Koc   +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), 2002
We 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), 2017
The 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, Bing-Zhao Li 0001, Qi-Yuan Cheng   +2 more
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A windowed graph Fourier transform

2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
The prevalence of signals on weighted graphs is increasing; however, because of the irregular structure of weighted graphs, classical signal processing techniques cannot be directly applied to signals on graphs. In this paper, we define generalized translation and modulation operators for signals on graphs, and use these operators to adapt the ...
David I. Shuman, Benjamin Ricaud, Pierre Vandergheynst   +2 more
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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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Discrete signal processing on graphs: Graph fourier transform

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
We 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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