Results 251 to 260 of about 4,335,273 (293)

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework. [PDF]

Front Comput Neurosci
Soundariya RS, Thangaraj P.
europepmc   +1 more source

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification. [PDF]

Front Comput Neurosci
Tian W, Yang J, Ju X, Li M, Hu D.
europepmc   +1 more source

Utilising Artificial Intelligence to Identify Ventricular Tachycardia Ablation Targets in Sinus Rhythm

Wang X, Mayer J, Dennis A, Chow AW, Al-Sheikhli J, Siang R, Winter J, O’Shea C, Dhanjal T, Lambiase PD, Orini M.   +10 more
europepmc   +1 more source

Graph Signal Processing in the Presence of Topology Uncertainties

IEEE Transactions on Signal Processing, 2020
The goal of this paper is to expand graph signal processing tools to deal with cases where the graph topology is not perfectly known. Assuming that the uncertainty affects only a limited number of edges, we make use of small perturbation analysis to derive closed form expressions instrumental to formulate signal processing algorithms that are ...
Ceci, Elena, Barbarossa, Sergio
openaire   +3 more sources

Graphon Filters: Graph Signal Processing in the Limit

IEEE Transactions on Signal Processing, 2021
Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure.
Matthew Morency, Geert Leus
exaly   +3 more sources

Graph Signal Processing: History, development, impact, and outlook

IEEE Signal Processing Magazine, 2023
Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space.
Antonio Ortega, Geert Leus, David Shuman   +2 more
exaly   +2 more sources

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SIGNAL PROCESSING ON GRAPHS

2023
Η παρούσα διατριβή πραγματεύτηκε μοντέλα επεξεργασίας σήματος που εφαρμόζονται σε γραφήματα. Η τετραγωνική κατά το ήμισυ ελαχιστοποίηση, οι εύρωστοι εκτιμητές, όπως ο διάμεσος και οι Μ-εκτιμητές, η κανονικοποίηση μέσω της l21 νόρμας, η ομαδική μέθοδος των ελαχίστων τετραγώνων αποτελούν τη βάση των προτεινόμενων προσεγγίσεων.
openaire   +2 more sources

GENERALIZED GRAPH SIGNAL PROCESSING

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018
Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a ...
Feng Ji, Wee Peng Tay
openaire   +2 more sources

Graph-Projected Signal Processing

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018
In the past few years, Graph Signal Processing (GSP) has attracted a lot of interest for its aim at extending Fourier analysis to arbitrary discrete topologies described by graphs. Since it is essentially built upon analogies between classical temporal Fourier transforms and ring graphs spectrum, these extensions do not necessarily yield expected ...
Grelier, Nicolas, Rosar Kós Lassance, Carlos Eduardo, Dupraz, Elsa, Gripon, Vincent   +3 more
openaire   +1 more source

Signal processing on graphs: Estimating the structure of a graph

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
This paper presents a computationally tractable algorithm for estimating the graph structure of graph signals is presented. The algorithm is demonstrated on simulated and real network time series datasets, and the performance of the new method is compared to that of related methods for estimating graph structure.
Jonathan Mei, José M. F. Moura
openaire   +1 more source