Results 41 to 50 of about 5,220,012 (288)

Brain fingerprinting using EEG graph inference

, 2023
Taking advantage of the human brain functional connectome as an individual's fingerprint has attracted great research in recent years. Conventionally, Pearson correlation between regional time-courses is used as a pairwise measure for each edge weight of
Saeedi-Sourck, Hamid, Van De Ville, Dimitri, Behjat, Hamid, Amico, Enrico; id_orcid, Abootalebi, Vahid, Amico, Enrico, Miri, Maliheh, Amico, Enrico,, Abootalebi, Vahid,, Van De Ville, Dimitri,, Saeedi-Sourck, Hamid,, Lund University., Miri, Maliheh,, Behjat, Hamid,   +13 more
core   +1 more source

Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

Sensors, 2023
In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +4 more
doaj   +1 more source

Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

IEEE Access, 2018
Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand.
Bochao Zhao, Kanghang He, Lina Stankovic, Vladimir Stankovic   +3 more
doaj   +1 more source

Gasper: GrAph Signal ProcEssing in R

CoRR, 2020
We present a short tutorial on to the use of the R gasper package. Gasper is a package dedicated to signal processing on graphs. It also provides an interface to the SuiteSparse Matrix Collection.
Basile de Loynes, Fabien Navarro, Baptiste Olivier   +2 more
openaire   +2 more sources

A Graph Signal Processing Framework for the Classification of Temporal Brain Data

, 2021
Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes.
Dorina Thanou, Itani, Sarah, Thanou, Dorina, Sarah Itani   +3 more
core   +1 more source

Extreme Learning Machine for Graph Signal Processing

, 2018
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal.
Peter Handel, Chatterjee, Saikat,, Händel, Peter,, Saikat Chatterjee, Arun Venkitararnan, Venkitaraman, Arun,   +5 more
core   +1 more source

An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

EURASIP Journal on Audio, Speech, and Music Processing, 2023
The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit from the learned multiple graphs’ property
Tingting Wang, Haiyan Guo, Zirui Ge, Qiquan Zhang, Zhen Yang   +4 more
doaj   +1 more source

Gradients of connectivity as graph Fourier bases of brain activity

Network Neuroscience, 2021
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience.
Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia   +4 more
doaj   +1 more source

Graph filter design using sum-of-squares representation

, 2019
Graph filters are an essential part of signal processing on graphs enabling one to modify the spectral content of the graph signals. This paper proposes a graph filter optimization method with an exact control of the ripple on the passband and the ...
Tuomas Aittomaki, Geert Leus, Aittomäki, Tuomas (author), Leus, G.J.T. (author), Aittomäki, Tuomas, Leus, Geert   +5 more
core   +1 more source

Optimized Quantization in Distributed Graph Signal Processing [PDF]

, 2019
Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization.
Pascal Frossard, Isabela Cunha Maia Nobre, Cunha Maia Nobre, Isabela, Frossard, Pascal   +3 more
core   +2 more sources