Results 41 to 50 of about 4,335,273 (293)

Dynamic Graph Learning: A Structure-Driven Approach

Mathematics, 2021
The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the ...
Bo Jiang, Yuming Huang, Ashkan Panahi, Yiyi Yu, Hamid Krim, Spencer L. Smith   +5 more
doaj   +1 more source

Node-Adaptive Regularization for Graph Signal Reconstruction

IEEE Open Journal of Signal Processing, 2021
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang, Mario Coutino, Geert Leus, Elvin Isufi   +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

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

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

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

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

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

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