Results 91 to 100 of about 4,180,610 (156)

Energy Disaggregation Using GSP (Graph Signal Processing)

, 2020
The use of computational devices eventually generated a large accumulation of data. This data ranges from purchase receipts to lawsuits. Therefore, there is a strong business and academic interest in what to do with such data in order to extract ...
Barbosa, Bruno Marques
core  

Introduction to the IEEE Journal on Selected Topics in Signal Processing and IEEE Transactions on Signal and Information Processing Over Networks Joint Special Issue on Graph Signal Processing

The papers in this special issue are intended to address some of the main research challenges in Graph Signal Processing by presenting a collection of the latest advances in the domain.
Rabbat, Michael, Ribeiro, Alejandro, Ortega, Antonio, Frossard, Pascal, Dragotti, Pier Luigi   +4 more
core   +1 more source

Topological Signal Processing Over Generalized Cell Complexes [PDF]

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices
Sergio Barbarossa, Stefania Sardellitti
core   +1 more source

Graph-Projected Signal Processing

, 2018
International audienceIn 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.
Grelier, Nicolas, Dupraz, Elsa, Gripon, Vincent, Rosar Kós Lassance, Carlos Eduardo   +3 more
core   +1 more source

Generalizing Graph Laplacian Learning: a Graph Signal Processing Perspective [PDF]

Graph Signal Processing (GSP) extends classical signal processing (SP) to non-Euclidean domains. For a complex system, GSP studies the matrix representation of its graph abstraction.
Shi, Changhao
core  

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing.
Giraldo, Jhony H., Mahmood, Arif, Bouwmans, Thierry, Thanou, Dorina, Garcia-Garcia, Belmar   +4 more
core   +1 more source

Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across ...
Kühn, Lukas, Fink, Olga, Faghih Niresi, Keivan, Frusque, Gaetan   +3 more
core   +1 more source

Graph Signal Reconstruction via Koopman Autoencoder

Real-world graph signals are inherently time-varying and evolve smoothly, making the characterization of such data challenging. We propose a novel approach for reconstructing missing time-varying graph data by leveraging the assumption that the latent ...
Krishnan, S., Park, J., Choi, J.
core   +1 more source

Enhancing EEG based cognitive state classification using graph Fourier transform. [PDF]

AIMS Neurosci
Sharma S, Kotwal A, Bijral RK, Sharma V, Manhas J.   +4 more
europepmc   +1 more source

Learning Laplacian Matrix in Smooth Graph Signal Representations

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing.
Dong, Xiaowen, Thanou, Dorina, Vandergheynst, Pierre, Frossard, Pascal   +3 more
core   +1 more source