Results 81 to 90 of about 4,180,610 (156)

Multi-Kernel Regression for Graph Signal Processing

, 2018
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel
Arun Venkitaraman, Peter Handel, Chatterjee, Saikat,, Händel, Peter,, Saikat Chatterjee, Venkitaraman, Arun,   +5 more
core   +1 more source

Level-Raising for GSp(4) [PDF]

, 2006
This thesis provides congruences between unstable and stable automorphic forms for the symplectic similitude group $GSp(4)$. More precisely, we raise the level of certain CAP representations $Pi$ of Saito-Kurokawa type, arising from classical modular ...
Sorensen, Claus Mazanti
core   +1 more source

Joint Topology Learning and Latent Input Identification Using Spatio-Temporally Linear Structured SEM

Mathematics
Topology identification and signal inference are cornerstone tasks in graph signal processing (GSP). Structural Equation Modeling (SEM) is particularly effective for network inference as it explicitly captures causal dependencies.
Jie Zhou, Rui Yang, Xintong Shi, Shuyang Feng   +3 more
doaj   +1 more source

Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis [PDF]

Multivariate signals measured simultaneously over time by sensor networks are becoming increasingly common. The emerging field of graph signal processing (GSP) promises to analyse spectral characteristics of these multivariate signals, while also taking ...
Wu, Min, Goerttler, Stephan; id_orcid, He, Fei; id_orcid   +2 more
core   +1 more source

Diffusion Inverse Filtering: Enhancing Functional Connectivity-Based Pattern Recognition by Counteracting Spatial Smoothing

Brain Sciences
Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their ...
Yuzeng Xu, Sho Otsuka, Seiji Nakagawa
doaj   +1 more source

Signal Processing [PDF]

, 2010
This book intends to provide highlights of the current research in signal processing area and to offer a snapshot of the recent advances in this field.

core   +1 more source

Graph neural networks for image processing

, 2021
Graph signal processing (GSP) has provided new powerful tools that are particularly suitable for visual data. Concurrent to the emergence of GSP, data-driven solutions, based on neural networks have shown impressive performances in a variety of tasks ...
Fracastoro G., Valsesia D.
core   +1 more source

A Preconditioned Graph Diffusion LMS for Adaptive Graph Signal Processing

, 2018
Graph filters, defined as polynomial functions of a graph-shift operator (GSO), play a key role in signal processing over graphs. In this work, we are interested in the adaptive and distributed estimation of graph filter coefficients from streaming graph
Sayed, Ali, Wang, Haiyan, Roula Nassif, Ali H. Sayed, Cedric Richard, Sayed, Ali H., Richard, Cedric, Fei Hua, Hua, Fei, Nassif, Roula, Haiyan Wang   +10 more
core   +1 more source

Non-intrusive load disaggregation using graph signal processing [PDF]

, 2018
With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand.
Stankovic, Lina, He, Kanghang, Liao, Jing, Stankovic, Vladimir   +3 more
core   +2 more sources

Time-varying graph learning from smooth and stationary graph signals with hidden nodes

EURASIP Journal on Advances in Signal Processing
Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available.
Rong Ye, Xue-Qin Jiang, Hui Feng, Jian Wang, Runhe Qiu, Xinxin Hou   +5 more
doaj   +1 more source