Graph signal processing: Overview, challenges, and applications
Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …
Graph signal processing, graph neural network and graph learning on biological data: a systematic review
Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …
span from population graphs (with patients as network nodes) to molecular graphs that …
A graph signal processing perspective on functional brain imaging
Modern neuroimaging techniques provide us with unique views on brain structure and
function; ie, how the brain is wired, and where and when activity takes place. Data acquired …
function; ie, how the brain is wired, and where and when activity takes place. Data acquired …
Localized spectral graph filter frames: A unifying framework, survey of design considerations, and numerical comparison
DI Shuman - IEEE Signal Processing Magazine, 2020 - ieeexplore.ieee.org
A major line of work in graph signal processing [2] during the past 10 years has been to
design new transform methods that account for the underlying graph structure to identify and …
design new transform methods that account for the underlying graph structure to identify and …
[HTML][HTML] Spectral representation of EEG data using learned graphs with application to motor imagery decoding
Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects
ongoing organization of brain activity. Characterization of the spatial patterns is an …
ongoing organization of brain activity. Characterization of the spatial patterns is an …
A review of graph-powered data quality applications for IoT monitoring sensor networks
The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
Graph signal processing: Overview, challenges and applications
Research in Graph Signal Processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper we first provide an overview of core ideas …
defined on irregular graph domains. In this paper we first provide an overview of core ideas …
Review of studies on recognition technologies and their applications used to assist learning and instruction
We reviewed studies on recognition technologies published in the last ten years. This review
study was aimed toward identifying, appraising, selecting, and synthesizing all high quality …
study was aimed toward identifying, appraising, selecting, and synthesizing all high quality …
Shapes of uncertainty in spectral graph theory
W Erb - IEEE Transactions on Information Theory, 2020 - ieeexplore.ieee.org
We present a flexible framework for uncertainty principles in spectral graph theory. In this
framework, general filter functions modeling the spatial and spectral localization of a graph …
framework, general filter functions modeling the spatial and spectral localization of a graph …
Covariation informed graph Slepians for motor imagery decoding
Graph signal processing (GSP) provides signal analytic tools for data defined in irregular
domains, as is the case of non-invasive electroencephalography (EEG). In this work, the …
domains, as is the case of non-invasive electroencephalography (EEG). In this work, the …