Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …

The smoothness of graph signals has found desirable real applications for processing
irregular (graph-based) signals. When the latent sources of the mixtures provided to us as …

Hyperspectral unmixing could provide decomposition for small units in hyperspectral images
(HSIs), allowing accurate analysis of ground objects. Unfortunately, interference such as …

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter
regions from an atlas, with edges reflecting the density of axonal projections between pairs …

Graph Learning (GL) is at the core of leveraging connections in machine learning (ML). By
observing a dataset of graph signals and considering specific assumptions, Graph Signal …

Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects
ongoing organization of brain activity. Characterization of the spatial patterns is an …

Blind source separation (BSS) is a signal processing subject that has recently been
extended to graph signals. Graph signals that are smooth on their own graphs provide an …

The smoothness of the graph signals on predefined/constructed graphs appears in many
natural applications of processing unstructured (ie, graph-based) data. In the case of latent …

The concept of Deep learning is a part of machine learning which is very useful nowadays to
achieve accurate voice and speech recognition based on the training data by creating …