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 …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Fog computing complements cloud computing by removing several limitations, such as
delays and network bandwidth. It emerged to support Internet of Things (IoT) applications …

In this paper, we present a novel generalization of the graph Fourier transform (GFT). Our
approach is based on separately considering the definitions of signal energy and signal …

We propose novel two-channel filter banks for signals on graphs. Our designs can be
applied to arbitrary graphs, given a positive semi definite variation operator, while using …

Graph signal sampling is the problem of selecting a subset of representative graph vertices
whose values can be used to interpolate missing values on the remaining graph vertices …

Graph signal reconstruction is a classic problem of graph signal processing. The ultimate
goal of signal reconstruction is to obtain an estimate as close as possible to the original …

We consider the problem of sampling signals defined on the nodes of a graph. This problem
arises in many contexts where the data is not structured and needs to be reconstructed from …

Signal or noise power is an important performance parameter or indicator used for many
applications in signal processing and wireless communications. This article investigates the …

This paper proposes a metric to measure the dissimilarity between graphs that may have a
different number of nodes. The proposed metric extends the generalised optimal subpattern …