Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …

Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …

Graph Fourier transform (GFT) has been proven to be an effective tool for impulse
component extraction of rolling bearings, but its performance is closely related to the …

Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in
a supervised learning setting, where the inputs are agnostic to the graph. KRG model …

The focus of Part I of this monograph has been on both the fundamental properties, graph
topologies, and spectral representations of graphs. Part II embarks on these concepts to …

In this paper, we propose a novel technique for the inspection of high-density polyethylene
(HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks …

We propose a kernel regression method to predict a target signal lying over a graph when
an input observation is given. The input and the output could be two different physical …

In this work we provide an extension to analytic signal method for multidimensional signals.
First, expressions for separate phase-shifted components are given. Second, we show that …

Standard signal processing techniques are implicitly based on the assumption that the
signal lies on a regular, homogeneous domain. In practice, however, many signals lie on an …