Deep learning (DL) based methods have swept the field of mechanical fault diagnosis,
because of the powerful ability of feature representation. However, many of existing DL …

Graph signal processing deals with signals which are observed on an irregular graph
domain. While many approaches have been developed in classical graph theory to cluster …

A random field is the representation of the joint probability distribution for a set of random
variables. Markov fields, in particular, have a long standing tradition as the theoretical …

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 …

Different from most of deep learning-based rotating machinery diagnosis methods, graph
convolutional network-based method can effectively mine relationship between nodes in the …

This article provides a new strategy for the heterogeneous change detection (HCD) problem:
solving HCD from the perspective of graph signal processing (GSP). We construct a graph to …

Single-cell technologies can measure the expression of thousands of molecular features in
individual cells undergoing dynamic biological processes. While examining cells along a …

Interpretation of guided wave signals is a central challenge for ultrasonic guided wave-
based damage detection and localization technology. Because of the complexity of the …

The area of Data Analytics on graphs promises a paradigm shift as we approach information
processing of classes of data, which are typically acquired on irregular but structured …

This paper considers a filter for smoothing spatial data. It can be used to smooth data on the
vertices of arbitrary undirected graphs with arbitrary non-negative spatial weights. It consists …