In this article, we present a novel approach to reconstruct the topology of networked linear
dynamical systems with latent nodes. The network is allowed to have directed loops and bi …

The era of “data deluge” has sparked renewed interest in graph-based learning methods
and their widespread applications ranging from sociology and biology to transportation and …

When facing graph signal processing tasks, it is typically assumed that the graph describing
the support of the signals is known. However, in many relevant applications the available …

Condition monitoring technology plays a crucial role in ensuring the reliable operation of
gas turbines. Digital twin has propelled condition monitoring research into a new phase …

We study blind deconvolution of signals defined on the nodes of an undirected graph.
Although observations are bilinear functions of both unknowns, namely the forward …

This article addresses the problem of reconstructing the topology of a network of agents
interacting via linear dynamics, while being excited by exogenous stochastic sources that …

Total least-squares (TLS) aims to estimate the unknown parameters of an errors-in-variables
(EIV) model from noisy observations when the coefficients are also perturbed by errors. It is …

The first step for any graph signal processing (GSP) procedure is to learn the graph signal
representation, ie, to capture the dependence structure of the data into an adjacency matrix …

Fault detection is crucial for gas turbine condition monitoring. This study proposes a fault
detection method for gas turbines based on model mismatch. Initially, a surrogate model of …

When approaching graph signal processing tasks, graphs are usually assumed to be
perfectly known. However, in many practical applications, the observed (inferred) network is …