The effective representation, processing, analysis, and visualization of large-scale structured
data, especially those related to complex domains, such as networks and graphs, are one of …

The quantum leap in mobile data traffic and high density of wireless electronic devices,
coupled with the advancements in industrial radio monitoring and autonomous systems …

Next-generation autonomous and networked industrial systems (ie, robots, vehicles, drones)
have driven advances in ultra-reliable low-laten-cy communications (URLLC) and …

A new robust continuous-time optimization algorithm for distributed problems is presented
which guarantees fixed-time convergence. The algorithm is based on a Lyapunov function …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Decentralized optimization with multiple networked clients/learners has advanced machine
learning significantly over the past few years. When data distributions at different …

In this article, we propose a multisensor cardinalized probability density hypothesis (CPHD)
filter for tracking an unknown number of targets that may maneuver over time by using a …

This paper proposes efficient batch-based and online strategies for kernel regression over
graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal …

This article introduces a novel physical-layer (PHY-layer) authentication scheme, called
transfer learning-based PHY-layer authentication (TL-PHA), aiming to achieve fast online …

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces.
We consider both centralized and fully distributed implementations. We first define nonlinear …