The increasing scarcity in the available spectrum for wireless communication is one of the
current bottlenecks impairing further deployment of services and coverage. The proper …

Adaptive networks (ANs) are effective real time techniques to process and track events
observed by sensor networks and, more recently, to equip Internet of Things (IoT) …

In this work, we present an energy-efficient distributed learning framework using coarsely
quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed …

The increase in life expectancy, according to the World Health Organization, is a fact, and
with it rises the incidence of age-related neurodegenerative diseases. The most recurrent …

Combinations of adaptive filters have attracted attention as a simple solution to improve filter
performance, including tracking properties. In this paper, we consider combinations of LMS …

We propose a normalized least mean squares algorithm with variable step size. Unlike other
solutions, it has low computational cost, only three parameters that are simple to choose …

We develop adaptive beamforming algorithms that are robust against sensor failure and ill-
conditioning in the autocorrelation matrix (common in low-rank interference scenarios). Both …

In this paper, we analyze the effects of random sampling on adaptive diffusion networks.
These networks consist in a collection of nodes that can measure and process data, and that …

Partial discharge (PD) is the primary factor causing insulation degradation in transformers.
However, the collected signals of partial discharge are often contaminated with significant …

Speaker recognition is challenging in real-world environments. Typically, studies approach
noises only in an additive manner. However, real environments commonly present …