The main challenge in the implementation of long-lasting vibration monitoring systems is to
tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design …

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can
be partitioned into a support estimation phase and a coefficient estimation phase. We …

The amount of data generated by distributed monitoring systems that can be exploited for
anomaly detection, along with real time, bandwidth, and scalability requirements leads to the …

Despite the universal nature of the compressed sensing mechanism, additional information
on the class of sparse signals to acquire allows adjustments that yield substantial …

The use of compressed sensing (CS) to achieve low-power consumptions in
electroencephalogram (EEG) mea-surement devices has attracted considerable research …

We investigate the advantage of a two-step approach in the recovery of Compressed
Sensing (CS) encoded signals in a realistic environment. First, the support of the signal is …

Compressed sensing (CS) for sensor-near vibration diagnostics represents a suitable
approach for the design of network-efficient structural health monitoring systems. This article …

This article introduces a multiemitter 2-D angle-of-arrival (AoA) estimation scheme. It
consists of an AoA estimation algorithm based on impinging signal spatial sparsity, Dantzig …

The increasing need for small and low-power Deep Neural Networks (DNNs) for edge
computing applications involves the investigation of new architectures that allow good …

Compressed sensing (CS) exploits signal sparsity in some domain to enable sub-Nyquist
sampling which increases the energy efficiency of analog-to-digital conversion (ADC) and …