In the era of Industry 4.0, digital transformation has spurred the swift advancement of
technologies, including intelligent predictive maintenance scheduling, prognostics and …

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

Internet-of-Things (IoT) is a key enabler for the transition to the Automatic Structural Health
Monitoring (ASHM) of technical facilities, thanks to the seamless flow of data from a …

The application of tiny machine learning (TinyML) in human-computer interaction is
revolutionizing gesture recognition technologies. However, there remains a significant gap …

In the context of TinyML, many research efforts have been devoted to designing forward
topologies to support On-Device Learning. Reaching this target would bring numerous …

Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of
it deployed on an edge device and the rest on a remote server is emerging as a promising …

Defect detection is the task of identifying defects in production samples. Usually, defect
detection classifiers are trained on ground-truth data formed by normal samples (negative …

Benchmarking plays a pivotal role in assessing and enhancing the performance of compact
deep learning models designed for execution on resource-constrained devices, such as …

In this work, a novel approach for the detection and localisation of nonlinear guided waves
often associated with the presence of damage in structural components is proposed. The …

The world's first law governing" artificial inelegance" has arrived! The emergence of Artificial
Intelligence (AI) technologies has prompted a global discourse on the necessity of …