This paper presents an eddy current approach for testing, estimating, and classifying CFRP plate sub-surface defects, mainly due to delamination, through specific 2D magnetic induction field amplitude maps. These maps, showing marked fuzziness content, require the development of a procedure based on a fuzzy approach being efficiently classified. Since similar defects produce similar maps, we propose a method based on innovative fuzzy similarity formulations. This procedure can collect maps similar to each other in particular defect classes. In addition, a low-cost analysis system, including the probe, has been implemented in hardware. The developed tool can detect and evaluate the extent of surface defects with the same performance as a hardware tool of higher specifications, and it could be fruitfully employed by airline companies to maintain aircraft in compliance with safety standards.

The evolution of electronic systems has significantly improved the monitoring of electrical consumption, ensuring precise measurement of energy assets. This paper proposes a three-step approach to improving the energy system management of an industrial plant. The first phase involves the identification of hot surfaces in electrical panels using non-destructive instrumentation for thermographic measurements. The second phase develops an electronic system consisting of current sensors and a temperature control system, a control unit made with Raspberry Pi integrated with Arduino, and a data transmission system using a 4G LTE Internet device capable of measuring the electrical current used by a machine in the plant. The electronic system measures current absorption (CA) by the lines of the electrical switchboards. The final phase focuses on the implementation of deep learning method based on the integration of an LSTM model for the forecasting of hourly CA and a U-Net model for supporting the classification and detection of anomalies identified during thermographic assessment. The results demonstrated substantial improvements in the accuracy of power consumption classification and the potential to optimise power distribution and reduce consumption accordingly. This classification results in reduced energy consumption and improved process quality.

Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics^® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies.

The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring device that captures the incidence of specific absorption rate (SAR) and temperature variation (∆T) on the human body. The system transmits data to a cloud platform, where a feedforward neural network (FFNN) processes the received information. SAR and surface temperature values are detected in an indoor environment, monitoring stationary and moving subjects. The results effectively assess temperature distribution due to electromagnetic fields. The prototype detected temperature peaks and high SAR values when the subjects remained motionless. Predictive analysis confirms the need for workplaces with materials shielding external electromagnetic signals and attenuating internal sources. Moderate mobile phone use could lower SAR and temperature values.

3D modelling of archaeological and historical structures is the new frontier in the field of conservation science. Similarly, the identification of buried finds, which enhances their multimedia diffusion and restoration, has gained relevance. As such sites often have a high level of structural complexity and complicated territorial geometries, accuracy in the creation of 3D models and the use of sophisticated algorithms for georadar data analysis are crucial. This research is the first step in a larger project aimed at reclaiming the ancient villages located in the Greek area of southern Italy. The present study focuses on the restoration of the village of Africo (RC), a village hit by past flooding. The survey began with a laser scan of the church of St. Nicholas, using both the Faro Focus3D and the Riegl LMS-Z420i laser scanner. At the same time, georadar analyses were carried out in order to pinpoint any buried objects. In the processing phase, our own MATLAB algorithms were used for both laser scanner and georadar datasets and the results compared with those obtained from the scanners’ respective proprietary software. We are working to develop a tourism app in both augmented and virtual reality environments, in order to disseminate and improve access to cultural heritage. The app allows users to see the 3D model and simultaneously access information on the site integrated from a variety of repositories. The aim is to create an immersive visit, in this case, to the church of St. Nicholas. Highlights: Use of different algorithms for registration of terrestrial laser scans and analysis of the data obtained. 3D acquisition, processing and restitution methodology from georadar data. Implementation of a tourist app in both virtual and augmented reality by integrating geomatics methodologies.

Monitoring machinery in wastewater treatment plants is crucial due to its impact on sensitive issues such as environmental health and quality of life. A designed and fabricated monitoring system ensures compliance with all operating and safety conditions. In the initial phase, the assessment of the electrical panel using non-destructive methodology was conducted. A thermographic survey, combined with an artificial intelligence algorithm, verified thermal dissipation and led to the creation of an current consumption database. Information derived from the measurement campaign assists in establishing a control system for machinery operation. Upon identifying the causes of malfunctions, data were sent in real-time to a monitoring platform consisting of current sensors and a control unit based on a Raspberry Pi and an Arduino board. Real-time transmission of data to the monitoring platform occurred via a 4G L TE internet stick. The collected data and results optimize consumption by managing the on/off switching of monitored machinery. Activation times can be set to control electrical overload values. The proposed system provides more accurate consumption and operating forecasts than existing systems. Finally, the system offers end-users suggestions on classifying thermal energy dissipated on the switchboard, distinguishing machinery or devices presenting overload issues. This classification translates into information on lower current consumption and enhanced process quality.