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 development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis.

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.

This paper presents an approach based on eddy currents induced by suitable magnetic induction fields to test, estimate, and classify subsurface delaminations in Carbon Fibre Reinforced Polymer (CFRP) plates for biomedical devices. The two-dimensional maps obtained, characterised by high fuzziness, required the software development of a procedure based on a highly efficient fuzzy classifier that exploits fuzzy similarity computations with reduced computational load by collecting similar maps (deriving from equally similar defects) in specific defects. The hardware implementation of what is designed in software (plate-probe system) detects and evaluates the entity of the defects due to delaminations by a classification percentage comparable with the performances obtained from more sophisticated classifiers, providing a possible tool for evaluating the delaminations potentially useful to assess aircraft compliance with applicable safety standards.