Development of fast and accurate models of microwave devices and antennas is of paramount importance in computer-aided design and circuit analysis. At this purpose, artificial neural networks (ANNs) have been extensively exploited in technical literature. However, in the last years support vector machines (SVMs) developed by Vapnik are gaining popularity due to many attractive features capable to overcome the limitations connected to ANNs. In this work, support vector regression machines (SVRMs) modelling performances are investigated and compared with ANNs performances by means of several cases of study

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 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.

A new fuzzy procedure for adaptive gray level image contrast enhancement is presented in this paper. Starting from the pixels belonging to a normalized gray level image, an appropriate smooth S-shaped fuzzy membership function (MF) is considered for gray-scale transformation and is adaptively developed through noise reduction and information loss minimization. Then, a set of fuzzy patches is extracted from the MF, and for each support of each patch, we compute four ascending order statistics that become points inside a 4-dimensional fuzzy unit hypercube after a suitable fuzzification step. Contrast enhancement is performed by computing the distances among the above points and the points of maximum darkness and maximum brightness (special vertexes in the hypercube) and by determining the rotation of the tangent line to the MF around a crucial point where fuzzy patches and the MF coexist. The proposed procedure enables high contrast enhancement in all the treated images with performance that is fully comparable to that obtained by three more sophisticated fuzzy techniques and by standard histogram equalization.

In this work, Characteristic Modes in conjunction with Spectral Domain Moment Method are used for the efficient analysis of the scattering from arrays of arbitrarily shaped microstrip patches. In the following the Theory of Characteristic Modes for a general arrays of patches is presented. The method is tested by considering simple structure composed by two patches only but it can be extended to larger arrays. Computed results are found to be in excellent agreement with results obtained by standard MoM with subdomain basis functions. Since a small number of characteristic modes are necessary to yield a good approximation of the induced current on patches, the proposed method dramatically reduce the number of unknowns and the corresponding computational effort in the MoM solution of the problem. For this reason it allows the analysis of larger arrays with direct inversion methods.