The application and comparison of U-Net convolutional neural network architectures is proposed in this work to guarantee a fast and accurate convergence of inverse scattering problems solved by Born iterative method, even in the presence of strong scatterers. Starting from a preliminary configuration proposed by the authors in some recent papers, two variants are introduced and discussed to significantly reduce the computational cost, while guaranteeing convergence with very high accuracy in the dielectric profiles reconstruction when considering strong scattereres, such as tumors, thus working as a regularization process to mitigate the induced non-linearity. As a further enhancement, a novel approach is introduced which integrates U-Net and Resnet models to realize a segmentation process, thus leading to the effective feature extraction and the accurate identification of anomalies within healthy tissue. Numerical assessments on a variety of breast models including abnormal lesions are discussed to successfully validate the proposed machine learning approach, through the adoption of properly defined evaluation metrics.

Microwave sensors are gaining increasing interest in blood glucose detection, for their potential ability to perform a continuous non-invasive monitoring of the glucose concentration, by relating the change in the blood dielectric properties to a variation in the glucose level. Usually, the involved body part (phantom) is placed on the sensor to perform the reading. However, the placement modality, as well as other external factors not related to the blood glucose concentrations (BGC) (i.e. system noise, environmental temperature, human tissues variations other than blood tissue) may also have an effect on the sensor response, due to the change in the propagation path of the electromagnetic field inside the body part under test. In this work, the variation effects induced on the microwave sensor response by the changes in the thickness and the dielectric properties of skin and fat tissues are analyzed and faced. In particular, to mitigate the above drawback in terms of sensor instability, a solid ”matching layer” is interposed between the resonant sensor and the phantom under test. A specific optimization procedure is performed to design microwave sensors with a stable response not influenced by variations in tissues different from the blood. Various sensors configurations with related resolution metrics are considered to assess the proposed idea and design methodology. Numerical results confirm the possibility to achieve a good trade-off between the measurement stability against undesired phantom variations and the sensitivity toward blood glucose levels, allowing to discriminate concentrations in the range of [100-300] mg/dL.

In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.

Statistically thinned arrays are obtained by thinning a reference filled array, according to a probabilistic law which is dictated by the reference current. The remaining radiators are still separated by commensurable distances, even if randomly located. Hence, wide-band operations are prevented by grating lobes occurrence. To overcome the above limitation, a new and simple strategy is presented in this paper for randomly deploying the elemental radiators across the array aperture. More in detail, unlike classic thinned arrays, elements surviving the thinning are not positioned on a priori fixed deterministic lattice. Instead, their positions are modeled as random variables, which are determined by exploiting the binned strategy. Numerical examples reveal that the new proposed scheme allows for a considerable enlargement of the frequency band, without incurring in grating lobes appearance.

Curve-fitting means the determination of the set of parameters that best fit the input data set as expressed by a given function that is usually non-linear. The paper addresses the curve fitting of Debye and Cole–Cole models to a dielectric permittivity spectrum. The success of a nonlinear curve fit heavily depends on the choice of the algorithm and how close the starting point is to the solution. For these reasons, two different algorithms, the Levenberg–Marquardt and the Variable Projection algorithms, were used for constrained optimization and compared, with particular reference to robustness with respect to the choice of the starting point of the reconstruction procedure. The dielectric spectrum of blood plasma with different glucose concentrations is taken as reference data and a Monte Carlo analysis was conducted to evaluate accuracy and precision in the two methods provided as the distance of the initial parameters from the true value’s changes. In general, both algorithms with constraints on the parameters provide good results for practical situations, although the Variable Projection Algorithm has a greater computational burden for large data sets.

Statistically thinned array antennas are usually employed to form single-beam radiation patterns. In this work, the possibility to adopt such type of antennas to obtain multiple-beam patterns is successfully explored. In particular, two schemes are proposed and compared. In the first one, multiple-beam patterns are realized by considering each beam corresponding to a different feeding network. In the second scheme, multiple-beam behavior is achieved by a single feeding network. A key question addressed in this manuscript is given by the analysis of the statistical deviation of the synthesized radiation pattern, as compared to the reference one. To this end, the up-crossing method is employed. In particular, the assumption of symmetric thinned arrays leads to analytical results, but avoids the adoption of the simplified hypothesis which usually give inaccuracy. The proposed approach is verified by a Monte Carlo analysis, and shows very good agreement between empirical data and theoretical predictions.