A Physics-Driven AI Approach for Microwave Imaging of Breast Tumors

In this paper, an innovative microwave imaging (MI) approach for breast tumor diagnosis is proposed that employs a differential formulation of the inverse scattering problem (ISP) at hand to exploit arbitrary-fidelity priors on the inhomogeneous reference/healthy tissues. The quantitative imaging of the unknown tumor is then rephrased into a global optimization problem, which is efficiently solved with an ad-hoc physics-driven artificial intelligence (AI) strategy inspired by the concepts and guidelines of the System-by-Design (SbD) paradigm. The effectiveness, the robustness, the reliability, and the efficiency of the proposed method are assessed against both synthetic and experimental data.

Multi-Scaling Differential Contraction Integral Method for Inverse Scattering Problems with Inhomogeneous Media

Practical applications of microwave imaging often require the solution of inverse scattering problems with inhomogeneous backgrounds. Towards this end, a novel inversion strategy, which combines the multi-scaling (MS) regularization scheme and the Difference Contraction Integral Equation (DCIE) formulation, is proposed. Such an integrated approach mitigates the non-linearity and the ill-posedness of the problem to obtain reliable high-resolution reconstructions of the unknown scattering profiles. The arising algorithmic implementation, denoted as MS-DCIE, does not require the computation of the Green's function of the inhomogeneous background, thus it provides an efficient and effective way to deal with complex scenarios. The performance of the MS-DCIE are assessed by means of numerical and experimental tests, in comparison with competitive state-of-the-art inversion strategies, as well.

Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors

Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well.

Spline-Shaped Microstrip Edge-Fed Antenna for 77 GHz Automotive Radar Systems

An innovative millimeter-wave (mm-wave) microstrip edge-fed antenna (EFA) for 77 GHz automotive radars is proposed. The radiator contour is modeled with a sinusoidal spline-shaped (SS) profile characterized by a reduced number of geometrical descriptors, but still able to guarantee a high flexibility in the modeling for fulfilling challenging user-defined requirements. The SS-EFA descriptors are effectively and efficiently optimized with a customized implementation of the System-by-Design (SbD) paradigm. The synthesized EFA layout, integrated within a linear arrangement of identical replicas to account for the integration into the real radar system, exhibits suitable impedance matching, isolation, polarization purity, and stability of the beam shaping/pointing within the target band [76:78][GHz]. The experimental assessment, carried out with a Compact Antenna Test Range (CATR) system on a printed circuit board (PCB)-manufactured prototype, assess the reliability of the outcomes from the full-wave (FW) simulations as well as the suitability of the synthesized SS-EFA for automotive radars.

On the Design of Complex EM Devices and Systems through the System-by-Design Paradigm -- A Framework for Dealing with the Computational Complexity

The System-by-Design (SbD) is an emerging engineering framework for the optimization-driven design of complex electromagnetic (EM) devices and systems. More specifically, the computational complexity of the design problem at hand is addressed by means of a suitable selection and integration of functional blocks comprising problem-dependent and computationally-efficient modeling and analysis tools as well as reliable prediction and optimization strategies. Thanks to the suitable re-formulation of the problem at hand as an optimization one, the profitable minimum-size coding of the degrees-of-freedom (DoFs), the "smart" replacement of expensive full-wave (FW) simulators with proper surrogate models (SMs), which yield fast yet accurate predictions starting from minimum size/reduced CPU-costs training sets, a favorable "environment" for an optimal exploitation of the features of global optimization tools in sampling wide/complex/nonlinear solution spaces is built. This research summary is then aimed at (i) providing a comprehensive description of the SbD framework and of its pillar concepts and strategies, (ii) giving useful guidelines for its successful customization and application to different EM design problems characterized by different levels of computational complexity, (iii) envisaging future trends and advances in this fascinating and high-interest (because of its relevant and topical industrial and commercial implications) topic. Representative benchmarks concerned with the synthesis of complex EM systems are presented to highlight advantages and potentialities as well as current limitations of the SbD paradigm.

A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization

A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm and it exploits some a-priori information on the antenna under test (AUT) to generate an over-complete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally-sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data and then it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the "burden/cost" of the acquisition process as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.

Holographic Smart EM Skins for Advanced Beam Power Shaping in Next Generation Wireless Environments

An innovative approach for the synthesis of inexpensive holographic smart electromagnetic (EM) skins with advanced beamforming features is proposed. The complex multiscale smart skin design is formulated within the Generalized Sheet Transition Condition (GSTC) framework as a combination of a mask-constrained isophoric inverse source problem and a micro-scale susceptibility dyadic optimization. The solution strategy integrates a local search procedure based on the iterative projection technique (IPT) and a System-by-Design (SbD)-based optimization loop for the identification of optimal metasurface descriptors matching the desired surface currents. The performance and the efficiency of the proposed approach are assessed in a set of representative test cases concerned with different smart skin apertures and target pattern masks.