Deep neural network (DNN) techniques have been applied to solve nonlinear electromagnetic inverse scattering problems (ISP) and shown potentials in superior imaging performances over traditional methods. In DNN ISP solvers, data features are learned from imaging samples to accurately represent the contrast distribution through the optimization of hyper-parameters of neural network, while the loss function which often quantifies discrepancies between the predicted imaging result and the ground truth is targeted to be minimized in the training process. In this paper, to further make use of the (piece-wise) smoothness of the contrast distribution, the desired solution is regularized by additionally penalized its total variation (TV) in the loss function. Effects of the TV regularization on the DNN ISP solver are studied and summarized with various simulation settings and the improvement in imaging performance is verified based on experiment data.

A computationally efficient modeling approach to analyze TM and TE electromagnetic scattering from periodic fiber-reinforced laminates is introduced, when they suffer from missing, shrunk/expanded, displaced fibers, and inclusions. Such damages inside the initially periodic structure (the fibers as organized in a given matrix) are seen as undamaged zones with fictitious line sources properly set inside them, and the field results as a summation of responses to the exterior source and equivalent ones. Then, methods for periodic structures can readily be used. Various numerical results are proposed that are evidencing the interest of this approach.

Human exposure to mobile devices is traditionally measured by a system in which the human body (or head) is modelled by a phantom and the energy absorbed from the device is estimated based on the electric fields measured with a single probe. Such a system suffers from low efficiency due to repeated volumetric scanning within the phantom needed to capture the absorbed energy throughout the volume. To speed up the measurement, fast SAR (specific absorption rate) measuring systems have been developed. However, discrepancies of measured results are observed between traditional and fast measuring systems. In this paper, the discrepancies in terms of post-processing procedures after the measurement of electric field (or its amplitude) are investigated. Here, the concerned fast measuring system estimates SAR based on the reconstructed field of the region of interest while the amplitude and phase of the electric field are measured on a single plane with a probe array. The numerical results presented indicate that the fast SAR measuring system has the potential to yield more accurate estimations than the traditional system, but no conclusion can be made on which kind of system is superior without knowledge of the field-reconstruction algorithms and the emitting source.

Computational modeling of a composite laminate made of a homogeneous planar slab wherein circular cylindrical fibers are periodically embedded is carried out when various damages affect the structure and destroy periodicity. Methods based on the Floquet theorem are inapplicable in direct manner. Supercell methodology yields a fictitious periodic structure, so as the electromagnetic field solution everywhere in space can be accurately modeled, provided that the supercell is large enough. Damages include missing, displaced, shrinked and/or expanded fibers, and cylindrical homogeneous inclusions. Plane-wave and line-source illuminations are accounted for in both TE and TM polarizations. Since often a prerequisite to imaging of damages, Green's functions are considered in detail and computed with the array scanning method. A set of simulations is proposed, with comparison with a standard finite-element approach, and modeling accuracy and efficiency are discussed.

Electromagnetic non-destructive testing of damaged multi-layer fiber-reinforced laminates is of concern. Each layer involves periodically positioned circular cylindrical fibers embedded within a matrix medium. Missing, displaced, shrunk, expanded fibers as well as circular inclusions inside a fiber are modeled as properly designed equivalent sources inside the undamaged structure. Then, the location of damages is retrieved via simply searching for such equivalent sources. A sparsity-constrained solution as well as classical algorithms including truncated singular value decomposition (TSVD), multiple signal classification (MUSIC) and basic matching pursuit (BMP) are considered, and their pros and cons are illustrated by numerical results in various configurations.