An innovative inverse scattering (IS) technique for the simultaneous processing of multifrequency (MF) ground-penetrating radar (GPR) measurements is proposed. The nonlinear IS problem is solved by ...profitably integrating a customized MF version of the particle swarm optimizer (PSO) within the iterative multiscaling approach (IMSA) to jointly exploit the reduction of the ratio between unknowns and uncorrelated data with a pervasive exploration of the multidimensional search space for minimizing the probability that the solution is trapped into local minima corresponding to false solutions of the problem at hand. Both numerical and experimental test cases are reported to assess the reliability of the MF-IMSA-PSO method toward accurate GPR tomography as well as improvements with respect to the competitive state-of-the-art inversion approaches.
This paper describes a class of linear thinned arrays with predictable and well-behaved sidelobes. The element placement is based on almost difference sets and the array power pattern is forced to ...pass through N uniformly-spaced values that, although neither equal nor constant as for difference sets, are a-priori known from the knowledge of the aperture size, the number of active array elements K , and the features of the correlation function. Such a property allows one to predict the bounds of the confidence range of the peak sidelobe of the admissible arrays obtainable through simple shift operations on a binary sequence. The expected peak sidelobe performances turn out to be comparable with those from difference sets, even though obtainable in a wider set of array configurations, and improved in comparison with cut-and-try random-placements.
A generalized formulation is derived for the analysis of the field manipulation properties of electromagnetic skins (EMSs) in the working regimes of interest for wireless communications. Based on ...such a theoretical framework, a unified method for the design of anomalous-reflecting and focusing EMSs is presented. Representative results, from a wide set of numerical experiments, are reported and validated with full-wave HFSS simulations to give the interested readers some insights on the accuracy, effectiveness, and computational efficiency of the proposed analysis/synthesis tools.
There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultaneous PET/MR and radiotherapy planning. Deep ...learning has recently shown great potential for solving this problem. The goal of this research was to investigate the capability of four common clinical MRI sequences (T1-weighted gradient-echo T1, T2-weighted fat-suppressed fast spin-echo T2-FatSat, post-contrast T1-weighted gradient-echo T1-Post, and fast spin-echo T2-weighted fluid-attenuated inversion recovery CUBE-FLAIR) as inputs into a deep CT synthesis pipeline. Data were obtained retrospectively in 92 subjects who had undergone an MRI and CT scan on the same day. The patient's MR and CT scans were registered to one another using affine registration. The deep learning model was a convolutional neural network encoder-decoder with skip connections similar to the U-net architecture and Inception V3 inspired blocks instead of sequential convolution blocks. After training with 150 epochs and a batch size of 6, the model was evaluated using structural similarity index (SSIM), peak SNR (PSNR), mean absolute error (MAE), and dice coefficient. We found that feasible results were attainable for each image type, and no single image type was superior for all analyses. The MAE (in HU) of the resulting synthesized CT in the whole brain was 51.236 ± 4.504 for CUBE-FLAIR, 45.432 ± 8.517 for T1, 44.558 ± 7.478 for T1-Post, and 45.721 ± 8.7767 for T2, showing not only feasible, but also very compelling results on clinical images. Deep learning-based synthesis of CT images from MRI is possible with a wide range of inputs, suggesting that viable images can be created from a wide range of clinical input types.
The synthesis of radiating planar arrays for wireless power transmission (WPT) is discussed. The objective is the maximization of the ratio between the power radiated on a target area and the total ...transmitted power. The optimal tapering is analytically found as the solution of a generalized eigenvalue problem whose descriptive matrices are either computed in closed-form or obtained through numerical integration depending on the problem geometry. A set of representative numerical results concerned with different transmitting apertures and target areas is presented.
A hybrid approach for the synthesis of planar thinned antenna arrays is presented. The proposed solution exploits and combines the most attractive features of a particle swarm algorithm and those of ...a combinatorial method based on the noncyclic difference sets of Hadamard type. Numerical experiments validate the proposed solution, showing improvements with respect to previous results.
This study proposes a genetic algorithm (GA)-enhanced almost difference set (ADS)-based methodology to design thinned linear arrays with low-peak sidelobe levels (PSLs). The method allows one to ...overcome the limitations of the standard ADS approach in terms of flexibility and performances. The numerical validation, carried out in the far-field and for narrow-band signals, points out that with affordable computational efforts it is possible to design array arrangements that outperform standard ADS-based designs as well as standard GA approaches.
Wireless sensor networks (WSNs) have shown many attractive features in a lot of real‐world applications that motivate their rapid and wide diffusion. One of the most challenging topics when dealing ...with WSNs is the localization and tracking of objects from measurements collected by the nodes themselves. Once distributed in a region without the knowledge of their positions, the nodes actively take part in the localization of the network as well as to the detection and monitoring of the presence and movements of targets lying within the sensed area. This paper reviews state‐of‐the‐art systems and approaches developed for WSN‐based localization and tracking of active as well as passive targets. The main focus is on systems that exploit the strength of the received signal, always available at the WSN nodes, without ad hoc or additional hardware. Recent strategies for WSN‐based imaging are discussed as well.
Key Points
Localization and tracking in WSNs infrastructured environments
State‐of‐the‐art review
Localization and tracking of cooperative and noncooperative targets
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 overcomplete 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 and mitigate (possible) truncation errors when dealing with space-constrained probing systems.
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