Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like extended targets, since multiple parts of the vehicle’s body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles’ roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: 1) by introducing a compact high-reflectivity structure on the target, we make vehicles behave like point targets, avoiding perspective deformations and related ranging biases and 2) we increase the reflectivity of the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cramér-Rao bound analysis in both narrowband and spatial wideband operating conditions.

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices.

Compressive sensing (CS) is currently one the most active research field in information engineering and science. The CS features/properties (e.g., its flexibility, robustness, accuracy, and effectiveness) and the strong theoretical background have motivated a great interest in developing and applying CS to many domains including antenna arrays. Indeed, several problems arising in such a framework can be directly formulated or suitably recast for an effective solution within the CS environment. This work is aimed at presenting a review of the current and most recent advances of CS formulations for antenna arrays developed at the ELEDIA research center. Towards this end, a set of representative applicative scenarios are illustrated ranging from the diagnosis and synthesis of antenna arrays up to antenna array processing including the estimation of directions-of-arrival. Current challenges and trends in the application of CS to the solution of traditional and/or innovative antenna array problems are discussed, as well.

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.

The proliferation of wireless services is driving innovative phased array solutions that are able to provide better cost/performance tradeoffs. In this context, the use of irregular array architectures provides a viable solution. This paper reviews and highlights some of the most recent advances in this field, including clustered, thinned, sparse, and time-modulated arrays, and their proposed design methodologies.

The real-time retrieval of the characteristics of a defect with eddy current testing in a nondestructive testing and evaluation framework is addressed. An innovative statistical learning approach is developed to deal with the inversion problem at hand in a computationally efficient way. More in detail, a feature extraction technique based on partial least squares (PLS) is profitably combined with a customized output space filling (OSF) adaptive sampling scheme for generating optimal training databases, while accurate and robust reconstructions are performed with a support vector regression (SVR) algorithm. A selected set of numerical and experimental results is reported to assess the effectiveness as well as the efficiency of the proposed PLS-OSF/SVR approach.

The introduction of electromagnetic (EM) media with unique field manipulation properties, collectively labelled as metamaterials, has boosted the interest toward the design, the fabrication, and the testing of artificial materials whose features can be engineered according to the applicative requirements and user objectives. In this framework, the long-term promise of reconfigurable metamaterial theory (i.e., the possibility to change the EM response of a 2-D/3-D material arbitrarily and in real time) has given the designers an extremely wide number of new degrees of freedom for the synthesis of innovative adaptive systems. Moreover, successful experimental validations of reconfigurable metamaterials in the entire EM spectrum from microwaves to optical frequencies have further stimulated academic and industrial interests in developing devices with enhanced performances, efficiency, and robustness. Nevertheless, the exploitation of reconfigurable metamaterials in commercial devices is still an open problem with several challenges from both the theoretical and technological viewpoints. This paper is then aimed at reviewing the latest advances on reconfigurable metamaterial engineering from the methodological perspective also providing a comprehensive and balanced survey on latest concepts, current trends, and envisaged future developments on this active field of research.

An innovative information-acquisition approach to 2-D ground-penetrating radar (GPR) prospecting is presented. A microwave inverse-scattering nested scheme combining a frequency hopping (FH) procedure and a multifocusing (MF) technique is proposed. On the one hand, the FH scheme effectively handles multifrequency GPR data, whereas on the other hand, MF techniques have been proven to be effective tools in reducing the occurrence of multilocal minima affecting GPR investigations. This allows the use of a local search technique based on the conjugate gradient method to iteratively solve the inverse problem at hand. Selected results are reported and analyzed to give some insights to the interested readers on the advantages and limitations of such an approach when handling synthetically generated and experimental GPR data.

The concept of long-range wireless power transmission (WPT) has been formulated shortly after the invention of high power microwave amplifiers. The promise of WPT, energy transfer over large distances without the need to deploy a wired electrical network, led to the development of landmark successful experiments, and provided the incentive for further research to increase the performances, efficiency, and robustness of these technological solutions. In this framework, the key-role and challenges in designing transmitting and receiving antenna arrays able to guarantee high-efficiency power transfer and cost-effective deployment for the WPT system has been soon acknowledged. Nevertheless, owing to its intrinsic complexity, the design of WPT arrays is still an open research field whose importance is growing as the possibility to transfer energy by means of electromagnetic waves gathers more and more interest from the applicative viewpoint. This paper is aimed at reviewing the array design approaches proposed in the state of the art for long-range wireless power transmission, highlighting the latest advances and innovative solutions as well as envisaging possible future trends of the research in this area.

Application of wireless technologies in the smart home is dealt with by pointing out advantages and limitations of available approaches for the solution of heterogeneous and coexisting problems related to the distributed monitoring of the home and the inhabitants. Some hot challenges facing the exploitation of noninvasive wireless devices for user behavior monitoring are then addressed and the application fields of smart power management and elderly people monitoring are chosen as representative cases where the estimation of user activities improves the potential of location-aware services in the smart home. The problem of user localization is considered with great care to minimize the invasiveness of the monitoring system. Wireless architectures are reviewed and discussed as flexible and transparent tools toward the paradigm of a totally automatic/autonomic environment. With respect to available state-of-the-art solutions, our proposed architecture is based also on existing wireless devices and exploits, in an opportunistic way, the characteristics of wireless signals to estimate the presence, the movements, and the behaviors of inhabitants, reducing the system complexity and costs. Selected and representative examples from real implementations are presented to give some insight on state-of-the-art solutions also envisaging possible future trends.

In this paper, the reconstruction of sparse scatterers under multiview transverse-electric illumination is dealt with. Starting from a probabilistic formulation of the “inverse source” problem, two Bayesian compressive sensing approaches are introduced. The former is a suitable extension of the single-task method presented earlier for the transverse-magnetic scalar case, while the other exploits an innovative multitask implementation to take into account the relationships among the “contrast currents” at the different probing views. Representative numerical results are discussed to assess, also comparatively, the numerical efficiency, the accuracy, and the robustness of the proposed approaches.

We consider cross-borehole imaging of buried objects embedded in a large search area by using a time domain inverse analysis. For simplicity, a two-dimensional model is examined. In order to avoid trap into false solution and enhance the achievable spatial resolution, the iterative multiscaling strategy combined with the forward-backward time-stepping method is proposed. Preliminary results show the effectiveness of proposed method.

In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.

In the framework of inverse scattering techniques for microwave imaging, this paper proposes an approach based on the integration between a multiscaling procedure and the level-set-based optimization in order to properly deal with the shape reconstruction of multiple and disconnected homogeneous scatterers. The effectiveness and robustness of the proposed approach is assessed against both synthetic and experimental data. A selected set of results concerned with complex shapes is presented and discussed.

In this work, the problem of the localization and shaping of buried objects or voids is addressed considering an innovative two steps strategy in order to enhance the reconstruction accuracy. In particular, at the first step the approach is aimed at firstly estimating the region-of-interest where the defect is supposed to be located. Then at the second step, the qualitative imaging of the object is improved through a level-set based shaping procedure. In order to assess the effectiveness of the proposed approach, selected numerical results concerned with different scenarios and noisy data are presented and discussed.

In the last two decades, the wide diffusion of mobile phones and wireless technologies has brought many advantages in professional activities as well as in everyday life. In general, mobile communication networks are constituted by a limited number of base stations. The spatial distribution of the field radiated from base stations turns out to be non-uniform with variations both in time and space that can exceed the normative limits of electromagnetic emissions. In order to real-time and in a long-term monitor the electromagnetic emissions, an innovative low cost solution based on a wireless sensor network (WSN) is presented in this paper. Such a network consists of spatially distributed and wirelessly connected autonomous devices (called nodes) that use suitable sensor to cooperatively collect physical quantities. The proposed solution considers the deployment of a set of WSN nodes, equipped with a broadband field probe, to monitor the level of the electromagnetic radiation in a distributed and real-time fashion.

The detection of buried landmines or unexploded ordnances is a problem of great interest in both civilian and military applications. In such a framework, this paper presents an approach for real-time localization of unknown subsurface objects. Such a technique is aimed at defining high resolution risk maps by means of a classification strategy based on a support vector machine and a multi-resolution approach. In order to assess its effectiveness, a selected set of numerical results regarding multiple target scenarios are presented.

Driven by economic and environmental pressures, precision agriculture has brought many technological enhancements to traditional farm machinery and management tools. For a small tillage, manual control of inputs (such as water, fertilizer, pesticide) is still possible, but such an approach becomes unfeasible for larger cultivations. Furthermore, the manual control is based on the operator's opinion and often have no quantitative basis. In order to optimize the yield and the use of the available resources, wireless sensor networks can play a relevant role because of their ability of providing real-time data collected by spatially distributed sensors.

The rapid progress of wireless communication and embedded systems has made wireless sensor networks a valuable backbone for numerous applications, mainly with monitoring purposes. In this field, the need for location aware systems has growth rapidly in the last few years. Most research efforts have been done in node localization but less attention has been paid on the localization and tracking of passive (that do not belong to the network infrastructure) objects. In this paper, the problem of passive object localization is dealt with an innovative methodology based on support vector machine exploiting the received signal strength indicator measured by the nodes. Some preliminary results chosen from the assessment of the proposed approach are presented.

An enhanced multistep strategy based on a multiresolution particle swarm optimizer is proposed for 3-D microwave imaging. The aim of such an integration is to improve the convergence capabilities of the approach and to reduce the dimension of the search space and the computational burden of the optimization strategy, thanks to a constrained control of the particle velocities adaptively determined. This favors the exploitation of the global search capabilities of the particle swarms also in the framework of large-scale 3-D inverse scattering problems. The proposed technique is assessed by considering numerical tests concerned with single and multiple 3-D targets. The results of an experimental testing are also discussed.

In this letter a physical limit to the information capacity of a multiple-input multiple-output (MIMO) Gaussian channel is presented exploiting the theory of non-redundant sampling of scattered fields. For a MIMO narrow-band system of arbitrarily large spatial extension, the information capacity limit is the same as the one of a single-input single-output (SISO) ultra-wide band (UWB) system. For MIMO systems of finite size, transmitting over a range of frequencies, space and frequency diversities can be optimally combined by allocating the signal power uniformly across space, and increasing linearly across frequency.

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of edge potential functions (EPF) with a powerful matching tool based on genetic algorithms (GAs). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies

The application of a multiscale strategy integrated with a stochastic technique to the solution of nonlinear inverse scattering problems is presented. The approach allows the explicit and effective handling of many difficulties associated with such problems ranging from ill-conditioning to nonlinearity and false solutions drawback. The choice of a finite dimensional representation for the unknowns, due to the upper bound to the essential dimension of the data, is iteratively accomplished by means of an adaptive multiresolution model, which offers a considerable flexibility for the use of the information on the scattering domain acquired during the iterative steps of the multiscaling process. Even though a suitable representation of the unknowns could limit the local minima problem, the multiresolution strategy is integrated with a customized stochastic optimizer based on the behavior of a particle swarm, which prevents the solution from being trapped into false solutions without a large increasing of the overall computational burden. Selected examples concerned with a two-dimensional microwave imaging problem are presented for illustrating the key features of the integrated stochastic multiscaling strategy.

In this paper, a new approach for the quantitative electromagnetic imaging of unknown scatterers located in free space from amplitude-only measurements of the total field is proposed and discussed. The reconstruction procedure splits the problem into two steps. The method is based on the use of an inverse source algorithm to first complete the scattering data by estimating the distribution of the radiated field in the investigation domain. The object's function profile is then retrieved from the phaseless data via an iterative multiresolution procedure integrated with an effective minimization technique based on the particle swarm algorithm. Numerical examples are provided to assess the effectiveness of the whole two-step strategy in the presence of synthetic noise-corrupted data as well as in dealing with experimental data sets. Comparisons with full-data and "bare" approaches are reported as well

The presence of noise in measured data should be carefully considered in inverse scattering methodologies, because of the intrinsic ill-conditioning of the problem. To limit the effects of noise on the retrieval procedure, this letter presents an innovative fuzzy-logic-based approach. The proposed strategy allows one to take into account the corrupted nature of the data by fully exploiting all the available information content of the measurements. Selected synthetic and experimental test cases are considered for assessing the effectiveness of the proposed approach also in comparison with a reference inverse scattering technique.

In this letter, a multiscaling strategy for full-vectorial three-dimensional inverse scattering problems is presented. The approach is fully iterative, and it avoids solving any forward problem at each step. Thanks to the adaptive multiresolution model, which offers considerable flexibility for the inclusion of the a priori knowledge and of the knowledge acquired during the iterative steps of the multiscaling process, the overall computational burden as well as the dimension of the search-space is considerably reduced. This allows to balance effectively the tradeoff between computational costs and achievable resolution accuracy. The effectiveness of the proposed approach is demonstrated through a selected set of preliminary experiments using homogeneous dielectric scatterers in a noisy synthetic environment.

This paper presents an innovative microwave technique, which is suitable for the detection of defects in nondestructive-test and nondestructive-evaluation (NDT/NDE) applications where a lot of a priori information is available. The proposed approach is based on the equations of the inverse scattering problem, which are solved by means of a minimization procedure based on a genetic algorithm. To reduce the number of problem unknowns, the available a priori information is efficiently exploited by introducing an updating procedure for the electric field computation based on the Sherman-Morrison-Woodbury formula. The results of a representative set of numerical experiments as well as comparisons with state-of-the-art methods are reported. They confirm the effectiveness, feasibility, and robustness of the proposed approach, which shows some interesting features by a computational point of view as well.

In clearing terrains contaminated or potentially contaminated by landmines and/or unexploded ordnances (UXOs), a quick wide-area surveillance is often required. Nevertheless, the identification of dangerous areas (instead of the detection of each subsurface object) can be enough for some scenarios/applications, allowing a suitable level of security in a cost-saving way. In such a framework, this paper describes a probabilistic approach for the definition of risk maps. Starting from the measurement of the scattered electromagnetic field, the probability of occurrence of dangerous targets in an investigated subsurface area is determined through a suitably defined classifier based on a support vector machine. To assess the effectiveness of the proposed approach and to evaluate its robustness, selected numerical results related to a two-dimensional geometry are presented.

In this letter, a real-world working case of interfering signals coming to an antenna array with random arrivals modeled as a Poisson process is considered. A procedure based on a suitable genetic algorithm for adaptive array control is assessed by means of numerical simulations. Selected results clearly demonstrate the effectiveness and flexibility of the proposed procedure.

In this paper, a new approach to the image retrieval problem is presented, that uses edge potential functions (EPF) and genetic algorithms (GA). The method allows a user to draw a rough sketch of the shape and to find or rank the images in a database that contain a similar shape at any position, rotation and scaling factor. It is explained how GAs allow to exploit the capability of EPFs to attract a sketch contour as a result, the algorithm provides the set of geometrical transformations corresponding to the best match, and a confidence factor about the presence of a matching object. The method has been widely tested achieving very satisfactory results.

A new online inverse scattering methodology is proposed. The original problem is recast into a regression estimation and successively solved by means of a support vector machine (SVM). Although the approach can be applied to various inverse scattering applications, it is very suitable for dealing with buried object detection. The application of SVMs to the solution of such problems is firstly illustrated. Then some examples, concerning the localization of a given object from scattered field data acquired at a number of measurement points, are presented. The effectiveness of the SVM method is evaluated in comparison with classical neural network based approaches.

Phaseless data are used to evaluate the application of an electromagnetic inverse-scattering-based procedure for the detection of cylindrical inhomogeneities, which are schematized as multilayer infinite dielectric cylinders with elliptic cross sections. The electromagnetic inverse problem is recast as a global optimization problem and iteratively solved by an efficient memetic algorithm, which combines deterministic and stochastic concepts. Moreover, a recursive analytical procedure is used for the forward-scattering computation. The possibility of localizing and reconstructing the scatterers by using phaseless input data, which would greatly simplify the design of the imaging apparatus, is evaluated both with reference to synthetically produced data and by means of experimental data obtained by a microwave tomograph.

A computational approach based on a genetic algorithm is proposed for the solution of a nonlinear inverse scattering problem for short-range microwave imaging applications. Starting from an integral-equation formulation, the aim is to derive locations, shapes, and distributions of the dielectric parameters of cylindrical scatterers. Simultaneously, the approach also provides the distributions of the internal total electric field. After discretization, the problem is recast as a nonlinear optimization problem. The paper exploits the application of a real-coded genetic algorithm in order to minimize a suitable functional. The reconstruction of strong scatterers with a resolution beyond the Rayleigh criterion is shown, and computational aspects are discussed. Comparisons with results obtained by using approximated formulations and a binary-coded genetic algorithm are also provided. Finally, a hybrid version of the approach (based on the combined strategy of the genetic algorithm and a conjugate gradient method) is presented and preliminarily tested.