In this paper, we propose a novel pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery. The entire process is subdivided into four steps. After a preprocessing step, the GPR image is thresholded to put under light the regions containing potential objects. The third step of the system consists of automatically detecting the objects in the obtained binary image by means of a search of linear/hyperbolic patterns formulated within a genetic optimization framework. In the genetic optimizer, each chromosome models the apex position and the curvature associated with the candidate pattern, while the fitness function expresses the Hamming distance between that pattern and the binary image content. Finally, in the fourth step, the problem of the recognition of the material type of the identified objects is approached as a classification issue, which is solved by means of an opportune feature-extraction strategy and a support vector machine classifier. To illustrate the performances of the proposed system, we conducted a thorough experimental study based on GPR images generated by a GPR simulator based on the finite-difference time-domain method so as to construct different acquisition scenarios by varying the number of buried objects, their position, their size, their shape, and their material type. In general, the obtained experimental results show that the proposed system exhibits promising performances both in terms of object detection and material recognition.

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 computational approach based on an innovative stochastic algorithm, namely, the particle swarm optimizer (PSO), is proposed for the solution of the inverse-scattering problem arising in microwave-imaging applications. The original inverse-scattering problem is reformulated in a global nonlinear optimization one by defining a suitable cost function, which is minimized through a customized PSO. In such a framework, this paper is aimed at assessing the effectiveness of the proposed approach in locating, shaping, and reconstructing the dielectric parameters of unknown two-dimensional scatterers. Such an analysis is carried out by comparing the performance of the PSO-based approach with other state-of-the-art methods (deterministic, as well as stochastic) in terms of retrieval accuracy, as well as from a computational point-of-view. Moreover, an integrated strategy (based on the combination of the PSO and the iterative multiscaling method) is proposed and analyzed to fully exploit complementary advantages of nonlinear optimization techniques and multiresolution approaches. Selected numerical experiments concerning dielectric scatterers different in shape, dimension, and dielectric profile, are performed starting from synthetic, as well as experimental inverse-scattering data.

Radio-frequency identification (RFID) sensors are one of the fundamental components of the internet of things that aims at connecting every physical object to the cloud for the exchange of information. In this framework, chipless RFIDs are a breakthrough technology because they remove the cost associated with the chip, being at the same time printable, passive, low-power and suitable for harsh environments. After the important results achieved with multibit chipless tags, there is a clear motivation and interest to extend the chipless sensing functionality to physical, chemical, structural and environmental parameters. These potentialities triggered a strong interest in the scientific and industrial community towards this type of application. Temperature and humidity sensors, as well as localization, proximity, and structural health prototypes, have already been demonstrated, and many other sensing applications are foreseen soon. In this review, both the different architectural approaches available for this technology and the requirements related to the materials employed for sensing are summarized. Then, the state-of-the-art of categories of sensors and their applications are reported and discussed. Finally, an analysis of the current limitations and possible solution strategies for this technology are given, together with an overview of expected future developments.

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.