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G. Franceschini

Bio: G. Franceschini is an academic researcher from University of Trento. The author has contributed to research in topics: Microwave imaging & Inverse scattering problem. The author has an hindex of 9, co-authored 26 publications receiving 408 citations.

Papers
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Journal ArticleDOI
TL;DR: The application of a multiscale strategy integrated with a stochastic technique to the solution of nonlinear inverse scattering problems is presented and allows the explicit and effective handling of many difficulties associated with such problems ranging from ill-conditioning to nonlinearity and false solutions drawback.
Abstract: 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.

101 citations

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TL;DR: A parallel implementation of an inverse scattering procedure based on a suitable hybrid genetic algorithm is presented, aimed at reducing the overall clock time in order to make the approach competitive with gradient-based methods in terms of runtime, but preserving the capabilities of escaping from local minima.
Abstract: Genetic algorithms (GAs) are well-known optimization strategies able to deal with nonlinear functions as those arising in inverse scattering problems. However, they are computationally expensive, thus offering poor performances in terms of general efficiency when compared with inversion techniques based on deterministic optimization methods. In this paper, a parallel implementation of an inverse scattering procedure based on a suitable hybrid genetic algorithm is presented. The proposed strategy is aimed at reducing the overall clock time in order to make the approach competitive with gradient-based methods in terms of runtime, but preserving the capabilities of escaping from local minima. This result is achieved by exploiting the natural parallelism of evolutionary techniques and the searching capabilities of the hybrid approach . The effectiveness of the proposed implementation is demonstrated by considering a selected numerical benchmark related to two-dimensional scattering geometries.

73 citations

Journal ArticleDOI
TL;DR: 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, based on the use of an inverse source algorithm.
Abstract: 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

54 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented a numerical study on the reconstruction accuracy (quantitative imaging) of the integrated GA-based multicrack strategy, thus completing the assessment previously carried out and limited to verifying the accuracy of the qualitative imaging (i.e., crack detection, location, and size estimation).
Abstract: This letter is aimed at presenting a numerical study on the reconstruction accuracy (quantitative imaging) of the integrated genetic algorithm (GA)-based multicrack strategy, thus completing the assessment previously carried out and limited to verifying the accuracy of the qualitative imaging (i.e., crack detection, location, and size estimation). The obtained results prove an acceptable reliability and accuracy of the GA-based integrated strategy also in reconstructing multiple defective regions even though the resulting performances degrade in comparison with those achieved by the same approach when used for qualitative imaging purposes.

50 citations

Journal ArticleDOI
TL;DR: This paper presents an innovative microwave technique, which is suitable for the detection of defects in nondestructive-test and nondestructureive-evaluation applications where a lot of a priori information is available, and shows some interesting features by a computational point of view.
Abstract: 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.

45 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems is presented, focusing on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves.
Abstract: This review is aimed at presenting an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems. The focus of this work is on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves. Starting from a general description of the structure of EAs, the classical stochastic operators responsible for the evolution process are described. The extension to hybrid implementations when integrated with local search techniques and the exploitation of the 'domain knowledge', either a priori obtained or collected during the optimization process, are also presented. Some theoretical discussions concerned with the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported as well. Successively, a review on how various researchers have applied or customized different evolutionary approaches to inverse scattering problems is carried out ranging from the shape reconstruction of perfectly conducting objects to the detection of the dielectric properties of unknown scatterers up to applications to sub-surface or biomedical imaging. Finally, open problems and envisaged developments are discussed.

439 citations

Journal ArticleDOI
TL;DR: In this paper, the authors give an overview of recent techniques which use a level set representation of shapes for solving inverse scattering problems, including shape sensitivity analysis and topological derivatives, and various techniques for incorporating regularization into the shape inverse problem using level sets.
Abstract: We give an overview of recent techniques which use a level set representation of shapes for solving inverse scattering problems. The main focus is on electromagnetic scattering using different popular models, such as for example Maxwell's equations, TM-polarized and TE-polarized waves, impedance tomography, a transport equation or its diffusion approximation. These models are also representative of a broader class of inverse problems. Starting out from the original binary approach of Santosa for solving the corresponding shape reconstruction problem, we successively develop more recent generalizations, such as for example using colour or vector level sets. Shape sensitivity analysis and topological derivatives are discussed as well in this framework. Moreover, various techniques for incorporating regularization into the shape inverse problem using level sets are demonstrated, which also include the choice of subclasses of simple shapes, such as ellipsoids, for the inversion. Finally, we present various numerical examples in two dimensions and in three dimensions for demonstrating the performance of level set techniques in realistic applications.

400 citations

Journal Article
TL;DR: In this article, the experimental set-up of the Institut Fresnel used to measure the scattered fields of different elongated objects is precisely described and the two-dimensional inhomogeneous ones are presented.
Abstract: In the present paper, the experimental set-up of Institut Fresnel used to measure the scattered fields of different elongated objects is precisely described. Since the special issue on 'Testing inversion algorithms against experimental data', the modifications of this system, outlined here, have mostly been done to improve the synchronization of the apparatuses and the precision of our measurements. Due to a large number of requests from the inverse problem community, it has been decided to add new measurements to the Institut Fresnel's database. All the new targets presented here are two-dimensional inhomogeneous ones. They are made of different dielectrics or are mixing metal and dielectric parts. Both TE and TM polarizations are measured for each target, from 2 to 10 GHz and even 18 GHz for the most complex target. In the first part of this paper the set-up is described precisely. The second part is devoted to the presentation of the targets. Finally, some TE and TM comparisons of measurements and direct problem simulations are shown to accredit our experimental method and to give an idea of the accuracy of these measurements.

218 citations

Journal ArticleDOI
TL;DR: Particle swarm optimization (PSO) as mentioned in this paper is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food, and it can be applied to inversion of geophysical data.
Abstract: Particle swarm optimization (PSO) is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate the applicability of PSO to inversion of geophysical data, we inverted three noise-corrupted synthetic sounding data sets over a multilayered 1D earth model by using DC, induced polarization (IP), and magnetotelluric (MT) methods. The results show that acceptable solutions can be obtained with a swarm of about 300 particles and that convergence occurs in less than 100 iterations. The time required to execute a PSO algorithm is comparable to that of a genetic algorithm (GA). Similarly, the models estimated from PSO and GA are close to the true solutions. Whereas a ridge regression (RR) algorithm converges in four to eight iterations, it yields satisfactory re...

191 citations

Journal ArticleDOI
TL;DR: Stochastic methods are now very common in electromagnetics as discussed by the authors, they have been recently proposed for solving inverse problems arising in radio-frequency and microwave imaging, and the main features making these approaches useful for imaging purposes are discussed and the currently considered strategies to reduce the computational load associated with stochastic optimization procedures delineated.
Abstract: Stochastic methods are now very common in electromagnetics. Among the various applications, they have been recently proposed for solving inverse problems arising in radio-frequency and microwave imaging. Some of the recently proposed stochastic inversion procedures are critically discussed (e.g., genetic algorithms, differential evolution methods, memetic algorithms, particle swarm optimizations, hybrid techniques, etc.) and the way they have been applied in this area. The use of the ant colony optimization method, which is a relatively new method in electromagnetics, is also proposed. Various imaging modalities are considered (tomography, buried object detection, and borehole sensing). Finally, the main features making these approaches useful for imaging purposes are discussed and the currently considered strategies to reduce the computational load associated with stochastic optimization procedures delineated

181 citations