scispace - formally typeset
Search or ask a question
Author

Andrea Massa

Bio: Andrea Massa 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 69, co-authored 764 publications receiving 15897 citations. Previous affiliations of Andrea Massa include University of Toronto & Centre national de la recherche scientifique.


Papers
More filters
Journal ArticleDOI
TL;DR: A comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.
Abstract: In electromagnetics, optimization problems generally require high computational resources and involve a large number of unknowns. They are usually characterized by non-convex functionals and continuous spaces suitable for strategies based on Differential Evolution (DE). In such a framework, this paper is aimed at presenting an overview of Differential Evolution-based approaches used in electromagnetics, pointing out novelties and customizations with respect to other fields of application. Starting from a general description of the evolutionary mechanism of Differential Evolution, Differential Evolution-based techniques for electromagnetic optimization are presented. Some hints on the convergence properties and the sensitivity to control parameters are also given. Finally, a comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.

496 citations

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
27 Jan 2016
TL;DR: 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.
Abstract: 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.

331 citations

Journal ArticleDOI
TL;DR: A review of the state-of-the-art and most recent advances of compressive sensing and related methods as applied to electromagnetics can be found in this article, where a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed.
Abstract: Several problems arising in electromagnetics can be directly formulated or suitably recast for an effective solution within the compressive sensing (CS) framework. This has motivated a great interest in developing and applying CS methodologies to several conventional and innovative electromagnetic scenarios. This work is aimed at presenting, to the best of the authors’ knowledge, a review of the state-of-the-art and most recent advances of CS formulations and related methods as applied to electromagnetics. Toward this end, a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed. Current challenges and trends in the application of CS to the solution of traditional and new electromagnetic problems are also discussed.

318 citations

Journal ArticleDOI
TL;DR: A numerically-efficient technique based on the Bayesian compressive sampling (BCS) for the design of maximally-sparse linear arrays is introduced, based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine for the problem solution.
Abstract: A numerically-efficient technique based on the Bayesian compressive sampling (BCS) for the design of maximally-sparse linear arrays is introduced. The method is based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine (RVM) for the problem solution. The proposed approach allows the design of linear arrangements fitting desired power patterns with a reduced number of non-uniformly spaced active elements. The numerical validation assesses the effectiveness and computational efficiency of the proposed approach as a suitable complement to existing state-of-the-art techniques for the design of sparse arrays.

286 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

4,321 citations

Journal ArticleDOI
TL;DR: The behavior of the SVM classifier when these hyper parameters take very small or very large values is analyzed, which helps in understanding thehyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors.
Abstract: Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

1,586 citations

Journal ArticleDOI
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
Abstract: Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

1,265 citations

Journal ArticleDOI
TL;DR: A holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution is proposed.

1,003 citations