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Ilaria Catapano

Bio: Ilaria Catapano is an academic researcher from National Research Council. The author has contributed to research in topics: Ground-penetrating radar & Radar imaging. The author has an hindex of 25, co-authored 141 publications receiving 2206 citations.


Papers
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Journal ArticleDOI
TL;DR: A simple design tool is introduced to devise guidelines to properly set the working frequency as well as to choose the optimum matching medium to facilitate the penetration of the probing wave into the head.
Abstract: The adoption of microwave imaging as a tool for non- invasive monitoring of brain stroke has recently gained increasing attention. In this respect, the paper aims at providing a twofold contribution. First, we introduce a simple design tool to devise guidelines to properly set the working frequency as well as to choose the optimum matching medium needed to facilitate the penetration of the probing wave into the head. Second, we propose an imaging strategy based on a modifled formulation of the linear sampling method, which allows a quasi real time monitoring of the disease's evolution. The accuracy of the design guidelines and performance of the imaging strategy are assessed through numerical examples dealing with 2D anthropomorphic phantoms.

227 citations

Journal ArticleDOI
TL;DR: In this article, a physical interpretation of the linear sampling method is proposed and tested, which shows its relationship with electromagnetic focusing problems and discusses merits and limitations of the method and suggest new guidelines for a successful application.
Abstract: Efficient and reliable reconstruction of location and shape of dielectric targets via microwave imaging is relevant in many applications. In this respect, the linear sampling method is an effective candidate to pursue this task. However, despite its simplicity and computational effectiveness, still its use is restricted to the mathematical community wherein it has been originally developed. Starting from this observation, in this paper we propose and test a simple and original "physical" interpretation of the linear sampling methods, which shows its relationship with electromagnetic focusing problems. Taking advantage of this result we discuss merits and limitations of the method and suggest new guidelines for a successful application. The analysis is supported with results against experimental data

154 citations

Journal ArticleDOI
TL;DR: This paper addresses the problem of reconstructing geometrical features of 3-D targets embedded into a nonaccessible region from multiview multistatic scattered field data by proposing a simplified and improved formulation based on the physical interpretation of SM.
Abstract: This paper addresses the problem of reconstructing geometrical features of 3-D targets embedded into a nonaccessible region from multiview multistatic scattered field data. Sampling methods (SM) are simple and computationally effective approaches to pursue this task. However, their implementation requires a large number of multipolarization sources and probes. Moreover, their performances are often unsatisfactory for aspect-limited measurement configurations and lossy media. In order to tackle these drawbacks, usually faced in subsurface imaging, we propose a simplified and improved formulation based on the physical interpretation of SM. In particular, such a formulation relies on a small number of single polarization probes and exploits multifrequency data, for the first time in the framework of SM. The performances of the resulting approach are verified by monitoring 3-D regions of large extent.

105 citations

Journal ArticleDOI
TL;DR: The factors afiecting the complexity of the inverse problem are exploited to trace guidelines aimed at setting the matching ∞uid, the frequency range and the number of probes in such a way that the dielectric parameters of female breast tissues can be reliably retrieved.
Abstract: Microwave tomography deserves attention in biomedical imaging, owing to its potential capability of providing a morphological and functional assessment of the inspected tissues. However, such a goal requires the not trivial task of solving a non linear inverse scattering problem. In this paper, the factors afiecting the complexity of the inverse problem are exploited to trace guidelines aimed at setting the matching ∞uid, the frequency range and the number of probes in such a way that the dielectric parameters of female breast tissues can be reliably retrieved. Examples, concerning 2D realistic numerical phantoms obtained by NMR images, are given to asses a posteriori the efiectiveness of the proposed guidelines.

99 citations

Journal ArticleDOI
TL;DR: A microwave technique for breast cancer imaging based on the use of magnetic nanoparticles as contrast agent to induce a nonnull magnetic contrast selectively localized within the tumor allows to face cancer imaging as the reconstruction of a magnetic contrast from the corresponding scattered field.
Abstract: In this paper, a microwave technique for breast cancer imaging is presented. The approach is based on the use of magnetic nanoparticles as contrast agent to induce a nonnull magnetic contrast selectively localized within the tumor. This allows us to face cancer imaging as the reconstruction of a magnetic contrast from the corresponding scattered field. To extract, from the measured data the contribution due to the magnetic contrast, i.e., the signal meaningful for cancer imaging, the approach exploits the possibility of modulating the magnetic response of magnetic nanoparticles by means of a polarizing magnetic field. The achievable reconstruction capabilities and the robustness against uncertainties on the electric features of the surrounding electric scenario are assessed by means of numerical examples.

98 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 Jan 2016
TL;DR: In this paper, the authors present the principles of optics electromagnetic theory of propagation interference and diffraction of light, which can be used to find a good book with a cup of coffee in the afternoon, instead of facing with some infectious bugs inside their computer.
Abstract: Thank you for reading principles of optics electromagnetic theory of propagation interference and diffraction of light. As you may know, people have search hundreds times for their favorite novels like this principles of optics electromagnetic theory of propagation interference and diffraction of light, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their computer.

2,213 citations

Journal ArticleDOI
TL;DR: An overview on medical imaging using microwave imaging for breast cancer and its challenges, hopes, and outlook is presented.
Abstract: Microwaves and millimeter waves have been used extensively to image dielectric bodies. The application of microwaves in biomedical imaging and diagnostics, however, remains a field with many uncharted territories. This article is an overview on medical imaging using microwave imaging for breast cancer and its challenges, hopes, and outlook.

532 citations

Journal ArticleDOI
TL;DR: This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures.
Abstract: Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate and flexible methods for data analysis. In this respect, the use of deep learning methods is trend-setting, enabling high predictive accuracy, while learning the relevant data features independently in an end-to-end fashion. Very recently, a series of studies have demonstrated that the deep learning method of Convolutional Neural Networks (CNN) is very effective to represent spatial patterns enabling to extract a wide array of vegetation properties from remote sensing imagery. This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing. The main part synthesizes current trends and developments, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures. The literature review showed that CNN can be applied to various problems, including the detection of individual plants or the pixel-wise segmentation of vegetation classes, while numerous studies have evinced that CNN outperform shallow machine learning methods. Several studies suggest that the ability of CNN to exploit spatial patterns particularly facilitates the value of very high spatial resolution data. The modularity in the common deep learning frameworks allows a high flexibility for the adaptation of architectures, whereby especially multi-modal or multi-temporal applications can benefit. An increasing availability of techniques for visualizing features learned by CNNs will not only contribute to interpret but to learn from such models and improve our understanding of remotely sensed signals of vegetation. Although CNN has not been around for long, it seems obvious that they will usher in a new era of vegetation remote sensing.

473 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