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Loreto Di Donato

Bio: Loreto Di Donato is an academic researcher from University of Catania. The author has contributed to research in topics: Inverse scattering problem & Microwave imaging. The author has an hindex of 15, co-authored 58 publications receiving 884 citations. Previous affiliations of Loreto Di Donato include Mediterranea University of Reggio Calabria & National Research Council.


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: 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
12 Jun 2019-Sensors
TL;DR: A novel architecture is introduced that integrates an ultra-low power intelligent power management, an RF to DC converter with very low power sensitivity and high power conversion efficiency (PCE), an Amplitude-Shift-Keying/Frequency- shift- Keying/FSK receiver and digital circuitry to achieve the advantage to cope with the wide variety of energy sources and use cases.
Abstract: The continuous development of internet of things (IoT) infrastructure and applications is paving the way for advanced and innovative ideas and solutions, some of which are pushing the limits of state-of-the-art technology. The increasing demand for Wireless Sensor Nodes (WSNs) able to collect and transmit data through wireless communication channels, while often positioned in locations that are difficult to access, is driving research into innovative solutions involving energy harvesting (EH) and wireless power transfer (WPT) to eventually allow battery-free sensor nodes. Due to the pervasiveness of radio frequency (RF) energy, RF EH and WPT are key technologies with the potential to power IoT devices and smart sensing architectures involving nodes that need to be wireless, maintenance free, and sufficiently low in cost to promote their use almost anywhere. This paper presents a state-of-the-art, ultra-low power 2.5 μ W highly integrated mixed signal system on chip (SoC), for multi-source energy harvesting and wireless power transfer. It introduces a novel architecture that integrates an ultra-low power intelligent power management, an RF to DC converter with very low power sensitivity and high power conversion efficiency (PCE), an Amplitude-Shift-Keying/Frequency-Shift-Keying (ASK/FSK) receiver and digital circuitry to achieve the advantage to cope, in a versatile way and with minimal use of external components, with the wide variety of energy sources and use cases. Diverse methods for powering Wireless Sensor Nodes through energy harvesting and wireless power transfer are implemented providing related system architectures and experimental results.

74 citations

Journal ArticleDOI
TL;DR: The possibility of mimicking different types of breast tissues to realize experimental phantoms has been investigated by measuring the complex permittivity of polyethylene glycol mono phenyl ether (Triton X-100) and distilled water solutions.
Abstract: The possibility of mimicking different types of breast tissues to realize experimental phantoms has been investigated by measuring the complex permittivity of polyethylene glycol mono phenyl ether (Triton X-100) and distilled water solutions. In this respect, broad band electromagnetic characterization of several mixtures, at different concentrations, has been carried out in the 0.5-12 GHz frequency range. The good fitting between the dielectric properties of mammary tissues and that of the proposed mixtures, and the stability against temperature in the range 18-30 � C, suggest the possibility of mimicking the dielectric characteristics of breast tissues using easily available and low cost materials. V C 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1276-1280, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26001

66 citations

Journal ArticleDOI
TL;DR: In this paper, a method based on the aperture antennas theory is proposed to understand the limitations of OAM antennas in far-field links and additional insight is also given by analyzing the properties of the operators relating source and farfield distributions for a given order of the vortex.
Abstract: In this article, we propose a method based on the aperture antennas theory to understand the limitations of orbital angular momentum (OAM) antennas in far-field links. Additional insight is also given by analyzing the properties of the operators relating source and farfield distributions for a given order of the vortex and emphasizing additional drawbacks. The degrees-of-freedom (DoF) of the fields associated with the different orders of the vortices are also discussed.

63 citations


Cited by
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Book ChapterDOI
01 Jan 2018
TL;DR: Ground penetrating radar (GPR) is a tool for indirectly looking at underground objects (such as graves, gravel and sand layers, and other underground structures) using radio waves, which have a longer wavelength than x-rays.
Abstract: Ground penetrating radar (GPR) is a tool for indirectly looking at underground objects (such as graves), gravel and sand layers, and other underground structures. The information or data received by GPR is like an x-ray or map of the underground. In fact, GPR uses electromagnetic (EM) waves, as xray machines do, but GPR uses radio waves, which have a longer wavelength (see Figure A1). The wavelength, or the length of one wave, is the fundamental difference between the forms of electromagnetic energy. For example, the wavelength of x-rays range from about 10 billionths of a meter to about 10 trillionths of a meter, whereas radio waves can be a few meters long.

428 citations

Journal ArticleDOI
TL;DR: In this article, the authors exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering, and propose DeepNIS, which consists of a cascade of multilayer complex-valued residual convolutional neural network modules.
Abstract: Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computational costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multilayer complex-valued residual convolutional neural network modules. We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, which are extremely hard and impractical to solve using conventional inverse scattering methods.

278 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed.
Abstract: Widely used medical imaging systems in clinics currently rely on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The aforementioned technologies provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. Microwave sensing and imaging (MSI) is an alternative method based on nonionizing electromagnetic (EM) signals operating over the frequency range covering hundreds of megahertz to tens of gigahertz. The advantages of using EM signals are low health risk, low cost implementation, low operational cost, ease of use, and user friendliness. Advancements made in microelectronics, material science, and embedded systems make it possible for miniaturization and integration into portable, handheld, mobile devices with networking capability. MSI has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, cancer detection, and localization of in-body RF sources. The fundamental notion of MSI is that it exploits the tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed.

274 citations

Journal ArticleDOI
TL;DR: A novel method that could be applied to the fleld of MR brain image classiflcation and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees is presented.
Abstract: Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of Kfold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer’s disease, Alzheimer’s disease plus visual agnosia, Pick’s disease, sarcoma, and Huntington’s disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256× 256 size image on a laptop of P4 IBM with 3GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees. Received 14 June 2012, Accepted 23 July 2012, Scheduled 19 August 2012 * Corresponding author: Yudong Zhang (zhangyudongnuaa@gmail.com).

230 citations

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