Computer-aided diagnosis (CAD) is the use of a computer software to help physicians having a better interpretation of medical images. CAD systems can be viewed as pattern recognition algorithms that identify suspicious signs on a medical image and complement physicians' judgments, by reducing inter-/intra-observer variability and subjectivity. The proposed CAD systems in this thesis have been designed based on the statistical approach to pattern recognition as the most successfully used technique in practice. The main focus of this thesis has been on designing (new) feature extraction and classification algorithms for ultrasound-based CAD purposes. Ultrasound imaging has a broad range of usage in medical applications because it is a safe device which does not use harmful ionizing radiations, it provides clinicians with real-time images, it is portable and relatively cheap. The thesis was concerned with developing new ultrasound-based systems for the diagnosis of prostate cancer (PCa) and myocardial infarction (MI) where these issues have been addressed in two separate parts. In the first part, 1) a new CAD system was designed for prostate cancer biopsy by focusing on handling uncertainties in labels of the ground truth data, 2) the appropriateness of the independent component analysis (ICA) method for learning features from radiofrequency (RF) signals, backscattered from prostate tissues, was examined and, 3) a new ensemble scheme for learning ICA dictionaries from RF signals, backscattered from a tissue mimicking phantom, was proposed. In the second part, 1) principal component analysis (PCA) was used for the statistical modeling of the temporal deformation patterns of the left ventricle (LV) to detect abnormalities in its regional function, 2) a spatio-temporal representation of LV function based on PCA parameters was proposed to detect MI and, 3) a local-to-global statistical shape model based on PCA was presented to detect MI.

Internet of Things (IoT) is a recent paradigm that envisions a near future, in which the objects of everyday life will communicate with one another and with the users, becoming an integral part of the Internet. The application of the IoT paradigm to an urban context is of particular interest, as it responds to the need to adopt ICT solutions in the city management, thus realizing the Smart City concept. Creating IoT and Smart City platforms poses many issues and challenges. Building suitable solutions that guarantee an interoperability of platform nodes and easy access, requires appropriate tools and approaches that allow to timely understand the effectiveness of solutions. This thesis investigates the above mentioned issues through two methodological approaches: mathematical modelling and experimenta- tion. On one hand, a mathematical model for multi-hop networks based on semi- Markov chains is presented, allowing to properly capture the behaviour of each node in the network while accounting for the dependencies among all links. On the other hand, a methodology for spatial downscaling of testbeds is proposed, implemented, and then exploited for experimental performance evaluation of proprietary but also standardised protocol solutions, considering smart lighting and smart building scenarios. The proposed downscaling procedure allows to create an indoor well-accessible testbed, such that experimentation conditions and performance on this testbed closely match the typical operating conditions and performance where the final solutions are expected to be deployed.

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

Graphene excellent properties make it a promising candidate for building future nanoelectronic devices. Nevertheless, the absence of an energy gap is an open problem for the transistor application. In this thesis, graphene nanoribbons and pattern-hydrogenated graphene, two alternatives for inducing an energy gap in graphene, are investigated by means of numerical simulations. A tight-binding NEGF code is developed for the simulation of GNR-FETs. To speed up the simulations, the non-parabolic effective mass model and the mode-space tight-binding method are developed. The code is used for simulation studies of both conventional and tunneling FETs. The simulations show the great potential of conventional narrow GNR-FETs, but highlight at the same time the leakage problems in the off-state due to various tunneling mechanisms. The leakage problems become more severe as the width of the devices is made larger, and thus the band gap smaller, resulting in a poor on/off current ratio. The tunneling FET architecture can partially solve these problems thanks to the improved subthreshold slope; however, it is also shown that edge roughness, unless well controlled, can have a detrimental effect in the off-state performance. In the second part of this thesis, pattern-hydrogenated graphene is simulated by means of a tight-binding model. A realistic model for patterned hydrogenation, including disorder, is developed. The model is validated by direct comparison of the momentum-energy resolved density of states with the experimental angle-resolved photoemission spectroscopy. The scaling of the energy gap and the localization length on the parameters defining the pattern geometry is also presented. The results suggest that a substantial transport gap can be attainable with experimentally achievable hydrogen concentration.

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

In this dissertation some novel indices for vulnerability and robustness assessment of power grids are presented. Such indices are mainly defined from the structure of transmission power grids, and with the aim of Blackout (BO) prevention and mitigation. Numerical experiments showing how they could be used alone or in coordination with pre-existing ones to reduce the effects of BOs are discussed. These indices are introduced inside 3 different sujects: The first subject is for taking a look into economical aspects of grids’ operation and their effects in BO propagation. Basically, simulations support that: the determination to operate the grid in the most profitable way could produce an increase in the size or frequency of BOs. Conversely, some uneconomical ways of supplying energy are shown to be less affected by BO phenomena. In the second subject new topological indices are devised to address the question of "which are the best buses to place distributed generation?". The combined use of two indices, is shown as a promising alternative for extracting grid’s significant features regarding robustness against BOs and distributed generation. For this purpose, a new index based on outage shift factors is used along with a previously defined electric centrality index. The third subject is on Static Robustness Analysis of electric networks, from a purely structural point of view. A pair of existing topological indices, (namely degree index and clustering coefficient), are combined to show how degradation of the network structure can be accelerated. Blackout simulations were carried out using the DC Power Flow Method and models of transmission networks from the USA and Europe.

In this thesis integration of high temperature superconductor technology in the future advanced power system will be investigated. In particular, superconducting magnetic energy storage system (SMES) for power quality of distribution grid and customer protection will be discussed. The complete design method, including the magnet and power electronic interface design will be discussed in more details. The method will be applied to the design of an industrial scale SMES system. Commercially available high temperature superconductor (HTS) material (YBCO) and magnesium diboride (MgB2) tapes will be considered for the design of the magnet. A multifunctional control algorithm for compensating voltage sag and improving power quality will be implemented, and the advantages of the SMES system and utilized control algorithm for this application will be illustrated. As a second part of the thesis, high temperature superconducting DC (HTS-DC) cables for transmission and distribution will be introduced. A method for both electromagnetic and thermo fluid-dynamic design of power cable will be developed. As a first case study superconducting DC collector grid for offshore wind-park will be technically and economically evaluated and the cost and loss model of the system will be discussed. Also, the transient behavior of the high temperature superconducting DC cable in high voltage DC (HVDC) system, which is crucial for stability, will be evaluated. Both line commutated converters (LCC) and voltage source converters (VSC) will be considered.

Despite the fast development of telecommunications systems experienced during the last two decades, much progress is expected in the coming years with the introduction of new solutions capable of delivering fast data-rates and ubiquitous connectivity. However, this development can only happen through the evolution of radio-frequency systems, which should be capable of working at high-power and high-speed. At the same time, the power dissipation of these systems should be minimized. In this dissertation, methods for the characterization and modeling of transistors and power amplifiers are presented, along with the necessary nonlinear measurements techniques. In particular, dynamic electrical effects, originated by the properties of the semiconductor materials and by the system configurations, are investigated. Consequently, these phenomena, which cannot be ignored to obtain the wanted performance, are empirically identified and included in models for Gallium Nitride (GaN) transistors and power amplifiers driven by a dynamic voltage supply.

This thesis exhibits the results of my PhD Apprenticeship Program, carried out at the “Marposs S.p.a.” firm, in the electronic research division, and at the Department of Physics and Astronomy of the Bologna University, in the INFN's electronics laboratories of the ATLAS group. During these three years of research, I worked on the development and realization of electronic boards dedicated to flexible data acquisition, designed to be applied in several contexts, that need to share high performance FPGAs and high-speed serial communications. The thesis describes the successful application of high-speed configurable electronic devices to two different fields, firstly developed in the particle physics scenario, and then the industrial measurement of mechanical pieces, reaching the main goal of the PhD Apprenticeship Program. The common denominator is the development of high speed electronics based on FPGAs for demanding data acquisition and data processing applications. The thesis describes the contribution to the luminosity monitor of LHC at CERN and illustrates a multi-camera system developed for automatic inspection of mechanical pieces made by a machine tool. The Apprenticeship Program allowed me to continue my academic course in parallel with my working activity, giving me the opportunity to finalize the project started during my internship and thesis for my master degree. It also allowed me to achieve a higher level in education and training in two different contexts of excellence, i.e. the industrial company and the academic research, where I concretely learned the best technical knowledge. The chance of bringing together two distant worlds was the most enthusiastic aspect of this PhD research. The world of industry and academic research face similar problems but with different points of view and goals. I had the opportunity to explore pure academic research, and also to apply the knowledge acquired in these years to the industrial research.

In a future perspective, the need of mapping an unknown indoor environment, of localizing and retrieving information from objects with zero costs and efforts could be satisfied by the adoption of next 5G technologies. Thanks to the mix of mmW and massive arrays technologies, it will be possible to achieve a higher indoor localization accuracy without relying on a dedicated infrastructure for localization but exploiting that designed for communication purposes. Besides users localization and navigation objectives, mapping and thus, the capability of reconstructing indoor scenarios, will be an important field of research with the possibility of sharing environmental information via crowd-sourcing mechanisms between users. Finally, in the Internet of Things vision, it is expected that people, objects and devices will be interconnected to each other with the possibility of exchanging the acquired and estimated data including those regarding objects identification, positioning and mapping contents. To this end, the merge of RFID, WSN and UWB technologies has demonstrated to be a promising solution. Stimulated by this framework, this work describes different technological and signal processing approaches to ameliorate the localization capabilities and the user awareness about the environment. From one side, it has been focused on the study of the localization and mapping capabilities of multi-antenna systems based on 5G technologies considering different technological issues, as for example those related to the existing available massive arrays. From the other side, UWB-RFID systems relying on passive communication schemes have been investigated in terms of localization coverage and by developing different techniques to improve the accuracy even in presence of NLOS conditions.