This work presents developments of Guidance, Navigation and Control (GNC) systems with application to autonomous Unmanned Aerial Vehicle (UAV). Precisely, this work shows the development of navigation system based on nonlinear complementary filters for position, velocity and attitude estimation using low-cost sensors. The proposed filtering method provides attitude estimates in quaternion representations and position and velocity estimates by fusing measurements from Inertial Measurement Unit (IMU), GPS, and a barometer. Least Square Method (LSM) was used in gains tuning to find the best-fitting of the estimated states with precise measurements obtained by a vision based motion capture system. A complete navigation system was produced by integrating both the attitude and the position filters. The integration of the filtering approach based primarily on the ease of design and computational load. Furthermore, the structure of the filtering design allow for straightforward implementation without a need of high performance signal processing. Moreover, the filters can be tuned totally independent of each other. This work also introduces a nonlinear flight controller for stability and trajectory tracking that is practical for real-time implementation. This controller is also demonstrated the ability of a supervisory controller to provide effective waypoint navigation capabilities in autonomous UAV. The implementation of the guidance, navigation, and control algorithms were adopted in the design of a novel smartphone based autopilot for particular quadrotor aerial platforms. The performances of the proposed work are then evaluated by means of several flight tests. The work also includes a design of advanced navigation and guidance systems based on Robot Operating System (ROS) for Search And Rescue (SAR) missions. Primarily, the performance of the navigation and guidance systems were tested in laboratory by simulating GPS measurements in Linux computer mounted on the top of a quadrotor. This activity facilitates moving by the experiments from indoor to outdoor.

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.

Many problems arising in several and different areas of human knowledge share the characteristic of being intractable in real cases. The relevance of the solution of these problems, linked to their domain of action, has given birth to many frameworks of algorithms for solving them. Traditional solution paradigms are represented by exact and heuristic algorithms. In order to overcome limitations of both approaches and obtain better performances, tailored combinations of exact and heuristic methods have been studied, giving birth to a new paradigm for solving hard combinatorial optimization problems, constituted by model-based metaheuristics. In the present thesis, we deepen the issue of model-based metaheuristics, and present some methods, belonging to this class, applied to the solution of combinatorial optimization problems.

In this thesis, it is examined the issue of equivalence between linear terms in higher order languages, that is, in languages which allow to use functions as variables, and where variables which appear in the terms must be used exactly once. The work is developed focusing on the bisimulation method, with the purpose to compare this technique with that which has become the standard for the comparison between the terms of a language, i.e. the context equivalence. The thesis is divided into three parts: in the first one, the introduction of the bisimulation and context equivalence techniques takes place within a deterministic linear and typed language. In the second part, the same techniques are reformulated for a language that, while preserving the linearity, loses the deterministic connotation, allowing the terms to evaluate to a set of values each one having a certain probability to appear in the end of calculation. In the last part, a quantum language is examined, discussing the advantages of quantum computation, which allows to speed-up many of the algorithms of computation. Here one gives the concept of quantum program, which is inextricably linked to the (quantum) register where the qubits used in the computation are stored, entailing a more complex notion of equivalence between terms. The techniques to demonstrate that bisimulation is a congruence are not standard and have been used for the first time by Howe for untyped languages: within the thesis, one shows that bisimulation is a congruence in all considered languages but it coincides with the context equivalence relation only for the deterministic one. Indeed, extending the techniques already used by Howe to the probabilistic and quantum environment, it is shown, as non trivial result, that in probabilistic and quantum linear languages the bisimulation is contained in context equivalence relation.

With an increasing number of people who live in cities, urban mobility becomes one of the most important research fields in the so-called smart city environments. Urban mobility can be defined as the ability of people to move around the city, living and interacting with the space. For these reasons, urban accessibility represents a primary factor to keep into account for social inclusion and for the effective exercise of citizenship. In this thesis, we researched how to use crowdsourcing and participative sensing to effectively and efficiently collect data about aPOIs (accessible Point Of Interests) with the aim of obtaining an updated, trusted and completed accessible map of the urban environment. The data gathered in such a way, was integrated with data retrieved from external open dataset and used in computing personalized accessible urban paths. In order to deeply investigate the issues related to this research, we designed and prototyped mPASS, a context-aware and location-based accessible way-finding system.

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.