An Analysis of Living Conditions in Rural Villages in China. A New Geography of the Country.

Analysts, politicians and international players from all over the world look at China as one of the most powerful countries on the international scenario, and as a country whose economic development can significantly impact on the economies of the rest of the world. However many aspects of this country have still to be investigated. First the still fundamental role played by Chinese rural areas for the general development of the country from a political, economic and social point of view. In particular, the way in which the rural areas have influenced the social stability of the whole country has been widely discussed due to their strict relationship with the urban areas where most people from the countryside emigrate searching for a job and a better life. In recent years many studies have mostly focused on the urbanization phenomenon with little interest in the living conditions in rural areas and in the deep changes which have occurred in some, mainly agricultural provinces. An analysis of the level of infrastructure is one of the main aspects which highlights the principal differences in terms of living conditions between rural and urban areas. In this thesis, I first carried out the analysis through the multivariate statistics approach (Principal Component Analysis and Cluster Analysis) in order to define the new map of rural areas based on the analysis of living conditions. In the second part I elaborated an index (Living Conditions Index) through the Fuzzy Expert/Inference System. Finally I compared this index (LCI) to the results obtained from the cluster analysis drawing geographic maps. The data source is the second national agricultural census of China carried out in 2006. In particular, I analysed the data refer to villages but aggregated at province level.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Reduced-order modelling, circuit-level design and SOI fabrication of microelectromechanical resonators

This thesis deals with two important research aspects concerning radio frequency (RF) microresonators and switches. First, a new approach for compact modeling and simulation of these devices is presented. Then, a combined process flow for their simultaneous fabrication on a SOI substrate is proposed. Compact models for microresonators and switches are extracted by applying mathematical model order reduction (MOR) to the devices finite element (FE) description in ANSYS c° . The behaviour of these devices includes forms of nonlinearities. However, an approximation in the creation of the FE model is introduced, which enables the use of linear model order reduction. Microresonators are modeled with the introduction of transducer elements, which allow for direct coupling of the electrical and mechanical domain. The coupled system element matrices are linearized around an operating point and reduced. The resulting macromodel is valid for small signal analysis around the bias point, such as harmonic pre-stressed analysis. This is extremely useful for characterizing the frequency response of resonators. Compact modelling of switches preserves the nonlinearity of the device behaviour. Nonlinear reduced order models are obtained by reducing the number of nonlinearities in the system and handling them as input to the system. In this way, the system can be reduced using linear MOR techniques and nonlinearities are introduced directly in the reduced order model. The reduction of the number of system nonlinearities implies the approximation of all distributed forces in the model with lumped forces. Both for microresonators and switches, a procedure for matrices extraction has been developed so that reduced order models include the effects of electrical and mechanical pre-stress. The extraction process is fast and can be done automatically from ANSYS binary files. The method has been applied for the simulation of several devices both at devices and circuit level. Simulation results have been compared with full model simulations, and, when available, experimental data. Reduced order models have proven to conserve the accuracy of finite element method and to give a good description of the overall device behaviour, despite the introduced approximations. In addition, simulation is very fast, both at device and circuit level. A combined process-flow for the integrated fabrication of microresonators and switches has been defined. For this purpose, two processes that are optimized for the independent fabrication of these devices are merged. The major advantage of this process is the possibility to create on-chip circuit blocks that include both microresonators and switches. An application is, for example, aswitched filter bank for wireless transceiver. The process for microresonators fabrication is characterized by the use of silicon on insulator (SOI) wafers and on a deep reactive ion etching (DRIE) step for the creation of the vibrating structures in single-crystal silicon and the use of a sacrificial oxide layer for the definition of resonator to electrode distance. The fabrication of switches is characterized by the use of two different conductive layers for the definition of the actuation electrodes and by the use of a photoresist as a sacrificial layer for the creation of the suspended structure. Both processes have a gold electroplating step, for the creation of the resonators electrodes, transmission lines and suspended structures. The combined process flow is designed such that it conserves the basic properties of the original processes. Neither the performance of the resonators nor the performance of the switches results affected by the simultaneous fabrication. Moreover, common fabrication steps are shared, which allows for cheaper and faster fabrication.

Modulation Techniques for Multi-Phase Converters and Control Strategies for Multi-Phase Electric Drives

The ever-increasing spread of automation in industry puts the electrical engineer in a central role as a promoter of technological development in a sector such as the use of electricity, which is the basis of all the machinery and productive processes. Moreover the spread of drives for motor control and static converters with structures ever more complex, places the electrical engineer to face new challenges whose solution has as critical elements in the implementation of digital control techniques with the requirements of inexpensiveness and efficiency of the final product. The successfully application of solutions using non-conventional static converters awake an increasing interest in science and industry due to the promising opportunities. However, in the same time, new problems emerge whose solution is still under study and debate in the scientific community During the Ph.D. course several themes have been developed that, while obtaining the recent and growing interest of scientific community, have much space for the development of research activity and for industrial applications. The first area of research is related to the control of three phase induction motors with high dynamic performance and the sensorless control in the high speed range. The management of the operation of induction machine without position or speed sensors awakes interest in the industrial world due to the increased reliability and robustness of this solution combined with a lower cost of production and purchase of this technology compared to the others available in the market. During this dissertation control techniques will be proposed which are able to exploit the total dc link voltage and at the same time capable to exploit the maximum torque capability in whole speed range with good dynamic performance. The proposed solution preserves the simplicity of tuning of the regulators. Furthermore, in order to validate the effectiveness of presented solution, it is assessed in terms of performance and complexity and compared to two other algorithm presented in literature. The feasibility of the proposed algorithm is also tested on induction motor drive fed by a matrix converter. Another important research area is connected to the development of technology for vehicular applications. In this field the dynamic performances and the low power consumption is one of most important goals for an effective algorithm. Towards this direction, a control scheme for induction motor that integrates within a coherent solution some of the features that are commonly required to an electric vehicle drive is presented. The main features of the proposed control scheme are the capability to exploit the maximum torque in the whole speed range, a weak dependence on the motor parameters, a good robustness against the variations of the dc-link voltage and, whenever possible, the maximum efficiency. The second part of this dissertation is dedicated to the multi-phase systems. This technology, in fact, is characterized by a number of issues worthy of investigation that make it competitive with other technologies already on the market. Multiphase systems, allow to redistribute power at a higher number of phases, thus making possible the construction of electronic converters which otherwise would be very difficult to achieve due to the limits of present power electronics. Multiphase drives have an intrinsic reliability given by the possibility that a fault of a phase, caused by the possible failure of a component of the converter, can be solved without inefficiency of the machine or application of a pulsating torque. The control of the magnetic field spatial harmonics in the air-gap with order higher than one allows to reduce torque noise and to obtain high torque density motor and multi-motor applications. In one of the next chapters a control scheme able to increase the motor torque by adding a third harmonic component to the air-gap magnetic field will be presented. Above the base speed the control system reduces the motor flux in such a way to ensure the maximum torque capability. The presented analysis considers the drive constrains and shows how these limits modify the motor performance. The multi-motor applications are described by a well-defined number of multiphase machines, having series connected stator windings, with an opportune permutation of the phases these machines can be independently controlled with a single multi-phase inverter. In this dissertation this solution will be presented and an electric drive consisting of two five-phase PM tubular actuators fed by a single five-phase inverter will be presented. Finally the modulation strategies for a multi-phase inverter will be illustrated. The problem of the space vector modulation of multiphase inverters with an odd number of phases is solved in different way. An algorithmic approach and a look-up table solution will be proposed. The inverter output voltage capability will be investigated, showing that the proposed modulation strategy is able to fully exploit the dc input voltage either in sinusoidal or non-sinusoidal operating conditions. All this aspects are considered in the next chapters. In particular, Chapter 1 summarizes the mathematical model of induction motor. The Chapter 2 is a brief state of art on three-phase inverter. Chapter 3 proposes a stator flux vector control for a three- phase induction machine and compares this solution with two other algorithms presented in literature. Furthermore, in the same chapter, a complete electric drive based on matrix converter is presented. In Chapter 4 a control strategy suitable for electric vehicles is illustrated. Chapter 5 describes the mathematical model of multi-phase induction machines whereas chapter 6 analyzes the multi-phase inverter and its modulation strategies. Chapter 7 discusses the minimization of the power losses in IGBT multi-phase inverters with carrier-based pulse width modulation. In Chapter 8 an extended stator flux vector control for a seven-phase induction motor is presented. Chapter 9 concerns the high torque density applications and in Chapter 10 different fault tolerant control strategies are analyzed. Finally, the last chapter presents a positioning multi-motor drive consisting of two PM tubular five-phase actuators fed by a single five-phase inverter.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

User scheduling for low earth orbit multi-user multiple-input-multiple-output non-terrestrial network systems

In rural and isolated areas without cellular coverage, Satellite Communication (SatCom) is the best candidate to complement terrestrial coverage. However, the main challenge for future generations of wireless networks will be to meet the growing demand for new services while dealing with the scarcity of frequency spectrum. As a result, it is critical to investigate more efficient methods of utilizing the limited bandwidth; and resource sharing is likely the only choice. The research community’s focus has recently shifted towards the interference management and exploitation paradigm to meet the increasing data traffic demands. In the Downlink (DL) and Feedspace (FS), LEO satellites with an on-board antenna array can offer service to numerous User Terminals (UTs) (VSAT or Handhelds) on-ground in FFR schemes by using cutting-edge digital beamforming techniques. Considering this setup, the adoption of an effective user scheduling approach is a critical aspect given the unusually high density of User terminals on the ground as compared to the on-board available satellite antennas. In this context, one possibility is that of exploiting clustering algorithms for scheduling in LEO MU-MIMO systems in which several users within the same group are simultaneously served by the satellite via Space Division Multiplexing (SDM), and then these different user groups are served in different time slots via Time Division Multiplexing (TDM). This thesis addresses this problem by defining a user scheduling problem as an optimization problem and discusses several algorithms to solve it. In particular, focusing on the FS and user service link (i.e., DL) of a single MB-LEO satellite operating below 6 GHz, the user scheduling problem in the Frequency Division Duplex (FDD) mode is addressed. The proposed State-of-the-Art scheduling approaches are based on graph theory. The proposed solution offers high performance in terms of per-user capacity, Sum-rate capacity, SINR, and Spectral Efficiency.