Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.

The energy harvesting research field has grown considerably in the last decade due to increasing interests in energy autonomous sensing systems, which require smart and efficient interfaces for extracting power from energy source and power management (PM) circuits. This thesis investigates the design trade-offs for minimizing the intrinsic power of PM circuits, in order to allow operation with very weak energy sources. For validation purposes, three different integrated power converter and PM circuits for energy harvesting applications are presented. They have been designed for nano-power operations and single-source converters can operate with input power lower than 1 μW. The first IC is a buck-boost converter for piezoelectric transducers (PZ) implementing Synchronous Electrical Charge Extraction (SECE), a non-linear energy extraction technique. Moreover, Residual Charge Inversion technique is exploited for extracting energy from PZ with weak and irregular excitations (i.e. lower voltage), and the implemented PM policy, named Two-Way Energy Storage, considerably reduces the start-up time of the converter, improving the overall conversion efficiency. The second proposed IC is a general-purpose buck-boost converter for low-voltage DC energy sources, up to 2.5 V. An ultra-low-power MPPT circuit has been designed in order to track variations of source power. Furthermore, a capacitive boost circuit has been included, allowing the converter start-up from a source voltage VDC0 = 223 mV. A nano-power programmable linear regulator is also included in order to provide a stable voltage to the load. The third IC implements an heterogeneous multisource buck-boost converter. It provides up to 9 independent input channels, of which 5 are specific for PZ (with SECE) and 4 for DC energy sources with MPPT. The inductor is shared among channels and an arbiter, designed with asynchronous logic to reduce the energy consumption, avoids simultaneous access to the buck-boost core, with a dynamic schedule based on source priority.

This thesis presents a new approach for the design and fabrication of bond wire magnetics for power converter applications by using standard IC gold bonding wires and micro-machined magnetic cores. It shows a systematic design and characterization study for bond wire transformers with toroidal and race-track cores for both PCB and silicon substrates. Measurement results show that the use of ferrite cores increases the secondary self-inductance up to 315 µH with a Q-factor up to 24.5 at 100 kHz. Measurement results on LTCC core report an enhancement of the secondary self-inductance up to 23 µH with a Q-factor up to 10.5 at 1.4 MHz. A resonant DC-DC converter is designed in 0.32 µm BCD6s technology at STMicroelectronics with a depletion nmosfet and a bond wire micro-transformer for EH applications. Measures report that the circuit begins to oscillate from a TEG voltage of 280 mV while starts to convert from an input down to 330 mV to a rectified output of 0.8 V at an input of 400 mV. Bond wire magnetics is a cost-effective approach that enables a flexible design of inductors and transformers with high inductance and high turns ratio. Additionally, it supports the development of magnetics on top of the IC active circuitry for package and wafer level integrations, thus enabling the design of high density power components. This makes possible the evolution of PwrSiP and PwrSoC with reliable highly efficient magnetics.

As human beings, we trust our five senses, that allow us to experience the world and communicate. Since our birth, the amount of data that every day we can acquire is impressive and such a richness reflects the complexity of humankind in arts, technology, etc. The advent of computers and the consequent progress in Data Science and Artificial Intelligence showed how large amounts of data can contain some sort of “intelligence” themselves. Machines learn and create a superimposed layer of reality. How data generated by humans and machines are related today? To give an answer we will present three projects in the context of “Mixed Reality”, the ideal place where Reality, Virtual Reality and Augmented Reality are increasingly connected as long as data enhance the digital experiences, making them more “real”. We will start with BRAVO, a tool that exploits the brain activity to improve the user’s learning process in real time by means of a Brain-Computer Interface that acquires EEG data. Then we will see AUGMENTED GRAPHICS, a framework for detecting objects in the reality that can be captured easily and inserted in any digital scenario. Based on the moments invariants theory, it looks particularly designed for mobile devices, as it assumes a light concept of object detection and it works without any training set. As third work, GLOVR, a wearable hand controller that uses inertial sensors to offer directional controls and to recognize gestures, particularly suitable for Virtual Reality applications. It features a microphone to record voice sequences that then are translated in tasks by means of a natural language web service. For each project we will summarize the main results and we will trace some future directions of research and development.

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.

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.

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.

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.

This thesis investigates the possibility of integrating the standard CMOS design process with additional microstructures enhancing circuit functionalities. More specifically, the thesis faces the problem of miniaturization of magnetic and piezoelectric devices mostly focused on the application field of EH (Energy Harvesting) systems and ultra-low power and ultra-low voltage systems. It shows all the most critical aspects which have to be taken into account during the design process of miniaturized inductors for PwrSoC (Power System on Chip) or transformers. Furthermore it shows that it is possible to optimize the inductance value and also performances by means of a proper choice of the size of the planar core or choosing a different layout shape such as a serpentine shape in place of the classic toroidal one. A new formula for the correct evaluation of the MPL (Magnetic Path Length) was also introduced. Concerning the piezoelectric counterpart, it is focused on the design and simulation of various MEMS PTs based on a SOI (Silicon on Insulator) structure with AlN (Alluminum Nitride) as active piezoelectric element, in perspective of having a SoC with embedded MEMS devices and circuitry. Furthermore it demonstrates for the first time the use of a PT (Piezoelectric Transformer) for ultra-low voltage EH applications. A new boost oscillator based on a discrete PZT (Lead Zirconate Titanate) PT instead of a MT (Magnetic Transformer) has been modelled and tested on a circuit made up by discrete devices, showing performances comparable to commercial solutions like the LTC3108 from Linear. Furthermore this novel boost oscillator has been designed in a 0.35μm technology by ST Microelectronics, showing better performances as intuitively expected by the developed mathematical model of the entire system.