Mobile robots gained lots of attention in the last decades. Because of its flexibility and increased capabilities of automation, mobile robots are used in many applications: from domotic, to search and rescue missions, to agriculture, environment protection and many more. The main capability of mobile robots to accomplish a mission is the mobility in the work environment. To move in a certain environment the robots should achieve: guidance, navigation and control. This thesis focuses on guidance and control of mobile robots, with application to certain classes of robots: Vertical Take Off and Landing Unmanned Aerial Vehicles (VTOL UAV) and Differential Wheel robots (DWR). The contribution of this thesis is on modeling and control of the two classes of robots, and on novel strategies of combined control and motion planning for kinodynamic systems. A new approach to model a class of multi-propeller VTOL is proposed, with the aim of generating a general model for a system as a composition of elementary modules such as actuators and payloads. Two control law for VTOL vehicles and DWR are proposed. The goal of the first is to generate a simple yet powerful control to globally asymptotically stabilize a VTOL for acrobatic maneuvers. The second is a simple saturated input control law for trajectory tracking of a DWR model in 2D. About planning, a novel approach to generate non-feasible trajectories for robots that still guarantees a correct path for kinodynamic planning is proposed. The goal is to reduce the runtime of planners to be used in real-time and realistic scenario. Moreover an innovative framework for mobile robots motion planning with the use of Discrete Event Systems theory is introduced. The two proposed approaches allow to build a global, robust, real-time, quasi-optimal, kinodynamic planner suitable for replanning.

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.

This thesis covers two main topics: the design and performance evaluation of Wireless Body Area Networks (WBANs), and the simulation and mathematical modeling of Delay Tolerant Networks (DTNs). Different Medium Access Control (MAC) protocols for WBANs are implemented on dedicated hardware in order to evaluate, through extensive measurement campaigns, the performance of the network in terms of packet loss rate, delay and energy consumption. Novel solutions to cope with body shadowing and to improve the coexistence with other wireless technologies, are presented and evaluated. An analytic model for the CSMA/CA protocol defined in the IEEE 802.15.6 standard is also presented. The benefits of offloading part of the traffic carried by a wireless backbone to a DTN composed of mobile nodes in a urban environment, is also investigated. A more analytic approach, mainly using tools from stochastic geometry and Markov chains theory, is used to develop a mathematical framework for the evaluation of the performance of routing rules for DTNs.

The design and development of wearable inertial sensor systems for health monitoring has garnered a huge attention in the scientific community and the industry during the last years. Such platforms have a typical architecture and common building blocks to enable data collection, data processing and feedback restitution. In this thesis we analyze power optimization techniques that can be applied to such systems. When reducing power consumption in a wearable system, different trade-offs have to be inevitably faced. We thus propose software techniques that span from well known duty cycling, frequency scaling, data compression to new paradigm such as radio triggering, heterogeneous multi-core and context aware power management.

The negotiation between power consumption, performance, programmability, and portability drives all computing industry designs, in particular the mobile and embedded systems domains. Two design paradigms have proven particularly promising in this context: architectural heterogeneity and many-core processors. Parallel programming models are key to effectively harness the computational power of heterogeneous many-core SoC. This thesis presents a set of techniques and HW/SW extensions that enable performance improvements and that simplify programmability for heterogeneous many-core platforms. The thesis contributions cover vertically the entire software stack for many-core platforms, from hardware abstraction layers running on top of bare-metal, to programming models; from hardware extensions for efficient parallelism support to middleware that enables optimized resource management within many-core platforms. First, we present mechanisms to decrease parallelism overheads on parallel programming runtimes for many-core platforms, targeting fine-grain parallelism. Second, we present programming model support that enables the offload of computational kernels within heterogeneous many-core systems. Third, we present a novel approach to dynamically sharing and managing many-core platforms when multiple applications coded with different programming models execute concurrently. All these contributions were validated using STMicroelectronics STHORM, a real embodiment of a state-of-the-art many-core system. Hardware extensions and architectural explorations were explored using VirtualSoC, a SystemC based cycle-accurate simulator of many-core platforms.

To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's computer architecture - and at the same time we show methodologies to introduce it with little or no loss in terms of flexibility. In particular, we show that heterogeneity can be employed to tackle the "walls" that impede further development of new computing applications: the utilization wall, i.e. the impossibility to keep all transistors on in deeply integrated chips, and the "data deluge", i.e. the amount of data to be processed that is scaling up much faster than the computing performance and efficiency. We introduce a methodology to improve heterogeneous design exploration of tightly coupled clusters; moreover we propose a fractal heterogeneity architecture that is a parallel accelerator for low-power sensor nodes, and is itself internally heterogeneous thanks to an heterogeneous coprocessor for brain-inspired computing. This platform, which is silicon-proven, can lead to more than 100x improvement in terms of energy efficiency with respect to typical computing nodes used within the same domain, enabling the application of complex algorithms, vastly more performance-hungry than the current state-of-the-art in the ULP computing domain.

Over the last years, consensus and synchronization problems have been a popular topic in the systems and control community. This interest is motivated by the fact that, in several fields of application, a certain number of agents is interacting or has to cooperate to achieve a certain task. Robotic swarms, sensor networks, power networks, biological networks are only few outstanding examples where networks of agents displays behaviors which can be modeled and studied by means of consensus and synchronisation techniques. In this thesis we consider a general class of networked nonlinear systems in different operating frameworks and design control architecture to force the systems to reach synchronization and consensus on a target behavior. In particular, we consider the case of homogeneous and heterogeneous nonlinear agents with a static communication topology and design a static high-gain-based diffusive coupling and an internal model-based regulator respectively, to solve the problem of consensus. Then, we analyze the case of dynamical links and show under which conditions, synchronization for homogeneous nonlinear systems can be achieved. Depending on the structure of the dynamic links at hand, static and dynamic regulators (based on the concept extended state observers) are proposed. Furthermore, we address the problem of disconnected topology and switching topology and derive under which conditions agents reach cluster synchronization and synchronization respectively. Last, we consider the problem of a sampled exchange of information between the agents and design a triggering rule locally at each agent such that the overall network reaches synchronization.

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply different from Human-to-Human communications both in terms of traffic patterns and requirements. This thesis investigate both concepts. In particular, here are studied short and long range solutions for Machine-to-machine applications. For this work a dual approach has been followed: for the short-range solutions analysis an experimental approach has been privileged; meanwhile for the long-range solutions analysis a theoretical and simulation approach has been preferred. In both case, a particular attention has been given to the feasibility of the solutions proposed, hence solutions based on products that already exist in the market have been privileged.

Performance and energy consumption in modern computing platforms is largely dominated by the memory hierarchy. The increasing computational power in the multiprocessors and accelerators, and the emergence of the data-intensive workloads (e.g. large-scale graph traversal and scientific algorithms) requiring fast transfer of large volumes of data, are two main trends which intensify this problem by putting even higher pressure on the memory hierarchy. This increasing gap between computation speed and data transfer speed is commonly referred as the “memory wall” problem. With the emergence of heterogeneous Three Dimensional (3D) Integration based on through-silicon-vias (TSV), this situation has started to recover in the past years. On one hand, it is now possible to improve memory access bandwidth and/or latency by either stacking memories directly on top of processors or through abstracted memory interfaces such as Micron’s Hybrid Memory Cube (HMC). On the other hand, near memory computation has become worthy of revisiting due to the cost-effective integration of logic and memory in 3D stacks. These two directions bring about several interesting opportunities including performance improvement, energy and cost reduction, product miniaturization, and modular design for improved time to market. In this research, we study the effectiveness of the 3D integration technology and the optimization opportunities which it can provide in the different layers of the memory hierarchy in cluster-based many-core platforms ranging from intra-cluster L1 to inter-cluster L2 scratchpad memories (SPMs), as well as the main memory. In addition, by moving a part of the computation to where data resides, in the 3D-stacked memory context, we demonstrate further energy and performance improvement opportunities.

The objective of this thesis is twofold: on one hand, the design of nonliner observers, on the other, the design of internal-model regulators to solve the robust output regulation problem. In the observer theory a key role is played by the so called high-gain observers. The purpose of the first part of the thesis is to propose novel techniques which allow to overcome or at least to mitigate some of the main drawbacks characterizing this class of observers. Firstly, we study the possibility of writing an observer for multi-input multi-output observable systems in the original coordinates. Then, we propose a novel class of high-gain observers, denoted as ``low-power'', which allows to overcome numerical problems, to avoid the peaking phenomenon and to improve the sensitivity properties to high-frequency measurement noise. The second part of the thesis addresses the output regulation problem, solved for linear systems during the 70's by Francis and Wonham who coined the celebrated ``internal model principle''. Constructive solutions have also been proposed in the nonlinear framework but under restrictive assumptions that reduce the class of systems to which this methodology can be applied. In this thesis we focus on the output regulation problem in presence of periodic disturbances and we propose a novel approach which allows to consider a broader class of nonlinear systems. With the proposed design the stabilization problem and the regulation problem are substantially decoupled and output regulation is achieved in presence of uncertainties or disturbances, as long as the trajectories of the resulting closed-loop system are bounded.