Il fascicolo sanitario elettronico tra Semantic Web e diritto alla privacy

Principale obiettivo della ricerca è quello di ricostruire lo stato dell’arte in materia di sanità elettronica e Fascicolo Sanitario Elettronico, con una precipua attenzione ai temi della protezione dei dati personali e dell’interoperabilità. A tal fine sono stati esaminati i documenti, vincolanti e non, dell’Unione europea nonché selezionati progetti europei e nazionali (come “Smart Open Services for European Patients” (EU); “Elektronische Gesundheitsakte” (Austria); “MedCom” (Danimarca); “Infrastruttura tecnologica del Fascicolo Sanitario Elettronico”, “OpenInFSE: Realizzazione di un’infrastruttura operativa a supporto dell’interoperabilità delle soluzioni territoriali di fascicolo sanitario elettronico nel contesto del sistema pubblico di connettività”, “Evoluzione e interoperabilità tecnologica del Fascicolo Sanitario Elettronico”, “IPSE - Sperimentazione di un sistema per l’interoperabilità europea e nazionale delle soluzioni di Fascicolo Sanitario Elettronico: componenti Patient Summary e ePrescription” (Italia)). Le analisi giuridiche e tecniche mostrano il bisogno urgente di definire modelli che incoraggino l’utilizzo di dati sanitari ed implementino strategie effettive per l’utilizzo con finalità secondarie di dati sanitari digitali , come Open Data e Linked Open Data. L’armonizzazione giuridica e tecnologica è vista come aspetto strategico per ridurre i conflitti in materia di protezione di dati personali esistenti nei Paesi membri nonché la mancanza di interoperabilità tra i sistemi informativi europei sui Fascicoli Sanitari Elettronici. A questo scopo sono state individuate tre linee guida: (1) armonizzazione normativa, (2) armonizzazione delle regole, (3) armonizzazione del design dei sistemi informativi. I principi della Privacy by Design (“prottivi” e “win-win”), così come gli standard del Semantic Web, sono considerate chiavi risolutive per il suddetto cambiamento.

The twofold role of Cloud Computing in Digital Forensics: target of investigations and helping hand to evidence analysis

This PhD thesis discusses the impact of Cloud Computing infrastructures on Digital Forensics in the twofold role of target of investigations and as a helping hand to investigators. The Cloud offers a cheap and almost limitless computing power and storage space for data which can be leveraged to commit either new or old crimes and host related traces. Conversely, the Cloud can help forensic examiners to find clues better and earlier than traditional analysis applications, thanks to its dramatically improved evidence processing capabilities. In both cases, a new arsenal of software tools needs to be made available. The development of this novel weaponry and its technical and legal implications from the point of view of repeatability of technical assessments is discussed throughout the following pages and constitutes the unprecedented contribution of this work

The Role of B5G UAV-Aided Mobile Networks

With the advent of 5G, several novel network paradigms and technologies have been proposed to fulfil the key requirements imposed. Flexibility, efficiency and scalability, along with sustainability and convenience for expenditure have to be addressed in targeting these brand new needs. Among novel paradigms introduced in the scientific literature in recent years, a constant and increasing interest lies in the use of unmanned aerial vehicles (UAVs) as network nodes supporting the legacy terrestrial network for service provision. Their inherent features of moving nodes make them able to be deployed on-demand in real-time. Which, in practical terms, means having them acting as a base station (BS) when and where there is the highest need. This thesis investigates then the potential role of UAV-aided mobile radio networks, in order to validate the concept of adding an aerial network component and assess the system performance, from early to later stages of its deployment. This study is intended for 5G and beyond systems, to allow time for the technology to mature. Since advantages can be manyfold, the aerial network component is considered at the network layer under several aspects, from connectivity to radio resource management. A particular emphasis is given to trajectory design, because of the efficiency and flexibility it potentially adds to the infrastructure. Two different frameworks have been proposed, to take into account both a re-adaptable heuristic and an optimal solution. Moreover, diverse use cases are taken under analysis, from mobile broadband to machine and vehicular communications. The thesis aim is thus to discuss the potential and advantages of UAV-aided systems from a broad perspective. Results demonstrate that the technology has good prospects for diverse scenarios with a few arrangements.

Safe and Collaborative Navigation & Interaction With Mobile Manipulators in Domestic Appliance Test Labs

Safe collaboration between a robot and human operator forms a critical requirement for deploying a robotic system into a manufacturing and testing environment. In this dissertation, the safety requirement for is developed and implemented for the navigation system of the mobile manipulators. A methodology for human-robot co-existence through a 3d scene analysis is also investigated. The proposed approach exploits the advance in computing capability by relying on graphic processing units (GPU’s) for volumetric predictive human-robot contact checking. Apart from guaranteeing safety of operators, human-robot collaboration is also fundamental when cooperative activities are required, as in appliance test automation floor. To achieve this, a generalized hierarchical task controller scheme for collision avoidance is developed. This allows the robotic arm to safely approach and inspect the interior of the appliance without collision during the testing procedure. The unpredictable presence of the operators also forms dynamic obstacle that changes very fast, thereby requiring a quick reaction from the robot side. In this aspect, a GPU-accelarated distance field is computed to speed up reaction time to avoid collision between human operator and the robot. An automated appliance testing also involves robotized laundry loading and unloading during life cycle testing. This task involves Laundry detection, grasp pose estimation and manipulation in a container, inside the drum and during recovery grasping. A wrinkle and blob detection algorithms for grasp pose estimation are developed and grasp poses are calculated along the wrinkle and blobs to efficiently perform grasping task. By ranking the estimated laundry grasp poses according to a predefined cost function, the robotic arm attempt to grasp poses that are more comfortable from the robot kinematic side as well as collision free on the appliance side. This is achieved through appliance detection and full-model registration and collision free trajectory execution using online collision avoidance.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Enabling 5G and beyond: millimeter wave propagation and cloud RAN evolution

In the last few years, mobile wireless technology has gone through a revolutionary change. Web-enabled devices have evolved into essential tools for communication, information, and entertainment. The fifth generation (5G) of mobile communication networks is envisioned to be a key enabler of the next upcoming wireless revolution. Millimeter wave (mmWave) spectrum and the evolution of Cloud Radio Access Networks (C-RANs) are two of the main technological innovations of 5G wireless systems and beyond. Because of the current spectrum-shortage condition, mmWaves have been proposed for the next generation systems, providing larger bandwidths and higher data rates. Consequently, new radio channel models are being developed. Recently, deterministic ray-based models such as Ray-Tracing (RT) are getting more attractive thanks to their frequency-agility and reliable predictions. A modern RT software has been calibrated and used to analyze the mmWave channel. Knowledge of the electromagnetic properties of materials is therefore essential. Hence, an item-level electromagnetic characterization of common construction materials has been successfully achieved to obtain information about their complex relative permittivity. A complete tuning of the RT tool has been performed against indoor and outdoor measurement campaigns at 27 and 38 GHz, setting the basis for the future development of advanced beamforming techniques which rely on deterministic propagation models (as RT). C-RAN is a novel mobile network architecture which can address a number of challenges that network operators are facing in order to meet the continuous customers’ demands. C-RANs have already been adopted in advanced 4G deployments; however, there are still some issues to deal with, especially considering the bandwidth requirements set by the forthcoming 5G systems. Open RAN specifications have been proposed to overcome the new 5G challenges set on C-RAN architectures, including synchronization aspects. In this work it is described an FPGA implementation of the Synchronization Plane for an O-RAN-compliant radio system.

Propagation modelling and resource allocation in mobile radio communications

Over the past years, ray tracing (RT) models popularity has been increasing. From the nineties, RT has been used for field prediction in environment such as indoor and urban environments. Nevertheless, with the advent of new technologies, the channel model has become decidedly more dynamic and to perform RT simulations at each discrete time instant become computationally expensive. In this thesis, a new dynamic ray tracing (DRT) approach is presented in which from a single ray tracing simulation at an initial time t0, through analytical formulas we are able to track the motion of the interaction points. The benefits that this approach bring are that Doppler frequencies and channel prediction can be derived at every time instant, without recurring to multiple RT runs and therefore shortening the computation time. DRT performance was studied on two case studies and the results shows the accuracy and the computational gain that derives from this approach. Another issue that has been addressed in this thesis is the licensed band exhaustion of some frequency bands. To deal with this problem, a novel unselfish spectrum leasing scheme in cognitive radio networks (CRNs) is proposed that offers an energy-efficient solution minimizing the environmental impact of the network. In addition, a network management architecture is introduced and resource allocation is proposed as a constrained sum energy efficiency maximization problem. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.

PCM-based in-memory computing: architectures, circuits and applications

Analog In-memory Computing (AIMC) has been proposed in the context of Beyond Von Neumann architectures as a valid strategy to reduce internal data transfers energy consumption and latency, and to improve compute efficiency. The aim of AIMC is to perform computations within the memory unit, typically leveraging the physical features of memory devices. Among resistive Non-volatile Memories (NVMs), Phase-change Memory (PCM) has become a promising technology due to its intrinsic capability to store multilevel data. Hence, PCM technology is currently investigated to enhance the possibilities and the applications of AIMC. This thesis aims at exploring the potential of new PCM-based architectures as in-memory computational accelerators. In a first step, a preliminar experimental characterization of PCM devices has been carried out in an AIMC perspective. PCM cells non-idealities, such as time-drift, noise, and non-linearity have been studied to develop a dedicated multilevel programming algorithm. Measurement-based simulations have been then employed to evaluate the feasibility of PCM-based operations in the fields of Deep Neural Networks (DNNs) and Structural Health Monitoring (SHM). Moreover, a first testchip has been designed and tested to evaluate the hardware implementation of Multiply-and-Accumulate (MAC) operations employing PCM cells. This prototype experimentally demonstrates the possibility to reach a 95% MAC accuracy with a circuit-level compensation of cells time drift and non-linearity. Finally, empirical circuit behavior models have been included in simulations to assess the use of this technology in specific DNN applications, and to enhance the potentiality of this innovative computation approach.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.