3D Surveying and Data Management towards the Realization of a Knowledge System for Cultural Heritage

The research activities involved the application of the Geomatic techniques in the Cultural Heritage field, following the development of two themes: Firstly, the application of high precision surveying techniques for the restoration and interpretation of relevant monuments and archaeological finds. The main case regards the activities for the generation of a high-fidelity 3D model of the Fountain of Neptune in Bologna. In this work, aimed to the restoration of the manufacture, both the geometrical and radiometrical aspects were crucial. The final product was the base of a 3D information system representing a shared tool where the different figures involved in the restoration activities shared their contribution in a multidisciplinary approach. Secondly, the arrangement of 3D databases for a Building Information Modeling (BIM) approach, in a process which involves the generation and management of digital representations of physical and functional characteristics of historical buildings, towards a so-called Historical Building Information Model (HBIM). A first application was conducted for the San Michele in Acerboli’s church in Santarcangelo di Romagna. The survey was performed by the integration of the classical and modern Geomatic techniques and the point cloud representing the church was used for the development of a HBIM model, where the relevant information connected to the building could be stored and georeferenced. A second application regards the domus of Obellio Firmo in Pompeii, surveyed by the integration of the classical and modern Geomatic techniques. An historical analysis permitted the definitions of phases and the organization of a database of materials and constructive elements. The goal is the obtaining of a federate model able to manage the different aspects: documental, analytic and reconstructive ones.

The FOOT experiment: Trigger and Data Acquisition (TDAQ) development and data analysis

Hadrontherapy employs high-energy beams of charged particles (protons and heavier ions) to treat deep-seated tumours: these particles have a favourable depth-dose distribution in tissue characterized by a low dose in the entrance channel and a sharp maximum (Bragg peak) near the end of their path. In these treatments nuclear interactions have to be considered: beam particles can fragment in the human body releasing a non-zero dose beyond the Bragg peak while fragments of human body nuclei can modify the dose released in healthy tissues. These effects are still in question given the lack of interesting cross sections data. Also space radioprotection can profit by fragmentation cross section measurements: the interest in long-term manned space missions beyond Low Earth Orbit is growing in these years but it has to cope with major health risks due to space radiation. To this end, risk models are under study: however, huge gaps in fragmentation cross sections data are currently present preventing an accurate benchmark of deterministic and Monte Carlo codes. To fill these gaps in data, the FOOT (FragmentatiOn Of Target) experiment was proposed. It is composed by two independent and complementary setups, an Emulsion Cloud Chamber and an electronic setup composed by several subdetectors providing redundant measurements of kinematic properties of fragments produced in nuclear interactions between a beam and a target. FOOT aims to measure double differential cross sections both in angle and kinetic energy which is the most complete information to address existing questions. In this Ph.D. thesis, the development of the Trigger and Data Acquisition system for the FOOT electronic setup and a first analysis of 400 MeV/u 16O beam on Carbon target data acquired in July 2021 at GSI (Darmstadt, Germany) are presented. When possible, a comparison with other available measurements is also reported.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Internet of healthcare (law): privacy and data protection aspects in an internet of everything

The purpose of this research study is to discuss privacy and data protection-related regulatory and compliance challenges posed by digital transformation in healthcare in the wake of the COVID-19 pandemic. The public health crisis accelerated the development of patient-centred remote/hybrid healthcare delivery models that make increased use of telehealth services and related digital solutions. The large-scale uptake of IoT-enabled medical devices and wellness applications, and the offering of healthcare services via healthcare platforms (online doctor marketplaces) have catalysed these developments. However, the use of new enabling technologies (IoT, AI) and the platformisation of healthcare pose complex challenges to the protection of patient’s privacy and personal data. This happens at a time when the EU is drawing up a new regulatory landscape for the use of data and digital technologies. Against this background, the study presents an interdisciplinary (normative and technology-oriented) critical assessment on how the new regulatory framework may affect privacy and data protection requirements regarding the deployment and use of Internet of Health Things (hardware) devices and interconnected software (AI systems). The study also assesses key privacy and data protection challenges that affect healthcare platforms (online doctor marketplaces) in their offering of video API-enabled teleconsultation services and their (anticipated) integration into the European Health Data Space. The overall conclusion of the study is that regulatory deficiencies may create integrity risks for the protection of privacy and personal data in telehealth due to uncertainties about the proper interplay, legal effects and effectiveness of (existing and proposed) EU legislation. The proliferation of normative measures may increase compliance costs, hinder innovation and ultimately, deprive European patients from state-of-the-art digital health technologies, which is paradoxically, the opposite of what the EU plans to achieve.

Dynamic services in mobile ad hoc networks

The increasing diffusion of wireless-enabled portable devices is pushing toward the design of novel service scenarios, promoting temporary and opportunistic interactions in infrastructure-less environments. Mobile Ad Hoc Networks (MANET) are the general model of these higly dynamic networks that can be specialized, depending on application cases, in more specific and refined models such as Vehicular Ad Hoc Networks and Wireless Sensor Networks. Two interesting deployment cases are of increasing relevance: resource diffusion among users equipped with portable devices, such as laptops, smart phones or PDAs in crowded areas (termed dense MANET) and dissemination/indexing of monitoring information collected in Vehicular Sensor Networks. The extreme dynamicity of these scenarios calls for novel distributed protocols and services facilitating application development. To this aim we have designed middleware solutions supporting these challenging tasks. REDMAN manages, retrieves, and disseminates replicas of software resources in dense MANET; it implements novel lightweight protocols to maintain a desired replication degree despite participants mobility, and efficiently perform resource retrieval. REDMAN exploits the high-density assumption to achieve scalability and limited network overhead. Sensed data gathering and distributed indexing in Vehicular Networks raise similar issues: we propose a specific middleware support, called MobEyes, exploiting node mobility to opportunistically diffuse data summaries among neighbor vehicles. MobEyes creates a low-cost opportunistic distributed index to query the distributed storage and to determine the location of needed information. Extensive validation and testing of REDMAN and MobEyes prove the effectiveness of our original solutions in limiting communication overhead while maintaining the required accuracy of replication degree and indexing completeness, and demonstrates the feasibility of the middleware approach.

Spectrum shaping and its application: spread-spectrum clock generator and circuits for ultra wide band

Electromagnetic spectrum can be identified as a resource for the designer, as well as for the manufacturer, from two complementary points of view: first, because it is a good in great demand by many different kind of applications; second, because despite its scarce availability, it may be advantageous to use more spectrum than necessary. This is the case of Spread-Spectrum Systems, those systems in which the transmitted signal is spread over a wide frequency band, much wider, in fact, than the minimum bandwidth required to transmit the information being sent. Part I of this dissertation deals with Spread-Spectrum Clock Generators (SSCG) aiming at reducing Electro Magnetic Interference (EMI) of clock signals in integrated circuits (IC) design. In particular, the modulation of the clock and the consequent spreading of its spectrum are obtained through a random modulating signal outputted by a chaotic map, i.e. a discrete-time dynamical system showing chaotic behavior. The advantages offered by this kind of modulation are highlighted. Three different prototypes of chaos-based SSCG are presented in all their aspects: design, simulation, and post-fabrication measurements. The third one, operating at a frequency equal to 3GHz, aims at being applied to Serial ATA, standard de facto for fast data transmission to and from Hard Disk Drives. The most extreme example of spread-spectrum signalling is the emerging ultra-wideband (UWB) technology, which proposes the use of large sections of the radio spectrum at low amplitudes to transmit high-bandwidth digital data. In part II of the dissertation, two UWB applications are presented, both dealing with the advantages as well as with the challenges of a wide-band system, namely: a chaos-based sequence generation method for reducing Multiple Access Interference (MAI) in Direct Sequence UWB Wireless-Sensor-Networks (WSNs), and design and simulations of a Low-Noise Amplifier (LNA) for impulse radio UWB. This latter topic was studied during a study-abroad period in collaboration with Delft University of Technology, Delft, Netherlands.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Statistical mechanics formalism and methods for the analysis of real networks

This thesis provides a thoroughly theoretical background in network theory and shows novel applications to real problems and data. In the first chapter a general introduction to network ensembles is given, and the relations with “standard” equilibrium statistical mechanics are described. Moreover, an entropy measure is considered to analyze statistical properties of the integrated PPI-signalling-mRNA expression networks in different cases. In the second chapter multilayer networks are introduced to evaluate and quantify the correlations between real interdependent networks. Multiplex networks describing citation-collaboration interactions and patterns in colorectal cancer are presented. The last chapter is completely dedicated to control theory and its relation with network theory. We characterise how the structural controllability of a network is affected by the fraction of low in-degree and low out-degree nodes. Finally, we present a novel approach to the controllability of multiplex networks

Cooperative Cognitive Wireless Networks over TV White and Gray Spaces

Wireless networks rapidly became a fundamental pillar of everyday activities. Whether at work or elsewhere, people often benefits from always-on connections. This trend is likely to increase, and hence actual technologies struggle to cope with the increase in traffic demand. To this end, Cognitive Wireless Networks have been studied. These networks aim at a better utilization of the spectrum, by understanding the environment in which they operate, and adapt accordingly. In particular recently national regulators opened up consultations on the opportunistic use of the TV bands, which became partially free due to the digital TV switch over. In this work, we focus on the indoor use of of TVWS. Interesting use cases like smart metering and WiFI like connectivity arise, and are studied and compared against state of the art technology. New measurements for TVWS networks will be presented and evaluated, and fundamental characteristics of the signal derived. Then, building on that, a new model of spectrum sharing, which takes into account also the height from the terrain, is presented and evaluated in a real scenario. The principal limits and performance of TVWS operated networks will be studied for two main use cases, namely Machine to Machine communication and for wireless sensor networks, particularly for the smart grid scenario. The outcome is that TVWS are certainly interesting to be studied and deployed, in particular when used as an additional offload for other wireless technologies. Seeing TVWS as the only wireless technology on a device is harder to be seen: the uncertainity in channel availability is the major drawback of opportunistic networks, since depending on the primary network channel allocation might lead in having no channels available for communication. TVWS can be effectively exploited as offloading solutions, and most of the contributions presented in this work proceed in this direction.