Proximity detection protocols for IoT devices

In recent years, we have witnessed the growth of the Internet of Things paradigm, with its increased pervasiveness in our everyday lives. The possible applications are diverse: from a smartwatch able to measure heartbeat and communicate it to the cloud, to the device that triggers an event when we approach an exhibit in a museum. Present in many of these applications is the Proximity Detection task: for instance the heartbeat could be measured only when the wearer is near to a well defined location for medical purposes or the touristic attraction must be triggered only if someone is very close to it. Indeed, the ability of an IoT device to sense the presence of other devices nearby and calculate the distance to them can be considered the cornerstone of various applications, motivating research on this fundamental topic. The energy constraints of the IoT devices are often in contrast with the needs of continuous operations to sense the environment and to achieve high accurate distance measurements from the neighbors, thus making the design of Proximity Detection protocols a challenging task.

NB-IoT via non terrestrial networks

Massive Internet of Things is expected to play a crucial role in Beyond 5G (B5G) wireless communication systems, offering seamless connectivity among heterogeneous devices without human intervention. However, the exponential proliferation of smart devices and IoT networks, relying solely on terrestrial networks, may not fully meet the demanding IoT requirements in terms of bandwidth and connectivity, especially in areas where terrestrial infrastructures are not economically viable. To unleash the full potential of 5G and B5G networks and enable seamless connectivity everywhere, the 3GPP envisions the integration of Non-Terrestrial Networks (NTNs) into the terrestrial ones starting from Release 17. However, this integration process requires modifications to the 5G standard to ensure reliable communications despite typical satellite channel impairments. In this framework, this thesis aims at proposing techniques at the Physical and Medium Access Control layers that require minimal adaptations in the current NB-IoT standard via NTN. Thus, firstly the satellite impairments are evaluated and, then, a detailed link budget analysis is provided. Following, analyses at the link and the system levels are conducted. In the former case, a novel algorithm leveraging time-frequency analysis is proposed to detect orthogonal preambles and estimate the signals’ arrival time. Besides, the effects of collisions on the detection probability and Bit Error Rate are investigated and Non-Orthogonal Multiple Access approaches are proposed in the random access and data phases. The system analysis evaluates the performance of random access in case of congestion. Various access parameters are tested in different satellite scenarios, and the performance is measured in terms of access probability and time required to complete the procedure. Finally, a heuristic algorithm is proposed to jointly design the access and data phases, determining the number of satellite passages, the Random Access Periodicity, and the number of uplink repetitions that maximize the system's spectral efficiency.

Radiation characteristics enhancement of planar structures using metasurfaces

In this thesis, the focus is on utilizing metasurfaces to improve radiation characteristics of planar structures. The study encompasses various aspects of metasurface applications, including enhancing antenna radiation characteristics and manipulating electromagnetic (EM) waves, such as polarization conversion and anomalous reflection. The thesis introduces the design of a single-port antenna with dual-mode operation, integrating metasurfaces. This antenna serves as the front-end for a next-generation tag, functioning as a position sensor with identification and energy harvesting capabilities. It operates in the lower European Ultra-Wideband (UWB) frequency range for communication/localization and the UHF band for wireless energy reception. The design aims for a low-profile stack-up that remains unaffected by background materials. Researchers worldwide are drawn to metasurfaces due to their EM wave manipulation capabilities. The thesis also demonstrates how a High-Impedance Surface (HIS) can enhance the antenna's versatility through metasurface application, including conformal design using 3D-printing technology, ensuring adaptability for various deformation and tracking/powering scenarios. Additionally, the thesis explores two distinct metasurface applications. One involves designing an angularly stable super-wideband Circular Polarization Converter (CPC) operating from 11 to 35GHz with an impressive relative impedance bandwidth of 104.3%. The CPC shows a stable response even at oblique incidences up to 40 degrees, with a Peak Cross-Polarization Ratio (PCR) exceeding 62% across the entire band. The second application focuses on an Intelligent Reflective Surface (IRS) capable of redirecting incoming waves in unconventional directions. Tunability is achieved through an artificially developed ferroelectric material (HfZrO) and distributed capacitive elements (IDC) to fine-tune impedance and phase responses at the meta-atom level. The IRS demonstrates anomalous reflection for normal incident waves. These innovative applications of metasurfaces offer promising advancements in antenna design, EM wave manipulation, and versatile wireless communication systems.

“Puta”, “travesti” e “brasiliana”: processos interseccionais de tradução e criminalização entre Brasil e Itália

Esta tese se debruça nas (im)possibilidades de tradução terminológica para demonstrar uma incomunicabilidade entre o contexto brasileiro e italiano, em termos de trabalho sexual e políticas travestis. Proponho uma análise etnográfica dos usos dos termos, efetivada pela pessoa antropóloga também corporificada e marcada contextualmente. Apresento como nos dois contextos há uma aproximação entre as noções “puta” e “travesti” que se materializa em processos interseccionais de criminalização. Demonstro como no contexto brasileiro mais do que termos, envolvem disputas, agenciamentos e vivências corporificadas que refletem ativismos protagonizados por pessoas diretamente engajadas na transformação política dessas nomenclaturas – movimentações intransponíveis para o contexto italiano. Ao mesmo tempo, “brasiliana” ativa um imaginário italiano local que enquadra a prostituição e vivências trans majoritariamente como um problema migratório, para o qual se mobilizam ostensivos recursos e financiamentos que ganham forma no combate à “tratta” / tráfico de pessoas” – todo um aparato de difícil tradução para o contexto brasileiro. Dessa forma, partindo dos termos locais mobilizados nos dois contextos, penso nos elementos culturais naturalizados e em seu diálogo transcultural. Os processos de tradução são, desse modo, epistemológicos e necessariamente políticos, uma vez que estão situados em uma geopolítica marcadamente desigual. Afirmo, portanto, que as (im)possibilidades de tradução cultural se materializam em ativismos, políticas institucionais e normativas legais que ativam diversas formas de criminalizar possibilidades de existência, criação de redes de afeto e de potência política em trânsito.

Long-term reliability of power GaN HEMTS

The world is quickly changing, and the field of power electronics assumes a pivotal role in addressing the challenges posed by climate change, global warming, and energy management. The introduction of wide-bandgap semiconductors, particularly gallium nitride (GaN), in contrast to the traditional silicon technology, is leading to lightweight, compact and evermore efficient circuitry. However, GaN technology is not mature yet and still presents reliability issues which constrain its widespread adoption. Therefore, GaN reliability is a hotspot for the research community. Extensive efforts have been directed toward understanding the physical mechanisms underlying the performance and reliability of GaN power devices. The goal of this thesis is to propose a novel in-circuit degradation analysis in order to evaluate the long-term reliability of GaN-based power devices accurately. The in-circuit setup is based on measure-stress-measure methodology where a high-speed synchronous buck converter ensures the stress while the measure is performed by means of full I-V characterizations. The switch from stress mode to characterization mode and vice versa is automatic thanks to electromechanical and solid-state relays controlled by external unit control. Because these relays are located in critical paths of the converter layout, the design has required a comprehensive study of electrical and thermal problems originated by the use of GaN technology. In addition, during the validation phase of the converter, electromagnetic-lumped-element circuit simulations are carried out to monitor the signal integrity and junction temperature of the devices under test. However, the core of this work is the in-circuit reliability analysis conducted with 80 V GaN HEMTs under several operating conditions of the converter in order to figure out the main stressors which contribute to the device's degradation.

Grant-free protocols for massive multiple access

In next generation Internet-of-Things, the overhead introduced by grant-based multiple access protocols may engulf the access network as a consequence of the proliferation of connected devices. Grant-free access protocols are therefore gaining an increasing interest to support massive multiple access. In addition to scalability requirements, new demands have emerged for massive multiple access, including latency and reliability. The challenges envisaged for future wireless communication networks, particularly in the context of massive access, include: i) a very large population size of low power devices transmitting short packets; ii) an ever-increasing scalability requirement; iii) a mild fixed maximum latency requirement; iv) a non-trivial requirement on reliability. To this aim, we suggest the joint utilization of grant-free access protocols, massive MIMO at the base station side, framed schemes to let the contention start and end within a frame, and succesive interference cancellation techniques at the base station side. In essence, this approach is encapsulated in the concept of coded random access with massive MIMO processing. These schemes can be explored from various angles, spanning the protocol stack from the physical (PHY) to the medium access control (MAC) layer. In this thesis, we delve into both of these layers, examining topics ranging from symbol-level signal processing to succesive interference cancellation-based scheduling strategies. In parallel with proposing new schemes, our work includes a theoretical analysis aimed at providing valuable system design guidelines. As a main theoretical outcome, we propose a novel joint PHY and MAC layer design based on density evolution on sparse graphs.

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.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

6G enabled IoV scenarios with distributed intelligence mechanisms

The Internet of Vehicles (IoV) paradigm has emerged in recent times, where with the support of technologies like the Internet of Things and V2X , Vehicular Users (VUs) can access different services through internet connectivity. With the support of 6G technology, the IoV paradigm will evolve further and converge into a fully connected and intelligent vehicular system. However, this brings new challenges over dynamic and resource-constrained vehicular systems, and advanced solutions are demanded. This dissertation analyzes the future 6G enabled IoV systems demands, corresponding challenges, and provides various solutions to address them. The vehicular services and application requests demands proper data processing solutions with the support of distributed computing environments such as Vehicular Edge Computing (VEC). While analyzing the performance of VEC systems it is important to take into account the limited resources, coverage, and vehicular mobility into account. Recently, Non terrestrial Networks (NTN) have gained huge popularity for boosting the coverage and capacity of terrestrial wireless networks. Integrating such NTN facilities into the terrestrial VEC system can address the above mentioned challenges. Additionally, such integrated Terrestrial and Non-terrestrial networks (T-NTN) can also be considered to provide advanced intelligent solutions with the support of the edge intelligence paradigm. In this dissertation, we proposed an edge computing-enabled joint T-NTN-based vehicular system architecture to serve VUs. Next, we analyze the terrestrial VEC systems performance for VUs data processing problems and propose solutions to improve the performance in terms of latency and energy costs. Next, we extend the scenario toward the joint T-NTN system and address the problem of distributed data processing through ML-based solutions. We also proposed advanced distributed learning frameworks with the support of a joint T-NTN framework with edge computing facilities. In the end, proper conclusive remarks and several future directions are provided for the proposed solutions.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Acquisizione di segnali EEG durante compiti di reaching e decodifica mediante rete neurale convoluzionale

La decodifica dei segnali elettroencefalografici (EEG) consiste nell’analisi del segnale per classificare le azioni o lo stato cognitivo di un soggetto. Questi studi possono permettere di comprendere meglio i correlati neurali alla base del movimento, oltre che avere un’applicazione pratica nelle Brain Computer Interfaces. In questo ambito, di rilievo sono le reti neurali convoluzionali (Convolutional Neural Networks, CNNs), che grazie alle loro elevate performance stanno acquisendo importanza nella decodifica del segnale EEG. In questo elaborato di tesi è stata addestrata una CNN precedentemente proposta in letteratura, EEGNet, per classificare i segnali EEG acquisiti durante movimenti di reaching del braccio dominante, sulla base della posizione del target da raggiungere. I dati sono stati acquisiti su dieci soggetti grazie al protocollo sviluppato in questo lavoro, in cui 5 led disposti su una semicirconferenza rappresentano i target del movimento e l’accensione casuale di un led identifica il target da raggiungere in ciascuna prova. I segnali EEG acquisiti sono stati quindi ricampionati, filtrati e suddivisi in epoche di due secondi attorno all’inizio di ciascun movimento, rimuovendo gli artefatti oculari mediante ICA. La rete è stata valutata in tre task di classificazione, uno a cinque classi (una posizione target per classe) e due a tre classi (raggruppando più posizioni target per classe). Per ogni task, la rete è stata addestrata in cross-validazione utilizzando un approccio within-subject. Con questo approccio sono state addestrate e validate 15 CNNs diverse per ogni soggetto. Infine, è stato calcolato l’F1 score per ciascun task di classificazione, mediando i risultati sui soggetti, per valutare quantitativamente le performance della CNN che sono risultati migliori nel classificare target disposti a destra e a sinistra.

Modelli Input-Output: un'analisi simultanea di shock domanda e offerta

La tesi analizza il modello Input-Output, introdotto da Leontief nel 1936, per studiare la reazione dei sistemi industriali di Germania, Spagna ed Italia alle restrizioni imposte dai governi per limitare la diffusione della pandemia da COVID-19. Si studiano le economie considerando gli scambi tra i settori produttivi intermedi e la domanda finale. La formulazione originale del modello necessita diverse modifiche per descrivere realisticamente le reti di produzione e comunque non è del tutto esaustiva in quanto si ipotizza che la produttività dei sistemi sia sempre tale da soddisfare pienamente la domanda che giunge per il prodotto emesso. Perciò si introduce una distinzione tra le variabili del problema, assumendo che alcune componenti di produzione siano indipendenti dalla richiesta e che altre componenti siano endogene. Le soluzioni di questo sistema tuttavia non sempre risultano appartenenti al dominio di definizione delle variabili. Dunque utilizzando tecniche di programmazione lineare, si osservano i livelli massimi di produzione e domanda corrisposta in un periodo di crisi anche quando i sistemi non raggiungono questa soglia poiché non pienamente operativi. Si propongono diversi schemi di razionamento per distribuire tra i richiedenti i prodotti emessi: 1) programma proporzionale in base alle domande di tutti i richiedenti; 2) programma proporzionale in base alle richieste, con precedenza ai settori intermedi; 3) programma prioritario in cui vengono riforniti i settori intermedi in base alla dimensione dell’ordine; 4) programma prioritario con fornitura totale degli ordini e ordine di consegna casuale. I risultati ottenuti dipendono dal modello di fornitura scelto, dalla dimensione dello shock cui i settori sono soggetti e dalle proprietà della rete industriale, descritta come grafo pesato.

Satellite IoT: a Study and Performance Analysis of GEO and LEO Systems

The study is divided into two main part: one focused on the GEO Satellite IoT and the other on the LEO Satellite IoT. Concerning the GEO Satellite IoT, the activity has been developed in the context of EUMETSAT Data Collection Service (DCS) by investigating the performance at the receiver within challenging scenarios. DCS are provided by several GEO Satellite operators, giving almost total coverage around the world. In this study firstly an overview of the DCS end-to-end architecture is given followed by a detailed description of both the tools used for the simulations: the DCP-TST (message generator and transmitter) and the DCP-RX (receiver). After generating several test messages, the performances have been evaluated with the addition of impairments (CW and sweeping interferences) and considerations in terms of BER and Good Messages are produced. Furthermore, a study on the PLL System is also conducted together with evaluations on the effectiveness of tuning the PLL Bw on the overall performance. Concerning the LEO Satellite IoT, the activity was carried out in the framework of the ASI Bidirectional IoT Satellite Service (BISS) Project. The elaborate covers a survey about the possible services that the project can accomplish and a technical analysis on the uplink MA. In particular, the LR-FHSS is proved to be a valid alternative for the uplink through an extensive analysis on its Network capacity and through the study of an analytic model for Success Probability with its Matlab implementation.

Geometric deep learning for polygonal mesh denoising

Il seguente lavoro si propone come analisi degli operatori convoluzionali che caratterizzano le graph neural networks. ln particolare, la trattazione si divide in due parti, una teorica e una sperimentale. Nella parte teorica vengono innanzitutto introdotte le nozioni preliminari di mesh e convoluzione su mesh. In seguito vengono riportati i concetti base del geometric deep learning, quali le definizioni degli operatori convoluzionali e di pooling e unpooling. Un'attenzione particolare è stata data all'architettura Graph U-Net. La parte sperimentare riguarda l'applicazione delle reti neurali e l'analisi degli operatori convoluzionali applicati al denoising di superfici perturbate a causa di misurazioni imperfette effettuate da scanner 3D.

Tèilifìs Na Gaelige. Il salvataggio delle lingue celtiche tramite tv e nuovi media

La mia tesi si occupa delle politiche culturali, di salvataggio delle lingue celtiche gaeliche scozzesi e irlandesi. In particolare, ho analizzato due canali dei rispettivi paesi: BBCalba (Scozia), e TG4 (Irlanda), prendendo in esame le differenze dei contesti politici, economici e culturali che hanno influenzato le due reti televisive. Lo scopo della mia tesi è dimostrare che TG4 sia riuscito a svolgere meglio il suo compito, essendo un canale televisivo pubblico di un paese indipendente e impegnato nella salvaguardia della sua lingua nativa; al contrario, BBCalba ha trovato molte più difficoltà sul suo cammino, perchè nel contesto televisivo del Regno Unito, è soltanto un canale minore regionale, il cui scopo è osteggiato da una parte della politica britannica, che lo vede come un focolaio indipendentista.