A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

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.

Fluidodinamica biomedica computazionale del flusso sanguigno in presenza di affezioni strumentali e/o patologiche

La tesi di Dottorato studia il flusso sanguigno tramite un codice agli elementi finiti (COMSOL Multiphysics). Nell’arteria è presente un catetere Doppler (in posizione concentrica o decentrata rispetto all’asse di simmetria) o di stenosi di varia forma ed estensione. Le arterie sono solidi cilindrici rigidi, elastici o iperelastici. Le arterie hanno diametri di 6 mm, 5 mm, 4 mm e 2 mm. Il flusso ematico è in regime laminare stazionario e transitorio, ed il sangue è un fluido non-Newtoniano di Casson, modificato secondo la formulazione di Gonzales & Moraga. Le analisi numeriche sono realizzate in domini tridimensionali e bidimensionali, in quest’ultimo caso analizzando l’interazione fluido-strutturale. Nei casi tridimensionali, le arterie (simulazioni fluidodinamiche) sono infinitamente rigide: ricavato il campo di pressione si procede quindi all’analisi strutturale, per determinare le variazioni di sezione e la permanenza del disturbo sul flusso. La portata sanguigna è determinata nei casi tridimensionali con catetere individuando tre valori (massimo, minimo e medio); mentre per i casi 2D e tridimensionali con arterie stenotiche la legge di pressione riproduce l’impulso ematico. La mesh è triangolare (2D) o tetraedrica (3D), infittita alla parete ed a valle dell’ostacolo, per catturare le ricircolazioni. Alla tesi sono allegate due appendici, che studiano con codici CFD la trasmissione del calore in microcanali e l’ evaporazione di gocce d’acqua in sistemi non confinati. La fluidodinamica nei microcanali è analoga all’emodinamica nei capillari. Il metodo Euleriano-Lagrangiano (simulazioni dell’evaporazione) schematizza la natura mista del sangue. La parte inerente ai microcanali analizza il transitorio a seguito dell’applicazione di un flusso termico variabile nel tempo, variando velocità in ingresso e dimensioni del microcanale. L’indagine sull’evaporazione di gocce è un’analisi parametrica in 3D, che esamina il peso del singolo parametro (temperatura esterna, diametro iniziale, umidità relativa, velocità iniziale, coefficiente di diffusione) per individuare quello che influenza maggiormente il fenomeno.