Il lavoro presentato ha come oggetto la ricostruzione tridimensionale della città di Bologna nella sua fase rinascimentale. Tale lavoro vuole fornire un modello 3D delle architetture e degli spazi urbani utilizzabile sia per scopi di ricerca nell’ambito della storia delle città sia per un uso didattico-divulgativo nel settore del turismo culturale. La base del lavoro è una fonte iconografica di grande importanza: l’affresco raffigurante Bologna risalente al 1575 e situato in Vaticano; questa è una veduta a volo d’uccello di grandi dimensioni dell’intero tessuto urbano bolognese all’interno della terza cerchia di mura. In esso sono rappresentate in maniera particolareggiata le architetture civili e ecclesiastiche, gli spazi ortivi e cortilivi interni agli isolati e alcune importanti strutture urbane presenti in città alla fine del Cinquecento, come l’area portuale e i canali interni alla città, oggi non più visibili. La ricostruzione tridimensionale è stata realizzata tramite Blender, software per la modellazione 3D opensource, attraverso le fasi di modellazione, texturing e creazione materiali (mediante campionamento delle principali cromie presenti nell’affresco), illuminazione e animazione. Una parte della modellazione è stata poi testata all’interno di un GIS per verificare l’utilizzo delle geometrie 3D come elementi collegabili ad altre fonti storiche relative allo sviluppo urbano e quindi sfruttabili per la ricerca storica. Grande attenzione infine è stata data all’uso dei modelli virtuali a scopo didattico-divulgativo e per il turismo culturale. La modellazione è stata utilizzata all’interno di un motore grafico 3D per costruire un ambiente virtuale interattivo nel quale un utente anche non esperto possa muoversi per esplorare gli spazi urbani della Bologna del Cinquecento. In ultimo è stato impostato lo sviluppo di un’applicazione per sistemi mobile (Iphone e Ipad) al fine di fornire uno strumento per la conoscenza della città storica in mobilità, attraverso la comparazione dello stato attuale con quello ricostruito virtualmente. / The present work concerns a three-dimensional reconstruction of the city of Bologna as it was in the Renaissance period; it provides a 3D model of buildings and their architecture and urban spaces that is ideally suited for research into the history of cities, educational purposes, and cultural tourism. The reconstruction is based on an iconographic source of primary importance: the 1575 fresco of Bologna situated in the Vatican. This fresco is a huge bird’s view of the whole urban fabric of Bologna within its third city walls; civil and religious buildings and their architecture are depicted with a high level of detail, featuring gardens and other green areas in city blocks and other important urban structures observable in the city in the late 1500’s, such as the dock area and the canals within the city, which today are no longer visible. The three-dimensional reconstruction was made using Blender, an opensource 3D modelling software program and involved various steps: modelling, the creation of textures and materials (sampling the main colours in the fresco), lighting and animation. A part of the virtual model was then imported into a GIS to test the use of 3D models as items to be linked to other historical documents related to urban development, thus making the reconstruction particularly suitable for historical research. Great emphasis was given to the employment of virtual models suitable for educational purposes and for cultural tourism. The three-dimensional model was imported into a 3D engine to create an interactive virtual environment in which the user can move about to explore the urban spaces of Bologna in the 1500’s. The last step was the development of an application for mobile devices (e.g. Iphone and Ipad). This app allows users to compare contemporary Bologna and the virtual reconstructions while on the move.

We investigate the benefits that emerge when the fields of constraint programming and concurrency meet. On one hand, constraints can be use in concurrency theory to increase the conciseness and the expressive power of concurrent languages from a pragmatic point of view. On the other hand, problems modeled by using constraints can be solved faster and more efficiently using a concurrent system. We explore both directions providing two separate lines of contribution. Firstly we study the expressive power of a concurrent language, namely Constraint Handling Rules, that supports constraints as a primitive construct. We show what features of this language make it Turing powerful. Then we propose a framework to solve constraint problems that is intended to be deployed on a concurrent system. For the development of this framework we used the concurrent language Jolie following the Service Oriented paradigm. Based on this experience, we also propose an extension to Service Oriented Languages to overcome some of their limitations and to improve the development of concurrent applications.

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Cost, performance and availability considerations are forcing even the most conservative high-integrity embedded real-time systems industry to migrate from simple hardware processors to ones equipped with caches and other acceleration features. This migration disrupts the practices and solutions that industry had developed and consolidated over the years to perform timing analysis. Industry that are confident with the efficiency/effectiveness of their verification and validation processes for old-generation processors, do not have sufficient insight on the effects of the migration to cache-equipped processors. Caches are perceived as an additional source of complexity, which has potential for shattering the guarantees of cost- and schedule-constrained qualification of their systems. The current industrial approach to timing analysis is ill-equipped to cope with the variability incurred by caches. Conversely, the application of advanced WCET analysis techniques on real-world industrial software, developed without analysability in mind, is hardly feasible. We propose a development approach aimed at minimising the cache jitters, as well as at enabling the application of advanced WCET analysis techniques to industrial systems. Our approach builds on:(i) identification of those software constructs that may impede or complicate timing analysis in industrial-scale systems; (ii) elaboration of practical means, under the model-driven engineering (MDE) paradigm, to enforce the automated generation of software that is analyzable by construction; (iii) implementation of a layout optimisation method to remove cache jitters stemming from the software layout in memory, with the intent of facilitating incremental software development, which is of high strategic interest to industry. The integration of those constituents in a structured approach to timing analysis achieves two interesting properties: the resulting software is analysable from the earliest releases onwards - as opposed to becoming so only when the system is final - and more easily amenable to advanced timing analysis by construction, regardless of the system scale and complexity.

Biomedical analyses are becoming increasingly complex, with respect to both the type of the data to be produced and the procedures to be executed. This trend is expected to continue in the future. The development of information and protocol management systems that can sustain this challenge is therefore becoming an essential enabling factor for all actors in the field. The use of custom-built solutions that require the biology domain expert to acquire or procure software engineering expertise in the development of the laboratory infrastructure is not fully satisfactory because it incurs undesirable mutual knowledge dependencies between the two camps. We propose instead an infrastructure concept that enables the domain experts to express laboratory protocols using proper domain knowledge, free from the incidence and mediation of the software implementation artefacts. In the system that we propose this is made possible by basing the modelling language on an authoritative domain specific ontology and then using modern model-driven architecture technology to transform the user models in software artefacts ready for execution in a multi-agent based execution platform specialized for biomedical laboratories.

The evolution of the electronics embedded applications forces electronics systems designers to match their ever increasing requirements. This evolution pushes the computational power of digital signal processing systems, as well as the energy required to accomplish the computations, due to the increasing mobility of such applications. Current approaches used to match these requirements relies on the adoption of application specific signal processors. Such kind of devices exploits powerful accelerators, which are able to match both performance and energy requirements. On the other hand, the too high specificity of such accelerators often results in a lack of flexibility which affects non-recurrent engineering costs, time to market, and market volumes too. The state of the art mainly proposes two solutions to overcome these issues with the ambition of delivering reasonable performance and energy efficiency: reconfigurable computing and multi-processors computing. All of these solutions benefits from the post-fabrication programmability, that definitively results in an increased flexibility. Nevertheless, the gap between these approaches and dedicated hardware is still too high for many application domains, especially when targeting the mobile world. In this scenario, flexible and energy efficient acceleration can be achieved by merging these two computational paradigms, in order to address all the above introduced constraints. This thesis focuses on the exploration of the design and application spectrum of reconfigurable computing, exploited as application specific accelerators for multi-processors systems on chip. More specifically, it introduces a reconfigurable digital signal processor featuring a heterogeneous set of reconfigurable engines, and a homogeneous multi-core system, exploiting three different flavours of reconfigurable and mask-programmable technologies as implementation platform for applications specific accelerators. In this work, the various trade-offs concerning the utilization multi-core platforms and the different configuration technologies are explored, characterizing the design space of the proposed approach in terms of programmability, performance, energy efficiency and manufacturing costs.

This work is concerned with the increasing relationships between two distinct multidisciplinary research fields, Semantic Web technologies and scholarly publishing, that in this context converge into one precise research topic: Semantic Publishing. In the spirit of the original aim of Semantic Publishing, i.e. the improvement of scientific communication by means of semantic technologies, this thesis proposes theories, formalisms and applications for opening up semantic publishing to an effective interaction between scholarly documents (e.g., journal articles) and their related semantic and formal descriptions. In fact, the main aim of this work is to increase the users' comprehension of documents and to allow document enrichment, discovery and linkage to document-related resources and contexts, such as other articles and raw scientific data. In order to achieve these goals, this thesis investigates and proposes solutions for three of the main issues that semantic publishing promises to address, namely: the need of tools for linking document text to a formal representation of its meaning, the lack of complete metadata schemas for describing documents according to the publishing vocabulary, and absence of effective user interfaces for easily acting on semantic publishing models and theories.

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

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).