This thesis adresses the problem of localization, and analyzes its crucial aspects, within the context of cooperative WSNs. The three main issues discussed in the following are: network synchronization, position estimate and tracking. Time synchronization is a fundamental requirement for every network. In this context, a new approach based on the estimation theory is proposed to evaluate the ultimate performance limit in network time synchronization. In particular the lower bound on the variance of the average synchronization error in a fully connected network is derived by taking into account the statistical characterization of the Message Delivering Time (MDT) . Sensor network localization algorithms estimate the locations of sensors with initially unknown location information by using knowledge of the absolute positions of a few sensors and inter-sensor measurements such as distance and bearing measurements. Concerning this issue, i.e. the position estimate problem, two main contributions are given. The first is a new Semidefinite Programming (SDP) framework to analyze and solve the problem of flip-ambiguity that afflicts range-based network localization algorithms with incomplete ranging information. The occurrence of flip-ambiguous nodes and errors due to flip ambiguity is studied, then with this information a new SDP formulation of the localization problem is built. Finally a flip-ambiguity-robust network localization algorithm is derived and its performance is studied by Monte-Carlo simulations. The second contribution in the field of position estimate is about multihop networks. A multihop network is a network with a low degree of connectivity, in which couples of given any nodes, in order to communicate, they have to rely on one or more intermediate nodes (hops). Two new distance-based source localization algorithms, highly robust to distance overestimates, typically present in multihop networks, are presented and studied. The last point of this thesis discuss a new low-complexity tracking algorithm, inspired by the Fano’s sequential decoding algorithm for the position tracking of a user in a WLAN-based indoor localization system.

The contemporary media landscape is characterized by the emergence of hybrid forms of digital communication that contribute to the ongoing redefinition of our societies cultural context. An incontrovertible consequence of this phenomenon is the new public dimension that characterizes the transmission of historical knowledge in the twenty-first century. Awareness of this new epistemic scenario has led us to reflect on the following methodological questions: what strategies should be created to establish a communication system, based on new technology, that is scientifically rigorous, but at the same time engaging for the visitors of museums and Internet users? How does a comparative analysis of ancient documentary sources form a solid base of information for the virtual reconstruction of thirteenth century Bologna in the Metaverse? What benefits can the phenomenon of cross-mediality give to the virtual heritage? The implementation of a new version of the Nu.M.E. project allowed for answering many of these instances. The investigation carried out between 2008 and 2010 has shown that, indeed, real-time 3D graphics and collaborative virtual environments can be feasible tools for representing philologically the urban medieval landscape and for communicating properly validated historical data to the general public. This research is focused on the study and implementation of a pipeline that permits mass communication of historical information about an area of vital importance in late medieval Bologna: Piazza di Porta Ravegnana. The originality of the developed project is not limited solely to the methodological dimension of historical research. Adopted technological perspective is an excellent example of innovation that digital technologies can bring to the cultural heritage. The main result of this research is the creation of Nu.ME 2010, a cross-media system of 3D real-time visualization based on some of the most advanced free software and open source technologies available today free of charge.

This thesis introduces new processing techniques for computer-aided interpretation of ultrasound images with the purpose of supporting medical diagnostic. In terms of practical application, the goal of this work is the improvement of current prostate biopsy protocols by providing physicians with a visual map overlaid over ultrasound images marking regions potentially affected by disease. As far as analysis techniques are concerned, the main contributions of this work to the state-of-the-art is the introduction of deconvolution as a pre-processing step in the standard ultrasonic tissue characterization procedure to improve the diagnostic significance of ultrasonic features. This thesis also includes some innovations in ultrasound modeling, in particular the employment of a continuous-time autoregressive moving-average (CARMA) model for ultrasound signals, a new maximum-likelihood CARMA estimator based on exponential splines and the definition of CARMA parameters as new ultrasonic features able to capture scatterers concentration. Finally, concerning the clinical usefulness of the developed techniques, the main contribution of this research is showing, through a study based on medical ground truth, that a reduction in the number of sampled cores in standard prostate biopsy is possible, preserving the same diagnostic power of the current clinical protocol. / Cette thèse introduit de nouvelles techniques de traitement pour l’interprétation guidée d’images ultrasons, dans le but de soutenir le diagnostic médical. L’objectif pratique de ce travail est l’amélioration du protocole standard pour la biopsie de la prostate, en fournissant au médecin une carte visuelle sur l’écographie qui marque les régions potentiellement malignes. En ce qui concerne les techniques d’analyse, la contribution principale de cette thèse est l’introduction de la déconvolution comme étape de pré-traitement dans la procédure standard de caractérisation des tissus par ultrasons, afin d’améliorer la valeur diagnostique des caractéristiques du signal. Cette thèse présente, en outre, des innovations dans la modélisation du signal ultrason, en particulier la proposition d’un modèle autorégressif à moyenne glissante en temps continu (CARMA), le développement d’un nouvel estimateur au maximum de vraisemblance de paramètres CARMA fondé sur les splines exponentielles et la définition des paramètres CARMA comme des nouvelles caractéristiques du signal ultrason capables de capturer l’information sur la concentration de scatterers. Enfin, concernant l’utilité clinique des techniques développées, la contribution principale de cette recherche est dans la démonstration, au travers d’une étude médicale, de la possibilité de réduire concrètement le nombre d'échantillons prélevés pendant la biopsie de la prostate standard, en préservant le même pouvoir diagnostique du protocole biopsique standard.

In the post genomic era with the massive production of biological data the understanding of factors affecting protein stability is one of the most important and challenging tasks for highlighting the role of mutations in relation to human maladies. The problem is at the basis of what is referred to as molecular medicine with the underlying idea that pathologies can be detailed at a molecular level. To this purpose scientific efforts focus on characterising mutations that hamper protein functions and by these affect biological processes at the basis of cell physiology. New techniques have been developed with the aim of detailing single nucleotide polymorphisms (SNPs) at large in all the human chromosomes and by this information in specific databases are exponentially increasing. Eventually mutations that can be found at the DNA level, when occurring in transcribed regions may then lead to mutated proteins and this can be a serious medical problem, largely affecting the phenotype. Bioinformatics tools are urgently needed to cope with the flood of genomic data stored in database and in order to analyse the role of SNPs at the protein level. In principle several experimental and theoretical observations are suggesting that protein stability in the solvent-protein space is responsible of the correct protein functioning. Then mutations that are found disease related during DNA analysis are often assumed to perturb protein stability as well. However so far no extensive analysis at the proteome level has investigated whether this is the case. Also computationally methods have been developed to infer whether a mutation is disease related and independently whether it affects protein stability. Therefore whether the perturbation of protein stability is related to what it is routinely referred to as a disease is still a big question mark. In this work we have tried for the first time to explore the relation among mutations at the protein level and their relevance to diseases with a large-scale computational study of the data from different databases. To this aim in the first part of the thesis for each mutation type we have derived two probabilistic indices (for 141 out of 150 possible SNPs): the perturbing index (Pp), which indicates the probability that a given mutation effects protein stability considering all the “in vitro” thermodynamic data available and the disease index (Pd), which indicates the probability of a mutation to be disease related, given all the mutations that have been clinically associated so far. We find with a robust statistics that the two indexes correlate with the exception of all the mutations that are somatic cancer related. By this each mutation of the 150 can be coded by two values that allow a direct comparison with data base information. Furthermore we also implement computational methods that starting from the protein structure is suited to predict the effect of a mutation on protein stability and find that overpasses a set of other predictors performing the same task. The predictor is based on support vector machines and takes as input protein tertiary structures. We show that the predicted data well correlate with the data from the databases. All our efforts therefore add to the SNP annotation process and more importantly found the relationship among protein stability perturbation and the human variome leading to the diseasome.

Ion channels are pore-forming proteins that regulate the flow of ions across biological cell membranes. Ion channels are fundamental in generating and regulating the electrical activity of cells in the nervous system and the contraction of muscolar cells. Solid-state nanopores are nanometer-scale pores located in electrically insulating membranes. They can be adopted as detectors of specific molecules in electrolytic solutions. Permeation of ions from one electrolytic solution to another, through a protein channel or a synthetic pore is a process of considerable importance and realistic analysis of the main dependencies of ion current on the geometrical and compositional characteristics of these structures are highly required. The project described by this thesis is an effort to improve the understanding of ion channels by devising methods for computer simulation that can predict channel conductance from channel structure. This project describes theory, algorithms and implementation techniques used to develop a novel 3-D numerical simulator of ion channels and synthetic nanopores based on the Brownian Dynamics technique. This numerical simulator could represent a valid tool for the study of protein ion channel and synthetic nanopores, allowing to investigate at the atomic-level the complex electrostatic interactions that determine channel conductance and ion selectivity. Moreover it will provide insights on how parameters like temperature, applied voltage, and pore shape could influence ion translocation dynamics. Furthermore it will help making predictions of conductance of given channel structures and it will add information like electrostatic potential or ionic concentrations throughout the simulation domain helping the understanding of ion flow through membrane pores.

This work has been realized by the author in his PhD course in Electrical, Computer Science and Telecommunication at the University of Bologna, Faculty of Engineering, Italy. All the documentation here reported is a summary of years of work, under the supervision of Prof. Oreste Andrisano, coordinator of Wireless Communication Laboratory - WiLab, in Bologna. The subject of this thesis is the transmission of video in a context of heterogeneous network, and in particular, using a wireless channel. All the instrumentation that has been used for the characterization of the telecommunication systems belongs to CNR (National Research Council), CNIT (Italian Inter- University Center), and DEIS (Dept. of Electrical, Computer Science, and Systems). From November 2009 to July 2010, the author spent his time abroad, working in collaboration with DLR - German Aerospace Center in Munich, Germany, on channel coding area, developing a general purpose decoder machine to decode a huge family of iterative codes. A patent concerning Doubly Generalized-Low Density Parity Check codes has been produced by the author as well as some important scientic papers, published on IEEE journals and conferences.

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This work has been realized by the author in his PhD course in Electronics, Computer Science and Telecommunication at the University of Bologna, Faculty of Engineering, Italy. The subject of this thesis regards important channel estimation aspects in wideband wireless communication systems, such as echo cancellation in digital video broadcasting systems and pilot aided channel estimation through an innovative pilot design in Multi-Cell Multi-User MIMO-OFDM network. All the documentation here reported is a summary of years of work, under the supervision of Prof. Oreste Andrisano, coordinator of Wireless Communication Laboratory - WiLab, in Bologna. All the instrumentation that has been used for the characterization of the telecommunication systems belongs to CNR (National Research Council), CNIT (Italian Inter-University Center), and DEIS (Dept. of Electronics, Computer Science, and Systems). From November 2009 to May 2010, the author spent his time abroad, working in collaboration with DOCOMO - Communications Laboratories Europe GmbH (DOCOMO Euro-Labs) in Munich, Germany, in the Wireless Technologies Research Group. Some important scientific papers, submitted and/or published on IEEE journals and conferences have been produced by the author.

In this thesis two major topics inherent with medical ultrasound images are addressed: deconvolution and segmentation. In the first case a deconvolution algorithm is described allowing statistically consistent maximum a posteriori estimates of the tissue reflectivity to be restored. These estimates are proven to provide a reliable source of information for achieving an accurate characterization of biological tissues through the ultrasound echo. The second topic involves the definition of a semi automatic algorithm for myocardium segmentation in 2D echocardiographic images. The results show that the proposed method can reduce inter- and intra observer variability in myocardial contours delineation and is feasible and accurate even on clinical data.

Electronic applications are nowadays converging under the umbrella of the cloud computing vision. The future ecosystem of information and communication technology is going to integrate clouds of portable clients and embedded devices exchanging information, through the internet layer, with processing clusters of servers, data-centers and high performance computing systems. Even thus the whole society is waiting to embrace this revolution, there is a backside of the story. Portable devices require battery to work far from the power plugs and their storage capacity does not scale as the increasing power requirement does. At the other end processing clusters, such as data-centers and server farms, are build upon the integration of thousands multiprocessors. For each of them during the last decade the technology scaling has produced a dramatic increase in power density with significant spatial and temporal variability. This leads to power and temperature hot-spots, which may cause non-uniform ageing and accelerated chip failure. Nonetheless all the heat removed from the silicon translates in high cooling costs. Moreover trend in ICT carbon footprint shows that run-time power consumption of the all spectrum of devices accounts for a significant slice of entire world carbon emissions. This thesis work embrace the full ICT ecosystem and dynamic power consumption concerns by describing a set of new and promising system levels resource management techniques to reduce the power consumption and related issues for two corner cases: Mobile Devices and High Performance Computing.