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Energy Efficiency and Privacy in Device-to-Device Communication
Get PDFMobile data traffic has increased many folds in recent years and current cellular networks are undeniably overloaded to meet the escalating user's demands of higher bandwidth and data rates. To meet such demands, Device-to-Device (D2D) communication is regarded as a potential solution to solve the capacity bottleneck problem in legacy cellular networks. Apart from offloading cellular traffic, D2D communication, due to its intrinsic property to rely on proximity, enables a broad range of proximity-based applications for both public safety and commercial users. Some potential applications, among others, include, proximity-based social interactions, exchange of information, advertisements and Vehicle-to-Vehicle (V2V) communication. The success of D2D communication depends upon the scenarios in which the users in the proximity interact with each other. Although there is a lot of work on resource allocation and interference management in D2D networks, very few works focus on the architectural aspects of D2D communication, emphasizing the benchmarking of energy efficiency for different application scenarios.
In this dissertation, we benchmark the energy consumption of D2D User Equipments (UEs) in different application scenarios. To this end, first we consider a scenario wherein different UEs, interested in sharing the same service, form a Mobile Cloud (MC). Since, some UEs can involve in multiple services/applications at a time, there is a possibility of interacting with multiple MCs. In this regard, we find that there is a threshold for the number of UEs in each MC, who can participate in multiple applications, beyond which legacy cellular communication starts performing better in terms of overall energy consumption of all UEs in the system. Thereafter, we extend the concept of MC to build a multi-hop D2D network and evaluate the energy consumption of UEs for a content distribution application across the network. In this work, we optimize the size of an MC to get the maximum energy savings.
Apart from many advantages, D2D communication poses potential challenges in terms of security and privacy. As a solution, we propose to bootstrap trust in D2D UEs before establishing any connection with unknown users. In particular, we propose Pretty Good Privacy (PGP) and reputation based mechanisms in D2D networks. Finally, to preserve user's privacy and to secure the contents, we propose to encrypt the contents cached at D2D nodes (or any other caching server). In particular, we leverage convergent encryption that can provide an extra benefit of eliminating duplicate contents from the caching server
MCDM methods based on pairwise comparison matrices and their fuzzy extension
No full textMethods based on pairwise comparison matrices (PCMs) form a significant part of multi-criteria decision making (MCDM) methods. These methods are based on structuring pairwise comparisons (PCs) of objects from a finite set of objects into a PCM and deriving priorities of objects that represent the relative importance of each object with respect to all other objects in the set. However, the crisp PCMs are not able to capture uncertainty stemming from subjectivity of human thinking and from incompleteness of information about the problem that are often closely related to MCDM problems. That is why the fuzzy extension of methods based on PCMs has been of great interest. In order to derive fuzzy priorities of objects from a fuzzy PCM (FPCM), standard fuzzy arithmetic is usually applied to the fuzzy extension of the methods originally developed for crisp PCMs.
%Fuzzy extension of the methods based on PCMs usually consists in simply replacing the crisp PCs in the given model by fuzzy PCs and applying standard fuzzy arithmetic to obtain the desired fuzzy priorities.
However, such approach fails in properly handling uncertainty of preference information contained in the FPCM. Namely, reciprocity of the related PCs of objects in a FPCM and invariance of the given method under permutation of objects are violated when standard fuzzy arithmetic is applied to the fuzzy extension. This leads to distortion of the preference information contained in the FPCM and consequently to false results.
Thus, the first research question of the thesis is:
``Based on a FPCM of objects, how should fuzzy priorities of these objects be determined so that they reflect properly all preference information available in the FPCM?''
This research question is answered by introducing an appropriate fuzzy extension of methods originally developed for crisp PCMs. That is, such fuzzy extension that does not violate reciprocity of the related PCs and invariance under permutation of objects, and that does not lead to a redundant increase of uncertainty of the resulting fuzzy priorities of objects. Fuzzy extension of three different types of PCMs is examined in this thesis - multiplicative PCMs, additive PCMs with additive representation, and additive PCMs with multiplicative representation. In particular, construction of PCMs, verifying consistency, and deriving priorities of objects from PCMs are studied in detail for each type of these PCMs.
First, well-known and in practice most often applied methods based on crisp PCMs are reviewed.
Afterwards, fuzzy extensions of these methods proposed in the literature are reviewed in detail and their drawbacks regarding the violation of reciprocity of the related PCs and of invariance under permutation of objects are pointed out. It is shown that these drawbacks can be overcome by properly applying constrained fuzzy arithmetic instead of standard fuzzy arithmetic to the computations.
In particular, we always have to look at a FPCM as a set of PCMs with different degrees of membership to the FPCM, i.e. we always have to consider only PCs that are mutually reciprocal. Constrained fuzzy arithmetic allows us to impose the reciprocity of the related PCs as a constraint on arithmetic operations with fuzzy numbers, and its appropriate application also guarantees invariance of the methods under permutation of objects.
Finally, new fuzzy extensions of the methods are proposed based on constrained fuzzy arithmetic and it is proved that these methods do not violate the reciprocity of the related PCs and are invariant under permutation of objects.
Because of these desirable properties, fuzzy priorities of objects obtained by the methods proposed in this thesis reflect the preference information contained in fuzzy PCMs better in comparison to the fuzzy priorities obtained by the methods based on standard fuzzy arithmetic.
Beside the inability to capture uncertainty, methods based on PCMs are also not able to cope with situations where it is not possible or reasonable to obtain complete preference information from DMs. This problem occurs especially in the situations involving large-dimensional PCMs.
When dealing with incomplete large-dimensional PCMs, compromise between reducing the number of PCs required from the DM and obtaining reasonable priorities of objects is of paramount importance.
This leads to the second research question:
``How can the amount of preference information required from the DM in a large-dimensional PCM be reduced while still obtaining comparable priorities of objects?''
This research question is answered by introducing an efficient two-phase method. Specifically, in the first phase, an interactive algorithm based on weak-consistency condition is introduced for partially filling an incomplete PCM. This algorithm is designed in such a way that minimizes the number of PCs required from the DM and provides sufficient amount of preference information at the same time. The weak-consistency condition allows for providing ranges of possible intensities of preference for every missing PC in the incomplete PCM. Thus, at the end of the first phase, a PCM containing intervals for all PCs that were not provided by the DM is obtained.
Afterward, in the second phase, the methods for obtaining fuzzy priorities of objects from fuzzy PCMs proposed in this thesis within the answer to the first research question are applied to derive interval priorities of objects from this incomplete PCM. The obtained interval priorities cover all weakly consistent completions of the incomplete PCM and are very narrow. The performance of the method is illustrated by a real-life case study and by simulations that demonstrate the ability of the algorithm to reduce the number of PCs required from the DM in PCMs of dimension 15 and greater by more than 60\% on average while obtaining interval priorities comparable with the priorities obtainable from the hypothetical complete PCMs
Speech Adaptation Modeling for Statistical Machine Translation
No full textSpoken language translation (SLT) exists within one of the most challenging intersections of speech and natural language processing. While machine translation (MT) has demonstrated its effectiveness on the translation of textual data, the translation of spoken language remains a challenge, largely due to the mismatch between the training conditions of MT and the noisy signal that is output by an automatic speech recognition (ASR) system. In the interchange between ASR and MT, errors propagated from noisy speech recognition outputs may become compounded, rendering the speech translation to be unintelligible. Additionally, aspects such as stylistic differences between written and spoken registers can lead to the generation of inadequate translations. This scenario is predominantly caused by a mismatch between the training conditions of ASR and MT. Due to the lack of training data that couples speech audio with translated transcripts, MT systems in the SLT pipeline must rely predominantly on textual data that does not represent well the characteristics of spoken language. Likewise, independence assumptions between each sentence results in ASR and MT systems that do not yield consistent outputs.
In this thesis develop techniques to overcome the mismatch between speech and textual data by improving the robustness of the MT system. Our work can be divided into three parts. First we analyze the effects the difference between spoken and written registers has on SLT quality. We additionally introduce a data analysis methodology to measure the impact of ASR errors on translation quality. Secondly, we propose several approaches to improve the MT component's tolerance of noisy ASR outputs: by adapting its models based on the bilingual statistics of each sentence's neighboring context, and through the introduction of a process by which textual resources can be transformed into synthetic ASR data to use when training a speech-centric MT system. In particular, we focus on the translation from spoken English to French and German -- the two parent languages of English -- and demonstrate that information about the types and frequency of ASR errors can improve the robustness of machine translation for SLT. Finally, we introduce and motivate several challenges in spoken language translation with neural machine translation models that are specific to their modeling architecture
Development of rate-dependent thermoplastic constitutive models for numerical analysis of ceramics at high-temperature
No full textThree constitutive models for the mechanical description of the behavior of ceramic materials are developed, implemented into a numerical code, calibrated on experimental data, and validated.
- The first model is elastic-plastic and addresses the cold compaction of ceramic powders, combining nonlinear elasticity, elasto-plastic coupling and increase of cohesion.
- The second model is thermal-viscous-elastic-plastic and is specifically tailored to describe the thermo-mechanical behavior of refractory devices under working conditions at high-temperature.
- The third model is thermal-elastic-plastic and implements a shape
evolution of the BP yield function, calibrated to simulate the forming of green bodies and their pre-sintering phase.
Three algorithms are developed for the integration of constitutive equations when pathological yield functions are involved. The algorithms are coded in user material subroutines to be used in commercial FE softwares and their accuracy is evaluated in model problems allowing for semi-analytical solutions. Material constitutive parameters are obtained from a combination of experimental tests and multi-objective optimization and employed in FE simulations of industrial processes, such as cold-forming of combed finish ceramic tiles and metal flow confinement by means of refractory devices.
This PhD thesis is the outcome of the secondment period at
the Vesuvius Group, within the framework of the IAPP European project HOTBRICKS (contract number PIAPP GA-2013-609758)
Parabolic flights in pico-g for space-based gravitational wave observatory: the free-fall experiment on LISA Pathfinder
No full textThis thesis reports on the results of the so-called free-fall experiment performed on LISA Pathfinder (LPF). After an introduction to the measurement of space-time curvature from space and its application to gravitational wave observation, overviews of LISA and the precursor mission, LISA Pathfinder, are described. Then a specific source of noise arising on LPF, the actuation noise, is investigated, also with reference to the free-fall experiment. Then, the physics and the design of the experiment are described as well as the analysis technique adopted to analyze the free-fall data. Finally, the results of the free-fall data campaign are shown, with a discussion regarding the possible implications for LISA and space-based gravity gradiometers
Analyzing ecosystem services and green urban infrastructures to support urban planning
No full textEcosystems contribute to human wellbeing though the provisioning of goods and services, also known as ecosystem services (ES). However not all ecosystems provide ES to the same extent and depending on physical characteristics of the ecosystems or their location within the city, ES flow differently. The consideration of ecosystems and ES in the planning practice can play an important role in coping with urban challenges, aside to their potential to ameliorate quality of life.
Urban planning represents one of the tools administrations have to influence the distribution of ecosystems and ES in a city, and to determine the benefits they provide and, more specifically, to re-determine the number, the location and type of beneficiaries reached. Inclusion of the ES concept in the planning practice can lead to strategic the creation or restoration of Green Urban Infrastructures in a city to maximize the provisioning of a specific ES.
Despite the awareness of advantages coming from the application of the ES concept in the planning, there is limited evidence about the application in the planning practice.
Goal of this work of this work is to contribute to mainstream ES knowledge into practice. Towards the achievement of this goal, it is crucial to understand the extent to which the ES concept is currently included in urban planning, and to identify the type of information that can most effectively support decision-makers and planners in adopting ES knowledge, and specifically Ecosystem-based measures in their “everyday” urban planning. The work is organized in four specific objectives: i) to provide an overview of the current state of the art related to inclusion of Ecosystem-based measures in urban planning and discuss, and use it identify and discuss the main shortcoming and propose possible solutions. ii)0 to develop an approach to estimate the cooling capacity provided by Green Urban Infrastructures to support urban planning. iii) to test the application of ES assessments in two case studies. iv) to develop guidance to support equitable distribution of ES in cities.
The ES concept represents a tool to understand the underlying links between ecosystems, benefits provided and human wellbeing: if effectively used and mainstreamed in the planning practice, can be one of the keys for more livable and equitable citie
A data analytics framework for physiological signals from wearable devices
Get PDFWearable devices have emerged as the most innovative opportunity to enable acquisition and quantification of physiological signals in real-world indoor or outdoor contexts. However, their use in research should be based on a reproducible analytics process, ensuring that all the critical steps in data collection and processing are managed in a reliable experimental setup.
The aim of this thesis is to investigate the actual value and technical limitations of wearable devices for their use in a research context, such as physiological monitoring of sleep and crying states in infants, of parenting of typical or atypical children, synchrony in educational contexts, and of fatigue patterns in outdoor sport activity, e.g. skiing.
The thesis describes an approach and solutions that aim to compensate the effects of such technical limits. Besides providing a set of appropriate signal processing algorithms, a real-life sensing architecture is designed and implemented enabling synchronized acquisition from multiple subjects and multiple sensors, including cardiac signals, electrodermal activity and inertial data streams. The signal processing pipeline and the real-life sensing architecture are merged in a unique data analytics framework (Physiolitix).
The framework is validated on a fairly wide range of sensors, including medical quality multi-sensor smartwatches and smart textile garments applied in diverse research contexts. In particular, a calibration dataset is developed to compare wearable and clinical devices in an affective computing task. We found that wearables can be employed as a valid substitute for medical quality devices with the help of adequate signal processing and machine learning solutions
Laicità e diritto penale
No full textScopo del presente studio è individuare e declinare i possibili àmbiti di interazione tra diritto penale e laicità: tale analisi muoverà, innanzitutto, da una ricerca, a livello definitorio, circa la nozione di “laicità” e circa il (fisiologico) carattere di “vaghezza” che connota il concetto de quo, con la conseguente difficoltà di individuarne un significato, se non unitario, quantomeno condiviso (si ricorrerà, a tal fine, anche ad un’indagine in chiave comparata). Si procederà quindi ad esaminare il diritto positivo e giurisprudenziale – in primis attraverso la lettura delle sentenze della Corte costituzionale, e della relativa elaborazione della “laicità” in termini di “equidistanza” ed “imparzialità” – ed effettuando poi una ricognizione sulle fonti del diritto penale (dove si registrerà la “crisi” del legislatore penale e la crescente centralità del giudice e della giurisprudenza, anche alla luce dell’incidenza e dell’influenza esercitata, sul diritto interno, dalle fonti sovranazionali, in particolare dal diritto dell’Unione europea e dal diritto convenzionale della CEDU). Lo studio si concentrerà sul tentativo di individuare alcune declinazioni di “laicità” nel peculiare àmbito del diritto penale, interrogandosi al contempo sul relativo status della/delle “laicità”, ossia la qualificazione come “carattere”, ovvero come “principio”.
Si proporrà una lettura – valorizzando la necessaria (anzi, pretesa) autonomia delle regole che debbono connotarlo (le «regole proprie del diritto penale»), dalle influenze “distorsive” ad opera di agenti “esterni” al diritto penale o al diritto stesso – in termini di “laicità” anzitutto «come metodo», quale frutto di un procedimento dialogico, nella ricerca di (ri)affermare il «fatto» quale “centro gravitazionale” verso cui orientare la politica criminale e l’attività di interpretazione. Il concetto di “laicità” verrà poi accostato alle condizioni di legittimazione del diritto penale, laicità «come canone politico-criminale» orientato alla individuazione ed all’affermazione di un diritto penale che rigetti forme di «paternalismo» e di «moralismo», ed alla concreta dimensione applicativa (interrogandosi sugli spazi della laicità «come ausilio interpretativo»), proponendosi infine un’analisi di istituti di parte generale e di fattispecie di parte speciale che si presentano (astrattamente e concretamente) idonee al penetrare di istanze “esterne” al diritto penale
Development of 3D Silicon radiation detectors for neutrons and high energy charged particles
Get PDFIn the past few years, several interesting developments in microstructured solid-state thermal neutron detectors have been pursued. These devices feature high aspect-ratio cavities, filled with neutron converter materials, so as to improve the neutron detection efficiency with respect to coated planar sensors. In the framework of the INFN HYDE (HYbrid Detectors for neutrons) project, we have designed new microstructured sensors aimed at thermal and fast neutron detection. Owing to the different cross section, neutron imaging is complementary to X-ray imaging allowing for a high contrast in soft materials. To this purpose, the possibility to have pixelated neutron detectors compatible with existing read-out chips (e.g., those from the Medipix/Timepix family) is an important goal that was achieved in this thesis. In this thesis the entire workflow will be described in detail, covering the design, simulations, fabrication and characterization of 3D neutron detectors for imaging produced at FBK.
As a related topic, new 3D sensors for the "Phase-2" upgrades at High Luminosity LHC have been developed, and some aspects relevant to the device simulation and characterization are also reported in this thesis
Predictive networks for multi meta-omics data integration
No full textThe role of microbiome in disease onset and in equilibrium is being exposed by a wealth of high-throughput omics methods. All key research directions, e.g., the study of gut microbiome dysbiosis in IBD/IBS, indicate the need for bioinformatics methods that can model the complexity of the microbial communities ecology and unravel its disease-associated perturbations. A most promising direction is the “meta-omics” approach, that allows a profiling based on various biological molecules at the metagenomic scale (e.g., metaproteomics, metametabolomics) as well as different “microbial” omes (eukaryotes and viruses) within a system biology approach. This thesis introduces a bioinformatic framework for microbiota datasets that combines predictive profiling, differential network analysis and meta-omics integration. In detail, the framework identifies biomarkers discriminating amongst clinical phenotypes, through machine learning techniques (Random Forest or SVM) based on a complete Data Analysis Protocol derived by two initiatives funded by FDA: the MicroArray Quality Control-II and Sequencing Quality Control projects. The biomarkers are interpreted in terms of biological networks: the framework provides a setup for networks inference, quantification of networks differences based on the glocal Hamming and Ipsen-Mikhailov (HIM) distance and detection of network communities. The differential analysis of networks allows the study of microbiota structural organization as well as the evolving trajectories of microbial communities associated to the dynamics of the target phenotypes. Moreover, the framework combines a novel similarity network fusion method and machine learning to identify biomarkers from the integration of multiple meta-omics data. The framework implementation requires only standard open source computational biology tools, as a combination of R/Bioconductor and Python functions. In particular, full scripts for meta-omics integration are available in a GitHub repository to ease reuse (https://github.com/AleZandona/INF). The pipeline has been validated on original data from three different clinical datasets. First, the predictive profiling and the network differential analysis have been applied on a pediatric Inflammatory Bowel Disease (IBD) cohort (in faecal vs biopsy environments) and controls, in collaboration with a multidisciplinary team at the Ospedale Pediatrico Bambino Gesú (Rome, I). Then, the meta-omics integration has been tested on a paired bacterial and fungal gut microbiota human IBD datasets from the Gastroenterology Department of the Saint Antoine Hospital (Paris, F), thanks to the collaboration with “Commensals and Probiotics-Host Interactions” team at INRA (Jouy-en-Josas, F). Finally, the framework has been validated on a bacterial-fungal gut microbiota dataset from children affected by Rett syndrome. The different nature of datasets used for validation naturally supports the extension of the framework on different omics datasets. Besides, clinical practice can take advantage of our framework, given the reproducibility and robustness of results, ensured by the adopted Data Analysis Protocol, as well as the biological relevance of the findings, confirmed by the clinical collaborators. Specifically, the omics-based dysbiosis profiles and the inferred biological networks can support the current diagnostic tools to reveal disease-associated perturbations at a much prodromal earlier stage of disease and may be used for disease prevention, diagnosis and prognosis