Reliable Broadcasting and Streaming of Multimedia Content

This thesis is focused on the study of techniques that allow to have reliable transmission of multimedia content in streaming and broadcasting applications, targeting in particular video content. The design of efficient error-control mechanisms, to enhance video transmission systems reliability, has been addressed considering cross-layer and multi-layer/multi-dimensional channel coding techniques to cope with bit errors as well as packet erasures. Mechanisms for unequal time interleaving have been designed as a viable solution to reduce the impact of errors and erasures by acting on the time diversity of the data flow, thus enhancing robustness against correlated channel impairments. In order to account for the nature of the factors which affect the physical layer channel in the evaluation of FEC schemes performances, an ad-hoc error-event modeling has been devised. In addition, the impact of error correction/protection techniques on the quality perceived by the consumers of video services applications and techniques for objective/subjective quality evaluation have been studied. The applicability and value of the proposed techniques have been tested by considering practical constraints and requirements of real system implementations.

Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.

Learning from errors: Psychological, relational, and cultural aspects

Although errors might foster learning, they can also be perceived as something to avoid if they are associated with negative consequences (e.g., receiving a bad grade or being mocked by classmates). Such adverse perceptions may trigger negative emotions and error-avoidance attitudes, limiting the possibility to use errors for learning. These students’ reactions may be influenced by relational and cultural aspects of errors that characterise the learning environment. Accordingly, the main aim of this research was to investigate whether relational and cultural characteristics associated with errors affect psychological mechanisms triggered by making mistakes. In the theoretical part, we described the role of errors in learning using an integrated multilevel (i.e., psychological, relational, and cultural levels of analysis) approach. Then, we presented three studies that analysed how cultural and relational error-related variables affect psychological aspects. The studies adopted a specific empirical methodology (i.e., qualitative, experimental, and correlational) and investigated different samples (i.e., teachers, primary school pupils and middle school students). Findings of study one (cultural level) highlighted errors acquire different meanings that are associated with different teachers’ error-handling strategies (e.g., supporting or penalising errors). Study two (relational level) demonstrated that teachers’ supportive error-handling strategies promote students’ perceptions of being in a positive error climate. Findings of study three (relational and psychological level) showed that positive error climate foster students’ adaptive reactions towards errors and learning outcomes. Overall, our findings indicated that different variables influence students’ learning from errors process and teachers play an important role in conveying specific meanings of errors during learning activities, dealing with students’ mistakes supportively, and establishing an error-friendly classroom environment.

Natural patterns and anthropogenic disturbance in north Adriatic marine benthic assemblages: descriptive and methodological studies

Marine soft bottom systems show a high variability across multiple spatial and temporal scales. Both natural and anthropogenic sources of disturbance act together in affecting benthic sedimentary characteristics and species distribution. The description of such spatial variability is required to understand the ecological processes behind them. However, in order to have a better estimate of spatial patterns, methods that take into account the complexity of the sedimentary system are required. This PhD thesis aims to give a significant contribution both in improving the methodological approaches to the study of biological variability in soft bottom habitats and in increasing the knowledge of the effect that different process (both natural and anthropogenic) could have on the benthic communities of a large area in the North Adriatic Sea. Beta diversity is a measure of the variability in species composition, and Whittaker’s index has become the most widely used measure of beta-diversity. However, application of the Whittaker index to soft bottom assemblages of the Adriatic Sea highlighted its sensitivity to rare species (species recorded in a single sample). This over-weighting of rare species induces biased estimates of the heterogeneity, thus it becomes difficult to compare assemblages containing a high proportion of rare species. In benthic communities, the unusual large number of rare species is frequently attributed to a combination of sampling errors and insufficient sampling effort. In order to reduce the influence of rare species on the measure of beta diversity, I have developed an alternative index based on simple probabilistic considerations. It turns out that this probability index is an ordinary Michaelis-Menten transformation of Whittaker's index but behaves more favourably when species heterogeneity increases. The suggested index therefore seems appropriate when comparing patterns of complexity in marine benthic assemblages. Although the new index makes an important contribution to the study of biodiversity in sedimentary environment, it remains to be seen which processes, and at what scales, influence benthic patterns. The ability to predict the effects of ecological phenomena on benthic fauna highly depends on both spatial and temporal scales of variation. Once defined, implicitly or explicitly, these scales influence the questions asked, the methodological approaches and the interpretation of results. Problem often arise when representative samples are not taken and results are over-generalized, as can happen when results from small-scale experiments are used for resource planning and management. Such issues, although globally recognized, are far from been resolved in the North Adriatic Sea. This area is potentially affected by both natural (e.g. river inflow, eutrophication) and anthropogenic (e.g. gas extraction, fish-trawling) sources of disturbance. Although few studies in this area aimed at understanding which of these processes mainly affect macrobenthos, these have been conducted at a small spatial scale, as they were designated to examine local changes in benthic communities or particular species. However, in order to better describe all the putative processes occurring in the entire area, a high sampling effort performed at a large spatial scale is required. The sedimentary environment of the western part of the Adriatic Sea was extensively studied in this thesis. I have described, in detail, spatial patterns both in terms of sedimentary characteristics and macrobenthic organisms and have suggested putative processes (natural or of human origin) that might affect the benthic environment of the entire area. In particular I have examined the effect of off shore gas platforms on benthic diversity and tested their effect over a background of natural spatial variability. The results obtained suggest that natural processes in the North Adriatic such as river outflow and euthrophication show an inter-annual variability that might have important consequences on benthic assemblages, affecting for example their spatial pattern moving away from the coast and along a North to South gradient. Depth-related factors, such as food supply, light, temperature and salinity play an important role in explaining large scale benthic spatial variability (i.e., affecting both the abundance patterns and beta diversity). Nonetheless, more locally, effects probably related to an organic enrichment or pollution from Po river input has been observed. All these processes, together with few human-induced sources of variability (e.g. fishing disturbance), have a higher effect on macrofauna distribution than any effect related to the presence of gas platforms. The main effect of gas platforms is restricted mainly to small spatial scales and related to a change in habitat complexity due to a natural dislodgement or structure cleaning of mussels that colonize their legs. The accumulation of mussels on the sediment reasonably affects benthic infauna composition. All the components of the study presented in this thesis highlight the need to carefully consider methodological aspects related to the study of sedimentary habitats. With particular regards to the North Adriatic Sea, a multi-scale analysis along natural and anthopogenic gradients was useful for detecting the influence of all the processes affecting the sedimentary environment. In the future, applying a similar approach may lead to an unambiguous assessment of the state of the benthic community in the North Adriatic Sea. Such assessment may be useful in understanding if any anthropogenic source of disturbance has a negative effect on the marine environment, and if so, planning sustainable strategies for a proper management of the affected area.

Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

Modello bayesiano per la riduzione dell'incertezza nella previsione delle piene del fiume Reno

Many efforts have been devoting since last years to reduce uncertainty in hydrological modeling predictions. The principal sources of uncertainty are provided by input errors, for inaccurate rainfall prediction, and model errors, given by the approximation with which the water flow processes in the soil and river discharges are described. The aim of the present work is to develop a bayesian model in order to reduce the uncertainty in the discharge predictions for the Reno river. The ’a priori’ distribution function is given by an autoregressive model, while the likelihood function is provided by a linear equation which relates observed values of discharge in the past and hydrological TOPKAPI model predictions obtained by the rainfall predictions of the limited-area model COSMO-LAMI. The ’a posteriori’ estimations are provided throw a H∞ filter, because the statistical properties of estimation errors are not known. In this work a stationary and a dual adaptive filter are implemented and compared. Statistical analysis of estimation errors and the description of three case studies of flood events occurred during the fall seasons from 2003 to 2005 are reported. Results have also revealed that errors can be described as a markovian process only at a first approximation. For the same period, an ensemble of ’a posteriori’ estimations is obtained throw the COSMO-LEPS rainfall predictions, but the spread of this ’a posteriori’ ensemble is not enable to encompass observation variability. This fact is related to the building of the meteorological ensemble, whose spread reaches its maximum after 5 days. In the future the use of a new ensemble, COSMO–SREPS, focused on the first 3 days, could be helpful to enlarge the meteorogical and, consequently, the hydrological variability.

The uncertainty in standardised sound power measurements: complying with ISO 17025

In the context of “testing laboratory” one of the most important aspect to deal with is the measurement result. Whenever decisions are based on measurement results, it is important to have some indication of the quality of the results. In every area concerning with noise measurement many standards are available but without an expression of uncertainty, it is impossible to judge whether two results are in compliance or not. ISO/IEC 17025 is an international standard related with the competence of calibration and testing laboratories. It contains the requirements that testing and calibration laboratories have to meet if they wish to demonstrate that they operate to a quality system, are technically competent and are able to generate technically valid results. ISO/IEC 17025 deals specifically with the requirements for the competence of laboratories performing testing and calibration and for the reporting of the results, which may or may not contain opinions and interpretations of the results. The standard requires appropriate methods of analysis to be used for estimating uncertainty of measurement. In this point of view, for a testing laboratory performing sound power measurement according to specific ISO standards and European Directives, the measurement of uncertainties is the most important factor to deal with. Sound power level measurement, according to ISO 3744:1994 , performed with a limited number of microphones distributed over a surface enveloping a source is affected by a certain systematic error and a related standard deviation. Making a comparison of measurement carried out with different microphone arrays is difficult because results are affected by systematic errors and standard deviation that are peculiarities of the number of microphones disposed on the surface, their spatial position and the complexity of the sound field. A statistical approach could give an overview of the difference between sound power level evaluated with different microphone arrays and an evaluation of errors that afflict this kind of measurement. Despite the classical approach that tend to follow the ISO GUM this thesis present a different point of view of the problem related to the comparison of result obtained from different microphone arrays.

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.

Essays in Behavioral Personnel Economics

The thesis contemplates 4 papers and its main goal is to provide evidence on the prominent impact that behavioral analysis can play into the personnel economics domain.The research tool prevalently used in the thesis is the experimental analysis.The first paper provide laboratory evidence on how the standard screening model–based on the assumption that the pecuniary dimension represents the main workers’choice variable–fails when intrinsic motivation is introduced into the analysis.The second paper explores workers’ behavioral reactions when dealing with supervisors that may incur in errors in the assessment of their job performance.In particular,deserving agents that have exerted high effort may not be rewarded(Type-I errors)and undeserving agents that have exerted low effort may be rewarded(Type-II errors).Although a standard neoclassical model predicts both errors to be equally detrimental for effort provision,this prediction fails when tested through a laboratory experiment.Findings from this study suggest how failing to reward deserving agents is significantly more detrimental than rewarding undeserving agents.The third paper investigates the performance of two antithetic non-monetary incentive schemes on schooling achievement.The study is conducted through a field experiment.Students randomized to the main treatments have been incentivized to cooperate or to compete in order to earn additional exam points.Consistently with the theoretical model proposed in the paper,the level of effort in the competitive scheme proved to be higher than in the cooperative setting.Interestingly however,this result is characterized by a strong gender effect.The fourth paper exploits a natural experiment setting generated by the credit crunch occurred in the UK in the2007.The economic turmoil has negatively influenced the private sector,while public sector employees have not been directly hit by the crisis.This shock–through the rise of the unemployment rate and the increasing labor market uncertainty–has generated an exogenous variation in the opportunity cost of maternity leave in private sector labor force.This paper identifies the different responses.

Decoding Agc1 deficiency: bioinformatics approaches to unveil the functional implications of Slc25a12 on the transcriptome and epigenome

In the brain, mutations in SLC25A12 gene encoding AGC1 cause an ultra-rare genetic disease reported as a developmental and epileptic encephalopathy associated with global cerebral hypomyelination. Symptoms of the disease include diffused hypomyelination, arrested psychomotor development, severe hypotonia, seizures and are common to other neurological and developmental disorders. Amongst the biological components believed to be most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination. Recent studies (Poeta et al, 2022) have also shown how altered levels of transcription factors and epigenetic modifications greatly affect proliferation and differentiation in oligodendrocyte precursor cells (OPCs). In this study we explore the transcriptomic landscape of Agc1 in two different system models: OPCs silenced for Agc1 and iPSCs from human patients differentiated to neural progenitors. Analyses range from differential expression analysis, alternative splicing, master regulator analysis. ATAC-seq results on OPCs were integrated with results from RNA-Seq to assess the activity of a TF based on the accessibility data from its putative targets, which allows to integrate RNA-Seq data to infer their role as either activators or repressors. All the findings for this model were also integrated with early data from iPSCs RNA-seq results, looking for possible commonalities between the two different system models, among which we find a downregulation in genes encoding for SREBP, a transcription factor regulating fatty acids biosynthesis, a key process for myelination which could explain the hypomyelinated state of patients. We also find that in both systems cells tend to form more neurites, likely losing their ability to differentiate, considering their progenitor state. We also report several alterations in the chromatin state of cells lacking Agc1, which confirms the hypothesis for which Agc1 is not a disease restricted only to metabolic alterations in the cells, but there is a profound shift of the regulatory state of these cells.