Implémentation sur FPGA de l’algorithme MUSIC sur antenne-réseau expérimentale à 10 GHz

Les techniques des directions d’arrivée (DOA) sont une voie prometteuse pour accroitre la capacité des systèmes et les services de télécommunications en permettant de mieux estimer le canal radio-mobile. Elles permettent aussi de suivre précisément des usagers cellulaires pour orienter les faisceaux d’antennes dans leur direction. S’inscrivant dans ce contexte, ce présent mémoire décrit étape par étape l’implémentation de l’algorithme de haut niveau MUSIC (MUltiple SIgnal Classification) sur une plateforme FPGA afin de déterminer en temps réel l’angle d’arrivée d’une ou des sources incidentes à un réseau d’antennes. Le concept du prototypage rapide des lois de commande (RCP) avec les outils de XilinxTM System generator (XSG) et du MBDK (Model Based Design Kit) de NutaqTM est le concept de développement utilisé. Ce concept se base sur une programmation de code haut niveau à travers des modèles, pour générer automatiquement un code de bas niveau. Une attention particulière est portée sur la méthode choisie pour résoudre le problème de la décomposition en valeurs et vecteurs propres de la matrice complexe de covariance par l’algorithme de Jacobi. L’architecture mise en place implémentant cette dernière dans le FPGA (Field Programmable Gate Array) est détaillée. Par ailleurs, il est prouvé que MUSIC ne peut effectuer une estimation intéressante de la position des sources sans une calibration préalable du réseau d’antennes. Ainsi, la technique de calibration par matrice G utilisée dans ce projet est présentée, en plus de son modèle d’implémentation. Enfin, les résultats expérimentaux du système mis à l’épreuve dans un environnement réel en présence d’une source puis de deux sources fortement corrélées sont illustrés et analysés.

Etapas de cambio comportamental frente al consumo de drogas ilegales, tabaco y alcohol en escolares de 9 a 17 años de Bogotá – Colombia

Objetivo: El propósito del estudio fue describir estadísticamente las etapas de cambio comportamental frente al consumo de sustancias psicoactivas –SPA– (alcohol, tabaco y drogas ilegales) en escolares entre 9 y 17 años de Bogotá- Colombia, pertenecientes al estudio FUPRECOL. Método: Se trata de un estudio descriptivo y transversal en 6.965 niños y adolescentes entre 9 y 17 años, pertenecientes a 24 instituciones educativas oficiales de Bogotá - Colombia. La medición de los procesos de cambio propuestos por el Modelo Transteórico (MTT), aplicados al consumo de drogas, tabaco y alcohol se aplicaron de manera auto-diligenciada mediante un cuestionario estructurado. Resultados: De la muestra evaluada, el 58,4% fueron mujeres con un promedio de edad 12,74 ± 2.38 años. En la población en general, frente al consumo de drogas, el 6% de los escolares se encontraban en etapa de pre-contemplación, 44 % en contemplación; 30% en preparación/acción, 20% en mantenimiento. Con relación al consumo de alcohol, el 5% de los niños y adolescentes se encontraban en etapa de pre-contemplación, 36 % en contemplación; 12% en preparación/acción, 46% en mantenimiento. Frente al tabaco, el 4% de los niños y adolescentes se encontraban en etapa de pre-contemplación, 33 % en contemplación; 12% en preparación/acción, 51% en mantenimiento. Conclusiones: En los escolares evaluados, un importante porcentaje se ubica en la etapa de mantenimiento frente a la intención de consumo de tabaco y alcohol. Frente al consumo de drogas ilegales los niños y adolescentes están en la etapa de contemplación. Se requieren esfuerzos mayores para fomentar programas preventivos que enseñen sobre el riesgo del abuso/dependencia de este tipo de sustancias psicoactiva sobre la salud; dándole prioridad en las agendas y políticas públicas dentro del ámbito escolar.

One-dimensional Model for Fluids of Third-grade in Tubes with Constant Radius

Our goal in this paper is to extend previous results obtained for Newtonian and secondgrade fluids to third-grade fluids in the case of an axisymmetric, straight, rigid and impermeable tube with constant cross-section using a one-dimensional hierarchical model based on the Cosserat theory related to fluid dynamics. In this way we can reduce the full threedimensional system of equations for the axisymmetric unsteady motion of a non-Newtonian incompressible third-grade fluid to a system of equations depending on time and on a single spatial variable. Some numerical simulations for the volume flow rate and the the wall shear stress are presented.

Avaliação do potencial de desenvolvimento em meninos e meninas dos 4 aos 6 anos

Todos nós, educadores, devemos prestar uma atenção aprofundada às potencialidades e inclinações das crianças que constituem os nossos grupos, para que possamos estimular e desenvolver as suas competências. É nesse sentido que desenvolvemos este Estudo Exploratório, com o qual se pretende analisar a adaptação da avaliação de potencial derivada da Teoria das Inteligência Múltiplas ao contexto da Educação Pré-Escolar, com o objectivo de contribuir para a qualidade da intervenção educativa, articulando conceitos como Inteligência, Competência, Potencial, Currículo, Avaliação Dinâmica. Participam no estudo 42 crianças que frequentam Jardim de Infância e integram dois grupos heterogéneos e as respectivas educadoras. A metodologia é de natureza qualitativa e quantitativa, apoiando-se na aplicação dos seguintes instrumentos: modelo de avaliação de potencial baseado no Spectrum; Inventário de Quociente Emocional, versão para crianças e jovens (Bar-On Emocional Quotient Inventory: Youth Version), adaptado a educadores por Candeias e Monteiro (2010); questionário de Caracterização do ambiente educativo - Sala de Actividades, adaptado por Candeias e lglésias (2010); Teste de Avaliação de Habilidades Cognitivas de Solução de Problemas lnterpessoais (EVHACOSPI). Os resultados obtidos sugerem a importância de uma avaliação do potencial, baseada na Teoria das Inteligências Múltiplas, que utiliza instrumentos adequados a cada um dos domínios do potencial e da competência humana propostos por H. Gardner: verbal, Lógico-Matemática, Musical, Corporal-Cinestésica, Visuo-espacial, lnterpessoal, Intrapessoal e Naturalista. Apontam também para o papel que este tipo de avaliação pode desempenhar na intervenção educativa que se objectiva intencional e fundamentada. ABSTRACT: Ali of us, educators, must pay a special attention to the potentialities and inclinations of children who make up our groups, so enabling the stimulation and development of their competences. ln this sense we developed this Exploratory Study, through which is intended to analyse the adaptation of potential evaluation derived from the Theory of Multiple intelligences in the range of Pre-School Education, aiming at the contribution for the educative intervention: articulating concepts such as Intelligence, Competence Potential, Curriculum, and Dynamic Evaluation. The study involves 42 children attending Infant School, belonging to two heterogeneous groups and respective educators. Methodology is of qualitative and quantitative nature, supported by the following instruments: evaluation model based on Spectrum; Inventory of Emotional Quotient, version for children and youths, adapted to educators by Candeias e Monteiro (2010), Query of Characterization of Educative Environment - Activities Room, adapted by Candeias e lglésias (2010); Test for Evaluation of Cognitive Abilities of Interpersonal Problems Solution. (EVHACOSPI). The results suggest the importance of an assessment, based on the Theory of Multiple Intelligence, which uses instruments fitted to each of the domains of human potential and competence proposed by H. Gardiner: Verbal, Logical-Maths, Musical, Corporal¬ Kinaesthetic, Visuo-Spatial, Interpersonal, Intrapersonal and Naturalist. They also refer to the role that this type of assessment can play in the educational intervention aiming to be intentional and grounded.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

GÉOARCHÉOLOGIE DU VILLAGE: Variabilité des modes d’occupation de la fin du Néolithique à l’âge du Bronze en Italie et en France méridionale. Implications culturelles

Entre la fin du Néolithique et l’âge du Bronze, la présence d’habitats groupés de type village est un phénomène diffus, tant en Italie qu’en France méridionale. Néanmoins, la prise en compte de la variabilité des formes de la stratification des sites interroge. En quoi l’enregistrement sédimentaire des sols d’habitat permet-il d’appréhender la question de l’organisation villageoise et de sa variabilité entre la fin du Néolithique et l’âge du Bronze ? Quelle image cet enregistrement sédimentaire donne-t-il de l’organisation sociale et économique du village ? Afin d’aborder ces questions, nous avons choisi de mener une étude géoarchéologique sur des sites de formes différentes, issus de contextes chrono-culturels et environnementaux variés. La démarche, fondée sur l’emploi de la micromorphologie des sols en tant qu’outil analytique, vise à caractériser l’organisation spatio-temporelle des sols d’occupation à l’échelle du site, selon une approche spatiale des processus de formation de la stratification archéologique. L’élaboration d’un modèle, qui repose sur une classification des micro-faciès sédimentaires selon le système d’activité, et son application à des sites-laboratoires permettent de qualifier les techniques de construction en terre, l’usage du sol et les dynamiques d’occupation propres à chaque site, dans le but de déterminer les comportements socio-économiques et les spécificités du mode de vie villageois enregistrées par les sols. Cette approche permet d’évaluer les constantes et les variables qui qualifient les différents types d’occupation. Le sol, conçu comme matérialité de l’espace villageois, devient ainsi un témoignage direct de la variabilité culturelle et des différentes formes d’organisation des communautés de la fin du Néolithique et de l’âge du Bronze.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

The integration of immigrant-origin youths in Japan

The steadily growing immigration phenomenon in today’s Japan is showing a tangible and expanding presence of immigrant-origin youths residing in the country. International research in the migration studies area has underlined the importance of focusing on immigrant-origin youths to shed light on the character of the way immigrant incorporate in countries of destinations. In-deed, immigrants’ offspring, the adults of tomorrow, embody the interlocutor between first-generation immigrants and the receiving societal context. The extent of the presence of immigrants’ children in countries of destination is also a reliable yardstick to assess the maturation of the migration process, transforming it from a temporary phenomenon to a long-term settlement. Within this framework, the school is a privileged site to observe and analyze immigrant-origin youths’ integration. Alongside their family and peers, school constitutes one of the main agents of socialization. Here, children learn norms and rules and acquire the necessary tools to eventually compete in the pursuit of an occupation, determining their future socioeconomic standing. This doctoral research aims to identify which theoretical model articulated in the area of migration studies best describes the adaptation process of immigrant-origin youths in Japan. In particular, it examines whether (and to what extent) any of the pre-existing frameworks can help explain the Japanese occurring circumstances, or whether further elaboration and adjustment are needed. Alternatively, it studies whether it is necessary to produce a new model based on the peculiarities of the Japanese social context. This study provides a theoretical-oriented contribution to the (mainly descriptive but maturing) literature on immigrant-origin youths’ integration in Japan. Considering past growth trends of Japanese immigration and its expanding prospective projections (Korekawa 2018c), this study might be considered pioneering for future development of the phenomenon.

Investigation of organic semiconductor/water interfaces for optobioelectronic devices

The thesis investigates the potential of photoactive organic semiconductors as a new class of materials for developing bioelectronic devices that can convert light into biological signals. The materials can be either small molecules or polymers. When these materials interact with aqueous biological fluids, they give rise to various electrochemical phenomena, including photofaradaic or photocapacitive processes, depending on whether photogenerated charges participate in redox processes or accumulate at an interface. The thesis starts by studying the behavior of the H2Pc/PTCDI molecular p/n thin-film heterojunction in contact with aqueous electrolyte. An equivalent circuit model is developed, explaining the measurements and predicting behavior in wireless mode. A systematic study on p-type polymeric thin-films is presented, comparing rr-P3HT with two low bandgap conjugated polymers: PBDB-T and PTB7. The results demonstrate that PTB7 has superior photocurrent performance due to more effective electron-transfer onto acceptor states in solution. Furthermore, the thesis addresses the issue of photovoltage generation for wireless photoelectrodes. An analytical model based on photoactivated charge-transfer across the organic-semiconductor/water interface is developed, explaining the large photovoltages observed for polymeric p-type semiconductor electrodes in water. Then, flash-precipitated nanoparticles made of the same three photoactive polymers are investigated, assessing the influence of fabrication parameters on the stability, structure, and energetics of the nanoparticles. Photocathodic current generation and consequent positive charge accumulation is also investigated. Additionally, newly developed porous P3HT thin-films are tested, showing that porosity increases both the photocurrent and the semiconductor/water interfacial capacity. Finally, the thesis demonstrates the biocompatibility of the materials in in-vitro experiments and shows safe levels of photoinduced intracellular ROS production with p-type polymeric thin-films and nanoparticles. The findings highlight the potential of photoactive organic semiconductors in the development of optobioelectronic devices, demonstrating their ability to convert light into biological signals and interface with biological fluids.

Analysis of earthquakes predictability

Earthquake prediction is a complex task for scientists due to the rare occurrence of high-intensity earthquakes and their inaccessible depths. Despite this challenge, it is a priority to protect infrastructure, and populations living in areas of high seismic risk. Reliable forecasting requires comprehensive knowledge of seismic phenomena. In this thesis, the development, application, and comparison of both deterministic and probabilistic forecasting methods is shown. Regarding the deterministic approach, the implementation of an alarm-based method using the occurrence of strong (fore)shocks, widely felt by the population, as a precursor signal is described. This model is then applied for retrospective prediction of Italian earthquakes of magnitude M≥5.0,5.5,6.0, occurred in Italy from 1960 to 2020. Retrospective performance testing is carried out using tests and statistics specific to deterministic alarm-based models. Regarding probabilistic models, this thesis focuses mainly on the EEPAS and ETAS models. Although the EEPAS model has been previously applied and tested in some regions of the world, it has never been used for forecasting Italian earthquakes. In the thesis, the EEPAS model is used to retrospectively forecast Italian shallow earthquakes with a magnitude of M≥5.0 using new MATLAB software. The forecasting performance of the probabilistic models was compared to other models using CSEP binary tests. The EEPAS and ETAS models showed different characteristics for forecasting Italian earthquakes, with EEPAS performing better in the long-term and ETAS performing better in the short-term. The FORE model based on strong precursor quakes is compared to EEPAS and ETAS using an alarm-based deterministic approach. All models perform better than a random forecasting model, with ETAS and FORE models showing better performance. However, to fully evaluate forecasting performance, prospective tests should be conducted. The lack of objective tests for evaluating deterministic models and comparing them with probabilistic ones was a challenge faced during the study.

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.