A presente dissertação traz a questão da aderência de viagens de veículos em monitoramentos inteligentes com sistemas Automatic Vehicle Location (AVL) que operam em rotas rodoviárias. Uma viagem é considerada como uma série de \"passadas\", que correspondem ao tempo em que o veículo está em movimento, mas excluindo os tempos gastos em paradas para atividades como carregamento/descarregamento entre outras. A partir de dados históricos coletados via Global Positioning System (GPS) pelo sistema AVL, uma metodologia de filtragem e aplicações estatísticas para geração das passadas é apresentada. Além disso, são propostos métodos para geração de padrões de viagem de referência, baseados em tempos de viagem e velocidades, desvios padrões, locais de descontinuidades entre outros parâmetros. A geração desses padrões em conjunto com procedimentos operacionais permite o monitoramento eficiente do progresso de viagens de frotas de veículos, para finalidades logísticas e de segurança. O progresso de um veículo ao longo de uma rota é analisado diante dos padrões de viagem de referência obtidos a partir de suas viagens prévias, de veículos similares na mesma rota ou de viagens em rotas de mesma classe, dependendo do que for mais adequado. A geração de padrões é um processo dinâmico que gera conhecimento sobre o veículo e comportamento da rodovia ao longo do tempo. Desenho do processo de monitoramento do progresso de viagem é apresentado, no qual, a cada nova coleta de dado GPS ou a cada instante solicitado pelo usuário, a aderência é medida, eventuais descontinuidades (saídas da rota, paradas ou mudança de sentido) são identificadas e avisos são gerados. Tal aderência é definida por índice de desempenho que considera os desvios de tempo em relação a valores de referência e respectivas tolerâncias. Para experimentação da metodologia, foi realizada simulação de viagem na rodovia BR116 na ligação São Paulo - Rio de Janeiro, sobre base com cerca de 130.000 registros de dados GPS associados. Com integração em Geographic Information System (GIS) para suporte de funcionalidades, foram gerados os padrões de viagem e simulado o processo de monitoramento com sucesso. / This dissertation addresses the question of vehicle travel adherence in intelligent monitoring with Automatic Transportation Location (AVL) operating in a regional environment. A trip is considered as series of runs, corresponding to time in movement but excluding time spent on activities such as loading/unloading and others. Based on historic data collected from AVL/GPS a statistical data filtering method to generate the runs is presented. Furthermore, statistical methods are proposed to generate travel patterns based on travel time, speed, standard deviation and other parameters. The pattern generation together with operational procedures allows effective monitoring of large fleets in logistics and safety. The progress of a vehicle along a route is evaluated face to the statistical patterns of its previous successful trips or against statistical patterns of similar vehicles on the same route, whichever appropriate. The generation of patterns is a dynamic continuous process that generates knowledge on vehicle and road behavior along time. A broad outline of the travel monitoring process is presented. Whenever the requested by user, the process calculates the travel adherence, identifies abnormalities and generates alarms. That adherence is defined by a performance index, which considers the travel time deviations from the reference values and the respective tolerances. Successful experimentation was carried out on the Rio de Janeiro - São Paulo motorway, with 130.000 Global Positioning System (GPS) positional data relayed from trucks to a Geographic Information System (GIS) based monitoring system in Brazil.

O crescente desenvolvimento de aplicações utilizadas por dispositivos móveis que fazem uso das tecnologias de posicionamento via satélite e comunicação móvel, juntamente com a popularização destes dispositivos, sejam eles celulares ou GPSs (Global Positioning System) automotivos reforçam ainda mais a necessidade de representação e o entendimento a cerca das entidades móveis retratadas nestes dispositivos e incentivam estudos que forneçam um significado maior do que a simples representação posicional destas entidades. Considera-se neste trabalho, que estas entidades móveis são veículos rastreados via satélite que fornecem sua posição espacial, determinada por um par de coordenadas geográficas (latitude e longitude), coletadas em intervalos de tempo regulares para sistemas AVL (Automatic Vehicle Location) que são responsáveis pelo monitoramento do estado destes veículos. Neste trabalho, foram desenvolvidas funções para a previsão da localização e geração de padrões dos veículos monitorados por sistemas AVL. Para tanto, as paradas efetuadas pelos veículos irão definir regiões comuns de parada ocorridas durante um intervalo de tempo passado e serão consideradas como um padrão de localização, enquanto que as trajetórias serão utilizadas para definir o padrão de movimentação. Os relacionamentos topológicos que estes padrões possuem irão definir por meio de condições espaciais/direcionais e temporais quais serão as regiões de paradas consideradas como prováveis destinos, a partir de outra região de parada, considerada como origem e, permitirão a geração de matrizes com valores de frequências relativas que consideram o número de visitas que uma região recebe a partir da outra. O que possibilita extrair valores de probabilidade condicional para cada destino provável. Portanto, a metodologia proposta e as funções desenvolvidas que foram validadas em experimentos, que utilizaram dados reais de rastreamento, fornecem uma camada inicial de inteligência aos sistemas AVL, que proporciona aos controladores destes sistemas utilizarem consultas preditivas, identificarem mais facilmente anomalias de comportamento, que possam evidenciar alguma ocorrência incomum na movimentação do veículo, além de aumentar a segurança dos veículos que possuem um dispositivo de rastreamento por meio da definição de padrões inerentes ao veículo. / The increasing development of applications used by mobile devices that make use of the technologies of satellite positioning and mobile communications, along with the popularity of these devices, whether cell phones or GPS\'s (Global Positioning System) automotive further reinforce the need for representation and understanding about the mobile entities represent in these devices and encourage studies that provide a greater meaning than the simple positional representation of these entities. It is considered in this work that these entities are tracked vehicles that provide satellite spatial position, determined by a pair of coordinates (latitude and longitude), collected at regular time intervals for systems AVL (Automatic Vehicle Location) that are responsible for monitoring the state of these vehicles. In this work, functions have been developed to predict the location and pattern generation of vehicles monitored by AVL systems. Accordingly, these stops will define common regions of the stop occurred during a period of time past and will be considered as the pattern location, while the trajectories are used to define the pattern of movement of the vehicle. The topological relationships that have these patterns define conditions through spatial/directional and temporal, which are stops regions considered as probable destinations from another stop region, regarded as origin and allow the generation of matrices with values of frequencies on considering the number of visits that region receives from the other. What makes it possible to extract values of conditional probability for each likely destination. Therefore, the proposed methodology and functions developed that been validated in experiments using real data to tracking provide a initial layer of intelligence to the AVL system that gives drivers of these systems use predictive queries, more easily identify behavioral abnormalities that may show some unusual occurrence in moving the vehicle, in addition to increasing the safety of vehicles which have a tracking device by setting patterns relating to the vehicle.

Satellites have revolutionized navigation by making it more universal, accessible and ac- curate. Global Positioning System (GPS) is the most widely used satellite navigation system in the world. However, it is prone to errors from various sources such as the ionosphere, troposphere and clock biases. In order to make the system very accurate and reliable, especially to meet the requirements of safety-critical applications, Satellite Based Augmentation Systems (SBAS) have recently been designed in various countries to augment the GPS by providing corrections for its errors. An Indian SBAS called GAGAN (GPS Aided Geo Augmented Navigation), developed for the Airports Authority of India (AAI) by Indian Space Research Organization (ISRO) is currently being installed and proven for aviation and other use. The uncertain propagation delay of signals through the ionosphere is the most important contributor of error in GPS positioning, its maximal elimination is a major task of SBAS overlays. Ionospheric delays have steady, cyclic, and irregular components. The last types are of particular concern because they are unpredictable. This thesis deals with ionospheric depletion, an important phenomenon of this class that is specific to tropical regions like India and hence have not been well studied in the context of other SBAS systems of the world which cover mid-latitude domains. Depletion is an ionospheric phenomenon in which the density of electrons dips suddenly and then returns close to the previous value. It poses a challenge to the model adopted for ionospheric delay estimation since it may not be detectable by ground systems be- cause of its localized nature, and its occurrence and intensity cannot be predicted. In this work we have analyzed the depletion characteristics over the Indian region such as its distribution, frequency of occurrence, and depth and duration parameters. We have then studied and implemented an existing algorithm to detect a depletion from the Total Electron Content (TEC) data. This algorithm has been found to be inaccurate for estimation of depletion duration, and we have proposed an improved algorithm for depletion detection and shown it to be more suitable for the Indian SBAS, GAGAN. The algorithm utilizes multiple thresholds for depletion detection in order to improve performance in the presence of irregularities including noise. These thresholds are determined by analyzing real TEC data containing depletion events over the Indian region. The detected depletion events are those that have a strong likelihood of contributing large range errors and degrading GAGAN's reliability. The thresholds include depletion parameters such as the depth, duration, rate of change of TEC, and the rate of change of slope of the TEC curve. The characterization of depletion events over the Indian region yielded useful insights into the behaviour of the phenomenon. It was observed that the depletion events were invariably present post-sunset, between 1900 and 0200 hrs. This observation is consistent with the other studies on plasma bubbles so far. The average depth of the depletion was found to be about 3.31 meters of propagation delay while the strongest depletion corresponds to about 5.04 meters of delay. The latter observation impresses upon the need to detect and study the phenomenon of depletion since it is capable of causing a significant loss of accuracy and reliability to the system. The duration of the depletion was found to range from about 10 min to 2.35 hours. In addition, a statistical study of the relationship among the different parameters and a study devoted to now-casting of depletion was made to get a more quantitative insight into the phenomenon of depletion. Scintillation is another phenomenon occurring in the ionosphere which causes rapid fluctuations of phase and amplitude of the signal due to TEC variations in the ionosphere. The occurrences of depletion were observed to be accompanied by scintillation, as also noted in previous studies. The correlation of depletion and scintillation was studied using the data available for this research. A spatial characterization of the depletion events was also investigated using the same temporal TEC data from neighbouring stations which were relatively close to each other. This study addressed the movement of the plasma bubble with respect to the advection speed and direction with definite results. Attention was also devoted to the spatial dimension of the bubble as observed from various stations. Contributions to this variability in the apparent spatial extent comes from the observation of the depletion event from varying lines-of-sight corresponding to different GPS satellites which are also moving, and the differential `slicing' effect because of the location of the stations with respect to the plasma bubble, in addition to the evolution of the bubble during transit. The detection of depletion and its temporal characterization, in addition to the knowledge of its spatial extent and motion, can provide very useful insights on the behaviour of a depletion event and over the ionosphere in general. This knowledge and the mechanism for detection can help to improve the quality and dependability of the information provided by SBAS systems, in particular the Indian GAGAN system, for improved navigation in this part of the world. The present thesis aims to make a significant contribution in this direction.

No description available.

Rychlý vývoj v oblasti mobilní informatiky vyústil v nový, alternativní způsob mobilní komunikace, v němž mobilní uzly tvoří samoorganizující se bezdrátovou síť, jíž se říká mobilní síť ad hoc (Mobile Ad hoc Network, MANET). Specifické vlastnosti sítí MANET stavějí návrh síťového protokolu před řadu problémů na všech vrstvách protokolové sady . Příčinou jsou nepředvídatelné změny topologie a mobilní povaha těchto sítí. Nástrojem, který řeší problémy plynoucí z mobility uzlů, je predikce budoucích změn v topologii sítě. To má zásadní význam pro různé úlohy jako přesměrování. Tato disertační práce se zabývá dvěma metodami predikce mobility pro sítě MANET. První metoda se nazývá „predikce mobility s využitím virtuální mapy“ (mobility prediction using virtual map) a předpokládá, že každý uzel si dokáže vybudovat svou virtuální mapu v závislosti na svém umístění v průběhu času. Vyvinutý predikční algoritmus byl implementován do síťového simulátoru NS-2, aby jej bylo možné vyhodnotit. V této práci zkoumám stávající modely mobility a způsob, jakým v nich lze aplikovat tuto metodu predikce. Simulace sledují zlepšení výkonnosti, co se týče průměrného zpoždění na bázi end-to-end, poměru doručených paketů a propustnosti sítě. Navržený koncept predikce byl implementován pomocí směrovacího protokolu AODV(Ad Hoc On-Demand Distance Vector). Pro druhou metodu jsem vyvinula umělou neuronovou síť pro predikci pohybů v sítích MANET. Model pro predikci mobility vznikl na základě dat shromážděných ze vzorců umístění. K učení či trénování ANN byl využit bayesovský přístup. Ten byl implementován v softwaru pro trénování bayesovských neuronových sítí s názvem Model Manager. Nejlepším způsobem hodnocení závěrečného modelu je provedení predikcí a jejich srovnání s cílovými daty. Predikce vznikají na základě 50 vzorců jako vstupních proměnných. Dosažené výsledky prezentované s diskutované v práci se vyznačují zlepšením zásadních parametrů komunikační sítě, jako jsou propustnost, zpoždění, Poměr doručených paketů, až o 30% v porovnání s klasickým směrovacím protokolem AODV, kde není implementován predikční model.

<p dir="ltr">This Research focuses on developing artificial intelligence tools to detect and mitigate cyber-attacks targeting unmanned aerial vehicles. </p>

Cette thèse vise à expliquer les fluctuations du bien-être momentané tout au long de la journée et de la semaine, en tenant compte de l'influence de l'environnement construit et social sur ces variations. Cette étude a évalué l'impact du moment de la journée, des interactions sociales, de la météo et des environnements bâtis et sociaux, sur le bien-être momentané à l’aide d’un devis longitudinal par évaluation écologique momentanée géographique (GEMA). Un total de 899 participants résident dans le Grand Montréal, âgés de 18 à 80 ans (Âge : M = 41,71, md = 39 ; femmes = 55,7%) ont rempli une échelle brève de l'humeur trois fois par jour pendant sept jours consécutifs sur leurs téléphones intelligents (application EthicaData). Lors des réponses, la coordonnée GPS de leur localisation a également été captée, et a servi à mesurer diverses expositions environnementales dans un système d’information géographique. Un modèle à effets mixtes à trois niveaux avec des effets aléatoires a montré une corrélation positive entre le bien-être et l'âge, les après-midis, les week-ends et les interactions sociales impliquant la famille et les amis. En revanche, le bien-être était négativement associé aux soirées. Quelques variables de l’environnement bâti et social étaient significativement associées au bien-être. Ces liens ont persisté après contrôle des facteurs de confusion potentiels. De plus, un effet d'interaction a révélé que l'influence des interactions sociales momentanées différait entre les hommes et les femmes. Cette étude met en lumière le rôle des facteurs environnementaux et sociaux dans la compréhension du bien-être momentané. L'intégration de la technologie géospatiale et des évaluations écologiques momentanées offre des perspectives précieuses pour l'urbanisme et la santé publique dans l’exploration des liens entre contexte et santé. / This thesis aims to explain the fluctuations of momentary well-being throughout the day and week, taking into consideration how the built and social environment affects these variations. In this seven-day longitudinal study using GPS-enabled smartphones and EthicaData software with a geographic ecological momentary assessment (GEMA) approach, the real-time impact of built and social environments on self-reported momentary well-being of residents of Greater Montreal between 2018 and 2021 was investigated. A total of 889 participants aged 18–80 years (Age: M=41.71, md=39; females = 55.7%) completed the Short Mood Scale three times daily for seven consecutive days. A three-level mixed-effects model with random effects showed a positive correlation between well-being and age, afternoons, weekends, and social interactions involving family and friends. On the other hand, well-being was negatively associated with evenings. Only a few built and social environmental variables were found to be significantly associated with well-being. These links remained after controlling for potential confounding factors. Moreover, an interaction effect revealed that the influence of momentary social interactions differed for men and women. This study highlights the significance of environmental and social factors in comprehending momentary well-being, which has important implications for urban planning and public health initiatives. Integrating geospatial technology and EMA provides valuable insights into this intricate relationship.

[ES] La falta de empatía en niños y adolescentes son fuertes predictores de desarrollar conductas antisociales, emitir juicios morales inadecuados, dificultades en las relaciones interpersonales o conductas de acoso escolar, por lo que es necesario disponer de programas educativos que promuevan la empatía y una respuesta "orientada hacia los demás" desde edades tempranas para ayudar a fomentar un mayor bienestar social en los estudiantes. Un enfoque ampliamente utilizado para promover el aprendizaje en el contexto educativo es la gamificación dado que mejora en los estudiantes la motivación para aprender, la participación activa y la satisfacción, compartiendo la idea de utilizar experiencias de juego positivas para un propósito serio, como lograr un cambio de comportamiento, en lugar de centrarse en el entretenimiento. Se dispone en la literatura de algunas propuestas gamificadas tecnológicas para promover la empatía en la educación, sin embargo pocos trabajos integran la Realidad Aumentada (RA), una forma de juego radicalmente nueva que permite cultivar experiencias de aprendizaje inmersivas y estimulantes al combinar contenido digital superpuesto en el mundo real. Esta tecnología emergente e innovadora es compatible con los dispositivos móviles, siendo unidades asequibles con capacidades de reconocimiento de imágenes, seguimiento de objetos, detección de ubicación y orientación, siendo una solución práctica para ofrecer al usuario una experiencia RA no necesitando requisitos de hardware sofisticados. Esta tesis revela la falta de investigación sobre entornos sociales digitales para promover la empatía, existiendo evidencias de que la empatía y la respuesta prosocial/prosocialidad están significativamente relacionadas. Por ello, esta tesis propone un modelo circular de empatía híbrido derivado de los modelos existentes en la literatura y que involucra los procesos y componentes principales de esta habilidad, para promover acciones conductuales dirigidas a beneficiar o ayudar a otros, como el comportamiento prosocial. Este modelo circular se transforma en dinámicas de juego operativas a través de un modelo gamificado de aprendizaje de tareas y pasos para llevar al jugador a experimentar diversidad de escenarios o historias reflexivas (observación), organizadas en torno a un desafío o misión central desarrollada de forma individual (monousuario) o grupal (multiusuario), buscando promover emociones positivas como la empatía en términos del comportamiento prosocial (actuación). El modelo de aprendizaje está definido por espacios aumentados basados en la ubicación siendo coordenadas GPS o anclajes espaciales (espacios físicos previamente escaneados) y que puede ser adaptado a múltiples contextos de aprendizaje. En esta tesis se presentan dos estrategias gamificadas basadas en la ubicación con Realidad Aumentada Móvil. La primera EmoFindAR, con "anclajes espaciales" diseñado a nivel multiusuario con dinámica competitiva vs. colaborativa para la identificación y manipulación de estados emocionales básicos, evaluada en la educación primaria donde se observa que promueve en los estudiantes la socialización, las habilidades comunicativas y la inteligencia emocional. La segunda propuesta diseñada con el "Sistema de Posicionamiento Global (GPS)", siendo EmpathyAR para un aprendizaje individualizado y su versión multiusuario SocialTaskAR para un aprendizaje en equipo. / [CA] La falta d'empatia en xiquets i adolescents és un fort predictor de desenvolupar conductes antisocials, emetre judicis morals inadequats, dificultats en les relacions interpersonals o conductes d'assetjament escolar, per la qual cosa és necessari disposar de programes educatius que promouen l'empatia i una resposta "orientada cap als altres" des d'edats primerenques per a ajudar a fomentar un major benestar social en els estudiants. Un enfocament àmpliament utilitzat per a promoure l'aprenentatge en el context educatiu és la ludificació donat que millora en els estudiants la motivació per a aprendre, la participació activa i la satisfacció, compartint la idea d'utilitzar experiències de joc positives per a un propòsit seriós, com aconseguir un canvi de comportament, en lloc de centrar-se en l'entreteniment. Es disposa en la literatura d'algunes propostes lúdiques tecnològiques per a promoure l'empatia en l'educació, no obstant això pocs treballs integren la Realitat Augmentada (RA), una forma de joc radicalment nova que permet cultivar experiències d'aprenentatge immersives i estimulants en combinar contingut digital superposat en el món real. Aquesta tecnologia emergent i innovadora és compatible amb els dispositius mòbils, sent unitats assequibles amb capacitats de reconeixement d'imatges, seguiment d'objectes, detecció d'ubicació i orientació, sent una solució pràctica per a oferir a l'usuari una experiència RA no necessitant requisits de maquinari sofisticat. Aquesta tesi revela la falta d'investigació sobre entorns socials digitals per a promoure l'empatia, existint evidències que l'empatia i la resposta pro social estan significativament relacionades. Per això, aquesta tesi proposa un model circular d'empatia híbrid derivat dels models existents en la literatura i que involucra els processos i components principals d'aquesta habilitat, per a promoure accions conductuals dirigides a beneficiar o ajudar a uns altres, com el comportament pro social. Aquest model circular es transforma en dinàmiques de joc operatives a través d'un model ludificat d'aprenentatge de tasques i passos per a portar al jugador a experimentar diversitat d'escenaris o històries reflexives (observació), organitzades entorn d'un desafiament o missió central desenvolupada de manera individual (monousuari) o grupal (multiusuari), buscant promoure emocions positives com l'empatia en termes del comportament pro social (actuació). El model d'aprenentatge està definit per espais augmentats basats en la ubicació sent coordenades GPS o ancoratges espacials (espais físics prèviament escanejats) i que pot ser adaptat a múltiples contextos d'aprenentatge. En aquesta tesi es presenten dues estratègies lúdiques basades en la ubicació amb Realitat Augmentada Mòbil. La primera EmoFindAR, amb "ancoratges espacials" dissenyat a nivell multiusuari amb dinàmica competitiva vs. col.laborativa per a la identificació i manipulació d'estats emocionals bàsics, avaluada en l'educació primària on s'observa que promou en els estudiants la socialització, les habilitats comunicatives i la intel·ligència emocional. La segona proposta dissenyada amb el "Sistema de Posicionament Global (GPS)", sent EmpathyAR per a un aprenentatge individualitzat i la seua versió multiusuari SocialTaskAR per a un aprenentatge en equip. / [EN] The lack of empathy in children and adolescents is a strong predictor of developing antisocial behavior, resulting in inappropriate moral judgments, difficulties in interpersonal relationships or bullying behavior, which is why it is necessary to have educational programs that promote empathy and an response "oriented towards others" from an early age to help foster greater social well-being in students. A widely used approach to promote learning in the educational context is gamification since it improves students' motivation to learn, active participation and satisfaction, sharing the idea of using positive game experiences for a serious purpose, such as achieving a behavioral change, instead of focusing on entertainment. There are some technological gamified proposals in the literature to promote empathy in education, however few works integrate Augmented Reality (AR), a radically new form of gaming that allows cultivating immersive and stimulating learning experiences by combining digital content superimposed in the real world. This emerging and innovative technology is compatible with mobile devices, which are affordable units with image recognition, object tracking, location detection and orientation capabilities, a practical solution to offer the user an AR experience without needing sophisticated hardware requirements. This thesis reveals the lack of research on digital social environments to promote empathy, with evidence that empathy and prosocial response/pro-sociality are significantly related. Therefore, this thesis proposes a hybrid circular model of empathy derived from existing models in the literature involving the main processes and components of this ability, to promote behavioral actions aimed at benefiting or helping others, such as prosocial behavior. This circular model is transformed into operational game dynamics through a gamified model of learning tasks and steps to lead the player to experience a diversity of scenarios or reflective stories (observation), organized around a central challenge or mission developed in an individual (single-user) or group (multi-user), seeking to promote positive emotions such as empathy in terms of prosocial behavior (performance). The learning model is defined in terms of location-based augmented spaces being GPS coordinates or spatial anchors (previously scanned physical spaces) and that can be adapted to multiple learning contexts. In this thesis two gamified strategies based on location with Mobile Augmented Reality are presented. The first EmoFindAR, with "spatial anchors" designed at a multi-user level with competitive vs. collaborative dynamics for the identification and manipulation of basic emotional states, evaluated in primary education where it is observed that it promotes socialization, communication skills and emotional intelligence in students. The second proposal designed with the "Global Positioning System (GPS)", is EmpathyAR for individualized learning and its multi-user version SocialTaskAR for team learning. / Gracias a todos los miembros del proyecto R+D+i PID2019-108915RB-I00 por sus contribuciones brindadas y en particular a Jorge Montaner Marco por su colaboración incondicional. / López Faicán, LG. (2023). Desarrollo de habilidades empáticas mediante estrategias de gamificación basadas en realidad aumentada móvil multiusuario para el contexto educativo [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/200583