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    Authors: Dadiala, Rhea;

    Aging infrastructure, along with the escalating occurrence and severity of extreme weather events linked to climate change, and the growing demands of an increasing population, have placed significant strains on wastewater and stormwater systems. Consequently, there has been a rise in instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs), among other related problems. These challenges have intensified the impact of sewershed issues on both society and the environment. Fortunately, recent advancements in technology, such as sophisticated sensor technologies, more powerful processors, and advanced mathematical modeling techniques, have opened up new possibilities for developing intelligent water systems in the United States that are capable of making well-informed, data driven decisions. While the technological capabilities of these tools are advancing, their application in the water sector is limited and often siloed. Water utilities face a variety of challenges related to digitalization of sewershed management and require a more structured approach for their digital transformation. This research aims to present a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) that will serve as a blueprint to facilitate the implementation of Intelligent Sewersheds for water utilities across the country. The proposed framework will focus on enhancing our understanding of various aspects, including system of systems thinking, data management, modeling techniques, decision-making processes, and service delivery, in order to adopt a more intelligent and efficient approach to managing sewersheds. This framework was piloted with small, medium and large scale utilities to capture feedback on the proposed building blocks from a real world perspective and the findings from these interviews reveal that most utilities are in the preliminary stages of intelligent water systems implementation, and is more common among large utilities as compared to their small and medium counterparts. Master of Science Water infrastructure in the United States is facing several challenges like the deterioration of infrastructure with time, increasing extreme weather events like flooding due to climate change, and growing population demands. These issues are putting a lot of pressure on wastewater and stormwater systems, leading to more instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs) and other related problems that have significant negative impacts on both the society and the natural environment. Fortunately, recent advancements in technology like remote sensing, internet of things, increased computing power and advanced data analysis tools like artificial intelligence and machine learning, have provided opportunities for water utilities to improve their sewershed management practices. This research introduces a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) to help implement the new technologies and practices available for water utilities nationwide to improve the efficiency and reliability of sewershed management. The framework focuses on improving our understanding of different aspects like how a sewershed and its components are defined, collecting and managing data parameters, modeling techniques, decision-making, and service delivery. The framework was piloted with small, medium, and large scale utilities to get real-world feedback. The findings showed that most utilities are in the early stages of adopting intelligent water systems, and larger utilities are more likely to use these technologies compared to smaller ones.

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    Authors: Khabbazan, S. (author);

    Agriculture plays a critical role in the economy and environment worldwide, and the provision of real-time, reliable information on large-scale agricultural activity is essential for precision agriculture and global economic prosperity. In this context, remote sensing, especially through Synthetic Aperture Radar (SAR), can play an important role by offering accurate estimation of crop biophysical parameters such as Leaf Area Index (LAI), crop height, dry biomass, and Vegetation Water Content (VWC). Unlike traditional high-resolution optical imagery, which is often undermined by cloud cover, SAR microwave remote sensing overcomes these limitations by generating and transmitting longer wavelengths (300MHz – 10 GHz) that penetrate clouds and aerosols and allowing data acquisition both day and night. However, SAR data is influenced by various factors such as sensor characteristics such as frequency, polarization, and incidence angle, as well as target characteristics like the size and shape distribution of crop constituents, and more importantly, the water content of crop constituents. In the path towards precision farming and more sustainable and efficient farming using SAR data, understanding the role of these factors, particularly the dynamics of external and internal vegetation water content on radar backscatter, is vital.To date, however, the potentially confounding effects of both internal and, particularly, external water dynamics in vegetation on radar backscatter have not been adequately addressed. Existing studies have indeed illustrated the effects of SCW on radar backscatter, but the degree to which it influences different frequencies and polarizations, and the subsequent impact on crop bio-geophysical parameters remains unclear. Therefore, the main goal of this thesis is to expand our knowledge of the relationship between radar backscatter, vegetation dynamics, and surface canopy water (SCW) in agricultural monitoring. In this thesis we utilized statistical analysis and radiative transfer modeling in combination with fully polarimetric L-band data from a truck-mounted scatterometer and C-band data from Sentinel-1, along with extensive field data… Mathematical Geodesy and Positioning

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    Doctoral thesis . 2024
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      Doctoral thesis . 2024
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    Authors: Rosén, Julia;

    We live in a complex world where we proactively plan and execute various behaviors by forming expectations in real time. Expectations are beliefs regarding the future state of affairs and they play an integral part of our perception, attention, and behavior. Over time, our expectations become more accurate as we interact with the world and others around us. People interact socially with other people by inferring others' purposes, intentions, preferences, beliefs, emotions, thoughts, and goals. Similar inferences may occur when we interact with social robots. With anthropomorphic design, these robots are designed to mimic people physically and behaviorally. As a result, users predominantly infer agency in social robots, often leading to mismatched expectations of the robots' capabilities, which ultimately influences the user experience. In this thesis, the role and relevance of users' expectations in first-hand social human-robot interaction (sHRI) was investigated. There are two major findings. First, in order to study expectations in sHRI, the social robot expectation gap evaluation framework was developed. This framework supports the systematic study and evaluation of expectations over time, considering the unique context where the interaction is unfolding. Use of the framework can inform sHRI researchers and designers on how to manage users’ expectations, not only in the design, but also during evaluation and presentation of social robots. Expectations can be managed by identifying what kinds of expectations users have and aligning these through design and dissemination which ultimately creates more transparent and successful interactions and collaborations. The framework is a tool for achieving this goal. Second, results show that previous experience has a strong impact on users’ expectations. People have different expectations of social robots and view social robots as both human-like and as machines. Expectations of social robots can vary according to the source of the expectation, with those who had previous direct experiences of robots having different expectations than those who relied on indirect experiences to generate expectations. One consequence of these results is that expectations can be a confounding variable in sHRI research. Previous experience with social robots can prime users in future interactions with social robots. These findings highlight the unique experiences users have, even when faced with the same robot. Users' expectations and how they change over time shapes the users’ individual needs and preferences and should therefore be considered in the interpretation of sHRI. In doing so, the social robot expectation gap can be reduced. Vi lever i en komplex värld och för att kunna hantera denna komplexitet formar vi förväntningar. Förväntningar är antaganden om framtida tillstånd och är en vital del av vår perception, uppmärksamhet och beteende. Genom att interagera med omvärlden och andra människor blir våra förväntningar mer precisa och korrekta över tid. I en social interaktion behöver vi förstå den andra personens syften, avsikter, preferenser, övertygelser, känslor, tankar och mål. Sociala robotar är utformade för att skapa liknande inferenser när användare interagerar med dem. Detta kan leda till missbedömningar mellan vad vi förväntar oss av sociala robotar och vad dessa artefakter är kapabla till, vilket påverkar användarupplevelsen av sociala robotar. I den här avhandlingen presenteras den forskning som har utförts för att studera rollen och relevansen av människors förväntningar i social människa-robotinteraktion (sMRI). Resultaten kan delas in i två större fynd. Det första fyndet är ett utvärderingsramverk som ämnar att systematiskt studera användares förväntningar av sociala robotar i en interaktion, med fokus på hur förväntningar ändras över tid i en interaktion, med interaktionens unika kontext i åtanke. Ramverket är menat för designers av sociala robotar och forskare inom sMRI-fältet för att bättre studera, hantera, och förstå förväntningar, både i robotarnas design och i robotarnas agerande. Det andra fyndet består av de empiriska resultat som visar hur tidigare erfarenheter påverkar användares förväntningar. Förväntningarna baseras till stor del på vilka typer av tidigare erfarenheter användare har, där de med direkta erfarenheter av robotar har andra förväntningar än de med indirekta erfarenheter. Vidare visar resultaten att användare ser sociala robotar både som människolika och som maskiner samtidigt. Förväntningar kan också ses som en bakomliggande variabel inom sMRI-forskning eftersom tidigare erfarenheter kan påverka deltagare i kommande interaktioner med sociala robotar. Resultaten visar även att användarupplevelsen är unik för varje användare, även om roboten är densamma, vilket bör tas i åtanke när resultat tolkas i en sMRI-kontext. Genom att ha förväntningar i åtanke kan vi minska det gap som uppstår mellan människors förväntningar av sociala robotar och robotarnas faktiska förmågor. På så sätt kan vi främja positiva användarupplevelser och förbättra interaktionen mellan människa och robot. Två av sju delarbeten (övriga se rubriken Delarbeten/List of papers):VI Rosén, Julia, Lindblom, Jessica, Lamb, Maurice, and Billing, Erik (Under review). “Previous Experience Matters: An In-Person Investigation of Expectations in Human-Robot Interaction”. In: Under review for scientific journal, pp. 1–19.VII Lindblom, Jessica, Rosén, Julia, Lamb, Maurice, and Billing, Erik (Manuscript). “Disentangling People’s Experiences and Expectations when Interacting with the Social Robot Pepper: A Qualitative Analysis”. In: Manuscript for scientific journal, pp. 1–41.

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    Thesis . 2024
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    Authors: Nic R?amoinn, Susan;

    APPROVED This thesis presents a sociocultural theory (SCT) investigation of the use of robotics for second language learning (SLL) and computational thinking (CT) skills development in the early years of primary school. The research intervention combines a playful storytelling robotics-based activity with the Irish language within a play session. Jeanette Wing, revived the area of CT in (2006, p. 44) noting that CT should be added "to every child's analytical thinking." Current research indicates that technological tools have a positive impact on the development of CT skills when integrated with other curricular areas in primary school (Alimisis & Moro, 2016; Angeli & Valanides, 2020; Angeli et al., 2016; Bers, 2020; Bers et al., 2012; Hassenfeld & Bers, 2020). This research intervention contributes to the field of CT skills development as well as second language learning in the early years of primary school, through the integration of robotics and Irish, in an English medium classroom. The researcher deemed design-based research (DBR) as the most appropriate methodology to answer the research questions while using both qualitative and quantitative data collection methods. The research intervention was developed through an iterative design cycle of two one pilot cycles and a third pilot cycle over six weeks. The final iteration, cycle four, comprised of a six-week intervention using Bee-Bot (programmable floor robot) as part of a playful activity to promote second language learning with 22 children in a junior infant class (aged 5-6 years). The children worked in small groups to programme Bee-Bot to support their storytelling through Irish. The results of this research activity were positive with feedback from the children and the teacher of increased interest and use of the Irish language, observed language gains; identification and development of CT skills; and positive meaningful engagement through Irish amongst the children. The findings provide an insight into the opportunities that a technological tool provides during a playful activity with this young age group as well as outlining the potential parallels between the language learning process and CT skills development. As the Irish curriculum moves towards a more integrated model (NCCA, 2023) the analysis of the children's experiences during this research activity along with their language and CT skill development offers an interesting insight for early year's language educators and curriculum developers in Ireland.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Burger, Tim;

    This ethnography of rural social life on São Jorge Island, Azores, examines the interconnections between depopulation, people’s practices of material livelihood, and their understandings of their overall historical and social condition. Following decades of transatlantic outmigration and demographic decline, subsistence farmers in my main fieldsite, the village of ‘Fajã’, shared a focus on overall decay. However, they evaluated and lived out what I call ‘depopulated social relations’ in different and conflicting ways. This study explores the heterogeneous range of reflexive, hands-on, and often counterintuitive modes in which island residents organized their historical consciousness around the social reproduction of agrarian livelihoods in unfavourable circumstances. My interlocutors’ central predicament was that their intensely valued agrarian land was being overgrown by brushwood. They often described this land as “already lost” (*já perdido*). Watching the physical change of their gardens and terraces was an unsettling and confusing experience that metonymically condensed broader concerns about the moral imperative of agricultural labour, the lack of desirable conviviality, the gendered dynamics of household subsistence, or sacred spacetime. A collective sense of failure regarding agrarian cultivation was the key category through which island residents made sense of depopulation. While gardening hence became a positively imbued activity in which people acted out their historical consciousness, other economic practices such as renting out houses to tourists or intensive cattle farming were more ambivalent in moral quality. Chapters One and Two approach these conflicting forms of livelihood-making through the lenses of land and households, respectively. I argue that the dominant asset for realizing wealth, public distinction, and moral selfhood has shifted from subsistence gardens to houses, producing novel forms of inequality and social heterogeneity. Chapter Three examines outmigration, which people held to be the leading cause for demographic change. I further develop the theme of heterogeneity by juxtaposing different migrants’ life-histories and varied understandings of emplacement. Chapter Four examines the intrinsic value residents frequently attributed to agrarian labour – portraying it as an end in itself, not a means to other ends – since working together consciously counteracted the impacts of depopulation in a convivial way. Chapter Five returns to general perceptions of land and landscape, now exploring models of masculinity and the violent potential of ideals of agrarian cultivation that take shape in relation to experience of ‘lost’ land as uncontrollable environmental change. Chapter Six explores the Holy Ghost Festival (*Festa do Espírito Santo*) as a disputed spatiotemporal model that both mediated decline and provided an iconic form to orient economic activities. The theoretical arguments that emerge from these chapters contribute to anthropological debates on value, space, and depopulation. I suggest that an action-centred theory of value requires attention to spatial semiotics in order to conceptualize how everyday practices give form to larger social and cultural wholes.

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    Authors: Freeman, Erika;

    Forest management and harvest offer a promising means of combating climate change by removing CO2 from the atmosphere. However, most forest carbon (C) is held in soils. Thus, by disturbing soils and altering hydrology, forest management and harvest potentially displace large amounts of C from forest soils into aquatic ecosystems. My dissertation seeks to understand the fate of this forest C as dissolved organic matter (DOM) into various aquatic endpoints by tracing its molecular composition along the ephemeral water film that begins in upland soils and ends in streams. The fate and function of DOM in aquatic ecosystems are strongly affected by its chemical properties. Thus, recent progress in the molecular characterization of DOM has opened a new line of inquiry into harvest impacts on aquatic ecosystem functioning. My thesis advances this line of inquiry by applying high-resolution mass spectrometry to study the effect of forest disturbance on DOM in soils and connected streams. Chapter 1 of this thesis gives a general overview of the molecular nature of terrestrial DOM sources, how these sources may be altered by harvest, and the subsequent transfer, fate, and properties of DOM once in streams. It also outlines the specific objectives I address with a combination of field experiments and synoptic surveys in the Batchewana watershed in Ontario, Canada. In Chapter 2, I tracked DOM along soil depths, and hillslope positions in four replicate forest headwater catchments of the Canadian hardwood forest. I related DOM composition to soil microbiomes and physical chemistry to establish baseline conditions before a harvest experiment. I found that DOM changed similarly along soil-aquatic gradients, irrespective of differences in environmental conditions. My results implicated continuous microbial reworking that shifts DOM towards a shared pool of compounds in soils. Such general degradation patterns can inform the management of soil-to-stream carbon losses by predicting DOM composition and its downstream reactivity along environmental gradients. In Chapter 3, I quantified and characterised the effects of logging on DOM composition over three years using the four experimental catchments from Chapter 2. Two catchments were experimentally logged, while the remaining two were left as controls. I found that DOM concentration in stream water from logged catchments increased in a pulse during the first year, but only the changes in the quality of DOM persisted. Using ultrahigh-resolution mass spectrometry, I showed that DOM released from deforested catchments was energy-rich and more chemically diverse, likely because of higher hydrological connectivity with intermediate and deep soil layers. I estimated that while logging increased the overall annual flux of dissolved organic carbon by approximately 8.5% of the extracted wood carbon, the exposure of deeper soil through logging released previously stable soil organic carbon to streams. The resulting changes to the molecular composition of DOM within headwater streams persisted for at least two years after logging, potentially disrupting aquatic ecosystems and making streams more likely to release terrestrial C into the atmosphere. In Chapter 4, I examined the chemical properties of DOM in stream water from over 200 Canadian headwater streams in an area with historical forest harvest. I demonstrated that using the fluorescence properties of streams, the effect of harvest, although detectable on a large spatial scale, is relatively minor compared to the effects of forest types and wetness gradients. These results have implications for land-water linkages under a changing climate that shift terrestrial sources of DOM. Finally, in Chapter 5, I discuss the implications of my findings for understanding the coupling between terrestrial and aquatic ecosystems and propose avenues for future research.

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    Authors: Izquierdo Córdoba, Luis Miguel, 1986-;

    Orientadores: João Maurício Rosário, Dario Amaya Hurtado Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica Resumo: A robótica bípede tem adquirido uma importância relevante nos últimos anos devido a suas principais aplicações na robótica de assistência e reabilitação física, como são os exoesqueletos ou dispositivos robóticos usáveis (wearable devices), assim como na robótica colaborativa. Em razão disso, fazem-se necessários avanços significativos em termos de estratégias de controle, custo de desenvolvimento e segurança para melhorar o desempenho de robôs bípedes na execução dos movimentos. Este trabalho de pesquisa apresenta os fundamentos teóricos e a metodologia para desenvolver um sistema de controle híbrido bioinspirado para um exoesqueleto robótico de membro inferior, com o propósito de garantir uma execução do ciclo de marcha próxima da dinâmica natural. O mecanismo robótico que representa o exoesqueleto é projetado para reproduzir o movimento da marcha no plano sagital considerando três graus de liberdade, correspondentes às juntas do quadril, joelho e tornozelo. O ciclo de marcha é modelado por uma dinâmica híbrida, isto é, a dinâmica do ciclo da marcha bípede é analisada como uma ocorrência de eventos discretos por meio do formalismo de Máquina de Estado Finito (Finite State Machine - FSM), e como um modelo dinâmico contínuo por meio da formulação de Euler-Lagrange. Projeta-se um controlador por eventos para agir como supervisor dos controladores locais de junta, que são principalmente controladores PID. As trajetórias de marcha são geradas para cada grau de liberdade utilizando-se um Centro Gerador de Padrões (Central Pattern Generator - CPG) baseado em uma rede acoplada de osciladores neurais de Matsuoka. As trajetórias são geradas de forma a se adaptarem à inclinação da superfície e à velocidade média da marcha, por meio do algoritmo de aprendizado por reforço Deep Deterministic Policy Gradient (DDPG) dentro da arquitetura de controle. O principal objetivo desta pesquisa será aplicar os resultados em um sistema robótico bípede capaz de reproduzir a marcha e as estratégias de controle motor humano, para projetar dispositivos robóticos de elevado desempenho em tarefas de reabilitação motora, assistência física ou em robótica colaborativa Abstract: Bipedal robotics has acquired relevance in recent years due to its main application, related to physical rehabilitation robotic assistance, such as exoskeletons or wearable robotic devices, and collaborative robotics. However, significant advances are still needed in terms of control and safety strategies, to improve the locomotion and performance of bipedal robots. The cost of development of such strategies is also required to significantly decrease. This research work presents the theoretical foundations, framework, and methodology to develop a bioinspired hybrid control system for a lower limb exoskeleton, to guarantee a bipedal gait cycle execution that is close to the natural dynamics, and with the ability to adapt gait trajectories to environmental conditions, specifically conditions of surface slope and average gait speed. The robotic mechanism used in this work to represent the exoskeleton is designed to reproduce movement in the sagittal plane considering three degrees of freedom, corresponding to the hip, knee, and ankle joints. Hybrid dynamics models the gait cycles, i.e., the bipedal gait cycle dynamics are analyzed as an occurrence of discrete events through the Finite State Machine (FSM) formalism, and as a model continuous dynamic through the Euler-Lagrange formulation. The gait trajectories, determined by the dynamics of events, will be generated for each degree of freedom of the exoskeleton using a Central Pattern Generator (CPG) based on coupled Matsuoka neural oscillators. Also, an event control design acts as a supervisor of the local joint controllers, which are mainly PID controllers. Trajectories are generated by applying Reinforcement Learning to Bipedal Robotics, to adapt to surface slope and average gait speed. Trajectory adaptation of each freedom degree is implemented using the Deep Deterministic Policy Gradient (DDPG) algorithm within the control architecture. The main objective of this research will be to apply the results in a real bipedal robotic system capable of reproducing gait and human motor control strategies, to design high-performance robotic devices in tasks of motor rehabilitation, physical assistance, or collaborative robotics Doutorado Mecatrônica Doutor em Engenharia Mecânica CAPES 88882.435184/2019-01

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    Authors: Contini, Denis, 1990-;

    Orientador: Edmundo Roberto Mauro Madeira, Luiz Fernando Bittencourt Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação Resumo: Com o surgimento da Quarta Revolução Industrial, diversas fábricas passaram a adotar um modelo de produção que, além de automatizado, opera com a coleta e análise dos dados por meio de diversos sensores e aplicações a fim de melhorar a eficiência e produtividade. Com isso, a quantidade de dados coletados e transmitidos aumentou consideravelmente. Nesse contexto, este trabalho propõe um mecanismo orquestrador de serviços e dados (MSOFEC) baseado na análise periódica das métricas de rede e dos recursos de processamento de maneira que a aplicação obtenha níveis maiores de desempenho, confiabilidade e disponibilidade na criação da infraestrutura e no direcionamento dos dados de uma aplicação de Internet das Coisas Industrial em um cenário multicamada borda-névoa-nuvem. Os resultados demonstraram que, em cenários com grandes quantidades de mensagens sendo transmitidas em curtos períodos de tempo, é possível diminuir os atrasos na latência e melhorar a utilização de recursos de processamento, mantendo-se bons níveis de disponibilidade e confiabilidade para a aplicação, quando comparado com soluções disponíveis em apenas uma camada Abstract: With the emergence of the Fourth Industrial Revolution, several factories began to adopt a production model that, in addition to being automated, operates with the collection and analysis of data through various sensors and applications in order to improve efficiency and productivity. As a result, the amount of data collected and transmitted has increased considerably. In this context, this work proposes a service and data orchestrator mechanism (MSOFEC) based on the periodic analysis of network metrics and processing resources so that the application obtains higher levels of performance, reliability and availability in infrastructure creation and targeting data from an Industrial Internet of Things application in a multilayer edge-fog-cloud scenario. The results showed that, in scenarios with large amounts of messages being transmitted in short periods of time, it is possible to reduce latency delays and improve the use of processing resources, maintaining good levels of availability and reliability for the application, when compared to solutions available in only one layer Mestrado Ciência da Computação Mestre em Ciência da Computação CNPQ 465446 / 2014-0 CAPES 001 FAPESP 2014 / 50937-1; 2015 / 24485-9

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    https://doi.org/10.47749/t/uni...
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    Authors: Cleveston, Iury, 1994-;

    Orientador: Esther Luna Colombini Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação Resumo: Construir veículos capazes de operar sem supervisão humana é um grande desafio, exigindo percepção e compreensão adequadas do mundo. Neste sentido, determinar a posição espacial do veículo é fundamental. Algoritmos de Odometria Visual (VO) estimam a postura do agente usando apenas alterações visuais nas imagens de entrada. Os métodos de odometria visual mais recentes implementam técnicas de deep learning usando redes neurais convolucionais (CNN) extensivamente, o que adiciona um custo substancial ao lidar com imagens de alta resolução. Em tarefas de VO, mais dados de entrada não significa uma melhor previsão; pelo contrário, a rede pode ter que aprender a filtrar informações desnecessárias. Portanto, a implementação de arquiteturas computacionalmente leves e eficientes despertou o interesse em abordar o problema a partir de uma nova perspectiva. Neste contexto, o Modelo Recorrente Atencional (RAM) surge como uma nova arquitetura, que implementa o conceito de atenção através da seleção de partes essenciais da informação usando aprendizado por reforço (RL). No entanto, o RAM foi introduzido principalmente como prova de conceito para tarefas de classificação no conjunto de dados MNIST. Neste trabalho, propomos o RAM-VO, que é a extensão do RAM para tarefas de regressão e odometria visual. A nova arquitetura modifica a arquitetura base e melhora a representação visual e temporal das informações, incluindo o fluxo ótico como informação contextual para inicialização do agente de RL. Além disso, o RAM-VO implementa o algoritmo Proximal Policy Optimization (PPO) no lugar do algoritmo REINFORCE, o que garante o aprendizado de uma política mais robusta. Os resultados indicam que o RAM-VO pode realizar regressões com seis graus de liberdade a partir de imagens de entrada monoculares usando aproximadamente 3 milhões de parâmetros. Além disso, experimentos no conjunto de dados KITTI demonstram que o RAM-VO alcança resultados competitivos utilizando apenas 5.7% da informação visual disponível. Abstract: Building vehicles capable of operating without human supervision is challenging, requiring a proper perception and understanding of the world. Mainly, determining the vehicle's pose is fundamental. Visual Odometry (VO) algorithms estimate the agent's egomotion using only visual changes from the input images. The most recent visual odometry methods implement deep-learning techniques using convolutional neural networks (CNN) extensively, which add a substantial cost when dealing with high-resolution images. In VO tasks, more input data does not mean a better prediction; on the contrary, the network may have to learn how to filter out useless information. Therefore, the implementation of computationally efficient and lightweight architectures has sparked an interest in approaching the problem from a new perspective. In this context, the Recurrent Attention Model (RAM) has emerged as a novel architecture, which implements the concept of attention by incrementally selecting the essential pieces of information using reinforcement learning. However, RAM was introduced mainly as a concept proof for classification tasks on the MNIST dataset. In this work, we propose the RAM-VO, which is the RAM's extension to regression and visual odometry tasks. Our novel model modifies the basic RAM architecture and improves the visual and temporal representation of information, including the optical flow as contextual information for initializing the RL agent. Also, RAM-VO implements the Proximal Policy Optimization (PPO) algorithm in place of the REINFORCE algorithm, which guarantees the learning of a robust policy. The results indicate that RAM-VO can perform regressions with six degrees of freedom from monocular input images using approximately 3 million parameters. In addition, experiments on the KITTI dataset demonstrate that RAM-VO achieves competitive results using only 5.7% of the available visual information. Mestrado Ciência da Computação Mestre em Ciência da Computação CNPQ 130834/2019-0

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    Authors: Paulo, Rodrigo Leme de, 1991-;

    Orientador: Daniel Albiero Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola Resumo: O monitoramento do estresse hídrico é uma etapa importante da Irrigação de Precisão (IP) e a identificação desse estresse usa técnicas promissoras para o desenvolvimento de sistemas inteligentes de irrigação, racionalizando o uso dos recursos hídricos. O estresse hídrico provoca o fechamento estomatal das plantas e com isso as trocas gasosas são reduzidas fazendo com que a temperatura da planta, geralmente mais baixa, aumente em relação ao ambiente. Uma das maneiras de detectar esse estresse é através da temperatura de dossel, que pode ser medida pela radiação infravermelho emitida pela planta durante a transpiração. O Crop Water Stress Index (CWSI), índice quantitativo que utiliza essa diferença entre temperaturas e os limites máximos e mínimos de temperatura em condições de total estresse ou quando a planta não está sob déficit hídrico. Com o objetivo de detectar o estresse hídrico na cultura da rúcula, o presente trabalho busca cruzar informações de sensores infravermelho (IR), que fazem leituras de temperatura numa varredura por toda a área de testes da cultura, com o processamento de imagens RGB, que identifica e segmenta apenas as folhas da planta, gerando um mapa de temperaturas foliares das plantas, um dos parâmetros necessários para o cálculo do CWSI. A altura do conjunto foi verificada através de uma pré-varredura do sensor Ultrassom. Os mapas gerados foram validados através de uma câmera térmica, apresentando erro médio de aproximadamente 0,2ºC. A parametrização da equação Non Water-Stressed Baseline (NWSB) foi realizada no mesmo microclima em que o experimento, gerando os coeficientes angular e linear iguais a -0,782 e 0,102, respectivamente, fazendo com que os limites máximos e mínimos de temperatura ficassem ligeiramente deslocados da faixa real de temperaturas da planta. Os mapas de estresse gerados apresentaram diferentes valores de CWSI para cada tratamento, mantidos sob diferentes condições hídricas ao longo de todo o ciclo da cultura e o sistema proposto foi capaz de detectar estresse hídrico causado pela disponibilidade de água para a planta, possibilitando a definição do manejo de irrigação pelo CWSI Abstract: Water stress monitoring it is an important step of Precision Irrigation (IP) and its identification uses promising techniques for smart irrigation systems development, rationalizing the use of water resources. Water stress causes the plants stomatal closures, so gas exchanges are reduced, causing the plant's temperature, generally lower, to increase in relation to the environment. It is possible to identify water stress by canopy temperature, which can be measured by infrared radiation emitted by the plant during transpiration. The Crop Water Stress Index (CWSI), a quantitative index that uses this difference between temperatures and the maximum and minimum temperature limits under of total stress conditions or when the plant is not under water deficit. To detect water stress in Arugula (Eruca sativa Miller), this work seeks to cross-reference information from infrared sensors, which take temperature readings in a scan across the entire crop experimental area, with RGB image processing, which identifies and segments only the leaves of the plant, generating a leaf temperatures map, one of the parameters necessary for the calculation of the CWSI. The system height was verified trough a pre-scan by the ultrasonic sensor. The maps were validated using a thermal camera, with an average error of approximately 0.2ºC. The parameterization of the Non Water-Stressed Baseline (NWSB) equation was performed in the same microclimate as the experiment, resulting in angular and linear coefficients equal to -0.782 and 0.102, respectively, causing the maximum and minimum temperature limits to be slightly displaced the actual temperature range of the plant. The stress maps showed different CWSI values for each treatment, maintained under different water conditions throughout the entire crop cycle and the proposed system was able to detect water stress caused by the availability of water for the plant, enabling the irrigation scheduling by CWSI method Mestrado Máquinas Agrícolas Mestre em Engenharia Agrícola CAPES 88882.434667/2019-01

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      https://doi.org/10.47749/t/uni...
      Thesis . 2023 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dadiala, Rhea;

    Aging infrastructure, along with the escalating occurrence and severity of extreme weather events linked to climate change, and the growing demands of an increasing population, have placed significant strains on wastewater and stormwater systems. Consequently, there has been a rise in instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs), among other related problems. These challenges have intensified the impact of sewershed issues on both society and the environment. Fortunately, recent advancements in technology, such as sophisticated sensor technologies, more powerful processors, and advanced mathematical modeling techniques, have opened up new possibilities for developing intelligent water systems in the United States that are capable of making well-informed, data driven decisions. While the technological capabilities of these tools are advancing, their application in the water sector is limited and often siloed. Water utilities face a variety of challenges related to digitalization of sewershed management and require a more structured approach for their digital transformation. This research aims to present a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) that will serve as a blueprint to facilitate the implementation of Intelligent Sewersheds for water utilities across the country. The proposed framework will focus on enhancing our understanding of various aspects, including system of systems thinking, data management, modeling techniques, decision-making processes, and service delivery, in order to adopt a more intelligent and efficient approach to managing sewersheds. This framework was piloted with small, medium and large scale utilities to capture feedback on the proposed building blocks from a real world perspective and the findings from these interviews reveal that most utilities are in the preliminary stages of intelligent water systems implementation, and is more common among large utilities as compared to their small and medium counterparts. Master of Science Water infrastructure in the United States is facing several challenges like the deterioration of infrastructure with time, increasing extreme weather events like flooding due to climate change, and growing population demands. These issues are putting a lot of pressure on wastewater and stormwater systems, leading to more instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs) and other related problems that have significant negative impacts on both the society and the natural environment. Fortunately, recent advancements in technology like remote sensing, internet of things, increased computing power and advanced data analysis tools like artificial intelligence and machine learning, have provided opportunities for water utilities to improve their sewershed management practices. This research introduces a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) to help implement the new technologies and practices available for water utilities nationwide to improve the efficiency and reliability of sewershed management. The framework focuses on improving our understanding of different aspects like how a sewershed and its components are defined, collecting and managing data parameters, modeling techniques, decision-making, and service delivery. The framework was piloted with small, medium, and large scale utilities to get real-world feedback. The findings showed that most utilities are in the early stages of adopting intelligent water systems, and larger utilities are more likely to use these technologies compared to smaller ones.

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    Authors: Khabbazan, S. (author);

    Agriculture plays a critical role in the economy and environment worldwide, and the provision of real-time, reliable information on large-scale agricultural activity is essential for precision agriculture and global economic prosperity. In this context, remote sensing, especially through Synthetic Aperture Radar (SAR), can play an important role by offering accurate estimation of crop biophysical parameters such as Leaf Area Index (LAI), crop height, dry biomass, and Vegetation Water Content (VWC). Unlike traditional high-resolution optical imagery, which is often undermined by cloud cover, SAR microwave remote sensing overcomes these limitations by generating and transmitting longer wavelengths (300MHz – 10 GHz) that penetrate clouds and aerosols and allowing data acquisition both day and night. However, SAR data is influenced by various factors such as sensor characteristics such as frequency, polarization, and incidence angle, as well as target characteristics like the size and shape distribution of crop constituents, and more importantly, the water content of crop constituents. In the path towards precision farming and more sustainable and efficient farming using SAR data, understanding the role of these factors, particularly the dynamics of external and internal vegetation water content on radar backscatter, is vital.To date, however, the potentially confounding effects of both internal and, particularly, external water dynamics in vegetation on radar backscatter have not been adequately addressed. Existing studies have indeed illustrated the effects of SCW on radar backscatter, but the degree to which it influences different frequencies and polarizations, and the subsequent impact on crop bio-geophysical parameters remains unclear. Therefore, the main goal of this thesis is to expand our knowledge of the relationship between radar backscatter, vegetation dynamics, and surface canopy water (SCW) in agricultural monitoring. In this thesis we utilized statistical analysis and radiative transfer modeling in combination with fully polarimetric L-band data from a truck-mounted scatterometer and C-band data from Sentinel-1, along with extensive field data… Mathematical Geodesy and Positioning

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    TU Delft Repository
    Doctoral thesis . 2024
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      TU Delft Repository
      Doctoral thesis . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rosén, Julia;

    We live in a complex world where we proactively plan and execute various behaviors by forming expectations in real time. Expectations are beliefs regarding the future state of affairs and they play an integral part of our perception, attention, and behavior. Over time, our expectations become more accurate as we interact with the world and others around us. People interact socially with other people by inferring others' purposes, intentions, preferences, beliefs, emotions, thoughts, and goals. Similar inferences may occur when we interact with social robots. With anthropomorphic design, these robots are designed to mimic people physically and behaviorally. As a result, users predominantly infer agency in social robots, often leading to mismatched expectations of the robots' capabilities, which ultimately influences the user experience. In this thesis, the role and relevance of users' expectations in first-hand social human-robot interaction (sHRI) was investigated. There are two major findings. First, in order to study expectations in sHRI, the social robot expectation gap evaluation framework was developed. This framework supports the systematic study and evaluation of expectations over time, considering the unique context where the interaction is unfolding. Use of the framework can inform sHRI researchers and designers on how to manage users’ expectations, not only in the design, but also during evaluation and presentation of social robots. Expectations can be managed by identifying what kinds of expectations users have and aligning these through design and dissemination which ultimately creates more transparent and successful interactions and collaborations. The framework is a tool for achieving this goal. Second, results show that previous experience has a strong impact on users’ expectations. People have different expectations of social robots and view social robots as both human-like and as machines. Expectations of social robots can vary according to the source of the expectation, with those who had previous direct experiences of robots having different expectations than those who relied on indirect experiences to generate expectations. One consequence of these results is that expectations can be a confounding variable in sHRI research. Previous experience with social robots can prime users in future interactions with social robots. These findings highlight the unique experiences users have, even when faced with the same robot. Users' expectations and how they change over time shapes the users’ individual needs and preferences and should therefore be considered in the interpretation of sHRI. In doing so, the social robot expectation gap can be reduced. Vi lever i en komplex värld och för att kunna hantera denna komplexitet formar vi förväntningar. Förväntningar är antaganden om framtida tillstånd och är en vital del av vår perception, uppmärksamhet och beteende. Genom att interagera med omvärlden och andra människor blir våra förväntningar mer precisa och korrekta över tid. I en social interaktion behöver vi förstå den andra personens syften, avsikter, preferenser, övertygelser, känslor, tankar och mål. Sociala robotar är utformade för att skapa liknande inferenser när användare interagerar med dem. Detta kan leda till missbedömningar mellan vad vi förväntar oss av sociala robotar och vad dessa artefakter är kapabla till, vilket påverkar användarupplevelsen av sociala robotar. I den här avhandlingen presenteras den forskning som har utförts för att studera rollen och relevansen av människors förväntningar i social människa-robotinteraktion (sMRI). Resultaten kan delas in i två större fynd. Det första fyndet är ett utvärderingsramverk som ämnar att systematiskt studera användares förväntningar av sociala robotar i en interaktion, med fokus på hur förväntningar ändras över tid i en interaktion, med interaktionens unika kontext i åtanke. Ramverket är menat för designers av sociala robotar och forskare inom sMRI-fältet för att bättre studera, hantera, och förstå förväntningar, både i robotarnas design och i robotarnas agerande. Det andra fyndet består av de empiriska resultat som visar hur tidigare erfarenheter påverkar användares förväntningar. Förväntningarna baseras till stor del på vilka typer av tidigare erfarenheter användare har, där de med direkta erfarenheter av robotar har andra förväntningar än de med indirekta erfarenheter. Vidare visar resultaten att användare ser sociala robotar både som människolika och som maskiner samtidigt. Förväntningar kan också ses som en bakomliggande variabel inom sMRI-forskning eftersom tidigare erfarenheter kan påverka deltagare i kommande interaktioner med sociala robotar. Resultaten visar även att användarupplevelsen är unik för varje användare, även om roboten är densamma, vilket bör tas i åtanke när resultat tolkas i en sMRI-kontext. Genom att ha förväntningar i åtanke kan vi minska det gap som uppstår mellan människors förväntningar av sociala robotar och robotarnas faktiska förmågor. På så sätt kan vi främja positiva användarupplevelser och förbättra interaktionen mellan människa och robot. Två av sju delarbeten (övriga se rubriken Delarbeten/List of papers):VI Rosén, Julia, Lindblom, Jessica, Lamb, Maurice, and Billing, Erik (Under review). “Previous Experience Matters: An In-Person Investigation of Expectations in Human-Robot Interaction”. In: Under review for scientific journal, pp. 1–19.VII Lindblom, Jessica, Rosén, Julia, Lamb, Maurice, and Billing, Erik (Manuscript). “Disentangling People’s Experiences and Expectations when Interacting with the Social Robot Pepper: A Qualitative Analysis”. In: Manuscript for scientific journal, pp. 1–41.

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    Thesis . 2024
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    Authors: Nic R?amoinn, Susan;

    APPROVED This thesis presents a sociocultural theory (SCT) investigation of the use of robotics for second language learning (SLL) and computational thinking (CT) skills development in the early years of primary school. The research intervention combines a playful storytelling robotics-based activity with the Irish language within a play session. Jeanette Wing, revived the area of CT in (2006, p. 44) noting that CT should be added "to every child's analytical thinking." Current research indicates that technological tools have a positive impact on the development of CT skills when integrated with other curricular areas in primary school (Alimisis & Moro, 2016; Angeli & Valanides, 2020; Angeli et al., 2016; Bers, 2020; Bers et al., 2012; Hassenfeld & Bers, 2020). This research intervention contributes to the field of CT skills development as well as second language learning in the early years of primary school, through the integration of robotics and Irish, in an English medium classroom. The researcher deemed design-based research (DBR) as the most appropriate methodology to answer the research questions while using both qualitative and quantitative data collection methods. The research intervention was developed through an iterative design cycle of two one pilot cycles and a third pilot cycle over six weeks. The final iteration, cycle four, comprised of a six-week intervention using Bee-Bot (programmable floor robot) as part of a playful activity to promote second language learning with 22 children in a junior infant class (aged 5-6 years). The children worked in small groups to programme Bee-Bot to support their storytelling through Irish. The results of this research activity were positive with feedback from the children and the teacher of increased interest and use of the Irish language, observed language gains; identification and development of CT skills; and positive meaningful engagement through Irish amongst the children. The findings provide an insight into the opportunities that a technological tool provides during a playful activity with this young age group as well as outlining the potential parallels between the language learning process and CT skills development. As the Irish curriculum moves towards a more integrated model (NCCA, 2023) the analysis of the children's experiences during this research activity along with their language and CT skill development offers an interesting insight for early year's language educators and curriculum developers in Ireland.

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