85 resultados para Visual identification tasks
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6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.
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27th Euromicro Conference on Real-Time Systems (ECRTS 2015), Lund, Sweden.
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11th IEEE World Conference on Factory Communication Systems (WFCS 2015). 27 to 29, May, 2015, TII-SS-2: Scheduling and Performance Analysis. Palma de Mallorca, Spain.
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International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015). 7 to 9, Apr, 2015. Singapure, Singapore.
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Distributed real-time systems such as automotive applications are becoming larger and more complex, thus, requiring the use of more powerful hardware and software architectures. Furthermore, those distributed applications commonly have stringent real-time constraints. This implies that such applications would gain in flexibility if they were parallelized and distributed over the system. In this paper, we consider the problem of allocating fixed-priority fork-join Parallel/Distributed real-time tasks onto distributed multi-core nodes connected through a Flexible Time Triggered Switched Ethernet network. We analyze the system requirements and present a set of formulations based on a constraint programming approach. Constraint programming allows us to express the relations between variables in the form of constraints. Our approach is guaranteed to find a feasible solution, if one exists, in contrast to other approaches based on heuristics. Furthermore, approaches based on constraint programming have shown to obtain solutions for these type of formulations in reasonable time.
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This work presents a hybrid coordinated manoeuvre for docking an autonomous surface vehicle with an autonomous underwater vehicle. The control manoeuvre uses visual information to estimate the AUV relative position and attitude in relation to the ASV and steers the ASV in order to dock with the AUV. The AUV is assumed to be at surface with only a small fraction of its volume visible. The system implemented in the autonomous surface vehicle ROAZ, developed by LSA-ISEP to perform missions in river environment, test autonomous AUV docking capabilities and multiple AUV/ASV coordinated missions is presented. Information from a low cost embedded robotics vision system (LSAVision), along with inertial navigation sensors is fused in an extended Kalman filter and used to determine AUV relative position and orientation to the surface vehicle The real time vision processing system is described and results are presented in operational scenario.
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Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.
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Aprender a ler é um dos maiores desafios que as crianças enfrentam quando entram para a escola. A dificuldade no domínio do código alfabético, nos níveis da consciência fonológica e a falta de fluência na leitura são fatores que interferem em larga escala na aprendizagem global dos alunos. Habilitar um aluno para a prática da leitura é um estímulo que tem vindo a dar origem a várias investigações e intervenções no campo da educação. Este projeto descreve dois programas de treino: “Programa de treino da percepção Visual” e “Programa de promoção do desenvolvimento da consciência fonológica”, num aluno do 2º ciclo do ensino básico com dificuldade de fluência na leitura, ao longo de quinze aulas de 90 minutos. No que respeita aos resultados do primeiro estudo, que teve por base o “Programa de treino da percepção visual”, não foram encontradas diferenças relevantes quanto ao seu efeito na fluência da leitura do aluno. No entanto, no segundo estudo, que se centrou na aplicação do “Programa de promoção do desenvolvimento da consciência fonológica” em complemento com o “Programa de treino da percepção visual”, mostrou que o aluno ficou mais fluente na leitura diminuindo o número de erros de precisão (substituições, omissões, inversões, adições e erros complexos). Assim, sugere-se uma monotorização sistemática das aprendizagens dos alunos para que as intervenções possam ser cada vez mais precoces e direcionadas para as suas necessidades.
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13th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2015). 21 to 23, Oct, 2015, Session W1-A: Multiprocessing and Multicore Architectures. Porto, Portugal.
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Paper/Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.
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Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.
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The underground scenarios are one of the most challenging environments for accurate and precise 3d mapping where hostile conditions like absence of Global Positioning Systems, extreme lighting variations and geometrically smooth surfaces may be expected. So far, the state-of-the-art methods in underground modelling remain restricted to environments in which pronounced geometric features are abundant. This limitation is a consequence of the scan matching algorithms used to solve the localization and registration problems. This paper contributes to the expansion of the modelling capabilities to structures characterized by uniform geometry and smooth surfaces, as is the case of road and train tunnels. To achieve that, we combine some state of the art techniques from mobile robotics, and propose a method for 6DOF platform positioning in such scenarios, that is latter used for the environment modelling. A visual monocular Simultaneous Localization and Mapping (MonoSLAM) approach based on the Extended Kalman Filter (EKF), complemented by the introduction of inertial measurements in the prediction step, allows our system to localize himself over long distances, using exclusively sensors carried on board a mobile platform. By feeding the Extended Kalman Filter with inertial data we were able to overcome the major problem related with MonoSLAM implementations, known as scale factor ambiguity. Despite extreme lighting variations, reliable visual features were extracted through the SIFT algorithm, and inserted directly in the EKF mechanism according to the Inverse Depth Parametrization. Through the 1-Point RANSAC (Random Sample Consensus) wrong frame-to-frame feature matches were rejected. The developed method was tested based on a dataset acquired inside a road tunnel and the navigation results compared with a ground truth obtained by post-processing a high grade Inertial Navigation System and L1/L2 RTK-GPS measurements acquired outside the tunnel. Results from the localization strategy are presented and analyzed.
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13th International Conference on Autonomous Robot Systems (Robotica), 2013, Lisboa
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Proceedings of the International Conference on Computer Vision Theory and Applications, 361-365, 2013, Barcelona, Spain