969 resultados para 3D sensor


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1st ASPIC International Congress

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XXXIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2015). 15 to 19, May, 2015, III Workshop de Comunicação em Sistemas Embarcados Críticos. Vitória, Brasil.

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III Jornadas de Electroquímica e Inovação (Electroquímica e Nanomateriais), na Universidade de Trás-os-Montes e Alto Douro, Vila Real, 16 a 17 de Setembro de 2013

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Graduate Student Symposium on Molecular Imprinting 2013, na Queen’s University, Belfast, United Kingdom, 15 a 17 de Agosto de 2013

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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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IEEE International Conference on Pervasive Computing and Communications (PerCom). 23 to 26, Mar, 2015, PhD Forum. Saint Louis, U.S.A..

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Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

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4º Festival Nacional de Robótica - Actas do Encontro Científico - Proceedings of the Scientific Meeting, Biblioteca Almeida Garrett, Palácio de Cristal – Porto, 23-24 Abril 2004

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Poster in 12th European Conference on Wireless Sensor Networks (EWSN 2015). 9 to 11, Feb, 2015, pp 24-25. Porto, Portugal.

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Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN'15), 9-11 Feb 2015, Porto-Portugal.

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Poster presented in 12th European Conference on Wireless Sensor Network (EWSN 2015). 9 to 11, Feb, 2015. Porto, Portugal.

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XXXIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2015), III Workshop de Comunicação em Sistemas Embarcados Críticos. Vitória, Brasil.

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This paper analyzes the signals captured during impacts and vibrations of a mechanical manipulator. The Fourier Transform of eighteen different signals are calculated and approximated by trendlines based on a power law formula. A sensor classification scheme based on the frequency spectrum behavior is presented.

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Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with incertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.