11 resultados para Garcia, Diego-Pleitos de hidalguia

em Universidad de Alicante


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Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.

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Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

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Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

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Background: To develop and validate an item bank to measure mobility in older people in primary care and to analyse differential item functioning (DIF) and differential bundle functioning (DBF) by sex. Methods: A pool of 48 mobility items was administered by interview to 593 older people attending primary health care practices. The pool contained four domains based on the International Classification of Functioning: changing and maintaining body position, carrying, lifting and pushing, walking and going up and down stairs. Results: The Late Life Mobility item bank consisted of 35 items, and measured with a reliability of 0.90 or more across the full spectrum of mobility, except at the higher end of better functioning. No evidence was found of non-uniform DIF but uniform DIF was observed, mainly for items in the changing and maintaining body position and carrying, lifting and pushing domains. The walking domain did not display DBF, but the other three domains did, principally the carrying, lifting and pushing items. Conclusions: During the design and validation of an item bank to measure mobility in older people, we found that strength (carrying, lifting and pushing) items formed a secondary dimension that produced DBF. More research is needed to determine how best to include strength items in a mobility measure, or whether it would be more appropriate to design separate measures for each construct.

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El presente estudio muestra los resultados de la adaptación de las escalas de actitudes hacia el tabaco (López-Sánchez, Garcia-Rodriguez, Mira y Estévez, 2000), el alcohol (Garcia-Rodriguez y López-Sánchez, 2001) y otras drogas (López-Sánchez et al., 2000) en población portuguesa. Se trata de tres escalas, cada una con trece ítems, de respuesta tipo Likert con cinco opciones de respuesta, que diferencian tres sub-escalas. Para realizar el estudio de adaptación de las escalas a la población portuguesa, se han utilizado dos muestras independientes, constituidas por 329 y 443 adolescentes, alumnos de bachillerato del norte del país, a quienes se administraron los instrumentos. Los datos recogidos, después de ser sometidos a un análisis factorial exploratorio y confirmatorio, permitieron encontrar una solución adecuada para cada una de las escalas, dando como resultando una buena validez y fiabilidad. En la validez concurrente, los resultados sugieren diferencias de género con todas las sub-escalas y una relación negativa con la edad, así como diferencias en las actitudes de acuerdo con la experiencia y el consumo.

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Pd and bimetallic Ni50Pd50 nanoparticles protected by polyvinylpyrrolidone (PVP) have been synthesized by the reduction-by-solvent method and deposited on single wall carbon nanotubes (SWCNTs) to be tested as H2 sensors. The SWCNTs were deposited by drop casting from different suspensions. The Pd nanoparticles-based sensors show a very reproducible performance with good sensitivity and very low response times (few seconds) for different H2 concentrations, ranging from 0.2% to 5% vol. H2 in air at atmospheric pressure. The influence of the metal nanoparticle composition, the quality of SWCNTs suspension and the metal loading have been studied, observing that all these parameters play an important role in the H2 sensor performance. Evidence for water formation during the H2 detection on Pd nanoparticles has been found, and its repercussion on the behaviour of the assembled sensors is discussed. The sensor preparation procedure detailed in this work has proven to be simple and reproducible to prepare cost-effective and highly efficient H2 sensors that perform very well under real application conditions.

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The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

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En esta memoria se describe el proyecto llevado para establecer los mecanismos y procedimientos para el seguimiento y control de calidad de las asignaturas del segundo curso del Grado en Ingeniería Multimedia, curso 2013/2014. En concreto, los mecanismos de control se centran en la planificación de las sesiones docentes y las actividades de evaluación llevadas a cabo en cada una de dichas asignaturas.

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The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods.

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En el curso docente 2010-2011 se inició la implantación del grado en Ingeniería Multimedia, título próximo a la Ingeniería Informática, pero enfocada a formar a profesionales capaces de gestionar proyectos Multimedia tanto en el ámbito del ocio como en el de la gestión de contenidos en redes de información. Esta implantación ha sido progresiva, de manera que cada año se iniciaba un curso nuevo de esta titulación, motivo por el cual este año, 2014-2015, es el primer año en el que el título está completamente implantado desde el inicio del curso. Esto nos ha llevado a plantearnos realizar un estudio sobre como están interconectadas las asignaturas en los distintos cursos. Este estudio ha tenido como objetivo averiguar los problemas o carencias de conocimientos que, por un lado tienen los alumnos en 2º curso, y por otro los que se pueden encontrar en 3º, así como establecer las posibles vías de solución a estos problemas, con la finalidad de mejorar el rendimiento en el aprendizaje de los alumnos. También se ha realizado un seguimiento sobre la evaluación de los alumnos realizada en las asignaturas de 2º para contrastar su adecuación al sistema de evaluación continua promovido por el Plan Bolonia.

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Olga Diego Freises (Alacant 1969) estudià Belles Arts a les facultats de València (Universitat Politècnica) i d’Altea (Universitat Miguel Hernández), on es titulà el 2006. La seua activitat artística ―crítica, compromesa i innovadora― s’ha orientat bàsicament a la performança, encara que amb un important suport gràfic i escultòric de dibuixos i de maquetes. Com es habitual, Olga Diego fa servir el vídeo com a medi de registre i/o de suport de les accions. En ocasions el vídeo arriba a ser la base o el format de presentació d’una acció. És, doncs, un registre fotogràfic ―foto mòbil que pot esdevenir foto fixa― allò que conserva la memòria del que es féu o del que passà. En ocasions la gravació és manual i en ocasions la càmera va lligada a un artefacte. S’ofereixen, així, visions diferents de l’acció. En ocasions es treballa amb llum de dia i en ocasions amb llum de foc o de focus, oferint també així visions diferents de l’acció. Per tant, el vídeo es treballa en funció del suggeriment, de la claredat expositiva o de l’ambigüitat que s’intenta. Proposem aquí una introducció als vídeos vinculats a algunes de les accions d’Olga Diego desenvolupades a llocs tan diferents com ara Espanya, Sàhara Occidental Libre i Egipte.