Memoria de la red docente “Metodologías Docentes en asignaturas de Economía”. Curso académico 2013-2014

El objetivo de la red “Metodologías docentes en asignaturas de Economía” es investigar sobre metodologías docentes que impliquen una participación activa del estudiante, con el fin de mejorar el proceso enseñanza-aprendizaje. En este trabajo se exponen las metodologías docentes que los profesores integrantes de la red han aplicado en sus respectivas asignaturas durante el curso académico 2013-2014. Estas metodologías son el resultado de la experiencia adquirida en los años precedentes en los que han estado investigando e innovando con criterios de calidad sobre metodologías docentes en el seno de la red. Asimismo, en el trabajo se exponen los principales resultados obtenidos en las diferentes asignaturas, entre los que cabe señalar la elevada asistencia de los alumnos a las clases y el alto porcentaje de estudiantes que se presentan al examen final, resultados que contrastan con lo que ocurría en las antiguas licenciaturas. Asimismo, también se pone de manifiesto el efecto positivo que los cambios introducidos por el Plan Bolonia han tenido sobre la calificación final de los alumnos, que ha aumentado de manera significativa.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.