Tod@s iguales, tod@s diferentes.

Seleccionado en la convocatoria: Ayudas para proyectos de temática educativa, Gobierno de Aragón 2009-10

Tutorias personales 'te acompa??amos'

Seleccionado en la convocatoria: Ayudas a la innovaci??n e investigaci??n educativa en centros docentes de niveles no universitarios, Gobierno de Arag??n 2009-10

Actividades para prevenir el acoso escolar a través de las nuevas tecnologías.

Participante en la convocatoria: Premios de buenas prácticas en materia de convivencia, Gobierno de Aragón 2009-10

Proyecto habla bla bla.

Seleccionado en la convocatoria: Ayudas a la innovación e investigación educativa en centros docentes de niveles no universitarios, Gobierno de Aragón 2010-11

Protectoras virtuales de animales dirigidas por niños.

Seleccionado en la convocatoria: Ayudas a la innovación e investigación educativa en centros docentes de niveles no universitarios, Gobierno de Aragón 2010-2011

Convivenvia entre g??neros

Participante en la convocatoria: Premios de buenas pr??cticas en materia de convivencia, Gobierno de Arag??n 2011-2012

Cella en el mundo ??A qu?? te apuntas?

Seleccionado en la convocatoria: Ayudas para desarrollar proyectos de convivencia, Gobierno de Arag??n 2008-09

An??lisis psicom??trico de la escala multidimensional de autoconcepto AF5 en una muestra de adolescentes y adultos de Catalu??a

Resumen tomado de la publicaci??n

Indicadores de calidad en la docencia virtual : adaptaci??n de los entornos a la diversidad cognitiva de los estudiantes

Resumen tomado de la publicaci??n

Un nuevo modelo de aprendizaje basado en problemas, el ABP 4x4, es eficaz para desarrollar competencias profesionales valiosas en asignaturas con m??s de 100 alumnos

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Evaluating the probability and the impact of a failure in GMPLS based networks

In this paper, we define a new scheme to develop and evaluate protection strategies for building reliable GMPLS networks. This is based on what we have called the network protection degree (NPD). The NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability, and an a posteriori evaluation, the failure impact degree (FID), which determines the impact on the network in case of failure, in terms of packet loss and recovery time. Having mathematical formulated these components, experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms in order to offer a certain degree of protection

Enhancing MPLS QoS routing algorithms by using the network protection degree paradigm

IP based networks still do not have the required degree of reliability required by new multimedia services, achieving such reliability will be crucial in the success or failure of the new Internet generation. Most of existing schemes for QoS routing do not take into consideration parameters concerning the quality of the protection, such as packet loss or restoration time. In this paper, we define a new paradigm to develop new protection strategies for building reliable MPLS networks, based on what we have called the network protection degree (NPD). This NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability and an a posteriori evaluation, the failure impact degree (FID), to determine the impact on the network in case of failure. Having mathematical formulated these components, we point out the most relevant components. Experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms to offer a certain degree of protection

M??todos de escalamiento aplicados a la priorizaci??n de necesidades de formaci??n en organizaciones

Resumen tomado de la publicaci??n

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation