HidroSIG: un sistema de información geográfica para la gestión de recursos naturales y modelación del medio ambiente

This paper describes HidroGIS, a GIS platform developed by Water Resources Program at Universidad Nacional de Colombia at Medellín. HidroSIG is a tool for hydrological variables visualization and analysis, using a set of modules that make this software a powerful tool for hydrological modeling. HidroSIG has tools for digital terrain models processing, water supply estimation using long term water balance in watersheds, a rainfall-runoff model, a model for landslide susceptibility estimation, an one-dimensional pollutant transport model, tools for homogeneity analysis in time series and tools for satellite images classification. The tools in development status are also described

Attention training in Attention Deficit Hyperactivity Disorder

Resumen tomado de la publicaci??n

Lliçó inaugural: "Girona, universitat europea"

En l'acte d'inauguració del curs acadèmic de la Universitat de Girona 2008-2009, el senador i president de l'Assemblea del Consell d'Europa, Lluís Maria de Puig, parla de la construcció europea i del desig que la Universitat de Girona s'insereixi plenament en aquest procés. Desenvolupa aquesta idea a partir de tres aspectes: el paper de la universitat avui, el procés de Bolonya, i la Universitat de Girona i Europa

Caracterització i diagnosi de masses en imatges de mamografia

Els objectius del projecte es divideixen en tres blocs: Primerament, realitzar una segmentació automàtica del contorn d'una imatge on hi ha una massa central. Tot seguit, a partir del contorn trobat, caracteritzar la massa. I finalment, utilitzant les característiques anteriors classificar la massa en benigne o maligne. En el projecte s'utilitza el Matlab com a eina de programació. Concretament les funcions enfocades al processat de imatges del toolbox de Image processing (propi de Matlab) i els classificadors de la PRTools de la Delft University of Technology

Immersió lingüística, cultural i acadèmica per a estudiants de Telecomunicació

L’Escola Tècnica Superior d’Enginyers de Telecomunicació (ETSET) de la Universitat Politècnica de València (UPV) promou la mobilitat dels seus estudiants i professors, no només a través de programes nacionals –SICUE-Sèneca– o internacionals (ERASMUS, PROMOE), sinó també per la realització de viatges de pràctiques de llengua estrangera. D’aquesta manera, els estudiants matriculats en les assignatures de francès, anglès o alemany poden beneficiar- se d’una estada a França, Gran Bretanya o Alemanya, visitant-ne les universitats i empreses del sector. En aquesta comunicació ens remetem a l’últim viatge realitzat a París al març passat, durant el qual quinze estudiants de l’ETSET, matriculats en els cinc grups de les assignatures de francès (nivell bàsic, intermedi i avançat), van visitar una Grande École, l’ENSEA (École Nationale Supérieure de l’Électronique et de ses Applications). La visita, coordinada i programada entre els professors responsables d’ambdues escoles, va consistir a efectuar presentacions institucionals (escola, universitat d’origen) i generals (informació sobre la ciutat i la seua cultura) per part dels mateixos estudiants espanyols i francesos en els diferents grups de les assignatures d’espanyol. Per a fer-ho, cada grup havia preparat la presentació en PowerPoint de la seua escola o ciutat en la llengua d’estudi. Aquest tipus d’activitats facilita la immersió lingüística, cultural i acadèmica dels estudiants, de manera que els permet rebre informació de primera mà sobre el panorama dels seus estudis en un altre país, i alhora patrocinen la seua pròpia escola entre els estudiants estrangers

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Las competencias gen??ricas en la educaci??n superior : estudio comparativo entre la opini??n de empleadores y acad??micos

Resumen tomado de la publicaci??n

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

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

La discapacidad en la literatura.

Las creencias que se han mantenido en nuestro entorno cultural sobre las personas con discapacidad han contribuido a construir una identidad ambigua de la discapacidad tanto desde el mundo real como desde el ficticio de la literatura. En la literatura, el modo en que tradicionalmente se ha abordado la discapacidad, la caracterización de los personajes con alguna discapacidad, ha reforzado los prejuicios, provocando hacia estos personajes, en los lectores, sentimientos de rechazo o burla, o bien, de sobreprotección y de piedad o un rancio sentimentalismo. Se reflexiona sobre la necesidad de un cambio a la hora de abordar la discapacidad, una nueva sensibilidad que trate y la represente teniendo en cuenta los contextos culturales en los que vive la persona discapacitada. Se cita la obra del neurólogo Oliver Sacks como ejemplo de este nuevo enfoque que tendría que incorporarse a la literatura sobre la discapacidad; una visión que nos permita reconocer que con las personas discapacitadas son más las semejanzas que las diferencias lo que compartimos como seres humanos .

Aproximació pedagògica a la cultura romana de les termes. 'Aproximación pedagógica a la cultura romana de las termas'.

Las Termas son uno de los aspectos que más caracterizan la civilización romana. El baño en termas podía ser una ocupación fija de un romano durante todo el dia, donde encontraban lo necesario para el culto al cuerpo y el espíritu. Se hace una aproximación pedagógica a esta civilización.

Aprendre art : sensibilitzar cap al respecte i la valoració del patrimoni històric de la ciutat d'Inca.'Aprender arte : sensibilizar hacia el respeto y la valoración del patrimonio histórico de la ciudad de Inca'.

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