PFC - Competencias Profesionales - Memoria

Análisis de experiencias de enseñanza-aprendizaje de la competencia comunicativaescrita a nivel de Ingeniería Informática.

Interhemispheric coupling between the posterior sylvian regions impacts successful auditory temporal order judgment

Introduction: Accurate registration of the relative timing between the occurrence of sensory events on a sub-second time scale is crucial for both sensory-motor and cognitive functions (Mauk and Buonomano, 2004; Habib, 2000). Support for this assumption comes notably from evidence that temporal processing impairments are implicated in a range of neurological and psychiatric conditions (e.g. Buhusi & Meck, 2005). For instance, deficits in fast auditory temporal integration have been regularly put forward as resulting in phonologic discrimination impairments at the basis of speech comprehension deficits characterizing e.g. dyslexia (Habib, 2000). At least two aspects of the brain mechanisms of temporal order judgment remain unknown. First, it is unknown when during the course of stimulus processing a temporal ,,stamp‟ is established to guide TOJ perception. Second, the extent of interplay between the cerebral hemispheres in engendering accurate TOJ performance is unresolved Methods: We investigated the spatiotemporal brain dynamics of auditory temporal order judgment (aTOJ) using electrical neuroimaging analyses of auditory evoked potentials (AEPs) recorded while participants completed a near-threshold task requiring spatial discrimination of left-right and right-left sound sequences. Results: AEPs to sound pairs modulated topographically as a function of aTOJ accuracy over the 39-77ms post-stimulus period, indicating the engagement of distinct configurations of brain networks during early auditory processing stages. Source estimations revealed that accurate and inaccurate performance were linked to bilateral posterior sylvian regions activity (PSR). However, activity within left, but not right, PSR predicted behavioral performance suggesting that left PSR activity during early encoding phases of pairs of auditory spatial stimuli appears critical for the perception of their order of occurrence. Correlation analyses of source estimations further revealed that activity between left and right PSR was significantly correlated in the inaccurate but not accurate condition, indicating that aTOJ accuracy depends on the functional de-coupling between homotopic PSR areas. Conclusions: These results support a model of temporal order processing wherein behaviorally relevant temporal information - i.e. a temporal 'stamp'- is extracted within the early stages of cortical processes within left PSR but critically modulated by inputs from right PSR. We discuss our results with regard to current models of temporal of temporal order processing, namely gating and latency mechanisms.

Uso de tecnologías abiertas (Processing) en el desarrollo de interfaces avanzadas de visualización y acceso de datos : Estudio del caso Organic.Edunet

El artículo muestra, a través del estudio del caso del buscador semántico del portal Organic.Edunet, cómo el uso de tecnologías cerradas en la creación de interfaces avanzadas de visualización de datos impide su desarrollo y evolución. En el artículo se mostrará también cómo, combinado con técnicas para la medición y valoración de la usabilidad de las aplicaciones, el uso de tecnologías abiertas permite detectar los problemas del interface, proponer soluciones o alternativas, e implementarlas rápidamente.

GESDEPIN : Gestión del Departamento de Informática

Projecte per a la Gestió del Departament d'Informàtica en la web de l'Institut d'Educació Secundària Eduardo Merello de Port de Sagunt. L'objectiu últim del projecte és gestionar tota la informació relacionada amb el departament d'Informàtica, desenvolupant un eina senzilla que, integrada en la web del Centre, resulti còmoda, pràctica i útl, tant per als professors com per als alumnes.

Actualització del sistema informàtic de l'escola Da Vinci School

Projecte d'actualització del sistema informàtic d'un centre escolar.

Biomedical nanoparticles modulate specific CD4(+) T cell stimulation by inhibition of antigen processing in dendritic cells

Understanding how nanoparticles may affect immune responses is an essential prerequisite to developing novel clinical applications. To investigate nanoparticle-dependent outcomes on immune responses, dendritic cells (DCs) were treated with model biomedical poly(vinylalcohol)-coated super-paramagnetic iron oxide nanoparticles (PVA-SPIONs). PVA-SPIONs uptake by human monocyte-derived DCs (MDDCs) was analyzed by flow cytometry (FACS) and advanced imaging techniques. Viability, activation, function, and stimulatory capacity of MDDCs were assessed by FACS and an in vitro CD4(+) T cell assay. PVA-SPION uptake was dose-dependent, decreased by lipopolysaccharide (LPS)-induced MDDC maturation at higher particle concentrations, and was inhibited by cytochalasin D pre-treatment. PVA-SPIONs did not alter surface marker expression (CD80, CD83, CD86, myeloid/plasmacytoid DC markers) or antigen-uptake, but decreased the capacity of MDDCs to process antigen, stimulate CD4(+) T cells, and induce cytokines. The decreased antigen processing and CD4(+) T cell stimulation capability of MDDCs following PVA-SPION treatment suggests that MDDCs may revert to a more functionally immature state following particle exposure.

Colour image processing

In the context of the round table the following topics related to image colour processing will be discussed: historical point of view. Studies of Aguilonius, Gerritsen, Newton and Maxwell. CIE standard (Commission International de lpsilaEclaraige). Colour models. RGB, HIS, etc. Colour segmentation based on HSI model. Industrial applications. Summary and discussion. At the end, video images showing the robustness of colour in front of B/W images will be presented

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

On the way to solve lighting problems in underwater imaging

A major obstacle to processing images of the ocean floor comes from the absorption and scattering effects of the light in the aquatic environment. Due to the absorption of the natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion, and, as the vehicle moves, induce shadows in the scene. For this reason, the first step towards application of standard computer vision techniques to underwater imaging requires dealing first with these lighting problems. This paper analyses and compares existing methodologies to deal with low-contrast, nonuniform illumination in underwater image sequences. The reviewed techniques include: (i) study of the illumination-reflectance model, (ii) local histogram equalization, (iii) homomorphic filtering, and, (iv) subtraction of the illumination field. Several experiments on real data have been conducted to compare the different approaches

Detection of matchings in a sequence of underwater images through texture analysis

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach