Geolocalizador de noticias temáticas: el caso de los riesgos naturales

Las bases de datos geoespaciales temáticas en distintas escalas geográficas y temporales, son necesarias en multitud de líneas de investigación. Una de ellas es la gestión y alerta temprana de riesgos de desastres por amenazas naturales (inundaciones, huracanes, terremotos, etc.). Las noticias sobre éste tema se publican habitualmente en periódicos digitales de todo el mundo y comportan un alto contenido geográfico. Este trabajo pretende extraer automáticamente las noticias emitidas por canales de re-difusión web (conocidos por las siglas RSS en inglés) para georreferenciarlas, almacenarlas y distribuirlas como datos geoespaciales. Mediante técnicas de procesamiento de lenguaje natural y consultas a bases de datos de topónimos realizaremos la extracción de la información. El caso de estudio se aplicará para México y todos los componentes utilizados serán de código abierto

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.