A Hardware-Software Approach for On-Line Soft Error Mitigation in Interrupt-Driven Applications

Integrity assurance of configuration data has a significant impact on microcontroller-based systems reliability. This is especially true when running applications driven by events which behavior is tightly coupled to this kind of data. This work proposes a new hybrid technique that combines hardware and software resources for detecting and recovering soft-errors in system configuration data. Our approach is based on the utilization of a common built-in microcontroller resource (timer) that works jointly with a software-based technique, which is responsible to periodically refresh the configuration data. The experiments demonstrate that non-destructive single event effects can be effectively mitigated with reduced overheads. Results show an important increase in fault coverage for SEUs and SETs, about one order of magnitude.

Edición de videos y diagramas en Básquetbol

La edición de videos proporciona al entrenador un elemento interesante para seleccionar imágenes y momentos clave del juego. Aquí se muestra como los registros audiovisuales seleccionados resultan útiles para entrenadores y jugadores en un contexto de práctica o de rendimiento deportivo, y para profesores y estudiantes en un contexto de enseñanza. La educación superior y las prácticas vinculadas a la dirección de equipos en una liga de básquetbol son los escenarios elegidos para revelar cómo el entrenador convive con el uso de herramientas informáticas que le permiten relacionarse con sus jugadores, con colegas y con otros integrantes de la liga. Aquí se considera la asociación del uso de internet, editor de videos, y repositorios de documentos. Se analizan las ventajas del uso de esta conjunción de herramientas aplicadas al entrenamiento, la práctica y la enseñanza del básquetbol. Además, se explica el equipamiento necesario para el ingreso de la tecnología informática propuesta, vinculada a la práctica y enseñanza del básquetbol

Edición de videos y diagramas en Básquetbol

La edición de videos proporciona al entrenador un elemento interesante para seleccionar imágenes y momentos clave del juego. Aquí se muestra como los registros audiovisuales seleccionados resultan útiles para entrenadores y jugadores en un contexto de práctica o de rendimiento deportivo, y para profesores y estudiantes en un contexto de enseñanza. La educación superior y las prácticas vinculadas a la dirección de equipos en una liga de básquetbol son los escenarios elegidos para revelar cómo el entrenador convive con el uso de herramientas informáticas que le permiten relacionarse con sus jugadores, con colegas y con otros integrantes de la liga. Aquí se considera la asociación del uso de internet, editor de videos, y repositorios de documentos. Se analizan las ventajas del uso de esta conjunción de herramientas aplicadas al entrenamiento, la práctica y la enseñanza del básquetbol. Además, se explica el equipamiento necesario para el ingreso de la tecnología informática propuesta, vinculada a la práctica y enseñanza del básquetbol

Edición de videos y diagramas en Básquetbol

La edición de videos proporciona al entrenador un elemento interesante para seleccionar imágenes y momentos clave del juego. Aquí se muestra como los registros audiovisuales seleccionados resultan útiles para entrenadores y jugadores en un contexto de práctica o de rendimiento deportivo, y para profesores y estudiantes en un contexto de enseñanza. La educación superior y las prácticas vinculadas a la dirección de equipos en una liga de básquetbol son los escenarios elegidos para revelar cómo el entrenador convive con el uso de herramientas informáticas que le permiten relacionarse con sus jugadores, con colegas y con otros integrantes de la liga. Aquí se considera la asociación del uso de internet, editor de videos, y repositorios de documentos. Se analizan las ventajas del uso de esta conjunción de herramientas aplicadas al entrenamiento, la práctica y la enseñanza del básquetbol. Además, se explica el equipamiento necesario para el ingreso de la tecnología informática propuesta, vinculada a la práctica y enseñanza del básquetbol

Caracterización del consumidor en la adopción del comercio electrónico B2C en la ciudad de Manizales – Colombia

En los últimos años, el desarrollo creciente de la tecnología informática ha generado cambios en diferentes aspectos de la vida contemporánea. Las negociaciones, comunicaciones, el entretenimiento, la forma de relacionarse, entre otros aspectos, se han visto afectados por el uso de Internet, obligando a realizar ajustes a las tendencias que vienen transformando al mundo y sus aspectos determinantes.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.

Interfaces for Intelligent Environments

Presentaciones de la asignatura Interfaces para Entornos Inteligentes del Máster en Tecnologías de la Informática/Machine Learning and Data Mining.