mpiGL : un projecte d'utilització del programari lliure en la visualització gràfica en xarxes de càlcul científic

S'analitzen les problemàtiques relacionades amb la presentació d'informació gràfica en temps real durant un càlcul paral·lel o col·laboratiu en un entorn distribuït, i es fa una proposta de toolkit obert que estén el llenguatge OpenGL per la seva resolució.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

ServEngine : Framework pel desenvolupament de serveis en línia

En aquest projecte es desenvoluparà ServEngine, un framework per al desenvolupament de serveis de portals, comunitats virtuals i sistemes de comerç electrònic basat en el patró MVC i, com s'elegirà més endavant després d'un procés d'anàlisi i comparació, utilitzant Struts com a base de desenvolupament.

Implementació d'un esquema criptogràfic per gestionar de forma segura els historials mèdics dels pacients a traves d'una xarxa de comunicacions

Aquest treball presenta una solució basada en criptosistemes de clau pública, certificats i signatures digitals, emprant Java com a llenguatge de programació. Per a estendre la funcionalitat quant a seguretat del Java Developer Kit (JDK) s'utilitza la llibreria criptogràficaIAIK (Institute for Applied Information Processing and Communication).

Abstracting qualitative information for process supervision

Supervisory systems evolution makes the obtaining of significant information from processes more important in the way that the supervision systems' particular tasks are simplified. So, having signal treatment tools capable of obtaining elaborate information from the process data is important. In this paper, a tool that obtains qualitative data about the trends and oscillation of signals is presented. An application of this tool is presented as well. In this case, the tool, implemented in a computer-aided control systems design (CACSD) environment, is used in order to give to an expert system for fault detection in a laboratory plant

Sistema de control per a línies de producció

Creació d'un sistema per al control d'una línia de producció des del punt de vista de l'enginyeria del programari.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we de- velop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional mul- tilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrec- tional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimiza- tion search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow com- putation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Trust region versus line search for computing the optical flow

We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.

Formalizing defeasible argumentation using a labeled deductive system

In the last years there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI, logic programming and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. In the last decade, defeasible argumentation has proven to be a confluence point for many approaches to formalizing commonsense reasoning. Different formalisms have been developed, many of them sharing common features. This paper presents a formalization of an LDS for defensible argumentation, in which the main issues concerning defeasible argumentation are captured within a unified logical framework. The proposed framework is defined in two stages. First, defeasible inference will be formalized by characterizing an argumentative LDS. That system will be then extended in order to capture conflict among arguments using a dialectical approach. We also present some logical properties emerging from the proposed framework, discussing also its semantical characterization.

Bystander responses to a violent incident in an immersive virtual environment

Under what conditions will a bystander intervene to try to stop a violent attack by one person on another? It is generally believed that the greater the size of the crowd of bystanders, the less the chance that any of them will intervene. A complementary model is that social identity is critical as an explanatory variable. For example, when the bystander shares common social identity with the victim the probability of intervention is enhanced, other things being equal. However, it is generally not possible to study such hypotheses experimentally for practical and ethical reasons. Here we show that an experiment that depicts a violent incident at life-size in immersive virtual reality lends support to the social identity explanation. 40 male supporters of Arsenal Football Club in England were recruited for a two-factor between-groups experiment: the victim was either an Arsenal supporter or not (in-group/out-group), and looked towards the participant for help or not during the confrontation. The response variables were the numbers of verbal and physical interventions by the participant during the violent argument. The number of physical interventions had a significantly greater mean in the ingroup condition compared to the out-group. The more that participants perceived that the Victim was looking to them for help the greater the number of interventions in the in-group but not in the out-group. These results are supported by standard statistical analysis of variance, with more detailed findings obtained by a symbolic regression procedure based on genetic programming. Verbal interventions made during their experience, and analysis of post-experiment interview data suggest that in-group members were more prone to confrontational intervention compared to the out-group who were more prone to make statements to try to diffuse the situation.

Trust region versus line search for computing the optical flow

We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.

Optimización y aproximación al problema de la regresión simbólica a través de Straight Line Programs (SLP's) y algoritmos genéticos : Entrenamiento genético de SLP's

El problema de la regresión simbólica consiste en el aprendizaje, a partir de un conjunto muestra de datos obtenidos experimentalmente, de una función desconocida. Los métodos evolutivos han demostrado su eficiencia en la resolución de instancias de dicho problema. En este proyecto se propone una nueva estrategia evolutiva, a través de algoritmos genéticos, basada en una nueva estructura de datos denominada Straight Line Program (SLP) y que representa en este caso expresiones simbólicas. A partir de un SLP universal, que depende de una serie de parámetros cuya especialización proporciona SLP's concretos del espacio de búsqueda, la estrategia trata de encontrar los parámetros óptimos para que el SLP universal represente la función que mejor se aproxime al conjunto de puntos muestra. De manera conceptual, este proyecto consiste en un entrenamiento genético del SLP universal, utilizando los puntos muestra como conjunto de entrenamiento, para resolver el problema de la regresión simbólica.

Escola a prop

'Escola a prop' és una aplicació pensada per connectar pares i escola; desenvolupada amb tècniques d'orientació a objectes, ha de facilitar la comunicació entre aquestes dues entitats.