DI - CAOS - Comunicaciones en Congresos y otros eventos

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Computer vision and laser scanner road environment perception
(IEEE, 2014-05-12) García, Fernando; Ponz Vila, Aurelio; Martín Gómez, David; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Data fusion procedure is presented to enhance classical Advanced Driver Assistance Systems (ADAS). The novel vehicle safety approach, combines two classical sensors: computer vision and laser scanner. Laser scanner algorithm performs detection of vehicles and pedestrians based on pattern matching algorithms. Computer vision approach is based on Haar-Like features for vehicles and Histogram of Oriented Gradients (HOG) features for pedestrians. The high level fusion procedure uses Kalman Filter and Joint Probabilistic Data Association (JPDA) algorithm to provide high level detection. Results proved that by means of data fusion, the performance of the system is enhanced.
Context Aided Multilevel Pedestrian Detection
(IEEE, 2013) García, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Abstract: The proposed work, depicts a novel algorithm able to provide multiple pedestrian detection, based on the use of classical sensors in modern automotive application and context information. The work takes advantage of the use of Joint Probabilities Data Association (JPDA) and context information to enhance the classic performance of the pedestrian detection algorithms. The combination of the different information sources with powerful tracking algorithms helps to overcome the difficulties of this processes, providing a trustable tool that improves performance of the single sensor detection algorithms. Context in a rich information source, able to improve the fusion process in all levels by the use of a priori knowledge of the application. In the present work multilevel fusion solution is provided for road safety application. Context is used in all the fusion levels, helping to improve the perception of the road environment and the relations among detections. By the fusion of all information sources, accurate and trustable detection is provided and complete situation assessment obtained, with estimation of the danger that involves any detection.
Joint Probabilistic Data Association fusion approach for pedestrian detection
(Ieee - The Institute Of Electrical And Electronics Engineers, Inc, 2013) García, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María
Abstract: Fusion is becoming a classic topic in Intelligent Transport System (ITS) society. The lack of trustworthy sensors requires the combination of several devices to provide reliable detections. In this paper a novel approach, that takes advantage of the Joint Probabilistic Data Association technique (JPDA) for data association, is presented. The approach uses one of the most powerful techniques of Multiple Target Tracking theory and adapts it to fulfill the strong requirements of road safety applications. The different test performed proved that a powerful association technique can enhance the capacity of Advance Driver Assistance Systems. Two main sensors are used for pedestrian detection: laser scanner and computer vision. Furthermore, the approach takes advantage of the availability of other information sources i.e. context information and online information (GPS). The detections are fused using JPDA, enhancing the capacities of classical pedestrian detection systems, mainly based in visual information. The test performed also showed that JPDA improved the results offered by other data association techniques, e.g. Global Nearest Neighbors.
Ensemble method based on individual evolving classifiers
(IEEE Computer Society, 2013) Iglesias Martínez, José Antonio; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
Abstract: Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary.
Evolving Systems for Computer User Behavior Classification
(IEEE, 2013) Iglesias Martínez, José Antonio; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
A computer can keep track of computer users to improve the security in the system. However, this does not prevent a user from impersonating another user. Only the user behavior recognition can help to detect masqueraders. Under the UNIX operating system, users type several commands which can be analyzed in order to create user profiles. These profiles identify a specific user or a specific computer user behavior. In addition, a computer user behavior changes over time. If the behavior recognition is done automatically, these changes need to be taken into account. For this reason, we propose in this paper a simple evolving method that is able to keep up to date the computer user behavior profiles. This method is based on Evolving Fuzzy Systems. The approach is evaluated using real data streams.
Coordinación global basada en controladores locales reactivos en la RoboCup
(Universitat Rovira i Virgili, Escola Técnica Superior d'Enginyeria, 2000) Fernández, Fernando; Gutiérrez, Germán; Molina, José M.
El principal problema a que nos enfrentamos al diseñar sistemas multi-agente es cómo coordinar los agentes que pertenecen a este sistema para obtener un comportamiento global eficiente. En algunos trabajos, el comportamiento coordinado de los agentes se obtiene gracias a un conocimiento total del dominio en el que se pueden aplicar planificadores tradicionales. Sin embargo, en muchos dominios esta aproximación no es posible por tratarse de dominios demasiado grandes y la comunicación y capacidad de memoria de los agentes muy limitada. En este trabajo se presenta un diseño multi-agente que permite obtener un comportamiento global coordinado basado en los comportamientos reactivos de los agentes que son controlados o dirigidos por la información local que poseen. En el diseño se ha minimizado la comunicación entre los agentes, ya que el proceso de coordinación se basa en reglas locales reactivas. Esta propuesta tiene una primera aplicación en el dominio de la RoboCup (Robot World Cup Iniciative), proyecto ampliamente utilizado por la comunidad investigadora. Palabras Clave: Inteligencia artificial distribuida, sistemas multi-agente, comportamientos cooperativos, comportamientos reactivos, RoboCup
Automatic design of artificial neural networks to forecast time series
(Ibergarceta, 2010) Peralta, Juan; Gutiérrez Sánchez, Germán; Sanchis de Miguel, María Araceli
In this work an approach to design Artificial Neural Networks (ANN) to forecast Time Series is tackled. The approach is an automatic method that is carried out by an Evolutionary Algorithm (as a search algorithm) to design ANN. A key issue for these kinds of approaches is what information is included into the chromosome that represents an ANN There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the ANN. The results using a parameter Encoding Scheme to design ANN for a Time Series Competition are shown
Testing feature selection in traffic signs
(Universidad de Las Palmas de Gran Canaria, 2007) Sesmero Lorente, María Paz; Alonso Weber, Juan Manuel; Gutiérrez Sánchez, Germán; Sanchis de Miguel, María Araceli
Road signs carry essential information for successful driving. Therefore, if we are interested in developing a Driver Support Systems, both, detection and classification of road signs are essential tasks for an autonomous system. However, both tasks are some of the less studied subjects in the field of Intelligent Transport systems. In this research we lay the foundations of a software implementation for a classifier system that will be implemented in hardware and will be able to be used for real-time traffic sign categorization. The selected classification method is a Multilayer Perceptron trained with Back-Propagation algorithm. The reason of this selection is, on one hand, that for certain types of problems, such as object recognition in natural environments, neural network learning methods provide a robust approach. On the other hand, and under certain, limitations related mainly to the number of units, a hardware implementation on FPGA of ANN is possible. Therefore, ANNs are a good method for real-time processing in real-word problems
Cooperación en sistemas distribuidos de robots reactivos minimizando la cantidad de información comunicada
(Universidad de Vigo, 2000) Fernández, Fernando; Gutiérrez, Germán; Molina, José M.
La coordinación emergente pretende obtener comportamientos colaborativos entre diversos agentes sin que eso implique que cada individuo deba tener un conocimiento global del dominio, y sin que ese conocimiento deba estar centralizado. Al no requerir conocimiento global, se minimiza la comunicación entre los agentes de forma que cada uno de ellos puede comportarse de forma reactiva y totalmente autónoma. En este trabajo se presenta una primera aproximación a este modelo de coordinación aplicado al dominio de la RoboCup.
MMRF for proteome annotation applied to human protein disease prediction
(Springer, 2011) García Jiménez, Beatriz; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
Biological processes where every gene and protein participates is an essential knowledge for designing disease treatments. Nowadays, these annotations are still unknown for many genes and proteins. Since making annotations from in-vivo experiments is costly, computational predictors are needed for different kinds of annotation such as metabolic pathway, interaction network, protein family, tissue, disease and so on. Biological data has an intrinsic relational structure, including genes and proteins, which can be grouped by many criteria. This hinders the possibility of finding good hypotheses when attribute-value representation is used. Hence, we propose the generic Modular Multi-Relational Framework (MMRF) to predict different kinds of gene and protein annotation using Relational Data Mining (RDM). The specific MMRF application to annotate human protein with diseases verifies that group knowledge (mainly protein-protein interaction pairs) improves the prediction, particularly doubling the area under the precision-recall curve
Point of care medical device communication standars (ISO11073/IEEE1073) in patient telemonitoring
(2005) Galarraga, M.; Martínez, I.; Toledo Heras, María Paula de; Serrano, L.; García, J.; Jiménez, Silvia
This paper reviews the use of ISO11073/ IEEE1073 international standard in patient telemonitoring. The purpose of this family of standards is to allow interoperability between medical instrumentation devices and medical information systems. Its application in the field of telemonitoring can encourage telemedicine services and e-care, preventing failures and problems that are making difficult its spread (use problems, high costs of reconfigurations and actualizations). An application guide for the system engineer that want to apply them is proposed, showing the steps to follow, the benefits and handicaps in the standard implementation for different telemonitoring scenarios. The study also includes the conformity levels that have to be fulfilled, the main application points of the standard.
Caos Online Coach 2006 Team Description
(2006) Iglesias Martínez, José Antonio; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
This paper describes the main features of the Caos Coach 2006 Simulation Team. This Coach focuses on the challenge of the opponent modelling using sequential events of the players, from observations of their main features. Also, it is able to translate observations of a dynamic and complex environment into a time-serie of recognized events. Finally, our coach implements a mechanism to compare different time-series.
The RoboCup agent behavior modeling challenge
(2010) Iglesias Martínez, José Antonio; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
RoboCup is an international joint project that aims to foster Arti cial Intelligence (AI) and intelligent robotics research by providing a standard problem. RoboCup offers different challenges for intelligent agent researchers in a dynamic, real-time and multi-agent domain. One of these challenges, especially in the Simulation League, is the opponent modeling, which is crucial for the ultimate goal of the RoboCup project: develop a team of fully autonomous. In order to emphasize opponent-modeling approaches, the RoboCup Coach Competition was created and it was held every year (with some changes) from 2001 to 2006. Although there were several interesting research works about the agent modeling challenge during that time, several considerations were not well de ned and the competition was suspended after RoboCup Coach Competition 2006. In this paper, we propose a new approach for the competition to face the opponent modeling challenge in the RoboCup competition.
Designing human-like video game synthetic characters through machine consciousness
(2009) Arrabales, Raúl; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
La aplicación de modelos de consciencia artificial en los sistemas multiagente
(Universidad de Castilla-La Mancha. Departamento de Sistemas Informáticos, 2006) Arrabales, Raúl; Sanchis de Miguel, María Araceli
Durante la última década han aparecido algunas implementaciones de modelos científicos de la consciencia basados en sistemas multiagente. El propósito de este artículo es recopilar y describir estos sistemas, determinando hasta que punto estas implementaciones satisfacen los modelos correspondientes, y analizando si proporcionan realmente las supuestas ventajas de usar consciencia artificial en la resolución de problemas. También se analizan en general las funciones de la consciencia y los beneficios que éstas pueden aportar en el rendimiento de los sistemas multiagente.
Implementación integrada de una plataforma telemática basada en estándares para monitorización de pacientes
(2007) Martínez, I.; Fernández, J.; Galarraga, M.; Serrano, L.; Toledo Heras, María Paula de; García, J.
This paper presents a proof-of-concept design of an integrated solution of a telematic platform for home telemonitoring. It is end-to-end standards-based, using ISO/IEEE11073 in the client environment and EN13606 to send the information to an Electronic Healthcare Record (EHR) server. This solution has been implemented to comply with the standards available versions and tested in a laboratory environment to demonstrate the feasibility of an end-to-end standards-based platform.
Reducing the amount of input data in traffic sign classification
(2006) Granados, Ana; Ledezma Espino, Agapito Ismael; Gutiérrez Sánchez, Germán; Sanchis de Miguel, María Araceli
Several complex problems have to be solved in order to build Intelligent Transport Systems. Among them, it is worth mentioning the detection and classification of tra±c signs which could appear at any position within a captured image. This paper analyzes the influence of the number of attributes in the field of classification of tra±c signs when automatic learning techniques are used. In order to face this task, four different approaches have been considered, three of them symbolic and one sub-symbolic. These techniques have been applied using two different input pattern dimensions and their performances have been compared.
An evolving framework for clustering computer users
(2010) Iglesias Martínez, José Antonio; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
The idea of clustering computer users is very beneficial for making recommendations to a user based on the histories of other users with similar preferences, detecting changes in the behavior of a user, and so on. However, computer users have different needs as they learn to use a software system or their goals changes. Although there are several approaches for clustering users, most of them do not consider the changes in their behavior. In this paper, we present an approach for clustering automatically the behavior profile of a computer user and an evolving method based on Evolving Systems to keep up to date the created profile clusters.
CERA-CRANIUM: a test bed for machine consciousness research
(2009) Arrabales, Raúl; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
This paper describes a novel framework designed as a test bed for machine consciousness cognitive models (MCCM). This MCCM experimentation framework is based on a generalpurpose cognitive architecture that can be integrated in different environments and confronted with different problem domains. The definition of a generic cognitive control system for abstract agents is the root of the versatility of the presented framework. The proposed control system, which is inspired in the major cognitive theories of consciousness, provides mechanisms for both sensory data acquisition and motor action execution. Sensory and motor data is represented in the proposed architecture using different level workspaces where percepts and actions are generated thanks to the competition and collaboration of specialized processors. Additionally, this cognitive architecture provides the means to modulate perception and behavior; in other words, it offers an interface for a higher control layer to drive the way percepts and actions are generated and how they interact with each other. This mechanism permits the experimentation with virtually any high level cognitive model of consciousness. An illustrative application scenario, autonomous explorer robots, is also reviewed in this work.
VIENA: VIsual ENvironment for Analyzing RoboCup Soccer Teams
(2010) Iglesias Martínez, José Antonio; Palacín, Miguel Ángel; Ledezma Espino, Agapito Ismael; Sanchis de Miguel, María Araceli
Nowadays, one of the main subjects of research in multi-agent systems deals with the development of autonomous agents that can interac efficiently. Opponent modeling is a technique in game-playing which attempts to create a model of behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. RoboCup is an international joint project that aims to foster Artificial Intelligence (AI) and intelligent robotics research by providing a standard problem. RoboCup offers different challenges for intelligent agent researches in a dynamic, real-time and multi-agent domain. One of these challenges, especially in the Simulation League, is the opponent modeling, which is crucial for the ultimate goal of the RoboCup project: develop a team of fully autonomous. This aspect has motivated the construction of a tool for analyzing the behavoir of a soccer team: VIENA. Although there are several tools in the RoboCup enviroment for analyzing a soccer games from a loglife and displaying statictical data (a survey of these tools is presented in this paper), VIENA analyzes a soccer team behavoir using different methods proposed for user.

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