Implementación de una herramienta para el estudio rápido de sistemas cuasi-ópticos

Los programas comerciales de análisis de onda completa requieren, en el rango de la cuasi-óptica, un elevado número de recursos computacionales y, relativamente, grandes periodos de tiempo para llevar a cabo los cálculos oportunos. Este proyecto fin de carrera aparece como respuesta a la necesidad del Grupo de Radiofrecuencia de la Universidad Carlos III de Madrid, de disponer de una aplicación capaz de proporcionar una aproximación rápida y precisa del comportamiento de un sistema electromagnético en el rango de la cuasi-óptica. El objetivo no es resolver el problema electromagnético completo, sino que, basándose en la teoría de propagación y transformación de haces gaussianos, se ha desarrollado una aplicación que reduce notablemente los tiempos de cálculo. ___________________________________________________________________________________________________________Trade full-wave analysis programs require, in the quasi-optics range, a large number of computational resources and relatively long periods of time to carry out appropriate calculations. This final project stems from the need of the Radiofrequency Group of the Carlos III University of Madrid, to have an application capable of providing fast and accurate approximation of the behavior of an electromagnetic system in the range of quasi-optics. The goal is not completely solve the electromagnetic problem, but develope an application that greatly reduces computational time, based on the theory of propagation and transformation of Gaussian beams.Ingeniería de Telecomunicació

Valoración de cultivares tradicionales de tomate negro como herramienta de sustentabilidad en el entorno de las sierras de Segura

Premio extraordinario de Trabajo Fin de Máster curso 2016-2017. Máster en AgroecologíaEste trabajo trata de valorar el uso de la biodiversidad cultivada, recursos fitogenéticos tradicionales o variedades locales, concretamente: tomates negros segureños (propios de las sierras de Segura y su entorno) como herramienta para la articulación de sistemas agroalimentarios alternativos que permitan la sustentabilidad tanto del medio rural como del urbano, ya que ambos evolucionan de forma solidaria. Al mismo tiempo, se fomenta su cultivo y conocimiento contribuyendo a frenar la erosión genética que les afecta y a proteger la memoria biocultural del territorio. Para ello, se han recopilado distintas muestras de semillas de tomates negros segureños. Se ha llevado a cabo su cultivo experimental para conocer de primera mano sus características y comportamiento agronómico. Se han caracterizado los distintos cultivares y se han multiplicado sus semillas, que estarán disponibles para continuar su cultivo. Se ha entrevistado a personas que conservan y cultivan estas semillas con el fin de recoger información relativa ellas y el conocimiento campesino que tienen asociado. Se han llevado a cabo evaluaciones participativas de los frutos por parte de personas consumidoras. Y se han desarrollado experiencias relacionadas con la comercialización en circuitos cortos para determinar su encaje en este tipo de canales de distribución alternativos. La información recogida se presenta en fichas para cada cultivar

A Practical Approach to the Development of Ontology-Based Information Fusion Systems

Proceedings of: NATO Advanced Study Institute (ASI) on Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, Salamanca, 19-30 September, 2011Ontology-based representations are gaining momentum among other alternatives to implement the knowledge model of high-level fusion applications. In this paper, we provide an introduction to the theoretical foundations of ontology-based knowledge representation and reasoning, with a particular focus on the issues that appear in maritime security –where heterogeneous regulations, information sources, users, and systems are involved. We also present some current approaches and existing technologies for high-level fusion based on ontological representations. Unfortunately, current tools for the practical implementation of ontology-based systems are not fully standardized, or even prepared to work together in medium-scale systems. Accordingly, we discuss different alternatives to face problems such as spatial and temporal knowledge representation or uncertainty management. To illustrate the conclusions drawn from this research, an ontology-based semantic tracking system is briefly presented. Results and latent capabilities of this framework are shown at the end of the paper, where we also envision future opportunities for this kind of applications.This research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS 2008-07029-C02-02.Publicad

Topological Properties in Ontology-based Applications

Proceedings of: 11th International Conference on Intelligent Systems Design and Applications, Córdoba, Spain, 22 – 24 November, 2011.Representation and reasoning with spatial properties is essential in several application domains where ontologies are being successfully applied; e.g., Information Fusion systems. This requires a full characterization of the semantics of relations such as adjacent, included, overlapping, etc. Nevertheless, ontologies are not expressive enough to directly support widely-use spatial or topological theories, such as the Region Connection Calculus (RCC). In addition, these properties must be properly instantiated in the ontology, which may require expensive calculations. This paper presents a practical approach to represent and reason with topological properties in ontology-based systems, as well as some optimization techniques that have been applied in a video-based Information Fusion application.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC,CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.Publicad

Context-based scene recognition from visual data in smart homes: an Information Fusion approach

Ambient Intelligence (AmI) aims at the development of computational systems that process data acquired by sensors embedded in the environment to support users in everyday tasks. Visual sensors, however, have been scarcely used in this kind of applications, even though they provide very valuable information about scene objects: position, speed, color, texture, etc. In this paper, we propose a cognitive framework for the implementation of AmI applications based on visual sensor networks. The framework, inspired by the Information Fusion paradigm, combines a priori context knowledge represented with ontologies with real time single camera data to support logic-based high-level local interpretation of the current situation. In addition, the system is able to automatically generate feedback recommendations to adjust data acquisition procedures. Information about recognized situations is eventually collected by a central node to obtain an overall description of the scene and consequently trigger AmI services. We show the extensible and adaptable nature of the approach with a prototype system in a smart home scenario.This research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008- 06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Ontological representation of time-of-flight camera data to support vision-based AmI

Proceedings of: 4th International Workshop on Sensor Networks and Ambient Intelligence, 19-23 March 2012, Lugano ( Switzerland)Recent advances in technologies for capturing video data have opened a vast amount of new application areas. Among them, the incorporation of Time-of-Flight (ToF) cameras on Ambient Intelligence (AmI) environments. Although theperformance of tracking algorithms have quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted for smart environments. This paper presents an extension of a previous system in the area of videobased AmI to incorporate ToF information to enhance sceneinterpretation. The framework is founded on an ontologybased model of the scene, which is extended to incorporate ToF data. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Applying the Dynamic Region Connection Calculus to Exploit Geographic Knowledge in Maritime Surveillance

Proceedings of: 15th International Conference on Information Fusion (FUSION 2012), Singapore, 9-12 July 2012.Concerns about the protection of the global transport network have risen the need of new security and surveillance systems. Ontology-based and fusion systems represent an attractive alternative for practical applications focused on fast and accurate responses. This paper presents an architecture based on a geometric model to efficiently predict and calculate the topological relationships between spatial objects. This model aims to reduce the number of calculations by relying on a spatial data structure. The goal is the detection of threatening behaviors next to points of interest without a noticeable loss of efficiency. The architecture has been embedded in an ontology-based prototype compliant with the Joint Directors of Laboratories (JDL) model for Information Fusion. The prototype capabilities are illustrated by applying international protection rules in maritime scenarios.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Ontological Representation of Light Wave Camera Data to Support Vision-Based AmI

This article belongs to the Special Issue Sensors and Wireless Sensor Networks for Novel Concepts of Things, Interfaces and Applications in Smart SpacesRecent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although the performance of tracking algorithms has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This paper advocates for the introduction of a part-based representational level in cognitive-based systems in order to accurately represent the novel sensors' knowledge. The paper also reviews the theoretical and practical issues in part-whole relationships proposing a specific taxonomy for computer vision approaches. General part-based patterns for human body and transitive part-based representation and inference are incorporated to an ontology-based previous framework to enhance scene interpretation in the area of video-based AmI. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application for the elaboration of live market researches.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Computer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008–07029-C02–02.Publicad

Ontological representation of context knowledge for visual data fusion

8 pages, 4 figures.-- Contributed to: 12th International Conference on Information Fusion, 2009 (FUSION '09, Seattle, Washington, US, Jul 6-9, 2009).Context knowledge is essential to achieve successful information fusion, especially at high JDL levels. Context can be used to interpret the perceived situation, which is required for accurate assessment. Both types of knowledge, contextual and perceptual, can be represented with formal languages such as ontologies, which support the creation of readable representations and reasoning with them. In this paper, we present an ontology-based model compliant with JDL to represent knowledge in cognitive visual data fusion systems. We depict the use of the model with an example on surveillance. We show that such a model promotes system extensibility and facilitates the incorporation of humans in the fusion loop.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.Publicad