Prospectives of monitoring biological activity in a red-legged partridge incubator with a carbon dioxide probe

This study focuses on the relationship between CO2 production and the ultimate hatchability of the incubation. A total amount of 43316 eggs of red-legged partridge (Alectoris rufa) were supervised during five actual incubations: three in 2012 and two in 2013. The CO2 concentration inside the incubator was monitored over a 20-day period, showing sigmoidal growth from ambient level (428 ppm) up to 1700 ppm in the incubation with the highest hatchability. Two sigmoid growth models (logistic and Gompertz) were used to describe the CO2 production by the eggs, with the result that the logistic model was a slightly better fit (r2=0.976 compared to r2=0.9746 for Gompertz). A coefficient of determination of 0.997 between the final CO2 estimation (ppm) using the logistic model and hatchability (%) was found.

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Efficiency improvement of HF communications using cognitive radio principles

Cognitive Radio principles can be applied to HF communications to make a more efficient use of the extremely scarce spectrum. In this contribution we focus on analyzing the usage of the available channels done by the legacy users, which are regarded as primary users since they are allowed to transmit without resorting any smart procedure, and consider the possibilities for our stations -over the HFDVL (HF Data+Voice Link) architecture- to participate as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes, and is trained with real measurements from the 14 MHz band.

A framework for building distributed social network websites

Las redes son la esencia de comunidades y sociedades humanas; constituyen el entramado en el que nos relacionamos y determinan cómo lo hacemos, cómo se disemina la información o incluso cómo las cosas se llevan a cabo. Pero el protagonismo de las redes va más allá del que adquiere en las redes sociales. Se encuentran en el seno de múltiples estructuras que conocemos, desde las interaciones entre las proteínas dentro de una célula hasta la interconexión de los routers de internet. Las redes sociales están presentes en internet desde sus principios, en el correo electrónico por tomar un ejemplo. Dentro de cada cliente de correo se manejan listas contactos que agregadas constituyen una red social. Sin embargo, ha sido con la aparición de los sitios web de redes sociales cuando este tipo de aplicaciones web han llegado a la conciencia general. Las redes sociales se han situado entre los sitios más populares y con más tráfico de la web. Páginas como Facebook o Twitter manejan cifras asombrosas en cuanto a número de usuarios activos, de tráfico o de tiempo invertido en el sitio. Pero las funcionalidades de red social no están restringidas a las redes sociales orientadas a contactos, aquellas enfocadas a construir tu lista de contactos e interactuar con ellos. Existen otros ejemplos de sitios que aprovechan las redes sociales para aumentar la actividad de los usuarios y su involucración alrededor de algún tipo de contenido. Estos ejemplos van desde una de las redes sociales más antiguas, Flickr, orientada al intercambio de fotografías, hasta Github, la red social de código libre más popular hoy en día. No es una casualidad que la popularidad de estos sitios web venga de la mano de sus funcionalidades de red social. El escenario es más rico aún, ya que los sitios de redes sociales interaccionan entre ellos, compartiendo y exportando listas de contactos, servicios de autenticación y proporcionando un valioso canal para publicitar la actividad de los usuarios en otros sitios web. Esta funcionalidad es reciente y aún les queda un paso hasta que las redes sociales superen su condición de bunkers y lleguen a un estado de verdadera interoperabilidad entre ellas, tal como funcionan hoy en día el correo electrónico o la mensajería instantánea. Este trabajo muestra una tecnología que permite construir sitios web con características de red social distribuída. En primer lugar, se presenta una tecnología para la construcción de un componente intermedio que permite proporcionar cualquier característica de gestión de contenidos al popular marco de desarrollo web modelo-vista-controlador (MVC) Ruby on Rails. Esta técnica constituye una herramienta para desarrolladores que les permita abstraerse de las complejidades de la gestión de contenidos y enfocarse en las particularidades de los propios contenidos. Esta técnica se usará también para proporcionar las características de red social. Se describe una nueva métrica de reusabilidad de código para demostrar la validez del componente intermedio en marcos MVC. En segundo lugar, se analizan las características de los sitios web de redes sociales más populares, con el objetivo de encontrar los patrones comunes que aparecen en ellos. Este análisis servirá como base para definir los requisitos que debe cumplir un marco para construir redes sociales. A continuación se propone una arquitectura de referencia que proporcione este tipo de características. Dicha arquitectura ha sido implementada en un componente, Social Stream, y probada en varias redes sociales, tanto orientadas a contactos como a contenido, en el contexto de una asociación vecinal tanto como en proyectos de investigación financiados por la UE. Ha sido la base de varios proyectos fin de carrera. Además, ha sido publicado como código libre, obteniendo una comunidad creciente y está siendo usado más allá del ámbito de este trabajo. Dicha arquitectura ha permitido la definición de un nuevo modelo de control de acceso social que supera varias limitaciones presentes en los modelos de control de acceso para redes sociales. Más aún, se han analizado casos de estudio de sitios de red social distribuídos, reuniendo un conjunto de caraterísticas que debe cumplir un marco para construir redes sociales distribuídas. Por último, se ha extendido la arquitectura del marco para dar cabida a las características de redes sociales distribuídas. Su implementación ha sido validada en proyectos de investigación financiados por la UE. Abstract Networks are the substance of human communities and societies; they constitute the structural framework on which we relate to each other and determine the way we do it, the way information is diseminated or even the way people get things done. But network prominence goes beyond the importance it acquires in social networks. Networks are found within numerous known structures, from protein interactions inside a cell to router connections on the internet. Social networks are present on the internet since its beginnings, in emails for example. Inside every email client, there are contact lists that added together constitute a social network. However, it has been with the emergence of social network sites (SNS) when these kinds of web applications have reached general awareness. SNS are now among the most popular sites in the web and with the higher traffic. Sites such as Facebook and Twitter hold astonishing figures of active users, traffic and time invested into the sites. Nevertheless, SNS functionalities are not restricted to contact-oriented social networks, those that are focused on building your own list of contacts and interacting with them. There are other examples of sites that leverage social networking to foster user activity and engagement around other types of content. Examples go from early SNS such as Flickr, the photography related networking site, to Github, the most popular social network repository nowadays. It is not an accident that the popularity of these websites comes hand-in-hand with their social network capabilities The scenario is even richer, due to the fact that SNS interact with each other, sharing and exporting contact lists and authentication as well as providing a valuable channel to publize user activity in other sites. These interactions are very recent and they are still finding their way to the point where SNS overcome their condition of data silos to a stage of full interoperability between sites, in the same way email and instant messaging networks work today. This work introduces a technology that allows to rapidly build any kind of distributed social network website. It first introduces a new technique to create middleware that can provide any kind of content management feature to a popular model-view-controller (MVC) web development framework, Ruby on Rails. It provides developers with tools that allow them to abstract from the complexities related with content management and focus on the development of specific content. This same technique is also used to provide the framework with social network features. Additionally, it describes a new metric of code reuse to assert the validity of the kind of middleware that is emerging in MVC frameworks. Secondly, the characteristics of top popular SNS are analysed in order to find the common patterns shown in them. This analysis is the ground for defining the requirements of a framework for building social network websites. Next, a reference architecture for supporting the features found in the analysis is proposed. This architecture has been implemented in a software component, called Social Stream, and tested in several social networks, both contact- and content-oriented, in local neighbourhood associations and EU-founded research projects. It has also been the ground for several Master’s theses. It has been released as a free and open source software that has obtained a growing community and that is now being used beyond the scope of this work. The social architecture has enabled the definition of a new social-based access control model that overcomes some of the limitations currenly present in access control models for social networks. Furthermore, paradigms and case studies in distributed SNS have been analysed, gathering a set of features for distributed social networking. Finally the architecture of the framework has been extended to support distributed SNS capabilities. Its implementation has also been validated in EU-founded research projects.

Cooperative Multi-robot Patrolling: A study of distributed approaches based on mathematical models of game theory to protect infrastructures

El principio de Teoría de Juegos permite desarrollar modelos estocásticos de patrullaje multi-robot para proteger infraestructuras criticas. La protección de infraestructuras criticas representa un gran reto para los países al rededor del mundo, principalmente después de los ataques terroristas llevados a cabo la década pasada. En este documento el termino infraestructura hace referencia a aeropuertos, plantas nucleares u otros instalaciones. El problema de patrullaje se define como la actividad de patrullar un entorno determinado para monitorear cualquier actividad o sensar algunas variables ambientales. En esta actividad, un grupo de robots debe visitar un conjunto de puntos de interés definidos en un entorno en intervalos de tiempo irregulares con propósitos de seguridad. Los modelos de partullaje multi-robot son utilizados para resolver este problema. Hasta el momento existen trabajos que resuelven este problema utilizando diversos principios matemáticos. Los modelos de patrullaje multi-robot desarrollados en esos trabajos representan un gran avance en este campo de investigación. Sin embargo, los modelos con los mejores resultados no son viables para aplicaciones de seguridad debido a su naturaleza centralizada y determinista. Esta tesis presenta cinco modelos de patrullaje multi-robot distribuidos e impredecibles basados en modelos matemáticos de aprendizaje de Teoría de Juegos. El objetivo del desarrollo de estos modelos está en resolver los inconvenientes presentes en trabajos preliminares. Con esta finalidad, el problema de patrullaje multi-robot se formuló utilizando conceptos de Teoría de Grafos, en la cual se definieron varios juegos en cada vértice de un grafo. Los modelos de patrullaje multi-robot desarrollados en este trabajo de investigación se han validado y comparado con los mejores modelos disponibles en la literatura. Para llevar a cabo tanto la validación como la comparación se ha utilizado un simulador de patrullaje y un grupo de robots reales. Los resultados experimentales muestran que los modelos de patrullaje desarrollados en este trabajo de investigación trabajan mejor que modelos de trabajos previos en el 80% de 150 casos de estudio. Además de esto, estos modelos cuentan con varias características importantes tales como distribución, robustez, escalabilidad y dinamismo. Los avances logrados con este trabajo de investigación dan evidencia del potencial de Teoría de Juegos para desarrollar modelos de patrullaje útiles para proteger infraestructuras. ABSTRACT Game theory principle allows to developing stochastic multi-robot patrolling models to protect critical infrastructures. Critical infrastructures protection is a great concern for countries around the world, mainly due to terrorist attacks in the last decade. In this document, the term infrastructures includes airports, nuclear power plants, and many other facilities. The patrolling problem is defined as the activity of traversing a given environment to monitoring any activity or sensing some environmental variables If this activity were performed by a fleet of robots, they would have to visit some places of interest of an environment at irregular intervals of time for security purposes. This problem is solved using multi-robot patrolling models. To date, literature works have been solved this problem applying various mathematical principles.The multi-robot patrolling models developed in those works represent great advances in this field. However, the models that obtain the best results are unfeasible for security applications due to their centralized and predictable nature. This thesis presents five distributed and unpredictable multi-robot patrolling models based on mathematical learning models derived from Game Theory. These multi-robot patrolling models aim at overcoming the disadvantages of previous work. To this end, the multi-robot patrolling problem was formulated using concepts of Graph Theory to represent the environment. Several normal-form games were defined at each vertex of a graph in this formulation. The multi-robot patrolling models developed in this research work have been validated and compared with best ranked multi-robot patrolling models in the literature. Both validation and comparison were preformed by using both a patrolling simulator and real robots. Experimental results show that the multirobot patrolling models developed in this research work improve previous ones in as many as 80% of 150 cases of study. Moreover, these multi-robot patrolling models rely on several features to highlight in security applications such as distribution, robustness, scalability, and dynamism. The achievements obtained in this research work validate the potential of Game Theory to develop patrolling models to protect infrastructures.

Métodos diferenciales e interferométricos para la evaluación de deformaciones de la corteza terrestre mediante técnicas GNSS e InSAR = Differential and interferometric methods for evaluation of crustal deformation using GNSS and InSAR techniques

La actividad volcánica interviene en multitud de facetas de la propia actividad humana, no siempre negativas. Sin embargo, son más los motivos de peligrosidad y riesgo que incitan al estudio de la actividad volcánica. Existen razones de seguridad que inciden en el mantenimiento del seguimiento y monitorización de la actividad volcánica para garantizar la vida y la seguridad de los asentamientos antrópicos en las proximidades de los edificios volcánicos. En esta tesis se define e implementa un sistema de monitorización de movimientos de la corteza en las islas de Tenerife y La Palma, donde el impacto social que representa un aumento o variación de la actividad volcánica en las islas es muy severo. Aparte de la alta densidad demográfica del Archipiélago, esta población aumenta significativamente, en diferentes periodos a lo largo del año, debido a la actividad turística que representa la mayor fuente de ingresos de las islas. La población y los centros turísticos se diseminan predominantemente a lo largo de las costas y también a lo largo de los flancos de los edificios volcánicos. Quizá el mantenimiento de estas estructuras sociales y socio-económicas son los motivos más importantes que justifican una monitorización de la actividad volcánica en las Islas Canarias. Recientemente se ha venido trabajando cada vez más en el intento de predecir la actividad volcánica utilizando los nuevos sistemas de monitorización geodésica, puesto que la actividad volcánica se manifiesta anteriormente por deformación de la corteza terrestre y cambios en la fuerza de la gravedad en la zona donde más tarde se registran eventos volcánicos. Los nuevos dispositivos y sensores que se han desarrollado en los últimos años en materias como la geodesia, la observación de la Tierra desde el espacio y el posicionamiento por satélite, han permitido observar y medir tanto la deformación producida en el terreno como los cambios de la fuerza de la gravedad antes, durante y posteriormente a los eventos volcánicos que se producen. Estos nuevos dispositivos y sensores han cambiado las técnicas o metodologías geodésicas que se venían utilizando hasta la aparición de los mismos, renovando métodos clásicos y desarrollando otros nuevos que ya se están afianzando como metodologías probadas y reconocidas para ser usadas en la monitorización volcánica. Desde finales de la década de los noventa del siglo pasado se han venido desarrollando en las Islas Canarias varios proyectos que han tenido como objetivos principales el desarrollo de nuevas técnicas de observación y monitorización por un lado y el diseño de una metodología de monitorización volcánica adecuada, por otro. Se presenta aquí el estudio y desarrollo de técnicas GNSS para la monitorización de deformaciones corticales y su campo de velocidades para las islas de Tenerife y La Palma. En su implementación, se ha tenido en cuenta el uso de la infraestructura geodésica y de monitorización existente en el archipiélago a fin de optimizar costes, además de complementarla con nuevas estaciones para dar una cobertura total a las dos islas. Los resultados obtenidos en los proyectos, que se describen en esta memoria, han dado nuevas perspectivas en la monitorización geodésica de la actividad volcánica y nuevas zonas de interés que anteriormente no se conocían en el entorno de las Islas Canarias. Se ha tenido especial cuidado en el tratamiento y propagación de los errores durante todo el proceso de observación, medida y proceso de los datos registrados, todo ello en aras de cuantificar el grado de fiabilidad de los resultados obtenidos. También en este sentido, los resultados obtenidos han sido verificados con otros procedentes de sistemas de observación radar de satélite, incorporando además a este estudio las implicaciones que el uso conjunto de tecnologías radar y GNSS tendrán en un futuro en la monitorización de deformaciones de la corteza terrestre. ABSTRACT Volcanic activity occurs in many aspects of human activity, and not always in a negative manner. Nonetheless, research into volcanic activity is more likely to be motivated by its danger and risk. There are security reasons that influence the monitoring of volcanic activity in order to guarantee the life and safety of human settlements near volcanic edifices. This thesis defines and implements a monitoring system of movements in the Earth’s crust in the islands of Tenerife and La Palma, where the social impact of an increase (or variation) of volcanic activity is very severe. Aside from the high demographic density of the archipelago, the population increases significantly in different periods throughout the year due to tourism, which represents a major source of revenue for the islands. The population and the tourist centres are mainly spread along the coasts and also along the flanks of the volcanic edifices. Perhaps the preservation of these social and socio-economic structures is the most important reason that justifies monitoring volcanic activity in the Canary Islands. Recently more and more work has been done with the intention of predicting volcanic activity, using new geodesic monitoring systems, since volcanic activity is evident prior to eruption because of a deformation of the Earth’s crust and changes in the force of gravity in the zone where volcanic events will later be recorded. The new devices and sensors that have been developed in recent years in areas such as geodesy, the observation of the Earth from space, and satellite positioning have allowed us to observe and measure the deformation produced in the Earth as well as the changes in the force of gravity before, during, and after the volcanic events occur. The new devices and sensors have changed the geodetic techniques and methodologies that were used previously. The classic methods have been renovated and other newer ones developed that are now vouched for as proven recognised methodologies to be used for volcanic monitoring. Since the end of the 1990s, in the Canary Islands various projects have been developed whose principal aim has been the development of new observation and monitoring techniques on the one hand, and the design of an appropriate volcanic monitoring methodology on the other. The study and development of GNSS techniques for the monitoring of crustal deformations and their velocity field is presented here. To carry out the study, the use of geodetic infrastructure and existing monitoring in the archipelago have been taken into account in order to optimise costs, besides complementing it with new stations for total coverage on both islands. The results obtained in the projects, which are described below, have produced new perspectives in the geodetic monitoring of volcanic activity and new zones of interest which previously were unknown in the environment of the Canary Islands. Special care has been taken with the treatment and propagation of errors during the entire process of observing, measuring, and processing the recorded data. All of this was done in order to quantify the degree of trustworthiness of the results obtained. Also in this sense, the results obtained have been verified with others from satellite radar observation systems, incorporating as well in this study the implications that the joint use of radar technologies and GNSS will have for the future of monitoring deformations in the Earth’s crust.

Combining Wireless Sensor Networks and Semantic Middleware for an Internet of Things-Based Sportsman/Woman Monitoring Application.

Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

Non-invasive user monitoring in daily tasks for cognitive rehabilitation

El uso de técnicas para la monitorización del movimiento humano generalmente permite a los investigadores analizar la cinemática y especialmente las capacidades motoras en aquellas actividades de la vida cotidiana que persiguen un objetivo concreto como pueden ser la preparación de bebidas y comida, e incluso en tareas de aseo. Adicionalmente, la evaluación del movimiento y el comportamiento humanos en el campo de la rehabilitación cognitiva es esencial para profundizar en las dificultades que algunas personas encuentran en la ejecución de actividades diarias después de accidentes cerebro-vasculares. Estas dificultades están principalmente asociadas a la realización de pasos secuenciales y al reconocimiento del uso de herramientas y objetos. La interpretación de los datos sobre la actitud de este tipo de pacientes para reconocer y determinar el nivel de éxito en la ejecución de las acciones, y para ampliar el conocimiento en las enfermedades cerebrales, sus consecuencias y severidad, depende totalmente de los dispositivos usados para la captura de esos datos y de la calidad de los mismos. Más aún, existe una necesidad real de mejorar las técnicas actuales de rehabilitación cognitiva contribuyendo al diseño de sistemas automáticos para crear una especie de terapeuta virtual que asegure una vida más independiente de estos pacientes y reduzca la carga de trabajo de los terapeutas. Con este objetivo, el uso de sensores y dispositivos para obtener datos en tiempo real de la ejecución y estado de la tarea de rehabilitación es esencial para también contribuir al diseño y entrenamiento de futuros algoritmos que pudieran reconocer errores automáticamente para informar al paciente acerca de ellos mediante distintos tipos de pistas como pueden ser imágenes, mensajes auditivos o incluso videos. La tecnología y soluciones existentes en este campo no ofrecen una manera totalmente robusta y efectiva para obtener datos en tiempo real, por un lado, porque pueden influir en el movimiento del propio paciente en caso de las plataformas basadas en el uso de marcadores que necesitan sensores pegados en la piel; y por otro lado, debido a la complejidad o alto coste de implantación lo que hace difícil pensar en la idea de instalar un sistema en el hospital o incluso en la casa del paciente. Esta tesis presenta la investigación realizada en el campo de la monitorización del movimiento de pacientes para proporcionar un paso adelante en términos de detección, seguimiento y reconocimiento del comportamiento de manos, gestos y cara mediante una manera no invasiva la cual puede mejorar la técnicas actuales de rehabilitación cognitiva para la adquisición en tiempo real de datos sobre el comportamiento del paciente y la ejecución de la tarea. Para entender la importancia del marco de esta tesis, inicialmente se presenta un resumen de las principales enfermedades cognitivas y se introducen las consecuencias que tienen en la ejecución de tareas de la vida diaria. Más aún, se investiga sobre las metodologías actuales de rehabilitación cognitiva. Teniendo en cuenta que las manos son la principal parte del cuerpo para la ejecución de tareas manuales de la vida cotidiana, también se resumen las tecnologías existentes para la captura de movimiento de manos. Una de las principales contribuciones de esta tesis está relacionada con el diseño y evaluación de una solución no invasiva para detectar y seguir las manos durante la ejecución de tareas manuales de la vida cotidiana que a su vez involucran la manipulación de objetos. Esta solución la cual no necesita marcadores adicionales y está basada en una cámara de profundidad de bajo coste, es robusta, precisa y fácil de instalar. Otra contribución presentada se centra en el reconocimiento de gestos para detectar el agarre de objetos basado en un sensor infrarrojo de última generación, y también complementado con una cámara de profundidad. Esta nueva técnica, y también no invasiva, sincroniza ambos sensores para seguir objetos específicos además de reconocer eventos concretos relacionados con tareas de aseo. Más aún, se realiza una evaluación preliminar del reconocimiento de expresiones faciales para analizar si es adecuado para el reconocimiento del estado de ánimo durante la tarea. Por su parte, todos los componentes y algoritmos desarrollados son integrados en un prototipo simple para ser usado como plataforma de monitorización. Se realiza una evaluación técnica del funcionamiento de cada dispositivo para analizar si es adecuada para adquirir datos en tiempo real durante la ejecución de tareas cotidianas reales. Finalmente, se estudia la interacción con pacientes reales para obtener información del nivel de usabilidad del prototipo. Dicha información es esencial y útil para considerar una rehabilitación cognitiva basada en la idea de instalación del sistema en la propia casa del paciente al igual que en el hospital correspondiente. ABSTRACT The use of human motion monitoring techniques usually let researchers to analyse kinematics, especially in motor strategies for goal-oriented activities of daily living, such as the preparation of drinks and food, and even grooming tasks. Additionally, the evaluation of human movements and behaviour in the field of cognitive rehabilitation is essential to deep into the difficulties some people find in common activities after stroke. This difficulties are mainly associated with sequence actions and the recognition of tools usage. The interpretation of attitude data of this kind of patients in order to recognize and determine the level of success of the execution of actions, and to broaden the knowledge in brain diseases, consequences and severity, depends totally on the devices used for the capture of that data and the quality of it. Moreover, there is a real need of improving the current cognitive rehabilitation techniques by contributing to the design of automatic systems to create a kind of virtual therapist for the improvement of the independent life of these stroke patients and to reduce the workload of the occupational therapists currently in charge of them. For this purpose, the use of sensors and devices to obtain real time data of the execution and state of the rehabilitation task is essential to also contribute to the design and training of future smart algorithms which may recognise errors to automatically provide multimodal feedback through different types of cues such as still images, auditory messages or even videos. The technology and solutions currently adopted in the field don't offer a totally robust and effective way for obtaining real time data, on the one hand, because they may influence the patient's movement in case of marker-based platforms which need sensors attached to the skin; and on the other hand, because of the complexity or high cost of implementation, which make difficult the idea of installing a system at the hospital or even patient's home. This thesis presents the research done in the field of user monitoring to provide a step forward in terms of detection, tracking and recognition of hand movements, gestures and face via a non-invasive way which could improve current techniques for cognitive rehabilitation for real time data acquisition of patient's behaviour and execution of the task. In order to understand the importance of the scope of the thesis, initially, a summary of the main cognitive diseases that require for rehabilitation and an introduction of the consequences on the execution of daily tasks are presented. Moreover, research is done about the actual methodology to provide cognitive rehabilitation. Considering that the main body members involved in the completion of a handmade daily task are the hands, the current technologies for human hands movements capture are also highlighted. One of the main contributions of this thesis is related to the design and evaluation of a non-invasive approach to detect and track user's hands during the execution of handmade activities of daily living which involve the manipulation of objects. This approach does not need the inclusion of any additional markers. In addition, it is only based on a low-cost depth camera, it is robust, accurate and easy to install. Another contribution presented is focused on the hand gesture recognition for detecting object grasping based on a brand new infrared sensor, and also complemented with a depth camera. This new, and also non-invasive, solution which synchronizes both sensors to track specific tools as well as recognize specific events related to grooming is evaluated. Moreover, a preliminary assessment of the recognition of facial expressions is carried out to analyse if it is adequate for recognizing mood during the execution of task. Meanwhile, all the corresponding hardware and software developed are integrated in a simple prototype with the purpose of being used as a platform for monitoring the execution of the rehabilitation task. Technical evaluation of the performance of each device is carried out in order to analyze its suitability to acquire real time data during the execution of real daily tasks. Finally, a kind of healthcare evaluation is also presented to obtain feedback about the usability of the system proposed paying special attention to the interaction with real users and stroke patients. This feedback is quite useful to consider the idea of a home-based cognitive rehabilitation as well as a possible hospital installation of the prototype.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

A Model-Based Monitoring Architecture for Heterogeneous Enterprise Services and Information Systems

Runtime management of distributed information systems is a complex and costly activity. One of the main challenges that must be addressed is obtaining a complete and updated view of all the managed runtime resources. This article presents a monitoring architecture for heterogeneous and distributed information systems. It is composed of two elements: an information model and an agent infrastructure. The model negates the complexity and variability of these systems and enables the abstraction over non-relevant details. The infrastructure uses this information model to monitor and manage the modeled environment, performing and detecting changes in execution time. The agents infrastructure is further detailed and its components and the relationships between them are explained. Moreover, the proposal is validated through a set of agents that instrument the JEE Glassfish application server, paying special attention to support distributed configuration scenarios.

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information.

A Light-Weight On-Chip Monitoring Network for Dynamic Adaptation and Calibration

Current nanometer technologies suffer within-die parameter uncertainties, varying workload conditions, aging, and temperature effects that cause a serious reduction on yield and performance. In this scenario, monitoring, calibration, and dynamic adaptation become essential, demanding systems with a collection of multi purpose monitors and exposing the need for light-weight monitoring networks. This paper presents a new monitoring network paradigm able to perform an early prioritization of the information. This is achieved by the introduction of a new hierarchy level, the threshing level. Targeting it, we propose a time-domain signaling scheme over a single-wire that minimizes the network switching activity as well as the routing requirements. To validate our approach, we make a thorough analysis of the architectural trade-offs and expose two complete monitoring systems that suppose an area improvement of 40% and a power reduction of three orders of magnitude compared to previous works.

On the monitoring of surface displacement in connection with volcano reactivation in Tenerife, Canary Islands, using space techniques

Geodetic volcano monitoring in Tenerife has mainly focused on the Las Cañadas Caldera, where a geodetic micronetwork and a levelling profile are located. A sensitivity test of this geodetic network showed that it should be extended to cover the whole island for volcano monitoring purposes. Furthermore, InSAR allowed detecting two unexpected movements that were beyond the scope of the traditional geodetic network. These two facts prompted us to design and observe a GPS network covering the whole of Tenerife that was monitored in August 2000. The results obtained were accurate to one centimetre, and confirm one of the deformations, although they were not definitive enough to confirm the second one. Furthermore, new cases of possible subsidence have been detected in areas where InSAR could not be used to measure deformation due to low coherence. A first modelling attempt has been made using a very simple model and its results seem to indicate that the deformation observed and the groundwater level variation in the island may be related. Future observations will be necessary for further validation and to study the time evolution of the displacements, carry out interpretation work using different types of data (gravity, gases, etc) and develop models that represent the island more closely. The results obtained are important because they might affect the geodetic volcano monitoring on the island, which will only be really useful if it is capable of distinguishing between displacements that might be linked to volcanic activity and those produced by other causes. One important result in this work is that a new geodetic monitoring system based on two complementary techniques, InSAR and GPS, has been set up on Tenerife island. This the first time that the whole surface of any of the volcanic Canary Islands has been covered with a single network for this purpose. This research has displayed the need for further similar studies in the Canary Islands, at least on the islands which pose a greater risk of volcanic reactivation, such as Lanzarote and La Palma, where InSAR techniques have been used already.

Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

A Method for Developing Ontologies from User-Generated Classication Systems

Abstract Web 2.0 applications enabled users to classify information resources using their own vocabularies. The bottom-up nature of these user-generated classification systems have turned them into interesting knowledge sources, since they provide a rich terminology generated by potentially large user communities. Previous research has shown that it is possible to elicit some emergent semantics from the aggregation of individual classifications in these systems. However the generation of ontologies from them is still an open research problem. In this thesis we address the problem of how to tap into user-generated classification systems for building domain ontologies. Our objective is to design a method to develop domain ontologies from user-generated classifications systems. To do so, we rely on ontologies in the Web of Data to formalize the semantics of the knowledge collected from the classification system. Current ontology development methodologies have recognized the importance of reusing knowledge from existing resources. Thus, our work is framed within the NeOn methodology scenario for building ontologies by reusing and reengineering non-ontological resources. The main contributions of this work are: An integrated method to develop ontologies from user-generated classification systems. With this method we extract a domain terminology from the classification system and then we formalize the semantics of this terminology by reusing ontologies in the Web of Data. Identification and adaptation of existing techniques for implementing the activities in the method so that they can fulfill the requirements of each activity. A novel study about emerging semantics in user-generated lists. Resumen La web 2.0 permitió a los usuarios clasificar recursos de información usando su propio vocabulario. Estos sistemas de clasificación generados por usuarios son recursos interesantes para la extracción de conocimiento debido principalmente a que proveen una extensa terminología generada por grandes comunidades de usuarios. Se ha demostrado en investigaciones previas que es posible obtener una semántica emergente de estos sistemas. Sin embargo la generación de ontologías a partir de ellos es todavía un problema de investigación abierto. Esta tesis trata el problema de cómo aprovechar los sistemas de clasificación generados por usuarios en la construcción de ontologías de dominio. Así el objetivo de la tesis es diseñar un método para desarrollar ontologías de dominio a partir de sistemas de clasificación generados por usuarios. El método propuesto reutiliza conceptualizaciones existentes en ontologías publicadas en la Web de Datos para formalizar la semántica del conocimiento que se extrae del sistema de clasificación. Por tanto, este trabajo está enmarcado dentro del escenario para desarrollar ontologías mediante la reutilización y reingeniería de recursos no ontológicos que se ha definido en la Metodología NeOn. Las principales contribuciones de este trabajo son: Un método integrado para desarrollar una ontología de dominio a partir de sistemas de clasificación generados por usuarios. En este método se extrae una terminología de dominio del sistema de clasificación y posteriormente se formaliza su semántica reutilizando ontologías en la Web de Datos. La identificación y adaptación de un conjunto de técnicas para implementar las actividades propuestas en el método de tal manera que puedan cumplir automáticamente los requerimientos de cada actividad. Un novedoso estudio acerca de la semántica emergente en las listas generadas por usuarios en la Web.