Integration of research advances in modelling and monitoring in support of WFD river basin management planning in the context of climate change

The integration of scientific knowledge about possible climate change impacts on water resources has a direct implication on the way water policies are being implemented and evolving. This is particularly true regarding various technical steps embedded into the EU Water Framework Directive river basin management planning, such as risk characterisation, monitoring, design and implementation of action programmes and evaluation of the "good status" objective achievements (in 2015). The need to incorporate climate change considerations into the implementation of EU water policy is currently discussed with a wide range of experts and stakeholders at EU level. Research trends are also on-going, striving to support policy developments and examining how scientific findings and recommendations could be best taken on board by policy-makers and water managers within the forthcoming years. This paper provides a snapshot of policy discussions about climate change in the context of the WFD river basin management planning and specific advancements of related EU-funded research projects. Perspectives for strengthening links among the scientific and policy-making communities in this area are also highlighted.

Independence, Global analysis, and parallelism, in dinamically scheduled constraint logic programming.

El punto de vista de muchas otras aplicaciones que modifican las reglas de computación. En segundo lugar, y una vez generalizado el concepto de independencia, es necesario realizar un estudio exhaustivo de la efectividad de las herramientas de análisis en la tarea de la paralelizacion automática. Los resultados obtenidos de dicha evaluación permiten asegurar de forma empírica que la utilización de analizadores globales en la tarea de la paralelizacion automática es vital para la consecución de una paralelizarían efectiva. Por último, a la luz de los buenos resultados obtenidos sobre la efectividad de los analizadores de flujo globales basados en la interpretación abstracta, se presenta la generalización de las herramientas de análisis al contexto de los lenguajes lógicos restricciones y planificación dinámica.

Broadband access in complex environments: LTE on railway

This paper assesses the main challenges associated with the propagation and channel modeling of broadband radio systems in a complex environment of high speed and metropolitan railways. These challenges comprise practical simulation, modeling interferences, radio planning, test trials and performance evaluation in different railway scenarios using Long Term Evolution (LTE) as test case. This approach requires several steps; the first is the use of a radio propagation simulator based on ray-tracing techniques to accurately predict propagation. Besides the radio propagation simulator, a complete test bed has been constructed to assess LTE performance, channel propagation conditions and interference with other systems in real-world environments by means of standard-compliant LTE transmissions. Such measurement results allowed us to evaluate the propagation and performance of broadband signals and to test the suitability of LTE radio technology for complex railway scenarios.

Rule Representation in Distributed Environments with Accepting Networks of Splicing Processors.

This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem solver inspired in the biological DNA operations. A processor has a rules set, splicing rules in this model,an object multiset and a filters set. Rules can be applied in parallel since there exists a large number of copies of objects in the multiset. Processors can form a graph in order to solve a given problem. This paper shows the network configuration in order to solve the SAT problem using linear resources and time. A rule representation arquitecture in distributed environments can be easily implemented using these networks of processors, such as decision support systems, as shown in the paper.

Energy Management Module for Mobile Robots in Hostile Environments

In hostile environments at CERN and other similar scientific facilities, having a reliable mobile robot system is essential for successful execution of robotic missions and to avoid situations of manual recovery of the robots in the event that the robot runs out of energy. Because of environmental constraints, such mobile robots are usually battery-powered and hence energy management and optimization is one of the key challenges in this field. The ability to know beforehand the energy consumed by various elements of the robot (such as locomotion, sensors, controllers, computers and communication) will allow flexibility in planning or managing the tasks to be performed by the robot.

Classical vs. biometric features in the 2013 speaker recognition evaluation in mobile environments

MFCC coefficients extracted from the power spectral density of speech as a whole, seems to have become the de facto standard in the area of speaker recognition, as demonstrated by its use in almost all systems submitted to the 2013 Speaker Recognition Evaluation (SRE) in Mobile Environment [1], thus relegating to background this component of the recognition systems. However, in this article we will show that selecting the adequate speaker characterization system is as important as the selection of the classifier. To accomplish this we will compare the recognition rates achieved by different recognition systems that relies on the same classifier (GMM-UBM) but connected with different feature extraction systems (based on both classical and biometric parameters). As a result we will show that a gender dependent biometric parameterization with a simple recognition system based on GMM- UBM paradigm provides very competitive or even better recognition rates when compared to more complex classification systems based on classical features

Temporal task allocation in periodic environments. An approach based on synchronization

In this paper, we study a robot swarm that has to perform task allocation in an environment that features periodic properties. In this environment, tasks appear in different areas following periodic temporal patterns. The swarm has to reallocate its workforce periodically, performing a temporal task allocation that must be synchronized with the environment to be effective. We tackle temporal task allocation using methods and concepts that we borrow from the signal processing literature. In particular, we propose a distributed temporal task allocation algorithm that synchronizes robots of the swarm with the environment and with each other. In this algorithm, robots use only local information and a simple visual communication protocol based on light blinking. Our results show that a robot swarm that uses the proposed temporal task allocation algorithm performs considerably more tasks than a swarm that uses a greedy algorithm.

Modelling and Control of Stepper Motors for High Accuracy Positioning Systems Used in Radioactive Environments

Hybrid Stepper Motors are widely used in open-loop position applications. They are the choice of actuation for the collimators in the Large Hadron Collider, the largest particle accelerator at CERN. In this case the positioning requirements and the highly radioactive operating environment are unique. The latter forces both the use of long cables to connect the motors to the drives which act as transmission lines and also prevents the use of standard position sensors. However, reliable and precise operation of the collimators is critical for the machine, requiring the prevention of step loss in the motors and maintenance to be foreseen in case of mechanical degradation. In order to make the above possible, an approach is proposed for the application of an Extended Kalman Filter to a sensorless stepper motor drive, when the motor is separated from its drive by long cables. When the long cables and high frequency pulse width modulated control voltage signals are used together, the electrical signals difer greatly between the motor and drive-side of the cable. Since in the considered case only drive-side data is available, it is therefore necessary to estimate the motor-side signals. Modelling the entire cable and motor system in an Extended Kalman Filter is too computationally intensive for standard embedded real-time platforms. It is, in consequence, proposed to divide the problem into an Extended Kalman Filter, based only on the motor model, and separated motor-side signal estimators, the combination of which is less demanding computationally. The efectiveness of this approach is shown in simulation. Then its validity is experimentally demonstrated via implementation in a DSP based drive. A testbench to test its performance when driving an axis of a Large Hadron Collider collimator is presented along with the results achieved. It is shown that the proposed method is capable of achieving position and load torque estimates which allow step loss to be detected and mechanical degradation to be evaluated without the need for physical sensors. These estimation algorithms often require a precise model of the motor, but the standard electrical model used for hybrid stepper motors is limited when currents, which are high enough to produce saturation of the magnetic circuit, are present. New model extensions are proposed in order to have a more precise model of the motor independently of the current level, whilst maintaining a low computational cost. It is shown that a significant improvement in the model It is achieved with these extensions, and their computational performance is compared to study the cost of model improvement versus computation cost. The applicability of the proposed model extensions is demonstrated via their use in an Extended Kalman Filter running in real-time for closed-loop current control and mechanical state estimation. An additional problem arises from the use of stepper motors. The mechanics of the collimators can wear due to the abrupt motion and torque profiles that are applied by them when used in the standard way, i.e. stepping in open-loop. Closed-loop position control, more specifically Field Oriented Control, would allow smoother profiles, more respectful to the mechanics, to be applied but requires position feedback. As mentioned already, the use of sensors in radioactive environments is very limited for reliability reasons. Sensorless control is a known option but when the speed is very low or zero, as is the case most of the time for the motors used in the LHC collimator, the loss of observability prevents its use. In order to allow the use of position sensors without reducing the long term reliability of the whole system, the possibility to switch from closed to open loop is proposed and validated, allowing the use of closed-loop control when the position sensors function correctly and open-loop when there is a sensor failure. A different approach to deal with the switched drive working with long cables is also presented. Switched mode stepper motor drives tend to have poor performance or even fail completely when the motor is fed through a long cable due to the high oscillations in the drive-side current. The design of a stepper motor output fillter which solves this problem is thus proposed. A two stage filter, one devoted to dealing with the diferential mode and the other with the common mode, is designed and validated experimentally. With this ?lter the drive performance is greatly improved, achieving a positioning repeatability even better than with the drive working without a long cable, the radiated emissions are reduced and the overvoltages at the motor terminals are eliminated.

Distributed control strategies for photovoltaic systems in urban environments

Este trabajo es una contribución a los sistemas fotovoltaicos (FV) con seguimiento distribuido del punto de máxima potencia (DMPPT), una topología que se caracteriza porque lleva a cabo el MPPT a nivel de módulo, al contrario de las topologías más tradicionales que llevan a cabo el MPPT para un número más elevado de módulos, pudiendo ser hasta cientos de módulos. Las dos tecnologías DMPPT que existen en el mercado son conocidos como microinversores y optimizadores de potencia, y ofrecen ciertas ventajas sobre sistemas de MPPT central como: mayor producción en situaciones de mismatch, monitorización individual de cada módulo, flexibilidad de diseño, mayor seguridad del sistema, etc. Aunque los sistemas DMPPT no están limitados a los entornos urbanos, se ha enfatizado en el título ya que es su mercado natural, siendo difícil una justificación de su sobrecoste en grandes huertas solares en suelo. Desde el año 2010 el mercado de estos sistemas ha incrementado notablemente y sigue creciendo de una forma continuada. Sin embargo, todavía falta un conocimiento profundo de cómo funcionan estos sistemas, especialmente en el caso de los optimizadores de potencia, de las ganancias energéticas esperables en condiciones de mismatch y de las posibilidades avanzadas de diagnóstico de fallos. El principal objetivo de esta tesis es presentar un estudio completo de cómo funcionan los sistemas DMPPT, sus límites y sus ventajas, así como experimentos varios que verifican la teoría y el desarrollo de herramientas para valorar las ventajas de utilizar DMPPT en cada instalación. Las ecuaciones que modelan el funcionamiento de los sistemas FVs con optimizadores de potencia se han desarrollado y utilizado para resaltar los límites de los mismos a la hora de resolver ciertas situaciones de mismatch. Se presenta un estudio profundo sobre el efecto de las sombras en los sistemas FVs: en la curva I-V y en los algoritmos MPPT. Se han llevado a cabo experimentos sobre el funcionamiento de los algoritmos MPPT en situaciones de sombreado, señalando su ineficiencia en estas situaciones. Un análisis de la ventaja del uso de DMPPT frente a los puntos calientes es presentado y verificado. También se presenta un análisis sobre las posibles ganancias en potencia y energía con el uso de DMPPT en condiciones de sombreado y este también es verificado experimentalmente, así como un breve estudio de su viabilidad económica. Para ayudar a llevar a cabo todos los análisis y experimentos descritos previamente se han desarrollado una serie de herramientas software. Una siendo un programa en LabView para controlar un simulador solar y almacenar las medidas. También se ha desarrollado un programa que simula curvas I-V de módulos y generador FVs afectados por sombras y este se ha verificado experimentalmente. Este mismo programa se ha utilizado para desarrollar un programa todavía más completo que estima las pérdidas anuales y las ganancias obtenidas con DMPPT en instalaciones FVs afectadas por sombras. Finalmente, se han desarrollado y verificado unos algoritmos para diagnosticar fallos en sistemas FVs con DMPPT. Esta herramienta puede diagnosticar los siguientes fallos: sombras debido a objetos fijos (con estimación de la distancia al objeto), suciedad localizada, suciedad general, posible punto caliente, degradación de módulos y pérdidas en el cableado de DC. Además, alerta al usuario de las pérdidas producidas por cada fallo y no requiere del uso de sensores de irradiancia y temperatura. ABSTRACT This work is a contribution to photovoltaic (PV) systems with distributed maximum power point tracking (DMPPT), a system topology characterized by performing the MPPT at module level, instead of the more traditional topologies which perform MPPT for a larger number of modules. The two DMPPT technologies available at the moment are known as microinverters and power optimizers, also known as module level power electronics (MLPE), and they provide certain advantages over central MPPT systems like: higher energy production in mismatch situations, monitoring of each individual module, system design flexibility, higher system safety, etc. Although DMPPT is not limited to urban environments, it has been emphasized in the title as it is their natural market, since in large ground-mounted PV plants the extra cost is difficult to justify. Since 2010 MLPE have increased their market share steadily and continuing to grow steadily. However, there still lacks a profound understanding of how they work, especially in the case of power optimizers, the achievable energy gains with their use and the possibilities in failure diagnosis. The main objective of this thesis is to provide a complete understanding of DMPPT technologies: how they function, their limitations and their advantages. A series of equations used to model PV arrays with power optimizers have been derived and used to point out limitations in solving certain mismatch situation. Because one of the most emphasized benefits of DMPPT is their ability to mitigate shading losses, an extensive study on the effects of shadows on PV systems is presented; both on the I-V curve and on MPPT algorithms. Experimental tests have been performed on the MPPT algorithms of central inverters and MLPE, highlighting their inefficiency in I-V curves with local maxima. An analysis of the possible mitigation of hot-spots with DMPPT is discussed and experimentally verified. And a theoretical analysis of the possible power and energy gains is presented as well as experiments in real PV systems. A short economic analysis of the benefits of DMPPT has also been performed. In order to aide in the previous task, a program which simulates I-V curves under shaded conditions has been developed and experimentally verified. This same program has been used to develop a software tool especially designed for PV systems affected by shading, which estimates the losses due to shading and the energy gains obtained with DMPPT. Finally, a set of algorithms for diagnosing system faults in PV systems with DMPPT has been developed and experimentally verified. The tool can diagnose the following failures: fixed object shading (with distance estimation), localized dirt, generalized dirt, possible hot-spots, module degradation and excessive losses in DC cables. In addition, it alerts the user of the power losses produced by each failure and classifies the failures by their severity and it does not require the use of irradiance or temperature sensors.

Distributed control strategies for photovoltaic systems in urban environments : Versión definitiva

Este trabajo es una contribución a los sistemas fotovoltaicos (FV) con seguimiento distribuido del punto de máxima potencia (DMPPT), una topología que se caracteriza porque lleva a cabo el MPPT a nivel de módulo, al contrario de las topologías más tradicionales que llevan a cabo el MPPT para un número más elevado de módulos, pudiendo ser hasta cientos de módulos. Las dos tecnologías DMPPT que existen en el mercado son conocidos como microinversores y optimizadores de potencia, y ofrecen ciertas ventajas sobre sistemas de MPPT central como: mayor producción en situaciones de mismatch, monitorización individual de cada módulo, flexibilidad de diseño, mayor seguridad del sistema, etc. Aunque los sistemas DMPPT no están limitados a los entornos urbanos, se ha enfatizado en el título ya que es su mercado natural, siendo difícil una justificación de su sobrecoste en grandes huertas solares en suelo. Desde el año 2010 el mercado de estos sistemas ha incrementado notablemente y sigue creciendo de una forma continuada. Sin embargo, todavía falta un conocimiento profundo de cómo funcionan estos sistemas, especialmente en el caso de los optimizadores de potencia, de las ganancias energéticas esperables en condiciones de mismatch y de las posibilidades avanzadas de diagnóstico de fallos. El principal objetivo de esta tesis es presentar un estudio completo de cómo funcionan los sistemas DMPPT, sus límites y sus ventajas, así como experimentos varios que verifican la teoría y el desarrollo de herramientas para valorar las ventajas de utilizar DMPPT en cada instalación. Las ecuaciones que modelan el funcionamiento de los sistemas FVs con optimizadores de potencia se han desarrollado y utilizado para resaltar los límites de los mismos a la hora de resolver ciertas situaciones de mismatch. Se presenta un estudio profundo sobre el efecto de las sombras en los sistemas FVs: en la curva I-V y en los algoritmos MPPT. Se han llevado a cabo experimentos sobre el funcionamiento de los algoritmos MPPT en situaciones de sombreado, señalando su ineficiencia en estas situaciones. Un análisis de la ventaja del uso de DMPPT frente a los puntos calientes es presentado y verificado. También se presenta un análisis sobre las posibles ganancias en potencia y energía con el uso de DMPPT en condiciones de sombreado y este también es verificado experimentalmente, así como un breve estudio de su viabilidad económica. Para ayudar a llevar a cabo todos los análisis y experimentos descritos previamente se han desarrollado una serie de herramientas software. Una siendo un programa en LabView para controlar un simulador solar y almacenar las medidas. También se ha desarrollado un programa que simula curvas I-V de módulos y generador FVs afectados por sombras y este se ha verificado experimentalmente. Este mismo programa se ha utilizado para desarrollar un programa todavía más completo que estima las pérdidas anuales y las ganancias obtenidas con DMPPT en instalaciones FVs afectadas por sombras. Finalmente, se han desarrollado y verificado unos algoritmos para diagnosticar fallos en sistemas FVs con DMPPT. Esta herramienta puede diagnosticar los siguientes fallos: sombras debido a objetos fijos (con estimación de la distancia al objeto), suciedad localizada, suciedad general, posible punto caliente, degradación de módulos y pérdidas en el cableado de DC. Además, alerta al usuario de las pérdidas producidas por cada fallo y no requiere del uso de sensores de irradiancia y temperatura. ABSTRACT This work is a contribution to photovoltaic (PV) systems with distributed maximum power point tracking (DMPPT), a system topology characterized by performing the MPPT at module level, instead of the more traditional topologies which perform MPPT for a larger number of modules. The two DMPPT technologies available at the moment are known as microinverters and power optimizers, also known as module level power electronics (MLPE), and they provide certain advantages over central MPPT systems like: higher energy production in mismatch situations, monitoring of each individual module, system design flexibility, higher system safety, etc. Although DMPPT is not limited to urban environments, it has been emphasized in the title as it is their natural market, since in large ground-mounted PV plants the extra cost is difficult to justify. Since 2010 MLPE have increased their market share steadily and continuing to grow steadily. However, there still lacks a profound understanding of how they work, especially in the case of power optimizers, the achievable energy gains with their use and the possibilities in failure diagnosis. The main objective of this thesis is to provide a complete understanding of DMPPT technologies: how they function, their limitations and their advantages. A series of equations used to model PV arrays with power optimizers have been derived and used to point out limitations in solving certain mismatch situation. Because one of the most emphasized benefits of DMPPT is their ability to mitigate shading losses, an extensive study on the effects of shadows on PV systems is presented; both on the I-V curve and on MPPT algorithms. Experimental tests have been performed on the MPPT algorithms of central inverters and MLPE, highlighting their inefficiency in I-V curves with local maxima. An analysis of the possible mitigation of hot-spots with DMPPT is discussed and experimentally verified. And a theoretical analysis of the possible power and energy gains is presented as well as experiments in real PV systems. A short economic analysis of the benefits of DMPPT has also been performed. In order to aide in the previous task, a program which simulates I-V curves under shaded conditions has been developed and experimentally verified. This same program has been used to develop a software tool especially designed for PV systems affected by shading, which estimates the losses due to shading and the energy gains obtained with DMPPT. Finally, a set of algorithms for diagnosing system faults in PV systems with DMPPT has been developed and experimentally verified. The tool can diagnose the following failures: fixed object shading (with distance estimation), localized dirt, generalized dirt, possible hot-spots, module degradation and excessive losses in DC cables. In addition, it alerts the user of the power losses produced by each failure and classifies the failures by their severity and it does not require the use of irradiance or temperature sensors.

On the role of global flow instability analysis in closed loop flow control

Control of linear flow instabilities has been demonstrated to be an effective theoretical flow control methodology, capable of modifying transitional flows on canonical geometries such as the plane channel and the flat-plate boundary layer. Extending the well-developed theoretical flow control techniques to flows over or through complex geometries requires addressing the issue of efficient capturing of the leading members of the global eigenspectrum pertinent to such flows. The present contribution describes state-of-the-art modal global instability analysis methodologies recently developed in our group, based on matrix formation and time-stepping, respectively. The relative performance of these algorithms is assessed on the recovery of BiGlobal and TriGlobal eigenspectra in the spanwise periodic and the cubic lid-driven cavity, respectively; the adjoint eigenspectrum in the latter flow is recovered for the first time. For three-dimensional flows without any homogeneous spatial direction, the time-stepping methodology was found to outperform the matrix-forming approach and permit recovering the leading TriGlobal eigenmodes in an three-dimensional open cavity of aspect ratio L : D : W = 5 : 1 : 1; theoretical flow control of this configuration is underway.

Multiobjective structural damage identification in uncertain environments

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this paper, a statistic structural damage detection method formulated in a multiobjective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The presented method is verified by a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.

On the role of global flow instability analysis in closed loop flow control

Control of linear flow instabilities has been demonstrated to be an effective theoretical flow control methodology, capable of modifying transitional flow on canonical geometries such as the plane channel and the flat-plate boundary layer.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Accessible interaction solution based on confidence for the deployment of pervasive sensitive services in intelligent environments

Los servicios basados en las Tecnologías de la Información y las Comunicaciones (TIC) están cada vez más presentes en la vida de las personas. El avance de las TIC en términos técnicos y de aceptación social ha dado lugar la creación de nuevos modelos de provisión de servicios. Estos modelos de servicio implican una mayor integración con las actividades de las personas de forma que ya no solo están presentes en su ámbito profesional o de espacio ciudadano sino también en un ámbito más íntimo relacionado con su propia identidad. Así en la actualidad es común encontrar servicios conocedores del estado de salud de las personas, sus hábitos domésticos, su ideología, etc. Por tanto el análisis de los servicios actuales no puede limitarse a una componente técnica sino que es más necesario que nunca la inclusión de aspectos más relacionados con la forma de ser y sentir de sus usuarios. De esta forma no solo se garantizará la corrección técnica de sus funcionalidades sino que también se fomentará la generación de soluciones cívicamente seguras y respetuosas tanto con los derechos como con la forma de ser y sentir de sus usuarios. Desde el punto de vista de ingeniería, la perspectiva del usuario se ha englobado históricamente bajo el concepto de aceptación tecnológica. Dentro de este ámbito se puede interpretar que soluciones respetuosas y adaptadas a los usuarios fomentarán la aceptación por parte de éstos. La aceptación de una solución es algo deseable si bien es difícil de asegurar. Esta dificultad es debida al desconocimiento del número de variables que afectan a la aceptación de soluciones tecnológicas y a la dificultad de optimización de las variables conocidas. En esta tesis doctoral se estudia y caracteriza una de las variables que afecta a la aceptación de los servicios actuales: la confianza. Se define la confianza en términos psicológicos caracterizándola para permitir su uso en los métodos propios de la ingeniería. Además se proponen distintas herramientas que facilitan la optimización de la confianza en servicios cuya complejidad convierte a esta variable en una cuestión básica para mejorar la aceptación. Como contexto de trabajo para la tesis se ha escogido un servicio de salud desplegado en un hogar. Este escenario presenta una serie de restricciones de aceptación relativas a la tecnología utilizada para la creación de los servicios y la forma en que éstos gestionan la información adquirida del usuario. Se trata de servicios altamente sensibles y deslocalizados que pueden afectar a la percepción del usuario sobre el entorno, el hogar, y generar temores o rechazos que impidan su adopción final como solución válida. Una vez definido el marco genérico de trabajo, el objetivo principal de esta tesis doctoral se concreta en contribuir al fomento de la aceptación de nuevos servicios de salud pervasivos y personalizados y su despliegue en entornos inteligentes domésticos mediante un marco de diseño que promueva un estado psicológico de confianza en los usuarios. Para lograr abordar correctamente este objetivo se han proporcionado una serie de resultados tanto a nivel conceptual como tecnológico y experimental. En concreto se ha ofrecido una caracterización completa del sentimiento de confianza desde un punto de vista de ingeniería y una definición del concepto de servicio sensible deslocalizado o pervasivo. Además se ofrece un método para la inclusión del Diseño de Interacción, herramienta muy relacionada con la mejora de las variables de aceptación de tecnología, en los procesos de ingeniería de este tipo de servicios mediante un conjunto de patrones de interacción Persona – Entorno Inteligente. Finalmente se ha proporcionado el desarrollo de una arquitectura software para garantizar el correcto despliegue de estos servicios sensibles pervasivos en espacios inteligentes de una forma confiable. La discusión de los resultados obtenidos sugiere la extensión del modelo a diferentes servicios de la Sociedad de la Información que manejen datos sensibles tanto en el contexto del Hogar Digital como otros contextos donde el usuario realice actividades cotidianas, como los espacios de trabajo o los centros educativos. Las líneas de trabajo futuras contemplan la necesidad inminente de aplicar los resultados a desarrollos en curso, dentro de proyectos de investigación en los que participa el autor así como el desarrollo de nuevas líneas de investigación orientadas a la generación de nuevos espacios y tecnologías de interacción como el Colegio Digital, juguetes del futuro, sistemas de visualización confiables o sistemas de seguridad basados en el estado de las personas.