Modelado de un servicio de M-Salud basado en dinámica de sistemas

En términos generales, m-salud puede definirse como el conjunto de sistemas de información, sensores médicos y tecnologías de comunicaciones móviles para el cuidado de la salud. La creciente disponibilidad, miniaturización, comportamiento, velocidades de transmisión de datos cada vez mayores y la esperada convergencia de tecnologías de red y comunicaciones inalámbricas en torno a los sistemas de salud móviles están acelerando el despliegue de estos sistemas y la provisión de servicios de m-salud, como por ejemplo, la teleasistencia móvil. El concepto emergente de m-salud conlleva retos importantes (estudios técnicos, análisis, modelado de la provisión de servicios, etc.) que hay que afrontar para impulsar la evolución de los sistemas y servicios de e-salud ofrecidos desde tecnologías de telecomunicación que utilizan acceso por cable y redes fijas, hacia configuraciones móviles e inalámbricas de última generación. En este trabajo se analizará primeramente el significado e implicaciones de m-salud y la situación en la que se encuentra; los retos a los que hay que enfrentarse para su implantación y provisión así como su tendencia. De los múltiples y diferentes servicios que se pueden proveer se ha identificado el servicio de Localización de Personas LoPe, lanzado por Cruz Roja en febrero de 2007, para teleasistencia móvil y que permite conocer en todo momento la ubicación de la persona que porta su dispositivo asociado. Orientado a personas con discapacidad, en situación de riesgo o dependencia por deterioro cognitivo, tiene como objetivo ayudarlas a recuperar su autonomía personal. La provisión de este servicio se modelará mediante dinámica de sistemas, ya que esta teoría se considera idónea para modelar sistemas complejos que evolucionan con el tiempo. El resultado final es un modelo que implementado a través de la herramienta Studio 8® de la compañía noruega Powersim Software AS nos ha permitido analizar y evaluar su comportamiento a lo largo del tiempo, además de permitirnos extraer conclusiones sobre el mismo y plantear futuras mejoras sobre el servicio. ABSTRACT. In general terms, m-health can be defined as “mobile computing, medical sensor, and communications technologies for health care.” The increased availability, miniaturization, performance, enhanced data rates, and the expected convergence of future wireless communication and network technologies around mobile health systems are accelerating the deployment of m-health systems and services, for instance, mobile telecare. The emerging concept of m-health involves significant challenges (technical studies, analysis, modeling of service provision, etc.) that must be tackled to drive the development of e-health services and systems offered by telecommunication technologies that use wired and fixed networks towards wireless and mobile new generation networks. Firstly, in this master’s thesis, the meaning and implications of m-health and its current situation are analyzed. This analysis also includes the challenges that must be tackled for the implementation and provision of m-health technologies and services and the m-health trends. Among the many different m-health services already delivered, the Localización de Personas LoPe service has been identified to work with it. This service, launched by Spanish Red Cross in February 2007, enables to locate people who carry the associated device. It’s aimed at people with disabilities, at risk or dependency due to cognitive impairment and helps them to recover their personal autonomy. The provision of this service will be modeled with system dynamics considering that this theory suits very well the modeling of complex systems which evolve over time. The final result is a system dynamics model of the service implemented with Studio 8® tool developed by Powersim Software AS, a Norwegian company. This model has allowed us to analyze and evaluate its behaviour over time, as well as to draw conclusions and to consider some future improvements in the service.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Non-linear gain - look-up table based approach for modeling a family of DC to DC converters based on transient response analysis

The purpose of this work is to propose a structure for simulating power systems using behavioral models of nonlinear DC to DC converters implemented through a look-up table of gains. This structure is specially designed for converters whose output impedance depends on the load current level, e.g. quasi-resonant converters. The proposed model is a generic one whose parameters can be obtained by direct measuring the transient response at different operating points. It also includes optional functionalities for modeling converters with current limitation and current sharing in paralleling characteristics. The pusposed structured also allows including aditional characteristics of the DC to DC converter as the efficency as a function of the input voltage and the output current or overvoltage and undervoltage protections. In addition, this proposed model is valid for overdamped and underdamped situations.

Analysis of Contributions to NO2 Ambient Air Quality Levels in Madrid City (Spain) through Modeling. Implications for the Development of Policies and Air Quality Monitoring

As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large cities across Europe, particularly for NO2. Modeling air quality in urban areas is rather complex since observed concentration values are a consequence of the interaction of multiple sources and processes that involve a wide range of spatial and temporal scales. Besides a consistent and robust multi-scale modeling system, comprehensive and flexible emission inventories are needed. This paper discusses the application of the WRF-SMOKE-CMAQ system to the Madrid city (Spain) to assess the contribution of the main emitting sectors in the region. A detailed emission inventory was compiled for this purpose. This inventory relies on bottom-up methods for the most important sources. It is coupled with the regional traffic model and it makes use of an extensive database of industrial, commercial and residential combustion plants. Less relevant sources are downscaled from national or regional inventories. This paper reports the methodology and main results of the source apportionment study performed to understand the origin of pollution (main sectors and geographical areas) and define clear targets for the abatement strategy. Finally the structure of the air quality monitoring is analyzed and discussed to identify options to improve the monitoring strategy not only in the Madrid city but the whole metropolitan area.

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

Analysis of house prices to assess economic impacts of new public transport infrastructure: Madrid metro line 12.

Transportation infrastructure is known to affect the value of real estate property by virtue of changes in accessibility. The impact of transportation facilities is highly localized as well, and it is possible that spillover effects result from the capitalization of accessibility. The objective of this study was to review the theoretical background related to spatial hedonic models and the opportunities that they provided to evaluate the effect of new transportation infrastructure. An empirical case study is presented: the Madrid Metro Line 12, known as Metrosur, in the region of Madrid, Spain. The effect of proximity to metro stations on housing prices was evaluated. The analysis took into account a host of variables, including structure, location, and neighborhood and made use of three modeling approaches: linear regression estimation with ordinary least squares, spatial error, and spatial lag. The results indicated that better accessibility to Metrosur stations had a positive impact on real estate values and that the effect was marked in cases in which a house was for sale. The results also showed the presence of submarkets, which were well defined by geographic boundaries, and transport fares, which implied that the economic benefits differed across municipalities.

A graph-based approach for latency modeling and optimization in multiview video encoding

We present a novel framework for encoding latency analysis of arbitrary multiview video coding prediction structures. This framework avoids the need to consider an specific encoder architecture for encoding latency analysis by assuming an unlimited processing capacity on the multiview encoder. Under this assumption, only the influence of the prediction structure and the processing times have to be considered, and the encoding latency is solved systematically by means of a graph model. The results obtained with this model are valid for a multiview encoder with sufficient processing capacity and serve as a lower bound otherwise. Furthermore, with the objective of low latency encoder design with low penalty on rate-distortion performance, the graph model allows us to identify the prediction relationships that add higher encoding latency to the encoder. Experimental results for JMVM prediction structures illustrate how low latency prediction structures with a low rate-distortion penalty can be derived in a systematic manner using the new model.

Automated attribute inference in complex service workflows based on sharing analysis

The properties of data and activities in business processes can be used to greatly facilítate several relevant tasks performed at design- and run-time, such as fragmentation, compliance checking, or top-down design. Business processes are often described using workflows. We present an approach for mechanically inferring business domain-specific attributes of workflow components (including data Ítems, activities, and elements of sub-workflows), taking as starting point known attributes of workflow inputs and the structure of the workflow. We achieve this by modeling these components as concepts and applying sharing analysis to a Horn clause-based representation of the workflow. The analysis is applicable to workflows featuring complex control and data dependencies, embedded control constructs, such as loops and branches, and embedded component services.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Efficient numerical methods for global linear instability. Analysis and modeling of non-orthogonal swept attachment-line boundary layer flow

The aim of this thesis is to study the mechanisms of instability that occur in swept wings when the angle of attack increases. For this, a simplified model for the a simplified model for the non-orthogonal swept leading edge boundary layer has been used as well as different numerical techniques in order to solve the linear stability problem that describes the behavior of perturbations superposed upon this base flow. Two different approaches, matrix-free and matrix forming methods, have been validated using direct numerical simulations with spectral resolution. In this way, flow instability in the non-orthogonal swept attachment-line boundary layer is addressed in a linear analysis framework via the solution of the pertinent global (Bi-Global) PDE-based eigenvalue problem. Subsequently, a simple extension of the extended G¨ortler-H¨ammerlin ODEbased polynomial model proposed by Theofilis, Fedorov, Obrist & Dallmann (2003) for orthogonal flow, which includes previous models as particular cases and recovers global instability analysis results, is presented for non-orthogonal flow. Direct numerical simulations have been used to verify the stability results and unravel the limits of validity of the basic flow model analyzed. The effect of the angle of attack, AoA, on the critical conditions of the non-orthogonal problem has been documented; an increase of the angle of attack, from AoA = 0 (orthogonal flow) up to values close to _/2 which make the assumptions under which the basic flow is derived questionable, is found to systematically destabilize the flow. The critical conditions of non-orthogonal flows at 0 _ AoA _ _/2 are shown to be recoverable from those of orthogonal flow, via a simple analytical transformation involving AoA. These results can help to understand the mechanisms of destabilization that occurs in the attachment line of wings at finite angles of attack. Studies taking into account variations of the pressure field in the basic flow or the extension to compressible flows are issues that remain open. El objetivo de esta tesis es estudiar los mecanismos de la inestabilidad que se producen en ciertos dispositivos aerodinámicos cuando se aumenta el ángulo de ataque. Para ello se ha utilizado un modelo simplificado del flujo de base, así como diferentes técnicas numéricas, con el fin de resolver el problema de estabilidad lineal asociado que describe el comportamiento de las perturbaciones. Estos métodos; sin y con formación de matriz, se han validado utilizando simulaciones numéricas directas con resolución espectral. De esta manera, la inestabilidad del flujo de capa límite laminar oblicuo entorno a la línea de estancamiento se aborda en un marco de análisis lineal por medio del método Bi-Global de resolución del problema de valores propios en derivadas parciales. Posteriormente se propone una extensión simple para el flujo no-ortogonal del modelo polinomial de ecuaciones diferenciales ordinarias, G¨ortler-H¨ammerlin extendido, propuesto por Theofilis et al. (2003) para el flujo ortogonal, que incluye los modelos previos como casos particulares y recupera los resultados del analisis global de estabilidad lineal. Se han realizado simulaciones directas con el fin de verificar los resultados del análisis de estabilidad así como para investigar los límites de validez del modelo de flujo base utilizado. En este trabajo se ha documentado el efecto del ángulo de ataque AoA en las condiciones críticas del problema no ortogonal obteniendo que el incremento del ángulo de ataque, de AoA = 0 (flujo ortogonal) hasta valores próximos a _/2, en el cual las hipótesis sobre las que se basa el flujo base dejan de ser válidas, tiende sistemáticamente a desestabilizar el flujo. Las condiciones críticas del caso no ortogonal 0 _ AoA _ _/2 pueden recuperarse a partir del caso ortogonal mediante el uso de una transformación analítica simple que implica el ángulo de ataque AoA. Estos resultados pueden ayudar a comprender los mecanismos de desestabilización que se producen en el borde de ataque de las alas de los aviones a ángulos de ataque finitos. Como tareas pendientes quedaría realizar estudios que tengan en cuenta variaciones del campo de presión en el flujo base así como la extensión de éste al caso de flujos compresibles.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Physical and mathematical modeling of wave propagation in the Ariane 5 VEB structure

The separation of the lower stage of the ARIANE 5 Vehicle Equipment Bay (VEB) Structure is to be done using a pyrotechnic device. The wave propagation effects produced by the explosion can affect the electronic equipment, so it was decided to analyze, using both physical and numerical modeling, a small piece of the structure to determine the distribution of the accelerations and the relative importance of damping, stiffness, connections, etc. on the response of the equipment.

Analysis of the modified optical properties and band structure of GaAs12xSbx-capped InAs/GaAs quantum dots

The origin of the modified optical properties of InAs/GaAs quantum dots (QD) capped with a thin GaAs1−xSbx layer is analyzed in terms of the band structure. To do so, the size, shape, and composition of the QDs and capping layer are determined through cross-sectional scanning tunnelling microscopy and used as input parameters in an 8 × 8 k·p model. As the Sb content is increased, there are two competing effects determining carrier confinement and the oscillator strength: the increased QD height and reduced strain on one side and the reduced QD-capping layer valence band offset on the other. Nevertheless, the observed evolution of the photoluminescence (PL) intensity with Sb cannot be explained in terms of the oscillator strength between ground states, which decreases dramatically for Sb > 16%, where the band alignment becomes type II with the hole wavefunction localized outside the QD in the capping layer. Contrary to this behaviour, the PL intensity in the type II QDs is similar (at 15 K) or even larger (at room temperature) than in the type I Sb-free reference QDs. This indicates that the PL efficiency is dominated by carrier dynamics, which is altered by the presence of the GaAsSb capping layer. In particular, the presence of Sb leads to an enhanced PL thermal stability. From the comparison between the activation energies for thermal quenching of the PL and the modelled band structure, the main carrier escape mechanisms are suggested. In standard GaAs-capped QDs, escape of both electrons and holes to the GaAs barrier is the main PL quenching mechanism. For small-moderate Sb (<16%) for which the type I band alignment is kept, electrons escape to the GaAs barrier and holes escape to the GaAsSb capping layer, where redistribution and retraping processes can take place. For Sb contents above 16% (type-II region), holes remain in the GaAsSb layer and the escape of electrons from the QD to the GaAs barrier is most likely the dominant PL quenching mechanism. This means that electrons and holes behave dynamically as uncorrelated pairs in both the type-I and type-II structures.

Analysis and Quality Control from ARIMA Modeling

In this paper, we use ARIMA modelling to estimate a set of characteristics of a short-term indicator (for example, the index of industrial production), as trends, seasonal variations, cyclical oscillations, unpredictability, deterministic effects (as a strike), etc. Thus for each sector and product (more than 1000), we construct a vector of values corresponding to the above-mentioned characteristics, that can be used for data editing.