On the design and measurement of a novel non-orthogonal multi-beam network for triangular arrays of three radiating elements

An innovative dissipative multi-beam network for triangular arrays of three radiating elements is proposed. This novel network provides three orthogonal beams in θ0 elevation angle and a fourth one in the broadside steering direction. The network is composed of 90º hybrid couplers and fixed phase shifters. In this paper, a relation between network components, radiating element distance and beam steering directions will be shown. Application of the proposed dissipative network to the triangular cells of three radiating elements that integrate the intelligent antenna GEODA will be exhibited. This system works at 1.7 GHz, it has a 60º single radiating element beamwidth and a distance between array elements of 0.57 λ. Both beam patterns, theoretical and simulated, obtained with the network will be depicted. Moreover, the whole system, dissipative network built with GEODA cell array, has been measured in the anechoic chamber of the Radiation Group of Technical University of Madrid, demonstrating expected performance.

Study of a new 4×3 beam forming network for triangular arrays of three radiating elements

An innovative dissipative multi-beam network for triangular arrays of three radiating elements is proposed. This novel network provides three orthogonal beams in θ0 elevation angle and a fourth one in the broadside steering direction. The network is composed of 90º hybrid couplers and fixed phase shifters. In this paper, a relation between network components, radiating element distance and beam steering directions will be shown. Application of the proposed dissipative network to the triangular cells of three radiating elements that integrate the intelligent antenna GEODA will be exhibited. This system works at 1.7 GHz, it has a 60º single radiating element beamwidth and a distance between array elements of 0.57λ. Both beam patterns, theoretical and simulated, obtained with the network will be depicted. Moreover, the whole system, dissipative network built with GEODA cell array, has been measured in the anechoic chamber of the Radiation Group of Technical University of Madrid, demonstrating expected performance

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Implementation of the disruption predictor APODIS in JET Real Time Network using the MARTe framework

Disruptions in tokamaks devices are unavoidable, and they can have a significant impact on machine integrity. So it is very important have mechanisms to predict this phenomenon. Disruption prediction is a very complex task, not only because it is a multi-dimensional problem, but also because in order to be effective, it has to detect well in advance the actual disruptive event, in order to be able to use successful mitigation strategies. With these constraints in mind a real-time disruption predictor has been developed to be used in JET tokamak. The predictor has been designed to run in the Multithreaded Application Real-Time executor (MARTe) framework. The predictor ?Advanced Predictor Of DISruptions? (APODIS) is based on Support Vector Machine (SVM).

Structural health monitoring network system with wireless communications inside closed aerospace structures

Structural Health Monitoring (SHM) requires integrated "all in one" electronic devices capable of performing analysis of structural integrity and on-board damage detection in aircraft?s structures. PAMELA III (Phased Array Monitoring for Enhanced Life Assessment, version III) SHM embedded system is an example of this device type. This equipment is capable of generating excitation signals to be applied to an array of integrated piezoelectric Phased Array (PhA) transducers stuck to aircraft structure, acquiring the response signals, and carrying out the advanced signal processing to obtain SHM maps. PAMELA III is connected with a host computer in order to receive the configuration parameters and sending the obtained SHM maps, alarms and so on. This host can communicate with PAMELA III through an Ethernet interface. To avoid the use of wires where necessary, it is possible to add Wi-Fi capabilities to PAMELA III, connecting a Wi-Fi node working as a bridge, and to establish a wireless communication between PAMELA III and the host. However, in a real aircraft scenario, several PAMELA III devices must work together inside closed structures. In this situation, it is not possible for all PAMELA III devices to establish a wireless communication directly with the host, due to the signal attenuation caused by the different obstacles of the aircraft structure. To provide communication among all PAMELA III devices and the host, a wireless mesh network (WMN) system has been implemented inside a closed aluminum wingbox. In a WMN, as long as a node is connected to at least one other node, it will have full connectivity to the entire network because each mesh node forwards packets to other nodes in the network as required. Mesh protocols automatically determine the best route through the network and can dynamically reconfigure the network if a link drops out. The advantages and disadvantages on the use of a wireless mesh network system inside closed aerospace structures are discussed.

Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale

This paper analyzes the correlation between the fluctuations of the electrical power generated by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical power time series from a large collection of photovoltaic inverters of a same plant is an impor- tant contribution in the context of models built upon simplified assumptions to overcome the absence of such data. This data set is divided into three different fluctuation categories with a clustering proce- dure which performs correctly with the clearness index and the wavelet variances. Afterwards, the time dependent correlation between the electrical power time series of the inverters is esti- mated with the wavelet transform. The wavelet correlation depends on the distance between the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time scales below one minute are low without dependence on the daily fluctuation level. For time scales above 20 minutes, positive high correlation values are obtained, and the decay rate with the distance depends on the daily fluctuation level. At intermediate time scales the correlation depends strongly on the daily fluctuation level. The proposed methods have been implemented using free software. Source code is available as supplementary material.

Simulation Tool and Case Study for Planning Wireless Sensor Network

In this paper, a simulation tool for assisting the deployment of wireless sensor network is introduced and simulation results are verified under a specific indoor environment. The simulation tool supports two modes: deterministic mode and stochastic mode. The deterministic mode is environment dependent in which the information of environment should be provided beforehand. Ray tracing method and deterministic propagation model are employed in order to increase the accuracy of the estimated coverage, connectivity and routing; the stochastic mode is useful for large scale random deployment without previous knowledge on geographic information. Dynamic Source Routing protocol (DSR) and Ad hoc On-Demand Distance Vector Routing protocol (AODV) are implemented in order to calculate the topology of WSN. Hence this tool gives direct view on the performance of WSN and assists users in finding the potential problems of wireless sensor network before real deployment. At the end, a case study is realized in Centro de Electronica Industrial (CEI), the simulation results on coverage, connectivity and routing are verified by the measurement.

Tomas Lopez's Geographic Atlas of Spain (1787) and its Hydrographic Network: GIS analysis of the Reyno de Jaen

Knowledge of the development of hydrographic networks can be useful for a number of research works in hydraulic engineering. We thus, intend to analyse the cartography regarding the first work that systematically encompasses the entire hydrographic network: Tomas Lopez’s Geographic Atlas of Spain (1787). In order to achieve this goal, we will first analyze –by way of the Geographic Information System (GIS) – both the present and referred historical cartographies. In comparing them, we will use the then-existing population centres that correspond to modern ones. The aim is to compare the following research variables in the hydrographic network: former toponyms, length of riverbeds and distance to population centres. The results of this study will show the variation in the riverbeds and the probable change in their denomination.

Simulador WKM para gestión de conocimiento en una WSN

La gestión del conocimiento (KM) se basa en la captación, filtración, procesamiento y análisis de unos datos en bruto que con dicho refinamiento podrán llegar a convertirse en conocimiento o Sabiduría. Estas prácticas tendrán lugar en este PFC en una WSN (Wireless Sensor Network) compuesta de unos sofisticados dispositivos comúnmente conocidos como “motas” y cuya principal característica son sus bajas capacidades en cuanto a memoria, batería o autonomía. Ha sido objetivo primordial de este Proyecto de fin de Carrera aunar una WSN con la Gestión del Conocimiento así como demostrar que es posible llevar a cabo grandes procesamientos de información, con tan bajas capacidades, si se distribuyen correctamente los procesos. En primera instancia, se introducen conceptos básicos acerca de las WSN (Wireless Sensor Networks) así como de los elementos principales en dichas redes. Tras conocer el modelo de arquitectura de comunicaciones se procede a presentar la Gestión del Conocimiento de forma teórica y a continuación la interpretación que se ha hecho a partir de diversas referencias bibliográficas para llevar a cabo la implementación del proyecto. El siguiente paso es describir punto por punto todos los componentes del Simulador; librerías, funcionamiento y demás cuestiones sobre configuración y puesta a punto. Como escenario de aplicación se plantea una red de sensores inalámbricos básica cuya topología y ubicación es completamente configurable. Se lleva a cabo una configuración a nivel de red basada en el protocolo 6LowPAN pero con posibilidad de simplificarlo. Los datos se procesan de acuerdo a un modelo piramidal de Gestión de Conocimiento adaptable a las necesidades del usuario. Mediante la utilización de las diversas opciones que proporciona la interfaz gráfica implementada y los documentos de resultados que se van generando, se puede llevar a cabo un detallado estudio posterior de la simulación y comprobar si se cumplen las expectativas planteadas. Knowledge management (KM) is based on the collection, filtering, processing and analysis of some raw data which such refinement it can be turned into knowledge or wisdom. These practices will take place in a WSN (Wireless Sensor Network) consists of sophisticated devices commonly known as "dots" and whose main characteristics are its low capacity for memory, battery or autonomy. A primary objective of this Project will be to join a WSN with Knowledge Management and show that it is possible make largo information processing, with such low capacity if the processes are properly distributed. First, we introduce basic concepts about the WSN (Wireless Sensor Networks) and major elements of these networks. After meeting the communications architecture model, we proceed to show the Knowledge Management theory and then the interpretation of several bibliographic references to carry out the project implementation. The next step is discovering point by point all over the Simulator components; libraries, operation and the rest of points about configuration and tuning. As application scenario we propose a basic wireless sensor network whose topology and location is completely customizable. It will perform a network level configuration based in W6LowPAN Protocol. Data is processed according to a pyramidal pattern Knowledge Management adaptable to the user´s needs. The hardware elements will suffer more or less energy dependence depending on their role and activity in the network. Through the various options that provide the graphical interface has been implemented and results documents that are generated, can be carried out after a detailed study of the simulation and verify compliance with the expectations raised.

Ejercicio de Análisis de Redes con Network Analysis de ArcGIS 10.

Ejercicio de Análisis de Redes con Network Analysis de ArcGIS 10

The Phase Space as a New Representation of the Dynamical Behaviour of Temperature and Enthalpy in a Reefer monitored with a Multidistributed Sensors Network

The study of temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport, as well as forminimizing losses. The objective of this work is to develop a new methodology of data analysis based on phase space graphs of temperature and enthalpy, collected by means of multidistributed, low cost and autonomous wireless sensors and loggers. A transoceanic refrigerated transport of lemons in a reefer container ship from Montevideo (Uruguay) to Cartagena (Spain) was monitored with a network of 39 semi-passive TurboTag RFID loggers and 13 i-button loggers. Transport included intermodal transit from transoceanic to short shipping vessels and a truck trip. Data analysis is carried out using qualitative phase diagrams computed on the basis of Takens?Ruelle reconstruction of attractors. Fruit stress is quantified in terms of the phase diagram area which characterizes the cyclic behaviour of temperature. Areas within the enthalpy phase diagram computed for the short sea shipping transport were 5 times higher than those computed for the long sea shipping, with coefficients of variation above 100% for both periods. This new methodology for data analysis highlights the significant heterogeneity of thermohygrometric conditions at different locations in the container.

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.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Effect of reservoir tillage on rainwater harvesting and soil erosion control under a developed rainfall simulator.

Soil erosion is a serious environmental threat in the Mediterranean region due to torrential rainfalls, and it contributes to the degradation of agricultural land. Techniques such as rainwater harvesting may improve soil water storage and increase agricultural productivity, which could result in more effective land usage. Reservoir tillage is an effective system of harvesting rainwater, but it has not been scientifically evaluated like other tillage systems. Its suitability for the conditions in Spain has not been determined. To investigate and quantify water storage from reservoir tillage and how it could be adapted to improve infiltration of harvested rainwater, a laboratory-scale rainfall simulator was developed. Rainfall characteristics, including rainfall intensity, spatial uniformity and raindrop size, confirm that natural rainfall conditions are simulated with sufficient accuracy. The simulator was auto-controlled by a solenoid valve and three pressure nozzles were used to spray water corresponding to five rainfall intensities ranging from 36 to 112 mm h-1 for 3 to 101-year return period with uniformity coefficients between 83 and 94%. In order to assess the reservoir tillage method under surface slopes of 0, 5, and 10%, three soil scooping devices with identical volume were used to make depressions in the following forms: a) truncated square pyramid, b) triangular prism, and c) truncated cone. These depressions were compared to a control soil surface with no depression. For the loam soil used in this study, results show that reservoir tillage was able to reduce soil erosion and surface runoff and significantly increase infiltration. There was significant difference between the depressions and the control. Compared to the control, depression (a) reduced surface runoff by about 61% and the sediment yield concentration by about 79%.

Realización de Mapa Estratégico de Ruido de las Carreteras de la Red del Estado (A-3 - zona Campus Sur de la U.P.M.)

En cumplimiento de la Directiva 2002/49/CE en lo referente a carreteras, se han llevado a cabo en España, en el año 2007, los mapas estratégicos de ruido (MER) de los grandes ejes viarios de más de 6 millones de vehículos / año (datos 2006). En esta categoría se encuentra la carretera A-3 a su paso por el Campus Sur de la U.P.M. De acuerdo con el pliego de prescripciones técnicas, estos MER se dividen en fase A o de estudio básico y fase B o de estudio de detalle. En el proyecto denominado “Realización de Mapa Estratégico de Ruido de las carreteras de la Red del Estado (A-3 - Zona Campus sur de la U.P.M.)” se muestran los resultados de información pública de fase A del Mapa Estratégico de Ruido (MER) para la carretera A-3 en la Comunidad de Madrid. Teniendo en cuenta dichos resultados y la no elección del Campus Sur en la fase B (detalle) de dicho MER; en el presente proyecto se realiza el Mapa Estratégico de fase B del Campus Sur de la U.P.M. para el gran ejes viarios (A-3), de cumplimiento de la Directiva 2002/49/CE y siguiendo las indicaciones del pliego de prescripciones técnicas para esta primera fase de entregas de 2007 y utilizando para ello Sistemas de Información Geográfica (SIG). Se justifica, según los resultados obtenidos, si hubiera sido necesaria su inclusión en esta fase de estudio. Una vez establecidas las diferencias entre Mapa Estratégico de Ruido (MER) y Mapa de Ruido (MR), se analizan los diversos criterios técnicos que debe tomar un consultor acústico durante la realización de un MER y cómo estos pueden afectar al resultado final siendo todos ellos válidos pero creando una falta de homogeneidad entre los diferentes MER según el autor del estudio. Se describen las diferentes acciones que se están tomando tras la entrega de 2007 para intentar solucionar este problema de cara a las nuevas entregas. ABSTRACT: The environmental Noise Directive 2002/49/EC has clear requirements to Member States in terms of Strategic Noise Maps according to roads. So, have been carried out in Spain, in 2007, Strategic Noise Maps of the major roads which have more than six million vehicle passages a year (2006 data) where the A-3 road near “Campus Sur U.P.M.” is included. In accord with the statement of technical requirements, these Strategic Noise Maps are divided into A phase (Basic study) and B phase (detailed study). The present project by the name of "Implementation of Strategic Noise Map of Spanish Roads (A-3 – Campus Sur U.P.M. area)" shows the results of Strategic Noise Map for the A-3 in the Community of Madrid (A phase, public information – EGRA project Spanish Ministry of Transport). According to de study results and the not election of “Campus Sur” to B phase (detail), in this project has been made the B phase of Strategic Map in compliance with Directive 2002/49/EC, following the technical requirements specifications for the first phase of deliveries (2007) and using Geographic Information Systems (GIS) to carry on them. Is justified, in accordance with the results, if it was necessary to include “Campus Sur” in this B phase of study. Once established the differences between Strategic Noise Map and Noise Map, the present project examines the technical criteria that must take an acoustic consultant for the realization of a Strategic Map and how these criteria could affect the quality of the final results being all of them valid but it generate a lack of homogeneity between the different Strategic Noise Map by the author of the study. There wasn’t so much experience in Spain in the methodology proposed by the European Noise Directive. The actions that are being taken after delivery of 2007 to try to solve this problem and get harmonized the results among the whole network for the new deliveries each five years are shown at the present project.