Generic multisensor multitarget bias estimation architecture

Current bias estimation algorithms for air traffic control (ATC) surveillance are focused on radar sensors, but the integration of new sensors (especially automatic dependent surveillance-broadcast and wide area multilateration) demands the extension of traditional procedures. This study describes a generic architecture for bias estimation applicable to multisensor multitarget surveillance systems. It consists on first performing bias estimations using measurements from each target, of a subset of sensors, assumed to be reliable, forming track bias estimations. All track bias estimations are combined to obtain, for each of those sensors, the corresponding sensor bias. Then, sensor bias terms are corrected, to subsequently calculate the target or sensor-target pair specific biases. Once these target-specific biases are corrected, the process is repeated recursively for other sets of less reliable sensors, assuming bias corrected measures from previous iterations are unbiased. This study describes the architecture and outlines the methodology for the estimation and the bias estimation design processes. Then the approach is validated through simulation, and compared with previous methods in the literature. Finally, the study describes the application of the methodology to the design of the bias estimation procedures for a modern ATC surveillance application, specifically for off-line assessment of ATC surveillance performance.

A practical approach for outdoors distributed target localization in wireless sensor networks

Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

Estrategias de monitorización de CO2 y otros gases en los estudios de análogos naturales

La Universidad Politécnica de Madrid (UPM) y la Università degli Studi di Firenze (UniFi), bajo la coordinación técnica de AMPHOS21, participan desde 2009 en el proyecto de investigación “Estrategias de Monitorización de CO2 y otros gases en el estudio de Análogos Naturales”, financiado por la Fundación Ciudad de la Energía (CIUDEN) en el marco del Proyecto Compostilla OXYCFB300 (http://www.compostillaproject.eu), del Programa “European Energy Program for Recovery - EEPR”. El objetivo principal del proyecto fue el desarrollo y puesta a punto de metodologías de monitorización superficiales para su aplicación en el seguimiento y control de los emplazamientos donde se realice el almacenamiento geológico de CO2, analizando técnicas que permitan detectar y cuantificar las posibles fugas de CO2 a la atmósfera. Los trabajos se realizaron tanto en análogos naturales (españoles e italianos) como en la Planta de Desarrollo Tecnológico de Almacenamiento de CO2 de Hontomín. Las técnicas analizadas se centran en la medición de gases y aguas superficiales (de escorrentía y manantiales). En cuanto a la medición de gases se analizó el flujo de CO2 que emana desde el suelo a la atmósfera y la aplicabilidad de trazadores naturales (como el radón) para la detección e identificación de las fugas de CO2. En cuanto al análisis químico de las aguas se analizaron los datos geoquímicos e isotópicos y los gases disueltos en las aguas de los alrededores de la PDT de Hontomín, con objeto de determinar qué parámetros son los más apropiados para la detección de una posible migración del CO2 inyectado, o de la salmuera, a los ambientes superficiales. Las medidas de flujo de CO2 se realizaron con la técnica de la cámara de acúmulo. A pesar de ser una técnica desarrollada y aplicada en diferentes ámbitos científicos se estimó necesario adaptar un protocolo de medida y de análisis de datos a las características específicas de los proyectos de captura y almacenamiento de CO2 (CAC). Donde los flujos de CO2 esperados son bajos y en caso de producirse una fuga habrá que detectar pequeñas variaciones en los valores flujo con un “ruido” en la señal alto, debido a actividad biológica en el suelo. La medida de flujo de CO2 mediante la técnica de la cámara de acúmulo se puede realizar sin limpiar la superficie donde se coloca la cámara o limpiando y esperando al reequilibrio del flujo después de la distorsión al sistema. Sin embargo, los resultados obtenidos después de limpiar y esperar muestran menor dispersión, lo que nos indica que este procedimiento es el mejor para la monitorización de los complejos de almacenamiento geológico de CO2. El protocolo de medida resultante, utilizado para la obtención de la línea base de flujo de CO2 en Hontomín, sigue los siguiente pasos: a) con una espátula se prepara el punto de medición limpiando y retirando el recubrimiento vegetal o la primera capa compacta de suelo, b) se espera un tiempo para la realización de la medida de flujo, facilitando el reequilibrio del flujo del gas tras la alteración provocada en el suelo y c) se realiza la medida de flujo de CO2. Una vez realizada la medición de flujo de CO2, y detectada si existen zonas de anomalías, se debe estimar la cantidad de CO2 que se está escapando a la atmósfera (emanación total), con el objetivo de cuantificar la posible fuga. Existen un amplio rango de metodologías para realizar dicha estimación, siendo necesario entender cuáles son las más apropiadas para obtener el valor más representativo del sistema. En esta tesis se comparan seis técnicas estadísticas: media aritmética, estimador insegado de la media (aplicando la función de Sichel), remuestreo con reemplazamiento (bootstrap), separación en diferentes poblaciones mediante métodos gráficos y métodos basados en criterios de máxima verosimilitud, y la simulación Gaussiana secuencial. Para este análisis se realizaron ocho campañas de muestreo, tanto en la Planta de Desarrollo Tecnológico de Hontomón como en análogos naturales (italianos y españoles). Los resultados muestran que la simulación Gaussiana secuencial suele ser el método más preciso para realizar el cálculo, sin embargo, existen ocasiones donde otros métodos son más apropiados. Como consecuencia, se desarrolla un procedimiento de actuación para seleccionar el método que proporcione el mejor estimador. Este procedimiento consiste, en primer lugar, en realizar un análisis variográfico. Si existe una autocorrelación entre los datos, modelizada mediante el variograma, la mejor técnica para calcular la emanación total y su intervalo de confianza es la simulación Gaussiana secuencial (sGs). Si los datos son independientes se debe comprobar la distribución muestral, aplicando la media aritmética o el estimador insesgado de la media (Sichel) para datos normales o lognormales respectivamente. Cuando los datos no son normales o corresponden a una mezcla de poblaciones la mejor técnica de estimación es la de remuestreo con reemplazamiento (bootstrap). Siguiendo este procedimiento el máximo valor del intervalo de confianza estuvo en el orden del ±20/25%, con la mayoría de valores comprendidos entre ±3,5% y ±8%. La identificación de las diferentes poblaciones muestrales en los datos de flujo de CO2 puede ayudar a interpretar los resultados obtenidos, toda vez que esta distribución se ve afectada por la presencia de varios procesos geoquímicos como, por ejemplo, una fuente geológica o biológica del CO2. Así pues, este análisis puede ser una herramienta útil en el programa de monitorización, donde el principal objetivo es demostrar que no hay fugas desde el reservorio a la atmósfera y, si ocurren, detectarlas y cuantificarlas. Los resultados obtenidos muestran que el mejor proceso para realizar la separación de poblaciones está basado en criterios de máxima verosimilitud. Los procedimientos gráficos, aunque existen pautas para realizarlos, tienen un cierto grado de subjetividad en la interpretación de manera que los resultados son menos reproducibles. Durante el desarrollo de la tesis se analizó, en análogos naturales, la relación existente entre el CO2 y los isótopos del radón (222Rn y 220Rn), detectándose en todas las zonas de emisión de CO2 una relación positiva entre los valores de concentración de 222Rn en aire del suelo y el flujo de CO2. Comparando la concentración de 220Rn con el flujo de CO2 la relación no es tan clara, mientras que en algunos casos aumenta en otros se detecta una disminución, hecho que parece estar relacionado con la profundidad de origen del radón. Estos resultados confirmarían la posible aplicación de los isótopos del radón como trazadores del origen de los gases y su aplicación en la detección de fugas. Con respecto a la determinación de la línea base de flujo CO2 en la PDT de Hontomín, se realizaron mediciones con la cámara de acúmulo en las proximidades de los sondeos petrolíferos, perforados en los ochenta y denominados H-1, H-2, H-3 y H-4, en la zona donde se instalarán el sondeo de inyección (H-I) y el de monitorización (H-A) y en las proximidades de la falla sur. Desde noviembre de 2009 a abril de 2011 se realizaron siete campañas de muestreo, adquiriéndose más de 4.000 registros de flujo de CO2 con los que se determinó la línea base y su variación estacional. Los valores obtenidos fueron bajos (valores medios entre 5 y 13 g•m-2•d-1), detectándose pocos valores anómalos, principalmente en las proximidades del sondeo H-2. Sin embargo, estos valores no se pudieron asociar a una fuente profunda del CO2 y seguramente estuvieran más relacionados con procesos biológicos, como la respiración del suelo. No se detectaron valores anómalos cerca del sistema de fracturación (falla Ubierna), toda vez que en esta zona los valores de flujo son tan bajos como en el resto de puntos de muestreo. En este sentido, los valores de flujo de CO2 aparentemente están controlados por la actividad biológica, corroborado al obtenerse los menores valores durante los meses de otoño-invierno e ir aumentando en los periodos cálidos. Se calcularon dos grupos de valores de referencia, el primer grupo (UCL50) es 5 g•m-2•d-1 en las zonas no aradas en los meses de otoño-invierno y 3,5 y 12 g•m-2•d-1 en primavera-verano para zonas aradas y no aradas, respectivamente. El segundo grupo (UCL99) corresponde a 26 g•m-2•d- 1 durante los meses de otoño-invierno en las zonas no aradas y 34 y 42 g•m-2•d-1 para los meses de primavera-verano en zonas aradas y no aradas, respectivamente. Flujos mayores a estos valores de referencia podrían ser indicativos de una posible fuga durante la inyección y posterior a la misma. Los primeros datos geoquímicos e isotópicos de las aguas superficiales (de escorrentía y de manantiales) en el área de Hontomín–Huermeces fueron analizados. Los datos sugieren que las aguas estudiadas están relacionadas con aguas meteóricas con un circuito hidrogeológico superficial, caracterizadas por valores de TDS relativamente bajos (menor a 800 mg/L) y una fácie hidrogeoquímica de Ca2+(Mg2+)-HCO3 −. Algunas aguas de manantiales se caracterizan por concentraciones elevadas de NO3 − (concentraciones de hasta 123 mg/l), lo que sugiere una contaminación antropogénica. Se obtuvieron concentraciones anómalas de of Cl−, SO4 2−, As, B y Ba en dos manantiales cercanos a los sondeos petrolíferos y en el rio Ubierna, estos componentes son probablemente indicadores de una posible mezcla entre los acuíferos profundos y superficiales. El estudio de los gases disueltos en las aguas también evidencia el circuito superficial de las aguas. Estando, por lo general, dominado por la componente atmosférica (N2, O2 y Ar). Sin embargo, en algunos casos el gas predominante fue el CO2 (con concentraciones que llegan al 63% v/v), aunque los valores isotópicos del carbono (<-17,7 ‰) muestran que lo más probable es que esté relacionado con un origen biológico. Los datos geoquímicos e isotópicos de las aguas superficiales obtenidos en la zona de Hontomín se pueden considerar como el valor de fondo con el que comparar durante la fase operacional, la clausura y posterior a la clausura. En este sentido, la composición de los elementos mayoritarios y traza, la composición isotópica del carbono del CO2 disuelto y del TDIC (Carbono inorgánico disuelto) y algunos elementos traza se pueden considerar como parámetros adecuados para detectar la migración del CO2 a los ambientes superficiales. ABSTRACT Since 2009, a group made up of Universidad Politécnica de Madrid (UPM; Spain) and Università degli Studi Firenze (UniFi; Italy) has been taking part in a joint project called “Strategies for Monitoring CO2 and other Gases in Natural analogues”. The group was coordinated by AMPHOS XXI, a private company established in Barcelona. The Project was financially supported by Fundación Ciudad de la Energía (CIUDEN; Spain) as a part of the EC-funded OXYCFB300 project (European Energy Program for Recovery -EEPR-; www.compostillaproject.eu). The main objectives of the project were aimed to develop and optimize analytical methodologies to be applied at the surface to Monitor and Verify the feasibility of geologically stored carbon dioxide. These techniques were oriented to detect and quantify possible CO2 leakages to the atmosphere. Several investigations were made in natural analogues from Spain and Italy and in the Tecnchnological Development Plant for CO2 injection al Hontomín (Burgos, Spain). The studying techniques were mainly focused on the measurements of diffuse soil gases and surface and shallow waters. The soil-gas measurements included the determination of CO2 flux and the application to natural trace gases (e.g. radon) that may help to detect any CO2 leakage. As far as the water chemistry is concerned, geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of the PDT of Hontomín were analyzed to determine the most suitable parameters to trace the migration of the injected CO2 into the near-surface environments. The accumulation chamber method was used to measure the diffuse emission of CO2 at the soil-atmosphere interface. Although this technique has widely been applied in different scientific areas, it was considered of the utmost importance to adapt the optimum methodology for measuring the CO2 soil flux and estimating the total CO2 output to the specific features of the site where CO2 is to be stored shortly. During the pre-injection phase CO2 fluxes are expected to be relatively low where in the intra- and post-injection phases, if leakages are to be occurring, small variation in CO2 flux might be detected when the CO2 “noise” is overcoming the biological activity of the soil (soil respiration). CO2 flux measurements by the accumulation chamber method could be performed without vegetation clearance or after vegetation clearance. However, the results obtained after clearance show less dispersion and this suggests that this procedure appears to be more suitable for monitoring CO2 Storage sites. The measurement protocol, applied for the determination of the CO2 flux baseline at Hontomín, has included the following steps: a) cleaning and removal of both the vegetal cover and top 2 cm of soil, b) waiting to reduce flux perturbation due to the soil removal and c) measuring the CO2 flux. Once completing the CO2 flux measurements and detected whether there were anomalies zones, the total CO2 output was estimated to quantify the amount of CO2 released to the atmosphere in each of the studied areas. There is a wide range of methodologies for the estimation of the CO2 output, which were applied to understand which one was the most representative. In this study six statistical methods are presented: arithmetic mean, minimum variances unbiased estimator, bootstrap resample, partitioning of data into different populations with a graphical and a maximum likelihood procedures, and sequential Gaussian simulation. Eight campaigns were carried out in the Hontomín CO2 Storage Technology Development Plant and in natural CO2 analogues. The results show that sequential Gaussian simulation is the most accurate method to estimate the total CO2 output and the confidential interval. Nevertheless, a variety of statistic methods were also used. As a consequence, an application procedure for selecting the most realistic method was developed. The first step to estimate the total emanation rate was the variogram analysis. If the relation among the data can be explained with the variogram, the best technique to calculate the total CO2 output and its confidence interval is the sequential Gaussian simulation method (sGs). If the data are independent, their distribution is to be analyzed. For normal and log-normal distribution the proper methods are the arithmetic mean and minimum variances unbiased estimator, respectively. If the data are not normal (log-normal) or are a mixture of different populations the best approach is the bootstrap resampling. According to these steps, the maximum confidence interval was about ±20/25%, with most of values between ±3.5% and ±8%. Partitioning of CO2 flux data into different populations may help to interpret the data as their distribution can be affected by different geochemical processes, e.g. geological or biological sources of CO2. Consequently, it may be an important tool in a monitoring CCS program, where the main goal is to demonstrate that there are not leakages from the reservoir to the atmosphere and, if occurring, to be able to detect and quantify it. Results show that the partitioning of populations is better performed by maximum likelihood criteria, since graphical procedures have a degree of subjectivity in the interpretation and results may not be reproducible. The relationship between CO2 flux and radon isotopes (222Rn and 220Rn) was studied in natural analogues. In all emissions zones, a positive relation between 222Rn and CO2 was observed. However, the relationship between activity of 220Rn and CO2 flux is not clear. In some cases the 220Rn activity indeed increased with the CO2 flux in other measurements a decrease was recognized. We can speculate that this effect was possibly related to the route (deep or shallow) of the radon source. These results may confirm the possible use of the radon isotopes as tracers for the gas origin and their application in the detection of leakages. With respect to the CO2 flux baseline at the TDP of Hontomín, soil flux measurements in the vicinity of oil boreholes, drilled in the eighties and named H-1 to H-4, and injection and monitoring wells were performed using an accumulation chamber. Seven surveys were carried out from November 2009 to summer 2011. More than 4,000 measurements were used to determine the baseline flux of CO2 and its seasonal variations. The measured values were relatively low (from 5 to 13 g•m-2•day-1) and few outliers were identified, mainly located close to the H-2 oil well. Nevertheless, these values cannot be associated to a deep source of CO2, being more likely related to biological processes, i.e. soil respiration. No anomalies were recognized close to the deep fault system (Ubierna Fault) detected by geophysical investigations. There, the CO2 flux is indeed as low as other measurement stations. CO2 fluxes appear to be controlled by the biological activity since the lowest values were recorded during autumn-winter seasons and they tend to increase in warm periods. Two reference CO2 flux values (UCL50 of 5 g•m-2•d-1 for non-ploughed areas in autumn-winter seasons and 3.5 and 12 g•m-2•d-1 for in ploughed and non-ploughed areas, respectively, in spring-summer time, and UCL99 of 26 g•m-2•d-1 for autumn-winter in not-ploughed areas and 34 and 42 g•m-2•d-1 for spring-summer in ploughed and not-ploughed areas, respectively, were calculated. Fluxes higher than these reference values could be indicative of possible leakage during the operational and post-closure stages of the storage project. The first geochemical and isotopic data related to surface and spring waters and dissolved gases in the area of Hontomín–Huermeces (Burgos, Spain) are presented and discussed. The chemical and features of the spring waters suggest that they are related to a shallow hydrogeological system as the concentration of the Total Dissolved Solids approaches 800 mg/L with a Ca2+(Mg2+)-HCO3 − composition, similar to that of the surface waters. Some spring waters are characterized by relatively high concentrations of NO3 − (up to 123 mg/L), unequivocally suggesting an anthropogenic source. Anomalous concentrations of Cl−, SO4 2−, As, B and Ba were measured in two springs, discharging a few hundred meters from the oil wells, and in the Rio Ubierna. These contents are possibly indicative of mixing processes between deep and shallow aquifers. The chemistry of the dissolved gases also evidences the shallow circuits of the Hontomín– Huermeces, mainly characterized by an atmospheric source as highlighted by the contents of N2, O2, Ar and their relative ratios. Nevertheless, significant concentrations (up to 63% by vol.) of isotopically negative CO2 (<−17.7‰ V-PDB) were found in some water samples, likely related to a biogenic source. The geochemical and isotopic data of the surface and spring waters in the surroundings of Hontomín can be considered as background values when intra- and post-injection monitoring programs will be carried out. In this respect, main and minor solutes, the isotopic carbon of dissolved CO2 and TDIC (Total Dissolved Inorganic Carbon) and selected trace elements can be considered as useful parameters to trace the migration of the injected CO2 into near-surface environments.

Estimating the modal parameters from multiple measurement setups using a joint state space model

Computing the modal parameters of structural systems often requires processing data from multiple non-simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors, which are fixed for all measurements, while the other sensors change their position from one setup to the next. One possibility is to process the setups separately resulting in different modal parameter estimates for each setup. Then, the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global mode shapes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a new state space model that processes all setups at once. The result is that the global mode shapes are obtained automatically, and only a value for the natural frequency and damping ratio of each mode is estimated. We also investigate the estimation of this model using maximum likelihood and the Expectation Maximization algorithm, and apply this technique to simulated and measured data corresponding to different structures.

Beyond filtered backprojection: a reconstruction software package for ion beam microtomography data

A new version of the TomoRebuild data reduction software package is presented, for the reconstruction of scanning transmission ion microscopy tomography (STIMT) and particle induced X-ray emission tomography (PIXET) images. First, we present a state of the art of the reconstruction codes available for ion beam microtomography. The algorithm proposed here brings several advantages. It is a portable, multi-platform code, designed in C++ with well-separated classes for easier use and evolution. Data reduction is separated in different steps and the intermediate results may be checked if necessary. Although no additional graphic library or numerical tool is required to run the program as a command line, a user friendly interface was designed in Java, as an ImageJ plugin. All experimental and reconstruction parameters may be entered either through this plugin or directly in text format files. A simple standard format is proposed for the input of experimental data. Optional graphic applications using the ROOT interface may be used separately to display and fit energy spectra. Regarding the reconstruction process, the filtered backprojection (FBP) algorithm, already present in the previous version of the code, was optimized so that it is about 10 times as fast. In addition, Maximum Likelihood Expectation Maximization (MLEM) and its accelerated version Ordered Subsets Expectation Maximization (OSEM) algorithms were implemented. A detailed user guide in English is available. A reconstruction example of experimental data from a biological sample is given. It shows the capability of the code to reduce noise in the sinograms and to deal with incomplete data, which puts a new perspective on tomography using low number of projections or limited angle.

Operational Modal Analysis using Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

Operational modal analisys using joint statistical analysis of multiple records

In Operational Modal Analysis (OMA) of a structure, the data acquisition process may be repeated many times. In these cases, the analyst has several similar records for the modal analysis of the structure that have been obtained at di�erent time instants (multiple records). The solution obtained varies from one record to another, sometimes considerably. The differences are due to several reasons: statistical errors of estimation, changes in the external forces (unmeasured forces) that modify the output spectra, appearance of spurious modes, etc. Combining the results of the di�erent individual analysis is not straightforward. To solve the problem, we propose to make the joint estimation of the parameters using all the records. This can be done in a very simple way using state space models and computing the estimates by maximum-likelihood. The method provides a single result for the modal parameters that combines optimally all the records.

Fast human pose estimation using 3D Zernike descriptors

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

Using the EM algorithm to estimate the state space model for OMAX

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm that is applied to the estimation of modal parameters from system input and output data. The effectiveness of this structural identification method is evaluated through numerical simulation. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the simulated structure are estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates the modal parameters with precision in the presence of 20% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

Contribución a la caracterización espacial de canales con sistemas MIMO-OFDM en la banda de 2,45 Ghz

La tecnología de múltiples antenas ha evolucionado para dar soporte a los actuales y futuros sistemas de comunicaciones inalámbricas en su afán por proporcionar la calidad de señal y las altas tasas de transmisión que demandan los nuevos servicios de voz, datos y multimedia. Sin embargo, es fundamental comprender las características espaciales del canal radio, ya que son las características del propio canal lo que limita en gran medida las prestaciones de los sistemas de comunicación actuales. Por ello surge la necesidad de estudiar la estructura espacial del canal de propagación para poder diseñar, evaluar e implementar de forma más eficiente tecnologías multiantena en los actuales y futuros sistemas de comunicación inalámbrica. Las tecnologías multiantena denominadas antenas inteligentes y MIMO han generado un gran interés en el área de comunicaciones inalámbricas, por ejemplo los sistemas de telefonía celular o más recientemente en las redes WLAN (Wireless Local Area Network), principalmente por la mejora que proporcionan en la calidad de las señales y en la tasa de transmisión de datos, respectivamente. Las ventajas de estas tecnologías se fundamentan en el uso de la dimensión espacial para obtener ganancia por diversidad espacial, como ya sucediera con las tecnologías FDMA (Frequency Division Multiplexing Access), TDMA (Time Division Multiplexing Access) y CDMA (Code Division Multiplexing Access) para obtener diversidad en las dimensiones de frecuencia, tiempo y código, respectivamente. Esta Tesis se centra en estudiar las características espaciales del canal con sistemas de múltiples antenas mediante la estimación de los perfiles de ángulos de llegada (DoA, Direction-of- Arrival) considerando esquemas de diversidad en espacio, polarización y frecuencia. Como primer paso se realiza una revisión de los sistemas con antenas inteligentes y los sistemas MIMO, describiendo con detalle la base matemática que sustenta las prestaciones ofrecidas por estos sistemas. Posteriormente se aportan distintos estudios sobre la estimación de los perfiles de DoA de canales radio con sistemas multiantena evaluando distintos aspectos de antenas, algoritmos de estimación, esquemas de polarización, campo lejano y campo cercano de las fuentes. Así mismo, se presenta un prototipo de medida MIMO-OFDM-SPAA3D en la banda ISM (Industrial, Scientific and Medical) de 2,45 Ghz, el cual está preparado para caracterizar experimentalmente el rendimiento de los sistemas MIMO, y para caracterizar espacialmente canales de propagación, considerando los esquemas de diversidad espacial, por polarización y frecuencia. Los estudios aportados se describen a continuación. Los sistemas de antenas inteligentes dependen en gran medida de la posición de los usuarios. Estos sistemas están equipados con arrays de antenas, los cuales aportan la diversidad espacial necesaria para obtener una representación espacial fidedigna del canal radio a través de los perfiles de DoA (DoA, Direction-of-Arrival) y por tanto, la posición de las fuentes de señal. Sin embargo, los errores de fabricación de arrays así como ciertos parámetros de señal conlleva un efecto negativo en las prestaciones de estos sistemas. Por ello se plantea un modelo de señal parametrizado que permite estudiar la influencia que tienen estos factores sobre los errores de estimación de DoA, tanto en acimut como en elevación, utilizando los algoritmos de estimación de DOA más conocidos en la literatura. A partir de las curvas de error, se pueden obtener parámetros de diseño para sistemas de localización basados en arrays. En un segundo estudio se evalúan esquemas de diversidad por polarización con los sistemas multiantena para mejorar la estimación de los perfiles de DoA en canales que presentan pérdidas por despolarización. Para ello se desarrolla un modelo de señal en array con sensibilidad de polarización que toma en cuenta el campo electromagnético de ondas planas. Se realizan simulaciones MC del modelo para estudiar el efecto de la orientación de la polarización como el número de polarizaciones usadas en el transmisor como en el receptor sobre la precisión en la estimación de los perfiles de DoA observados en el receptor. Además, se presentan los perfiles DoA obtenidos en escenarios quasiestáticos de interior con un prototipo de medida MIMO 4x4 de banda estrecha en la banda de 2,45 GHz, los cuales muestran gran fidelidad con el escenario real. Para la obtención de los perfiles DoA se propone un método basado en arrays virtuales, validado con los datos de simulación y los datos experimentales. Con relación a la localización 3D de fuentes en campo cercano (zona de Fresnel), se presenta un tercer estudio para obtener con gran exactitud la estructura espacial del canal de propagación en entornos de interior controlados (en cámara anecóica) utilizando arrays virtuales. El estudio analiza la influencia del tamaño del array y el diagrama de radiación en la estimación de los parámetros de localización proponiendo, para ello, un modelo de señal basado en un vector de enfoque de onda esférico (SWSV). Al aumentar el número de antenas del array se consigue reducir el error RMS de estimación y mejorar sustancialmente la representación espacial del canal. La estimación de los parámetros de localización se lleva a cabo con un nuevo método de búsqueda multinivel adaptativo, propuesto con el fin de reducir drásticamente el tiempo de procesado que demandan otros algoritmos multivariable basados en subespacios, como el MUSIC, a costa de incrementar los requisitos de memoria. Las simulaciones del modelo arrojan resultados que son validados con resultados experimentales y comparados con el límite de Cramer Rao en términos del error cuadrático medio. La compensación del diagrama de radiación acerca sustancialmente la exactitud de estimación de la distancia al límite de Cramer Rao. Finalmente, es igual de importante la evaluación teórica como experimental de las prestaciones de los sistemas MIMO-OFDM. Por ello, se presenta el diseño e implementación de un prototipo de medida MIMO-OFDM-SPAA3D autocalibrado con sistema de posicionamiento de antena automático en la banda de 2,45 Ghz con capacidad para evaluar la capacidad de los sistemas MIMO. Además, tiene la capacidad de caracterizar espacialmente canales MIMO, incorporando para ello una etapa de autocalibración para medir la respuesta en frecuencia de los transmisores y receptores de RF, y así poder caracterizar la respuesta de fase del canal con mayor precisión. Este sistema incorpora un posicionador de antena automático 3D (SPAA3D) basado en un scanner con 3 brazos mecánicos sobre los que se desplaza un posicionador de antena de forma independiente, controlado desde un PC. Este posicionador permite obtener una gran cantidad de mediciones del canal en regiones locales, lo cual favorece la caracterización estadística de los parámetros del sistema MIMO. Con este prototipo se realizan varias campañas de medida para evaluar el canal MIMO en términos de capacidad comparando 2 esquemas de polarización y tomando en cuenta la diversidad en frecuencia aportada por la modulación OFDM en distintos escenarios. ABSTRACT Multiple-antennas technologies have been evolved to be the support of the actual and future wireless communication systems in its way to provide the high quality and high data rates required by new data, voice and data services. However, it is important to understand the behavior of the spatial characteristics of the radio channel, since the channel by itself limits the performance of the actual wireless communications systems. This drawback raises the need to understand the spatial structure of the propagation channel in order to design, assess, and develop more efficient multiantenna technologies for the actual and future wireless communications systems. Multiantenna technologies such as ‘Smart Antennas’ and MIMO systems have generated great interest in the field of wireless communications, i.e. cellular communications systems and more recently WLAN (Wireless Local Area Networks), mainly because the higher quality and the high data rate they are able to provide. Their technological benefits are based on the exploitation of the spatial diversity provided by the use of multiple antennas as happened in the past with some multiaccess technologies such as FDMA (Frequency Division Multiplexing Access), TDMA (Time Division Multiplexing Access), and CDMA (Code Division Multiplexing Access), which give diversity in the domains of frequency, time and code, respectively. This Thesis is mainly focus to study the spatial channel characteristics using schemes of multiple antennas considering several diversity schemes such as space, polarization, and frequency. The spatial characteristics will be study in terms of the direction-of-arrival profiles viewed at the receiver side of the radio link. The first step is to do a review of the smart antennas and MIMO systems technologies highlighting their advantages and drawbacks from a mathematical point of view. In the second step, a set of studies concerning the spatial characterization of the radio channel through the DoA profiles are addressed. The performance of several DoA estimation methods is assessed considering several aspects regarding antenna array structure, polarization diversity, and far-field and near-field conditions. Most of the results of these studies come from simulations of data models and measurements with real multiantena prototypes. In the same way, having understand the importance of validate the theoretical data models with experimental results, a 2,4 GHz MIMO-OFDM-SPAA2D prototype is presented. This prototype is intended for evaluating MIMO-OFDM capacity in indoor and outdoor scenarios, characterize the spatial structure of radio channels, assess several diversity schemes such as polarization, space, and frequency diversity, among others aspects. The studies reported are briefly described below. As is stated in Chapter two, the determination of user position is a fundamental task to be resolved for the smart antenna systems. As these systems are equipped with antenna arrays, they can provide the enough spatial diversity to accurately draw the spatial characterization of the radio channel through the DoA profiles, and therefore the source location. However, certain real implementation factors related to antenna errors, signals, and receivers will certainly reduce the performance of such direction finding systems. In that sense, a parameterized narrowband signal model is proposed to evaluate the influence of these factors in the location parameter estimation through extensive MC simulations. The results obtained from several DoA algorithms may be useful to extract some parameter design for directing finding systems based on arrays. The second study goes through the importance that polarization schemes can have for estimating far-field DoA profiles in radio channels, particularly for scenarios that may introduce polarization losses. For this purpose, a narrowband signal model with polarization sensibility is developed to conduct an analysis of several polarization schemes at transmitter (TX) and receiver (RX) through extensive MC simulations. In addition, spatial characterization of quasistatic indoor scenarios is also carried out using a 2.45 GHz MIMO prototype equipped with single and dual-polarized antennas. A good agreement between the measured DoA profiles with the propagation scenario is achieved. The theoretical and experimental evaluation of polarization schemes is performed using virtual arrays. In that case, a DoA estimation method is proposed based on adding an phase reference to properly track the DoA, which shows good results. In the third study, the special case of near-field source localization with virtual arrays is addressed. Most of DoA estimation algorithms are focused in far-field source localization where the radiated wavefronts are assume to be planar waves at the receive array. However, when source are located close to the array, the assumption of plane waves is no longer valid as the wavefronts exhibit a spherical behavior along the array. Thus, a faster and effective method of azimuth, elevation angles-of-arrival, and range estimation for near-field sources is proposed. The efficacy of the proposed method is evaluated with simulation and validated with measurements collected from a measurement campaign carried out in a controlled propagation environment, i.e. anechoic chamber. Moreover, the performance of the method is assessed in terms of the RMSE for several array sizes, several source positions, and taking into account the effect of radiation pattern. In general, better results are obtained with larger array and larger source distances. The effect of the antennas is included in the data model leading to more accurate results, particularly for range rather than for angle estimation. Moreover, a new multivariable searching method based on the MUSIC algorithm, called MUSA (multilevel MUSIC-based algorithm), is presented. This method is proposed to estimate the 3D location parameters in a faster way than other multivariable algorithms, such as MUSIC algorithm, at the cost of increasing the memory size. Finally, in the last chapter, a MIMO-OFDM-SPAA3D prototype is presented to experimentally evaluate different MIMO schemes regarding antennas, polarization, and frequency in different indoor and outdoor scenarios. The prototype has been developed on a Software-Defined Radio (SDR) platform. It allows taking measurements where future wireless systems will be developed. The novelty of this prototype is concerning the following 2 subsystems. The first one is the tridimensional (3D) antenna positioning system (SPAA3D) based on three linear scanners which is developed for making automatic testing possible reducing errors of the antenna array positioning. A set of software has been developed for research works such as MIMO channel characterization, MIMO capacity, OFDM synchronization, and so on. The second subsystem is the RF autocalibration module at the TX and RX. This subsystem allows to properly tracking the spatial structure of indoor and outdoor channels in terms of DoA profiles. Some results are draw regarding performance of MIMO-OFDM systems with different polarization schemes and different propagation environments.

The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small

In the maximum parsimony (MP) and minimum evolution (ME) methods of phylogenetic inference, evolutionary trees are constructed by searching for the topology that shows the minimum number of mutational changes required (M) and the smallest sum of branch lengths (S), respectively, whereas in the maximum likelihood (ML) method the topology showing the highest maximum likelihood (A) of observing a given data set is chosen. However, the theoretical basis of the optimization principle remains unclear. We therefore examined the relationships of M, S, and A for the MP, ME, and ML trees with those for the true tree by using computer simulation. The results show that M and S are generally greater for the true tree than for the MP and ME trees when the number of nucleotides examined (n) is relatively small, whereas A is generally lower for the true tree than for the ML tree. This finding indicates that the optimization principle tends to give incorrect topologies when n is small. To deal with this disturbing property of the optimization principle, we suggest that more attention should be given to testing the statistical reliability of an estimated tree rather than to finding the optimal tree with excessive efforts. When a reliability test is conducted, simplified MP, ME, and ML algorithms such as the neighbor-joining method generally give conclusions about phylogenetic inference very similar to those obtained by the more extensive tree search algorithms.

Calculating the probability of multitaxon evolutionary trees: bootstrappers Gambit.

The reconstruction of multitaxon trees from molecular sequences is confounded by the variety of algorithms and criteria used to evaluate trees, making it difficult to compare the results of different analyses. A global method of multitaxon phylogenetic reconstruction described here, Bootstrappers Gambit, can be used with any four-taxon algorithm, including distance, maximum likelihood, and parsimony methods. It incorporates a Bayesian-Jeffreys'-bootstrap analysis to provide a uniform probability-based criterion for comparing the results from diverse algorithms. To examine the usefulness of the method, the origin of the eukaryotes has been investigated by the analysis of ribosomal small subunit RNA sequences. Three common algorithms (paralinear distances, Jukes-Cantor distances, and Kimura distances) support the eocyte topology, whereas one (maximum parsimony) supports the archaebacterial topology, suggesting that the eocyte prokaryotes are the closest prokaryotic relatives of the eukaryotes.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Partitioned likelihood support and the evaluation of data set conflict

In simultaneous analyses of multiple data partitions, the trees relevant when measuring support for a clade are the optimal tree, and the best tree lacking the clade (i.e., the most reasonable alternative). The parsimony-based method of partitioned branch support (PBS) forces each data set to arbitrate between the two relevant trees. This value is the amount each data set contributes to clade support in the combined analysis, and can be very different to support apparent in separate analyses. The approach used in PBS can also be employed in likelihood: a simultaneous analysis of all data retrieves the maximum likelihood tree, and the best tree without the clade of interest is also found. Each data set is fitted to the two trees and the log-likelihood difference calculated, giving partitioned likelihood support (PLS) for each data set. These calculations can be performed regardless of the complexity of the ML model adopted. The significance of PLS can be evaluated using a variety of resampling methods, such as the Kishino-Hasegawa test, the Shimodiara-Hasegawa test, or likelihood weights, although the appropriateness and assumptions of these tests remains debated.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.