Differences into HT and HTO concentrations in air into the Western Mediterranean Basin and Continental Europe and Safety Related Issues.

Real time Tritium concentrations in air in two chemical forms, HT and HTO, coming from an ITER-like fusion reactor as source were coupled the European Centre Medium Range Weather Forecast (ECMWF) numerical model with the Lagrangian Atmospheric-particle dispersion model FLEXPART. This tool was analyzed in nominal tritium discharge operational reference and selected incidental conditions affecting the Western Mediterranean Basin during 45 days during summer 2010 together with surface “wind observations” or weather data based in real hourly observations of wind direction and velocity providing a real approximation of the tritium behavior after the release to the atmosphere from a fusion reactor. From comparison with NORMTRI - a code using climatologically sequences as input - over the same area, the real time results have demonstrated an apparent overestimation of the corresponding climatologically sequence of Tritium concentrations in air outputs, at several distances from the reactor. For this purpose two development patterns were established. The first one was following a cyclonic circulation over the Mediterranean Sea and the second one was based on the plume delivered over the Interior of the Iberian Peninsula and Continental Europe by another stabilized circulation corresponding to a High Pressure System. One of the important remaining activities defined then, was the qualification tool. In order to validate the model of ECMWF/FLEXPART we have developed of a new complete data base of tritium concentrations for the months from November 2010 to March 2011 and defined a new set of four patterns of HT transport in air, in each case using real boundary conditions: stationary to the North, stationary to the South, fast and very fast displacement. Finally the differences corresponding to those four early patterns (each one in assessments 1 and 2) has been analyzed in terms of the tuning of safety related issues and taking into account the primary phase o- - f tritium modeling, from its discharge to the atmosphere to the deposition on the ground, will affect to the complete tritium environmental pathway altering the chronic dose by absorption, reemission and ingestion both from elemental tritium, HT and from the oxide of tritium, HTO

Imposed Deformations Measured on a Real Integral Structure: New Airport Terminal Barajas, Madrid, Spain

Experimental research on imposed deformation is generally conducted on small scale laboratory experiments. The attractiveness of field research lies in the possibility to compare results obtained from full scale structures to theoretical prediction. Unfortunately, measurements obtained from real structures are rarely described in literature. The structural response of integral edifices depends significantly on stiffness changes and constraints. The New Airport Terminal Barajas in Madrid, Spain provides with large integral modules, partially post?tensioned concrete frames, cast monolithically over three floor levels and an overall length of approx. 80 m. The field campaign described in this article explains the instrumentation of one of these frames focusing on the influence of imposed deformations such as creep, shrinkage and temperature. The applied monitoring equipment included embedded strain gages, thermocouples, DEMEC measurements and simple displacement measurements. Data was collected throughout construction and during two years of service. A complete data range of five years is presented and analysed. The results are compared with a simple approach to predict the long?term shortening of this concrete structure. Both analytical and experimental results are discussed.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Towards a complete propagation of nuclear data uncertainties Fission yields and decay data

Propagation of nuclear data uncertainties to calculated values is interesting for design purposes and libraries evaluation. XSUSA, developed at GRS, propagates cross section uncertainties to nuclear calculations. In depletion simulations, fission yields and decay data are also involved and suppose a possible source of uncertainty that must be taken into account. We have developed tools to generate varied fission yields and decay libraries and to propagate uncertainties trough depletion in order to complete the XSUSA uncertainty assessment capabilities. A simple test to probe the methodology is defined and discussed.

Propagation of Nuclear Data Uncertainties in Transmutation Calculations Using ACAB Code

The assessment of the accuracy of parameters related to the reactor core performance (e.g., ke) and f el cycle (e.g., isotopic evolution/transmutation) due to the uncertainties in the basic nuclear data (ND) is a critical issue. Different error propagation techniques (adjoint/forward sensitivity analysis procedures and/or Monte Carlo technique) can be used to address by computational simulation the systematic propagation of uncertainties on the final parameters. To perform this uncertainty assessment, the ENDF covariance les (variance/correlation in energy and cross- reactions-isotopes correlations) are required. In this paper, we assess the impact of ND uncertainties on the isotopic prediction for a conceptual design of a modular European Facility for Industrial Transmutation (EFIT) for a discharge burnup of 150 GWd/tHM. The complete set of uncertainty data for cross sections (EAF2007/UN, SCALE6.0/COVA-44G), radioactive decay and fission yield data (JEFF-3.1.1) are processed and used in ACAB code.

A set of canonical problems in sloshing. Part 0: Experimental setup and data processing

In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

NetCDF based data archiving system applied to ITER Fast Plant System Control prototype

EURATOM/CIEMAT and Technical University of Madrid (UPM) have been involved in the development of a FPSC [1] (Fast Plant System Control) prototype for ITER, based on PXIe (PCI eXtensions for Instrumentation). One of the main focuses of this project has been data acquisition and all the related issues, including scientific data archiving. Additionally, a new data archiving solution has been developed to demonstrate the obtainable performances and possible bottlenecks of scientific data archiving in Fast Plant System Control. The presented system implements a fault tolerant architecture over a GEthernet network where FPSC data are reliably archived on remote, while remaining accessible to be redistributed, within the duration of a pulse. The storing service is supported by a clustering solution to guaranty scalability, so that FPSC management and configuration may be simplified, and a unique view of all archived data provided. All the involved components have been integrated under EPICS [2] (Experimental Physics and Industrial Control System), implementing in each case the necessary extensions, state machines and configuration process variables. The prototyped solution is based on the NetCDF-4 [3] and [4] (Network Common Data Format) file format in order to incorporate important features, such as scientific data models support, huge size files management, platform independent codification, or single-writer/multiple-readers concurrency. In this contribution, a complete description of the above mentioned solution is presented, together with the most relevant results of the tests performed, while focusing in the benefits and limitations of the applied technologies.

Complete and efficient methods for supporting side effects in independent/restricted and-parallelism

It has been shown that it is possible to exploit Independent/Restricted And-parallelism in logic programs while retaining the conventional "don't know" semantics of such programs. In particular, it is possible to parallelize pure Prolog programs while maintaining the semantics of the language. However, when builtin side-effects (such as write or assert) appear in the program, if an identical observable behaviour to that of sequential Prolog implementations is to be preserved, such side-effects have to be properly sequenced. Previously proposed solutions to this problem are either incomplete (lacking, for example, backtracking semantics) or they force sequentialization of significant portions of the execution graph which could otherwise run in parallel. In this paper a series of side-effect synchronization methods are proposed which incur lower overhead and allow more parallelism than those previously proposed. Most importantly, and unlike previous proposals, they have well-defined backward execution behaviour and require only a small modification to a given (And-parallel) Prolog implementation.

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

Assessing the dynamic behavior of wsn motes and rfid semi-passive tags for temperature monitoring.

A notorious advantage of wireless transmission is a significant reduction and simplification in wiring and harness. There are a lot of applications of wireless systems, but in many occasions sensor nodes require a specific housing to protect the electronics from hush environmental conditions. Nowadays the information is scarce and nonspecific on the dynamic behaviour of WSN and RFID. Therefore the purpose of this study is to evaluate the dynamic behaviour of the sensors. A series of trials were designed and performed covering temperature steps between cold room (5 °C), room temperature (23 °C) and heated environment (35 °C). As sensor nodes: three Crossbow motes, a surface mounted Nlaza module (with sensor Sensirion located on the motherboard), an aerial mounted Nlaza where the Sensirion sensor stayed at the end of a cable), and four tags RFID Turbo Tag (T700 model with and without housing), and 702-B (with and without housing). To assess the dynamic behaviour a first order response approach is used and fitted with dedicated optimization tools programmed in Matlab that allow extracting the time response (?) and corresponding determination coefficient (r2) with regard to experimental data. The shorter response time (20.9 s) is found for the uncoated T 700 tag which encapsulated version provides a significantly higher response (107.2 s). The highest ? corresponds to the Crossbow modules (144.4 s), followed by the surface mounted Nlaza module (288.1 s), while the module with aerial mounted sensor gives a response certainly close above to the T700 without coating (42.8 s). As a conclusion, the dynamic response of temperature sensors within wireless and RFID nodes is dramatically influenced by the way they are housed (to protect them from the environment) as well as by the heat released by the node electronics itself; its characterization is basic to allow monitoring of high rate temperature changes and to certify the cold chain. Besides the time to rise and to recover is significantly different being mostly higher for the latter than for the former.

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.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Design and Validation of an Optimized MB-OFDM Ultra Wideband Transceiver System

Esta tesis está incluida dentro del campo del campo de Multiband Orthogonal Frequency Division Multiplexing Ultra Wideband (MB-OFDM UWB), el cual ha adquirido una gran importancia en las comunicaciones inalámbricas de alta tasa de datos en la última década. UWB surgió con el objetivo de satisfacer la creciente demanda de conexiones inalámbricas en interiores y de uso doméstico, con bajo coste y alta velocidad. La disponibilidad de un ancho de banda grande, el potencial para alta velocidad de transmisión, baja complejidad y bajo consumo de energía, unido al bajo coste de implementación, representa una oportunidad única para que UWB se convierta en una solución ampliamente utilizada en aplicaciones de Wireless Personal Area Network (WPAN). UWB está definido como cualquier transmisión que ocupa un ancho de banda de más de 20% de su frecuencia central, o más de 500 MHz. En 2002, la Comisión Federal de Comunicaciones (FCC) definió que el rango de frecuencias de transmisión de UWB legal es de 3.1 a 10.6 GHz, con una energía de transmisión de -41.3 dBm/Hz. Bajo las directrices de FCC, el uso de la tecnología UWB puede aportar una enorme capacidad en las comunicaciones de corto alcance. Considerando las ecuaciones de capacidad de Shannon, incrementar la capacidad del canal requiere un incremento lineal en el ancho de banda, mientras que un aumento similar de la capacidad de canal requiere un aumento exponencial en la energía de transmisión. En los últimos años, s diferentes desarrollos del UWB han sido extensamente estudiados en diferentes áreas, entre los cuales, el protocolo de comunicaciones inalámbricas MB-OFDM UWB está considerado como la mejor elección y ha sido adoptado como estándar ISO/IEC para los WPANs. Combinando la modulación OFDM y la transmisión de datos utilizando las técnicas de salto de frecuencia, el sistema MB-OFDM UWB es capaz de soportar tasas de datos con que pueden variar de los 55 a los 480 Mbps, alcanzando una distancia máxima de hasta 10 metros. Se esperara que la tecnología MB-OFDM tenga un consumo energético muy bajo copando un are muy reducida en silicio, proporcionando soluciones de bajo coste que satisfagan las demandas del mercado. Para cumplir con todas estas expectativas, el desarrollo y la investigación del MBOFDM UWB deben enfrentarse a varios retos, como son la sincronización de alta sensibilidad, las restricciones de baja complejidad, las estrictas limitaciones energéticas, la escalabilidad y la flexibilidad. Tales retos requieren un procesamiento digital de la señal de última generación, capaz de desarrollar sistemas que puedan aprovechar por completo las ventajas del espectro UWB y proporcionar futuras aplicaciones inalámbricas en interiores. Esta tesis se centra en la completa optimización de un sistema de transceptor de banda base MB-OFDM UWB digital, cuyo objetivo es investigar y diseñar un subsistema de comunicación inalámbrica para la aplicación de las Redes de Sensores Inalámbricas Visuales. La complejidad inherente de los procesadores FFT/IFFT y el sistema de sincronización así como la alta frecuencia de operación para todos los elementos de procesamiento, se convierten en el cuello de la botella para el diseño y la implementación del sistema de UWB digital en base de banda basado en MB-OFDM de baja energía. El objetivo del transceptor propuesto es conseguir baja energía y baja complejidad bajo la premisa de un alto rendimiento. Las optimizaciones están realizadas tanto a nivel algorítmico como a nivel arquitectural para todos los elementos del sistema. Una arquitectura hardware eficiente en consumo se propone en primer lugar para aquellos módulos correspondientes a núcleos de computación. Para el procesado de la Transformada Rápida de Fourier (FFT/IFFT), se propone un algoritmo mixed-radix, basado en una arquitectura con pipeline y se ha desarrollado un módulo de Decodificador de Viterbi (VD) equilibrado en coste-velocidad con el objetivo de reducir el consumo energético e incrementar la velocidad de procesamiento. También se ha implementado un correlador signo-bit simple basado en la sincronización del tiempo de símbolo es presentado. Este correlador es usado para detectar y sincronizar los paquetes de OFDM de forma robusta y precisa. Para el desarrollo de los subsitemas de procesamiento y realizar la integración del sistema completo se han empleado tecnologías de última generación. El dispositivo utilizado para el sistema propuesto es una FPGA Virtex 5 XC5VLX110T del fabricante Xilinx. La validación el propuesta para el sistema transceptor se ha implementado en dicha placa de FPGA. En este trabajo se presenta un algoritmo, y una arquitectura, diseñado con filosofía de co-diseño hardware/software para el desarrollo de sistemas de FPGA complejos. El objetivo principal de la estrategia propuesta es de encontrar una metodología eficiente para el diseño de un sistema de FPGA configurable optimizado con el empleo del mínimo esfuerzo posible en el sistema de procedimiento de verificación, por tanto acelerar el periodo de desarrollo del sistema. La metodología de co-diseño presentada tiene la ventaja de ser fácil de usar, contiene todos los pasos desde la propuesta del algoritmo hasta la verificación del hardware, y puede ser ampliamente extendida para casi todos los tipos de desarrollos de FPGAs. En este trabajo se ha desarrollado sólo el sistema de transceptor digital de banda base por lo que la comprobación de señales transmitidas a través del canal inalámbrico en los entornos reales de comunicación sigue requiriendo componentes RF y un front-end analógico. No obstante, utilizando la metodología de co-simulación hardware/software citada anteriormente, es posible comunicar el sistema de transmisor y el receptor digital utilizando los modelos de canales propuestos por IEEE 802.15.3a, implementados en MATLAB. Por tanto, simplemente ajustando las características de cada modelo de canal, por ejemplo, un incremento del retraso y de la frecuencia central, podemos estimar el comportamiento del sistema propuesto en diferentes escenarios y entornos. Las mayores contribuciones de esta tesis son: • Se ha propuesto un nuevo algoritmo 128-puntos base mixto FFT usando la arquitectura pipeline multi-ruta. Los complejos multiplicadores para cada etapa de procesamiento son diseñados usando la arquitectura modificada shiftadd. Los sistemas word length y twiddle word length son comparados y seleccionados basándose en la señal para cuantización del SQNR y el análisis de energías. • El desempeño del procesador IFFT es analizado bajo diferentes situaciones aritméticas de bloques de punto flotante (BFP) para el control de desbordamiento, por tanto, para encontrar la arquitectura perfecta del algoritmo IFFT basado en el procesador FFT propuesto. • Para el sistema de receptor MB-OFDM UWB se ha empleado una sincronización del tiempo innovadora, de baja complejidad y esquema de compensación, que consiste en funciones de Detector de Paquetes (PD) y Estimación del Offset del tiempo. Simplificando el cross-correlation y maximizar las funciones probables solo a sign-bit, la complejidad computacional se ve reducida significativamente. • Se ha propuesto un sistema de decodificadores Viterbi de 64 estados de decisión-débil usando velocidad base-4 de arquitectura suma-comparaselecciona. El algoritmo Two-pointer Even también es introducido en la unidad de rastreador de origen con el objetivo de conseguir la eficiencia en el hardware. • Se han integrado varias tecnologías de última generación en el completo sistema transceptor basebanda , con el objetivo de implementar un sistema de comunicación UWB altamente optimizado. • Un diseño de flujo mejorado es propuesto para el complejo sistema de implementación, el cual puede ser usado para diseños de Cadena de puertas de campo programable general (FPGA). El diseño mencionado no sólo reduce dramáticamente el tiempo para la verificación funcional, sino también provee un análisis automático como los errores del retraso del output para el sistema de hardware implementado. • Un ambiente de comunicación virtual es establecido para la validación del propuesto sistema de transceptores MB-OFDM. Este método es provisto para facilitar el uso y la conveniencia de analizar el sistema digital de basebanda sin parte frontera analógica bajo diferentes ambientes de comunicación. Esta tesis doctoral está organizada en seis capítulos. En el primer capítulo se encuentra una breve introducción al campo del UWB, tanto relacionado con el proyecto como la motivación del desarrollo del sistema de MB-OFDM. En el capítulo 2, se presenta la información general y los requisitos del protocolo de comunicación inalámbrica MBOFDM UWB. En el capítulo 3 se habla de la arquitectura del sistema de transceptor digital MB-OFDM de banda base . El diseño del algoritmo propuesto y la arquitectura para cada elemento del procesamiento está detallado en este capítulo. Los retos de diseño del sistema que involucra un compromiso de discusión entre la complejidad de diseño, el consumo de energía, el coste de hardware, el desempeño del sistema, y otros aspectos. En el capítulo 4, se ha descrito la co-diseñada metodología de hardware/software. Cada parte del flujo del diseño será detallado con algunos ejemplos que se ha hecho durante el desarrollo del sistema. Aprovechando esta estrategia de diseño, el procedimiento de comunicación virtual es llevado a cabo para probar y analizar la arquitectura del transceptor propuesto. Los resultados experimentales de la co-simulación y el informe sintético de la implementación del sistema FPGA son reflejados en el capítulo 5. Finalmente, en el capítulo 6 se incluye las conclusiones y los futuros proyectos, y también los resultados derivados de este proyecto de doctorado. ABSTRACT In recent years, the Wireless Visual Sensor Network (WVSN) has drawn great interest in wireless communication research area. They enable a wealth of new applications such as building security control, image sensing, and target localization. However, nowadays wireless communication protocols (ZigBee, Wi-Fi, and Bluetooth for example) cannot fully satisfy the demands of high data rate, low power consumption, short range, and high robustness requirements. New communication protocol is highly desired for such kind of applications. The Ultra Wideband (UWB) wireless communication protocol, which has increased in importance for high data rate wireless communication field, are emerging as an important topic for WVSN research. UWB has emerged as a technology that offers great promise to satisfy the growing demand for low-cost, high-speed digital wireless indoor and home networks. The large bandwidth available, the potential for high data rate transmission, and the potential for low complexity and low power consumption, along with low implementation cost, all present a unique opportunity for UWB to become a widely adopted radio solution for future Wireless Personal Area Network (WPAN) applications. UWB is defined as any transmission that occupies a bandwidth of more than 20% of its center frequency, or more than 500 MHz. In 2002, the Federal Communications Commission (FCC) has mandated that UWB radio transmission can legally operate in the range from 3.1 to 10.6 GHz at a transmitter power of －41.3 dBm/Hz. Under the FCC guidelines, the use of UWB technology can provide enormous capacity over short communication ranges. Considering Shannon’s capacity equations, increasing the channel capacity requires linear increasing in bandwidth, whereas similar channel capacity increases would require exponential increases in transmission power. In recent years, several different UWB developments has been widely studied in different area, among which, the MB-OFDM UWB wireless communication protocol is considered to be the leading choice and has recently been adopted in the ISO/IEC standard for WPANs. By combing the OFDM modulation and data transmission using frequency hopping techniques, the MB-OFDM UWB system is able to support various data rates, ranging from 55 to 480 Mbps, over distances up to 10 meters. The MB-OFDM technology is expected to consume very little power and silicon area, as well as provide low-cost solutions that can satisfy consumer market demands. To fulfill these expectations, MB-OFDM UWB research and development have to cope with several challenges, which consist of high-sensitivity synchronization, low- complexity constraints, strict power limitations, scalability, and flexibility. Such challenges require state-of-the-art digital signal processing expertise to develop systems that could fully take advantages of the UWB spectrum and support future indoor wireless applications. This thesis focuses on fully optimization for the MB-OFDM UWB digital baseband transceiver system, aiming at researching and designing a wireless communication subsystem for the Wireless Visual Sensor Networks (WVSNs) application. The inherent high complexity of the FFT/IFFT processor and synchronization system, and high operation frequency for all processing elements, becomes the bottleneck for low power MB-OFDM based UWB digital baseband system hardware design and implementation. The proposed transceiver system targets low power and low complexity under the premise of high performance. Optimizations are made at both algorithm and architecture level for each element of the transceiver system. The low-power hardwareefficient structures are firstly proposed for those core computation modules, i.e., the mixed-radix algorithm based pipelined architecture is proposed for the Fast Fourier Transform (FFT/IFFT) processor, and the cost-speed balanced Viterbi Decoder (VD) module is developed, in the aim of lowering the power consumption and increasing the processing speed. In addition, a low complexity sign-bit correlation based symbol timing synchronization scheme is presented so as to detect and synchronize the OFDM packets robustly and accurately. Moreover, several state-of-the-art technologies are used for developing other processing subsystems and an entire MB-OFDM digital baseband transceiver system is integrated. The target device for the proposed transceiver system is Xilinx Virtex 5 XC5VLX110T FPGA board. In order to validate the proposed transceiver system in the FPGA board, a unified algorithm-architecture-circuit hardware/software co-design environment for complex FPGA system development is presented in this work. The main objective of the proposed strategy is to find an efficient methodology for designing a configurable optimized FPGA system by using as few efforts as possible in system verification procedure, so as to speed up the system development period. The presented co-design methodology has the advantages of easy to use, covering all steps from algorithm proposal to hardware verification, and widely spread for almost all kinds of FPGA developments. Because only the digital baseband transceiver system is developed in this thesis, the validation of transmitting signals through wireless channel in real communication environments still requires the analog front-end and RF components. However, by using the aforementioned hardware/software co-simulation methodology, the transmitter and receiver digital baseband systems get the opportunity to communicate with each other through the channel models, which are proposed from the IEEE 802.15.3a research group, established in MATLAB. Thus, by simply adjust the characteristics of each channel model, e.g. mean excess delay and center frequency, we can estimate the transmission performance of the proposed transceiver system through different communication situations. The main contributions of this thesis are: • A novel mixed radix 128-point FFT algorithm by using multipath pipelined architecture is proposed. The complex multipliers for each processing stage are designed by using modified shift-add architectures. The system wordlength and twiddle word-length are compared and selected based on Signal to Quantization Noise Ratio (SQNR) and power analysis. • IFFT processor performance is analyzed under different Block Floating Point (BFP) arithmetic situations for overflow control, so as to find out the perfect architecture of IFFT algorithm based on the proposed FFT processor. • An innovative low complex timing synchronization and compensation scheme, which consists of Packet Detector (PD) and Timing Offset Estimation (TOE) functions, for MB-OFDM UWB receiver system is employed. By simplifying the cross-correlation and maximum likelihood functions to signbit only, the computational complexity is significantly reduced. • A 64 state soft-decision Viterbi Decoder system by using high speed radix-4 Add-Compare-Select architecture is proposed. Two-pointer Even algorithm is also introduced into the Trace Back unit in the aim of hardware-efficiency. • Several state-of-the-art technologies are integrated into the complete baseband transceiver system, in the aim of implementing a highly-optimized UWB communication system. • An improved design flow is proposed for complex system implementation which can be used for general Field-Programmable Gate Array (FPGA) designs. The design method not only dramatically reduces the time for functional verification, but also provides automatic analysis such as errors and output delays for the implemented hardware systems. • A virtual communication environment is established for validating the proposed MB-OFDM transceiver system. This methodology is proved to be easy for usage and convenient for analyzing the digital baseband system without analog frontend under different communication environments. This PhD thesis is organized in six chapters. In the chapter 1 a brief introduction to the UWB field, as well as the related work, is done, along with the motivation of MBOFDM system development. In the chapter 2, the general information and requirement of MB-OFDM UWB wireless communication protocol is presented. In the chapter 3, the architecture of the MB-OFDM digital baseband transceiver system is presented. The design of the proposed algorithm and architecture for each processing element is detailed in this chapter. Design challenges of such system involve trade-off discussions among design complexity, power consumption, hardware cost, system performance, and some other aspects. All these factors are analyzed and discussed. In the chapter 4, the hardware/software co-design methodology is proposed. Each step of this design flow will be detailed by taking some examples that we met during system development. Then, taking advantages of this design strategy, the Virtual Communication procedure is carried out so as to test and analyze the proposed transceiver architecture. Experimental results from the co-simulation and synthesis report of the implemented FPGA system are given in the chapter 5. The chapter 6 includes conclusions and future work, as well as the results derived from this PhD work.