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.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification with super-classes

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.

Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Computational methods to create and analyze a digital gene expression atlas of embryo development from microscopy images

Abstract The creation of atlases, or digital models where information from different subjects can be combined, is a field of increasing interest in biomedical imaging. When a single image does not contain enough information to appropriately describe the organism under study, it is then necessary to acquire images of several individuals, each of them containing complementary data with respect to the rest of the components in the cohort. This approach allows creating digital prototypes, ranging from anatomical atlases of human patients and organs, obtained for instance from Magnetic Resonance Imaging, to gene expression cartographies of embryo development, typically achieved from Light Microscopy. Within such context, in this PhD Thesis we propose, develop and validate new dedicated image processing methodologies that, based on image registration techniques, bring information from multiple individuals into alignment within a single digital atlas model. We also elaborate a dedicated software visualization platform to explore the resulting wealth of multi-dimensional data and novel analysis algo-rithms to automatically mine the generated resource in search of bio¬logical insights. In particular, this work focuses on gene expression data from developing zebrafish embryos imaged at the cellular resolution level with Two-Photon Laser Scanning Microscopy. Disposing of quantitative measurements relating multiple gene expressions to cell position and their evolution in time is a fundamental prerequisite to understand embryogenesis multi-scale processes. However, the number of gene expressions that can be simultaneously stained in one acquisition is limited due to optical and labeling constraints. These limitations motivate the implementation of atlasing strategies that can recreate a virtual gene expression multiplex. The developed computational tools have been tested in two different scenarios. The first one is the early zebrafish embryogenesis where the resulting atlas constitutes a link between the phenotype and the genotype at the cellular level. The second one is the late zebrafish brain where the resulting atlas allows studies relating gene expression to brain regionalization and neurogenesis. The proposed computational frameworks have been adapted to the requirements of both scenarios, such as the integration of partial views of the embryo into a whole embryo model with cellular resolution or the registration of anatom¬ical traits with deformable transformation models non-dependent on any specific labeling. The software implementation of the atlas generation tool (Match-IT) and the visualization platform (Atlas-IT) together with the gene expression atlas resources developed in this Thesis are to be made freely available to the scientific community. Lastly, a novel proof-of-concept experiment integrates for the first time 3D gene expression atlas resources with cell lineages extracted from live embryos, opening up the door to correlate genetic and cellular spatio-temporal dynamics. La creación de atlas, o modelos digitales, donde la información de distintos sujetos puede ser combinada, es un campo de creciente interés en imagen biomédica. Cuando una sola imagen no contiene suficientes datos como para describir apropiadamente el organismo objeto de estudio, se hace necesario adquirir imágenes de varios individuos, cada una de las cuales contiene información complementaria respecto al resto de componentes del grupo. De este modo, es posible crear prototipos digitales, que pueden ir desde atlas anatómicos de órganos y pacientes humanos, adquiridos por ejemplo mediante Resonancia Magnética, hasta cartografías de la expresión genética del desarrollo de embrionario, típicamente adquiridas mediante Microscopía Optica. Dentro de este contexto, en esta Tesis Doctoral se introducen, desarrollan y validan nuevos métodos de procesado de imagen que, basándose en técnicas de registro de imagen, son capaces de alinear imágenes y datos provenientes de múltiples individuos en un solo atlas digital. Además, se ha elaborado una plataforma de visualization específicamente diseñada para explorar la gran cantidad de datos, caracterizados por su multi-dimensionalidad, que resulta de estos métodos. Asimismo, se han propuesto novedosos algoritmos de análisis y minería de datos que permiten inspeccionar automáticamente los atlas generados en busca de conclusiones biológicas significativas. En particular, este trabajo se centra en datos de expresión genética del desarrollo embrionario del pez cebra, adquiridos mediante Microscopía dos fotones con resolución celular. Disponer de medidas cuantitativas que relacionen estas expresiones genéticas con las posiciones celulares y su evolución en el tiempo es un prerrequisito fundamental para comprender los procesos multi-escala característicos de la morfogénesis. Sin embargo, el número de expresiones genéticos que pueden ser simultáneamente etiquetados en una sola adquisición es reducido debido a limitaciones tanto ópticas como del etiquetado. Estas limitaciones requieren la implementación de estrategias de creación de atlas que puedan recrear un multiplexado virtual de expresiones genéticas. Las herramientas computacionales desarrolladas han sido validadas en dos escenarios distintos. El primer escenario es el desarrollo embrionario temprano del pez cebra, donde el atlas resultante permite constituir un vínculo, a nivel celular, entre el fenotipo y el genotipo de este organismo modelo. El segundo escenario corresponde a estadios tardíos del desarrollo del cerebro del pez cebra, donde el atlas resultante permite relacionar expresiones genéticas con la regionalización del cerebro y la formación de neuronas. La plataforma computacional desarrollada ha sido adaptada a los requisitos y retos planteados en ambos escenarios, como la integración, a resolución celular, de vistas parciales dentro de un modelo consistente en un embrión completo, o el alineamiento entre estructuras de referencia anatómica equivalentes, logrado mediante el uso de modelos de transformación deformables que no requieren ningún marcador específico. Está previsto poner a disposición de la comunidad científica tanto la herramienta de generación de atlas (Match-IT), como su plataforma de visualización (Atlas-IT), así como las bases de datos de expresión genética creadas a partir de estas herramientas. Por último, dentro de la presente Tesis Doctoral, se ha incluido una prueba conceptual innovadora que permite integrar los mencionados atlas de expresión genética tridimensionales dentro del linaje celular extraído de una adquisición in vivo de un embrión. Esta prueba conceptual abre la puerta a la posibilidad de correlar, por primera vez, las dinámicas espacio-temporales de genes y células.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Run-Time Scalable Hardware for Reconfigurable Systems

La optimización de parámetros tales como el consumo de potencia, la cantidad de recursos lógicos empleados o la ocupación de memoria ha sido siempre una de las preocupaciones principales a la hora de diseñar sistemas embebidos. Esto es debido a que se trata de sistemas dotados de una cantidad de recursos limitados, y que han sido tradicionalmente empleados para un propósito específico, que permanece invariable a lo largo de toda la vida útil del sistema. Sin embargo, el uso de sistemas embebidos se ha extendido a áreas de aplicación fuera de su ámbito tradicional, caracterizadas por una mayor demanda computacional. Así, por ejemplo, algunos de estos sistemas deben llevar a cabo un intenso procesado de señales multimedia o la transmisión de datos mediante sistemas de comunicaciones de alta capacidad. Por otra parte, las condiciones de operación del sistema pueden variar en tiempo real. Esto sucede, por ejemplo, si su funcionamiento depende de datos medidos por el propio sistema o recibidos a través de la red, de las demandas del usuario en cada momento, o de condiciones internas del propio dispositivo, tales como la duración de la batería. Como consecuencia de la existencia de requisitos de operación dinámicos es necesario ir hacia una gestión dinámica de los recursos del sistema. Si bien el software es inherentemente flexible, no ofrece una potencia computacional tan alta como el hardware. Por lo tanto, el hardware reconfigurable aparece como una solución adecuada para tratar con mayor flexibilidad los requisitos variables dinámicamente en sistemas con alta demanda computacional. La flexibilidad y adaptabilidad del hardware requieren de dispositivos reconfigurables que permitan la modificación de su funcionalidad bajo demanda. En esta tesis se han seleccionado las FPGAs (Field Programmable Gate Arrays) como los dispositivos más apropiados, hoy en día, para implementar sistemas basados en hardware reconfigurable De entre todas las posibilidades existentes para explotar la capacidad de reconfiguración de las FPGAs comerciales, se ha seleccionado la reconfiguración dinámica y parcial. Esta técnica consiste en substituir una parte de la lógica del dispositivo, mientras el resto continúa en funcionamiento. La capacidad de reconfiguración dinámica y parcial de las FPGAs es empleada en esta tesis para tratar con los requisitos de flexibilidad y de capacidad computacional que demandan los dispositivos embebidos. La propuesta principal de esta tesis doctoral es el uso de arquitecturas de procesamiento escalables espacialmente, que son capaces de adaptar su funcionalidad y rendimiento en tiempo real, estableciendo un compromiso entre dichos parámetros y la cantidad de lógica que ocupan en el dispositivo. A esto nos referimos con arquitecturas con huellas escalables. En particular, se propone el uso de arquitecturas altamente paralelas, modulares, regulares y con una alta localidad en sus comunicaciones, para este propósito. El tamaño de dichas arquitecturas puede ser modificado mediante la adición o eliminación de algunos de los módulos que las componen, tanto en una dimensión como en dos. Esta estrategia permite implementar soluciones escalables, sin tener que contar con una versión de las mismas para cada uno de los tamaños posibles de la arquitectura. De esta manera se reduce significativamente el tiempo necesario para modificar su tamaño, así como la cantidad de memoria necesaria para almacenar todos los archivos de configuración. En lugar de proponer arquitecturas para aplicaciones específicas, se ha optado por patrones de procesamiento genéricos, que pueden ser ajustados para solucionar distintos problemas en el estado del arte. A este respecto, se proponen patrones basados en esquemas sistólicos, así como de tipo wavefront. Con el objeto de poder ofrecer una solución integral, se han tratado otros aspectos relacionados con el diseño y el funcionamiento de las arquitecturas, tales como el control del proceso de reconfiguración de la FPGA, la integración de las arquitecturas en el resto del sistema, así como las técnicas necesarias para su implementación. Por lo que respecta a la implementación, se han tratado distintos aspectos de bajo nivel dependientes del dispositivo. Algunas de las propuestas realizadas a este respecto en la presente tesis doctoral son un router que es capaz de garantizar el correcto rutado de los módulos reconfigurables dentro del área destinada para ellos, así como una estrategia para la comunicación entre módulos que no introduce ningún retardo ni necesita emplear recursos configurables del dispositivo. El flujo de diseño propuesto se ha automatizado mediante una herramienta denominada DREAMS. La herramienta se encarga de la modificación de las netlists correspondientes a cada uno de los módulos reconfigurables del sistema, y que han sido generadas previamente mediante herramientas comerciales. Por lo tanto, el flujo propuesto se entiende como una etapa de post-procesamiento, que adapta esas netlists a los requisitos de la reconfiguración dinámica y parcial. Dicha modificación la lleva a cabo la herramienta de una forma completamente automática, por lo que la productividad del proceso de diseño aumenta de forma evidente. Para facilitar dicho proceso, se ha dotado a la herramienta de una interfaz gráfica. El flujo de diseño propuesto, y la herramienta que lo soporta, tienen características específicas para abordar el diseño de las arquitecturas dinámicamente escalables propuestas en esta tesis. Entre ellas está el soporte para el realojamiento de módulos reconfigurables en posiciones del dispositivo distintas a donde el módulo es originalmente implementado, así como la generación de estructuras de comunicación compatibles con la simetría de la arquitectura. El router has sido empleado también en esta tesis para obtener un rutado simétrico entre nets equivalentes. Dicha posibilidad ha sido explotada para aumentar la protección de circuitos con altos requisitos de seguridad, frente a ataques de canal lateral, mediante la implantación de lógica complementaria con rutado idéntico. Para controlar el proceso de reconfiguración de la FPGA, se propone en esta tesis un motor de reconfiguración especialmente adaptado a los requisitos de las arquitecturas dinámicamente escalables. Además de controlar el puerto de reconfiguración, el motor de reconfiguración ha sido dotado de la capacidad de realojar módulos reconfigurables en posiciones arbitrarias del dispositivo, en tiempo real. De esta forma, basta con generar un único bitstream por cada módulo reconfigurable del sistema, independientemente de la posición donde va a ser finalmente reconfigurado. La estrategia seguida para implementar el proceso de realojamiento de módulos es diferente de las propuestas existentes en el estado del arte, pues consiste en la composición de los archivos de configuración en tiempo real. De esta forma se consigue aumentar la velocidad del proceso, mientras que se reduce la longitud de los archivos de configuración parciales a almacenar en el sistema. El motor de reconfiguración soporta módulos reconfigurables con una altura menor que la altura de una región de reloj del dispositivo. Internamente, el motor se encarga de la combinación de los frames que describen el nuevo módulo, con la configuración existente en el dispositivo previamente. El escalado de las arquitecturas de procesamiento propuestas en esta tesis también se puede beneficiar de este mecanismo. Se ha incorporado también un acceso directo a una memoria externa donde se pueden almacenar bitstreams parciales. Para acelerar el proceso de reconfiguración se ha hecho funcionar el ICAP por encima de la máxima frecuencia de reloj aconsejada por el fabricante. Así, en el caso de Virtex-5, aunque la máxima frecuencia del reloj deberían ser 100 MHz, se ha conseguido hacer funcionar el puerto de reconfiguración a frecuencias de operación de hasta 250 MHz, incluyendo el proceso de realojamiento en tiempo real. Se ha previsto la posibilidad de portar el motor de reconfiguración a futuras familias de FPGAs. Por otro lado, el motor de reconfiguración se puede emplear para inyectar fallos en el propio dispositivo hardware, y así ser capaces de evaluar la tolerancia ante los mismos que ofrecen las arquitecturas reconfigurables. Los fallos son emulados mediante la generación de archivos de configuración a los que intencionadamente se les ha introducido un error, de forma que se modifica su funcionalidad. Con el objetivo de comprobar la validez y los beneficios de las arquitecturas propuestas en esta tesis, se han seguido dos líneas principales de aplicación. En primer lugar, se propone su uso como parte de una plataforma adaptativa basada en hardware evolutivo, con capacidad de escalabilidad, adaptabilidad y recuperación ante fallos. En segundo lugar, se ha desarrollado un deblocking filter escalable, adaptado a la codificación de vídeo escalable, como ejemplo de aplicación de las arquitecturas de tipo wavefront propuestas. El hardware evolutivo consiste en el uso de algoritmos evolutivos para diseñar hardware de forma autónoma, explotando la flexibilidad que ofrecen los dispositivos reconfigurables. En este caso, los elementos de procesamiento que componen la arquitectura son seleccionados de una biblioteca de elementos presintetizados, de acuerdo con las decisiones tomadas por el algoritmo evolutivo, en lugar de definir la configuración de las mismas en tiempo de diseño. De esta manera, la configuración del core puede cambiar cuando lo hacen las condiciones del entorno, en tiempo real, por lo que se consigue un control autónomo del proceso de reconfiguración dinámico. Así, el sistema es capaz de optimizar, de forma autónoma, su propia configuración. El hardware evolutivo tiene una capacidad inherente de auto-reparación. Se ha probado que las arquitecturas evolutivas propuestas en esta tesis son tolerantes ante fallos, tanto transitorios, como permanentes y acumulativos. La plataforma evolutiva se ha empleado para implementar filtros de eliminación de ruido. La escalabilidad también ha sido aprovechada en esta aplicación. Las arquitecturas evolutivas escalables permiten la adaptación autónoma de los cores de procesamiento ante fluctuaciones en la cantidad de recursos disponibles en el sistema. Por lo tanto, constituyen un ejemplo de escalabilidad dinámica para conseguir un determinado nivel de calidad, que puede variar en tiempo real. Se han propuesto dos variantes de sistemas escalables evolutivos. El primero consiste en un único core de procesamiento evolutivo, mientras que el segundo está formado por un número variable de arrays de procesamiento. La codificación de vídeo escalable, a diferencia de los codecs no escalables, permite la decodificación de secuencias de vídeo con diferentes niveles de calidad, de resolución temporal o de resolución espacial, descartando la información no deseada. Existen distintos algoritmos que soportan esta característica. En particular, se va a emplear el estándar Scalable Video Coding (SVC), que ha sido propuesto como una extensión de H.264/AVC, ya que este último es ampliamente utilizado tanto en la industria, como a nivel de investigación. Para poder explotar toda la flexibilidad que ofrece el estándar, hay que permitir la adaptación de las características del decodificador en tiempo real. El uso de las arquitecturas dinámicamente escalables es propuesto en esta tesis con este objetivo. El deblocking filter es un algoritmo que tiene como objetivo la mejora de la percepción visual de la imagen reconstruida, mediante el suavizado de los "artefactos" de bloque generados en el lazo del codificador. Se trata de una de las tareas más intensivas en procesamiento de datos de H.264/AVC y de SVC, y además, su carga computacional es altamente dependiente del nivel de escalabilidad seleccionado en el decodificador. Por lo tanto, el deblocking filter ha sido seleccionado como prueba de concepto de la aplicación de las arquitecturas dinámicamente escalables para la compresión de video. La arquitectura propuesta permite añadir o eliminar unidades de computación, siguiendo un esquema de tipo wavefront. La arquitectura ha sido propuesta conjuntamente con un esquema de procesamiento en paralelo del deblocking filter a nivel de macrobloque, de tal forma que cuando se varía del tamaño de la arquitectura, el orden de filtrado de los macrobloques varia de la misma manera. El patrón propuesto se basa en la división del procesamiento de cada macrobloque en dos etapas independientes, que se corresponden con el filtrado horizontal y vertical de los bloques dentro del macrobloque. Las principales contribuciones originales de esta tesis son las siguientes: - El uso de arquitecturas altamente regulares, modulares, paralelas y con una intensa localidad en sus comunicaciones, para implementar cores de procesamiento dinámicamente reconfigurables. - El uso de arquitecturas bidimensionales, en forma de malla, para construir arquitecturas dinámicamente escalables, con una huella escalable. De esta forma, las arquitecturas permiten establecer un compromiso entre el área que ocupan en el dispositivo, y las prestaciones que ofrecen en cada momento. Se proponen plantillas de procesamiento genéricas, de tipo sistólico o wavefront, que pueden ser adaptadas a distintos problemas de procesamiento. - Un flujo de diseño y una herramienta que lo soporta, para el diseño de sistemas reconfigurables dinámicamente, centradas en el diseño de las arquitecturas altamente paralelas, modulares y regulares propuestas en esta tesis. - Un esquema de comunicaciones entre módulos reconfigurables que no introduce ningún retardo ni requiere el uso de recursos lógicos propios. - Un router flexible, capaz de resolver los conflictos de rutado asociados con el diseño de sistemas reconfigurables dinámicamente. - Un algoritmo de optimización para sistemas formados por múltiples cores escalables que optimice, mediante un algoritmo genético, los parámetros de dicho sistema. Se basa en un modelo conocido como el problema de la mochila. - Un motor de reconfiguración adaptado a los requisitos de las arquitecturas altamente regulares y modulares. Combina una alta velocidad de reconfiguración, con la capacidad de realojar módulos en tiempo real, incluyendo el soporte para la reconfiguración de regiones que ocupan menos que una región de reloj, así como la réplica de un módulo reconfigurable en múltiples posiciones del dispositivo. - Un mecanismo de inyección de fallos que, empleando el motor de reconfiguración del sistema, permite evaluar los efectos de fallos permanentes y transitorios en arquitecturas reconfigurables. - La demostración de las posibilidades de las arquitecturas propuestas en esta tesis para la implementación de sistemas de hardware evolutivos, con una alta capacidad de procesamiento de datos. - La implementación de sistemas de hardware evolutivo escalables, que son capaces de tratar con la fluctuación de la cantidad de recursos disponibles en el sistema, de una forma autónoma. - Una estrategia de procesamiento en paralelo para el deblocking filter compatible con los estándares H.264/AVC y SVC que reduce el número de ciclos de macrobloque necesarios para procesar un frame de video. - Una arquitectura dinámicamente escalable que permite la implementación de un nuevo deblocking filter, totalmente compatible con los estándares H.264/AVC y SVC, que explota el paralelismo a nivel de macrobloque. El presente documento se organiza en siete capítulos. En el primero se ofrece una introducción al marco tecnológico de esta tesis, especialmente centrado en la reconfiguración dinámica y parcial de FPGAs. También se motiva la necesidad de las arquitecturas dinámicamente escalables propuestas en esta tesis. En el capítulo 2 se describen las arquitecturas dinámicamente escalables. Dicha descripción incluye la mayor parte de las aportaciones a nivel arquitectural realizadas en esta tesis. Por su parte, el flujo de diseño adaptado a dichas arquitecturas se propone en el capítulo 3. El motor de reconfiguración se propone en el 4, mientras que el uso de dichas arquitecturas para implementar sistemas de hardware evolutivo se aborda en el 5. El deblocking filter escalable se describe en el 6, mientras que las conclusiones finales de esta tesis, así como la descripción del trabajo futuro, son abordadas en el capítulo 7. ABSTRACT The optimization of system parameters, such as power dissipation, the amount of hardware resources and the memory footprint, has been always a main concern when dealing with the design of resource-constrained embedded systems. This situation is even more demanding nowadays. Embedded systems cannot anymore be considered only as specific-purpose computers, designed for a particular functionality that remains unchanged during their lifetime. Differently, embedded systems are now required to deal with more demanding and complex functions, such as multimedia data processing and high-throughput connectivity. In addition, system operation may depend on external data, the user requirements or internal variables of the system, such as the battery life-time. All these conditions may vary at run-time, leading to adaptive scenarios. As a consequence of both the growing computational complexity and the existence of dynamic requirements, dynamic resource management techniques for embedded systems are needed. Software is inherently flexible, but it cannot meet the computing power offered by hardware solutions. Therefore, reconfigurable hardware emerges as a suitable technology to deal with the run-time variable requirements of complex embedded systems. Adaptive hardware requires the use of reconfigurable devices, where its functionality can be modified on demand. In this thesis, Field Programmable Gate Arrays (FPGAs) have been selected as the most appropriate commercial technology existing nowadays to implement adaptive hardware systems. There are different ways of exploiting reconfigurability in reconfigurable devices. Among them is dynamic and partial reconfiguration. This is a technique which consists in substituting part of the FPGA logic on demand, while the rest of the device continues working. The strategy followed in this thesis is to exploit the dynamic and partial reconfiguration of commercial FPGAs to deal with the flexibility and complexity demands of state-of-the-art embedded systems. The proposal of this thesis to deal with run-time variable system conditions is the use of spatially scalable processing hardware IP cores, which are able to adapt their functionality or performance at run-time, trading them off with the amount of logic resources they occupy in the device. This is referred to as a scalable footprint in the context of this thesis. The distinguishing characteristic of the proposed cores is that they rely on highly parallel, modular and regular architectures, arranged in one or two dimensions. These architectures can be scaled by means of the addition or removal of the composing blocks. This strategy avoids implementing a full version of the core for each possible size, with the corresponding benefits in terms of scaling and adaptation time, as well as bitstream storage memory requirements. Instead of providing specific-purpose architectures, generic architectural templates, which can be tuned to solve different problems, are proposed in this thesis. Architectures following both systolic and wavefront templates have been selected. Together with the proposed scalable architectural templates, other issues needed to ensure the proper design and operation of the scalable cores, such as the device reconfiguration control, the run-time management of the architecture and the implementation techniques have been also addressed in this thesis. With regard to the implementation of dynamically reconfigurable architectures, device dependent low-level details are addressed. Some of the aspects covered in this thesis are the area constrained routing for reconfigurable modules, or an inter-module communication strategy which does not introduce either extra delay or logic overhead. The system implementation, from the hardware description to the device configuration bitstream, has been fully automated by modifying the netlists corresponding to each of the system modules, which are previously generated using the vendor tools. This modification is therefore envisaged as a post-processing step. Based on these implementation proposals, a design tool called DREAMS (Dynamically Reconfigurable Embedded and Modular Systems) has been created, including a graphic user interface. The tool has specific features to cope with modular and regular architectures, including the support for module relocation and the inter-module communications scheme based on the symmetry of the architecture. The core of the tool is a custom router, which has been also exploited in this thesis to obtain symmetric routed nets, with the aim of enhancing the protection of critical reconfigurable circuits against side channel attacks. This is achieved by duplicating the logic with an exactly equal routing. In order to control the reconfiguration process of the FPGA, a Reconfiguration Engine suited to the specific requirements set by the proposed architectures was also proposed. Therefore, in addition to controlling the reconfiguration port, the Reconfiguration Engine has been enhanced with the online relocation ability, which allows employing a unique configuration bitstream for all the positions where the module may be placed in the device. Differently to the existing relocating solutions, which are based on bitstream parsers, the proposed approach is based on the online composition of bitstreams. This strategy allows increasing the speed of the process, while the length of partial bitstreams is also reduced. The height of the reconfigurable modules can be lower than the height of a clock region. The Reconfiguration Engine manages the merging process of the new and the existing configuration frames within each clock region. The process of scaling up and down the hardware cores also benefits from this technique. A direct link to an external memory where partial bitstreams can be stored has been also implemented. In order to accelerate the reconfiguration process, the ICAP has been overclocked over the speed reported by the manufacturer. In the case of Virtex-5, even though the maximum frequency of the ICAP is reported to be 100 MHz, valid operations at 250 MHz have been achieved, including the online relocation process. Portability of the reconfiguration solution to today's and probably, future FPGAs, has been also considered. The reconfiguration engine can be also used to inject faults in real hardware devices, and this way being able to evaluate the fault tolerance offered by the reconfigurable architectures. Faults are emulated by introducing partial bitstreams intentionally modified to provide erroneous functionality. To prove the validity and the benefits offered by the proposed architectures, two demonstration application lines have been envisaged. First, scalable architectures have been employed to develop an evolvable hardware platform with adaptability, fault tolerance and scalability properties. Second, they have been used to implement a scalable deblocking filter suited to scalable video coding. Evolvable Hardware is the use of evolutionary algorithms to design hardware in an autonomous way, exploiting the flexibility offered by reconfigurable devices. In this case, processing elements composing the architecture are selected from a presynthesized library of processing elements, according to the decisions taken by the algorithm, instead of being decided at design time. This way, the configuration of the array may change as run-time environmental conditions do, achieving autonomous control of the dynamic reconfiguration process. Thus, the self-optimization property is added to the native self-configurability of the dynamically scalable architectures. In addition, evolvable hardware adaptability inherently offers self-healing features. The proposal has proved to be self-tolerant, since it is able to self-recover from both transient and cumulative permanent faults. The proposed evolvable architecture has been used to implement noise removal image filters. Scalability has been also exploited in this application. Scalable evolvable hardware architectures allow the autonomous adaptation of the processing cores to a fluctuating amount of resources available in the system. Thus, it constitutes an example of the dynamic quality scalability tackled in this thesis. Two variants have been proposed. The first one consists in a single dynamically scalable evolvable core, and the second one contains a variable number of processing cores. Scalable video is a flexible approach for video compression, which offers scalability at different levels. Differently to non-scalable codecs, a scalable video bitstream can be decoded with different levels of quality, spatial or temporal resolutions, by discarding the undesired information. The interest in this technology has been fostered by the development of the Scalable Video Coding (SVC) standard, as an extension of H.264/AVC. In order to exploit all the flexibility offered by the standard, it is necessary to adapt the characteristics of the decoder to the requirements of each client during run-time. The use of dynamically scalable architectures is proposed in this thesis with this aim. The deblocking filter algorithm is the responsible of improving the visual perception of a reconstructed image, by smoothing blocking artifacts generated in the encoding loop. This is one of the most computationally intensive tasks of the standard, and furthermore, it is highly dependent on the selected scalability level in the decoder. Therefore, the deblocking filter has been selected as a proof of concept of the implementation of dynamically scalable architectures for video compression. The proposed architecture allows the run-time addition or removal of computational units working in parallel to change its level of parallelism, following a wavefront computational pattern. Scalable architecture is offered together with a scalable parallelization strategy at the macroblock level, such that when the size of the architecture changes, the macroblock filtering order is modified accordingly. The proposed pattern is based on the division of the macroblock processing into two independent stages, corresponding to the horizontal and vertical filtering of the blocks within the macroblock. The main contributions of this thesis are: - The use of highly parallel, modular, regular and local architectures to implement dynamically reconfigurable processing IP cores, for data intensive applications with flexibility requirements. - The use of two-dimensional mesh-type arrays as architectural templates to build dynamically reconfigurable IP cores, with a scalable footprint. The proposal consists in generic architectural templates, which can be tuned to solve different computational problems. •A design flow and a tool targeting the design of DPR systems, focused on highly parallel, modular and local architectures. - An inter-module communication strategy, which does not introduce delay or area overhead, named Virtual Borders. - A custom and flexible router to solve the routing conflicts as well as the inter-module communication problems, appearing during the design of DPR systems. - An algorithm addressing the optimization of systems composed of multiple scalable cores, which size can be decided individually, to optimize the system parameters. It is based on a model known as the multi-dimensional multi-choice Knapsack problem. - A reconfiguration engine tailored to the requirements of highly regular and modular architectures. It combines a high reconfiguration throughput with run-time module relocation capabilities, including the support for sub-clock reconfigurable regions and the replication in multiple positions. - A fault injection mechanism which takes advantage of the system reconfiguration engine, as well as the modularity of the proposed reconfigurable architectures, to evaluate the effects of transient and permanent faults in these architectures. - The demonstration of the possibilities of the architectures proposed in this thesis to implement evolvable hardware systems, while keeping a high processing throughput. - The implementation of scalable evolvable hardware systems, which are able to adapt to the fluctuation of the amount of resources available in the system, in an autonomous way. - A parallelization strategy for the H.264/AVC and SVC deblocking filter, which reduces the number of macroblock cycles needed to process the whole frame. - A dynamically scalable architecture that permits the implementation of a novel deblocking filter module, fully compliant with the H.264/AVC and SVC standards, which exploits the macroblock level parallelism of the algorithm. This document is organized in seven chapters. In the first one, an introduction to the technology framework of this thesis, specially focused on dynamic and partial reconfiguration, is provided. The need for the dynamically scalable processing architectures proposed in this work is also motivated in this chapter. In chapter 2, dynamically scalable architectures are described. Description includes most of the architectural contributions of this work. The design flow tailored to the scalable architectures, together with the DREAMs tool provided to implement them, are described in chapter 3. The reconfiguration engine is described in chapter 4. The use of the proposed scalable archtieectures to implement evolvable hardware systems is described in chapter 5, while the scalable deblocking filter is described in chapter 6. Final conclusions of this thesis, and the description of future work, are addressed in chapter 7.

Morphological Functions to Quantify Three-Dimensional Tomograms of Macropore Structure in a Vineyard Soil with Two Different Management Regimes

Soil tomography and morphological functions built over Minkowski functionals were used to describe the impact on pore structure of two soil management practices in a Mediterranean vineyard. Soil structure controls important physical and biological processes in soil–plant–microbial systems. Those processes are dominated by the geometry of soil pore structure, and a correct model of this geometry is critical for understanding them. Soil tomography has been shown to provide rich three-dimensional digital information on soil pore geometry. Recently, mathematical morphological techniques have been proposed as powerful tools to analyze and quantify the geometrical features of porous media. Minkowski functionals and morphological functions built over Minkowski functionals provide computationally efficient means to measure four fundamental geometrical features of three-dimensional geometrical objects, that is, volume, boundary surface, mean boundary surface curvature, and connectivity. We used the threshold and the dilation and erosion of three-dimensional images to generate morphological functions and explore the evolution of Minkowski functionals as the threshold and as the degree of dilation and erosion changes. We analyzed the three-dimensional geometry of soil pore space with X-ray computed tomography (CT) of intact soil columns from a Spanish Mediterranean vineyard by using two different management practices (conventional tillage versus permanent cover crop of resident vegetation). Our results suggested that morphological functions built over Minkowski functionals provide promising tools to characterize soil macropore structure and that the evolution of morphological features with dilation and erosion is more informative as an indicator of structure than moving threshold for both soil managements studied.

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.

Global Linear Instability at the Dawn of its 4th Decade: A List of Challenges (A Practical Guide on how to Contain the Euphoria and Avoid the Oversell)

Global linear instability theory is concerned with the temporal or spatial development of small-amplitude perturbations superposed upon laminar steady or time-periodic threedimensional flows, which are inhomogeneous in two (and periodic in one) or all three spatial directions.1 The theory addresses flows developing in complex geometries, in which the parallel or weakly nonparallel basic flow approximation invoked by classic linear stability theory does not hold. As such, global linear theory is called to fill the gap in research into stability and transition in flows over or through complex geometries. Historically, global linear instability has been (and still is) concerned with solution of multi-dimensional eigenvalue problems; the maturing of non-modal linear instability ideas in simple parallel flows during the last decade of last century2–4 has given rise to investigation of transient growth scenarios in an ever increasing variety of complex flows. After a brief exposition of the theory, connections are sought with established approaches for structure identification in flows, such as the proper orthogonal decomposition and topology theory in the laminar regime and the open areas for future research, mainly concerning turbulent and three-dimensional flows, are highlighted. Recent results obtained in our group are reported in both the time-stepping and the matrix-forming approaches to global linear theory. In the first context, progress has been made in implementing a Jacobian-Free Newton Krylov method into a standard finite-volume aerodynamic code, such that global linear instability results may now be obtained in compressible flows of aeronautical interest. In the second context a new stable very high-order finite difference method is implemented for the spatial discretization of the operators describing the spatial BiGlobal EVP, PSE-3D and the TriGlobal EVP; combined with sparse matrix treatment, all these problems may now be solved on standard desktop computers.

Estimación de la Reflectividad del Multitrayecto en Entornos Urbanos en Radares de Apertura Sintética

Synthetic Aperture Radar (SAR) images a target region reflectivity function in the multi-dimensional spatial domain of range and cross-range. SAR synthesizes a large aperture radar in order to achieve finer azimuth resolution than the one provided by any on-board real antenna. Conventional SAR techniques assume a single reflection of transmitted waveforms from targets. Nevertheless, today¿s new scenes force SAR systems to work in urban environments. Consequently, multiple-bounce returns are added to direct-scatter echoes. We refer to these as ghost images, since they obscure true target image and lead to poor resolution. By analyzing the quadratic phase error (QPE), this paper demonstrates that Earth¿s curvature influences the defocusing degree of multipath returns. In addition to the QPE, other parameters such as integrated sidelobe ratio (ISLR), peak sidelobe ratio (PSLR), contrast and entropy provide us with the tools to identify direct-scatter echoes in images containing undesired returns coming from multipath.