Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Using spatially distributed parameters and multi-response objective functions to solve parameterization of complex applications of semi-distributed hydrological models

Application of semi-distributed hydrological models to large, heterogeneous watersheds deals with several problems. On one hand, the spatial and temporal variability in catchment features should be adequately represented in the model parameterization, while maintaining the model complexity in an acceptable level to take advantage of state-of-the-art calibration techniques. On the other hand, model complexity enhances uncertainty in adjusted model parameter values, therefore increasing uncertainty in the water routing across the watershed. This is critical for water quality applications, where not only streamflow, but also a reliable estimation of the surface versus subsurface contributions to the runoff is needed. In this study, we show how a regularized inversion procedure combined with a multiobjective function calibration strategy successfully solves the parameterization of a complex application of a water quality-oriented hydrological model. The final value of several optimized parameters showed significant and consistentdifferences across geological and landscape features. Although the number of optimized parameters was significantly increased by the spatial and temporal discretization of adjustable parameters, the uncertainty in water routing results remained at reasonable values. In addition, a stepwise numerical analysis showed that the effects on calibration performance due to inclusion of different data types in the objective function could be inextricably linked. Thus caution should be taken when adding or removing data from an aggregated objective function.

Optimization in multi-implant placement for immediate loading in edentulous arches using a modified surgical template and prototyping: A case report

Immediate loading of dental implants shortens the treatment time and makes it possible to give the patient an esthetic appearance throughout the treatment period. Placement of dental implants requires precise planning that accounts for anatomic limitations and restorative goals. Diagnosis can be made with the assistance of computerized tomographic scanning, but transfer of planning to the surgical field is limited. Recently, novel CAD/CAM techniques such as stereolithographic rapid prototyping have been developed to build surgical guides in an attempt to improve precision of implant placement. The aim of this case report was to show a modified surgical template used throughout implant placement as an alternative to a conventional surgical guide.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Cross-Layer Packet Size Optimization for Wireless Terrestrial, Underwater, and Underground Sensor Networks

In this paper, a cross-layer solution for packet size optimization in wireless sensor networks (WSN) is introduced such that the effects of multi-hop routing, the broadcast nature of the physical wireless channel, and the effects of error control techniques are captured. A key result of this paper is that contrary to the conventional wireless networks, in wireless sensor networks, longer packets reduce the collision probability. Consequently, an optimization solution is formalized by using three different objective functions, i.e., packet throughput, energy consumption, and resource utilization. Furthermore, the effects of end-to-end latency and reliability constraints are investigated that may be required by a particular application. As a result, a generic, cross-layer optimization framework is developed to determine the optimal packet size in WSN. This framework is further extended to determine the optimal packet size in underwater and underground sensor networks. From this framework, the optimal packet sizes under various network parameters are determined.

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the new many-core design paradigm. Despite the extraordinarily high computing power, the complexity of many-core chips opens the door to several challenges. As a result of the increased silicon density of modern Systems-on-a-Chip (SoC), the design space exploration needed to find the best design has exploded and hardware designers are in fact facing the problem of a huge design space. Virtual Platforms have always been used to enable hardware-software co-design, but today they are facing with the huge complexity of both hardware and software systems. In this thesis two different research works on Virtual Platforms are presented: the first one is intended for the hardware developer, to easily allow complex cycle accurate simulations of many-core SoCs. The second work exploits the parallel computing power of off-the-shelf General Purpose Graphics Processing Units (GPGPUs), with the goal of an increased simulation speed. The term Virtualization can be used in the context of many-core systems not only to refer to the aforementioned hardware emulation tools (Virtual Platforms), but also for two other main purposes: 1) to help the programmer to achieve the maximum possible performance of an application, by hiding the complexity of the underlying hardware. 2) to efficiently exploit the high parallel hardware of many-core chips in environments with multiple active Virtual Machines. This thesis is focused on virtualization techniques with the goal to mitigate, and overtake when possible, some of the challenges introduced by the many-core design paradigm.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

High Efficiency Envelope Amplifier based on a Ripple Cancellation Buck Converter. Design, Optimization and Integration in an EER RFPA

Durante los últimos años la tendencia en el sector de las telecomunicaciones ha sido un aumento y diversificación en la transmisión de voz, video y fundamentalmente de datos. Para conseguir alcanzar las tasas de transmisión requeridas, los nuevos estándares de comunicaciones requieren un mayor ancho de banda y tienen un mayor factor de pico, lo cual influye en el bajo rendimiento del amplificador de radiofrecuencia (RFPA). Otro factor que ha influido en el bajo rendimiento es el diseño del amplificador de radiofrecuencia. Tradicionalmente se han utilizado amplificadores lineales por su buen funcionamiento. Sin embargo, debido al elevado factor de pico de las señales transmitidas, el rendimiento de este tipo de amplificadores es bajo. El bajo rendimiento del sistema conlleva desventajas adicionales como el aumento del coste y del tamaño del sistema de refrigeración, como en el caso de una estación base, o como la reducción del tiempo de uso y un mayor calentamiento del equipo para sistemas portátiles alimentados con baterías. Debido a estos factores, se han desarrollado durante las últimas décadas varias soluciones para aumentar el rendimiento del RFPA como la técnica de Outphasing, combinadores de potencia o la técnica de Doherty. Estas soluciones mejoran las prestaciones del RFPA y en algún caso han sido ampliamente utilizados comercialmente como la técnica de Doherty, que alcanza rendimientos hasta del 50% para el sistema completo para anchos de banda de hasta 20MHz. Pese a las mejoras obtenidas con estas soluciones, los mayores rendimientos del sistema se obtienen para soluciones basadas en la modulación de la tensión de alimentación del amplificador de potencia como “Envelope Tracking” o “EER”. La técnica de seguimiento de envolvente o “Envelope Tracking” está basada en la modulación de la tensión de alimentación de un amplificador lineal de potencia para obtener una mejora en el rendimiento en el sistema comparado a una solución con una tensión de alimentación constante. Para la implementación de esta técnica se necesita una etapa adicional, el amplificador de envolvente, que añade complejidad al amplificador de radiofrecuencia. En un amplificador diseñado con esta técnica, se aumentan las pérdidas debido a la etapa adicional que supone el amplificador de envolvente pero a su vez disminuyen las pérdidas en el amplificador de potencia. Si el diseño se optimiza adecuadamente, puede conseguirse un aumento global en el rendimiento del sistema superior al conseguido con las técnicas mencionadas anteriormente. Esta técnica presenta ventajas en el diseño del amplificador de envolvente, ya que el ancho de banda requerido puede ser menor que el ancho de banda de la señal de envolvente si se optimiza adecuadamente el diseño. Adicionalmente, debido a que la sincronización entre la señal de envolvente y de fase no tiene que ser perfecta, el proceso de integración conlleva ciertas ventajas respecto a otras técnicas como EER. La técnica de eliminación y restauración de envolvente, llamada EER o técnica de Kahn está basada en modulación simultánea de la envolvente y la fase de la señal usando un amplificador de potencia conmutado, no lineal y que permite obtener un elevado rendimiento. Esta solución fue propuesta en el año 1952, pero no ha sido implementada con éxito durante muchos años debido a los exigentes requerimientos en cuanto a la sincronización entre fase y envolvente, a las técnicas de control y de corrección de los errores y no linealidades de cada una de las etapas así como de los equipos para poder implementar estas técnicas, que tienen unos requerimientos exigentes en capacidad de cálculo y procesamiento. Dentro del diseño de un RFPA, el amplificador de envolvente tiene una gran importancia debido a su influencia en el rendimiento y ancho de banda del sistema completo. Adicionalmente, la linealidad y la calidad de la señal de transmitida deben ser elevados para poder cumplir con los diferentes estándares de telecomunicaciones. Esta tesis se centra en el amplificador de envolvente y el objetivo principal es el desarrollo de soluciones que permitan el aumento del rendimiento total del sistema a la vez que satisfagan los requerimientos de ancho de banda, calidad de la señal transmitida y de linealidad. Debido al elevado rendimiento que potencialmente puede alcanzarse con la técnica de EER, esta técnica ha sido objeto de análisis y en el estado del arte pueden encontrarse numerosas referencias que analizan el diseño y proponen diversas implementaciones. En una clasificación de alto nivel, podemos agrupar las soluciones propuestas del amplificador de envolvente según estén compuestas de una o múltiples etapas. Las soluciones para el amplificador de envolvente en una configuración multietapa se basan en la combinación de un convertidor conmutado, de elevado rendimiento con un regulador lineal, de alto ancho de banda, en una combinación serie o paralelo. Estas soluciones, debido a la combinación de las características de ambas etapas, proporcionan un buen compromiso entre rendimiento y buen funcionamiento del amplificador de RF. Por otro lado, la complejidad del sistema aumenta debido al mayor número de componentes y de señales de control necesarias y el aumento de rendimiento que se consigue con estas soluciones es limitado. Una configuración en una etapa tiene las ventajas de una mayor simplicidad, pero debido al elevado ancho de banda necesario, la frecuencia de conmutación debe aumentarse en gran medida. Esto implicará un bajo rendimiento y un peor funcionamiento del amplificador de envolvente. En el estado del arte pueden encontrarse diversas soluciones para un amplificador de envolvente en una etapa, como aumentar la frecuencia de conmutación y realizar la implementación en un circuito integrado, que tendrá mejor funcionamiento a altas frecuencias o utilizar técnicas topológicas y/o filtros de orden elevado, que permiten una reducción de la frecuencia de conmutación. En esta tesis se propone de manera original el uso de la técnica de cancelación de rizado, aplicado al convertidor reductor síncrono, para reducir la frecuencia de conmutación comparado con diseño equivalente del convertidor reductor convencional. Adicionalmente se han desarrollado dos variantes topológicas basadas en esta solución para aumentar la robustez y las prestaciones de la misma. Otro punto de interés en el diseño de un RFPA es la dificultad de poder estimar la influencia de los parámetros de diseño del amplificador de envolvente en el amplificador final integrado. En esta tesis se ha abordado este problema y se ha desarrollado una herramienta de diseño que permite obtener las principales figuras de mérito del amplificador integrado para la técnica de EER a partir del diseño del amplificador de envolvente. Mediante el uso de esta herramienta pueden validarse el efecto del ancho de banda, el rizado de tensión de salida o las no linealidades del diseño del amplificador de envolvente para varias modulaciones digitales. Las principales contribuciones originales de esta tesis son las siguientes: La aplicación de la técnica de cancelación de rizado a un convertidor reductor síncrono para un amplificador de envolvente de alto rendimiento para un RFPA linealizado mediante la técnica de EER. Una reducción del 66% en la frecuencia de conmutación, comparado con el reductor convencional equivalente. Esta reducción se ha validado experimentalmente obteniéndose una mejora en el rendimiento de entre el 12.4% y el 16% para las especificaciones de este trabajo. La topología y el diseño del convertidor reductor con dos redes de cancelación de rizado en cascada para mejorar el funcionamiento y robustez de la solución con una red de cancelación. La combinación de un convertidor redactor multifase con la técnica de cancelación de rizado para obtener una topología que proporciona una reducción del cociente entre frecuencia de conmutación y ancho de banda de la señal. El proceso de optimización del control del amplificador de envolvente en lazo cerrado para mejorar el funcionamiento respecto a la solución en lazo abierto del convertidor reductor con red de cancelación de rizado. Una herramienta de simulación para optimizar el proceso de diseño del amplificador de envolvente mediante la estimación de las figuras de mérito del RFPA, implementado mediante EER, basada en el diseño del amplificador de envolvente. La integración y caracterización del amplificador de envolvente basado en un convertidor reductor con red de cancelación de rizado en el transmisor de radiofrecuencia completo consiguiendo un elevado rendimiento, entre 57% y 70.6% para potencias de salida de 14.4W y 40.7W respectivamente. Esta tesis se divide en seis capítulos. El primer capítulo aborda la introducción enfocada en la aplicación, los amplificadores de potencia de radiofrecuencia, así como los principales problemas, retos y soluciones existentes. En el capítulo dos se desarrolla el estado del arte de amplificadores de potencia de RF, describiéndose las principales técnicas de diseño, las causas de no linealidad y las técnicas de optimización. El capítulo tres está centrado en las soluciones propuestas para el amplificador de envolvente. El modo de control se ha abordado en este capítulo y se ha presentado una optimización del diseño en lazo cerrado para el convertidor reductor convencional y para el convertidor reductor con red de cancelación de rizado. El capítulo cuatro se centra en el proceso de diseño del amplificador de envolvente. Se ha desarrollado una herramienta de diseño para evaluar la influencia del amplificador de envolvente en las figuras de mérito del RFPA. En el capítulo cinco se presenta el proceso de integración realizado y las pruebas realizadas para las diversas modulaciones, así como la completa caracterización y análisis del amplificador de RF. El capítulo seis describe las principales conclusiones de la tesis y las líneas futuras. ABSTRACT The trend in the telecommunications sector during the last years follow a high increase in the transmission rate of voice, video and mainly in data. To achieve the required levels of data rates, the new modulation standards demand higher bandwidths and have a higher peak to average power ratio (PAPR). These specifications have a direct impact in the low efficiency of the RFPA. An additional factor for the low efficiency of the RFPA is in the power amplifier design. Traditionally, linear classes have been used for the implementation of the power amplifier as they comply with the technical requirements. However, they have a low efficiency, especially in the operating range of signals with a high PAPR. The low efficiency of the transmitter has additional disadvantages as an increase in the cost and size as the cooling system needs to be increased for a base station and a temperature increase and a lower use time for portable devices. Several solutions have been proposed in the state of the art to improve the efficiency of the transmitter as Outphasing, power combiners or Doherty technique. However, the highest potential of efficiency improvement can be obtained using a modulated power supply for the power amplifier, as in the Envelope Tracking and EER techniques. The Envelope Tracking technique is based on the modulation of the power supply of a linear power amplifier to improve the overall efficiency compared to a fixed voltage supply. In the implementation of this technique an additional stage is needed, the envelope amplifier, that will increase the complexity of the RFPA. However, the efficiency of the linear power amplifier will increase and, if designed properly, the RFPA efficiency will be improved. The advantages of this technique are that the envelope amplifier design does not require such a high bandwidth as the envelope signal and that in the integration process a perfect synchronization between envelope and phase is not required. The Envelope Elimination and Restoration (EER) technique, known also as Kahn’s technique, is based on the simultaneous modulation of envelope and phase using a high efficiency switched power amplifier. This solution has the highest potential in terms of the efficiency improvement but also has the most challenging specifications. This solution, proposed in 1952, has not been successfully implemented until the last two decades due to the high demanding requirements for each of the stages as well as for the highly demanding processing and computation capabilities needed. At the system level, a very precise synchronization is required between the envelope and phase paths to avoid a linearity decrease of the system. Several techniques are used to compensate the non-linear effects in amplitude and phase and to improve the rejection of the out of band noise as predistortion, feedback and feed-forward. In order to obtain a high bandwidth and efficient RFPA using either ET or EER, the envelope amplifier stage will have a critical importance. The requirements for this stage are very demanding in terms of bandwidth, linearity and quality of the transmitted signal. Additionally the efficiency should be as high as possible, as the envelope amplifier has a direct impact in the efficiency of the overall system. This thesis is focused on the envelope amplifier stage and the main objective will be the development of high efficiency envelope amplifier solutions that comply with the requirements of the RFPA application. The design and optimization of an envelope amplifier for a RFPA application is a highly referenced research topic, and many solutions that address the envelope amplifier and the RFPA design and optimization can be found in the state of the art. From a high level classification, multiple and single stage envelope amplifiers can be identified. Envelope amplifiers for EER based on multiple stage architecture combine a linear assisted stage and a switched-mode stage, either in a series or parallel configuration, to achieve a very high performance RFPA. However, the complexity of the system increases and the efficiency improvement is limited. A single-stage envelope amplifier has the advantage of a lower complexity but in order to achieve the required bandwidth the switching frequency has to be highly increased, and therefore the performance and the efficiency are degraded. Several techniques are used to overcome this limitation, as the design of integrated circuits that are capable of switching at very high rates or the use of topological solutions, high order filters or a combination of both to reduce the switching frequency requirements. In this thesis it is originally proposed the use of the ripple cancellation technique, applied to a synchronous buck converter, to reduce the switching frequency requirements compared to a conventional buck converter for an envelope amplifier application. Three original proposals for the envelope amplifier stage, based on the ripple cancellation technique, are presented and one of the solutions has been experimentally validated and integrated in the complete amplifier, showing a high total efficiency increase compared to other solutions of the state of the art. Additionally, the proposed envelope amplifier has been integrated in the complete RFPA achieving a high total efficiency. The design process optimization has also been analyzed in this thesis. Due to the different figures of merit between the envelope amplifier and the complete RFPA it is very difficult to obtain an optimized design for the envelope amplifier. To reduce the design uncertainties, a design tool has been developed to provide an estimation of the RFPA figures of merit based on the design of the envelope amplifier. The main contributions of this thesis are: The application of the ripple cancellation technique to a synchronous buck converter for an envelope amplifier application to achieve a high efficiency and high bandwidth EER RFPA. A 66% reduction of the switching frequency, validated experimentally, compared to the equivalent conventional buck converter. This reduction has been reflected in an improvement in the efficiency between 12.4% and 16%, validated for the specifications of this work. The synchronous buck converter with two cascaded ripple cancellation networks (RCNs) topology and design to improve the robustness and the performance of the envelope amplifier. The combination of a phase-shifted multi-phase buck converter with the ripple cancellation technique to improve the envelope amplifier switching frequency to signal bandwidth ratio. The optimization of the control loop of an envelope amplifier to improve the performance of the open loop design for the conventional and ripple cancellation buck converter. A simulation tool to optimize the envelope amplifier design process. Using the envelope amplifier design as the input data, the main figures of merit of the complete RFPA for an EER application are obtained for several digital modulations. The successful integration of the envelope amplifier based on a RCN buck converter in the complete RFPA obtaining a high efficiency integrated amplifier. The efficiency obtained is between 57% and 70.6% for an output power of 14.4W and 40.7W respectively. The main figures of merit for the different modulations have been characterized and analyzed. This thesis is organized in six chapters. In Chapter 1 is provided an introduction of the RFPA application, where the main problems, challenges and solutions are described. In Chapter 2 the technical background for radiofrequency power amplifiers (RF) is presented. The main techniques to implement an RFPA are described and analyzed. The state of the art techniques to improve performance of the RFPA are identified as well as the main sources of no-linearities for the RFPA. Chapter 3 is focused on the envelope amplifier stage. The three different solutions proposed originally in this thesis for the envelope amplifier are presented and analyzed. The control stage design is analyzed and an optimization is proposed both for the conventional and the RCN buck converter. Chapter 4 is focused in the design and optimization process of the envelope amplifier and a design tool to evaluate the envelope amplifier design impact in the RFPA is presented. Chapter 5 shows the integration process of the complete amplifier. Chapter 6 addresses the main conclusions of the thesis and the future work.

Automated wordlength optimization framework for multi-source statistical interval-based analysis of nonlinear systems with control-flow structures

El uso de aritmética de punto fijo es una opción de diseño muy extendida en sistemas con fuertes restricciones de área, consumo o rendimiento. Para producir implementaciones donde los costes se minimicen sin impactar negativamente en la precisión de los resultados debemos llevar a cabo una asignación cuidadosa de anchuras de palabra. Encontrar la combinación óptima de anchuras de palabra en coma fija para un sistema dado es un problema combinatorio NP-hard al que los diseñadores dedican entre el 25 y el 50 % del ciclo de diseño. Las plataformas hardware reconfigurables, como son las FPGAs, también se benefician de las ventajas que ofrece la aritmética de coma fija, ya que éstas compensan las frecuencias de reloj más bajas y el uso más ineficiente del hardware que hacen estas plataformas respecto a los ASICs. A medida que las FPGAs se popularizan para su uso en computación científica los diseños aumentan de tamaño y complejidad hasta llegar al punto en que no pueden ser manejados eficientemente por las técnicas actuales de modelado de señal y ruido de cuantificación y de optimización de anchura de palabra. En esta Tesis Doctoral exploramos distintos aspectos del problema de la cuantificación y presentamos nuevas metodologías para cada uno de ellos: Las técnicas basadas en extensiones de intervalos han permitido obtener modelos de propagación de señal y ruido de cuantificación muy precisos en sistemas con operaciones no lineales. Nosotros llevamos esta aproximación un paso más allá introduciendo elementos de Multi-Element Generalized Polynomial Chaos (ME-gPC) y combinándolos con una técnica moderna basada en Modified Affine Arithmetic (MAA) estadístico para así modelar sistemas que contienen estructuras de control de flujo. Nuestra metodología genera los distintos caminos de ejecución automáticamente, determina las regiones del dominio de entrada que ejercitarán cada uno de ellos y extrae los momentos estadísticos del sistema a partir de dichas soluciones parciales. Utilizamos esta técnica para estimar tanto el rango dinámico como el ruido de redondeo en sistemas con las ya mencionadas estructuras de control de flujo y mostramos la precisión de nuestra aproximación, que en determinados casos de uso con operadores no lineales llega a tener tan solo una desviación del 0.04% con respecto a los valores de referencia obtenidos mediante simulación. Un inconveniente conocido de las técnicas basadas en extensiones de intervalos es la explosión combinacional de términos a medida que el tamaño de los sistemas a estudiar crece, lo cual conlleva problemas de escalabilidad. Para afrontar este problema presen tamos una técnica de inyección de ruidos agrupados que hace grupos con las señales del sistema, introduce las fuentes de ruido para cada uno de los grupos por separado y finalmente combina los resultados de cada uno de ellos. De esta forma, el número de fuentes de ruido queda controlado en cada momento y, debido a ello, la explosión combinatoria se minimiza. También presentamos un algoritmo de particionado multi-vía destinado a minimizar la desviación de los resultados a causa de la pérdida de correlación entre términos de ruido con el objetivo de mantener los resultados tan precisos como sea posible. La presente Tesis Doctoral también aborda el desarrollo de metodologías de optimización de anchura de palabra basadas en simulaciones de Monte-Cario que se ejecuten en tiempos razonables. Para ello presentamos dos nuevas técnicas que exploran la reducción del tiempo de ejecución desde distintos ángulos: En primer lugar, el método interpolativo aplica un interpolador sencillo pero preciso para estimar la sensibilidad de cada señal, y que es usado después durante la etapa de optimización. En segundo lugar, el método incremental gira en torno al hecho de que, aunque es estrictamente necesario mantener un intervalo de confianza dado para los resultados finales de nuestra búsqueda, podemos emplear niveles de confianza más relajados, lo cual deriva en un menor número de pruebas por simulación, en las etapas iniciales de la búsqueda, cuando todavía estamos lejos de las soluciones optimizadas. Mediante estas dos aproximaciones demostramos que podemos acelerar el tiempo de ejecución de los algoritmos clásicos de búsqueda voraz en factores de hasta x240 para problemas de tamaño pequeño/mediano. Finalmente, este libro presenta HOPLITE, una infraestructura de cuantificación automatizada, flexible y modular que incluye la implementación de las técnicas anteriores y se proporciona de forma pública. Su objetivo es ofrecer a desabolladores e investigadores un entorno común para prototipar y verificar nuevas metodologías de cuantificación de forma sencilla. Describimos el flujo de trabajo, justificamos las decisiones de diseño tomadas, explicamos su API pública y hacemos una demostración paso a paso de su funcionamiento. Además mostramos, a través de un ejemplo sencillo, la forma en que conectar nuevas extensiones a la herramienta con las interfaces ya existentes para poder así expandir y mejorar las capacidades de HOPLITE. ABSTRACT Using fixed-point arithmetic is one of the most common design choices for systems where area, power or throughput are heavily constrained. In order to produce implementations where the cost is minimized without negatively impacting the accuracy of the results, a careful assignment of word-lengths is required. The problem of finding the optimal combination of fixed-point word-lengths for a given system is a combinatorial NP-hard problem to which developers devote between 25 and 50% of the design-cycle time. Reconfigurable hardware platforms such as FPGAs also benefit of the advantages of fixed-point arithmetic, as it compensates for the slower clock frequencies and less efficient area utilization of the hardware platform with respect to ASICs. As FPGAs become commonly used for scientific computation, designs constantly grow larger and more complex, up to the point where they cannot be handled efficiently by current signal and quantization noise modelling and word-length optimization methodologies. In this Ph.D. Thesis we explore different aspects of the quantization problem and we present new methodologies for each of them: The techniques based on extensions of intervals have allowed to obtain accurate models of the signal and quantization noise propagation in systems with non-linear operations. We take this approach a step further by introducing elements of MultiElement Generalized Polynomial Chaos (ME-gPC) and combining them with an stateof- the-art Statistical Modified Affine Arithmetic (MAA) based methodology in order to model systems that contain control-flow structures. Our methodology produces the different execution paths automatically, determines the regions of the input domain that will exercise them, and extracts the system statistical moments from the partial results. We use this technique to estimate both the dynamic range and the round-off noise in systems with the aforementioned control-flow structures. We show the good accuracy of our approach, which in some case studies with non-linear operators shows a 0.04 % deviation respect to the simulation-based reference values. A known drawback of the techniques based on extensions of intervals is the combinatorial explosion of terms as the size of the targeted systems grows, which leads to scalability problems. To address this issue we present a clustered noise injection technique that groups the signals in the system, introduces the noise terms in each group independently and then combines the results at the end. In this way, the number of noise sources in the system at a given time is controlled and, because of this, the combinato rial explosion is minimized. We also present a multi-way partitioning algorithm aimed at minimizing the deviation of the results due to the loss of correlation between noise terms, in order to keep the results as accurate as possible. This Ph.D. Thesis also covers the development of methodologies for word-length optimization based on Monte-Carlo simulations in reasonable times. We do so by presenting two novel techniques that explore the reduction of the execution times approaching the problem in two different ways: First, the interpolative method applies a simple but precise interpolator to estimate the sensitivity of each signal, which is later used to guide the optimization effort. Second, the incremental method revolves on the fact that, although we strictly need to guarantee a certain confidence level in the simulations for the final results of the optimization process, we can do it with more relaxed levels, which in turn implies using a considerably smaller amount of samples, in the initial stages of the process, when we are still far from the optimized solution. Through these two approaches we demonstrate that the execution time of classical greedy techniques can be accelerated by factors of up to ×240 for small/medium sized problems. Finally, this book introduces HOPLITE, an automated, flexible and modular framework for quantization that includes the implementation of the previous techniques and is provided for public access. The aim is to offer a common ground for developers and researches for prototyping and verifying new techniques for system modelling and word-length optimization easily. We describe its work flow, justifying the taken design decisions, explain its public API and we do a step-by-step demonstration of its execution. We also show, through an example, the way new extensions to the flow should be connected to the existing interfaces in order to expand and improve the capabilities of HOPLITE.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Multi-physics modelling for design optimization and manufacturing on high-performance parallel systems

Abstract not available

Development of new scenario decomposition techniques for linear and nonlinear stochastic programming

Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-étapes est d’utiliser l’analyse par scénario. Pour ce faire, l’incertitude de certaines données du problème est modélisée par vecteurs aléatoires avec des supports finis spécifiques aux étapes. Chacune de ces réalisations représente un scénario. En utilisant des scénarios, il est possible d’étudier des versions plus simples (sous-problèmes) du problème original. Comme technique de décomposition par scénario, l’algorithme de recouvrement progressif est une des méthodes les plus populaires pour résoudre les problèmes de programmation stochastique multi-étapes. Malgré la décomposition complète par scénario, l’efficacité de la méthode du recouvrement progressif est très sensible à certains aspects pratiques, tels que le choix du paramètre de pénalisation et la manipulation du terme quadratique dans la fonction objectif du lagrangien augmenté. Pour le choix du paramètre de pénalisation, nous examinons quelques-unes des méthodes populaires, et nous proposons une nouvelle stratégie adaptive qui vise à mieux suivre le processus de l’algorithme. Des expériences numériques sur des exemples de problèmes stochastiques linéaires multi-étapes suggèrent que la plupart des techniques existantes peuvent présenter une convergence prématurée à une solution sous-optimale ou converger vers la solution optimale, mais avec un taux très lent. En revanche, la nouvelle stratégie paraît robuste et efficace. Elle a convergé vers l’optimalité dans toutes nos expériences et a été la plus rapide dans la plupart des cas. Pour la question de la manipulation du terme quadratique, nous faisons une revue des techniques existantes et nous proposons l’idée de remplacer le terme quadratique par un terme linéaire. Bien que qu’il nous reste encore à tester notre méthode, nous avons l’intuition qu’elle réduira certaines difficultés numériques et théoriques de la méthode de recouvrement progressif.

Development of new scenario decomposition techniques for linear and nonlinear stochastic programming

Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-étapes est d’utiliser l’analyse par scénario. Pour ce faire, l’incertitude de certaines données du problème est modélisée par vecteurs aléatoires avec des supports finis spécifiques aux étapes. Chacune de ces réalisations représente un scénario. En utilisant des scénarios, il est possible d’étudier des versions plus simples (sous-problèmes) du problème original. Comme technique de décomposition par scénario, l’algorithme de recouvrement progressif est une des méthodes les plus populaires pour résoudre les problèmes de programmation stochastique multi-étapes. Malgré la décomposition complète par scénario, l’efficacité de la méthode du recouvrement progressif est très sensible à certains aspects pratiques, tels que le choix du paramètre de pénalisation et la manipulation du terme quadratique dans la fonction objectif du lagrangien augmenté. Pour le choix du paramètre de pénalisation, nous examinons quelques-unes des méthodes populaires, et nous proposons une nouvelle stratégie adaptive qui vise à mieux suivre le processus de l’algorithme. Des expériences numériques sur des exemples de problèmes stochastiques linéaires multi-étapes suggèrent que la plupart des techniques existantes peuvent présenter une convergence prématurée à une solution sous-optimale ou converger vers la solution optimale, mais avec un taux très lent. En revanche, la nouvelle stratégie paraît robuste et efficace. Elle a convergé vers l’optimalité dans toutes nos expériences et a été la plus rapide dans la plupart des cas. Pour la question de la manipulation du terme quadratique, nous faisons une revue des techniques existantes et nous proposons l’idée de remplacer le terme quadratique par un terme linéaire. Bien que qu’il nous reste encore à tester notre méthode, nous avons l’intuition qu’elle réduira certaines difficultés numériques et théoriques de la méthode de recouvrement progressif.