Driving assistance system applied in curves for real vehicles | Sistema de ayuda a la conducción en curvas para vehículos reales

Decreasing the accidents on highway and urban environments is the main motivation for the research and developing of driving assistance systems, also called ADAS (Advanced Driver Assistance Systems). In recent years, there are many applications of these systems in commercial vehicles: ABS systems, Cruise Control (CC), parking assistance and warning systems (including GPS), among others. However, the implementation of driving assistance systems on the steering wheel is more limited, because of their complexity and sensitivity. This paper is focused in the development, test and implementation of a driver assistance system for controlling the steering wheel in curve zones. This system is divided in two levels: an inner control loop which permits to execute the position and speed target, softening the action over the steering wheel, and a second control outer loop (controlling for fuzzy logic) that sends the reference to the inner loop according the environment and vehicle conditions. The tests have been done in different curves and speeds. The system has been proved in a commercial vehicle with satisfactory results.

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Development of a methodology and tool to evaluate the impact of ICT measures on road transport emissions

The paper presents the main elements of a project entitled ICT-Emissions that aims at developing a novel methodology to evaluate the impact of ICT-related measures on mobility, vehicle energy consumption and CO2 emissions of vehicle fleets at the local scale, in order to promote the wider application of the most appropriate ICT measures. The proposed methodology combines traffic and emission modelling at micro and macro scales. These will be linked with interfaces and submodules which will be specifically designed and developed. A number of sources are available to the consortium to obtain the necessary input data. Also, experimental campaigns are offered to fill in gaps of information in traffic and emission patterns. The application of the methodology will be demonstrated using commercially available software. However, the methodology is developed in such a way as to enable its implementation by a variety of emission and traffic models. Particular emphasis is given to (a) the correct estimation of driver behaviour, as a result of traffic-related ICT measures, (b) the coverage of a large number of current vehicle technologies, including ICT systems, and (c) near future technologies such as hybrid, plug-in hybrids, and electric vehicles. The innovative combination of traffic, driver, and emission models produces a versatile toolbox that can simulate the impact on energy and CO2 of infrastructure measures (traffic management, dynamic traffic signs, etc.), driver assistance systems and ecosolutions (speed/cruise control, start/stop systems, etc.) or a combination of measures (cooperative systems).The methodology is validated by application in the Turin area and its capacity is further demonstrated by application in real world conditions in Madrid and Rome.

Traffic jam driving with NMV avoidance

n recent years, the development of advanced driver assistance systems (ADAS) – mainly based on lidar and cameras – has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations—in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuators – brake and throttle pedals – were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging.

Arquitectura de Control para la Conducción Autónoma de Vehículos en Entornos Urbanos y Autovías

Hoy en día, el desarrollo tecnológico en el campo de los sistemas inteligentes de transporte (ITS por sus siglas en inglés) ha permitido dotar a los vehículos con diversos sistemas de ayuda a la conducción (ADAS, del inglés advanced driver assistance system), mejorando la experiencia y seguridad de los pasajeros, en especial del conductor. La mayor parte de estos sistemas están pensados para advertir al conductor sobre ciertas situaciones de riesgo, como la salida involuntaria del carril o la proximidad de obstáculos en el camino. No obstante, también podemos encontrar sistemas que van un paso más allá y son capaces de cooperar con el conductor en el control del vehículo o incluso relegarlos de algunas tareas tediosas. Es en este último grupo donde se encuentran los sistemas de control electrónico de estabilidad (ESP - Electronic Stability Program), el antibloqueo de frenos (ABS - Anti-lock Braking System), el control de crucero (CC - Cruise Control) y los más recientes sistemas de aparcamiento asistido. Continuando con esta línea de desarrollo, el paso siguiente consiste en la supresión del conductor humano, desarrollando sistemas que sean capaces de conducir un vehículo de forma autónoma y con un rendimiento superior al del conductor. En este trabajo se presenta, en primer lugar, una arquitectura de control para la automatización de vehículos. Esta se compone de distintos componentes de hardware y software, agrupados de acuerdo a su función principal. El diseño de la arquitectura parte del trabajo previo desarrollado por el Programa AUTOPIA, aunque introduce notables aportaciones en cuanto a la eficiencia, robustez y escalabilidad del sistema. Ahondando un poco más en detalle, debemos resaltar el desarrollo de un algoritmo de localización basado en enjambres de partículas. Este está planteado como un método de filtrado y fusión de la información obtenida a partir de los distintos sensores embarcados en el vehículo, entre los que encontramos un receptor GPS (Global Positioning System), unidades de medición inercial (IMU – Inertial Measurement Unit) e información tomada directamente de los sensores embarcados por el fabricante, como la velocidad de las ruedas y posición del volante. Gracias a este método se ha conseguido resolver el problema de la localización, indispensable para el desarrollo de sistemas de conducción autónoma. Continuando con el trabajo de investigación, se ha estudiado la viabilidad de la aplicación de técnicas de aprendizaje y adaptación al diseño de controladores para el vehículo. Como punto de partida se emplea el método de Q-learning para la generación de un controlador borroso lateral sin ningún tipo de conocimiento previo. Posteriormente se presenta un método de ajuste on-line para la adaptación del control longitudinal ante perturbaciones impredecibles del entorno, como lo son los cambios en la inclinación del camino, fricción de las ruedas o peso de los ocupantes. Para finalizar, se presentan los resultados obtenidos durante un experimento de conducción autónoma en carreteras reales, el cual se llevó a cabo en el mes de Junio de 2012 desde la población de San Lorenzo de El Escorial hasta las instalaciones del Centro de Automática y Robótica (CAR) en Arganda del Rey. El principal objetivo tras esta demostración fue validar el funcionamiento, robustez y capacidad de la arquitectura propuesta para afrontar el problema de la conducción autónoma, bajo condiciones mucho más reales a las que se pueden alcanzar en las instalaciones de prueba. ABSTRACT Nowadays, the technological advances in the Intelligent Transportation Systems (ITS) field have led the development of several driving assistance systems (ADAS). These solutions are designed to improve the experience and security of all the passengers, especially the driver. For most of these systems, the main goal is to warn drivers about unexpected circumstances leading to risk situations such as involuntary lane departure or proximity to other vehicles. However, other ADAS go a step further, being able to cooperate with the driver in the control of the vehicle, or even overriding it on some tasks. Examples of this kind of systems are the anti-lock braking system (ABS), cruise control (CC) and the recently commercialised assisted parking systems. Within this research line, the next step is the development of systems able to replace the human drivers, improving the control and therefore, the safety and reliability of the vehicles. First of all, this dissertation presents a control architecture design for autonomous driving. It is made up of several hardware and software components, grouped according to their main function. The design of this architecture is based on the previous works carried out by the AUTOPIA Program, although notable improvements have been made regarding the efficiency, robustness and scalability of the system. It is also remarkable the work made on the development of a location algorithm for vehicles. The proposal is based on the emulation of the behaviour of biological swarms and its performance is similar to the well-known particle filters. The developed method combines information obtained from different sensors, including GPS, inertial measurement unit (IMU), and data from the original vehicle’s sensors on-board. Through this filtering algorithm the localization problem is properly managed, which is critical for the development of autonomous driving systems. The work deals also with the fuzzy control tuning system, a very time consuming task when done manually. An analysis of learning and adaptation techniques for the development of different controllers has been made. First, the Q-learning –a reinforcement learning method– has been applied to the generation of a lateral fuzzy controller from scratch. Subsequently, the development of an adaptation method for longitudinal control is presented. With this proposal, a final cruise control controller is able to deal with unpredictable environment disturbances, such as road slope, wheel’s friction or even occupants’ weight. As a testbed for the system, an autonomous driving experiment on real roads is presented. This experiment was carried out on June 2012, driving from San Lorenzo de El Escorial up to the Center for Automation and Robotics (CAR) facilities in Arganda del Rey. The main goal of the demonstration was validating the performance, robustness and viability of the proposed architecture to deal with the problem of autonomous driving under more demanding conditions than those achieved on closed test tracks.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

VLC oriented energy efficient driver techniques

LEDs are substituting fluorescent and incandescent bulbs as illumination sources due to their low power consumption and long lifetime. Visible Light Communications (VLC) makes use of the LEDs short switching times to transmit information. Although LEDs switching speed is around Mbps range, higher speeds (hundred of Mbps) can be reached by using high bandwidth-efficiency modulation techniques. However, the use of these techniques requires a more complex driver which elevates drastically its power consumption. In this work an energy efficiency analysis of the different VLC modulation techniques and drivers is presented. Besides, the design of new schemes of VLC drivers is described.

PREDIRCAM eHealth platform for individualized telemedical assistance for lifestyle modification in the treatment of obesity, diabetes, and cardiometabolic risk prevention: a pilot study (PREDIRCAM 1)

Background: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. Methods: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. Results: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. Conclusions: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.

Ecología de la dispersión de plantas en los bosques secos del suroccidente Ecuatoriano

La importancia del proceso de dispersión de semillas en la estructura y dinámica de los ecosistemas es ampliamente reconocida. Sin embargo, para los bosques tropicales estacionalmente secos los estudios relacionados con este proceso son aún escasos y dispersos en comparación con los bosques tropicales lluviosos. En este trabajo se estudió la importancia de los síndromes de dispersión de semillas en la estructuración de comunidades, mediante el análisis de los patrones de dispersión de semillas en el espacio y tiempo para comunidades de leñosas en los bosques secos del suroccidente Ecuatoriano. Esta área forma parte de la región Tumbesina, una de las áreas de endemismo más importantes del mundo, pero también uno de los hotspots más amenazados. El clima se caracteriza por una estación seca que va de mayo a noviembre y una estación lluviosa que se extiende desde diciembre a abril. Para toda esta zona se estima una temperatura promedio anual entre 20° y 26°C y una precipitación promedio anual entre 300 y 700 mm. El trabajo de campo se desarrolló entre febrero de 2009 y septiembre de 2012. El primer paso fue la recopilación de información sobre las especies leñosas nativas de los bosques secos del suroccidente de Ecuador, que permitiera asignar a cada especie a un síndrome de dispersión para determinar el espectro de síndromes de dispersión de semillas. Luego, utilizando la información disponible de 109 parcelas establecidas previamente a lo largo de cuatro cantones de la provincia de Loja que conservan bosques secos en buen estado, se analizó la relación entre el síndrome de dispersión y condiciones ambientales. La relación de los síndromes de dispersión con los patrones espaciales de las especies y con los patrones de la lluvia y banco de semillas se estudió dentro de una parcela permanente de 9 ha, en la Reserva Ecológica Arenillas. Dentro de esta parcela se estableció un transecto de aproximadamente 3,4 km, que se recorrió mensualmente para colectar excretas de cérvidos y analizar el rol de este grupo como dispersor de semillas. Una gran variedad de plantas en los bosques secos tropicales del suroccidente de Ecuador requirió la asistencia de animales para la dispersión de semillas. Sin embargo, un análisis del espectro de dispersión considerando no solo la riqueza, sino también la abundancia relativa de especies, permitió determinar que a pesar de la alta variedad de especies zoócoras, la mayor parte de la comunidad correspondía a individuos anemócoros, que no proveen ninguna recompensa para la dispersión por animales. Este patrón puede deberse a la abundancia relativa de hábitats adecuados para especies con diferente síndrome de dispersión. Las condiciones ambientales afectaron la estructura del espectro de dispersión en la comunidad de bosque seco neotropical estudiada. El análisis de la importancia relativa del síndrome de dispersión y de la heterogeneidad espacial en la formación de patrones espaciales de árboles adultos permitió determinar que la heterogeneidad ambiental ejercía un efecto adicional (y en algunos el único) en la formación de patrones agregados de la mayoría de especies estudiadas. Los resultados señalaron diferencias en los patrones espaciales de las especies dependiendo del síndrome de dispersión, pero también una gran variación en los patrones espaciales incluso entre especies del mismo síndrome de dispersión. El análisis simultáneo de los patrones de la lluvia de semillas y banco de semillas de una comunidad de leñosas y su relación con la vegetación establecida indicaron que la lluvia de semillas era temporalmente variable en número de especies y abundancia de semillas, y dependía del síndrome de dispersión. El síndrome de dispersión también influyó en la formación de bancos de semillas, siendo las especies con capacidad de dispersión limitada (autócoras) las de mayor riqueza de especies y abundancia de semillas. Los cérvidos también se consideraron como un elemento clave en el proceso de dispersión de semillas. Al menos ocho especies leñosas fueron dispersadas legítimamente vía endozoócora. La mayoría de las especies dispersadas presentaron diásporas sin adaptaciones obvias para la dispersión, por lo que la ingestión de semillas por cérvidos se constituye en una vía potencial para la dispersión de sus semillas a largas distancias y, con ello, mejora la posibilidad de colonizar nuevos sitios y mantener el flujo genético. Los resultados de este estudio aportan nuevas evidencias para el entendimiento de la importancia de los procesos de dispersión de semillas en la estructura de los bosques secos neotropicales. Uno de los principales hallazgos a partir de estos cuatro capítulos es que los patrones espaciales de las especies, así como las estrategias que utilizan para dispersarse y hacer frente a las condiciones adversas (es decir, lluvia o banco de semillas) llevan consigo un efecto del síndrome de dispersión, y que la intensidad ese efecto depende a la vez de las condiciones ambientales del lugar. ABSTRACT The importance of seed dispersal process in the estructuring and ecosystem dynamic is widely recongnized. However, for seasonally tropical dry forest studies related to this process are still scarce and scattered compared to tropical rain forests. The present research deals with the importance of seed dispersal syndromes as a driver in the community structure, focusing its attention to temporal and spatial patterns of seed dispersal in woody communities of seasonally dry forest at Southwestern Ecuador. This area is part of the Tumbesian region, one of the most important areas of endemism, but also one of the most threatened areas around the world. Climate is characterized by a dry season from May to November, and a rainy season from December to April. For the whole area an average temperature between 20 ° and 26 ° C, and an average annual rainfall between 300 and 700 mm are estimated. Fieldwork was carried out between February 2009 and September 2012. During a first step information about native woody species of dry forests of southwestern Ecuador was gathered, enabling to assign a dispersal syndrome to each species to determine the seed dispersal spectrum. In a second step, available information from 109 established plots along four municipalities in Loja province, which hold the highest and best conserved dry forest remanants, was analyzed to establish the relationship between dispersal syndromes and environmental conditions. The relationships between dispersal syndromes and species spatial patterns; and between dispersal syndromes and seed rain and seed bank patterns, were studied within a permanent plot of 9 ha, in the Arenillas Ecological Reserve. Within this plot one transect of approximately 3.4 km was set to collect monthly deer droppings, which were used to latter analyze the rol of this group as seed dispersers. The results showed that a large variety of plants in tropical dry forest of Southwestern Ecuador require animal assistance to dispers their seeds. However, an analysis of seed dispersal spectrum considering not only species richness, but also the relative abundance of species, allowed to determine that despite the high variety of zoochorous species, most individuals in the community corresponds to anemochoruos species. This shift may be due to the relative abundance of habitats that are suitable for species with different dispersal syndromes. Moreover, quantitative data analysis showed that environmental conditions affect the structure of seed dispersal spectrum in the studied community. The analysis of relative importance of dispersal syndrome, and the environmental heterogeneity on formation of adult trees spatial patterns, indicated that environmental heterogeneity exert an additional (or was the only) effect limiting the distribution of most species in this forest. The findings showed differences in spatial patterns related to dispersal syndrome, but also showed a large variation in spatial patterns even among species sharing the same dispersal syndrome. Simultaneous analysis of seed rain and seed bank patterns of a woody community, and their relationship with established vegetation, suggested that seed rain is temporally variable in species number and seeds abundance, and that variation is related to the dispersal syndrome. Dispersal syndrome also influenced on the formation of seed banks, being species with limited dispersal abilities (autochorous) the ones with highest species richness and seed abundance. Deer were found as a key element in the seed dispersal process. At least to eight woody species were dispersed legitimately by ingestion. Diaspores of most dispersed species had no obvious adaptations to seed dispersal, therefore, seed ingestion by deer represents a potential pathway for long-distance dispersal, and hence, improves the chances to colonizing new sites and to maintain gene flow. Overall, these results provide new evidence for understanding the importance of seed dispersal processes in the structure of Neotropical dry forests. One of the major findings from these four chapters is that spatial patterns of species, and the strategies used to disperse their seeds and to deal with the adverse conditions (i.e. seed rain or seed bank) are related with dispersal syndromes, and the intensity of that relation depends in turn, on environmental conditions.

Embedded Linux integration using Linuxlink tool and peripheral driver development for Zedboard

This project is divided into two main parts: The first part shows the integration of an Embedded Linux operating system on a development hardware platform named Zedboard. This platform contains a Zynq-7000 System on Chip (Soc) which is composed by two dual core ARM Cortex-A9 processors and a FPGA Artix-7. The Embedded Linux is built with Linuxlink, a Timesys tool. Meanwhile, the platform hardware configuration is done with Xilinx Vivado. The system is loaded with an SD card which requires to have every files needed for the booting process and for the operation. Some of these files are generated with Xilinx SDK software. The second part starts up from the system already built to integrate a peripheral in the Zynq-7000 FPGA. Also the drivers for controlling the peripheral from the operating system are developed. Finally, a user space program is created to test both of them. RESUMEN. Este proyecto consta de dos partes: La primera muestra la integración de un sistema operativo Linux embebido en una plataforma de desarrollo hardware llamada Zedboard. Esta plataforma utiliza un System on Chip (SoC) Zynq-7000 que está formado por dos procesadores ARM Cortex-A9 de doble núcleo y una FPGA Artix-7. El Linux embebido se construye utilizando la herramienta Linuxlink de Timesys, mientras que el hardware de la plataforma de desarrollo se configura con Vivado de Xilinx. El sistema se carga en una tarjeta SD que debe tener todos los archivos necesarios para completar el arranque y hacer funcionar el sistema. Algunos de esos archivos se generan con la herramienta SDK de Xilinx. En la segunda parte se utiliza el sistema construido para integrar un periférico en la FPGA del Zynq-7000, haciendo uso de Vivado, y se desarrollan los drivers necesarios para utilizarlo mediante el sistema operativo. Para probar esta última parte se desarrolla un programa de espacio de usuario.