Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.

Seismic modelling and ava analysis of hidrocarbon traps

In this study, forward seismic modelling of four geological models with Hydrocarbon (HC) traps were performed by ray tracing method to produce synthetic seismogram of each model. The idea is to identify the Hydrocarbon Indicators (HCI‟s) such as bright spot, flat spot, dim spot and Bottom Simulating Reflector (BSR) in the synthethic seismogram. The modelling was performed in DISCO/FOCUS 5.0 seismic data processing programme. Strong positive and negative reflection amplitudes and some artifact reflection horizons were observed on produced seismograms due to rapid changes in subsurface velocity and geometry respectively Additionally, Amplitude-versus-angle (AVA) curves of each HCIs was calculated by the Crewes Zoeppritz Explorer programme. AVA curves show that how the reflection coefficients change with the density and the P and S wave velocities of each layer such as oil, gas, gas hydrate or water saturated sediments. Due to AVA curves, an increase in reflection amplitude with incident angle of seismic waves corresponds to an indicator of a hydrocarbon reservoir

Use of camera trapping in determining Iberian lynx population parameters: The use area and its limitations

Below are the results of the survey of the Iberian lynx obtained with camera-trapping between 2000 and 2007 in Sierra Morena. Two very important aspects of camera-trapping concerning its efficiency are also analyzed. The first is the evolution along years according to the camera-trapping type used of two efficiency indicators. The results obtained demonstrate that the most efficient lure is rabbit, though it is the less proven (92 trap-nights), followed by camera-trapping in the most frequent marking places (latrines). And, we propose as a novel the concept of use area as a spatial reference unit for the camera-trapping monitoring of non radio-marked animals is proposed, and its validity discussed.

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.

Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches.

A method for Kinematic Calibration of a Parallel Robot by using one camera in hand and a spherical object

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

Scalable software architecture for on-line multi-camera video processing

In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhead

Adaptive Multi-Camera System for Real Time Object Detection

In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy.

Using clustering techniques for intelligent camera-based user interfaces

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential.

Camera localization using trajectories and maps

We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.

An Inexpensive Method for Kinematic Calibration of a Parallel Robot by Using One Hand-Held Camera as Main Sensor

This paper presents a novel method for the calibration of a parallel robot, which allows a more accurate configuration instead of a configuration based on nominal parameters. It is used, as the main sensor with one camera installed in the robot hand that determines the relative position of the robot with respect to a spherical object fixed in the working area of the robot. The positions of the end effector are related to the incremental positions of resolvers of the robot motors. A kinematic model of the robot is used to find a new group of parameters, which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and thereby improve spatial measurements. Finally, several working tests, static and tracking tests are executed in order to verify how the robotic system behaviour improves by using calibrated parameters against nominal parameters. In order to emphasize that, this proposed new method uses neither external nor expensive sensor. That is why new robots are useful in teaching and research activities.

Adaptive spatio-temporal filter for low-cost camera depth maps

In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and video information and by adapting its parameters to different levels of estimated noise. Kalman filters are used to reduce the temporal random fluctuations of the measurements. Finally an interpolation algorithm is used to obtain consistent depth maps in the regions where the depth information is not available. Results show that this approach allows to considerably improve the depth maps quality by considering spatio-temporal information and by adapting its parameters to different levels of noise.

Multi-Camera very wide baseline feature matching based on view-adaptive junction detection

This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar-constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Thermal, stress, and traps effects in AlGaN/GaN HEMTs

GaN y AlN son materiales semiconductores piezoeléctricos del grupo III-V. La heterounión AlGaN/GaN presenta una elevada carga de polarización tanto piezoeléctrica como espontánea en la intercara, lo que genera en su cercanía un 2DEG de grandes concentración y movilidad. Este 2DEG produce una muy alta potencia de salida, que a su vez genera una elevada temperatura de red. Las tensiones de puerta y drenador provocan un stress piezoeléctrico inverso, que puede afectar a la carga de polarización piezoeléctrica y así influir la densidad 2DEG y las características de salida. Por tanto, la física del dispositivo es relevante para todos sus aspectos eléctricos, térmicos y mecánicos. En esta tesis se utiliza el software comercial COMSOL, basado en el método de elementos finitos (FEM), para simular el comportamiento integral electro-térmico, electro-mecánico y electro-térmico-mecánico de los HEMTs de GaN. Las partes de acoplamiento incluyen el modelo de deriva y difusión para el transporte electrónico, la conducción térmica y el efecto piezoeléctrico. Mediante simulaciones y algunas caracterizaciones experimentales de los dispositivos, hemos analizado los efectos térmicos, de deformación y de trampas. Se ha estudiado el impacto de la geometría del dispositivo en su auto-calentamiento mediante simulaciones electro-térmicas y algunas caracterizaciones eléctricas. Entre los resultados más sobresalientes, encontramos que para la misma potencia de salida la distancia entre los contactos de puerta y drenador influye en generación de calor en el canal, y así en su temperatura. El diamante posee une elevada conductividad térmica. Integrando el diamante en el dispositivo se puede dispersar el calor producido y así reducir el auto-calentamiento, al respecto de lo cual se han realizado diversas simulaciones electro-térmicas. Si la integración del diamante es en la parte superior del transistor, los factores determinantes para la capacidad disipadora son el espesor de la capa de diamante, su conductividad térmica y su distancia a la fuente de calor. Este procedimiento de disipación superior también puede reducir el impacto de la barrera térmica de intercara entre la capa adaptadora (buffer) y el substrato. La muy reducida conductividad eléctrica del diamante permite que pueda contactar directamente el metal de puerta (muy cercano a la fuente de calor), lo que resulta muy conveniente para reducir el auto-calentamiento del dispositivo con polarización pulsada. Por otra parte se simuló el dispositivo con diamante depositado en surcos atacados sobre el sustrato como caminos de disipación de calor (disipador posterior). Aquí aparece una competencia de factores que influyen en la capacidad de disipación, a saber, el surco atacado contribuye a aumentar la temperatura del dispositivo debido al pequeño tamaño del disipador, mientras que el diamante disminuiría esa temperatura gracias a su elevada conductividad térmica. Por tanto, se precisan capas de diamante relativamente gruesas para reducer ele efecto de auto-calentamiento. Se comparó la simulación de la deformación local en el borde de la puerta del lado cercano al drenador con estructuras de puerta estándar y con field plate, que podrían ser muy relevantes respecto a fallos mecánicos del dispositivo. Otras simulaciones se enfocaron al efecto de la deformación intrínseca de la capa de diamante en el comportamiento eléctrico del dispositivo. Se han comparado los resultados de las simulaciones de la deformación y las características eléctricas de salida con datos experimentales obtenidos por espectroscopía micro-Raman y medidas eléctricas, respectivamente. Los resultados muestran el stress intrínseco en la capa producido por la distribución no uniforme del 2DEG en el canal y la región de acceso. Además de aumentar la potencia de salida del dispositivo, la deformación intrínseca en la capa de diamante podría mejorar la fiabilidad del dispositivo modulando la deformación local en el borde de la puerta del lado del drenador. Finalmente, también se han simulado en este trabajo los efectos de trampas localizados en la superficie, el buffer y la barrera. Las medidas pulsadas muestran que tanto las puertas largas como las grandes separaciones entre los contactos de puerta y drenador aumentan el cociente entre la corriente pulsada frente a la corriente continua (lag ratio), es decir, disminuir el colapse de corriente (current collapse). Este efecto ha sido explicado mediante las simulaciones de los efectos de trampa de superficie. Por su parte, las referidas a trampas en el buffer se enfocaron en los efectos de atrapamiento dinámico, y su impacto en el auto-calentamiento del dispositivo. Se presenta también un modelo que describe el atrapamiento y liberación de trampas en la barrera: mientras que el atrapamiento se debe a un túnel directo del electrón desde el metal de puerta, el desatrapamiento consiste en la emisión del electrón en la banda de conducción mediante túnel asistido por fonones. El modelo también simula la corriente de puerta, debida a la emisión electrónica dependiente de la temperatura y el campo eléctrico. Además, también se ilustra la corriente de drenador dependiente de la temperatura y el campo eléctrico. ABSTRACT GaN and AlN are group III-V piezoelectric semiconductor materials. The AlGaN/GaN heterojunction presents large piezoelectric and spontaneous polarization charge at the interface, leading to high 2DEG density close to the interface. A high power output would be obtained due to the high 2DEG density and mobility, which leads to elevated lattice temperature. The gate and drain biases induce converse piezoelectric stress that can influence the piezoelectric polarization charge and further influence the 2DEG density and output characteristics. Therefore, the device physics is relevant to all the electrical, thermal, and mechanical aspects. In this dissertation, by using the commercial finite-element-method (FEM) software COMSOL, we achieved the GaN HEMTs simulation with electro-thermal, electro-mechanical, and electro-thermo-mechanical full coupling. The coupling parts include the drift-diffusion model for the electron transport, the thermal conduction, and the piezoelectric effect. By simulations and some experimental characterizations, we have studied the device thermal, stress, and traps effects described in the following. The device geometry impact on the self-heating was studied by electro-thermal simulations and electrical characterizations. Among the obtained interesting results, we found that, for same power output, the distance between the gate and drain contact can influence distribution of the heat generation in the channel and thus influence the channel temperature. Diamond possesses high thermal conductivity. Integrated diamond with the device can spread the generated heat and thus potentially reduce the device self-heating effect. Electro-thermal simulations on this topic were performed. For the diamond integration on top of the device (top-side heat spreading), the determinant factors for the heat spreading ability are the diamond thickness, its thermal conductivity, and its distance to the heat source. The top-side heat spreading can also reduce the impact of thermal boundary resistance between the buffer and the substrate on the device thermal behavior. The very low electrical conductivity of diamond allows that it can directly contact the gate metal (which is very close to the heat source), being quite convenient to reduce the self-heating for the device under pulsed bias. Also, the diamond coated in vias etched in the substrate as heat spreading path (back-side heat spreading) was simulated. A competing mechanism influences the heat spreading ability, i.e., the etched vias would increase the device temperature due to the reduced heat sink while the coated diamond would decrease the device temperature due to its higher thermal conductivity. Therefore, relative thick coated diamond is needed in order to reduce the self-heating effect. The simulated local stress at the gate edge of the drain side for the device with standard and field plate gate structure were compared, which would be relevant to the device mechanical failure. Other stress simulations focused on the intrinsic stress in the diamond capping layer impact on the device electrical behaviors. The simulated stress and electrical output characteristics were compared to experimental data obtained by micro-Raman spectroscopy and electrical characterization, respectively. Results showed that the intrinsic stress in the capping layer caused the non-uniform distribution of 2DEG in the channel and the access region. Besides the enhancement of the device power output, intrinsic stress in the capping layer can potentially improve the device reliability by modulating the local stress at the gate edge of the drain side. Finally, the surface, buffer, and barrier traps effects were simulated in this work. Pulsed measurements showed that long gates and distances between gate and drain contact can increase the gate lag ratio (decrease the current collapse). This was explained by simulations on the surface traps effect. The simulations on buffer traps effects focused on illustrating the dynamic trapping/detrapping in the buffer and the self-heating impact on the device transient drain current. A model was presented to describe the trapping and detrapping in the barrier. The trapping was the electron direct tunneling from the gate metal while the detrapping was the electron emission into the conduction band described by phonon-assisted tunneling. The reverse gate current was simulated based on this model, whose mechanism can be attributed to the temperature and electric field dependent electron emission in the barrier. Furthermore, the mechanism of the device bias via the self-heating and electric field impact on the electron emission and the transient drain current were also illustrated.