Simulated sensor data from active and passive sensors has numerous applications in target detection and tracking. Simulated data is particularly useful in performance evaluation of target tracking algorithms where the ground truth of a scenario must be known. For real sensor data it is impossible to know the ground truth so simulated data must be used. This paper discusses existing methods for simulation of data from active sensors and proposes a method that builds on existing techniques from the field of computer graphics. An extension to existing methods is proposed to accommodate the simulation of active sensor data for which timing and frequency information is required in addition to intensity. Results from an existing method of active sensor data simulation are compared to the results of the proposed method. Additionally, a cloud computing framework is proposed and its scalability evaluated to address the fairly large computational load of such a simulation. / Thesis / Master of Applied Science (MASc)

Over 90% of traffic accidents are caused by human error. Therefore, the realization of autonomous driving could save countless lives and drastically reduce the associated financial expenses. Moreover, the collective behavior of self-driving cars would avoid traffic jams and thus reduce fuel consumption and greenhouse gas emissions. The majority of concepts is based on Light Detection And Ranging (LiDAR), which is the most precise method to measure distances. Matched to the 95% of commercial LiDAR systems based on laser wavelengths of mostly 905nm, siliconbased photo sensors are used. Avalanche photo diodes (APD) are the only sensor solution in mass production [6]. Due to an internal multiplication mechanism based on impact ionization, high signal-noise-ratios (SNR) are achieved and provide the required resolution of low signals from more than 100m distant targets. Currently none of the LiDAR technologies meet the reliability requirements of the automotive industry concerning the aging of installed components. Consequently, autonomous driving cannot yet be realized for public use. Very little is known about the aging of APDs in general and nothing at all in the field of automotive LiDAR. In order to provide novel insights into APD aging that help designers to achieve more robust sensors and thus to enable a step closer to the realization of autonomous driving, it was the aim of this thesis prepared in the industrial environment to reveal the underlying physical aging mechanisms and their effects on the function of APDs in automotive LiDAR application. At first, a novel APD degradation model was developed encompassing a wide range of processes, treating numerous fundamental aspects of negative oxide charge generation and Si:SiO2 interface trap generation. So far, no model is known covering the kinetics of APD degradation comprehensively in such deep detail. Due to the feedback between degradation phenomena and sensor internal fields and currents, a coupled problem arose. It was tackled by a sophisticated numerical iteration approach which was tailor-made and solved this problem self-consistently in a tandem procedure combining the simulation of sensor degradation and the Silvaco Atlas device simulator. This led to novel insights into the APD degradation behavior. The generation of negative oxide charges was identified to cause a drift of the impact ionization rate in the sensor edge. The generation of interface traps promotes the accumulation of negative oxide charges by their supply of thermally generated dark current. In this way, degradation is about 14% faster. In order to reflect not only the causal relations of APD degradation, the model was calibrated on experimental degradation data. With the calibrated degradation model and its self-consistent simulation approach an elaborated powerful tool was available. Stress experiments have been performed on test sensors under a variation of operation conditions and on APDs. APDs of the studied design are currently tested and installed in automotive LiDAR modules. The entire set of experimental results found its complete physical interpretation in conjunction with the degradation model which achieved an excellent agreement. Thereby, numerous novel insights were revealed: The extent of degradation is induced by the properties of the sensors oxide layer. The degradation pace increases with temperature, voltage and intensity of illumination whereas the impact of temperature is particularly strong due to the significant participation of the dark current during degradation. The oxygen vacancy was proven to be the dominant trap in the oxide layer of the studied sensors. An empirical distribution of individual sensor properties was achieved. In some cases, the impact ionization rate in the sensor edge increased which indicates a major problem, as noise increases when the generation- recombination processes in the sensor become more pronounced during degradation. In order to estimate the impact of the degradation induced increase of noise on the LiDAR application, the empirical distribution of individual sensor properties was extrapolated to the tail where sensors are very prone to degradation. Furthermore, the available noise models were extended to cover the effect of degradation. Application of the calibrated APD degradation model revealed, that the APD noise is highly effected and even triples during aging. The origin was exclusively assigned to the edge contribution. There, the avalanche breakdown of the edge dark current caused by degradation is the main initiator. Consequently, for the first time ever, the signal-noise-ratio (SNR) degradation mode of APDs in LiDAR application was identified. During degradation, the SNR of small signals from 100m distant objects degrades to a value below 1, where even theoretically a resolution is impossible. Finally, the picture of APD degradation was completed by the estimation of lifetime. In the case of the most severe conditions in LiDAR operation, it amounts to only 1000 h, which falls much below the requirements of the automotive industry of several decades.

The mining industry uses vehicles for a wide range of applications, including excavation and transportation of rock and soil. Currently, this requires a lot of human labour, mainly drivers, but efforts are being made to increase automation, e.g. using autonomous vehicles. In order for a vehicle to reach any level of autonomy, it needs to be aware of its surroundings, for instance by using sensors. The placement of the sensors is a difficult problem. The goal of this project was to create a tool that would simplify the sensor placement process. The tool should simulate sensors on autonomous vehicles, both by visualizing their field of view and by generating synthetic data. The tool was created, including simulation environments, models of different types of sensors and tools to analyze the results of the simulation. Both the field of view visualization and the data analysis tools were shown to be powerful tools for evaluating sensor placements. All of the sensor models are able to generate data, with different levels of realism. The radar model and the camera model give a good estimation of what the sensors can detect, while the lidar model is capable of generating realistic data. / Gruvindustrin använder fordon till ett stort antal olika uppgifter, bland annat till att gräva ut och förflytta sten och jord. Detta kräver för tillfället mycket manuellt arbete, framförallt förare, men försök att automatisera delar av arbetet utförs, till exempel genom att använda autonoma fordon. För att ett fordon ska kunna bli autonomt krävs det att det kan känna av sin omgivning, exempelvis genom att använda sensorer. Sensorplacering är ett svårt problem. Målet med projektet var att skapa ett verktyg för att förenkla sensorplaceringsprocessen. Verktyget skulle simulera sensorer på autonoma fordon, både genom att visualisera deras synfält och genom att generera syntetisk data. Verktyget skapades, inklusive simuleringsmiljöer, modeller av olika typer av sensorer, och verktyg för att analysera genererad data. Både synfältsvisualiseringen och datagenereringen visades vara användbara verktyg för att utvärdera sensorplaceringar. Alla sensormodellerna kan generera data, med olika realistiska resultat. Radarmodellen och kameramodellen ger en bra uppskattning av vad sensorerna kan detektera, medan lidarmodellen kan generera realistisk data.

[EN] This thesis has been created within the framework of the interoperability of the networks of sensors in industrial environments, using standard SWE (Sensor Web Enablement). It has developed from the participation in different research projects, within the research group SATRD (Distributed Real Time Systems and Applications) of the Communications Department of the UPV. Has gone a long time since the appearance of the first sensors that were only able to respond to a stimulus, to the concept of sensor web, where sensors can be remotely configured, perform actions and process and integrate data from other sensors. Nowadays, the use of networks of sensors or WSN (Wireless Sensor Networks) is widespread in several fields, to gather information, which can then be used by other applications. As it increases its use, it is necessary to combine or add data from sensors of different WSN, which is not always possible, due to the lack of interoperability between different manufacturers. That is why OGC (Open Geospatial Consortium) founded SWE, in order to develop standards for access to sensors via the Internet and thus improve the interoperability. For these reasons, in this thesis is specified an IoT architecture (Internet of Things) generically, so that it can be extended to multiple environments. The I3WSN architecture is designed for monitoring and control systems, ensuring interoperability between the different elements through the integration of SWE standards. After the design, the application of the architecture is carried out in three different contexts, associated with three research projects. The first of them, in order to monitor and manage the workers' health in industrial environments, based on environmental and medical data, within the project FASyS (Fabrica Absolutamente Segura y Saludable). Secondly, to ensure the level of assurance of a smart grid environment, from the capture of the security information of all its elements, in the UniverSEC project. Finally, to manage efficiently and intelligently transport freight containers, from traffic data in real time, in the project STIMULO. / [ES] Esta tesis se ha creado en el marco de la interoperabilidad de las redes de sensores en entornos industriales, mediante la utilización del estándar SWE (Sensor Web Enablement). Se ha desarrollado a partir de la participación en diferentes proyectos de investigación, dentro del grupo de investigación SATRD (Sistemas y Aplicaciones de Tiempo Real Distribuidos) del Departamento de Comunicaciones de la UPV. Ha pasado mucho tiempo desde la aparición de los primeros sensores que únicamente eran capaces de responder frente a un estímulo, hasta el concepto de sensor web, donde los sensores pueden ser configurados de forma remota, realizar acciones y procesar e integrar datos de otros sensores. Hoy en día, la utilización de redes de sensores o WSN (Wireless Sensor Networks) está ampliamente extendida en diversos ámbitos, para recopilar información, que luego pueda ser utilizada por otras aplicaciones. A medida que aumenta su utilización, surge la necesidad de combinar o agregar datos de sensores de distintas WSN, lo cual no siempre es posible, debido a la falta de interoperabilidad entre los distintos fabricantes. Es por ello que OGC (Open Geospatial Consortium) fundó SWE, con el fin de desarrollar estándares para el acceso a sensores a través de Internet y así mejorar la interoperabilidad. Por estos motivos, en esta tesis se ha especificado una arquitectura IoT (Internet of Things) de forma genérica, para que se pueda extender a múltiples entornos. La arquitectura I3WSN se ha diseñado para la monitorización y el control de sistemas, garantizando la interoperabilidad entre los distintos elementos gracias a la integración de los estándares de SWE. Tras el diseño, se ha llevado a cabo la aplicación de la arquitectura en tres contextos distintos, asociados a tres proyectos de investigación. El primero de ellos, con el objetivo de monitorizar y gestionar la salud de los trabajadores en entornos industriales, a partir de datos del entorno y datos médicos, dentro del proyecto FASyS (Fabrica Absolutamente Segura y Saludable). El segundo, para garantizar el nivel de aseguramiento de un entorno smart grid, a partir de la captura de la información de seguridad de todos sus elementos, en el proyecto UniverSEC. Y por último, para gestionar de forma eficiente e inteligente el transporte de contenedores de mercancías, a partir de los datos de tráfico en tiempo real, en el proyecto STIMULO. / [CA] Aquesta tesi ha estat creada en el marc de la interoperabilitat de les xarxes de sensors en entorns industrialitzats, utilitzant l'estàndard SWE (Sensor Web capacitació). S'ha desenvolupat des de la participació en diversos projectes d'investigació, dins del grup d'investigació SATRD (Sistemes i Aplicacions de Temps Real Distribuïts) del Departament de Comunicació de la UPV. Ha passat molt temps des de l'aparició dels primers sensors que només eren capaços de respondre a un estímul, fins al concepte de sensor web, on els sensors es poden configurar remotament, realitzar accions i processar i integrar les dades d'altres sensors. Avui en dia, l'ús de xarxes de sensors o WSN (Wireless Sensor Networks) està àmpliament estesa en diversos àmbits, per a recollir informació, que desprès puga ser utilitzada per altres aplicacions. A mesura que augmenta el seu ús, sorgeix la necessitat de combinar o afegir dades de sensors de diferents WSN, que no sempre és possible, degut a la falta d'interoperabilitat entre els diferents fabricants. És per això que OGC (Open Geospatial Consortium) va fundar SWE, per desenvolupar estàndards per l'accés de sensors a Internet i així millorar la interoperabilitat. Per aquests motius, en aquesta tesi s'ha especificat una arquitectura IoT (Internet of Things) de forma genèrica, per tal que es puga estendre a múltiples entorns. L'arquitectura I3WSN s'ha dissenyat per la monitorització i el control de sistemes, garantint la interoperabilitat entre els diferents elements gràcies a la integració dels estàndards de SWE. Després el disseny, s'ha dut a terme l'aplicació de l'arquitectura en tres contextos diferents, associats amb tres projectes d'investigació. El primer d'ells, amb la finalitat de monitoritzar i gestionar la salut dels treballadors en entorns industrials, a partir de dades ambientals i dades mèdiques, dins del projecte FASyS (Fabrica Absolutamente Segura y Saludable). El segon, per garantir el nivell d'assegurament d'un entorn de smart grid, a partir de la captura de la informació de seguretat de tots els seus elements, en el projecte UniverSEC. I finalment, per gestionar de manera eficient i intel·ligent el transport de contenidors de mercaderies, a partir de les dades de trànsit en temps real, en el projecte STIMULO. / Giménez Salazar, P. (2015). Aplicaciones de SWE en entornos industriales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51282

This work deal with taking advatage of ballance thermodynamic sensors as the replacement of commercial temperature sensors.