This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction. / Doctor of Philosophy / In this dissertation, we first explore some promising substitutes for the Global Positioning System (GPS) for the autonomous navigation of drones and metaverse user positioning in indoor spaces. Then, we will make the scope of research more comprehensive and try to explore substitutes to GPS for autonomous navigation of drones in general, both in indoor environments and outdoors. For the first part, we make our small indoor GPS. Similar to GPS, in our system, a receiver onboard the drone or the metaverse user can receive signals from our small semi-satellites in the room, and with that, it can localize itself. The idea is very similar to how the well-known GPS works, with some modifications. Unlike the GPS, we are using acoustic ultrasound signals or some RF signal based on 5G or Wi-Fi for transmission. Also, we have more freedom compared to GPS because, in GPS, they have to transmit signals from far ahead distances, whereas, in our scenario, it is just a room in which we put all of our semi-satellite transmitters. Moreover, we can put them anywhere we want in the room. This is, in fact important, because the positions of these semi-satellites have a huge effect on the accuracy of our system. Also, we can decide how many of them we need to cover every point in the room and not have any blind spots. We propose our novel techniques for finding the optimal placement to improve localization accuracy. In GPS, they propose a technique that is suitable for the case of those satellites and their distance to the targets. Similarly, we offer our novel techniques to have a robust transmission against noise and other factors and guarantee a localization scheme with high accuracy. All being said, our proposed system for indoor localization of drones and metaverse users in three dimensions has considered all the possible sources of error and proposed solutions to conquer them; hence a robust system with high accuracy in three-dimensional space.

In recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. An increasing research effort is being exerted to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters together with Löwner-John ellipsoid to approximate the highest posterior density region for trajectory prediction and uncertainty quantification. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications. / Master of Science / In recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. Over the coming years, the number of UAVs will increase rapidly. As a result, the risk of mid-air collisions grows, leading to property damages and possible loss of life if a UAV collides with manned aircraft. An increasing research effort has been made to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters, a Bayesian inferencing technique for trajectory prediction. The use of minimum volume enclosing ellipsoid to approximate the highest posterior density region for prediction uncertainty quantification is also investigated. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications.

In most man-made flight, vehicle control is achieved by deflecting flaps. However, in nature, morphing surfaces are found on both flying and swimming creatures. Morphing is used in nature because it is a more efficient form of control. This thesis investigates using morphing flaps to control a class of UAVs known as ducted fan vehicles. Specifically, this thesis discusses both the challenges and benefits of using morphing control surfaces. To achieve morphing, a piezoelectric device known as Macro Fiber Composites is used. These devices are embedded in the skin of the vehicles control surface, and when actuated, they cause the control surface to increase or decrease camber. This thesis describes experiments that were performed to investigate the performance of this type of actuator. Specifically, the actuation bandwidth of these devices is presented and compared to a servo. Results show that the morphing control surfaces can actuate at frequencies twice as high as a servo. / Master of Science

El crecimiento industrial ha acarreado grandes avances tecnológicos para nuestra sociedad. Lamentablemente, el precio a pagar por estos avances ha sido un aumento significativo de los niveles de contaminación del aire en todo el mundo, afectando tanto a zonas urbanas como a las zonas rurales. Por lo general, la monitorización de la calidad aire se realiza mediante estaciones de monitorización fijas. Sin embargo, este método es demasiado costoso, poco escalable y difícil de implementar en nuestras ciudades, las cuales están cada vez más pobladas. El uso de Mobile CrowdSensing, paradigma en el cual la monitorización la realizan los propios usuarios, permite realizar monitorización ambiental utilizando sensores móviles integrados en vehículos. Los posibles escenarios se pueden dividir en dos: entornos urbanos, donde hay un amplio conjunto de vehículos disponibles, y entornos rurales o industriales, donde el tráfico vehicular es escaso y está limitado a las principales arterias de transporte. Teniendo en cuenta estos dos escenarios, esta tesis propone una arquitectura, llamada EcoSensor, que permite monitorizar la contaminación del aire utilizando pequeños sensores de bajo coste instalados en diferentes tipos de vehículos, tales como bicicletas, automóviles o autobuses del sistema de transporte público, en el caso de entornos urbanos, y en drones o UAS en entornos rurales. La arquitectura propuesta está compuesta por tres componentes: un sensor de bajo coste para capturar datos de contaminación, un smartphone para realizar un preprocesamiento de la información y para transmitir los datos hacia un servidor central, y el servidor central, encargado de almacenar y procesar la información de contaminación ambiental. Para entornos urbanos, analizamos diferentes alternativas con respecto al diseño de una unidad de monitorización de bajo coste basada en plataformas de prototipado comerciales como RaspberryPi o Arduino, junto con sensores también de precio reducido. En la tesis realizamos un análisis, y proponemos un proceso, para llevar a cabo la monitorización ambiental utilizando la arquitectura propuesta. Este proceso abarca cuatro operaciones básicas: captura de datos, conversión de unidades, reducción de la variabilidad temporal, e interpolación espacial. Para entornos rurales, proponemos el uso de drones como unidades de sensorización móviles. Específicamente, equipamos el drone con capacidades de monitorización a través de un microordenador RaspberryPi y sensores de calidad del aire de bajo coste. Finalmente, se propone un algoritmo llamado PdUC para controlar el vuelo del UAV con el objetivo de realizar monitorización ambiental, identificando las áreas más contaminadas, y tratando de ese modo de mejorar la precisión general y la velocidad de monitorización. Además, proponemos una mejora a este algoritmo, denominada PdUC-D, basada en la discretización del área a monitorizar dividiéndola en pequeñas áreas (tiles), donde cada tile se monitoriza una sola vez, evitando así realizar muestreos redundantes. En general, verificamos que la monitorización móvil es una aproximación eficiente y fiable para monitorizar la contaminación del aire en cualquier entorno, ya sea usando vehículos o bicicletas en entornos urbanos, o UAVs en entornos rurales. Con respecto al proceso de monitorización ambiental, validamos nuestra propuesta comparando los valores obtenidos por nuestros sensores móviles de bajo coste con respecto a los valores típicos de referencia ofrecidos por las estaciones de monitorización fijas para el mismo período y ubicación, comprobando que los resultados son semejantes, y están acuerdo a lo esperado. Además, demostramos que PdUC-D, permite guiar autónomamente un UAV en tareas de monitorización del aire, ofreciendo un mejor rendimiento que los modelos de movilidad típicos, reduciendo tanto los errores de predicción como el tiempo para cubrir el área completa, / Industrial growth has brought unforeseen technological advances to our society. Unfortunately, the price to pay for these advances has been an increase of the air pollution levels worldwide, affecting both urban and countryside areas. Typically, air pollution monitoring relies on fixed monitoring stations to carry out the pollution control. However, this method is too expensive, not scalable, and hard to implement in any city. The Mobile Crowdsensing (MCS) approach, a novel paradigm whereby users are in charge of performing monitoring tasks, allows environment monitoring to be made using small sensors embedded in mobile vehicles. The possible scenarios can be divided into two: urban scenarios, where a wide set of vehicles are available, and rural and industrial areas, where vehicular traffic is scarce and limited to the main transportation arteries. Considering these two scenarios, in this thesis we propose an architecture, called EcoSensor, to monitor the air pollution using small sensors installed in vehicles, such as bicycles, private cars, or the public transportation system, applicable to urban scenarios, and the use of an Unmanned Aerial System (UAS) in rural scenarios. Three main components compose our architecture: a low-cost sensor to capture pollution data, a smartphone to preprocess the pollution information and transmit the data towards a central server, and the central server, to store and process pollution information. For urban scenarios, we analyze different alternatives regarding the design of a low-cost sensing unit based on commercial prototyping platforms such as Raspberry Pi or Arduino, and Commercial Off-the-shelf (COTS) air quality sensors. Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation. For rural scenarios, we propose the use of an Unmanned Aerial Vehicle (UAV) as a mobile sensor. Specifically, we equip the UAV with sensing capabilities through a Raspberry Pi microcomputer and low-cost air quality sensors. Finally, we propose an algorithm, called Pollution-driven UAV Control (PdUC), to control the UAV flight for monitoring tasks by focusing on the most polluted areas, and thereby attempting to improve the overall accuracy while minimizing flight time. We then propose an improvement to this algorithm, called Discretized Pollution-driven UAV Control (PdUC-D), where we discretize the target area by splitting it into small tiles, where each tile is monitored only once, thereby avoiding redundant sampling. Overall, we found that mobile sensing is a good approach for monitoring air pollution in any environment, either by using vehicles or bicycles in urban scenarios, or an UAVs in rural scenarios. We validate our proposal by comparing obtained values by our mobile sensors against typical values reported by monitoring stations at the same time and location, showing that the results are right, matching the expected values with a low error. Moreover, we proved that PdUC-D, our protocol for the autonomous guidance of UAVs performing air monitoring tasks, has better performance than typical mobility models in terms of reducing the prediction errors and reducing the time to cover the whole area.Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation. / El creixement industrial ha implicat grans avanços tecnològics per a la nostra societat. Lamentablement, el preu que cal pagar per aquests avanços ha sigut un augment significatiu dels nivells de contaminació de l'aire a tot el món, que afecta tant zones urbanes com zones rurals. En general, el monitoratge de la qualitat aire es fa mitjançant estacions de monitoratge fixes. No obstant això, aquest mètode és massa costós, poc escalable i difícil d'implementar a les nostres ciutats, les quals estan cada vegada més poblades. L'ús de Mobile CrowdSensing (MCS), paradigma en el qual el monitoratge el duen a terme els mateixos usuaris, permet realitzar monitorització ambiental tenint sensors mòbils integrats en vehicles. Els possibles escenaris es poden dividir en dos: entorns urbans, on hi ha un ampli conjunt de vehicles disponibles, i entorns rurals o industrials, on el trànsit vehicular és escàs i està limitat a les principals artèries de transport. Tenint en compte aquests dos escenaris, aquesta tesi proposa una arquitectura, anomenada EcoSensor, que permet monitorar la contaminació de l'aire utilitzant petits sensors de baix cost instal·lats en diferents tipus de vehicles, com ara bicicletes, automòbils o autobusos del sistema de transport públic, en el cas d'entorns urbans, i en UAVs (Unmanned Aerial Vehicles) en entorns rurals. L'arquitectura proposada està composta per tres components: un sensor de baix cost per a capturar dades de contaminació, un smartphone per a realitzar un preprocessament de la informació i per a transmetre les dades cap a un servidor central, i el servidor central, encarregat d'emmagatzemar i processar la informació de contaminació ambiental. Per a entorns urbans, analitzem diferents alternatives pel que fa al disseny d'una unitat de monitoratge (sensor mòbil) de baix cost basada en plataformes de prototipatge comercials com Raspberry Pi o Arduino, juntament amb sensors també de preu reduït. En la tesi fem una anàlisi, i proposem un procés, per a dur a terme el monitoratge ambiental utilitzant l'arquitectura proposada. Aquest procés abasta quatre operacions bàsiques: captura de dades, conversió d'unitats, reducció de la variabilitat temporal, i interpolació espacial. Per a entorns rurals, proposem l'ús de drons o Unmanned Aerial Vehicles (UAVs) com a unitats de sensorització mòbils. Específicament, equipem el dron amb capacitats de monitoratge a través d'un microordinador Raspberry Pi i sensors de qualitat de l'aire de baix cost. Finalment, es proposa un algorisme anomenat PdUC (Pollution-driven UAV Control) per a controlar el vol del UAV amb l'objectiu de realitzar monitoratge ambiental, que identifica les àrees més contaminades i que, d'aquesta manera, tracta de millorar la precisió general i la velocitat de monitoratge. A més, proposem una millora a aquest algorisme, denominada PdUC-D, basada en la discretització de l'àrea a monitorar dividint-la en xicotetes àrees (tiles), on cada tile es monitora una sola vegada, fet que evita dur a terme mostrejos redundants. En general, verifiquem que el monitoratge mòbil és una aproximació eficient i fiable per a monitorar la contaminació de l'aire en qualsevol entorn, ja siga usant vehicles o bicicletes en entorns urbans, o UAVs en entorns rurals. Pel que fa al procés de monitoratge ambiental, validem la nostra proposta comparant els valors obtinguts pels nostres sensors mòbils de baix cost pel que fa als valors típics de referència oferits per les estacions de monitoratge fixes per al mateix període i ubicació, i es comprova que els resultats són semblants, i estan d'acord amb el resultat esperat. A més, es demostra que PdUC-D permet guiar autònomament un UAV en tasques de monitoratge de l'aire, oferint un millor rendiment que els models de mobilitat típics, reduint tant els errors de predicció com el temps per a cobrir l'àrea completa, i aconseguint una major precisió dins de les àrees més / Alvear Alvear, ÓP. (2018). Mobile Sensing Architecture for Air Pollution Monitoring [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107928

This thesis attempts to illustrate how texts engage in a struggle to define social reality. The academic and political debate on the drone provides a case study illustrating pro-drone, anti-drone and drone-deconstructivist text´s attempt to define social reality around the drone. Through this exploration the thesis contests the positivist notion that reality is fixed and can be discovered. The thesis utilizes IR poststructuralism as a theoretical guideline and engages with a range of poststructuralist concepts and sub-theories identified in previous research. Methodologically, the thesis conducts a qualitative content analysis on three journal articles, two speeches and two chapters of a report. Moreover, the thesis conducts a discourse analysis to contextualize findings and provide in-depth analysis of examples. The thesis presents and analyzes findings in accordance with the categories identified in coding, highlights an example through discourse analysis and links findings to the research question concerned with texts struggle to define social reality. The thesis concludes by arguing that the drone-debate is a “battlespace” for defining social reality, thus contesting the positivist notion that social reality is fixed.

<p>Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSS) and inertial navigation systems (INS) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS unit and hyperspectral scanner to be evaluated. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This research presents three alternative calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, the thesis starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using Ground Control Points (GCPs) and one using tie points. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for ortho-rectification of six hyperspectral UAV datasets acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the Ground Sampling Distance (GSD) of the used scanner (namely, 3-5 cm when flying at 60 m).</p>

Sistemas de Transportes Inteligentes (STIs) englobam um conjuntos de tecnologias (Sensoriamento Remoto, Tecnologia da Informação, Eletrônica, Sistemas de Comunicação de Dados entre outros) que visam oferecer serviços e gerenciamento de tráfego avançado para meios de transporte rodoviário, aéreo e outros. A obtenção de informações a respeito das características e das condições do pavimento das estradas constitui uma parte importante dentro do sensoriamento nesses STIs. Investigar novas técnicas, metodologias e meios de automatizar a obtenção dessas informações é parte deste trabalho. Uma vez que existem diferentes tipos de defeitos em vias pavimentadas, esta tese apresenta a proposta de uma metodologia que permite a obtenção, de forma automática, das condições dos pavimentos asfálticos. A obtenção dos dados foi realizada por meio do Sensoriamento Remoto com uso de Veículos Aéreos Não Tripulados. A utilização de técnicas de Aprendizado de Máquina na detecção automática possibilitou alcançar uma acurácia de 99% na detecção de pavimentos asfálticos flexíveis e 92% na identificação de defeitos em alguns experimentos. Como resultado obteve-se o diagnóstico automático, não só das condições da via, mas de diferentes tipos de defeitos presentes em pavimentos. / Intelligent Transport Systems (ITS) is a set of integrated technologies (Remote Sensing, Information Technology, Electronics, Data Communication Systems among others) that aims to provide services and advanced traffic management for road, air, rail and others transportation systems. Obtaining information about characteristics and road pavement conditions is an important part within the sensing these ITS. Investigating new techniques, methods and means to optimize and automate obtaining these information are part of this work, since there are different types of defects on paved roads. Thus, this thesis proposes a methodology that allows automatically obtain information about the condition of the pavement. Data collection was performed with remote sensing technology using Unmanned Aerial Vehicles. Automatic detection was possible through the use of Machine Learning techniques with 99% of accuracy in pavements and 92% in distress identification. As a result we obtained the self-diagnosis, not just the pavement, but different types of distress present in the pavement.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The development of a control allocation system for use as part of a fault-tolerant control (FTC) system in unmanned aerial vehicles (UAVs) is presented. This system plays a vital role in minimising the possibility that a fault will necessitate the reconfiguration of the control, guidance or navigation systems of the aircraft by minimising the difference between the desired and achievable aircraft performance parameters. This is achieved by optimising the allocation of control effort commanded by the virtual actuators to the physical actuators present on the aircraft. A simple general six degree of freedom aircraft model is presented that contains all of the relevant terms needed to find the trim biases of the aircraft actuators and evaluate the performance of the virtual actuators. This model was used to develop a control allocation formulation that optimises the performance of the virtual actuators of the aircraft while minimising adverse effects and avoiding actuator saturation. The resulting problem formulation was formulated as a multi-objective optimisation problem which was solved using the sequential quadratic programming method. The control allocation system was practically implemented and tested. A number of failure categories of varying severity were defined and two aircraft with different levels of actuator redundancy were used to test the system. The control allocation algorithm was evaluated for each failure category, aircraft test case and for a number of differing control allocation system configurations. A number of enhancements were then made to the control allocation system which included adding frequency-based allocation and adapting the algorithm for an unconventional ducted-fan UAV. The control allocation system is shown to be applicable to a number of different conventional aircraft configurations with no alterations as well as being applicable to unconventional aircraft with minor alterations. The control allocation system is shown to be capable of handling both single and multiple actuator failures and the importance of actuator redundancy is highlighted as a factor that influences the effectiveness of control allocation. The control allocation system can be effectively used as part of a FTC system or as a tool that can be used to investigate control allocation and aircraft redundancy. / AFRIKAANSE OPSOMMING: Die ontwikkeling van ’n beheertoekenning sisteem vir gebruik as deel van ’n fout verdraagsame beheersisteem in onbemande lugvaartuie word voorgelê. Hierdie sisteem speel ’n essensiële rol in die vermindering van die moontlikheid dat ’n fout die herkonfigurasie van die beheer, bestuur of navigasiesisteme van die vaartuig tot gevolg sal hê, deur die verskil te verminder tussen die verlangde en bereikbare werkverrigtingsraamwerk van die vaartuig. Dit word bereik deur die optimisering van die toekenning van beheerpoging aangevoer deur die virtuele aktueerders na die fisiese aktueerders teenwoordig op die vaartuig. ’n Eenvoudige algemene ses grade van vryheid lugvaartuig model word voorgestel wat al die relevante terme bevat wat benodig word om die onewewigtigheid verstelling van die vaartuig se aktueerders te vind en die werksverrigting van die virtuele aktueerders te evalueer. Hierdie model is gebruik om ’n beheer toekenning formulering te ontwikkel wat die werkverrigting van die virtuele aktueerders van die vaartuig optimiseer terwyl nadelige gevolge verminder word asook aktueerder versadiging vermy word. Die gevolglike probleem formulering is omskryf as ’n multi-doel optimiserings probleem wat opgelos is deur gebruik van die sekwensiële kwadratiese programmerings metode. Die beheertoekenning sisteem is prakties geïmplementeer en getoets. ’n Aantal fout kategorieë van verskillende grade van erns is gedefinieer en twee vaartuie met verskillende vlakke van aktueerder oortolligheid is gebruik om die sisteem te toets. Die beheer toekenning algoritme is geëvalueer vir elke fout kategorie, vaartuig toetsgeval, asook vir ’n aantal verskillende beheertoekenning sisteem konfigurasies. ’n Aantal verbeterings is aangebring aan die beheertoekenning sisteem, naamlik die toevoeging van frekwensie gebaseerde toekenning en wysiging van die algoritme vir ’n onkonvensionele onbemande geleide waaier lugvaartuig. Die beheertoekenning sisteem is van toepassing op ’n aantal verskillende konvensionele vaartuig konfigurasies met geen verstellings asook van toepassing op onkonvensionele vaartuie met geringe verstellings. Die beheertoekenning sisteem kan beide enkel- en veelvoudige aktueerder tekortkominge hanteer en die belangrikheid van aktueerder oortolligheid is beklemtoon as ’n faktor wat die effektiwiteit van beheertoekenning beïnvloed. Die beheertoekenning sisteem kan effektief geïmplementeer word as deel van ’n fout verdraagsame beheersisteem of as ’n werktuig om beheertoekenning en vaartuig oortolligheid te ondersoek.

Sistemas de Transportes Inteligentes (STIs) englobam um conjuntos de tecnologias (Sensoriamento Remoto, Tecnologia da Informação, Eletrônica, Sistemas de Comunicação de Dados entre outros) que visam oferecer serviços e gerenciamento de tráfego avançado para meios de transporte rodoviário, aéreo e outros. A obtenção de informações a respeito das características e das condições do pavimento das estradas constitui uma parte importante dentro do sensoriamento nesses STIs. Investigar novas técnicas, metodologias e meios de automatizar a obtenção dessas informações é parte deste trabalho. Uma vez que existem diferentes tipos de defeitos em vias pavimentadas, esta tese apresenta a proposta de uma metodologia que permite a obtenção, de forma automática, das condições dos pavimentos asfálticos. A obtenção dos dados foi realizada por meio do Sensoriamento Remoto com uso de Veículos Aéreos Não Tripulados. A utilização de técnicas de Aprendizado de Máquina na detecção automática possibilitou alcançar uma acurácia de 99% na detecção de pavimentos asfálticos flexíveis e 92% na identificação de defeitos em alguns experimentos. Como resultado obteve-se o diagnóstico automático, não só das condições da via, mas de diferentes tipos de defeitos presentes em pavimentos. / Intelligent Transport Systems (ITS) is a set of integrated technologies (Remote Sensing, Information Technology, Electronics, Data Communication Systems among others) that aims to provide services and advanced traffic management for road, air, rail and others transportation systems. Obtaining information about characteristics and road pavement conditions is an important part within the sensing these ITS. Investigating new techniques, methods and means to optimize and automate obtaining these information are part of this work, since there are different types of defects on paved roads. Thus, this thesis proposes a methodology that allows automatically obtain information about the condition of the pavement. Data collection was performed with remote sensing technology using Unmanned Aerial Vehicles. Automatic detection was possible through the use of Machine Learning techniques with 99% of accuracy in pavements and 92% in distress identification. As a result we obtained the self-diagnosis, not just the pavement, but different types of distress present in the pavement.

Conservation of elasmobranch populations is often inhibited by a lack of data, particularly in understudied regions like the Red Sea. Survey efforts in this region have been infrequent and often highly localized. Establishing a broad baseline for elasmobranch diversity and abundance along the Saudi Arabian Red Sea coast could inform both conservation efforts and a nascent ecotourism industry. In this thesis, I describe a pilot study comparing biodiversity data from baited remote underwater video stations (BRUVS), unoccupied aerial vehicle surveys (UAVs), and eDNA sequencing at five islands in the Farasan Banks region of the Saudi Arabian Red Sea. Estimates of relative abundance were also compared between the BRUVS and UAVs. Each method identified species missed by the other two, but all three techniques exhibited clear habitat- and taxa-specific biases. I was able to identify key concerns for each approach that need to be addressed before large-scale implementation. If carefully planned and executed well, a full assessment of the Saudi Arabian coastline could establish a true baseline for shallow water elasmobranchs in the eastern Red Sea. Informing best conservation practices and identifying potential ecological attractions in accordance the environmental and economic goals of Saudi Arabia’s Vision 2030.