The work presented in this dissertation on piezoelectric inchworm motors (IWM) is part of a process to gain an understanding of the design, analysis and testing of this smart actuator technology. This work will form the foundation of what will hopefully lead to the realisation of a production-ready IWM design to be used in energy-scarce, battery-operated Unmanned Aerial Vehicles (UAVs), and forms part of a larger national drive to expand the UAV industry in South Africa. Although the principles used in the design of IWMs are well known, a new innovation is employed. A novel way to increase the force capacity of IWMs without compromising on the speed or displacement when compared to conventional methods is shown to be effective, and was used for the first time on IWMs. The use of a simple design equation is demonstrated to be useful in predicting the load limits and step displacements. Challenges of finding a correlation between predicted and measured performance values are discussed and solutions are presented. The history of IWMs and some background on piezoelectricity are given for the reader not familiar with these. The use of micro ridges on the clamp mechanisms is explored. The effects of the control signals on the mechanism of the motor are discussed in detail and some important comments on electrical controllers are made. The emphasis is on designing a strong motor that capitalises on the high-force density of piezoelectric material. / Dissertation (MEng)--University of Pretoria, 2014. / gm2014 / Mechanical and Aeronautical Engineering / unrestricted

A new Trapped Vortex Combustor (TVC) concept has been studied for applications such as those in Unmanned Aerial Vehicles (UAVs) as it offers potential for superior flame stability and low pressure loss. Flame stability is ensured by a strong vortex in a physical cavity attached to the combustor wall, and low pressure loss is due to the absence of swirl. Earlier studies on a compact combustor concept showed that there are issues with ensuring stable combustion over a range of operating conditions. The present work focuses on experimental studies and numerical simulations to study the stability issues and performance optimization in this compact single-cavity TVC configuration. For performing numerical simulations, an accurate and yet computationally affordable Modified Eddy Dissipation Concept combustion model is built upon the KIVA-3V platform to account for turbulence-chemistry interactions. Detailed validation with a turbulent non-premixed CH4/H2/N2 flame from literature showed that the model is sufficiently accurate and the effect of various simulation strategies is assessed. Transient flame simulation capabilities are assessed by comparison with experimental data from an acoustically excited oscillatory H2-air diffusion flame reported in literature. Subsequent to successful validation of the model, studies on basic TVC flow oscillations are performed. Frequencies of flow oscillations are found to be independent of flow velocities and cavity length, but dependent on the cavity depth. Cavity injection and combustion individually affect the magnitude of flow oscillations but do not significantly alter the resonant frequencies. Reacting flow experiments and flow visualization studies in an existing experimental TVC rig with optical access and variable cavity L/D ratio show that TVC flame stability depends strongly on the cavity air velocity. A detailed set of numerical simulations also confirms this and helps to identify three basic modes of TVC flame stabilization. A clockwise cavity vortex stabilized flame is formed at low cavity air velocities relative to the mainstream, while a strong anticlockwise cavity vortex is formed at high cavity air velocities and low L/Ds. At intermediate conditions, the cavity vortex structure is found to be in a transition state which leads to large scale flame instabilities and flame blow-out. For solving the flame instability problem, a novel strategy of incorporating a flow guide vane is proposed to establish the advantageous anticlockwise vortex without the use of cavity air. Experimental results with the modified configuration are quite encouraging for TVC flame stability at laboratory conditions, while numerical results show good stability even at extreme operating conditions. Further design optimization studies are performed in a multi-parameter space using detailed simulations. From the results, a strategy of using inclined struts in the main flow path along with the flow guide vane seems most promising. This configuration is tested experimentally and results pertaining to pressure drop, pattern factor and flame stability are found to be satisfactory.

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 no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107928 / TESIS

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

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To meet the latest requirements of the 6G standards, several techniques have been proposed in the open literature, such as millimeter waves, terahertz communication, and massive MIMO. In addition to these recent technologies, the use of unmanned aerial vehicles (UAVs) is strongly advocated for 6G networks, as the 6G standard will not be dedicated to broadband services, but will rather be oriented towards reduced geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. Although UAVs are characterized by their high mobility and their ability to establish line-of-sight links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage and provide some useful insights. To this end, we use tractable tools from stochastic geometry to model the complex communication system.

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In the current context of the energy transition, it has been argued by researchers and authors that the demand for raw materials for the necessary green technologies can’t be met without the input of primary raw materials. These materials can only be supplied through the mining cycle: exploration, mining, and processing. The mining cycle, however, can pose risks to the environment, which could be in contradiction with the motivation behind the implementation of green technologies. It is then society’s duty to strive for a constant reduction of the environmental impact of the mining cycle, or else, we would be in a paradoxical situation where, by mining materials to power the energy transition, if not done with care, we could be also risking the environment.  While this megatrend of the energy transition occurs, Unmanned Aerial Vehicles (UAVs) also known as drones, have reached a significant level of development which together with the miniaturization of geoscientific sensors, has opened the door to interesting fast, agile, and non-invasive ways of obtaining geological information. This has bridged gaps between the traditional scales of airborne and ground surveying and holds the potential of contributing to a less environmentally harmful mining cycle.  This thesis work intends to be a useful reference for anyone interested in working with UAVs in geosciences, especially for the exploration and management of raw materials from an entrepreneurial point of view. Here, a brief review of the current state of the art through the recent scientific literature on applications of drones in the mining cycle, including but not limited to geophysics and hyperspectral imaging is presented. Using this state of the art as a point of departure, semi-structured interviews with different stakeholders in the mining cycle were conducted to answer the research questions. The concept of value, ubiquitously present in the business research literature, was used to analyze the benefits that the use of UAVs can bring to the raw materials industry and the efforts to reduce its environmental footprint. The opportunities for entrepreneurs to be the conduit to deploy such benefits in society were also analyzed.  The work ends with a summary of the qualitative research findings, highlighting how drones constitute an optimization tool that can be used in all the stages of the mining cycle. Additionally, it highlights that UAV gravity and electromagnetic methods, together with better data processing software for hyperspectral imaging, are currently some of the most sought out and/or needed solutions by users.

Soybean Cyst Nematode (SCN) poses a significant threat to soybean production in North America and the world at large. Early and accurate detection of SCN infestations is crucial for implementing effective management strategies and minimizing yield losses. The conventional method of SCN detection involves uprooting plants to examine the roots and collecting soil samples. Drone-based multispectral imaging has been used as a viable alternative for crop monitoring due to its detailed spatial and spectral information and scheduling flexibility. This thesis aims to examine the potential of using multispectral drone images for SCN detection in a soybean production field and develop a non-destructive approach to support improved precision agricultural management practices. Using the DJI Matrice 210 drone and a MicaSense Altum sensor, at a height of 50 meters above ground level and top speed of 6 meters/second, a total of 2,550 multispectral images per flight were collected for a total of fourteen flights beginning in June 2022 up to September 2022 from a production field with variable SCN infestation levels located in Carmi, IL. These images were postprocessed with geometric and radiometric correction to produce orthomosaic photos.   Ten vegetation indices namely, NDRE, NDVI, EVI, GNDVI, BNDVI, SIPI, R-EDGE/G, NIR/G, R-EDGE/R and MSR, were computed for each flight date and study plot. The count of SCN eggs was appended to each study plot to find the correlation between the vegetation indices and the field parameters. The VIs having the highest correlation with the eggs and also having the highest number of correlation coefficients significantly different from zero were NDRE, NDVI and GNDVI. I computed the mean values of these VIs for each study plot and flight date which resulted into a time-series trend analysis. To identify study plots with similar trends, an agglomerative hierarchical clustering was performed which resulted into two clusters for each VI. After conducting the ANOVA test, NDVI returned statistically significant results for all the field parameters, GNDVI returned one while NDRE returned three outcomes that were not statistically significant. The study plots belonging to Cluster 1 had a higher mean of SCN count while those in Cluster 2 portrayed little or no SCN. I found NDVI to be the optimal VI because the results from statistical tests and modeling techniques conducted were significant for all SCN parameters, such as cyst and egg count for the plots clustered based on the NDVI trend. Therefore, I used the plots clustered based on the NDVI trend to train and test six ML classification models (Support Vector Classifier, Naïve Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, MLP-Neural Network and Gradient Boost) such that when presented with information in a format like that used in training, it becomes possible to identify plots with high or no SCN. Gradient Boost, MLP-NN and LDA performed with 89%, 82% and 80% accuracy respectively.