This thesis deals with the application of unmanned aerial vehicles (UAV) in photogrammetric purposes useful in geodesy. The UAV that was used for imaging is described in the first part, also there are introduced two programs which were used for data processing. A system of mission planning and data collecting is shown on the ortofoto creating as an example. The second part of this thesis deals with testing of the accuracy through terrestrially independently measured points. The main aim of the work is to demonstrate the high potential of UAV devices for geodesy purposes.

The future of aviation technology is transitioning to cleaner, more efficient and higher endurance aircraft solutions. As fully electric propulsion systems still fall short of the operational requirements of modern day aircraft, there is increasing pressure and demand for the aviation industry to explore alternatives to fossil fuel driven propulsion systems. The primary focus of this research is to experimentally evaluate hybrid electric propulsion systems (HEPS) for Unmanned Aerial Vehicles (UAV) which combine multiple power sources to improve performance. HEPS offer several potential benefits over more conventional propulsion systems such as a smaller environmental impact, lower fuel consumption, higher endurance and novel configurations through distributed propulsion. Advanced operating modes are also possible with HEPS, increasing the vehicle’s versatility and redundancy in case of power source failure. The primary objective of the research is to combine all of the components of a small-scale HEPS together in a modular test bench for evaluation. The test bench uses components sized for a small-scale UAV including a 2.34kW two-stroke 35cc engine and a 1.65kW brushless DC motor together with an ESC capable of regenerative braking. Individual components were first tested to characterize performance, and then all components were assembled together in a parallel configuration to observe system-level performance. The parallel HEPS is capable of functioning in the four required operating modes: EM Only, ICE Only, Dash Mode (combined EM and ICE power) as well as Regenerative Mode where the onboard batteries get recharged. Further, the test bench was implemented with a supervisory controller to optimize system performance and run each component in the most efficient region to achieve torque requirements programmed into mission profiles. The logic based controller operates with the ideal operating line (IOL) concept and is implemented with a custom LabView GUI. The system is able to run on electric power or ICE power interchangeably without making any modifications to the transmission as the one-way bearing assembly engages for whichever power source is rotating at the highest speed. The most impressive of these sets of tests is the Dash mode testing where the output torque of the propeller is supplied from both the EM and ICE. Working in tandem, it was proved that the EM was drawing 19.9A of current which corresponds to an estimated 0.57Nm additional torque to the propeller for a degree of hybridization of 49.91%. Finally, the regenerative braking mode was proven to be operational, capable of recharging the battery systems at 13A. All of these operating modes attest to the flexibility and convenience of having a hybrid-electric propulsion system. The results collected from the test bench were validated against the models created in the aircraft simulation framework. This framework was created in MATLAB to simulate the performance of a small UAV and compare the performance by swapping in various propulsion systems. The purpose of the framework is to make direct comparisons of HEPS performance for parallel and series architectures against conventional electric and gasoline configuration UAVs, and explore the trade-offs. Each aircraft variable in the framework was modelled parametrically so that parameter sweeps could be run to observe the impact on the aircraft’s performance. Finally, rather than comparing propulsion systems in steady-state, complex mission profiles were created that simulate real life applications for UAVs. With these experiments, it was possible to observe which propulsion configurations were best suited for each mission type, and provide engineers with information about the trade-offs or advantages of integrating hybrid-electric propulsion into UAV design. In the Pipeline Inspection mission, the exact payload capacities of each aircraft configuration could be observed in the fuel burn versus CL,cruise parameter sweep exercise. It was observed that the parallel HEPS configuration has an average of 3.52kg lower payload capacity for the 35kg aircraft (17.6%), but has a fuel consumption reduction of up to 26.1% compared to the gasoline aircraft configuration. In the LIDAR Data collection mission, the electric configuration could be suitable for collection ranges below 100km but suffers low LIDAR collection times. However, at 100km LIDAR collection range, the series HEPS has an endurance of 16hr and the parallel configuration has an endurance of 19hr. In the Interceptor mission, at 32kg TOW, the parallel HEPS configuration has an endurance/TOW of 1.3[hr/kg] compared to 1.15[hr/kg] for the gasoline aircraft. This result yields a 13% increase in endurance from 36.8hr for gasoline to 41.6hr for the parallel HEPS. Finally, in the Communications Relay mission, the gasoline configuration is recommended for all TOW above 28kg as it has the highest loiter endurance. / Graduate

Fusarium is one of the most important genera of fungi on earth. Many species of Fusarium are well-suited for atmospheric dispersal, yet little is known about their aerobiology. Previous research has shown that large-scale features known as atmospheric transport barriers (Lagrangian coherent structures) guide the transport and mixing of atmospheric populations of Fusarium. The overall goal of this work is to expand our knowledge on the movement and structure of atmospheric populations of Fusarium. The first objective was to monitor changes in colony forming units (CFUs) in atmospheric populations of Fusarium over small time intervals (10 min to several hours). We hypothesized that consecutive collections of Fusarium with unmanned aerial vehicles (UAVs) demonstrate small variations in colony counts. To test this hypothesis, sampling devices on UAVs were separated into two groups, four inner sampling devices opened during the first 10 minutes and four outer sampling devices opened during the second 10 minutes. Results indicated that (1) consecutive collections of Fusarium at 100 m demonstrated small variations in counts and (2) the similarity between collections decreased as the time between sampling intervals increased. The second objective was to determine the structure of atmospheric populations of Fusarium species and relate this to potential source regions. We hypothesized that diverse atmospheric populations of Fusarium are associated with multiple source regions. To test this hypothesis, Fusarium samples were collected with UAVs and identified to the level of species by sequencing a portion of the translation elongation factor 1-alpha gene (TEF-1•). Potential source regions were identified using the atmospheric transport model HYSPLIT. Results indicated that (1) diverse atmospheric populations of Fusarium appeared to be associated with multiple source regions, and (2) the number of Fusarium species collected with UAVs increased with back-trajectory distance of the sampled air. The third objective was to examine the associations between concentrations of populations of Fusarium at ground level (1 m) and in the lower atmosphere (100 m). We hypothesized that concentrations of Fusarium in the atmosphere vary between 1m and 100m. To test this hypothesis, Fusarium was collected with a Burkard volumetric sampler (BVS) and UAVs. Colony counts were converted to spore concentrations (spores per cubic meter of air). Sampling efficiency was used to correct spore concentrations. Results indicated that (1) the distribution of spore concentrations was similar for both samplers over different times of the day, (2) spore concentrations were generally higher in the fall, spring, and summer, and lower in the winter, and (3) spore concentrations were generally higher with BVS samplers than those with UAVs for both hourly and seasonal data. The fourth objective was to assess the ability of strains of Fusarium collected in the lower atmosphere to cause plant disease. We hypothesized that certain isolates of Fusarium collected with UAVs cause plant diseases. To test this hypothesis, we randomly selected isolates of three different species (F. circinatum, F. avenaceum, and F. sporotrichioides) of Fusarium collected with UAVs to inoculate three different hosts (wheat, corn, and pine). Known Fusarium strains were obtained from J. Leslie at Kansas State University as controls. Results indicated showed that the three different isolates tested were able to cause plant diseases in three different hosts (wheat, corn, and pine), confirming that these were potential agents of disease. This work sets the stage for future work examining potential source regions, transport distances, and seasonal patterns of Fusarium. An increased understanding of the dynamics and population structure of plant pathogenic Fusarium in the lower atmosphere is essential for predicting the spread of plant disease and optimizing disease management strategies in the future. / Ph. D.

In this degree project in aerospace engineering, a preliminary design of a UAV (Unmanned Aerial Vehicle) was performed. The UAV was intended to be used as a complement to the Swedish maritime administration’s helicopters, which cannot operate under limited visibility conditions. Its main mission would consist of surveillance. The UAV was therefore designed for some series criteria that were based on the customers’ requirements. The primary literature that was used was John D. Andersons Aircraft performance and design. Otherwise, historical statistical data from other aircraft was used and numerous assumptions were made. The result was a relatively small UAV named The Condor, weighing 25.6 kg with a wingspan of 2.5 m and operational in an altitude of 3500 m with a cruise speed of 81 knots. The UAV’s range is 70 nautical miles and is also able to operate in up to six hours. It should be able to manage a 300 m long runway. The chosen wing profile was the NACA 1412 with a maximal thickness and camber of 12 % and 1 % of the chord length, respectively. As for the stabilizer, the symmetric wing profile NACA 0012 was chosen. A so called constraint analysis was performed in order to determine the engine choice and thewing loading. The chosen engine was a 3.1 horsepower piston engine provided by Ricardo. The dimensions of the fuselage were designed only to fit the payload and no detailed analysis was done. It became 2.3 m long and with a maximal diameter of 0.3 m. / I det här kandidatexamensarbetet i flygteknik gjordes en preliminär design av en drönare. Drönaren skulle användas som komplettering till sjöfartsverkets helikoptrar, som inte kan användas vid mycket begränsad sikt. Dess huvudsakliga uppdrag skulle bestå av övervakning. Drönarens utformades därför efter en rad kriterier som baserades på uppdragsgivarens krav. Den huvudsakliga litteraturen som användes var John D. Andersons Aircraft performanceand design. I övrigt användes historisk statistisk data från andra flygplan och ett flertal antaganden gjordes. Resultatet blev en relativt liten drönare som döptes till The Condor och fick en vikt på 25.6 kg, med ett vingspann på 2.5 m och som opererar på 3500 m flyghöjd med en marschfart på 81 knop. Drönarens räckvidd är 70 nautiska mil och den kan därutöver operera i upp till sex timmar. Den bör klara av en landningsbana på 300 m. Som vingprofil valdes NACA 1412 med en maximal tjocklek och camber på 12 % respektive 1 % av kordalängden. För stabilisatorn valdes den symmetriska profilen NACA 0012. En så kallad ”constraint analysis” genomfördes för fastställande av motorval och vingbelastning. Motorn som valdes blev en 3.1 hästkrafters pistongmotor från Ricardo. Flygplanskroppens dimensioner utformades endast för att få plats med nyttolasten och ingen noggrannare analys genomfördes. Den blev 2.3 m lång med en maximal diameter på0.3 m.

A system was developed to perform real-time biological threat agent (BTA) detection with a small autonomous unmanned aerial vehicle (UAV). Biological sensors just recently reached a level of miniaturization and sensitivity that made UAV integration a feasible task. A Surface Plasmon Resonance (SPR) biosensor was integrated for the first time into a small UAV platform, allowing the UAV platform to collect and then quantify the concentration of an aerosolized biological agent in real-time. A sensor operator ran the SPR unit through a groundstation laptop and was able to wirelessly view detection results in real time. An aerial sampling mechanism was also developed for use with the SPR sensor. The collection system utilized a custom impinger setup to collect and concentrate aerosolized particles. The particles were then relocated and pressurized for use with the SPR sensor. The sampling system was tested by flying the UAV through a ground based plume of water soluble dye. During a second flight test utilizing the onboard SPR sensor, a sucrose solution was autonomously aerosolized, collected, and then detected by the combined sampling and SPR sensor subsystems, validating the system\'s functionality. The real-time BTA detection system has paved the way for future work quantifying biological agents in the atmosphere and performing source localization procedures. / Master of Science

Wireless sensor networks (WSNs) have been a topic of research for decades. Researchers have been exploring different uses for UAVs with their growing popularity. In this thesis I develop a wireless sensor network (WSN) and introduce the theoretical effects of an unmanned aerial vehicle (UAV) for wireless recharging of individual nodes in the WSN. My research focuses on understanding how to use wireless recharging technology to maximize the lifetime of a WSN by simulating recharging on the physical nodes. Using a three by three grid of nine sensor nodes, I proved that recharging the lowest powered node in the network at each sink iteration increased the lifetime of the WSN by 538% when compared to no recharging. I also further investigate the potential uses of a WSN and UAV for detecting and deterring animals. Using wireless sensor nodes to initially detect movement, and the UAV to find the object proved to be a viable solution for offloading the more power intensive tasks from the WSN to the UAV.

Ever since Marconi succeeded in his first demonstration on the possibility to communicate over the air overseas about a century ago, wireless communications have experienced dramatic improvements. Today's world sees the penetration of wireless communications into human life almost everywhere, from a simple remote control for TV to a cellular phone. With a better understanding of the adverse nature of the wireless propagation channels, engineers have been able to invent various clever techniques, i.e. Multiple Input Multiple Output (MIMO) technology, spread spectrum communications, Orthogonal Frequency Division Multiplexing (OFDM) to name a few, to achieve fast and reliable communications over each point-to-point link. Communications between multiple parties create networks. Limited Radio Frequency (RF) resources, e.g. transmit power, channel bandwidth, signaling time slots, etc., call for an optimal distribution of these resources among the users in the network. In this dissertation, two types of communication networks are of particular interest: cellular networks and mobile-relay-aided networks. For a symmetric cellular network, where a fixed communication infrastructure is assumed and each user has similar average Signal-to-Noise Ratio (SNR), we study the performance of a Maximum SNR (Max-SNR) scheduler, which schedules the strongest user for service, with the effects of channel estimation error, the Modulation and Coding Scheme (MCS), channel feedback delay, and Doppler shift all taken into account. The degradation of the throughput of a Max-SNR scheduler due to outdated channel knowledge for a system with large Doppler shift and asymmetric users is analyzed and mathematical derivations of the capacity of the system based upon an Auto-Regressive (AR) channel model are presented in the dissertation as well. Unlike the schedulers proposed in the literature, which instantaneously keep track of the strongest user, an optimal scheduler that operates on the properties of Doppler and the average SNR of each user is proposed. The high flexibility and easy deployment characteristics that Unmanned Aerial Vehicles (UAVs) possess endow them with the possibility to act as mobile relays to create secure and reliable communication links in severe environments. Unlike cellular communications, where the base stations are stationary, the mobility in a UAV-assisted network can be exploited to improve the quality of the communications. Herein, the deployment and optimal motion control problem for a mobile-relay-aided network is considered. A network protocol which achieves optimal throughput and maintains a certain Quality of Service (QoS) requirement is proposed from a cross-layer perspective. The handoff problem of the Access Point (AP) between various relays is studied and the effect of the mobility on the handoff algorithm is addressed.

Under ever intensifying pressures from land use, climate change, and erosion, tropical islands are among the most vulnerable systems in the world. Terrestrial systems are weakened by intensifying land use patterns, the weakening of which is highlighted when high intensity rainfall events erode sediment and leads to sediment deposition on the marine system. The deposition of sediment on the marine system is a major stressor that can lead to weakened coral reefs and a decrease in marine resources commonly gathered for food. These interactions have led to the emergence of biocultural resource management strategies, one of which is the ahupua'a system. The ahupua'a system, at some scales, is an example of a resilient resource management strategy that has held up despite the pressures and challenges of living on a tropical island. Here we utilize a combination of unmanned aerial vehicles (UAVs or drones) and autonomous surface vehicles (ASV) to gather imagery that is then used in geospatial analyses to better understand the ahupua'a of Ka'amola as well as evaluate coral reef structure along the south shore of Molokai. Our terrestrial work using UAVs and geospatial analyses supports qualitative data from community members and local land managers regarding sediment movement trends they have noticed. Steep slopes coupled with a weakened landscape and decreasing vegetative cover due to ungulate grazing has primed the area for erosion during high intensity rainfall events. Our marine work matches trends observed in previous studies and highlights the value in utilizing an ASV to perform marine remote sensing while also acknowledging the limitations associated with a system such as the one built for our research work.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have become increasingly prevalent in various applications. However, the localization and payload detection of drones is crucial for ensuring safety and security. This thesis proposes a vision-based solution using deep learning techniques to address these challenges. Existing solutions like radars and acoustic sensors have limitations, including high costs, limited accuracy, and the need for prior knowledge of the drone's model. Normal radars lack angle estimation accuracy and rely on known micro-Doppler features for payload detection, while acoustic sensors are restricted to close ranges for payload analysis. In contrast, cameras offer a cost-effective alternative as they have become widely available and can capture visual data. In addition, due to resource constraints, mounting multiple sensors on the UAV along with the camera is impractical, making reliance on cameras alone essential for addressing the mentioned problems. Recent advancements in deep learning algorithms enable regression and classification tasks, making vision data a promising choice for solving drone localization and payload detection problems. The proposed solution leverages convolutional neural networks (CNNs) for regression tasks, estimating the distance of a drone from the captured image. The CNN takes a cropped image within the drone's bounding box as input and outputs the estimated distance. Additionally, the drone's azimuth and elevation angles have been estimated based on its position in the captured image using a simple pinhole model for the camera. Also, the ResNet and EfficientNet classifiers could accurately classify drones as loaded or unloaded, even without prior knowledge of their shape. Due to a scarcity of publicly available datasets, this study developed the first air-to-air simulated dataset specifically for the classification of loaded versus unloaded drones. To evaluate the performance of the proposed solution, both simulated and experimental tests were conducted. The results showcased promising accuracy, with a root mean square error (RMSE) of less than 10 meters for distance estimation and an RMSE of less than 3 degrees for angle estimation. Furthermore, the payload detection problem was effectively addressed, achieving a classification accuracy of over 85\% for distinguishing between loaded and unloaded drones using the trained network based on the simulated dataset. The numerical highlights demonstrate the effectiveness of using camera sensors for 3D localization, with accurate distance and angle estimations. The high accuracy achieved in payload classification showcases the potential of the proposed solution for detecting drone payloads at distances up to 100 meters. These results pave the way for enhanced safety and security in drone environments.

The Sensor Analysis and Intelligence Laboratory (SAIL) at Mississippi State University's (MSU's) Center for Advanced Vehicular Systems (CAVS) incorporated sensors with unmanned aerial vehicles (UAVs). Mounting plates were created to secure the sensors to the UAVs for data collection. This study’s purpose was to detail the process that went in to creating two different versions of the mount plates. One version of the mounting system was cut from G10 fiberglass sheets, and the other version was made from 3D printing with polylactic acid (PLA). Characteristics such as cost, time, and simplicity of the manufacturing methods are compared in this study. Plate performance characteristics such as compatibility, weight, and success/failure are also discussed. Detailing the advantages and limitations of either approach will aid future researchers’ decision-making process for their own studies. They can use this study as a foundational framework for deciding which mount would best fit with their system requirements.