This work concentrates on the topic of remote sensing using a multispectral imag-ing system for water management and agriculture applications. The platform, which is alight-weight inexpensive runway-free unmanned aerial vehicle (UAV), namely, AggieAir, ispresented initially. A major portion of this work focuses on the development of a light-weight multispectral imager payload for the AggieAir platform, called GhostFoto. Theimager is band-recongurable, covering both visual red, green, and blue (RGB) and nearinfrared (NIR) spectrum, and interfaced with UAV on-board computer. The developmentof the image processing techniques, which are based on the collected multispectral aerialimages, is also presented in this work. One application is to perform fully autonomous rivertracking for applications such as river water management. Simulation based on aerial mul-tispectral images is done to demonstrate the feasibility of the developed algorithm. Othereort is made to create a systematic method to generate normalized difference vegetationindex (NDVI) using the airborne imagery. The GhostFoto multispectral imaging systembased on AggieAir architecture is proven to be an innovative and useful tool.

This thesis has been written at Linköping University for the company Instrument Control Sweden AB (ICS). ICS is a small company located in Linköping that develops software and hardware for Unmanned Aerial Vehicles, UAV. At present, ICS has a fully functional autopilot called EasyPilot but they want to reduce the autopilot’s size to make it more attractive. The purpose of this thesis was to investigate if it was possible to reduce the size of the autopilot and how, in that case, it would be done. It was also necessary to examine whether the old processors should be replaced by new ones and how hard it would be to convert the old software to these new processors. To succeed with the goals many of the old components had to be changed for new, smaller ones. Some less necessary parts were also completely removed. The results showed that the size could be reduced quite a bit, exactly how much is hard to say since no PCB-layout were done. By doing some programming tests on the new components it could be shown that some parts of the old code could be reused on the new design. It was mainly algorithms and other calculations. However, a lot of new code still had to be written in order to successfully convert the old software to the new hardware.

This thesis provides an assessment of the effectiveness of micro unmanned aerial vehicles (MUAVs) as a tool for collecting condition data for transportation infrastructure based on multiple field experiments. The primary experiment entails performing a level of service (LOS) condition assessment on multiple roadside sample units at various locations across the state of Texas. A secondary field experiment entails performing a pavement condition index (PCI) survey on airfield pavements. The condition of these sample units were assessed twice: onsite (i.e., ground truth) and by observing digital images (still and video) collected via a MUAV. The results of these surveys are then analyzed to determine if there are statistically significant differences in the standard deviation and mean values of the condition ratings. This study shows that in favorable site conditions, the MUAV demonstrates promise for improving current roadway inspection methods. However, limitations of the MUAVs field performance show that there is need for improvement in this technology before it can be implemented.

The ANGEL project (Aerial Network Guided Electronic Lookout) takes a systems engineering approach to the design, development, testing and implementation of a quadrotor unmanned aerial vehicle. Many current research endeavors into the field of quadrotors for use as unmanned vehicles do not utilize the broad systems approach to design and implementation. These other projects use pre-fabricated quadrotor platforms and a series of external sensors in a mock environment that is unfeasible for real world use. The ANGEL system was designed specifically for use in a combat theater where robustness and ease of control are paramount. A complete simulation model of the ANGEL system dynamics was developed and used to tune a custom controller in MATLAB and Simulink®. This controller was then implemented in hardware and paired with the necessary subsystems to complete the ANGEL platform. Preliminary tests show successful operation of the craft, although more development is required before it is deployed in field. A custom high-level controller for the craft was written with the intention that troops should be able to send commands to the platform without having a dedicated pilot. A second craft that exhibits detachable limbs for greatly enhanced transportation efficiency is also in development.

Ke studiu migrace hmyzu a jiných členovců unášených větrnými proudy je výhodné moci sbírat jejich vzorky za letu. Použití bezpilotního letadla s pastí by mohlo být dobrou cestou, jak toho dosáhnout. Tato metoda byla zkoumána a bylo navrženo bezpilotní letadlo v podobě bezocasého dvojploŠníku s pastí umístěnou mezi křídly.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / This article presents the design of an unmanned aerial vehicle manufactured in aramid, through the use of sensors and actuators for flight stabilization, capturing the images through a thermal imager and its wireless transmission for ground processing for application in the social security area used in fire accidents. The work shows that it is feasible to use the aramid material for the construction of the prototype, since it is a high temperature resistant material, also the integration of neural networks for semi-automatic flight control. The results of this research will serve to develop more advanced control devices, with simple components and controls so that people with technological limitations can use it, so that they can save lives in danger, that of their colleagues or themselves. / Revisión por pares

Aircraft modeling and simulation have become increasingly important in the areas of pilot training, safety and aircraft design, especially for unmanned aerial vehicles (UAVs). A userriendly, easily expandable, nonlinear six degree-ofreedom aircraft simulator for the Xipiter X-2C Xawk UAV was created to address these issues. The simulator will allow pilots to have an opportunity to train and gain experience in flying the aircraft even before it leaves the ground. In addition, it will allow for design modifications or new aircraft designs to be evaluated before time and money are spent on their implementation. This work can also serve as the basis for the development of control systems for the aircraft, such as a control augmentation system or autopilot.

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Unmanned aerial vehicles (UAVs) attract increasing attention for various wireless network applications by using UAVs' reliable line-of-sight (LoS) paths in air-ground connections and their flexible placement and movement. As such, the wireless network architecture is becoming three-dimensional (3D), incorporating terrestrial and aerial network nodes, which is more dynamic than the traditional terrestrial communications network. Despite the UAVs' advantages of high LoS path probability and flexible mobility, the challenges of UAV communications need to be considered in the design of integrated air-ground networks, such as spectrum sharing, air-ground interference management, energy-efficient and cost-effective UAV-assisted communications. On the other hand, in wireless networks, users request not only reliable communication services but also execute computation-intensive and latency-sensitive tasks. As one of the enabling technologies in wireless networks, edge computing is proposed to offload users' computation tasks to edge servers to reduce users' latency and energy consumption. However, this requires efficient utilization of both communication resources and computation resources. Furthermore, integrating UAVs into edge computing networks brings many benefits, such as enhancing offloading ability and extending offloading coverage region. This dissertation makes a series of fundamental contributions to UAVs and edge computing in wireless networks that include: 1) Reliable UAV communications, 2) Efficient edge computing schemes, and 3) Integration of UAV and edge computing. In the first contribution, we investigate UAV spectrum access and UAV swarm-enabled aerial reconfigurable intelligent surface (SARIS) for achieving reliable UAV communications. On the one hand, we study a 3D spectrum sharing between device-to-device (D2D) and UAVs communications. Specifically, UAVs perform spatial spectrum sensing to opportunistically access the licensed channels occupied by the D2D communications of ground users. The results show that UAVs' optimal spatial spectrum sensing radius can be obtained given specific network parameters. On the other hand, we study the beamforming and placement design for SARIS networks in downlink transmissions. We consider that the direct links between the ground base station (BS) and mobile users are blocked due to obstacles in the urban environment. SARIS assists the BS in reflecting the signals to randomly distributed mobile users. The results show that the proposed SARIS network significantly improves the weighted sum-rate for ground users, and the placement design plays an essential role in the overall system performance. In the second contribution, we develop a joint communication and computation resource allocation scheme for vehicular edge computing (VEC) systems. The full channel state information (CSI) in VEC systems is not always available at roadside units (RSUs). The channel varies fast due to vehicles' mobility, and it is pretty challenging to estimate CSI and feed back the RSUs for processing the VEC algorithms. To address the above problem, we introduce a large-scale CSI-based partial computation offloading scheme for VEC systems. Using deep learning and optimization tools, we minimize the users' energy consumption while guaranteeing their offloading latency and outage constraints. The results demonstrate that the introduced resource allocation scheme can significantly reduce the total energy consumption of users compared with other computation offloading schemes. In the third contribution, we present novel frameworks for integrating UAVs to edge computing networks to achieve improved computing performance. We study mobile edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and user equipment (UE), where UAVs and GCAPs cooperatively provide computation resources for UEs. The resource allocation algorithm is developed based on the block coordinate descent method by optimizing the subproblems of users' association, power control, bandwidth allocation, computation capacity allocation, and UAV placement. The results show the advantages of the introduced iterative algorithm regarding the reduced total energy consumption of UEs. Finally, we highlight directions for future works to advance the research presented in this dissertation and discuss its broader impact across the wireless networks industry and standard-making. / Doctor of Philosophy / The fifth-generation (5G) cellular network aims to achieve a high data rate by having greater bandwidth, deploying denser networks, and multiplying the antenna links' capacity. However, the current wireless cellular networks are fixed on the ground and thus pose many disadvantages. Moreover, the improved system performance comes at the cost of increased capital expenditures and operating expenses in wireless networks due to the enormous energy consumption at base stations (BS) and user equipment (UE). More spectrum and energy-efficient yet cost-effective technologies need to be developed in next-generation wireless networks, i.e., beyond-5G or sixth-generation (6G) networks. Recently, unmanned aerial vehicle (UAV) has attracted significant attention in wireless communications. Due to UAVs' agility and mobility, UAVs can be quickly deployed to support reliable communications, resorting to its line-of-sight-dominated connections in the air-ground channels. However, the sufficient available spectrum for extensive UAV communications is scarce, and the co-channel interference in air-air and air-ground connections need to be considered in the design of UAV networks. In addition to users' communication requests, users also need to execute intensive computation tasks with specific latency requirements. As such, edge computing has been proposed to integrate wireless communications and computing by offloading users' computation tasks to edge servers in proximity, reducing users' computation energy consumption and latency. Besides, integrating UAVs into edge computing networks makes efficient offloading schemes by leveraging the advantages of UAV communications. This dissertation makes several contributions that enhance UAV communications and edge computing systems performance, respectively, and present novel frameworks for UAV-assisted three-dimensional (3D) edge computing systems. This dissertation addresses the fundamental challenges in UAV communications, including spectrum sharing, interference management, UAV 3D placement, and beamforming, allowing broadband, wide-scale, cost-effective, and reliable wireless connectivity. Furthermore, this dissertation focuses on the energy-efficient vehicular edge computing systems and mobile edge computing systems, where the UAVs are applied to achieve 3D edge computing systems. To this end, various mathematical frameworks and efficient joint communication and computation resource allocation algorithms are proposed to design, analyze, optimize, and deploy UAV and edge computing systems. The results show that the proposed air-ground integrated networks can deliver spectrum-and-energy-efficient yet cost-effective wireless services, thus providing ubiquitous wireless connectivity and green computation offloading in the future beyond-5G or 6G wireless networks.

The task of railroad inspection is a tedious one. It requires a lot of skilled experts and long hours of frequent on-field inspection. Automated ground equipment systems that have been developed to address this problem have the drawback of blocking the rail service during inspection process. As an alternative, using aerial imagery from a UAV, Computer Vision and Machine Learning based techniques were developed in this thesis to analyze two kinds of defects on the rail tracks. The defects targeted were missing spikes on tie plates and cracks on ties. In order to perform this inspection, the rail region was identified in the image and then the tie plate and tie regions on the track were detected. These steps were performed using morphological operations, filtering and intensity analysis. Once the tie plate was localized, the regions of interest on the plate were used to train a machine learning model to recognize missing spikes. Classification using SVM resulted in an accuracy of around 96% and varied greatly based on the tie plate illumination and ROI alignment for Lampasas and Chickasha subdivision datasets. Also, many other different classifiers were used for training and testing and an ensemble method with majority vote scheme was also explored for classification. The second category of learning model used was a multi-layered neural network. The major drawback of this method was, it required a lot of images for training. However, it performed better than feature based classifiers with availability of larger training dataset. As a second kind of defect, tie conditions were analyzed. From the localized tie region, the tie cracks were detected using thresholding and morphological operations. A machine learning classifier was developed to predict the condition of a tie based on training examples of images with extracted features. The multi-class classification accuracy obtained was around 83% and there were no misclassifications seen between two extreme classes of tie condition on the test data. / Master of Science