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.

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