Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Mrinal Kumar (Committee Member); Bilin Aksun-Guvenc (Committee Member) Subjects: Automotive Engineering; Computer Science; Design; Energy; Engineering; Experiments; Mechanical Engineering; Systems Design; Technology; Transportation

Master of Science, Miami University, 2024, Electrical and Computer Engineering

In this thesis, a thorough investigation into the optimization of user connectivity in ad hoc communication networks using robust policy creation and intelligent UAV location in stochastic environments is presented. In order to handle the dynamic and decentralized character of ad hoc networks, we identified the optimal UAV positions by applying a multi-agent deep Q-learning technique. To train stochastic environment-adaptive policies, a novel simple algorithm was devised with an emphasis on the usefulness of these policies under different scenarios. Through an empirical investigation, the study offered information on the generalizability and adaptability of learnt behaviors by examining how well policies based on one distribution of settings performed when applied to different, unseen distributions. In this thesis, we also explored the resilience of UAV networks against jamming attempts and propose a method for unaffected UAVs to self-adjust their placements. This approach ensured optimal user coverage even in adversarial situations. By demonstrating the potential of machine learning techniques to maximize network performance and enhance user connectivity in the face of environmental uncertainties and security risks, these contributions will collectively advance the field of UAV-assisted communication.

Committee: Dr. Bryan Van Scoy (Advisor); Dr. Mark Scott (Committee Member); Dr. Veena Chidurala (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Science, Miami University, 2024, Electrical and Computer Engineering

In wireless communications, traditional base stations provide network connectivity to users. Static base stations, however, require significant time and money to construct and are therefore not suitable for remote areas and disaster scenarios. An alternative uses mobile base stations attached to UAVs. Such UAV-based communication networks can be rapidly deployed and adapt to their environment. The goal of this research is to position the UAVs to maximize user coverage. One approach treats UAVs as independent agents and uses multi-agent reinforcement learning to design policies that move the UAVs to positions that increase coverage; each UAV, however, must train its own policy and optimality is not guaranteed. Instead, we consider two centralized approaches to place the UAVs. The first uses centralized reinforcement learning to design a joint policy over all UAVs, but training the policy is not computationally tractable for large problems. The second approach uses mixed-integer optimization to find the UAV positions that maximize user coverage. While this yields the optimal solution, the computational time does not scale well with the problem size. Therefore, we first group users into clusters and then optimize UAV positions with respect to the clusters. The number of clusters trades off computational time with optimality.

Committee: Bryan Van Scoy (Advisor); Gokhan Sahin (Committee Member); Veena Chidurala (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering

Master of Science, The Ohio State University, 2023, Aero/Astro Engineering

The Parametric Flutter Margin (PFM) method is utilized to identify the aeroelastic characteristics of an Unmanned Aerial Vehicle's wing in a series of Ground Vibration Tests (GVTs) and initial flight-testing study. A self-contained electromechanical excitation system developed to excite and measure the resultant wing dynamics such that Frequency Response Functions (FRF) can be identified from recorded inertial data is detailed. The results of bench-top testing performed to characterize the purpose-built excitation pods is discussed. Specifically, ground-testing indicates that the excitation pod's instrumentation could accurately measure a perturbation force generated by a moving mass within 3% error of directly measured values. Further testing reveals that the excitation pod's instrumentation could measure the resultant wing acceleration response within 1 ms-2 error. A series of GVTs performed on various wing configurations demonstrate that the self-contained excitation pods generate a sufficiently powerful perturbation force to energize an elastic response. The structural modes experimentally identified through the excitation system and PFM estimation method closely match corresponding Finite Element Analysis (FEA) predictions. A flight-testing campaign was conducted to demonstrate the excitation system and PFM method in a free-flight environment. The results of this preliminary flight-test study indicate that the frequency characteristics associated with the flexible wing's symmetric and anti-symmetric modes can be identified. Initial flight-testing results reveal the importance of properly defining the excitation signals such that the symmetric and anti-symmetric forcing functions are of comparable magnitude. After identifying the phase-crossover frequencies from FRFs measured through flight-testing, a flutter margin versus airspeed curve is developed for both the wing's symmetric and anti-symmetric modes. From these curves, an anti-symmetric flutter mechanism wi (open full item for complete abstract)

Committee: Matthew McCrink (Advisor); Moti Karpel (Committee Member); Jack McNamara (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2023, Environmental Science

As increases in frequency, duration, intensity, and geographical location of cyanobacterial harmful algal blooms (HABs) have been observed, more timely monitoring and targeted treatment of HABs and their cyanotoxins are crucial for freshwater bodies that are used for drinking water, recreation, and food production sources. To combat this, new management practices with tools that can handle the spatial and temporal variability of HABs are needed for water treatment plants and other sectors to ensure human health and ecosystem health. Unmanned Aerial Vehicles (UAVs), also known as drones, serve as one solution for near real-time monitoring of HABs. Recently, UAVs have gained increasing interest in research and development due to their many applications, efficiency in data collection, and the ability to customize these systems to specific needs. While research has shown that UAVs can accurately estimate chl-a and phycocyanin values -HAB indicators- little research has been conducted analyzing UAV imagery in parallel with microbiome data. In chapter 1, I summarize major relevant topics are summarized related to cyanoHABs, public health, and UAV systems. This provides a holistic view of current knowledge, methods, and limitations in cyanoHAB monitoring and detection. Chapter 2 seeks to explore the microbial community in parallel with environmental data, by analyzing seasonal dynamics, composition, and interactions within the microbial community in a hypereutrophic urban lake. In chapter 3, the feasibility and accuracy of using an UAV system for monitoring a hypereutrophic, urban water body was assessed. Objectives of this chapter include 1) proposing an UAV system and imagery processing framework that can be utilized by non-geospatial experts, 2) assess the accuracy of UAV derived chlorophyll-a values by regressing ground sampled fluorometer values and remotely sensed values, and 3) determine what algorithms and buffer sizes perform the best for cyanobacteria quantific (open full item for complete abstract)

Committee: Jiyoung Lee (Advisor); Joseph Ortiz (Committee Member); Motomu Ibaraki (Committee Member); Rongjun Qin (Committee Member) Subjects: Environmental Science; Microbiology

Master of Science (MS), Wright State University, 2021, Computer Science

Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part of an effort to develop and test an on-board Sense and Avoid system for assisted and/or autonomous UAS operations. In particular, this work focuses on applying OpenCV and established computer vision algorithms to implement an object detection capability, which is a critical component of an Adaptive Two-Stage Edge-Centric Sense-and-Avoidance system. Additional efforts were made for integrating this capability into the overall system operations. Furthermore, two implements of the detection system are completed: one in C/C++ and the other in Python with an aim to compare their efficiency. It is found that both implements meet the real-time operation requirements, and experimental studies show little to no difference in processing time for object detection.

Committee: Yong Pei Ph.D. (Advisor); Mateen M. Rizki Ph.D. (Committee Member); Nicholas A. Speranza Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy (PhD), Wright State University, 2021, Computer Science and Engineering PhD

With the growing number of Unmanned Aircraft Systems, current network-centric architectures present limitations in meeting real-time and time-critical requirements. Current methods utilizing centralized off-platform processing have inherent energy inefficiencies, scalability challenges, performance concerns, and cyber vulnerabilities. In this dissertation, an adaptive, two-stage, energy-efficient, edge-centric architecture is proposed to address these limitations. A novel, edge-centric Sense-and-Avoidance architecture framework is presented, and a corresponding prototype is developed using commercial hardware to validate the proposed architecture. Instead of a network-centric approach, processing is distributed at the logical edge of the sensors, and organized as Detection and Classification Subsystems. Classical machine vision algorithms are used to detect and produce a region of interest. The region of interest is then segmented and fed to the Classification Subsystem to be classified using optimized neural networks. A compressed frame from the Detection Subsystem, along with the region of interest and classification results from the Classification Subsystem, can be sent to the Ground Control Station to produce an Artificial Intelligence enhanced view to increase operator comprehension. Experimentation and testing indicate this approach is feasible for real-time operations supporting throughput of at least three 4K frames per second. Additionally, on-platform detection and classification can occur without offloading large amounts of imagery to ground processing, thereby reducing unnecessary network transmissions and associated energy consumption. The sufficient processing frame rate effectively eliminates any hover during sensing processing, demonstrating how the architecture can reduce energy consumption for battery-powered electric unmanned aerial vehicles. This novel approach opens new opportunities to reduce power consumption in future electric transpo (open full item for complete abstract)

Committee: Yong Pei Ph.D. (Advisor); Jack S. Jean Ph.D. (Committee Member); Mateen M. Rizki Ph.D. (Committee Member); Ross McNutt Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

MS, Kent State University, 2019, College of Arts and Sciences / Department of Earth Sciences

This study was undertaken to compare discontinuity orientation data collected by unmanned aerial vehicle (UAV), terrestrial LiDAR (light detection and ranging), and transit compass methods at two study sites in the state of Virginia. These included a cut along state route 629 in Deerfield Township, Augusta County (Site 1) and an abandoned shale quarry at the foot of Cove Mountain in Wythe County, near Interstate 77 (Site 2). Transit compass measurements and UAV and LiDAR scans were taken at both field sites. Scans from both remote sensing methods were used to create 3D point cloud models using Pix4DMapper Pro software for UAV data and Cyclone software for LiDAR data. These 3D point clouds were imported into Split-FX to identify and extract discontinuity orientation data for use in Dips 7.0, RocPlane 3.0, and Swedge 6.0 computer programs. Orientation data from all three methods was used to generate stereonet plots, statistical plots and tables, and to perform kinematic analysis. In addition to structural data analysis, laboratory investigations were performed to determine engineering properties of the rock from each site. Statistical evaluation of the three data collection methods reveals that results from Site 1 UAV and LiDAR data do not match those of the transit compass data, and thus are not accurate. Site 2 results indicate that UAV data are more accurate than LiDAR data when compared to compass data. Compass data indicate the presence of four principal discontinuity sets (PDS) for Site 1 and three PDS for Site 2. In comparison, UAV data indicate three PDS for Site 1 and four PDS for Site 2; none of the Site 1 PDS centers match those of the compass data, but two of the Site 2 PDS centers do. LiDAR data indicate three PDS at Sites 1 and 2. The Site 1 PDS centers do not match those of the compass data, but one of the Site 2 PDS centers do. Results of kinematic analysis from compass data indicate that the main failure type present at both Sites 1 and 2 is plane (open full item for complete abstract)

Committee: Abdul Shakoor (Advisor); Neil Wells (Committee Member); Chester "Skip" Watts (Committee Member) Subjects: Engineering; Geological; Geology

MS, University of Cincinnati, 2019, Engineering and Applied Science: Aerospace Engineering

Autonomous vehicles have become a reality in many military and civilian applications. The ability to deploy them in constrained environments, such as regions with limited or no Global Positioning System (GPS) access, or no a-priori map information, can not only further their application space but also add capability to successfully complete tasks that are otherwise considered dull, dirty or dangerous for humans. This Thesis proposes and implements an autonomous navigation solution for an Unmanned Aerial Vehicle (UAV) using a robot-centered reference frame. An Extended Kalman Filter (EKF) is used to estimate the relative orientation of the UAV with respect to an user-defined goal and other objects of interest in the environment (called landmarks). A visual tracker continuously tracks these objects and based on the camera parameters, calculates their bearing measurement with respect to the UAV. This method uses a bearing-only measurement model to update the states of the system. The goal is selected real-time from the video feed provided by the UAV's onboard camera and the UAV has to navigate to it while avoiding obstacles along its path. A combined PN-guidance and obstacle avoidance controller is used for this purpose. A detailed 2D observability analysis is performed to find the sufficient conditions required for the system to be observable. The problem formulation is corroborated through extensive simulation and hardware results.

Committee: Rajnikant Sharma Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member) Subjects: Engineering

Master of Science, University of Toledo, 2018, Electrical Engineering

UAVs are used for many purposes including agriculture, industry, law enforcement, and defense. These autonomous systems have several advantages over manned aerial vehicles as not only they reduce expenses by avoiding human error, but they also save the lives of fighter jet pilots. Nowadays black-box machine learning algorithms are used to train unmanned vehicles to make decisions on their own. However, while these techniques give good predictive abilities, they fail to provide the reasoning behind decisions, thus rendering them untrustworthy. To address that concern, in this thesis, an intelligent rule based model that explains the logic behind the decisions of a UAV while it is on a predefined mission, has been developed. An effective XAI should be able to deliver explanation with high level of accuracy, handle uncertainty, and learn from experience. To address these points and provide meticulous explanation, this thesis utilizes a hybrid learning technique that combines explanation ability of Fuzzy logic which incorporates uncertainty with learning abilities of nature inspired Artificial Neural Networks. Before developing an explainable artificial intelligence (XAI), first model of UAV missions are created using Mamdani fuzzy inference system (FIS). Various patterns of paths for UAV mission are defined. On each path, weather conditions and enemies are placed at random locations. During a mission, UAV navigates through these predefined paths taking into consideration adverse weather patterns and its distance from a nearby enemy. UAV deviates from the predefined path and engages in attacking an enemy when the conditions demand. Data is gathered regarding the actual route the UAV took under those weather and enemy conditions and the actions it engaged in while traversing the planned route. The data gathered from UAV missions is used to create a reverse model. The model is Sugeno type fuzzy inference system based on subtractive clustering. It has seven inp (open full item for complete abstract)

Committee: Devinder Kaur (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ahmad Javaid (Committee Member); Richard Molyet (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering

Unmanned Aerial Vehicles (UAV) have continued to receive attention from corporations and governmental agencies due to their wide range of potential applications and hybrid nature. More Electric Aircraft (MEA) promise many benefits (e.g., reduced weight, decreased fuel consumption, and high reliability) and their development continues to be the trend. Hybrid UAVs are an ideal prototype to implement concepts of aircraft electrification due to their small size and the DC nature of their power systems. However, papers addressing the energy optimization UAV electric power systems fail to consider the importance of high accuracy and computational speed. This thesis proposes an energy optimization method to enhance the energy durability of a UAV through a novel approach integrating an optimization formulation and a detailed UAV simulation model, with physical circuitry characteristics. This approach allows for increased computation efficiency while still capturing physical system constraints experienced during real world flight, which are complex and highly nonlinear due to aerial, thermal, and electrical dynamics. Optimization formulations created within this work are based on dynamic programming and moving-horizon model predictive control (MPC). The efficacy of this method is proven on a realistic UAV system. Within the MPC formulation, various charge strategies are implemented and fuel consumption is calculated to provide insight into the trade-offs inherent within the UAV system, wherein battery discharging is required for high demand dash periods, but additional charge can only be supplied via increased output engine power. That is, minimal fuel consumption must be considered in light of the need for non-optimal output engine power to charge the battery such that a total mission can be completed. Algorithmic considerations regarding horizon size for MPC and algorithmic enhancements, considering random loads and renewable generation capacity on-board the UAV are pre (open full item for complete abstract)

Committee: Jiankang Wang (Advisor); Mahesh Illindala (Committee Member) Subjects: Electrical Engineering

MA, Kent State University, 2017, College of the Arts / School of Art

Specific to this discussion are the philosophical implications of surveillance, war and consumption; the decision making of the few adjusting the course of the many over time; and the endless possibilities of artificial autonomy that lie on the horizon. This art installation 'Rehabilitation Center' proposes an alternative reality within the context of the Anthropocene. One where there are no dark corners for closed-door decision making, where intrigue and play can heal the scars of deception and greed, where divisional rhetoric cannot withstand compassion and togetherness.

Committee: Andrew Kuebeck (Committee Chair) Subjects: Art Criticism

Master of Science (MS), Bowling Green State University, 2017, Geology

Suspended sediment in water bodies is a considerable environmental concern. Traditional sampling methods for suspended sediment are time-consuming as they involve vertical and spatial point-sampling. Remote sensing (RS) is an alternative to in-situ measurements and it is capable of monitoring suspended sediments in shallow waters spatially at large scales. Use of RS technology to map suspended sediment concentrations (SSC) depends on sensor type and its capability `to see through' the water column at given surface and water column conditions. This study examined the capabilities of RS technology to spatially quantify SSC at multi-depth intervals within the Maumee River, Ohio. Water samples were collected and analyzed for SSC in May, June, and October at depths of 0.5 ft., 2 ft., 3 ft., and 6 ft. Landsat 8, surface hyperspectral measurements (aggregated to simulate sensors), and MicaSense Sequoia camera onboard an unmanned aerial vehicle (UAV) were used. Single spectral bands, ratios, and multiple bands/ratios were examined in developing algorithms relating RS and field measurements. Linear regression models provided the best relationship for surface, Landsat 8, and UAV data throughout all depths. A 6 ft. depth had the highest correlation for surface (R2adj=0.93) and Landsat 8 (R2adj=0.79) data. For UAV a 3 ft. depth provided the best relationship (R2adj=0.52). Band ratios using nonlinear fitting provided good relationships (surface R2adj=0.72 and Landsat 8 R2adj=0.54) at 6 ft. as well. Results showed Landsat 8 more accurately measured suspended solids at 6 ft. than shallower depths. Regression equations and band ratios showed increasing relationships with SSC with increasing depth for Landsat 8 with an exception for 3 ft., which can occur due to stratification. UAV measurements produced best results for 3 ft. Algorithms with best results included ultra blue, blue, and green bands which are not typically used for quantifying SSC. Shorter wavelength bands (400 nm-5 (open full item for complete abstract)

Committee: Anita Simic (Advisor); Robert Vincent (Committee Member); James Evans (Committee Member) Subjects: Geological; Geology; Geotechnology; Hydrology; Remote Sensing; Sedimentary Geology

MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Engineering

The use of small and miniature Unmanned Aerial Vehicles (UAVs) for remote sensing and surveillance applications has become increasingly popular in the last two decades. Networks of UAVs, capable of providing flexible aerial views over large areas, are playing important roles in today's distributed sensing systems. Since camera sensors are sensitive to occlusions, it is more challenging to deploy UAVs for sensing in geometrically complex environments, such as dense urban areas and mountainous terrains. The intermittent connectivity in a sparse UAV network also makes it challenging to efficiently gather sensed multimedia data. This thesis is composed of two pieces of work. In the first piece of work, a new occlusion-aware UAV coverage technique with the objective of sensing a target area with satisfactory spatial resolution subject to the energy constraints of UAVs is proposed. An occlusion-aware waypoint generation algorithm is first designed to find the best set of waypoints for taking pictures in a target area. The selected waypoints are then assigned to multiple UAVs by solving a vehicle routing problem (VRP), which is formulated to minimize the maximum energy for the UAVs to travel through the waypoints. A genetic algorithm is designed to solve the VRP problem. Evaluation results show that the proposed coverage technique can reduce energy consumption while achieving better coverage than traditional coverage path planning techniques for UAVs. In the second piece of work, a communication scheme is designed to deliver the images sensed by a set of mobile survey UAVs to a static base station through the assistance of a relay UAV. Given the planned routes of the survey UAVs, a set of relay waypoints are found for the relay UAV to meet the survey UAVs and receive the sensed images. An Online Message Relaying technique (OMR) is proposed to schedule the relay UAV to collect images. Without any global collaboration between the relay UAV and the survey UAVs, OMR utilizes a (open full item for complete abstract)

Committee: Rui Dai Ph.D. (Committee Chair); Dharma Agrawal D.Sc. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Engineering

Doctor of Philosophy, The Ohio State University, 2016, Geography

The glaciers of the Cordillera Blanca Peru are rapidly retreating as a result of climate change, altering the timing, quantity and quality of water available to downstream users. Changes in water availability have serious implications for ecosystems, human livelihoods and regional economies. This dissertation investigates spatiotemporal changes in the glacier hydrologic system of the Cordillera Blanca Peru. It includes three major components. First, I develop multispectral unmanned aerial vehicles (UAV) and kite platforms capable of operating at over 5000m in mountain regions. Secondly, I deploy these platforms to investigate processes of glacier change and surface/subsurface hydrology within the glacial valleys of the Cordillera Blanca. Finally, I integrate UAV datasets with traditional field hydrology to improve our understanding of the spatiotemporal variability in soil moisture and its role in moderating groundwater storage within the Cordillera Blanca. I designed and deployed UAVs on multiple missions at over 5000masl in the Cordillera Blanca, Peru. After describing the UAV design in Chapter 2, this dissertation reports on results of four studies that utilise the UAV to address research questions within the region. Chapter 3 comprehensively assesses the accuracy of photogrammetrically derived structure from motion (SfM) digital elevation models (DEMs), by quantitatively and qualitatively comparing the data against surveyed GPS positions and LiDAR DEMs. Finding that accuracy is as good if not superior to low density LiDAR, with the high density SfM point clouds retaining unique surface details. Chapter 4 investigates the dynamics of glacier change over the debris covered Llaca glacier. I document the importance of debris cover and surface features such as ice cliffs in controlling melt rates. Average glacier downwasting is 0.75m over one year but is highly heterogeneous. Ice cliff horizontal recession rates of up to 25m annual were measured illustrating the i (open full item for complete abstract)

Committee: Bryan Mark PhD (Advisor); Darla Munroe PhD (Committee Member); Michael Durand PhD (Committee Member); Liu Desheng PhD (Committee Member) Subjects: Geography; Geomorphology; Hydrologic Sciences; Hydrology; Physical Geography; Remote Sensing; Robotics; Soil Sciences; Technology; Water Resource Management

Master of Science in Engineering (MSEgr), Wright State University, 2012, Electrical Engineering

Ground and marine based acoustic arrays are currently employed in a variety of military and civilian applications for the purpose of locating and identifying sources of interest. An airborne acoustic array could perform an identical role, while providing the ability to cover a larger area and pursue a target. In order to implement such a system, steps must be taken to attenuate environmental noise that interferes with the signal of interest. In this thesis, we discuss the noise sources present in an airborne environment, present currently available methods for mitigation of these sources, and propose the use of adaptive noise cancellation techniques for removal of unwanted wind and engine noise. The least mean squares, affine projection, and extended recursive least squares algorithms are tested on recordings made aboard an airplane in-flight, and the results are presented. The algorithms provide upwards of 37dB of noise cancellation, and are able to filter the noise from a chirp with a signal to noise ratio of -20db with minimal mean square error. The experiment demonstrates that adaptive noise cancellation techniques are an effective method of suppressing unwanted acoustic noise in an airborne environment, but due to the complexity of the environment more sophisticated algorithms may be warranted.

Committee: Brian Rigling PhD (Committee Chair); Kefu Xue PhD (Committee Member); Fred Garber PhD (Committee Member) Subjects: Acoustics; Aerospace Engineering; Applied Mathematics; Electrical Engineering; Engineering; Remote Sensing

Master of Science in Engineering (MSEgr), Wright State University, 2012, Mechanical Engineering

Today's unmanned aerial vehicles are being utilized by numerous groups around the world for various missions. Most of the smaller vehicles that have been developed use commercially-off-the-shelf parts, and little information about the performance characteristics of the propulsion systems is available in the archival literature. In light of this, the aim of the present research was to determine the performance of various small-scale propellers in the 4.0 to 6.0 inch diameter range driven by an electric motor. An experimental test stand was designed and constructed in which the propeller/electric motor was mounted in a wind tunnel for both static and dynamic testing. Both static and dynamic results from the present experiment were compared to those from previous studies. For static testing, the coefficient of thrust, the coefficient of propeller power, and the overall efficiency, defined as the ratio of the propeller output power to the electrical input power, were plotted versus the propeller rotational speed. For dynamic testing, the rotational speed of the propeller was held constant at regular intervals while the freestream airspeed was increased from zero to the windmill state. The coefficient of thrust, the coefficient of power, the propeller efficiency and the overall efficiency were plotted versus the advance ratio for various rotational speeds. The thrust and torque were found to increase with rotational speed, propeller pitch and diameter, and decrease with airspeed. Using the present data and data from the archival and non-archival sources, it was found that the coefficient of thrust increases with propeller diameter for square propellers where D = P. The coefficient of thrust for a family of propellers (same manufacturer and application) was found to have a good correlation from static conditions to the windmill state. While the propeller efficiency was well correlated for this family of propellers, the goodness of fit parameter was improved by modifying t (open full item for complete abstract)

Committee: Scott K. Thomas PhD (Committee Chair); Haibo Dong PhD (Committee Member); Zifeng Yang PhD (Committee Member); Mitch Wolff PhD (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Master of Science (MS), Wright State University, 2008, Human Factors and Industrial/Organizational Psychology MS

Predator Unmanned Aerial Vehicle assets are in high demand in the theater of operations for supporting the Global War on Terror and this demand is expected to increase. This work involved exploratory case study research into the envisioned world design problem of networked Predator multi-UAV control, as a candidate for meeting higher Predator sortie requirements without the need for a one for one increase in pilots. The concept involves the development of a potential new position for controlling multiple UAVs, called the Multi-Aircraft Manager (MAM). The goal was to analyze work requirements and develop representational models of the structure of this new work domain and develop an initial MAM display design representation (with a temporal emphasis) as a first hypothesis for an iterative program of evaluation and refinement. An additional goal was to discover and document, through this case study, what analysis methods explored helped to inform the design of the display representations. The MAM Tasking and Timeline Display was ecologically designed and mapped from the MAM cognitive work analysis (CWA) as a hypothesis of the work support the MAM will need to perform multi-aircraft management within a Global Unmanned Air System (UAS) work environment. This display includes timeline, status, and workload management vantages intended to complement the traditional geospatial map-based displays used by UAV pilots. This conceptual low fidelity display was used to both further the discussion of MAM among domain practitioners in a concrete way, enrich the work analysis, as well as to gather more display design requirements. The display concept served as an artifact to assist potential future users of MAM displays in envisioning the possibilities for supporting MAM. This is only the first step in an iterative program of evaluation and display refinement research needed for evolving the MAM vision concept and developing advanced human computer interface (HCI) displays in suppo (open full item for complete abstract)

Committee: John Flach PhD (Committee Chair); Valerie Shalin PhD (Committee Member); Mark Draper PhD (Committee Member) Subjects: Design

Master of Science in Engineering (MSEgr), Wright State University, 2007, Human Factors Engineering

In many current agent-based modeling systems, it is difficult for a domain-expert user to define and implement agent behaviors without possessing extensive programming knowledge. MUAVES is an existing simulation environment that serves as a research testbed for examining command and control issues with unmanned aerial vehicle (UAV) systems containing many vehicle agents. In its previous form, defining agent behaviors required knowledge of the C# programming language that some MUAVES users did not have. This thesis presents a new methodology for the definition and implementation of UAV agent behaviors in MUAVES. The new methodology is based on diagramming an agent's controller state. No programming knowledge is required to reuse modular behaviors and trigger conditions specified by previous researchers. The definition of novel behaviors has also been improved by placing behavioral code in external library files, away from the main simulation code. These novel behaviors can be implemented at any desired level of abstraction. After describing the methodology, some sample scenarios are presented as proofs-of-concept.

Committee: Raymond Hill (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2004, Computer and Information Science

We define a game of pursuers seeking evaders on a grid, originally motivated by, but not limited to, the problem of unmanned aerial vehicles searching for moving targets. The pursuers have a speed advantage over the evaders but are incapable of determining an evader's location unless a pursuer occupies the same location as that evader. The evaders, on the other hand, operate with unlimited luck and cunning: if it is possible for them to avoid the pursuers, they will. By treating the players as nondeterministic finite automata, we can model the game and use it as the input for a model checker. While model checkers normally are used to verify program correctness, we use the model checker to generate a pursuer-winning search strategy. We demonstrate this technique for the full model and then develop heuristics to reduce the model so we can address larger problem sizes. We further prove an upper bound on the minimum pursuer-winning speed; moreover, we show the bound is tight for a particular class of search strategies.

Committee: Paolo Sivilotti (Advisor) Subjects: Computer Science