https://etd.auburn.edu/handle/10415/2 2026-05-19T15:49:07Z https://etd.auburn.edu/handle/10415/10415 Design and Analysis of a Hot Start Vector Tracking Receiver Hubbard, Hendrix This thesis presents a novel method of initializing vector processing on a software-defined Global Positioning System receiver. By leveraging observables and ephemeris data obtained from a base station, the proposed receiver is able to instantly begin vector tracking. Conventional vector tracking approaches require an initial position estimate and decoded ephemeris data to commence processing, which is typically provided by scalar tracking. The proposed method overcomes this limitation and generates an initial position estimate without prior knowledge of the signal or receiver states. The receiver architecture integrates additional correlators located beyond the conventional half-chip range of the GPS L1 replica spacing vector. These extended correlators provide key insight into the signal power distribution, allowing the tracking loop to accurately align the code replica even when the initial position estimate is inadequate or the code phase lies outside the typical Delay Lock Loop pull-in region. This capability is particularly advantageous in dynamic or degraded environments where robust tracking is essential. By combining base station assisted initialization with extended correlator design, the proposed approach effectively merges the rapid time-to-first-fix of hot start methods with the robustness of vector tracking. The resulting system provides a computationally efficient, practical, and reliable solution for GPS receivers operating in a variety of signal conditions. In this thesis, the HSVT algorithm results show state convergence in under 1 second for static receivers and under 2 seconds for dynamic receivers, demonstrating performance comparable to existing hot start methods. These convergence times are validated through both experimental live-sky scenarios and simulated Monte Carlo analyses. 2026-05-14T00:00:00Z https://etd.auburn.edu/handle/10415/10414 Exploring the Metacognitive Model: A Comparative Study of Anxiety, Cannabis Use, and Comorbid Conditions Gorday, Julia The metacognitive model of emotional disorders is a transdiagnostic theory that suggests that psychopathology (e.g., anxiety- and fear-related disorders) occurs as a result of metacognitive beliefs (i.e., beliefs about one’s own thinking) and the subsequent activation of the Cognitive Attentional Syndrome (CAS; i.e., set of maladaptive self-regulation strategies; Wells & Matthews, 1996). A recent adaption of the metacognitive model (i.e., the metacognitive formulation of substance use) proposes that specific metacognitive beliefs about substance use lead to the development and maintenance of problematic substance use. Despite the abundance of literature that has examined the metacognitive model, no known study has compared the components of the metacognitive model across individuals with anxiety- and fear-related pathology and cannabis users. Further, no study to date has examined the metacognitive formulation of substance use (i.e., negative and positive metacognitive beliefs) in cannabis users. In an effort to fill these gaps in the literature, the present study sought to compare components of the metacognitive model (i.e., metacognitive beliefs and CAS activation) across participants with anxiety- and fear-related pathology, regular and frequent cannabis users, and healthy controls. Further, the present study aimed to evaluate the psychometric properties of an adapted measure of metacognitive beliefs about cannabis use. Adult participants (N = 46) completed a clinical interview to determine eligibility for one of three diagnostic groups and completed a subsequent battery of self-report measures. Results revealed that individuals with anxiety- and fear-related psychopathology and regular, frequent cannabis users may experience greater levels of metacognitive model components compared to healthy controls. However, this effect may be driven by anxiety- and fear-related pathology. Moreover, the present study evidenced positive associations between negative metacognitions about cannabis use and cannabis use frequency and problems. Present findings point to the potential benefits of treatment options that target generic metacognitive beliefs, CAS activation, and negative metacognitions about cannabis use. Given notable study limitations, future work is needed to confirm and expand upon present findings. 2026-05-13T00:00:00Z https://etd.auburn.edu/handle/10415/10413 Adaptive Human–Robot Collaborative Assembly: Integrating Planning, Behavior, and Gaze Schirmer, Fabian Human–robot collaborative assembly has high potential for manufacturing with high product variety, frequent product changes, and small production volumes. In such settings, assembly systems must be fexible. Rigid and highly specialized automation is often not suitable. Instead, assembly processes must be adapted quickly to new products, variants, and changing task conditions. Human–robot collaboration combines human fexibility and decision-making with robotic precision, repeatability, and physical support. However, this requires effcient planning, reliable interpretation of human behavior, and adaptive robot responses in dynamic environments. This thesis addresses these challenges and presents key approaches for adaptive human–robot collaborative assembly. First, an Extract–Enrich–Assess–Plan–Review (E2APR) framework is introduced to automate the generation of assembly sequence plans from heterogeneous engineering data, including CAD models, technical drawings, and assembly instructions. The framework supports task allocation between humans and robots, expert-guided refnement, and the generation of multiple collaborative assembly strategies. Second, this thesis presents an anomaly detection framework for collaborative assembly based on an LSTM autoencoder. Instead of explicitly classifying all possible worker actions, the system learns normal assembly behavior and detects deviations during execution. By combining reconstruction-error-based anomaly detection with object detection and the Assembly Sequence Plan, the framework distinguishes between valid alternative assembly paths and actual assembly errors. Third, a gaze-based intention recognition approach is proposed to enable more proactive collaboration. Eye gaze is interpreted as a non-verbal signal of worker attention and intention and is categorized into fxation, scanning, and task-switching behaviors. Experimental results demonstrate promising classifcation performance across all three categories, indicating that gaze behavior can provide useful contextual information for anticipating human actions. Finally, the thesis investigates adaptive robot path planning and communication in shared workspaces. Human arm movements are integrated as dynamic obstacles into the planning scene, and robot state changes are communicated through visual, auditory, and light-based modalities. A pilot user study indicates that communication does not negatively affect execution time, while light-based feedback reduces perceived frustration. In total, this thesis contributes a coherent approach for linking planning, perception, and interaction in human-robot collaborative assembly. The presented methods provide a foundation for collaborative systems that are not only automatically planned, but also capable of interpreting human behavior and adapting robot actions in a transparent and human-centered manner. 2026-05-11T00:00:00Z https://etd.auburn.edu/handle/10415/10412 Propeller slipstream effects on wing aerodynamics at low Reynolds numbers Ahmed, Luqman This dissertation investigates the effects of propeller slipstream on the aerodynamic performance of a downstream wing at low Reynolds numbers (70,000–130,000). The study addresses three objectives: quantifying aerodynamic loads under slipstream conditions, evaluating longitudinal riblets as a passive flow-control strategy, and comparing the effects of isotropic and propeller-induced turbulence on wing performance. Wind tunnel experiments were conducted using a two-bladed propeller and a NACA 0012 wing in a tractor configuration, with synchronized aerodynamic load and hot-wire measurements. The results show that propeller slipstream modifies both the steady-state and unsteady aerodynamic response of the wing. For the conventional midspan-aligned configuration, the slipstream increased lift and delayed stall, with larger gains at lower advance ratios; however, drag also increased, reducing (CL/CD)max relative to the isolated wing. The unsteady response exhibited a U-shaped variation with advance ratio, with lift fluctuations decreasing at intermediate values and increasing toward both lower and higher advance ratios, primarily due to the phase-locked component. Propeller placement and rotation direction also influenced aerodynamic performance. A wingtip-aligned propeller with inboard-up rotation reduced drag and achieved the highest aerodynamic efficiency but produced larger unsteady lift fluctuations at higher advance ratios. Longitudinal riblets reduced drag and suppressed phase-locked boundary-layer velocity fluctuations but did not reduce the integrated unsteady loading on the wing. Comparisons between grid-generated turbulence and propeller slipstream at nominally matched conditions further showed that turbulence intensity alone is insufficient to represent propeller slipstream inflow, as the slipstream cases exhibited different stall characteristics, larger unsteady lift fluctuations, and discrete spectral peaks at the blade passage frequency. 2026-05-11T00:00:00Z