VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Investigating OpenPilot as a Research Tool
Miller, Marty (National Surface Transportation Safety Center for Excellence, 2026-03-05)
This report investigates the feasibility of using OpenPilot, an open-source driver assistance system, and a Comma 3, the hardware that runs this software, as a research tool for data collection and analysis at the Virginia Tech Transportation Institute (VTTI). The study explores OpenPilot’s data logging capabilities, camera views, and driver monitoring assessments while identifying potential applications and challenges for research integration. OpenPilot, when paired with Comma 3/3X hardware, offers real-time logging of vehicle kinematics, GPS data, and video feeds, making it a potential alternative to traditional data acquisition systems. This study successfully developed methods to extract and integrate OpenPilot data into VTTI’s research pipeline, enabling seamless analysis of driver behavior and advanced driver assistance system performance. A pilot evaluation demonstrated OpenPilot’s ability to collect high-quality data that is suitable for analyzing Level 2 systems usage, driver-initiated disengagements, and driver monitoring system (DMS) effectiveness. The study also conducted a structured assessment of OpenPilot’s DMS, revealing that while it effectively detects gross head movements, it struggles to identify subtle eye glances, limiting its reliability for distraction research. Additionally, concerns regarding data privacy and storage limitations on newer Comma devices present potential barriers to large-scale deployment. Despite these challenges, OpenPilot shows promise as a research tool with further development. Ongoing work at VTTI aims to address data privacy and storage issues while leveraging OpenPilot for the study of an intelligent speed assist system. With continued refinement, OpenPilot and Comma 3/3X devices could become valuable assets for cost-efficient driving data collection and advanced driver assistance system research.
Bonding & Bridging Social Capital in Teams
Kaufman, Eric K. (2026-03-04)
Guest lecture for Virginia Tech's LDRS 5544 class on "Leading Teams Through Change."
Physiological Sensing for Driver State Monitoring: Technology Scan and Pilot Evaluation
Jain, Sparsh; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2026-03-04)
As advanced driver assistance systems and automated vehicle technologies evolve, the ability to monitor and assess driver readiness remains critical. In support of that need, this report describes several efforts to evaluate the feasibility and reliability of capturing physiological signals during real-world driving. The goal was to examine whether signals from respiration, cardiac activity, and brain function, captured via wearable and non-contact sensors, could complement existing driver monitoring methods and provide useful input for future in-vehicle systems. A review of the literature supporting respiration, cardiac, and brain activity as relevant domains for driver state monitoring was the initial step in this process. These physiological channels are discussed as potentially useful complements to traditional measures like eye behavior, especially given their links to autonomic and cognitive changes under various degraded states. The literature review was complemented by a technology scan of commercial and research-grade devices capable of measuring these signals in mobile contexts. A structured protocol for exercising an initial subset of these systems during impaired driving in a closed-course environment was also developed. This protocol was then executed in a pilot test with five participants, who completed standardized baseline and post-alcohol drives while instrumented with electrocardiogram (ECG), respiration, and electroencephalography (EEG) sensors in addition to sensors capturing driving performance and glance behavior. In this pilot study, alcohol was used as a convenient physiological stressor given its well-understood dosage effects on driving and physiology. Results indicated that alcohol consumption consistently altered some behavioral physiological signals across all three domains. Physiological responses were more robust and consistent than the observable driving metrics, potentially highlighting the complementary value of these signals. The small sample size, however, resulted in a lack of power to detect statistical significance between sober and impaired driving for most metrics. Respiration and ECG signals were captured with high reliability, while EEG results provided informative patterns but suffered from variable signal quality and motion artifacts. These findings support the initial viability of cardiac and respiratory sensing in mobile in-vehicle settings and highlight practical limitations that must be addressed for EEG. Altogether, the effort demonstrates that it is technically feasible to capture and interpret physiological signals in a real-world driving context using wearable and embedded sensors. While further validation is needed, the results provide a foundation for integrating such signals into future driver state monitoring systems, not as standalone indicators, but as part of a multimodal approach that reflects the complexity of driver physiology and behavior.
Likelihood-Free Bayesian Inference with Efficient Uncertainty Quantification
Nouri, Arash (Virginia Tech, 2026-02-09)
Uncertainty quantification (UQ) in inverse problems is essential for reliable parameter estimation in scientific and engineering applications. This thesis presents a study on two frameworks that separately quantifies two fundamental types of uncertainty: aleatoric uncertainty, arising from inherent measurement noise and non-identifiability in the inverse mapping, and epistemic uncertainty, stemming from limited training data and model inadequacy.
For aleatoric uncertainty quantification, a conditional Wasserstein Generative Adversarial Network with Full Gradient Penalty (cWGAN-GP) is employed to approximate the posterior distribution over parameters given observations. The trained generator enables efficient posterior sampling through a single forward pass, providing credible intervals and capturing potential multimodality in the solution space. A physics-informed extension, SGML-cWGAN, incorporates domain knowledge through physics-based loss terms to improve estimation accuracy. For epistemic uncertainty quantification, Prediction with Neural Network Corrections (PNC) is utilized, leveraging Neural Tangent Kernel theory to provide theoretically grounded uncertainty estimates. Bootstrap and stacking resampling methods generate multiple model instances, with prediction variance across instances serving as the epistemic uncertainty measure. The framework is evaluated on two benchmark problems: the FitzHugh-Nagumo (FHN) dynamical system and the Pacejka tire model. Results demonstrate that PNC achieves excellent performance on clean and structured noisy datasets, while cWGAN scales efficiently to large datasets containing up to 864,000 samples. The physics informed SGML-cWGAN achieves up to 33% improvement in mean squared error over the baseline cWGAN on the Pacejka dataset. However, a fundamental trade-off emerges: PNC faces computational constraints limiting applicability to datasets smaller than approximately 7,000 samples, while cWGAN requires a minimum of 8,000 samples for reliable performance. This incompatibility highlights the need for scalable epistemic uncertainty methods that complement data-hungry generative models. The findings demonstrate the viability of neural network-based approaches for uncertainty quantification in inverse problems, while identifying key limitations and directions for future research, including alternative simulation-based inference methods and improved posterior evaluation metrics
The necessity for skeletal muscle contractile assays to assess treatment efficacy in DMD
Yuan, Claire; Sweeten, Amanda; Grange, Robert W. (OAE Publishing, 2025-03-05)
Body movement relies on skeletal muscles generating power to move limbs effectively. Power is defined as force multiplied by velocity: a muscle produces force at a specific velocity (the speed of muscle shortening) and this results in power. In diseases like Duchenne Muscular Dystrophy (DMD), the absence of dystrophin weakens muscles and impairs their shortening velocity, leading to decreased power and consequently, impaired movement. Additionally, the diaphragm and heart muscles are also affected in DMD, causing difficulty breathing and impaired cardiac function. Compromised cardiorespiratory function can ultimately lead to death. Given the complex etiology of DMD and the essential role of power in all affected muscles, it is crucial to assess potential treatments for their effectiveness in improving muscle function. This review focuses on fundamental physiological assays used to evaluate muscle function in skeletal and diaphragm muscles. Common assays include force-frequency, force-velocity, power, and eccentric protocols, which are conducted ex vivo, in situ, and in vivo in small rodents (such as mice and rats) and larger intermediate animal models such as the Golden Retriever Muscular Dystrophy dog. Existing data support the use of skeletal muscle contractile assays as objective tools for assessing the efficacy of treatments.