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
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Recent Submissions
Communication Strategies for Cognitive Radar Networks
Esser, Joseph Max (Virginia Tech, 2026-10-14)
Distributed radar networks provide the ability to monitor large and dynamic scenes by uti- lizing multiple spatially separated sensors. However, the effectiveness of such networks are constrained by limited communication resources if a central fusion center needs to collect raw data from all the radar nodes. This thesis investigates adaptive radar resource manage- ment for multi-radar multi-target tracking through the application of multi-armed bandit based scheduling methods. A simulated radar network is developed where multiple radars observe maneuvering targets while providing track updates to a centralized fusion center.
Several radar selection strategies based on ϵ-Greedy and Upper Confidence Bound (UCB) multi-armed bandits are investigated and compared against a baseline communication strat- egy. Radar selection is performed by utilizing contextual information at the fusion center derived from the current tracking state and spatial distribution of targets, which allows communication resources to be dedicated to radars of interest. The performance of the re- sulting developed tracking system is evaluated using the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric. Simulation results demonstrate that the adaptive scheduling methods improve tracking performance by concentrating resources on regions of high target density, while the relative results between communication methods depends highly on target conditions and communication constraints.
In-Situ Perception-Guided Autonomy in Robotic Additive and Hybrid Manufacturing
Kosmal, Tadeusz Adlai Perry (Virginia Tech, 2026-10-14)
Additive Manufacturing (AM) provides geometric flexibility, but its autonomy remains constrained by stochastic process outcomes and reliance on skilled operators. Layer-wise deposition can introduce variations unique to each part, yet most AM systems execute predetermined toolpaths without observing or responding to the evolving as-built state. Inspection, corrective processing, and assembly of printed parts consequently require human interpretation and coordination across disconnected manufacturing systems. This dissertation develops a framework for reducing this burden by converging in-situ layer-wise sensing, probabilistic geometric state estimation, adaptive additive and subtractive toolpath generation, and robotic fabrication.
First, modular arrays of stereo structured-light scanners are embedded within AM environments and localized to a common machine coordinate frame. Measurements from multiple viewpoints are combined and evaluated against target geometry to identify a component's as-printed error state. The perception architecture is extended by calibrating long-wave infrared cameras to the stereo scanners, producing spatially registered thermal point clouds.
The measured geometric state is then converted into corrective action. A prior-guided stochastic surface-reconstruction method combines noisy and sparse scan data with geometric priors derived from expected geometry. The resulting posterior implicit field provides a closed estimate of the as-built surface for subtractive toolpath generation. A complementary additive repair framework identifies damaged regions, generates multi-axis deposition paths, and optimizes tool orientations for collision avoidance. Iterative application reduces a component's geometric deficit while remaining compatible with layer-wise replanning.
Finally, a robotic workcell integrating material extrusion, component placement, electrical connection, embedding, and part removal extends this autonomy to functional mechatronic fabrication by constructing flight-capable UASs without human involvement during robotic printing or assembly. Collectively, these contributions advance AM from open-loop execution toward adaptive manufacturing systems that observe, interpret, and act on the physical state of a printed component.
The Effects of Anti-DEI Legislation on Equity-Minded Engineering Faculty and Staff
Bigby Jr, Paul C. (Virginia Tech, 2026-10-14)
The welfare value of forecasting accuracy in electricity markets: Evidence from ERCOT
Singh, Gurkirat; Khansa, Lara Z.; Stewart, Shamar L.; Eldardiry, Hoda (2026-11-23)
Short-term load forecasting is central to electricity market operations, yet its significance depends on how forecast errors propagate through market design. This paper examines welfare implications of forecasting accuracy in ERCOT, where scarcity pricing and ancillary service procurement translate uncertainty into consumer costs. Using zonal data from 2019 to 2024, weather-augmented generalized additive models (GAM) outperform machine learning and deep learning alternatives, achieving MAPE of 0.81 percent versus 1.45 percent for XGBoost. Embedding these gains into ERCOT’s institutional framework highlights potentially large, nonlinear welfare implications concentrated in scarcity-proximate hours, positioning forecast accuracy as a policy lever in energy-only market design.
What MAPE Cannot See: Directional Forecast Bias and Operational Risk in Electricity Markets
Singh, Gurkirat; Khansa, Lara Z.; Stewart, Shamar L.; Eldardiry, Hoda (2026-11-23)
Forecast accuracy metrics such as Mean Absolute Percentage Error are incomplete guides to operational risk when prediction errors are asymmetric. Using hourly ERCOT data from January 2019 through January 2024, we evaluate seven forecasting models. Accuracy and directional bias can diverge: the lowest-MAPE specification has MPE of 0.23% versus 0.03% for its seasonal counterpart, while tree-based MPE reaches 2.22% in August and 4.6% to 6.3% in FARWEST. We develop a four-type bias typology and a governance framework for bias-aware forecasting that generalize to settings where prediction errors carry asymmetric costs.