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Access status: Open Access ,
CResMOT: Continuous RGB–Event Stateful Memory for Multi-Object Tracking
(University of Oklahoma – Graduate College, 2026) McCulley, Evan; Habibi, Golnaz; Xu, Shuozhi; Ghamarian, Iman
Recent advances in machine learning have made RGB-camera-only approaches increasingly viable for multi-object tracking. In the frame-based RGB setting considered here, however, detections arrive per frame at 20 Hz to 30 Hz and tracks are carried between frames by a motion model, so a track receives no new evidence between observations. Additional temporally resolved sensing is therefore needed if the tracker is to incorporate new measurements, rather than prediction alone, during those intervals. The usual remedy is a second sensor, but the established alternatives are themselves sampled. LiDAR, long-wave infrared, and radar all report at discrete instants tens of milliseconds apart. Continuous-time formulations model the motion between those instants, but they interpolate rather than observe. An event camera differs in kind rather than in rate. Each pixel reports independently when its log-luminance crosses a threshold, giving microsecond-timestamped output four to five orders of magnitude finer than the frame interval. It therefore supplies measurement during the interval, not only at its endpoints. This thesis investigates CResMOT (Continuous RGB–Event Stateful Memory for Multi-Object Tracking), whose defining requirement is that local event-derived state persist across RGB intervals and condition persistent per-object memory rather than being rebuilt as a frame-rate event image. A frozen YOLOX-m detector supplies RGB detections, a recurrent EVA-derived encoder supplies event features, and ROI-local fusion forms the observation. Each track carries a matrix-valued latent state propagated between observations by a Neural Ordinary Differential Equation and corrected at matched observations by an RWKV-7-style structured jump. The study establishes RGB and Speed-Invariant Frame ByteTrack baselines on DSEC-MOT, then evaluates detector-matched implementations of the event pathway. On a held-out sequence, an adapter acting on motion and covariance before association improves HOTA by 1.757, AssA by 4.421, and IDF1 by 2.574 over matched RGB ByteTrack. Memory added after candidate formation does not improve on it, and ground-truth oracles at that point are similarly limited. This locates the depth at which event state must enter. The complete architecture remains to be trained and evaluated as one system.
Access status: Embargo ,
Performance Analysis of Electrothermal Membrane Distillation with Carbon Nanotube-based Composite Spacers for Hypersaline Water Treatment
(University of Oklahoma – Graduate College, 2026) Shokrollahi, Milad; Bui, Ngoc T; Galizia, Michele; Papavassiliou, Dimitrios V
Membrane distillation (MD) is a thermally driven separation process operating at moderate temperatures and near-atmospheric pressures, offering advantages such as low fouling and scaling tendencies. Despite its potential for hypersaline water treatment, the industrial application of MD remains limited by low energy efficiency. Recent efforts have therefore focused on improving thermal utilization and energy recovery. In this thesis, electrothermal membrane distillation (ETMD) is investigated in two sections of experimental and numerical using carbon nanotube (CNTs) composite spacers as electrically conductive layers to improve thermal efficiency. A comprehensive computational fluid dynamics (CFD)–based sensitivity analysis was performed to optimize both operating and material parameters, identifying conductive-layer characteristics and design guidelines that advance ETMD toward practical, energy-efficient desalination. At optimal operating conditions, with a power density of up to 60 kW/m², feed flow rate of 2 mL/ min, and feed salinity as high as 100 g/ L NaCl, the ETMD configuration exhibits stable performance and excellent heat-utilization efficiency. The carbonized CNT composite exhibits high electrical conductivity, corrosion resistance, and mechanical stability, significantly enhancing interfacial heat transfer through localized Joule heating. Large-scale modeling further revealed that employing a CNT layer with an electrical conductivity of 360,000 S/m enables a membrane length of 1.4 m, achieving permeate fluxes of approximately 35 kg/m².h. These findings demonstrate the strong potential of CNT-based ETMD systems for scalable, energy-efficient desalination of hypersaline water, paving the way for industrial deployment and zero-liquid-discharge (ZLD) applications.
Access status: Embargo ,
ASSESSING TILLAGE PRACTICES AND WATER USE EFFICIENCY ACROSS WINTER WHEAT FARMS IN CANADIAN COUNTY, OKLAHOMA
(University of Oklahoma – Graduate College, 2026) Kafle, Aarati; Bhattarai, Nishan; Xie, Yanhua; Wimberly, Michael; Deng, Chengbin; Wagle, Pradeep
Improving crop productivity and water use efficiency (WUE) under a changing climate is a challenge for winter wheat (Triticum aestivum L.) systems across the globe, including those in the Southern Great Plains. This study used multi-sensor satellite imagery, machine learning, and field observations to map till and no-till winter wheat fields and assess gross primary productivity (GPP), evapotranspiration (ET), and WUE in Canadian County, a key wheat-growing region in Oklahoma. Sentinel-2, Landsat 8/9, PlanetScope, Sentinel-1 Synthetic Aperture Radar (SAR), field observations, and United States Department of Agriculture (USDA) Cropland Data Layer were used in the analysis. The random forest model showed good performance in training (overall accuracy, OA, of 66 to 86%) but showed limited ability to characterize out-of-sample plots (OA of 57% from Sentinel-1 to 66% from PlanetScope). Till farms were more widespread across the county, while no-till farms were more localized and spatially clustered in the 2025 growing season. Validation of seasonal GPP, ET, and WUE with flux tower observations showed good agreement between modeled and observed values, suggesting the potential of using satellite-derived products to assess field-level variability in crop water use and efficiencies. Notably, seasonal GPP, ET, and WUE varied between till and no-till winter wheat fields across growing seasons, with no-till fields generally showing higher GPP and WUE across several years, while ET differences between management practices were smaller. This study demonstrates the potential of multi-sensor remote sensing and machine learning for monitoring tillage practices and assessing crop-water productivity in winter wheat systems.
Access status: Open Access ,
Title Tectonic evolution and basin analysis of the Hanover Block in western Jamaica
(2012) Yalcin, Bora; Pigott, John D.; Slatt, Roger M.; Keller, G. Randy
Access status: Open Access ,
Microphysical Studies of Melting Hail Using In Situ Aircraft Measurements, Cloud Modeling, and Polarimetric Radar Data
(University of Oklahoma – Graduate College, 2026) Southward, Savannah; Bodine, David; Ryzhkov, Alexander; Carlin, Jacob; Lebo, Zachary; McFarquhar, Greg
Hail is a highly destructive severe weather hazard, causing over $10 billion USD in insured losses annually. Beyond impacts to personal and commercial property, industries such as agriculture and aviation are also highly vulnerable to the damaging nature of hail. Advanced polarimetric weather radars provide the primary means for remotely detecting hail, estimating its size, and issuing timely warnings. However, existing methodologies for quantifying and forecasting large hail remain limited, particularly due to uncertainties in relating radar signatures to hail microphysics and storm dynamics. This research leverages unique in situ observations from the armored T-28 aircraft, which has sampled hail-bearing storms across multiple field campaigns, to better constrain these relationships. Throughout this work, I investigate the sensitivity of polarimetric radar variables to hail size aloft and the role of downdraft dynamics in shaping radar signatures in hail-producing storms. Using a one-dimensional downdraft model with spectral bin microphysics initialized from T-28 aircraft observations, I examine how different definitions of maximum hail size (Dmax) influence simulated vertical profiles of reflectivity (ZH), differential reflectivity (ZDR), and specific differential phase (KDP). The model framework enables controlled exploration of how microphysical assumptions and environmental conditions influence both radar presentation and downdraft evolution. By linking in situ measurements, modeling, and radar observations, this work aims to improve understanding of hail microphysics and support the development of more optimized radar-based hail detection and prediction methodologies. The results demonstrate that uncertainty in the assumed maximum hail size propagates directly into simulated polarimetric radar signatures. While the inclusion of modeled downdrafts produced measurable reductions in hydrometeor concentration and corresponding decreases in radar variables, these effects were secondary to the influence of the particle size distribution and environmental thermodynamic structure. Comparison with S-Pol observations from the 29 June 2000 STEPS field campaign showed that the model successfully reproduced the primary characteristics of the observed vertical profiles of ZH, ZDR, and KDP, providing confidence that the coupled aircraft-model-radar framework captures the dominant physical processes governing melting hail. These findings improve understanding of how particle size distribution uncertainty and vertical transport influence polarimetric radar observations and provide guidance for the continued development of radar-based hail detection and quantitative precipitation estimation algorithms.

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