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Now showing 1 - 10 of 19

Recent Submissions

Access status: Open Access ,
2026 International Coastal Access Symposium Summary Report
(Dalhousie University, Marine Affairs Program, 2026-06-09) Winkler, Nicolas; Harrison, Hannah L.; Hull, Lily; Manuel, Patricia; Kofahl, Mike
Access status: Open Access ,
Combining multiple groundwater hydrograph analyses to characterize aquifer dynamics and drivers in complex hydrogeological settings
(Elsevier, 2026-08-01) Chai, Z.; LeRoux, N.K.; Jamieson, R.C.; Hill, A.M.R.; Somers, L.D.; Kurylyk, B.L.
Access status: Open Access ,
Deep Learning for Field-Based Cereal Phenomics
(2026-05-29) Ravichandran, Prabahar; Yes; Doctor of Philosophy; Department of Mechanical Engineering; Not Applicable; Dr. Hao Gan; Yes; Dr. Clifton Johnston; Dr. Vasantha Rupasinghe; Dr. Ya-Jun Pan; Dr. Young Ki Chang
Plant phenomics has become a critical bottleneck in crop improvement, as the capacity to generate high-quality phenotypic data has not kept pace with advances in genotyping. This thesis integrates deep learning into cereal phenomics through end-to-end, non-destructive pipelines that combine proximal and remote sensing with modern neural network architectures. Near-infrared spectroscopy (NIRS) coupled with convolutional neural networks (CNNs) is applied to rice for predicting amylose content, chalkiness, grain dimensions, and grain shape. Visible to near-infrared (VIS-NIR) hyperspectral imaging is investigated for wheat grain phenotyping using grain-level patches and one-, two-, and three-dimensional CNN architectures, where reformulating cultivar-independent prediction as a classification task improves robustness for moisture and protein. For field-based phenotyping, a deep learning framework estimates rice blast severity from canopy-level images acquired in the Universal Blast Nursery (UBN), and foundation and instance segmentation models are fine-tuned to delineate breeding plots from uncrewed aerial vehicle (UAV) imagery, supporting reliable plot-level trait extraction. The work also treats phenomics models as production systems: pipelines are containerised and deployed across workstations, high-performance computing, and cloud platforms, with systematic evaluation of inference latency and system-level bottlenecks. Together, these contributions demonstrate that deep learning can convert plant phenomics from a limiting factor into a scalable, decision-ready component of modern cereal breeding programs.
Access status: Open Access ,
Narratives of Multispecies Assemblages: Human Engagement with Roadkilled Animals
(2026) Perez-Haynes, Saylor
Access status: Open Access ,
Special or Restricted? Autistic People's Experiences of Special Interests
(2026-05) Aydogan, Clover
Access status: Embargo ,
Terrain-Adaptive Compensation for Rover Wheel-Slip with Controllers Selected by Quality Diversity
(2026-05-29) Grant, Jasper; Yes; Master of Applied Science; Department of Electrical & Computer Engineering; Not Applicable; n/a; Not Applicable; Jason Gu; Hany El Naggar; Mae Seto
Wheel-slip in planetary rovers creates localization error, wasted power, worn tires and occasionally, mission failure. While wheel-slip estimation and sensing has advanced, active online compensation for wheel-slip has received less attention. Existing approaches use real-time terrain measurements or respond to proprioceptive feedback with only adjusted wheel speeds and torques. Therefore, there is potential to improve proprioceptive-only strategies by not only leveraging wheel speeds, but also potentially steering angles and active suspension, to actively respond to slip. A solution which uses these additional inputs is not confined to conventional driving and can consider unconventional gaits such as ``walking” or ``inch worm” style locomotion. Unconventional gaits can increase the range of navigable slopes beyond limits established by previous rovers. Despite their improved performance, unconventional gaits still rely on terrain parameter knowledge. Existing physics-based models also require knowledge of the soil properties of the navigated terrain. A proposed two-stage offline then online learning framework generates wheel-slip compensation controllers that are not confined to conventional gaits and iterates until a controller is identified to be high-performing on the terrain considered without directly sensing the terrain parameters. Results of this adaptation successfully converge on controllers that perform well on the simulated terrain. This contribution includes a software framework to evaluate and validate rover wheel-slip compensation solutions on realistic deformable terrain. Future work will trial this system on a rover model on real terrain.