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Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
LSU Master's Theses
Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …
The Design Of A Computer System To Determine The Causes Of Edema Using Magnetic Resonance Spectroscopy, Carl Allen Fink
The Design Of A Computer System To Determine The Causes Of Edema Using Magnetic Resonance Spectroscopy, Carl Allen Fink
LSU Master's Theses
Diabetes is a growing problem in the U.S.A., closely linked to the current obesity epidemic. Two common complications of diabetes, osteomyelitis of the foot, and Charcot's joint, are impossible to differentiate via traditional Magnetic Resonance Imaging. The background of Magnetic Resonance Spectroscopy, which transforms the time-domain MRI signal into the frequency domain spectrum, is explored, and its use to aid in this differentiation is proposed. Artificial Neural Networks can be employed to evaluate the MRS data and to automate the process.