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Design Of Plastic Contaminant Eliminator In Seed Cotton, Joshua H. Tandio
Design Of Plastic Contaminant Eliminator In Seed Cotton, Joshua H. Tandio
Theses and Dissertations
Plastic contamination in cotton is a problem in cotton industry and researchers have worked on this problem with different approaches. This thesis documents the design of mechanical and electronic real-time systems for detecting and removing plastic contaminants. The mechanical system was designed to expose plastic embedded inside the seed cotton to the sensor and to separate plastic contaminated cotton from the process stream. The detection system consisted of an embedded computer interfaced with a USB camera and Neural Network (NN) software running in it. Two NN models were tested, a transfer learning model and a built-from-scratch original model. The original …
3d Shape Estimation Of Negative Obstacles Using Lidar Point Cloud Data, Viswadeep Lebakula
3d Shape Estimation Of Negative Obstacles Using Lidar Point Cloud Data, Viswadeep Lebakula
Theses and Dissertations
Obstacle detection and avoidance plays a crucial role in the autonomous navigation of unmanned ground vehicles (UGV). Information about the obstacles decreases as the distance between the UGV and obstacles increases. However, this information decreases much more rapidly for negative obstacles than for positive obstacles. UGV navigation becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., potholes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road environments avoiding negative obstacles in all situations is not possible. …
Deep Learning Assisted Intelligent Visual And Vehicle Tracking Systems, Liang Xu
Deep Learning Assisted Intelligent Visual And Vehicle Tracking Systems, Liang Xu
Theses and Dissertations
Sensor fusion and tracking is the ability to bring together measurements from multiple sensors of the current and past time to estimate the current state of a system. The resulting state estimate is more accurate compared with the direct sensor measurement because it balances between the state prediction based on the assumed motion model and the noisy sensor measurement. Systems can then use the information provided by the sensor fusion and tracking process to support more-intelligent actions and achieve autonomy in a system like an autonomous vehicle. In the past, widely used sensor data are structured, which can be directly …