khang nguyen | bscs'24 @ uta
We present an auto-generated dataset of deformed objects specific for assistive mobile cobot manipulation using an intuitive Laplacian-based mesh deformation procedure. We first determine the graspable region of the robot hand on the given object's mesh. Then, we uniformly sample handle points within the graspable region and perform deformation with multiple handle points based on the robot gripper configuration. In each deformation, we identify the orientation of handle points and prevent self-intersection
We present an end-to-end software-hardware framework that supports both conventional hardware and software components and integrates machine learning object detectors without requiring an additional dedicated graphic processor unit (GPU). We first design our framework to achieve real-time performance on the robot system, guarantee configuration optimization, and concentrate on code reusability. We then mathematically model and utilize our transfer learning strategies for 2D object detection and fuse them in
We present a low-maintenance, wind-powered, batteryfree, biocompatible, tree wearable, and intelligent sensing system , namely IoTree, to monitor water and nutrient levels inside a living tree. IoTree system includes tiny-size, biocompatible, and implantable sensors that continuously measure the impedance variations inside the living tree’s xylem, where water and nutrients are transported from the root to the upper parts.
khang nguyen | bscs'24 @ uta