Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Hardware Implementation Of Assistive Technology Robot, Joycephine Li
Hardware Implementation Of Assistive Technology Robot, Joycephine Li
Publications and Research
SuperHERO is an on-going research project in Computer Engineering Technology department which involves upgrading Heathkit Education Robot (HERO) hardware circuits and features by using modern hardware devices and sensors. The current phase of the project will focus on upgrading the motor drive system hardware as well as implementation and testing of features such as mobile robot obstacle detection and other assistive technologies to help people with disabilities. This involves the reattachment of the robot arm after repairing and updating with 3D printing and using modern hardware and software technology. We observed that the robotic arm has rotary and translation movements …
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Publications and Research
Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.
Unsupervised Feature Learning For Point Cloud By Contrasting And Clustering With Graph Convolutional Neural Network, Ling Zhang
Dissertations and Theses
Recently, deep graph neural networks (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and time-consuming. To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with GNNs. In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a …