With the rising popularity of online and hybrid learning, this study explores an innovative
method to improve students’ learning experiences with Electrical and Computer Engineering lab
equipment by employing cutting-edge technologies in augmented reality (AR) and artificial
intelligence (AI). Automatic object detection component, aligned with AR application, is
developed to recognize equipment, including multimeter, oscilloscope, wave generator, and power
supply. The deep neural network model, namely MobileNet SSD v2, is implemented in the study
for equipment recognition. We used object detection API from TensorFlow (TF) framework to
build the neural network model. When a piece of equipment is detected, the corresponding
augmented reality (AR) based tutorial will be displayed on the screen. In this study, a tutorial for
multi-meter is implemented. In order to provide users an intuitive and easy-to-follow tutorial, we
superimpose virtual models on the real multimeter. In addition, images and web links are added in
the tutorial to facilitate users with a better learning experience. Unity3D game engine is used as
the primary development tool to merge both framework systems and build immersive scenarios in
the tutorial.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17203550 |
| Date | 20 December 2021 |
| Creators | John Luis Estrada (11836646) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ARTIFICIAL_INTELLIGENCE_EMPOWERED_AUGMENTED_REALITY_APPLICATION_FOR_ELECTRICAL_ENGINEERING_LAB_EDUCATION/17203550 |
Page generated in 0.002 seconds