Enhancing Security on Embedded Systems Using TinyML/EdgeAI
Electrical Engineering
Team 41
William Reinhart, Benjamin Hogan, Ethan Toy, Gunnar Stirrett, Josh Gray
Summary
This capstone project demonstrated how artificial intelligence can run directly on resource constrained hardware. The team developed a proof-of-concept platform that captures signal data and uses on board AI to help identify unusual activity in real time. By combining embedded computing, RF technology, and machine learning, the project highlights a more efficient approach to security on devices at the edge.
Demo Day Zoom link: https://asu.zoom.us/rec/share/x1Xh8xyOUQyNS-si-wKnNGNKa1lPT3OaOt7puUC7T94u8ftnRR5udRNRdjiZLPbx.wdssmrngZ4d3O1Lg