The departmental Expo Q&A sessions are scheduled for the week of December 1st, 2024, online. This event is open to all as well. Use the links on the schedule page to join the conversation. We recommend the use of the WebEx desktop app for the best experience, but you can also join using your browser.
Team HNT – Course Planner Student View
Team Heads ‘N Tails VISION Have an efficient, user-friendly scheduling system that will function for all majors at Miami; It should be an elegant replacement to the spreadsheets currently used for planning and advising. MISSION Provide a comprehensive and operational system that streamlines the process of planning classes at Miami University with proper documentation and […]
Automatic Composition
CLIENT Chi-Hao Cheng, ECE TEAM Longze Li (CSE), Daniel Wood (ECE)
Millet Hall Maps
Millet Hall Expo map and project list
Self-Driving Car
CLIENT Jim Leonard, ECE PRODUCT DESCRIPTION The car uses YOLOv8 for object detection to predict where the person is in the frame. The server calculates the prediction of each frame and determines where the person is. The prediction then creates a boundbox of where the person is in the frame. With the bounding box enabled, […]
Spring 2024 Expo
The CEC Expo is scheduled for May 9th, 2024, in Millett Hall, 5:00 – 7:00pm. This event is open to all. Outstanding Project awards will be revealed at the Alumni & Friends Conference breakfast Friday morning. The departmental Expo Q&A sessions are scheduled for the week of April 29th, 2024, online. This event is open […]
QuickQuit
develop a robust browser extension that empowers users to access sensitive websites, such as LifeShare, with confidence and discretion by providing them with the tools to delete browsing data, modify URL history, and enhance privacy settings within Chrome.
Unpaper
Make PDF documents or printed text without accessibility information easier to read on small screens and with screen readers or other assistive tech.
Computer Vision For HealthCare
Research and create an application that combines computer vision and machine learning to allow users to quickly identify dental issues.
Security of Machine Learning II
Integrate model extraction attacks with membership inference techniques to enhance the efficacy of membership inference.