EfficientAI: Compressive Sensing and AI Integration for Low-Power, Energy-Harvesting IoT Systems in Data Science Applications
This project focuses on implementing compressed sensing and AI algorithms on battery-less IoT device efficiently to reduce the need of sampling and storing large amount of signals for health monitoring applications while addressing the intrinsic weakness of battery-less IoT devices. One of the potential data applications is to design and implement an AI/ML algorithm that can automatically discriminate life-threatening ventricular arrhythmias (VAs) (i.e., binary classification: VAs or non-VAs) from sparse (labeled one-channel intracardiac electrograms recordings while being able to be deployed and run on the given microcontroller platform.
- Faculty: Mimi Xie
- Department: Computer Science
- Open Positions: 2
- Mode: Hybrid
- Hours per week: 20
Responsibilities and Requirements:
Needed Skills: Programming skills with Python and C language; knowledge of AI/ML; Analytical thinking
Student Responsibilities: The student will be advised to work independently on data processing, ML/AL algorithm design, and experimental evaluation of proposed methods.
