Optimizing Electrical Impedance Tomography for Biometric and Battery Health Monitoring
Project Description
Electrical Impedance Tomography (EIT) offers a low-cost, noninvasive way to image internal conductivity distributions using boundary electrodes. In biomedicine, EIT can monitor respiration, hydration, and peripheral circulation; in energy systems, it can track defects and degradation in batteries. This project advances EIT reconstruction for real-time monitoring by co-designing sensor layouts, drive/measurement protocols, and ML-accelerated solvers that handle motion, contact impedance, and nonlinearities. The outcome is a robust EIT pipeline validated on phantoms, human-subject surrogates, and battery mockups, targeting a joint publication.
Supervisor
ZHENG, Qiye
Quota
2
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
• Implement baseline and improved EIT reconstructions; integrate data acquisition and preprocessing.
• Perform experiments on phantoms or safe human-equivalent setups; analyze battery mock data.
• Quantify spatial resolution, sensitivity, and real-time performance; contribute figures and methods sections.
• Perform experiments on phantoms or safe human-equivalent setups; analyze battery mock data.
• Quantify spatial resolution, sensitivity, and real-time performance; contribute figures and methods sections.
Applicant's Learning Objectives
• Fundamentals of EIT physics, forward/inverse problems, and regularization.
• Experience with hardware-in-the-loop sensing, DAQ systems, and algorithm evaluation.
• Exposure to biomedical and energy applications, including signal quality and safety considerations.
• Experience with hardware-in-the-loop sensing, DAQ systems, and algorithm evaluation.
• Exposure to biomedical and energy applications, including signal quality and safety considerations.
Complexity of the project
Moderate