Electrical and Computer Engineering ETDs

Publication Date

Summer 6-25-2025

Abstract

Electromagnetic interference (EMI) from radio frequency (RF) sources poses a major challenge to digital systems, especially in high-electromagnetic environments. Tradi- tional electromagnetic compatibility (EMC) analysis often focuses on continuous wave (CW) interference and overlooks the effects of waveform modulation on EMI coupling. This thesis explores how modulated waveforms influence EMI coupling and introduces a Generative Adversarial Network (GAN)-based classification framework to distinguish between harmful and non-harmful EMI signals. The study extends conventional EMC analysis using machine learning (ML), showing that modulated EMI waveforms can in- crease coupling by up to 27% compared to CW signals. This highlights the need for waveform-aware EMI mitigation strategies. The proposed GAN-based framework offers an accurate, automated method for evaluating EMI risk. Beyond EMC, the research applies EMI mitigation to medical asset management and mobile edge computing. Using predictive analytics and reinforcement learning, it enhances hospital resource manage- ment and improves the reliability of cloud-connected systems, contributing to resilient and secure digital infrastructure.

Keywords

EMI, GAN, AI, Recommendation System, Machine Learning

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Payman Zarkesh-Ha

Second Committee Member

Edl Schamiloglu

Third Committee Member

Sameer D. Hemmady

Third Advisor

Christos Christodoulou

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