Doctoral Dissertations

Abstract

"An autonomous vehicle (AV) may employ various sensors to detect the environment, and the hardware for self-driving should be able to process large volume of sensor data and make real-time decisions. For signal integrity (SI), both the bandwidths and data rates of the high-speed channels should meet the requirements. The electromagnetic interference (EMI) noise may become a major concern with the ever-increasing number of electric modules. This research focuses on both SI analysis and EMI modeling for AVs. The first topic is accurate and broadband three-phase motor modeling. The novel modeling methodology for a typical three-phase motor is presented, using which the motor characteristics can be reproduced. The second topic is related to SI and titled time-efficient worst-case glass weave skew (GWS) estimation for microstrip-lines. GWS is the dominant source of total line-to-line skew for differential signaling. Using the divide-and-conquer strategy, only the design information are needed for the calculations. Good correlation is reached, and it is more efficient to estimate the GWS. The third topic is titled a comprehensive study about inhomogeneous dielectric layers and the impacts on far-end crosstalk (FEXT) of high-speed PCB strip lines. FEXT in PCB strip lines is primarily attributed to dielectric inhomogeneity. The inhomogeneity problem is addressed by the novel algorithm that characterizes the Dks of glass fibers and epoxy resin. Full-wave simulations and real-board measurements are conducted to verify the proposed approach"--Abstract, p. iv

Advisor(s)

Kim, DongHyun (Bill)
Fan, Jun, 1971-

Committee Member(s)

Drewniak, James L.
Hwang, Chulsoon
Deng, Shaowei

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2022

Pagination

xii, 60 pages

Note about bibliography

Includes_bibliographical_references_(pages 58-59)

Rights

© 2022 Yuandong Guo, All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12277

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