@inproceedings{0071c0468af44987842818efff70b04d,
title = "Statistical Characterization of Cavity Quality Factor via the Stochastic Green's Function Approach",
abstract = "There has been a strong interest in statistically characterizing the cavity quality factor (Q-factor) for large, complex enclosures. While there are existing methods for analyzing the Q-factor statistics due to distributed losses, there is currently little discussion about the statistical cavity Q-factor caused by localized losses, such as aperture leakage and absorptive loading. This paper presents a physics-oriented, hybrid deterministic-stochastic model that calculates the probability distribution of cavity Q-factor. The research work is evaluated and validated through representative experiments.",
keywords = "Chaos, Green function, mode-stirred reverberation chambers, quality factor, statistical analysis",
author = "Shen Lin and Yang Shao and Zhen Peng and Addissie, \{Bisrat D.\} and Drikas, \{Zachary B.\}",
note = "The work is supported by NSF CAREER award, \#1750839, Office of Naval Research (ONR) Award \#N00014-20-1-2835, and by the Defense Advanced Research Projects Agency (DARPA) Award \#HR0011-21-2-00211.; 2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023 ; Conference date: 29-07-2023 Through 04-08-2023",
year = "2023",
doi = "10.1109/EMCSIPI50001.2023.10241638",
language = "English (US)",
series = "2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "183--188",
booktitle = "2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023",
address = "United States",
}