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New modeling methods for bounded Gaussian jitter (BGJ)/noise (BGN) and their applications in jitter/noise estimation/testing

Authors: Masashi Shimanouchi; Mike Peng Li; Daniel Chow;

New modeling methods for bounded Gaussian jitter (BGJ)/noise (BGN) and their applications in jitter/noise estimation/testing

Abstract

We propose a new method for modeling and quantifying bounded Gaussian jitter (BGJ), as well as bounded Gaussian noise (BGN). The validity and accuracy of the method are illustrated and verified both in theory and experiments. We then demonstrate the applications of this new method for jitter and noise estimation and testing, especially for total jitter (TJ) and total noise (TN) at a targeting bit error rate (BER) level. We illustrate the accuracy improvements with this new method over the conventional methods that do not take BGJ or BGN into account.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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