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Related Experiment Video

Updated: Jun 21, 2025

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Published on: May 6, 2009

Statistics for quantifying aging in time transfer system delays.

T E Parker¹, R C Brown¹, J A Sherman¹

¹National Institute of Standards and Technology, Boulder, CO, United States of America.

|July 10, 2024

Summary

This summary is machine-generated.

Aging in time transfer systems like TWSTFT and GPSCP can be over four times larger than indicated by TDEV or ADEVS. This study recommends ADEVS for better aging estimation due to its sensitivity to time drift.

Keywords:

ADEV TDEV aging time dispersion time transfer system delays

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Last Updated: Jun 21, 2025

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

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Published on: May 6, 2009

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

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Published on: June 15, 2022

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

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Published on: July 15, 2014

Area of Science:

Metrology
Time and Frequency Transfer
Satellite Navigation Systems

Background:

Residual time delays in time transfer systems (e.g., TWSTFT, GPSCP) exhibit temporal variations known as aging or time dispersion.
Double differencing TWSTFT and GPSCP data reveals changes in relative time delays, providing insights into system aging.

Purpose of the Study:

To develop and present analytical and Monte Carlo methods for estimating time dispersion (aging) in time transfer systems.
To compare the effectiveness of Time Deviation (TDEV) and a variation of Allan Deviation (ADEVS) in characterizing aging.

Main Methods:

Utilized analytical techniques to model aging in time transfer systems.
Employed Monte Carlo simulations to estimate aging from TDEV and ADEVS statistics.
Analyzed the sensitivity of TDEV and ADEVS to time drift and aging characteristics.

Main Results:

Aging in time transfer systems can be significantly underestimated, exceeding TDEV or ADEVS values by more than a factor of four.
ADEVS demonstrates greater sensitivity to time drift compared to TDEV, making it a more suitable metric for aging analysis.

Conclusions:

The study highlights the potential underestimation of aging in time transfer systems when relying solely on TDEV or ADEVS.
Recommends the use of ADEVS for more accurate assessment of aging due to its enhanced sensitivity to time drift.