Hardware-Limited Time Constant Estimation Using a Weighted Linear Regression - Séminaires Parisiens en Réseaux
Communication Dans Un Congrès Année : 2024

Hardware-Limited Time Constant Estimation Using a Weighted Linear Regression

Résumé

Accurately determining the time constant of a circuit enables IoT nodes to easily read out resistive or capacitive sensors. However, power and cost constraints lead to hardware limitations that complicate such measurements, including ADC noise, sampling clock jitter, poor voltage control over temperature and process, and a low power microprocessor without a fast multiplier or floating point support. This work discusses estimating the time constant of a decaying exponential's ADC samples using a simple weighted linear regression and describes the on-chip implementation of the regression on a low-cost, low-power microprocessor. Experimental results with an imperfect ADC show that time constants over more than two orders of magnitude can be accurately estimated within 5% of the nominal value with a mean standard error of about 1% of the nominal value.
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hal-04834529 , version 1 (12-12-2024)

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Titan Yuan, Filip Maksimovic, David C Burnett, Kristofer S.J. Pister. Hardware-Limited Time Constant Estimation Using a Weighted Linear Regression. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2024, Seoul, South Korea. pp.151-155, ⟨10.1109/ICASSP48485.2024.10446713⟩. ⟨hal-04834529⟩

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