Hardware-Limited Time Constant Estimation Using a Weighted Linear Regression
Published In
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Document Type
Citation
Publication Date
3-18-2024
Abstract
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.
Rights
© 2024 IEEE
Locate the Document
DOI
10.1109/ICASSP48485.2024.10446713
Persistent Identifier
https://archives.pdx.edu/ds/psu/41255
Publisher
IEEE
Citation Details
Yuan, T., Maksimovic, F., Burnett, D. C., & Pister, K. S. (2024, April). Hardware-Limited Time Constant Estimation Using a Weighted Linear Regression. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 151-155). IEEE.