Calibration and Characterization of a Reduced Form-Factor High Accuracy Three-Axis Teslameter

Cassar, Johann; Sammut, Andrew; Sammut, Nicholas; Calvi, M.; Mitrovic, Zarko; Popović, R.S.

doi:10.3390/electronics9010151

Cited by 3 publications

(2 citation statements)

References 8 publications

(23 reference statements)

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“…Using LI, it is possible to solve nonlinearity calibration when order of polynomial is higher than three. It is discussed that higher order regression represents more precise fit for calibration data since it will get very close to original function, meaning higher convergency [25,31,33,64,65]. However, higher orders also have tendency to oscillate which may give erratic value between the points.…”

Section: Theoretical Basics Of Non-linearity Calibrationmentioning

confidence: 99%

“…Thus, the sensor input-output's relationship can be solved using interpolation or least square approximation to define polynomial function for such a set of data in order to reach an efficient and accurate outcome (e.g. [23,[30][31][32][33][34][35]). Lagrange interpolation (LI) method is a proper choice for low-cost calibration scheme where the order of polynomial is higher degree.…”

Section: Introductionmentioning

confidence: 99%

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Calibration approach to quantify nonlinearity of MEMS pore pressure sensors using optimal interpolation

Barzegar¹,

Tadich²,

Sainsbury³

et al. 2022

Meas. Sci. Technol.

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MEMS-based instruments have become more attractive in recent years for many industries, particularly geotechnical monitoring owing to their small size and low capital cost. However, overcoming nonlinearity errors is a major concern to ensure accuracy, precision, and repeatability of measurement. Nonlinearity error in measuring instruments can be solved using polynomial function of different degree based on severity of error. In this study, Lagrange polynomial fitting method is applied for nonlinearity calibration of a newly developed MEMS pore pressure sensor by means of optimum calibration points. A procedure for optimum selection of the calibration points to get the best calibration characteristics of a pore pressure sensor is investigated. For this work, the calibration characteristics are evaluated by Lagrange interpolation using special set of Chebyshev nodes, D, A and R-optimum points. The D-A-R optimum points are constructed by Imperialist Competitive Algorithm (ICA). The value of the optimal approach is also compared with a uniform approach using equidistant points through actual readings. The results show the increased accuracy and precision of measurement using optimum approach. This increased accuracy allows the application of MEMs to sense smaller changes in pore pressure readings providing unique opportunity for passive estimation of subsurface properties.

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