Air-Coupled Ultrasound Resonant Spectroscopy Sensitivity Study in Plant Leaf Measurements

Svilainis, Linas; Nakutis, Žilvinas; Eidukynas, Valdas; Liaukonis, Dobilas; Aleksandrovas, Arturas; Chaziachmetovas, Andrius; Álvarez-Arenas, Tomás Gómez

doi:10.1109/sensors47087.2021.9639612

Cited by 1 publication

(4 citation statements)

References 14 publications

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“…A typical air-coupled RUS system for the plant leaf properties measurement was reported in [17,18]. The sensor contains the transmitting and the receiving transducers fixed at the known distance D from of each other.…”

Section: Sample Parameters Estimation Using Rusmentioning

confidence: 99%

“…(1) where T(ω, y, x) is the transfer function dependent on the sample parameter vector y and the air parameter vector x, R(ω) is the spectrum of the calibration signal received in the absence of the sample between the transducers [18], =2f is angular frequency, h is sample thickness, cair is ultrasound velocity in the air.…”

Section: Sample Parameters Estimation Using Rusmentioning

confidence: 99%

“…Signals were sampled using 14 bit, 10 MHz ADC. Detailed system description is available in [17], [18].…”

Section: A Experiments Setupmentioning

confidence: 99%

“…Once addressing the problem of miniature device [17] we have concentrated on another issue: environment conditions can differ significantly from those in the lab. In [18] it was demonstrated that disregarding the environment conditions (sound velocity in the air and air density) leads to a bias error in the leaf parameters estimation. The expected outdoor temperature variation in the range -10-+40 °C and pressure variation in the range 94-105 kPa will cause -3.2-+3.6% errors for sample thickness, -0.1-+ 15.1% errors for density and -6.8-+ 6.8% errors for ultrasound velocity in the sample.…”

Section: Introductionmentioning

confidence: 99%

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Air-Coupled Ultrasound Spectroscopy Air Parameters Compensation Technique

Nakutis,

Kaškonas,

Liaukonis

et al. 2024

IEEE Sensors J.

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The air-coupled resonance ultrasound spectroscopy (RUS) of thin plate thickness, density, ultrasound velocity and attenuation measurement are affected by air parameters. If air parameters are left unaccounted errors will occur. Conventional thermometer measurements are not efficient because temperature can vary faster than temperature sensor response. Technique for air parameters estimation and compensation of the RUS inverse solution results is proposed. The ultrasound delay over known distance is used for velocity in air estimation. There is no need for the additional measurement: the propagation time between transducers can be obtained from RUS calibration measurement. Ultrasound velocity in air is then used for temperature estimation. These measurements are augmented by pressure sensor measurement for air density estimation. Evaluation of the attainable measurement errors and analysis of uncertainties under such compensation was carried out using simulated signals. Sensitivity coefficients for every parameter were derived and attainable errors evaluated for temperature range from -5 °C to +40 °C and atmospheric pressure range from 94 kPa to 105 kPa. It was concluded that the relative uncertainty of sample attenuation, ultrasound velocity attenuation, density and thickness could be reduced approximately 22 times compared to case when air parameters are assumed to be equal to those in normal conditions. Experimental verification used 2 mm polycarbonate plate, measured values were compared against reported data. Experiment confirmed the efficiency of the proposed compensation: thickness estimation bias errors were reduced 17 times, bias errors for density were reduced 15 times and velocity estimation bias errors were reduced 5 times.

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Section: Sample Parameters Estimation Using Rusmentioning

confidence: 99%

Section: Sample Parameters Estimation Using Rusmentioning

confidence: 99%

“…Signals were sampled using 14 bit, 10 MHz ADC. Detailed system description is available in [17], [18].…”

Section: A Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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