On-line Magazine 'Sensors & Transducers' (S&T e-Digest)
(ISSN 1726- 5479)
Vol. 62, Issue 12, December 2005, pp. 462-472
Using a Dual-Channel FDC Device and ANN Techniques to Improve Measurements Accuracy
J.M. Dias Pereira1,2, O. Postolache1,2, P. Silva Girão2
1) LabIM/ESTSetúbal, Instituto Politécnico de Setúbal, Portugal, +351.265.790000, +351.265.721869,
2) Instituto de Telecomunicações, DEEC, IST, Lisboa, Portugal, +351.21.8417289, +351.21.8417672,
Received: 17 December 2005 /Accepted: 23 December 2005 /Published: 27 December 2005
Abstract: Frequency-to-Digital Conversion (FDC) and Artificial Neural Networks (ANNs) techniques are two powerful tools that can be used to improve the performance of measurement systems. The main advantages associated with frequency output transducers include their high noise immunity, high output signal power, wide dynamic range and simplicity of signal interfacing and coding. The frequency-to-digital conversion is easily performed by any low-cost microcontroller, or circuits based on commercial off-the-shelf (COTS) components, without need of any analog-to-digital converter (ADC). Considering the software component, artificial neural network techniques can easily be adapted to linearize transducer’s characteristics or to compensate the disturbances caused by influence quantities. In this paper a dual-channel FDC is used to measure relative humidity (RH) and temperature, and ANN techniques are applied to compensate RH measurements errors caused by temperature variations. Particular attention is also dedicated to the statistical behavior of the calibration data in order to select a suitable conversion accuracy for a given signal-to-noise ratio.
Keywords: frequency-to-digital conversion, calibration, error compensation, accuracy, neural network processing
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