The paper deals with the problem of bias randomization in evaluation of the measuring instrument capability. The bias plays a significant role in assessment of the measuring instrument quality. Because the measurement uncertainty is a comfortable parameter for evaluation in metrology, the bias may be treated as a component of the uncertainty associated with the measuring instrument. The basic method for calculation of the uncertainty in modern metrology is propagation of distributions. Any component of the uncertainty budget should be expressed as a distribution. Usually, in the case of a systematic effect being a bias, the rectangular distribution is assumed. In the paper an alternative randomization method using the Flatten-Gaussian distribution is proposed.
Monitoring of permanent stations that make up the reference frame is an integral part of the geodesists work. Selection of reference stations is based on analysis of parameters characterizing them (hardware, coordinates’ stability, mounting, location). In this paper, we took into account phase residual as an indicator of unmodelled signal. Phase residuals were computed based on ASG-EUPOS and EPN observation processing. The results show the connection between the method of mounting the antenna and the residuals. We have reviewed multipath effect at ASG-EUPOS stations, and chosen those which are characterized by the highest value of phase residual. The results show that LC phase residual is a good factor to characterize site’s solutions’ reliability. For majority of sites RMS values were less than 10 mm. Modulations associated with multipath effect were observed for few ASG-EUPOS sites only. Phase residuals are distributed specifically for sites, which antennas are mounted on pillars (more common for EPN sites). For majority of analysed sites phase residual distribution was similar for different days and did not depend directly on atmosphere condition.
The error reduction technique, based on inverse transformation, for a shunt active resistance measurement using an ammeter and voltmeter is considered. When computing a corrected reading only multiplicative operations on two measurement results are used, namely squaring and division. The proposed method allows to increase re-sistance measurement accuracy by about two orders of magnitude what has been validated by both theoretical and experimental outcomes.
This paper is devoted to a detailed experimentally based analysis of applicability of vector network analyzers for measuring impedance of surface mount inductors with and without DC bias. The measurements are made using custommade bias tees and a test fixture with an ordinary vector network analyzer. The main attention in the analysis is focused on measurement accuracy of an impedance of surface mount inductors. Measurement results obtained with a vector network analyzer will also be compared to those obtained by using an impedance analyzer based on auto-balancing bridge method.
Determination of the phase difference between two sinusoidal signals with noise components using samples of these signals is of interest in many measurement systems. The samples of signals are processed by one of many algorithms, such as 7PSF, UQDE and MSAL, to determine the phase difference. The phase difference result must be accompanied with estimation of the measurement uncertainty. The following issues are covered in this paper: the MSAL algorithm background, the ways of treating the bias influence on the phase difference result, comparison of results obtained by applying MSAL and the other mentioned algorithms to the same real signal samples, and evaluation of the uncertainty of the phase difference.
The Histogram Test method is a popular technique in analog-to-digital converter (ADC) testing. The presence of additive noise in the test setup or in the ADC itself can potentially affect the accuracy of the test results. In this study, we demonstrate that additive noise causes a bias in the terminal based estimation of the gain but not in the estimation of the offset. The estimation error is determined analytically as a function of the sinusoidal stimulus signal amplitude and the noise standard deviation. We derive an exact but computationally difficult expression as well as a simpler closed form approximation that provides an upper bound of the bias of the terminal based gain. The estimators are validated numerically using a Monte Carlo procedure with simulated and experimental data.