The paper makes a comparison of the results of the application of two-sided and one-sided versions of the Hodrick-Prescott filter on GDP data concerning 27 EU Member States. Based on the results, the overall finding is that, contrary to its assumed advantages, the one-sided filter does not help overcome endpoint unbiasedness. Quite the opposite, it rather spreads and consolidates the endpoint bias that plagues the two-sided version over the entire filtered data. In addition, regression-based results on the influence of the second, third, and fourth moments of the GDP acceleration rates on the differences between onesided and two-sided HP trends are presented.
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.
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.
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.