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The article presents an intelligent temperature transducer (ITT), which can work with a two-wire or a three-wire platinum resistance temperature detector (RTD). The ITT design allowed for compensation of the RTD’s lead wire resistance. The ITT used the author’s auto-calibration procedure, which minimized linearity errors of the ITT and RTD processing characteristics, ITT offset and gain errors, and errors resulting from changes in the ITT operating conditions concerning the nominal conditions. The presented results of a simulation and experimental studies confirmed the high effectiveness of this procedure. The determined uncertainty of temperature measurement using the Monte Carlo method and the obtained experimental results confirmed the possibility of measuring temperatures in the range of 0–200 °C with an expanded uncertainty of 0.02 °C at a 99% confidence level.

The article presents new tools for investigating the statistical properties of the harmonic signal autocorrelation function (ACF). These tools enable identification of the ACF estimator errors in measurements in which the triggering of the measurements is non-synchronized. This is important because in many measurement situations the initial phase of the measured signal is random. The developed tools enable testing the ACF estimator of a harmonic signal in the presence of Gaussian noise. These are the formulas on the basis of which the statistical properties of the estimator can be determined, including the bias, the variance and the mean squared error (MSE). For comparison, the article also presents the ACF statistical analysis tools used in the conditions of synchronized measurement triggering, known from the literature. Operation of the new tools is verified by simulation and experimental studies. The conducted research shows that differences between the MSE results obtained with the use of the developed formulas and those attained from simulations and experimental tests are not greater than 1 dB.

The paper presents a new and original method of -point estimation of sinusoidal signal amplitude. In this method, an -point estimator is calculated on the basis of initial signal samples. The way the estimator is constructed is explained. It is shown that the starting point for constructing the estimator is two initial signal samples. Next, in order to determine the estimator general form, three and subsequent initial signal samples appearing in a signal period are used. Some special cases of an estimator are considered. Such an estimator is compared with a four-point estimator proposed by Vizireanu and Halunga. It is shown that the -point estimator makes it possible to estimate the signal amplitude more accurately.

This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA). The designed estimator was compared with frequency estimators well-known from the literature, such as the modified covariance for half-length autocorrelation (MC-HLA), reformed Pisarenko harmonic decomposition for half-length autocorrelation(RPHD-HLA), modified Pisarenko harmonic decomposition for half-length autocorrelation (MPHD-HLA), zero-crossing (ZC), and iterative interpolated DFT (IpDFT-IR) estimators. We determined the samples of the ACF of a sinusoidal signal disturbed by Gaussian noise (simulations studies) and the samples of the ACF of a sinusoidal voltage(experimental studies), calculated estimators based on the obtained samples, and computed the mean squared error(MSE) to compare the estimators. The errorswere juxtaposed with the Cramér–Rao lower bound (CRLB). The research results have shown that the proposed estimator is one of the most accurate, especially for SNR > 25 dB. Then the RC-HLA estimator errors are comparable to the MPHD-HLA estimator errors. However, the biggest advantage of the developed estimator is the ability to quickly and accurately determine the frequency based on samples collected from no more than five signal periods. In this case, the RC-HLA estimator is the most accurate of the estimators tested.

This paper presents a new algorithm for fast uncertainty evaluation of root mean square (RMS) voltage measurement. It enables the evaluation of the expanded measurement uncertainty and partial uncertainties, which are useful in metrological analysis of the measurement. It can be used for any measurement system in which the RMS value is determined based on voltage samples. Various sources of uncertainty have been considered for this measurement system. The proposed algorithm is easier to implement than the commonly used uncertainty propagation method. Its operating principle is based on the Monte Carlo method. However, it allows the computation of the RMS measurement uncertainty within a significantly shorter time compared to the classical Monte Carlo method. The simulation and experimental results presented in this paper confirm the correct operation of the new algorithm and the acceleration of uncertainty computations up to 200 times in RMS measurement based on 1000 voltage samples.

In this paper, two new sinusoidal signal frequency estimators calculated on the basis of four equally spaced signal samples are presented. These estimators are called four-point estimators. Simulation and experimental research consisting in signal frequency estimation using the invented estimators have been carried out. Simulation has also been performed for frequency tracking. The simulation research was carried out applying the MathCAD computer program that determined samples of a sinusoidal signal disturbed by Gaussian noise. In the experimental research, sinusoidal signal samples were obtained by means of a National Instruments PCI-6024E data acquisition card and an Agilent 33220A function generator. On the basis of the collected samples, the values of four-point estimators invented by the authors and, for comparison, the values of three- and four-point estimators proposed by Vizireanu were determined. Next, estimation errors of the signal frequency were determined. It has been shown that the invented estimators can estimate a signal frequency with greater accuracy.

The paper is concerned with issues of the estimation of random variable distribution parameters by the Monte Carlo method. Such quantities can correspond to statistical parameters computed based on the data obtained in typical measurement situations. The subject of the research is the mean, the mean square and the variance of random variables with uniform, Gaussian, Student, Simpson, trapezoidal, exponential, gamma and arcsine distributions.