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The objective of this paper is to present a methodology for the evaluation of uncertainties in the measurements results obtained during the calibration of a digital manovacuometer prototype (DM) with a load cell sensor pressure device incorporated. Calibration curves were obtained for both pressure sensors of the DM using linear regression by weighted least squares method (WLS). Two models were built to evaluate uncertainty. One takes into account the information listed in the sensor datasheet, resulting in the maximum permissible measurement error of the manovacuometer, and the other on the WLS implemented during calibration. Considering a range of ten calibration points, it was found that calibration procedure designed using WLS modeling indicates that the range of measurement uncertainty extends from 0.2 up to 0.5 kPa. This is inside the manufacter range that extends from 1.5 up 3.5 kPa, showing adequacy for use

This paper presents a comparison of three feature extraction methods to denoise partial discharge (PD) signals. The denoising technique employs the Stationary Wavelet Transform (SWT) associated to a spatially-adaptive selection procedure based on the coefficients propagation along decomposition levels (scales). The PD and noise related coefficients are identified and separated by an automatic data classifier using Support Vector Machines (SVM). The first and second feature extraction methods act directly on the SWT coefficients and differ only on the procedures to characterize the propagation. The third method relies on Cycle Spinning (CS) on the several translated Discrete Wavelet Transform (DWT) obtained from SWT. We conducted an empirical study using Analysis of Variance (ANOVA) to evaluate the influence of the methods on denoising performance and to guarantee the statistical significance of the tests. Afterwards, performance was evaluated considering real PD signals measured in air and in solid dielectrics, corrupted by several types of interferences, both stationary and time-varying. The results show that the three approaches allow robust signal recovering and significant noise rejection, but differ substantially on the quality of the reconstructed signals.

This paper proposes a distributed measuring system based on wireless sensor networks (WSN) employed to estimate plant water-content in agricultural fields. The WSN is present in a crop field in order to measure environmental variables, like soil moisture. Water-content is obtained by measuring the attenuation of the network communication signal. The need for distributed measurements to estimate agricultural crop parameters is pointedout and a mathematical model of the radio-wave propagation through the vegetation is developed. This model is used to estimate plant water-content. Experimental results are also presented.

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This paper describes a partial discharge measurement system based on a programmable digital oscilloscope. The system enables the acquisition of partial discharge data both on phase-resolved and time-resolved basis, thus allowing these two analyses. After the acquisition of partial discharge data, statistical operators are applied over the phase distributions; as a way to identib the type of defect present on the equipment under test and its corresponding degradation level. To check for the system performance and develop an initial database on partial discharges, it was used to measure partial discharges in air and solid insulation, and their main characteristics were determined. These measurements were carried out in a measurement cell developed with great care to prevent signal deterioration, allowing the registration of almost distortion-free PD pulses.

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