Worldwide Interoperability for Microwave Access (WiMAX), based on the IEEE 802.16 standards, is a technology that offers mobile broadband access to multimedia and internet applications at low cost for operators and end-users. Similarly to cellular phone or other Radio Frequency devices, WiMAX has to be considered as a possible source of electromagnetic pollution and so, monitoring its emission, could be necessary to verify the compliance with the applicable limits. Generally, the monitoring of the electromagnetic pollution is performed by means of a suitable measurement chain constituted by an antenna connected to a traditional spectrum analyzer. The use of this kind of device to measure the power of digital modulated noise-like signals, such as WiMAX, requires to carefully set many instrument parameters to obtain reliable measurement results, otherwise a significant underestimate or overestimate of the human exposure can be obtained.
In this framework, this paper presents a suitable measurement method and spectrum analyzer proper settings able to warrant reliable measurements of electromagnetic emissions due to WiMAX devices.

The conventional concept of consistency in multiple evaluations of the same measurand is based on statistical error analysis. This concept is based on regarding the evaluations as realizations from sampling probability distributions of potential evaluations which might be obtained in contemplated replications. The expected values of the sampling distributions are regarded as unknown but the standard deviations are assumed to be known. The multiple evaluations are said to be statistically consistent if their dispersion agrees with the hypothesis that the sampling distributions of potential evaluations have equal expected values. As the science and technology of measurement advanced, the limitations of the statistical error analysis view of uncertainty in measurement became a hindrance to communication of scientific and technical measurements. Therefore, a new concept of uncertainty in measurement was established by the Guide to the Expression of Uncertainty in Measurement (GUM). In the GUM view, an evaluation and uncertainty are, respectively, measures of centrality and dispersion of a state-of-knowledge probability distribution for the measurand. Statistical consistency is not compatible with the GUM concept of uncertainty in measurement; however, metrologists continue to use it as an approximate rule of thumb because no suitable alternative has been available until recently. The concept of metrological consistency is compatible with the GUM concept of uncertainty in measurement. It is a pair-wise concept. A pair of state-of-knowledge distributions are said to be metrologically consistent if the ratio of the absolute difference between evaluations and the standard uncertainty of the difference is less than some chosen benchmark. As the concept of metrological consistency becomes more widely known and its benefits realized, it should become the dominant approach to test consistency of multiple evaluations of the same measurand.

This paper proposes the use of feedback linearization for the characterization of thermoresistive sensors and for the control of measurement systems based on thermoresistive sensors kept at constant temperature. Two important benefits brought by feedback linearization are: regarding sensor characterization, it allows the determination of static and dynamic parameters by a single experimental test; concerning temperature control of the measurement system, it makes the controller design simpler and prevents linear controllers from losing performance due to changes in the operation point. A simple PI controller in combination with feedback linearization is then applied to the system. Experimental results for sensor characterization and temperature control are presented.

Nowadays measurement data coming from all kinds of measurement systems is usually processed by algorithms. These algorithms are often delivered to the user as complex program and their numerical structure is not known. Therefore also influence of algorithm on an accuracy of processed data is not known. Coefficient matrix of algorithm represents its numerical operations and it can be a basis to algorithm accuracy evaluation. The paper presents a method how to identify this coefficient matrix. As an example this method is used to identify an FFT algorithm implemented in LabVIEW.

The measurement of the differential pressure is widely employed in the petrol industry for determining the flow rate, level, blockage of filters and the oil-water interface. The calibration of the differential pressure transmitters is usually made at atmospheric pressure, since there is little concern about the static pressure influence on the transducer performance. As a main contribution, this paper presents a calibration methodology of differential pressure transmitters. The ranges of the studied static pressure (from 0 to 20000 kPa) and differential pressure (from 40 to 250 kPa) cover Petrobras production and exploration operating conditions. To achieve the results, a pressure amplification device was developed and used at each port of the pressure transmitter. Thus, during the calibration of a pressure transmitter, the pressure differential at the transmitter ports is deduced from the measured value at nearly atmospheric pressure and the amplification factor. The uncertainty of the results was estimated and the methodology was used for the calibration of a pressure transmitter, showing that its calibrating curve varies with the operating pressure.