Signals peculiar to modern telecommunication systems are characterized by higher peak-to-average power ratios than in the past and can potentially lead to higher distortion in amplification stages if signal power statistics are not accounted for properly. CCDF (Complementary Cumulative Distribution Function) curves are an effective way of fully specifying signal power characteristics. Some high performance analysers, currently available on the market, carry out CCDF measurements through a demodulation of the radiofrequency signal and the application of custom digital signal-processing algorithms to the samples of the baseband components thus retrieved. Two alternative digital signal-processing approaches for measuring CCDF curves are presented in the paper. The major advantage they offer consists in the absence of demodulation; they are, in fact, based on original algorithms that are directly applied to the samples of the radiofrequency signal.

An algorithm to improve the estimation of the active power of electrical systems in the non-coherent sampling is proposed in this paper. It is based on smoothing sampled data by windowing, and then averaging of the DFT (discrete Fourier transform) coefficients in the frequency domain to reduce leakage effects. The simulation and experimental results are presented showing that averaging of DFT coefficients provides better estimation of the active power then only by windowing. The use of a suitable algorithm depends on positions of the frequency components, on a number of observed periods, and on signal to noise ratio.

Fast and accurate measurement of the instantaneous average active power is useful for building Energy Management System (EMS) in order to assure quality of service such as continuity, optimize energy consumption and reduce carbon dioxide emission.
In this paper the problems connected to the measurement of the instantaneous average active power for energy usage improving are discussed, also as the operational processes which deal to solve such problems.

In the paper modern CAD (computer aided design) techniques will be used for the analysis and design of a 20 kV combined current-voltage instrument transformer (CCVIT). The magnetic phe-nomena in the complex non-linear electromagnetic CCVIT system will be studied by using the finite element method in the three-dimensional domain and the original program package FEM-3D. The magnetic field analysis will derive the exact CCVIT metrological characteristics. The FEM-3D results are basis for further optimal CCVIT design from metrological aspect by using the stochastic method-genetic algorithm introduced in the original program as an instrument transformer design tool. The objective function during the optimization process is a combination of the CCVIT metrological parameters. Higher accuracy class will be achieved with the CCVIT optimal solution. The CAD results of the finite element method magnetic field analysis and the genetic algorithm optimal design will be experimentally verified through testing of the realized CCVIT prototype in a laboratory.

In this paper we propose a methodology for intelligent analysis of genomic measurements. It is based on a sequential scheme of Support Vector Machines and it can be used for class prediction of multiclass genomic samples. The proposed methodology was evaluated using two lung cancer datasets. The results are comparable and in many cases higher to the accuracy of relevant methodologies that have been proposed in the literature.