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.

The heart rate and blood pressure power spectrum, especially the power of the low frequency (LF) and high frequency (HF) components, have been widely used in the last decades for quantification of both autonomic function and respiratory activity. Discrete Wavelet Transform (DWT) and the Fast Fourier Transform (FFT) represent important tolls in this field. The paper presents a new solution for LF and HF evaluation that combines the Daubechies DWT with neural processing techniques. Several types of neural networks (Radial Basis Function and Multilayer Perceptron) capable of evaluating LF and HF values were designed and implemented. The training values to design the network were obtained after heart rate and blood pressure wavelets processing. The designed neural structures assure a faster evaluation tool of the sympathetic and parasympathetic autonomic nervous system control of cardiovascular function.

In this paper is proposed an algorithm of prediction fuzzy for chaotic time series. This approach has been select because, in presence of specific pathologies, biomedical data may be represented as a chaotic time series. In particular, we are interested in monitoring the intracranial pressure (IP) of some patients in a state of coma who were suffering from intracranial hypertension syndrome. In these particular cases, prediction is necessary (from a diagnostic point of view) if you want to operate at the right moment on IP abnormal conditions. The proposed approach is based on a prediction multi-factor algorithm which doesn’t need the knowledge of the mathematical working model of the biologic phenomenon, translating the real time series into a fuzzy time series.