Pulsed eddy current (PEC) is a new technique of non-destructive material evaluation (NDE). In comparison with other eddy current techniques, PEC uses pulsed excitation that is characterized by wide frequency content and thus the response carries more complex information about a tested object. PEC method is used in many NDE common applications such as inspection of defects, measurement of conductivity and estimation of material thickness. This article deals with the conventional and the pulsed eddy testing method as a new trend in electromagnetic nondestructive evaluation of conductive materials. Numerical simulations as well as selected experimental results are carried out to study correlations between the driving and the response signals regarding the defect classification. The gained numerical and experimental results are presented and discussed in the paper.

A microcomputer-controlled measuring instrument for phase angle measurement in low frequency range is described. It is based on the digital measurement of the time shift between input signals. To avoid the necessity of calculation of the measured phase shift, measurement of the time shift is repeated during a constant time interval to get the average value of the phase shift independent on the period of the input signals. The measured results are displayed on the display of the instrument and can be transferred into a personal computer (PC) which can process and display these results and control the instrument via USB serial interface. The method of the average value of the phase angle measurement is described and the error sources are briefly mentioned. The construction of the instrument and the software in the instrument and in the PC are briefly described. Measured results show sufficient accuracy of the instrument.

Biomedical signals are relentlessly superimposed with interferences and noise. The nonlinear processes which generate the signals, and the interferences, regularly exclude or limit the usage of classical linear techniques, and even wavelet transforms, to decompose the signal. Empirical Mode Decomposition (EMD) is a recently proposed method for analyzing signals from a nonlinear viewpoint. EMD is not defined by a formal mathematical analysis, instead the decomposition is obtained by following an algorithm, requiring experimental investigation, thus being a fully data-driven technique.
Hence, this work evaluates the impact of EMD implementation in the processing of biomedical signals, namely on electrocardiogram, impedance and photoplethysmogram, and ballistocardiogram. All these signals are very sensible to motion artefacts, and therefore mode decomposition is important to separate the representative component from the pure noise. EMD was never applied to most of these signals, which are very physiologically meaningful, so the implementation’s impact is assessed.

Rapid increase of computational performance of personal computers has enabled employment of automatic hardness measurement in the last decade. It reduces the human factor in measurement and speeds up the measurement process. On the other hand, the automatic measurement often fails, when the specimen surface is not well prepared. Robust image processing algorithm is crucial for the automatic hardness measurement. The article introduces a combination of an advanced image processing technique called Active Shape Modeling (ASM) and a sophisticated model estimation technique called Particle Filtering (PF). It enables measurement of rough polished specimens and minimizes the effect of specimen surface properties.

This paper presents an overview of algorithms for one-phase active power estimation using digital signal processing in time domain and in frequency domain and compares properties of these algorithms. Besides comparing already published algorithms also a new algorithm and additional information concerning some known algorithms is included. Results of computer simulations in Matlab and results of measurement by means of computer plug-in boards, both multiplexed and using simultaneous signal sampling is presented. Using new cosine windows designed by iterative algorithm recently published by this paper authors is also included.