This paper examines the possibility to set up standardized sampling strategies for circular features, which are tolerated according to the international system of limits and fits. The measuring process is simulated by a mathematical model and the sampling strategy derived for various measurement conditions. One important aim of the proposed model is to control the measurement uncertainty of the measurement process within certain limits of the feature tolerance. To do so the measurement uncertainty is esti-mated according to international standards. The model of the estimation process considers as uncertainty contributors the coordinate measuring machine, environmental conditions and workpiece deviations. Simulations have shown that it is possible to derive standardized sampling strategies for circular features tolerated according to the ISO system of limits and fits if measurement process parameters are classified.

This paper presents a temporal information modeling and describes its use in the context of intelligent instruments. First, we propose an information modeling for the use of measurement information that takes into account the response time. This model offers the advantage of computing the uncertainty associated with the information. Information can be obtained from several sources. From the proposed model, the uncertainty associated with information coming from each source is computable at any time. We present a structure that makes it possible to use the knowledge of a consumer needs in order to obtain the best quality of required information.

The mathematical model of an acoustic wave field in the measuring cavity of pressure calibrator is established. Two ways to the problem solution are posed. The system of two neural networks – RBF and perceptron – is applied to the working hole optimization and the wave field approximation. This new approach based on neural networks methodology seems to be adequate, effective and powerful: it is weakly sensitive to some entrance data perturbation, it gives trained neural networks for a set of problems solution, it is possible to use the same ideas in case of nonlinearity modeling.

Distributed processing of measurement results poses a number of challenges. Among important requirements are security, performance, robustness and ability to withstand network failures, need for flexibility and elegance of a proposed solution. We propose a novel approach to distributed processing, which capitalises on locality of data access. We propose a distributed software architecture that leverages a flexible consistency checking engine and takes advantage of software agency features in order to meet the mentioned distributed processing requirements.

This research investigates the problem of the movement classification by surface myoelectrical signals (MES), used for electromyographical (EMG) control of powered upper limbs, and also for biometric identification of the person. On of the solutions in this task is using pattern recognition approach. In this case the success of the myoelectric control scheme depends largely on the classification accuracy. The main target of the research was comparison of various neural network classifiers, such as multi layer perceptron with back-propagation learning algorithm (BPG), neural networks with radial-basis functions (RBF), probabilistic neural networks (PNN) and Kohonen`s self-organised maps (SOM). Fundamental to the success of chosen method was the sheme, which involves a wavelet based feature set, dimensionally reduced by principal components analysis (PCA), and classified by SOM classifier. It was also detected that the best accurate performance is possible when using 30 components as input vector for classifier, and four channels of myoelectric data greatly improve the classification accuracy, as compared to one channel.