The polysemic nature of words in natural languages causes a lot of problems in the process of translating texts, especially in automatic translation. The first part of the following paper is devoted to a brief analysis of the above mentioned phenomenon. The analysis is based on different kinds of chosen monolingual dictionaries. Next, the author shows the way in which automatic translator, placed on Google website, translates simple sentences including polysemic words from French into Polish. In the subsequent section of the paper the most important principles of the description of words based on the object oriented approach proposed by W. Banyś are briefly presented. The object oriented approach makes it possible to eliminate the mistakes whose coming into being results from the polysemic nature of words and which are done by a machine in the process of translating texts. The author has chosen several polysemic words which are then described with the use of object oriented approach.

One of the problems in Russia Power Sector strategy until 2035 is the technologies development for mitigation of harmful emissions by the heat and power production industry. This goal may be reached by the transition to environmentally friendly generation units such as oxy-fuel combustion power cycles that burn organic fuels in pure oxygen. This paper provides the results of research on one of the most efficient oxy-fuel combustion power cycle, which was modified by the usage of nitrogen for turbine cooling. The computer simulation and parametric optimization approaches are described in detail. The net efficiency of the oxy-fuel combustion power cycle in relationship to the carbon dioxide turbine exhaust pressure is shown. Moreover, the influence of the regenerator scheme and modeling parameters on heat performance is obtained. Particularly, it was found that the transition to a scheme with five two-threaded heat exchangers decrease cycle efficiency by 4.2% compare to a scheme with a multi-stream regenerator.

The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below.

The article presents measurement results of prototype integrated circuits for acquisition and processing of images in real time. In order to verify a new concept of circuit solutions of analogue image processors, experimental integrated circuits were fabricated. The integrated circuits, designed in a standard 0.35 μm CMOS technology, contain the image sensor and analogue processors that perform low-level convolution-based image processing algorithms. The prototype with a resolution of 32 × 32 pixels allows the acquisition and processing of images at high speed, up to 2000 frames/s. Operation of the prototypes was verified in practice using the developed software and a measurement system based on a FPGA platform.