The idea of active noise control is an attenuation of unwanted noise with an additionally generated acoustic wave using the phenomenon of interference. Its technical realization employs advanced control algorithms. Active noise control is an area of intense research and practical engineering applications. In the paper a new structure of adaptive active noise control systems is proposed. Compared with classical control systems used for active noise control, the proposed structure contains in an error signal measurement path an additional discrete-time filter that estimates signal values at the input of this path. These estimates are then used to tune the corresponding adaptive filter. Properties of the proposed adaptive active noise control structure are illustrated by simulation examples in which a feedforward control system equipped with this additional filter is used to attenuate unwanted wide-sense stationary random noises with continuous and mixed spectra.

Accurate and fast measurements are important in many areas of everyday engineering and research activities. This paper proposes a method that gives such measurements, additionally shortening the time in which they are obtained. To achieve this, a supplementary discrete-time filter, estimating values of delayed samples of the measured signal, is attached to the output of the data acquisition system. This filter is identified with the use of classical estimation methods, based on a dynamical model of the data acquisition system. The definition of the cost function minimised during filter identification depends on the nature of the environment in which measurements are acquired. The considerations presented in this paper are illustrated with four corresponding simulated case study examples. They show how, in a very simple way, dynamical properties of data acquisition systems may be corrected, and thus improved, using the corresponding supplementary discrete-time filters. The improvement, measured by the correction quality index introduced in the paper, was from a few times up to more than 100. The paper also raises the issue of obtaining models for tuning of the supplementary discrete-time filter. The considerations presented may be applied to formulate the artificial intelligence of data acquisition systems as well as sensors. Finally, the paper proposes to implement this intelligence as a knowledge base of the expert system.

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For environments subject to high level acoustic noise communication between people is difficult. This bemuses workers, limits working efficiency, and may even lead to accidents. Additionally, prolonged exposure to such noise results in damage to the human hearing system. A better isolation of people from the noisy machines is frequently technologically unfeasible or very expensive. Communication headsets available on the market are not ergonomic. On the other hand, they are not accepted in warm and humid environments because wearing them may cause skin galls. Therefore, at some places in mines, power plants, car factories, assembly lines, etc., the workers use earplugs made from an elastic noise absorbing material, and communication between them is limited to a set of gestures only. The aim of the paper is to present NoiseCom -a complex ergonomic earplug-based communication-improvement solution, integrating high passive and active noise reduction, enhancement of speech intelligibility and wireless speech transmission for a group of workers. Developed algorithms are presented and obtained results are reported based on recordings in real industrial conditions.