The paper presents a new approach to solving the problem of water quality control in rivers. We proposed an intelligent system that monitors and controls the quality of water in a river. The distributed measuring system works with a central control system that uses the intelligent analytical computing system. The Biochemical Oxygen Demand (BOD) and Dissolved Oxygens (DO) index was used to assess the state of water quality. Because the results for the DO measurement are immediate, while the measurement of the BOD parameter is performed in a laboratory environment over a period of several days, we used Artificial Neural Networks (ANN) for immediate estimation BOD to overcome the problem of controlling river water quality in real time. Mathematical models of varying complexity that represent indicators of water quality in the form of BOD and DO were presented and described with ordinary and distributed-parameters differential equations. The two-layered feed-forward neural network learned with supervised strategy has been tasked with estimating the BOD state coordinate. Using classic ANN properties, the difficult-to-measure river ecological state parameters interpolation effect was achieved. The quality of the estimation obtained in this way was compared to the quality of the estimation obtained using the Kalman-Bucy filter. Based on the results of simulation studies obtained, it was proved that it is possible to control river aeration based on the measurements of particular state coordinates and the use of an intelligent module that completes the "knowledge" concerning unmeasured data. The presented models can be further applied to describe other cascade objects.

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Recent progress in the development of mobile Eye Tracking (ET) systems shows that there is a demand for modern flexible solutions that would allow for dynamic tracking of objects in the video stream. The paper describes a newly developed tool for work with ET glasses, and its advantages are outlined with the example of a pilot study. A flight task is performed on the FNTP II MCC simulator, and the pilots are equipped with the Mobile Tobii Glasses. The proposed Smart Trainer tool performs dynamic object tracking in a registered video stream, allowing for an interactive definition of Area of Interest (AOI) with blurred contours for the individual cockpit instruments and for the construction of corresponding histograms of pilot attention. The studies are carried out on a group of experienced pilots with a professional pilot CPL(A) license with instrumental flight (Instrument Rating (IR)) certification and a group of pilots without instrumental training. The experimental section shows the differences in the perception of the flight process between two distinct groups of pilots with varying levels in flight training for the ATPL(A) line pilot license. The proposed Smart Trainer tool might be exploited in order to assess and improve the process of training operators of advanced systems with human machine interfaces.

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