Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information received from financial markets still requires physical attention and labor, which increases the risks associated primarily with the human factor and corresponding errors. An algorithm has been developed and tested for the automated collection of statistics from graphs of stock quotes, including data on the development and context of various figures (patterns) of technical analysis, as well as an improved adaptation and tracking system for the trend. The modeling process, analysis and the results of applying the analysis algorithm and statistics collection are presented. The developed algorithm works in conjunction with the previously created neural network pattern detector, which allows to automatically search for the exact boundaries of technical analysis figures of various sizes, analyze the context in front of them and play the patterns. This makes it possible to obtain important statistics that allow one to determine the degree of confidence in emerging patterns, taking into account their type, context, and other factors. In terms of accuracy and efficiency, the developed algorithm meets the existing challenges in the financial markets and can significantly increase the efficiency of the trader or investor through the automated processing of graphic and visual data. The created solution is universal in nature and can be applied to any capital market, regardless of the location and nature of the assets placed. The results can be used both to improve the accuracy of existing trading strategies, and for the analytical work of financial market participants. The use of new technologies for statistical processing of information can significantly improve the accuracy of investment and trade decisions

To improve efficiency of non-invasive monitoring of the internal brain temperature, a small-size single-channel microwave radiothermograph consisting of a miniature radiometer and a radiometric sensor based on a printed antenna was developed. Such solution is necessary to provide physicians with a system of non-invasive monitoring of diagnosis and treatment processes. Mathematical modeling and experimental verification of the technical solutions obtained are described in this paper. A miniature radiothermometer was developed. It is a balance modulation radiometer designed on the basis of the R.H. Dicke’s circuit with two loads. Taking into account the requirements of miniaturization, a radiometric sensor was developed by means of numerical simulation. As a result of calculations, optimum antenna dimensions were determined (the total size: ø30 mm, the size of the foil flane substrate: ø23 mm, dimensions of the emitter slot: 16 mm×2 mm). According to the mathematical modeling, the depth of detection of thermal anomalies was not less than 20 mm for the printed antenna which is practically the same as for the waveguide antenna successfully used at present in brain radiothermometry.The standing wave coefficient was determined for various head regions: frontal, temporal, parietal, occipital and the transient between the occipital and parietal regions. Experimental tests of the radiothermograph on water phantoms and biological objects have been carried out. A very good coincidence between the data of numerical simulation and the physical SWR experiment in a range of 1.04–1.8 was obtained. As a result of the study, it has been found that the radiothermograph with a printed slot antenna enabled measurement of internal brain temperature with an acceptable accuracy (±0.2 °C). This will ensure control of craniocerebral hypothermia in patients with brain stroke and allow doctors to promptly change the hypothermia tactics. Small size of the created unit will make it possible to combine it with medical robotic systems to improve treatment effectiveness.