The information technology that implements evaluation of redundant information using the methods of preprocessing and segmentation of digital images has been devised. The metrics for estimating redundant information containing a photo image using the approach based on texture variability were proposed. Using the example of aerial photography data, practical testing and research into the proposed assessment were carried out. Digital images, formed by various optoelectronic facilities, are distorted under the influence of obstacles of various nature. These obstacles complicate both the visual analysis of images by a human and their automatic processing. A solution to the problem can be obtained through preprocessing, which will lead to an increase in the informativeness of digital image data at a general decrease in content. An experimental study of the dependence of image informativeness on the results of overlaying previous filters for processing digital images, depending on the values of parameters of methods, was carried out. It was established that the use of algorithms sliding window analysis can significantly increase the resolution of analysis in the time area while maintaining a fairly high ability in the frequency area. The introduced metrics can be used in problems of computer vision, machine and deep learning, in devising information technologies for image recognition. The prospect is the task of increasing the efficiency of processing the monitoring results by automating the processing of the received data in order to identify informative areas. This will reduce the time of visual data analysis. The introduced metrics can be used in the development of automated systems of air surveillance data recognition.

Temperature is one of the most important indicators of human health. Therefore, the study of the effect of temperature on the human body is necessary.The approach of modeling heat transfer between a human and the environment with dispersed parameters is presented in this article. This approach is the development of a model heat transfer between a human and the environment with lumped parameters. In the process of modeling, the Fourier law of thermal conductivity, Newton Richman's law and mathematical apparatus of partial differential equations are used.

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The object of this work is the recognition algorithms of aerial photography objects, namely, the analysis of recognition accuracy based on data sets with different aggregation classes. To solve this problem, an information system for object recognition based on aerial photography data has been developed. An architecture based on neural network architectures of the ConvNets group with structural modifications was chosen and used to create the information system. The use of a convolutional neural network of the ConvNets group in the architecture of the information system for the recognition of objects of aerial photography gives high accuracy rates when training the information system and validating its results. But the authors did not find any studies on the learning of the neural network of the ConvNets group. Therefore, it was decided to conduct an analysis in which case the ConvNets network will provide validation results with higher accuracy when the training takes place on datasets with or without class aggregation. The authors performed an analysis of the accuracy of recognition of aerial photography objects based on data sets with different aggregation classes. The dataset used for neural network training consisted of 3-channel labeled images of 64x64 pixels size. Based on the analysis, the optimal number of epochs for training is selected, which makes it possible to recognize aerial photography objects with greater accuracy and speed. It was concluded that greater accuracy in image classification is achieved for sampling without crossing data from different classes (without aggregation of classes). The result of the work is recommended for use in the automation of dataset filling and information filtering of visual images

The paper considers a new direction of scientific research - "synergetics". The key provisions and its development as a science are considered. The focus is on open feedback systems as objects of research. The properties of these systems - openness, nonlinearity, dissipation and multidimensionality, allow the use of a synergistic approach in the study. Due to new trends in information technology in recent years, interest in the new architecture of Software Defined Networks has grown. A programmable controller is used as a control mechanism for SDN networks. The connection between the logical controller and the physical network is made using the OpenFlow protocol. The graph of the network topology is presented as a set of key parameters that come to the controller. From the set of parameters, the key ones used in the study are selected. The dynamics of the ratio of key parameters under the condition of optimizing the network infrastructure is studied. The dynamics of the network corresponding to the stability condition is investigated by the methods of synergetic control theory. SDN network control is formed by methods based on the principle of self-organization of nonlinear systems. As a result, synergetic control is synthesized to increase the resistance of the control system to destructive influences. Based on the selected dynamic invariant, the possibility of providing the selection of the parameter of the SDN network management system for the transition to a controlled state is shown.