The purpose of the research is to develop a method for assessing the severity of ischemic processes in multifocal atherosclerosis against the background of chronic cerebrovascular insufficiency and chronic ischemia of the lower extremities.
Methods. The main methods used in this study are the methodology for the synthesis of hybrid fuzzy decision rules, exploratory analysis, and the Delphi expert assessment method; calculations were based on the G. Rush model and the E. Shortliff iterative model.
Results. In the course of the research, particular decisive rules were synthesized to assess violations of the regulatory functions of the brain, ischemic disorders of the brain and lower extremities, disorders of the motor functions of the lower extremities, on the basis of which, using the iterative model of E. Shortliff and aggregating decisive rules, intermediate decisive rules were formed, which, in turn, entered the final decisive the rule "severity of ischemic interaction". Further, based on the final decisive rule, an algorithm was developed to assess the severity of ischemic interactions for comorbid patients with disintegration of affective-effector mechanisms of interaction between peripheral organs and regulatory functions of the central nervous system, allowing to assess the severity of the ischemic process and the risk of fatal complications in patients with the pathology in question.
Conclusion. In the course of the conducted research, high results were demonstrated in the application of synthesized partial decision rules within the framework of the task set in this study, and the expediency of using the obtained models and methods in the practice of a neurologist, vascular surgeon, angioedrologist, neurosurgeon was also shown.

The purpose of research. Modern unmanned aerial vehicles of various types provide the ability to perform various tasks of operational information collection, monitoring the state of the environment, technological facilities and territories, updating data on these facilities, as well as for reconnaissance and monitoring their condition. The purpose of the study is to establish the maximum flight range and distance necessary for the organization of a communication channel through which the video stream and flight control commands using the KAM-16 Turbo ¾ method will be transmitted between the ground control complex and the unmanned aerial vehicle in a difficult meteorological situation.
Methods are based on the basics of radio electronics, diagnostics and forecasting of the technical condition of aircraft. The methods of multicriteria analysis, parametric and structural synthesis were used. The principles of transmitting video images from drones used to monitor emergency situations were studied. A critical assessment of the maximum flight range of an unmanned aerial vehicle in a difficult meteorological situation was carried out.
Results. Analytical formulas for determining the energy potential of a data transmission channel in line of sight are presented. Graphs of the dependence of the energy reserve in the data transmission channel between the unmanned aerial vehicle and the ground control system are presented, which make it possible to determine the maximum distance for transmitting video images in Full HD quality in the 2.4 GHz band. Calculations were carried out on the basis of complex analytical expressions and graphical dependences of the flight range of a micro UAV on the speed of tailwind and headwind were constructed. The maximum headwind speed is calculated, which does not allow the use of micro UAVs in blizzard conditions.
Conclusion. A promising area of study of the use of UAV in emergency monitoring are micro-class copters capable of detecting victims by transmitting video information from a thermal imager during search and rescue operations in difficult weather conditions, such as snowstorms.

The purpose of the research is identification of organizational and technical methods of countering attacks on command and telemetry data transmission lines between aviation robotic devices and ground control points, as well as the formation of comprehensive information protection of the communication channels under consideration.
Methods. The scientific article substantiates methods for mitigating and countering attacks at the physical level of the ISO/OSI open systems interaction model for organizing communication using the MAVLINK protocol for the purpose of controlling aviation robotic devices (noise-resistant coding with a high code rate (using the example of the most common code - Viterbi), extension method Frequency Hopping Spectrum (FHSS), Direct Sequence Spread Spectrum (DSSS), Multiple Input Multiple Output (MIMO) technology). A method for organizing the selection of protection and countermeasures when controlling aviation robotic devices is outlined, which consists of assessing the risks and consequences of the implementation of vulnerabilities in order to introduce countermeasures there and in such quantities that they have the greatest benefit.
Results. The article discusses the main possibilities for intentional interference on the control and data transmission channels of consumer aviation robotic devices, an algorithm for assessing the risk of "leaving the route" is considered; a risk assessment option is provided in relation to the risk of an aircraft going off route, indicating the name of the attack, its likelihood, impact and resulting risk, as well as the acceptability of this risk and recommendations regarding the urgency of mitigating it; recommendations for countermeasures are presented in accordance with the acceptability of likelihood and impact; describes the relationship between risk and countermeasures used to reduce unauthorized exposure, in accordance with weighting coefficients.
Conclusion. The scientific article discusses the main methods of countering attacks and unauthorized access when controlling autonomous robotic devices. The risk coefficients of attacks and countermeasures taken are considered.

The purpose of research. To date, neurointerfaces have not been unified to create combined prosthetic control systems. Based on this, this review is aimed at understanding the possibility of integrating neurointerfaces by clarifying the advantages and disadvantages of neurotechnologies related to prosthetics and the possible creation of a combined prosthesis control system.

Methods. Analysis of brain-computer interfaces available in the literature in combination with neuroimaging experiments, especially in a hybrid system. A number of databases of scientific literature were used for the analysis, namely Google Scholar, scopus, etc. Links to the database data on the Internet: https://scholar.google.com/, https://www.mdpi.com/journal/sensors, elibrary.ru, https://www.refseek.com, https://link.springer.com/, https://www.base-search.net

Results. Brain-computer interfaces are currently being used in a wide variety of fields, including to improve the lives of people with disabilities. However, individual neural interfaces have certain disadvantages that make it difficult to use them to control mechanical devices, including prosthetic limbs. Hybrid neural interface systems (as an integrated software and hardware complex) are significantly superior to those obtained using separate neural interfaces, and these systems can be used for medical purposes.

Conclusion. This review provides a brief overview of the disability of people with missing upper limbs and how to improve their lives with prosthetics. The analysis of various hybrid methods of brain research is given. It can be noted that fNIRS technology is the closest technology that can facilitate the integration of neural interfaces, since it has advantages that make it a tool that complements other technologies, its advantages make up for the inherent disadvantages of fNIRS. It has been established that the hybrid system provides a clear advantage over individual neural interfaces.

The purpose of research is automatic recognition of the speaker's emotions, based on the processing of sound recordings intended for use in alarm systems when working with operators of locomotive crews and dispatch services.
Methods. Human emotion recognition has been a rapidly developing area of research in recent years. Features of the vocal tract, such as sound power, formant frequencies, are used to detect certain emotions with good accuracy. A method was used to determine the signal energy by highlighting the dominant frequency. The work has developed a program code, on the basis of which an analysis of four emotions is given - anger, joy, fear and calm. The most important and difficult step is to determine the features most suitable for distinguishing emotions and the availability of databases. Collecting databases is a complex task requiring the manifestation of sincerity of emotions. Often, the collection of a database takes place in an artificial environment and the speech may sound staged; to eliminate such problems, it is necessary to use call center recordings.
Results. Recordings of basic emotional states, such as anger, joy, sadness, fear and surprise, which are the most common case of the study, were obtained and processed. The developed software code allows us to get closer to automatically determining emotions from a speech signal. To analyze speech recordings in samples, indicators of signal energy and identification of the dominant frequency were used.
Conclusion. The implemented method of monitoring the emotional state of a human operator using a speech signal is widely used in the prevention and improvement of indicators of the psychophysiological professional suitability of locomotive crew workers and the preservation of their professional health. Distinct differences are observed in the characteristics of all types of emotions.

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.
Methods. A method for classifying the contours of the boundaries of segments of a complexly structured image is described. The method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. The method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. To classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. The method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.
Results. Experimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. The purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". The training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. The quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.
Conclusion. The use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

The purpose of the research is to improve the quality of assessment of functional states of working memory by developing a method for synthesizing fuzzy decision rules.
Methods. To monitor the state of various RAM blocks, a set of techniques was selected: reaction time assessment; searching for a signal in noise; "identification"; full reproduction; identification of missing digits; Memory. To synthesize decision rules, additional methods were used to assess such properties of attention as switchability, concentration and stability, for the implementation of which a device for monitoring the properties of the attention and memory function was used. To select an adequate mathematical research apparatus, an exploratory analysis of the structure of the processed data was carried out, according to which it was established that the selected classes of RAM states are of a fuzzy nature with uncertain boundaries of their intersections. Taking this into account, the methodology for the synthesis of hybrid fuzzy decision rules was chosen as a mathematical research tool, which was modified by developing a new method for fuzzy assessment of the functional state of RAM based on the characteristics of its properties.
Results. In the course of the research, models were synthesized to assess such characteristics of the functional state as levels of fatigue, psycho-emotional stress and the functional state of RAM using the full reproduction method. The resulting models can be used to synthesize decision rules for forecasting, early and differential diagnostics of the functional state, assessing the quality of work of the operator of human-machine systems and the health status of RAM.
Conclusion. The paper proposes a method for synthesizing fuzzy decision rules for assessing the functional state of RAM based on the characteristics of its properties using techniques obtained from the results of microstructural analysis. Fuzzy decision rules were obtained for assessing the level of fatigue, psycho-emotional stress and the functional state of RAM using the full reproduction method. During expert assessment and mathematical modeling, it was shown that confidence in the correct assessment of the level of functional state exceeds 0,95.

The purpose of research is to develop and test a technique for forming informative features using descriptors for neural networks designed to assess medical risks based on the analysis of transient processes in biomaterial in a living organism (in vivo).
Methods. Studies suggest the use of test electrical effects on areas of the body with unusual conductivity to obtain the amplitude-phase-frequency characteristic of the impe-dance of the biomaterial on which the specified effect was performed. The coordinates of the Cole graph of this biomaterial were used as key para-meters. To form the Cole graph, the Carson transform was used, based on transient data obtained using a four-terminal, where the main element is the impedance of the studied biomaterial. The input signals for the four-terminal were a sequence of sinusoidal pulses.
Results. Based on the E20-10 data collection system manufactured by L-Card CJSC, a software and hardware complex has been developed for digitizing transient processes in four-terminal circuits, the element of which is the impedance of biomaterial in anatomical areas with abnormal electrical conductivity. Software in the Delphi programming language was developed to generate test signals and record biomaterial responses to these exposures. A theoretical model was also proposed explaining the conversion of the samples of the transition characteristic of the four-terminal with the impedance of the biomaterial to the Cole graph of this biomaterial.
Conclusion. The study confirms that the use of a linear biomaterial impedance model contributes to the formation of descriptors based on the amplitude-phase-frequency characteristic, taking into account its dissipative properties. Building a Cole graph taking into account these dissipative characteristics allows us to develop classifiers of medical risks of socially significant diseases.

Purpose of the research. Breast cancer is the most common malignant tumor among women in Europe and its early detection plays a leading role in reducing mortality rates. Currently, X-ray mammography is the standard screening method for detecting breast cancer. However, due to the morphological similarities between benign and malignant lesions, many of the positive screening mammograms are false positive (up to 40%). Therefore, automation and intellectualization of this process is an urgent task.
Methods. The presented studies examine the problems of finding new, highly sensitive, prompt and non-invasive methods for detecting malignant tumors, based on the use of modern computer and telecommunication technologies, which make it possible not only to identify early manifestations of a pathological focus, but also to monitor the process of the effectiveness of therapy without significant harm to the patient’s health.
Results. The presented model of a multi-channel classifier integrates the capabilities of multi-frequency bioimpedance measurements and matrix acquisition of information from the surface of human skin through multi-electrode matrix systems. To do this, based on a matrix of electrodes, 3D mapping of the skin surface in problem areas is carried out. Through multi-frequency scanning, we obtain a three-dimensional bioimpedance image, which is analyzed by a convolutional neural network and/or by a decision maker. The proposed solution allows simultaneous analysis of data by an expert (bioimpedance image) and a convolutional neural network (trained classifier), which leads to a reduction in false positive results.
Conclusion. The possibilities of multichannel monitoring open up prospects for constructing impedance multidimensional "portraits" of malignant tumors. To classify “portraits” (diagnostics and preclinical diagnostics), methods and algorithms for image recognition and classification are used.

The purpose of the research is to develop a method for synthesizing models for assessing the state of RAM of operators of human-machine systems, the use of which in the decisive rules for predicting and diagnosing the states of RAM and its blocks ensures an increase in the quality of decisions made.
Methods. To monitor the state of various RAM blocks, the following set of techniques was selected: searching for a signal in noise; "identification"; full reproduction; identification of missing digits; Memory. To select an adequate mathematical research apparatus, an exploratory analysis of the structure of the processed data was carried out, during which it was found that the selected classes of RAM states are of a fuzzy nature with uncertain boundaries of their intersections. Taking into account the peculiarities of the processed data, the selected methodology was modified by developing a new method for fuzzy assessment of the state of RAM based on the characteristics of its properties in combination with informative features characterizing the ergonomics of the workplace, the environmental component and individual risk factors.
Results. In the course of the research, a model was synthesized for predicting the appearance and development of dysfunctions of RAM in operators of information-rich systems, characterized by the use of indicators characterizing the state of RAM blocks as predictors, which allows one to obtain confidence in the correct decision-making of no worse than 0,85.
Conclusion. In the course of the studies, it was shown that in order to improve the quality indicators of forecasting and diagnosing the states of RAM and its blocks, when synthesizing the corresponding decision rules, indicators characterizing the state of RAM blocks, energy imbalance of BAP, ergonomic and individual risk factors should be taken into account. With this approach, in forecasting problems, confidence in the correct decision-making is achieved at least 0,85. In the tasks of diagnosing the early stages of RAM disorders among operators of information-rich systems, confidence in correct decision-making exceeds 0,95.

The purpose of the research consists of modeling an interpreter for a functional programming language with metaprogramming capabilities and analyzing ways to implement primitive operators based on macros.
Methods. A formal model of a functional language interpreter, which is a subset of Common Lisp, was developed with denotational semantics, which allows you to accurately describe the behavior of the interpreter when calculating language elements such as quoting, accessing variables, sequence of actions, branching, assignment, abstraction, application.
Results. Based on denotational semantics, the architecture of a functional language interpreter with metaprogramming capabilities was developed. Numbers, symbols, pairs, strings and arrays were chosen as the basic types of objects. To store objects, a tag architecture was used, where the low-order bits of the object address are always zero, so they can store the object type code and the tag bit. Objects are allocated and freed automatically: a mark and cleanup algorithm is used for garbage collection. Using macros, branching operators, complete and incomplete, selection operator, and block operators of related variables were implemented.
Conclusion. As a result of the work, a functional language interpreter with metaprogramming capabilities was implemented. Using macros, primitive operators of condition, selection, and a block of related variables were implemented. Using these operators as an example, it is shown that using metaprogramming, only basic forms and primitives can be built into the interpreter, and the other operators can be implemented using metaprogramming, which makes it possible to simplify and reduce the amount of interpreter code.

The purpose of the research is to develop a simulation model and tune the hyperparameters of a neural network to predict possible states of a network of small spacecraft.
The research methods are based on the concepts of the theory of artificial intelligence to control a group of small spacecraft (SSV) – the use of adaptive methods and tools to make decisions, similar to the mechanisms of human thinking. In relation to space communication systems with a heterogeneous structure, artificial intelligence methods and technologies are aimed at predicting the state of communication channels between network nodes and automatically reconfiguring a network of devices based on neural network learning processes. One of the most important functions of network software for the application of cognitive algorithms is to predict the quality of communication between pairs of SSVs.
Results. A method has been developed for using the Transformer architecture neural network to predict possible states of the SSV network, which provides aggregation and time synchronization of data on the state of the SSV network, their use for training the neural network, as well as using the neural network to predict the quality of communication. The data format for the training sample has been created, based on the representation of the state of the SSV network, which ensures the generation of the initial state of the network, modeling the proactive mode of its operation, collecting SSV network state markers to generate training data sets in the form of chronological sequences grouped into frames, and allowing to reduce the amount of data transferred between SSVs when creating a training set. A simulation model of the SSV network has been developed, which provides generation of the initial state of the network, modeling of the proactive mode of its operation, and collection of information about the state of the SSV network to generate sets of synthetic training data.
Conclusion. The article develops a simulation model of the SSV network for generating synthetic training data and predicting possible states of the SSV network, as well as a method for using a neural network to predict possible states of the SSV network.