Objectives. The problems of decomposition of a parallel automaton into a net of sequential automata at synchronous realization and low power race free state assignment of them are considered. The objective of the paper is to investigate the possibilities of applying decomposition in state assignment of partial states in order to decrease the problem dimension taking into account the peculiar properties of the asynchronous realization.

Methods. The given parallel automaton is decomposed into a net of sequential asynchronous automata whose states are assigned then with ternary vectors. The power consumption lowering of the designed device is achieved by lowering the intensity of its memory elements switching that is appreciated by probabilities of transitions between the states of the automaton. The state assignment is reduced to the problem of minimal weighted cover. The probabilities of transitions between sets are calculated by means of solving a system of linear equations according to the Chapmann – Kolmogorov method.

Results. A method to construct a net of sequential asynchronous automata that realizes the given parallel automaton is described. The paper touches upon the problem of minimization of interconnections in the net.

Conclusion. Applying parallel automaton decomposition allows decreasing the dimension of the laborious problem of state assignment. The proposed method is intended for application in computer aided systems for design of discrete devices.

Objectives. The problem of restoring the functional description of digital VLSI devices presented at the transistor level is considered. The objective of the work is to develop means for extraction of blocks representing logical networks from two-level descriptions of CMOS circuits at the transistor level, which were obtained as a result of recognition (extraction) of subcircuits that implement logic elements.

Methods. Graph based methods and software tools are proposed for extracting a connected blocks representing a logical network from two-level descriptions of a transistor circuits in SPICE format. In the graph interpretation, the task is reduced to constructing a labeled directed graph of a logical network based on a labeled undirected bipartite graph specifying a two-level description of the transistor circuit.

Results. The proposed method makes it possible to identify lexicographically ranked logical networks, from which a transition is made to logical equations that specify the functions implemented at the outputs of the resulting networks. Software tools have been developed that provide the generation of a hierarchical description in SPICE format that implements the original circuit at the transistor level, as well as descriptions of found logical networks in the SF language of hierarchical structural and functional descriptions of discrete devices and in high-level languages (VHDL and Verilog).

Conclusion. The developed methods are implemented in C++, included in the program for decompiling transistor CMOS circuits and tested within it on practical examples of transistor-level circuits. The paper provides examples of reverse engineering of some practical transistor circuits.

Objectives. The problem of developing the mathematical foundations of modular secret sharing in a special linear group over the ring of integers is being solved.

The relevance of the problem is reduced to the fact that a large number of requirements are imposed on secret sharing schemes. These include the ideality of the scheme, the possibility of verification, changing the threshold without the participation of the dealer, the implementation of a non-threshold access structure and some others. Every secret sharing scheme developed to date does not fully satisfy all these requirements. It only has a certain configuration of these properties. The development of a scheme on a new mathematical basis is intended to expand the list of these configurations, which creates more opportunities for the user in choosing the optimal option.

Methods. Group theory, modular arithmetic and theory of secret sharing schemes are used.

Results. A fundamental domain with respect to the action of the main congruence subgroup by right shifts in the special linear group of second-order matrices over the ring of integers is constructed. On this basis, methods for modular secret sharing and its threshold restoration are proposed.

Conclusion. A rigorous mathematical justification is given for the correctness of the algorithms for generating partial secrets and restoring the main secret in the special linear group over the ring of integers. These results will be used to study the configuration of secret sharing properties in this group.

Objectives. To develop a new method for training a mobile robot control system to use a maze solver algorithm based on reinforcement learning and the right-hand algorithm.

Methods. The work uses the method of computer modeling in the MATLAB/Simulink environment.

Results. A new method for training a mobile robot control system capable of implementing the right-hand algorithm for finding an exit from a maze is proposed. The proposed method is based on the work of two agents interacting with each other: the first directly implements the search algorithm and searches for an exit from the maze, and the second, following it, tries to learn using the imitation learning method. The expert agent, implementing a discrete algorithm for moving through the maze, makes precise discrete steps and moves almost independently of the second agent. The only limitation is its speed, which is directly proportional to the distance between the agents. The second agent, the student agent, tries to reduce the distance to the first agent by trial and error. The learning process was implemented using the reinforcement learning method, which was used in the imitation mode and for which a corresponding reward function was developed, allowing the robot's center of mass to be kept in the center of the corridor and, if necessary, to turn, following the expert agent. The agents move along a virtual polygon consisting of branched corridors wide enough to implement various movement maneuvers.

Conclusion. It was proven that, thanks to the proposed method of imitative learning, the student agent is able not only to adopt the required behavior patterns from the expert agent – to search for an exit in a previously unknown labyrinth using the right-hand algorithm, but also to independently acquire new ones (changing speed on a turn, bypassing small dead-end corridors), which positively influence the performance of the assigned task.

Objectives. A hybrid approach to the problem of searching and classifying defects in printed circuit boards (PCB) is proposed. Key factors and trends in the design and production of PCBs are considered. The relevance of the study is determined by the use of new materials and production technologies.

Methods. A hybrid approach based on the algorithm of comparison with reference and the use of the YOLO family of neural network models for detecting objects is used to solve the problem.

Results. Models were trained on public sets of PCB images with six classes of defects, and their accuracy was assessed using generally accepted metrics.

Conclusion. Experiments have shown that the YOLOv8 neural network architecture has high accuracy of defect detection, low sensitivity to image quality, presence of text and graphic objects on the PCB, but the low quality of training datasets imposes restrictions on the use of only neural networks for defect detection. It is proposed to use a hybrid approach to improve the quality of defect inspection by applying different methods depending on the quality assessment of the analyzed images.

Objectives. The main purpose of this work is to adapt the architecture of the REINVENT neural network to generate potential inhibitors of the HIV-1 envelope protein gp120 using in the learning process with reinforcement of molecular docking on GPUs.

Methods. To modify the initial network model, molecular docking on GPUs implemented in the learning process with reinforcement was used, and an algorithm was developed that allows converting the representations of connections generated by the SMILES network into the PDBQT format necessary for docking. To accelerate the learning of the neural network in the modified version of the REINVENT model, the AutoDock-Vina-GPU-2.1 docking program was used, and to clarify the results of its work, the procedure for revaluing the affinity of compounds to the target using the RFScore-4 evaluation function was used.

Results. Using a modified version of the REINVENT model, more than 60,000 compounds were obtained, of which about 52,000 molecules have a binding energy value to the HIV-1 gp120 protein comparable to the value calculated for the HIV-1 inhibitor NBD-14204, used in calculations as a positive control. Of the 52,000 compounds selected, about 34,000 molecules satisfy the restrictions imposed on a potential drug to ensure its bioavailability when taken orally.

Conclusion. The results obtained allow us to demonstrate the effectiveness of an adapted neural network by the example of designing new potential inhibitors of the gp120 HIV-1 protein capable of blocking the CD4- binding site of the gp120 virus envelope protein and preventing its penetration into host cells.

Objectives. The problem of the web search results processing in an information support system for decision[1]making is solved in order to create and correct a meaningful description of a problem situation. An approach to solving this problem is proposed based on the use of thematic text corpora (collections of texts on a specific topic) as knowledge about the subject area, as well as a knowledge representation model based on verbal associations. When solving problems of the web search results processing in a decision-making information support system, five main goals are pursued: the formation of an extended description of a problem situation, the synthesis of a search prescription, an Internet search for information about decisions made, the synthesis of a retelling of the information found, and an assessment of the quality of the found analogues of the decisions made.

Methods. Methods of set theory, graph theory and mathematical linguistics are used.

Results. A mathematical model has been developed for the web search results processing in an information support system for decision making. The concepts of verbal association of words and texts, as well as pragmatically complete lexical structure, are formalized. The proven properties of such structures provide algorithmization of information processes in the model under consideration.

Conclusion. The approach to modeling is based on the formalization of the concepts of the informativeness of words, sentences, texts and the informativeness of verbal associations between them. As an implementation of the model proposed in the article, algorithms have been developed for creating a dictionary of pragmatically complete lexical structures, creating structural-lexical templates for sentences, texts and subject areas, synthesizing a brief retelling of the information found, and assessing the quality of the found analogues of the decisions made.