Relevance. Boolean functions are the foundation of modern cryptographic systems and algorithms for encryption, hashing, and pseudorandom sequence generation. Their key properties are high nonlinearity and balancedness. However, traditional methods for constructing Boolean functions with high nonlinearity are based on the use of bent functions, which have ideal spectral properties but are limited in their domain of existence (only for an even number of variables) and require complex algebraic constructions. This creates a contradiction between the theoretical optimality of bent structures and their low practical feasibility. Therefore, a pressing scientific challenge is the development of methods for generating Boolean functions with spectral characteristics close to bent functions, but suitable for practical application. The aim of this study is to improve the nonlinearity of Boolean functions by developing a method for adaptively modifying the detailing coefficients of wavelet decomposition, allowing for the redistribution of spectral energy and enhancement of high-frequency components without complicating the algebraic structure of the functions.

Methods. Spectral analysis, the discrete Haar wavelet transform, algorithmization, and experimental modeling were used to achieve this goal. Wavelet analysis is used not only for function decomposition but also as a tool for controlled spectral transformation.

Solution. An algorithm for adaptively correcting the detailing coefficients of wavelet decomposition is proposed, ensuring the redistribution of spectral density toward high-frequency components. Experiments were conducted for Boolean functions of dimensions n = 8, 10, and 12, confirming an increase in spectral nonlinearity by 12–18 % compared to the original functions. Novelty. This paper proposes for the first time the use of a discrete wavelet transform to purposefully enhance the spectral nonlinearity of Boolean functions. Previously, it was used primarily for signal analysis. A formula for adaptive modification of detailing coefficients is introduced, allowing for control of the spectral structure of functions without resorting to complex algebraic transformations.

The theoretical significance of this work lies in the substantiation of a new approach to generating Boolean functions based on spectral modeling of wavelet coefficients.

The practical significance of the results lies in the fact that the proposed approach opens the possibility of further automating the synthesis of Boolean functions and S-boxes with specified spectral characteristics. This can be used in the development of new encryption standards and in assessing the resistance of algorithms to advanced types of cryptanalysis.

Relevance. The article considers the problem of computational cost reduction for signal superposition. It is being solved in various applications, such as delay estimation of the radio and satellite channels, navigation, medicine task, echolocation, measuring devices repeatability. The feature of the researchable task is processing of signals with gaps. If the existing methods are applied to the signal processing, then it leads to time processing increasing or insufficient superposition accuracy. The solution will allow reducing the requirement of calculation equipment (processor) and increasing the decision-making efficiency.  

Purpose of the work. Computational cost reduction by the decreasing of the number hypotheses (offsets) by pyramid representation for signal superposition.

Methods. An optimization method for extremum searching that corresponds to offset base on the pyramid representation, a modified Lucas-Kanade method for additive and multiplicative deviation of the signal.

Results. A metric for comparing the signal with gaps was proposed. The two-stage algorithm was developed. The first stage estimates the offsets with sampling step precision by pyramid representation. The second stage estimates the offset with subsampling precision by the Lucas-Kanade method. Recommendations of the hypotheses number (offsets) for global extremum determination are presented. The developed algorithm in comparison to the current algorithm allows decreasing the processing time by 10 times with similar precision.

The work's scientific novelty is the enhanced method for processing the signal with gaps by pyramid representation.

Practical significance. The result can be used to reduce the computational cost for one-dimensional signal processing. Also it can be used for image superposition with different scale and rotation for real-time machine vision systems.

Relevance is driven by the necessity to develop software-defined receivers capable of dynamically adapting to changes in navigation systems without hardware modifications, as traditional hardware-defined receivers based on fixed signal processing algorithms possess limited adaptability to evolving signal structures and new services.
The research aim is to develop an algorithm for the joint processing of navigation signals with frequency and code division multiple access, ensuring the universality of a software receiver under diverse Global Navigation Satellite System signal conditions.

The scientific objective is the experimental verification of digital signal processing algorithms that facilitate unified processing of multiple navigation signal types within a single software environment.

Methods employed in the study include algorithmic modeling, correlation processing based on the Fast Fourier Transform, and experimental validation using a software receiver prototype.

Results are as follows: possible approaches for joint processing of complex signals with frequency and code division in a GLONASS software receiver are presented; a concept for the development of user navigation equipment technology is outlined; experimental results for the reception and processing algorithm of code-division signals are provided; an algorithm for joint processing of frequency-division and code-division signals in a unified software receiver is proposed.

The scientific novelty lies in the substantiation and experimental confirmation of the feasibility of unifying reception algorithms for signals with frequency and code division multiple access within a unified computational core.

Theoretical significance lies in obtaining new knowledge about the principles of constructing universal processing algorithms for navigation signals.

The practical significance is that the proposed algorithm enables the development of universal navigation receivers with software-defined adaptability to Global Navigation Satellite Systems updates, significantly reducing hardware modernization costs. Its implementation on high-performance processors will provide real-time processing capabilities in prospective receiver designs.

Today, many services are emerging that transmit augmented reality traffic in real time. This leads to the complex task of formulating metrics for evaluating augmented reality services. This article examines a comparison of wireless augmented reality technologies for a mobile application. This leads to the difficult task of generating indicators that have the greatest impact on assessing the quality of augmented reality services. In this study, a comprehensive assessment of the quality of perception using subjective and objective methods was carried out for various wireless technologies used to provide augmented reality services through a mobile application. The application is highlighted, geographically distributed across different cities. The analysis of network parameters was performed and the results of calculations of the Hearst parameter and subjective quality assessment for various wireless technologies and different user densities were obtained. The relationship between subjective and objective assessments of the quality of augmented reality services has been established. The relevance of this work is determined by the need to determine subjective assessments and the Hurst exponent depending on the wireless augmented reality technologies used.

Method. When processing the experimental results, the method of analyzing the variance graph was used.

The results are substantiated and can be added to the network characteristics for evaluating the performance of a mobile augmented reality application.

The novelty of the obtained results lies in the fact that an approach to assessing the quality of augmented reality services is considered, based on the established relationship between subjective assessments and the Hurst exponent.

Practical significance: an application is geographically distributed across cities with different population densities. Network analysis is performed over different time periods and calculation results are obtained for Wi-Fi and 4G wireless technologies in order to determine the relationship between the time series coefficient and the average packet size intensity. A comprehensive assessment of the quality of perception by subjective and objective methods was carried out.

This article examines a mathematical model and an analytical method for obtaining upper bound estimates of end-to-end delays of medium-priority eCPRI traffic in the Fronthaul based on TSN Ethernet technology of 4G/5G networks using a credit shaper.

The relevance of this study stems from the fact that the rapid development of promising Industry 4.0 infocommunication applications has necessitated the transmission of diverse traffic requiring high quality of service. For these purposes, the transport capabilities of 4G/5G networks can be utilized, provided that their fronthaul segment is implemented using the advanced technology of time-sensitive TSN Ethernet networks. To service aperiodic eCPRI traffic in a TSN network, it is advisable to use a dedicated credit-based traffic shaper (CBS) to increase fronthaul throughput. Requirements for the boundary delays for the transmission of various types of fronthaul traffic are regulated by the IEEE 802.1CM standard; however, it lacks a methodology for determining them.

The aim of this study to develop a model and method for estimating boundary delays in the Fronthaul segment based on TSN Ethernet technology in 4G/5G networks using the theory of network calculus.

Methods include deriving analytical expressions for the Fronthaul traffic arrival curves and its service curves in the TSN Ethernet network using the CBS credit traffic shaper based on the basic approaches of network calculus theory.

Results. A model for serving heterogeneous eCPRI traffic in the Fronthaul based on TSN Ethernet was developed. A method for obtaining worst-case delay for CBS medium-priority traffic the was proposed, based on network calculus.

Scientific novelty. The conducted study is the first attempt to obtain a methodology for estimating upper bounds on end-to-end delays of medium priority eCPRI traffic, taking into account the requirements of IEEE 802.1CM.

The theoretical significance of the work lies in the development of a mathematical model and a method for estimating the boundary delays of medium-priority traffic in the Fronthaul segment of 4G/5G networks based on TSN Ethernet technology, served using a credit generator, using the mathematical apparatus of network calculus theory.

Relevance. Non-contact monitoring of fuel assembly (FA) head height differences is important for ensuring the safe operation of nuclear power plants. Deviations in FA positions may indicate deformation of reactor core elements and affect reactor performance. The parallax-shift method, based on sequential frames acquired by a single television camera, eliminates mechanical contact with the monitored object and uses camera motion to form quasi-stereo pairs. However, its practical application requires assessing the influence of television system parameters and imaging geometry on measurement accuracy.

The purpose of this study is to establish relationships between television camera parameters, quasi-stereo pair geometry, and the error in determining FA head height differences using the parallax-shift method, as well as to identify rational parameter ranges for the measuring system.

Results. It was established that disparity estimation error is the dominant factor affecting final accuracy. Method stability is achieved through the coordinated selection of focal length, baseline, camera suspension height, and conditions for subpixel localization of fuel assembly head centers. Rational parameter ranges ensuring the required accuracy were determined. The novelty lies in the comprehensive assessment of the influence of television system parameters and imaging geometry on the accuracy of the parallax-shift method and in substantiating a rational quasi-stereo measurement configuration formed by a single television camera during arc motion.

Theoretical significance. The theoretical significance is determined by the development of a computational model of the parallax-shift method and by establishing relationships between disparity estimation error and relative height reconstruction error.

Practical significance. The obtained results can be used to configure television systems for FA monitoring and to justify requirements for non-contact measuring systems operating in the confined space of a reactor core.

Relevance. In recent years, Intelligent Transportation Systems (ITS) have played a pivotal role in urban management and the reduction of transportation risks, having become a key infrastructure technology for future urban network universes (Citiverse). A key challenge is enhancing system efficiency through accurate, real-time traffic prediction. Traditional centralized deep learning models suffer from network propagation delays and vulnerability of the central server in terms of both security and computational overload.

Objective. To develop a decentralized cloud framework based on dynamic fog computing and federated machine learning for traffic prediction in ITS, eliminating dependency on a central server and ensuring system fault tolerance through the proposed architecture.

Methods. The study employs literature analysis in the subject area, mathematical modeling, and computational experiments for performance evaluation. The proposed cloud framework integrates three technologies: Decentralized Federated Learning, Fog Computing, and Adaptive Graph Convolutional Recurrent Networks.

Results. The proposed framework operates effectively without a central server. Experiments on the real-world datasets PeMSD4 and PeMSD7(M) show that the model reduces communication overhead by approximately 48% compared to traditional FL methods. Convergence speed is significantly faster (a 17.8% reduction in the loss function during initial training rounds), while prediction accuracy remains at a competitive level compared to models relying on a central server. A novel decentralized system architecture is proposed that eliminates the central server while maintaining a balance among prediction accuracy, model efficiency, and network resource consumption.

Theoretical Significance. The study confirms the theoretical validity of integrating dynamic fog computing with decentralized federated learning. Implementing this approach using AGCRN at the fog node level enables accurate modeling of complex spatio-temporal dependencies, while eliminating the need for raw data transmission and central server involvement.

Practical Significance. Experimental results on real-world datasets confirm the feasibility of deploying the proposed solution in large-scale ITS within smart cities. The solution is particularly effective under conditions of limited network bandwidth, connectivity disruptions with the cloud, and overload of the central server.

Relevance. The vulnerability of global positioning systems stimulates the deployment and practical operation of local radio navigation systems in order to solve specific navigation tasks for unmanned aerial systems. This approach is based on the successful operation of short-range navigation systems such as TACAN and VOR/DME. However, the use of unmanned aerial vehicles as platforms for unauthorized radio monitoring and jamming systems makes local radio navigation systems vulnerable to destructive interference. Therefore, it is necessary to develop a set of measures to assess the intelligence protection of radio navigation information lines and, in the future, to develop methods to improve their interference protection.

Objective: to create a mathematical model for researching and evaluating the intelligence protection of elements of local radio navigation systems based on the coincidence of impulse streams.

Methods: energy calculation methods for radio communication lines, probability theory, and formalization of signal search procedures based on pulse flow theory.

Results: a private model of the functioning of a local radio navigation system for servicing unmanned aircraft platforms has been developed; an analytical apparatus for a model of radio monitoring of radio source signals has been refined, taking into account the spatial, frequency and time coincidence of the antenna patterns of the search receiver and the radio navigation signal emitted in pulsed mode; dependences of the probability of electromagnetic availability of radio sources on the monitoring distance for different antenna pattern widths have been obtained. the search receiver; the procedures for searching for navigation signals in the form of a pulse stream are formalized; the dependences of the probability and time of coincidence of pulse streams characterizing the search procedures and the functioning of local radio navigation systems are calculated.

Practical significance: the results of the work can be used in the design of the deployment of local radio navigation systems, taking into account the dependence of the probability of revealing radio emission sources on the potential monitoring range at different antenna beam widths.

Background. Modern information systems operate under the permanent influence of hybrid destabilizing factors whose nature — ranging from targeted cyberattacks to stochastic technical failures, sabotage, key personnel departure, and sanctions imposition — is often a priori unknown. Existing methods for controlling information systems under destabilizing factors are fragmented: they either focus on narrow technical aspects or are limited to administrative regulations, failing to provide holistic coverage of all hierarchical levels of the system.

Objective. The objective is to develop a generic architectural control model, ONYX, representing an information system as a computable state space and ensuring verifiable adaptation to destabilizing factors of arbitrary nature in order to preserve its functionality.

Methods. The state of an information system is represented as an attributed multigraph. For hierarchical systems, the Hierarchical Organization Postulate is introduced, decomposing the graph into the following levels: Management, Personnel, Hardware, and Software. The validity and intendedness of states are determined by the predicates and , respectively, based on first-order logic. System control is implemented by the operator R using a database of verified templates.

Results. The ONYX model has been developed, representing an information system as a multigraph with a control operator. Theorems have been proved on the solution existence criterion, namely X-adaptivity, on the solvability of recovery by the operator , on safety invariance, and on the relative completeness of the operator. The following consequences have been derived: the “Cone of Influence” effect and the principle of layer-based threat neutralization. The scientific novelty lies in the universality of the formalism for hierarchical and swarm systems, the definition of necessary and sufficient conditions for the solvability of the recovery task, the proof of the consequences of hierarchy, the substantiation of cross-level recoverability, and the introduction of a validity invariant for automatic control.

Theoretical and Practical Significance. The theoretical significance consists in creating a mathematical apparatus for controlling the structural dynamics of complex systems under destabilizing factors of arbitrary nature. The practical significance lies in formalizing the requirements of information security standards, including ISO/IEC 27001, GOST R ISO/IEC 270xx, and NIST CSF, for building next-generation SOAR systems that integrate organizational and technical protection measures.

The relevance of this research is due to the fact that, on the one hand, the development of artificial intelligence and information technologies in general opens up new prospects for the implementation of innovative traffic management methods and algorithms in road regulation processes, and on the other hand, most existing solutions are not adaptive to changes in the intensity of vehicle traffic and are not designed to operate effectively under peak loads.

The purpose of this work is to select the boundary parameters of the developed algorithm and analyze its effectiveness in comparison with other methods of traffic control.

The paper uses methods of queuing theory and graph theory for the traffic model, and also applies a multi-agent approach and reinforcement learning for the algorithm of traffic light phases control, simulation modeling is used to evaluate the system efficiency.

Results. In the course of solving the scientific problem, the functional requirements of the developed software were formed. A series of experiments was conducted on the simulation of the traffic management process under various scenarios. A simulation model of a road section in the Nevsky District of St. Petersburg was implemented. A number of experiments were conducted on the simulation of traffic situations and the optimization of the duration of traffic light phases, as well as the identification of the boundary parameters for the effective functioning of the system. A comparative analysis of traffic management methods was conducted.

The scientific novelty of the work is determined by the author's approach to combining methods of road traffic management and creating an algorithm that identifies the most suitable methods for the current traffic situation.

The practical significance of the developed solution lies in the fact that it is more effective than other algorithms considered in the article and can be used for managing road traffic and analyzing the efficiency of the road network.