Electronics and Control Systems

Air Traffic Controller Workload as a Factor in Multi-criteria Arrival Sequencing within the Point Merge System

Daniil Marshalok — 2025-09-29

Gate-release strategy in the Point Merge System is crucial for reliable arrival sequencing and separation assurance in terminal areas. In this study, we examine three aggregation policies for the exit decision from the radius-to-fix arc conjunctive (AND), disjunctive (OR), and majority (MAJORITY) implemented with a non-compensatory safety barrier and S* speed-control variants. The objective is to assess each policy’s ability to regulate headways, maintain time-based separation, limit low-altitude level-offs, manage advisory demand, and mitigate environmental impact under varying weather conditions, and to identify their strengths and weaknesses. As an example, we conduct an experimental evaluation on the published geometry of Dublin (EIDW) RWY 28L, parameterising arrivals with realistic kinematics and stratifying by METAR; performance metrics include headways at arc/gate/final, spacing error relative to S*, a time-based loss-of-separation proxy, level-off time, and coarse terminal-area fuel / CO₂. Human factors are incorporated through a Human Workload Index combining expected speed-advisory count, level-off time, short-headway alarms, and weather difficulty markers. Alternatives are ranked using TOPSIS with AHP-like weights over Safety, Efficiency, Human, and Environment. The results show that policy choice is the primary driver of headway regularity, advisory load, and low-altitude behaviour; moreover, treating workload as a first-class criterion can overturn rankings obtained from an efficiency-only view. This evaluation helps practitioners select and gate-release policies to site-specific tolerances within an auditable framework.

About the Phase Noise of Frequency Synthesizers

Yaroslav Hrytsev — 2025-09-29

The paper proposes a variant of implementing a partial synthesizer with a small frequency step and preserving a sufficient level of phase noise in the X-band frequency range, which provides high frequency stability and low phase noise by combining 3 methods. A brief review of common methods for constructing frequency synthesizers, such as phase-locked loop, digital signal synthesis (DDS), dielectric resonator oscillator, is given. Their advantages and disadvantages were used and taken into account in the development of a new method for constructing a frequency synthesizer. The article compares the characteristics of the phase-locked loop frequency synthesizer on the ADF5355 chip with the developed method. The proposed method, which includes all 3 proposed methods, is presented in the form of a functional circuit containing two phase-locked loops and one DDS. The first PLL contains a dielectric resonator oscillator with an output signal of 8 GHz and a working frequency bandwidth of 1 kHz with a minimum phase noise equal to –132.85 dBc/Hz at a 1 kHz offset. The low-noise DDS signal is fed to the second phase-locked loop. The output signal is in the range of 9-9.5 GHz with a phase noise of –98.32 dBc/Hz at a 10 kHz offset.

Programmed Module for Transportation of Humanitarian Medical Cargo by Drones in Extreme Conditions

Vladimir Shutko — 2025-09-29

The structure and functions of various drones (UAVs) for medical purposes were observed, their general analysis was done. The main goal and objectives of the work were outlined: to develop the structure of a container for UAVs for medical purposes, and to clarify the possibility of its transportation to programmatically defined object. These were done basing on the analysis of prototypes – versions of previously developed UAV modules for providing various types of medical assistance in extreme conditions. Different versions of modules for medical UAVs were characterized. The need to deliver a container to a specific object or person requires the creation of appropriate software to automate this process; developed software was based on face recognition technology – convolutional neural network. The work on the development of a container structure for medical purposes was disclosed in details. Its technical task was to present a version of medical container for multirotor type UAV with vertical take-off and landing. The medical container itself was made in the form of a streamlined cylinder to improve its aerodynamic characteristics, which reduces the value of air resistance during flight and increases the overall maneuverability and stability of the structures. Developed medical UAV was equipped with modules for diagnosing person's condition and providing them with appropriate medical care. Developed UAV can be used repeatedly, and modular systems of medical equipment and first aid supplies can be parachuted over the location of potentially injured person. The modules can be conditionally divided into several groups: diagnostic, resuscitation, a subgroup for transporting biomaterials, devices for detecting possible chemical contamination, etc. In process of the work, there were subdivided and developed several such modules (groups of devices) in the newly developed UAV. The data about the practical application of work results were given: possibility of introducing a UAV with a first aid container into the activities of emergency services (ambulance, other emergency services); use for detecting of chemical substances, environmental pollutants using chemo-specific detectors in conditions of potential danger to human life and health; provision of medical care in extreme conditions; delivery of necessary medications (vaccines, medical preparations) or performing the function of a courier for delivering biological samples to the nearest laboratory.

Calibration of Pressure Measurement Channels in Wind Tunnels

Oleksander Zhdanov — 2025-09-29

The article deals with estimating errors in the measurement systems during experimental tests of unmanned aerial vehicles in the wind tunnel. The instrumentation and technique used for calibrating pressure parameters are described. Features of the measurement in the wind tunnel are characterized. The main sources of pressure measurement are listed. The features of measuring pressure in the wind tunnel are discussed. The structural diagram of the calibrating channel of pressure measurement. The transformation function of the measuring channel was proposed. The approach for estimating relative errors is represented. The estimation of high-velocity head measurement errors has been carried out. Relative and absolute errors of velocity head were estimated. Errors in measuring airflow speed have been estimated. It is shown that the velocity of the airflow is the result of an indirect nonlinear measurement. The obtained results can be useful for testing unmanned aerial vehicles of different types. They can also be applied to the measurement of the pressure in various experimental equipment.

Automated Technological Design оf Nanoscale Transistors

Oleksandr Melnyk — 2025-09-29

The article is devoted to the automation of technological preparation of modern high-frequency and energy-efficient bipolar transistors with nanoscale depths of impurity implantation into a nanoconductor substrate. At the stage of mathematical modeling of technological operations of multilayer casting, the known theoretical and empirical, developed by the authors of the article, high-temperature dependences of doping parameters, distributions of depths of boundary distances of emitter and collector junctions, which, as a result, determine the thickness of the electrically neutral base region of the transistor, are taken into account. Mathematical models of technological parameters of surface and volume concentrations of impurities, which cause degeneration and repeated inversion of the conductivity types of the initial crystalline substrate, are proposed. The maximum possible values of the tincture and solution of acceptor and donor impurities, which increase the gain coefficients and reduce the power consumption of bipolar nanotransistors, are determined. The drift components of the base and collector currents, which are caused by the internal electric field of the inhomogeneous base, are taken into account. The temperature and time dependences of technological doping operations are found, which primarily determine the creation of bipolar transistors with a base thickness from 100 nm to 10 nm. The values of the limiting concentrations of impurities in semiconductor structures are established. Examples are considered that confirm the effectiveness of the proposed methods for automated design of bipolar nanotransistors. In the future, it is planned to develop generalized algorithms for multi-level hierarchical modeling of transistor nanoelectronics components.

Optimizing Drone Coverage in Agriculture: an Overview and New Approaches

Victor Sineglazov — 2025-09-29

This article investigates the problem of trajectory optimization for unmanned aerial vehicles during multispectral imaging of agricultural lands within the framework of precision agriculture concepts. The main problems related to complex field geometry, presence of natural and artificial obstacles, as well as limited battery capacity of drones are considered. A new hybrid route optimization method is proposed that integrates the ant colony optimization algorithm for global planning of zone traversal sequence with the binary gridding method for detailed local replanning within complex areas and obstacle avoidance. A key feature of the method is an adaptive mission recovery mechanism that allows the drone to dynamically return to the charging station, save mission state, and automatically continue operation from the last uncovered area. Simulation and comparative analysis results demonstrate that the developed approach significantly reduces total traveled route length and optimizes mission execution time compared to traditional methods, confirming its effectiveness for increasing autonomy and productivity of agricultural unmanned aerial vehicles.

Decentralized Local-priority Communication Protocol for Small Unmanned Aerial Vehicle Swarms

Victor Sineglazov — 2025-09-29

The paper proposes a decentralized communication protocol for small swarms of unmanned aerial vehicles that provides prioritized access to the control channel with limited radio resources. The approach is based on local priority selection, agent slot mapping with seat rotation for long-term fairness, and probabilistic sparsity within. This combination manages the load in a mathematical expectation, reduces the probability of collisions, and ensures low latency delivery of priority control messages without a central dispatcher. The simulation results for a swarm of 12 unmanned aerial vehicles demonstrate an increase in usable throughput, median delay at the level of one epoch, and collision rate at the level of the baseline approach with a significantly higher number of successful transmissions.

A Classification Method for Optical Coherence Tomography Images Based on a Structure-oriented Adaptive Neural Network Architecture

Dmytro Prochukhan — 2025-09-29

The method of optical coherence tomography image classification for automated diagnosis of diabetic retinopathy and diabetic macular edema is proposed in the article. An innovative adaptive multi-task deep neural network is created. It simultaneously solves the problems of pathology classification and structural feature reconstruction. The neural network uses the pre-trained EfficientNetB7 model as an encoder for efficient extraction of high-level features. The structural feature learning branch (decoder) is responsible for restoring spatial information. It increases the resolution of feature maps to the original size of 224x224 pixels with a gradual decrease in the number of filters and the use of Batch Normalization to stabilize learning. The classification branch combines semantic and structural features. It uses the channel attention mechanism for dynamic weighting of informative channels. Dropout and Batch Normalization layers are used to prevent overtraining in the classification branch. The model is optimized using a multi-task loss function. It consists of a modified loss function for classification (with class weights to balance data imbalance) and a root-mean-square error for structural loss. Training is performed using the Adam optimizer and the EarlyStopping, ModelCheckpoint, and ReduceLROnPlateau callbacks. The experiment was conducted on the OCT Image Classification dataset. Data augmentation (horizontal reflections) was performed to increase the number of images. High accuracy rates and cost functions were obtained as a result of training. The multi-task method enables the encoder to learn details and boundaries of the retina through Canny edge reconstruction. It contributes to improved classification and provides a powerful internal regularization mechanism, increasing the generalization ability of the model.

Re-uploading Data in Tensor Network

Victor Sineglazov — 2025-09-29

In this paper, we present an approach for enhancing quantum tensor networks through the method of data re-uploading. The proposed framework integrates multiple layers of classical data encoding into tensor network architectures, thereby improving their approximation capacity and reducing the impact of barren plateaus in training. The model construction relies on tree tensor networks combined with RX, RZ, and RY rotational gates and CNOT entanglement, while optimization is performed using differential evolution as a gradient-free algorithm. Experimental evaluation was carried out on the iris and wine datasets, comparing baseline tensor networks with architectures incorporating one to three re-uploading layers. The results demonstrate a consistent reduction in training and test loss, with accuracy, recall, and precision reaching 100% on the iris dataset for three layers and improvements of up to 40% in prediction quality on the wine dataset. These findings confirm that data re-uploading significantly enhances the performance and expressiveness of tensor network-based quantum models.

A Formal Language for the Analysis of Graph Models and its Software Implementation

Michael Zgurovsky — 2025-09-29

The purpose of this paper is to develop a specialized language for processing graph data and its software implementation. The proposed solution ensures versatility, usability, and efficiency, enabling the execution of both basic graph operations and more complex procedures. The tool supports classical graph algorithms, including shortest path search, graph traversal, and minimum spanning tree construction, as well as applications in modeling and analyzing message transition processes and processing graph-based representations of textual data. A common drawback of many existing graph analysis tools—often implemented as libraries of general-purpose programming languages—is their limited usability. This limitation arises from the fact that the description of graph analysis procedures relies on data structure operations defined in terms of these general-purpose languages, which complicates perception and reduces the clarity of the mathematical methods being implemented. Developing a specialized domain-specific language based on high-level abstractions can address these shortcomings. Such a language will provide a formalized description of methods for analyzing and processing graph models, improving their comprehensibility and accessibility to users. Its software implementation will deliver ready-to-use solutions for executing graph analysis methods. Composing such methods will facilitate solving a wide range of tasks, including the analysis of natural language messages and the study of information publication and dissemination processes in online environments.

Intelligent System for Diagnosing Vestibular Schwannoma

Victor Sineglazov — 2025-09-29

This scientific work is dedicated to the development of an intelligent system for diagnosing vestibular schwannoma. A new approach to texture analysis of magnetic resonance imaging images of schwanomas has been proposed as a method for assessing the growth of swelling. The use of this approach will help to avoid the risks of the progression of the neoplasm and immediately eliminate the need for surgical intervention. At the boundaries of the research, a number of classes of texture descriptors were put together, including: first-order statistics (intensity histograms), grey-color consistency matrix, dovzhin sequence matrix Gray Rivne, zone size matrix, Gray Rivn deposit matrix, as well as hvillet-transformed signs. The comprehensive analysis of these descriptors made it possible to formalize the internal microstructure of the fluff and implement an effective model for predicting its growth.

Flight Safety Issues During Aircraft Landing Approach

Yurii Hryshchenko — 2025-06-30

This paper examines the psychophysiological stress experienced by flight crews during a standard landing approach, a phase considered one of the most critical in aviation operations. Elevated levels of cognitive and emotional strain can significantly influence pilot performance, including situational awareness, decision-making accuracy, and reaction time. The study emphasizes the pivotal role of timely and precise avionics diagnostics in mitigating risks associated with equipment failure or misinterpretation of instrument data. In this context, a method for calculating the reliability of critical onboard instruments during landing is proposed. The approach integrates both technical reliability factors and operational stressors to provide a comprehensive evaluation framework. The findings aim to support enhanced safety protocols and inform the development of more resilient avionics systems.

Mathematical Model of Gyroscope with Contactless Suspended Rotor and Three-component Accelerometer for Solving Problems of Autonomous Autonomous Navigation

Oleg Smirnov — 2025-06-30

The problem of creating a mathematical model of a free three-degree gyroscope with a contactless suspended rotor and a three-component accelerometer for solving problems of high-precision autonomous navigation is solved. The problem under consideration includes determination of kinematic relations of the gyro device and solution of a direct problem for finding navigation parameters for a stationary base. The suspension system of these gyroscopes is practically indifferent to the environment in which the gyro operates, but in order to reduce braking moments, the gyro rotor is placed in a vacuum chamber. The given structure of vector relationships between coordinate systems and model parameters allows us to write down several groups of equations regarding the angular positions of the gyro rotor and its angular velocities.

Classification of Sentinel-2 Imagery Using Rayleigh Distribution Modeling

Igor Prokopenko — 2025-06-30

Nowadays land cover classification from satellite imagery is one of most actual and important problems in remote sensing. Multispectral satellite images such as Sentinel-2 images provide high-resolution imagery in different spectral bands, enabling detailed distinguishing of surface objects. This study presents a method of multispectral satellite image classification based on Rayleigh distribution, maximum likelihood method and likelihood functions. It was considered three land cover classes, such as “Water”, “Vegetation”, and “Buildings”, applying three spectral bands (Red spectral band, Green spectral band and Blue spectral band). Proposed classification procedure includes modeling spectral distributions with the Rayleigh probability distribution. The Rayleigh distribution parameters for each class and each spectral band are estimated from training data via the proposed formula. The ESA SNAP software is applied for image processing. Maximum likelihood method is applied for classification procedure. In remote sensing this method is used to classify pixels in satellite imagery into different classes. This method is based on assigning each pixel to the class, for which has the highest probability of belonging. It was described the methodology, including data preparation using the ESA SNAP software and data analysis in Microsoft Excel. The mathematical formulation of the Rayleigh distribution and the mathematical algorithm of calculation of likelihood functions for each class and for each spectral band have been considered. Results include fitted Rayleigh distribution parameters for each class and for each spectral band, classification maps, calculation of likelihood functions and classification result. The classification result depends on which class the maximum likelihood function corresponds to. An example has been considered where the class “Vegetation” is determined using the maximum likelihood method and Rayleigh distribution. The proposed approach can be applied for land-cover classification, ecological monitoring, agriculture and geological tasks.

A Method for Biometric Coding of Speech Signals Based on Adaptive Empirical Wavelet Transform

Oleksandr Lavrynenko — 2025-06-30

In this research, a biometric speech coding method is developed where empirical wavelet transform is used to extract biometric features of speech signals for voice identification of the speaker. This method differs from existing methods because it uses a set of adaptive bandpass Meyer wavelet filters and Hilbert spectral analysis to determine the instantaneous amplitudes and frequencies of internal empirical modes. This makes it possible to use multiscale wavelet analysis for biometric coding of speech signals based on an adaptive empirical wavelet transform, which increases the efficiency of spectral analysis by 1.2 times or 14 % by separating high-frequency speech oscillations into their low-frequency components, namely internal empirical modes. Also, a biometric method for encoding speech signals based on mel-frequency cepstral coefficients has been improved, which uses the basic principles of adaptive spectral analysis using an empirical wavelet transform, which also significantly improves the separation of the Fourier spectrum into adaptive bands of the corresponding formant frequencies of the speech signal.

Prediction of Moving Targets and Adaptive Avoidance in Hybrid PSO-MPC for a Swarm of UAV’s

Victor Sineglazov — 2025-06-30

The paper proposes a hybrid approach to the safe control of a multicopter swarm in the presence of two moving obstacles based on a combination of the particle swarm algorithm and model predictive control. The first stage of the algorithm is a global search for new target positions of subgroup centers using particle swarm algorithm based on predicted data, which allows the front subgroup to smoothly climb and avoid the danger zone. The second stage is the local adjustment of the movement of each vehicle within the model predictive control, taking into account dynamic constraints, which ensures accurate adherence to the calculated targets and prevents formation disruption. Simulation experiments demonstrate that the developed algorithm ensures coordinated maneuvers of all subgroups, timely avoidance of both moving threats, and return to the original formation without sudden jumps in altitude or chaotic behavior.

Accuracy Research for Non-orthogonal Configuration of Inertial Sensors

Olha Sushchenko — 2025-06-30

This article deals with accuracy research of the non-orthogonal configuration of inertial sensors based on Allan variance. The influence of changes in the measurement range of the inertial module on the Allan variance was assessed. Based on an analysis of the results of the Allan variance assessment, a procedure for choosing multi-axis MEMS sensors with identical characteristics to create an inertial non-orthogonal measuring instrument is proposed. An example of compiling a data processing algorithm for an inertial measuring instrument with a non-orthogonal arrangement of sensitivity axes based on an assembly of 3-axis MEMS sensors is given. The simulation results for numerical estimates are represented. Improvement of the accuracy of the non-orthogonal inertial measuring instruments using the Allan variance is shown.

Security System for Office Premises with Use of Modern Information Technologies

Mykola Vasylenko — 2025-06-30

The paper analyzes the office premises and determines the required set of functions and structure of the security system, which includes a video surveillance subsystem, an access control subsystem, a burglar alarm subsystem, and a backup power supply subsystem. A detailed scheme of placement and interaction of components is proposed, and algorithms for the system operation are presented. The integration of the backup power supply subsystem with renewable energy sources ensures autonomous operation in the event of power grid failures.Office security is critical for protecting employees, property, and sensitive information by integrating physical and cybersecurity measures. Modern systems use advanced information technologies, such as remote, high-resolution video surveillance with autonomous analytics and biometric-based access control, to monitor, detect, and respond to threats efficiently. This technological integration enables real-time oversight and immediate action, significantly enhancing overall office protection.

Air Raid Alert Mesh Network System: Key Provision

Halyna Vlakh-Vyhrynovska — 2025-06-30

In the face of modern hybrid threats and infrastructure vulnerabilities, the timely and secure dissemination of air raid alerts is vital for civilian safety. Traditional centralized alert systems are susceptible to disruption, making decentralized wireless alternatives increasingly relevant. This paper presents a secure key provisioning framework for a decentralized air raid alert system built on LoRa-based mesh networking. Each node is equipped with a cryptographic identity stored in a hardware secure element (ATECC608A), enabling message authentication, signature verification, and node revocation without centralized control. The proposed system ensures that only trusted nodes can initiate or relay alert signals, effectively preventing spoofing, replay attacks, and unauthorized activations. A series of real-world tests and simulations demonstrates that the framework introduces minimal latency while significantly enhancing system resilience and trustworthiness. The results confirm the feasibility of a scalable, tamper-resistant alert network capable of operating under degraded or hostile conditions.

Diagnosis of Vestibular Schwannoma Based on Intelligent MRI Image Processing

Victor Sineglazov — 2025-06-30

The study identified the main clinical and diagnostic features of the disease, reviewed modern diagnostic methods for schwannoma, including magnetic resonance imaging and computed tomography, as well as the role of clinical examination, history and laboratory tests, analyzed available open data and proposed the concept of combining medical images with molecular indicators to build more effective diagnostic models based on semantic segmentation. Diagnostic and prognostic biomarkers were summarized, including TNF-α, CD68, CD163, IL-6, CCR2 and others, which may increase the accuracy of predicting the course of the disease.

A Comprehensive Benchmark of Collaborative Filtering Methods on Implicit Feedback Datasets

Ivan Pyshnograiev — 2025-06-26

Collaborative filtering is a foundational technique in modern recommender systems, especially when dealing with implicit feedback signals such as clicks, purchases, or listening behavior. Despite the abundance of сollaborative filtering models, including classical, probabilistic, and neural approaches, there is a lack of standardized, large-scale evaluations across diverse datasets. This study presents a comprehensive empirical benchmark of 13 сollaborative filtering algorithms encompassing matrix factorization, pairwise ranking, variational and non-variational autoencoders, graph-based neural models, and probabilistic methods. Using four representative implicit feedback datasets from different domains, we evaluate models under a unified experimental protocol using ranking-based metrics (MAP@10, NDCG@10, Precision@10, Recall@10, MRR), while also reporting training efficiency. Our results reveal that neural architectures such as NeuMF, VAECF, and LightGCN offer strong performance in dense and moderately sparse scenarios, but may face scalability constraints on larger datasets. Simpler models like EASEᴿ and BPR often achieve a favorable balance between performance and efficiency. This benchmark offers actionable insights into the trade-offs of modern сollaborative filtering methods and guides future research in implicit recommender systems.

Parametric Optimization of the Hierarchical Fuzzy Model of Control with Transfer of Fuzzy Values of Intermediate Data

Natalia Lazarieva — 2025-06-28

The subject of the study is the intellectualization of the technological process of controlling complex objects in order to intellectualize and replace the labor of a human operator. In conditions that are difficult to describe by mathematical methods due to incompleteness and uncertainty, a hybrid neuro-fuzzy model with a hierarchical structure is used to control the process. The aim of the article is to study and develop a learning algorithm for the Mamdani→Sugeno model with the transfer of fuzzy intermediate data between hierarchical levels, implemented by an adaptive neural network. To ensure the accuracy of real-time forecasting, an algorithm for parametric adaptation to operating conditions with the adjustment of the parameters of antecedents and consequences at two levels has been defined. When studying the methods of data transfer between levels, fuzzy logic and artificial neural networks methods, the gradient descent method, Mamdani and Takagi–Sugeno–Kang algorithms, etc. were used. The study confirms the possibility of using hybrid models to intellectualize the process of controlling complex objects. The scientific innovation of the obtained results is the construction of a neural network of a hierarchical control system and the development of a learning algorithm for the transfer of fuzzy intermediate variables with parametric model adaptation based on the gradient descent algorithm.

Orthophotomosaicing Framework for Thermal and Multispectral Images Collected with a UAV for Intelligent Systems

Victor Sineglazov — 2025-06-27

In this paper, a framework for orthophotomosaicing of multispectral and thermal images collected by unmanned aerial vehicles is presented. The proposed framework is based on a two-stage data preprocessing and mosaicing orthophotographic restoration of images captured with a route-planned unmanned aerial vehicle collection. The super-resolution and image restoration step is handled via a two-pathway U-net image restoration artificial neural network. The framework simplifies the process and makes the collected data less sensitive to noise via image restoration and upscaling steps. The framework was tested on visible, multispectral and thermal images and provides 3.5% and 5.34% improvements in peak signal-to-noise ratio for multispectral and thermal orthophotomosaics.

Multi-agent Control of UAVs Using Deep Reinforcement Learning

Ihnat Myroshychenko — 2025-06-28

This paper presents a novel control framework for managing a group of unmanned aerial vehicles using multi-agent deep reinforcement learning. The approach leverages actor–critic architectures, centralized training with decentralized execution, and shared experience replay to enable autonomous coordination in dynamic environments. Simulation results confirm improved tracking accuracy, reduced collision rates, and increased coverage efficiency. The study also compares the proposed system against baseline methods and outlines future work for real-world adaptation. The novelty lies in applying multi-agent deep reinforcement learning to a continuous unmanned aerial vehicle control task in cluttered environments with limited sensing.

Optimizing Kubernetes Autoscaling with Artificial Intelligence

Olha Tkhai — 2025-06-28

This study explores how to improve Kubernetes auto-scaling using artificial intelligence based forecasting. The authors emphasize the limitations of traditional, reactive auto-scaling methods that lag behind rapid changes in demand and propose a proactive approach that predicts future resource requirements. The paper presents a framework for integrating artificial intelligence based predictions into the Kubernetes ecosystem to improve operational efficiency and resource utilization. To address the main challenges, the authors focus on improving workload forecasting and mitigating the impact of random fluctuations in Kubernetes performance. To address this issue, they use time-series forecasting models combined with data preprocessing techniques to predict future CPU utilization and thus inform scaling decisions before peaks or troughs in demand occur. The results show that artificial intelligence based forecasting can significantly improve scaling accuracy, reduce latency, and optimize resource utilization in Kubernetes environments. Time-series models are developed and evaluated using real CPU utilization data from a Kubernetes cluster, including RNN, LSTM, and CNN-GRU. The study also explores new architectures such as Fourier Analysis Network and Kolmogorov–Arnold Network and their integration with the transformer model. In general, the proposed approach aims to improve resource efficiency and application reliability in Kubernetes through proactive automatic scaling.

Comparative Analysis of BRISK and ORB Methods for Local Feature Detection in Satellite Imagery

Artem Riabko — 2025-04-09

Binary local feature detection, which is very important for the satellite image processing of object recognition and image matching, was studied in this paper. In this examination, the BRISK and ORB methods, now used extensively for detecting features for the satellite image processing purpose, have been evaluated. The objective of this paper is investigation of the methods in respect of their ability to detect keypoints and their robustness against transformation and identify their strengths and weaknesses. As an example, an experimental comparison is put forward in the MATLAB environment for images from Vatican City and one of its buildings. This evaluation will help researchers in choosing the most appropriate method depending on their applications.

Comprehensive Security System of Data Transmission Networks of Civil Aviation Enterprises

Kostiantyn Dzhelalov — 2025-04-09

A comprehensive security system for data transmission networks in civil aviation enterprises is considered, which is aimed at overcoming the growing cybersecurity threats in modern aviation infrastructure. Vulnerabilities such as outdated systems, human errors and integration of IoT devices pose significant risks to data confidentiality, integrity and availability. To mitigate these challenges, the system integrates advanced cryptographic algorithms, DevOps methodologies for automated security updates and real-time monitoring tools such as Grafana and Prometheus. The use of fault tolerance mechanisms ensures uninterrupted operation and resilience during security incidents.

High-speed Underwinding of Fuel Hose of UAV Aerial Refueling System

Mykola Filyashkin — 2025-04-09

The paper is devoted to the issues of automation of refueling of unmanned aerial vehicles in the air. The problems of the docking stage are considered, in particular, the sagging of the fuel hose with its subsequent beating, which occurs immediately after contact. The main attention is paid to the stage of winding the sagging fuel hose after the drogue is docked with the probe of the refueled unmanned aerial vehicle. Options for improving the fuel hose winding drive are proposed. The emergency braking programs of the refueled unmanned aerial vehicle to equalize its speed with the speed of the tanker are studied. Options for changing the speed of reeling the fuel hose depending on the emergency braking parameters are proposed. The procedure for laying the fuel hose on the underwinding drum and its effect on changing the reeling speed are analyzed. Options are offered to prevent the drogue and probe from disengaging due to excessive hose tension.

Investigating the Influence of Communication Systems on the Performance of UAV Combat Missions

Vasyl Yehunko — 2025-04-09

This paper investigates the optimization of the communication system of unmanned aerial systems to improve the efficiency of combat operations. In particular, approaches to improving data transmission between unmanned aerial vehicles, manned aircraft and ground control points are analyzed, as well as routing algorithms to ensure control stability. The problem of countering electronic warfare and its impact on the effectiveness of unmanned aerial vehicles in combat conditions is considered separately. The article presents the results of evaluating the effectiveness of modern communication systems for unmanned aerial vehicles, develops mathematical models for calculating the cost and duration of a combat mission, and assesses the resistance of the communication system to electronic warfare. The results can be used to improve communication systems, increase the effectiveness of combat operations and develop a strategy for protection against electronic warfare.

Mathematical Models and Localization Algorithms Wireless Networks

Andriy Dudnik — 2025-04-09

This paper comprehensively analyzes mathematical models and localization algorithms for wireless sensor networks deployed in resource-constrained environments. Precise node localization is crucial in ensuring the efficiency and reliability of various systems, including environmental monitoring, disaster response, industrial automation, and logistics tracking. Accurate spatial information enables context-aware data processing, improves routing efficiency, and enhances overall network performance.

The study focuses on several established and emerging localization techniques, including the Distance Vector-Hop (DV-Hop) algorithm, anchor-based positioning methods, and the Multidimensional Scaling (MDS-MAP) approach. These algorithms are assessed regarding localization accuracy, computational complexity, scalability, and energy consumption. A detailed review of mathematical models used for estimating distances—based on signal strength (RSSI), time of arrival (ToA), and time difference of arrival (TDoA)—is provided. Particular emphasis is placed on error minimization strategies using Kalman filters, smoothing algorithms, and hybrid measurement techniques. Furthermore, the influence of deployment-specific parameters such as node density, radio signal multipath propagation, environmental interference, antenna specifications, and frequency band selection is thoroughly examined. The simulation results demonstrate that the MDS-MAP algorithm achieves the highest localization precision, with root mean square error (RMSE) values below 1%, although it demands considerable computational resources. In contrast, more straightforward methods such as Distance Vector-Hop or heuristic-based algorithms show moderate accuracy but require fewer resources, making them suitable for devices with limited processing power and battery capacity. The study offers practical recommendations for optimizing node placement and localization configurations to balance precision and system overhead in real-world applications. The results are particularly relevant to scenarios where the infrastructure is limited or temporary and adaptability and robustness to environmental dynamics are essential. This work will be of significant interest to researchers, engineers, and system architects working in wireless sensor networks, particularly those developing localization solutions under operational constraints or in unpredictable environments. It contributes theoretical insights and applied guidance for improving localization efficiency and reliability in low-power distributed systems.

Enhancement Arduplane Radio Failsafe Algorithm by Extending with a New Delegation Action

Yaroslav Sheyko — 2025-04-08

The article deals with the problem of the absence of a radio failsafe mechanism in the unmanned aerial vehicles' ArduPilot firmware that delegates the flight control management from manual mode to the onboard companion computer once the radio transmitter signal is lost due to natural reasons or caused by military-grade artificial radio interference. Algorithms for detecting signal loss and switching control to onboard computers are proposed. The proposed algorithms require knowledge adjustments and enhancements in three components: MAVlink protocol as a transport between parties, ArduPilot firmware running a flight controller, and a client library encapsulating MAVlink implementation. The proposed algorithms require knowledge of the principles of flight control approaches as well as programming skills in C++, and Python languages. To solve the problem adjustments and enhancements in three components are used: MAVlink protocol as a transport between parties, ArduPilot firmware running a flight controller, and a client library encapsulating MAVlink implementation. A brand-new short-radio failsafe action is added to keep ArduPilot firmware consistent. On top of that, the proposed problem-solving approach eliminates the potential misleading of pilots compared to modifying one of the existing failsafe scenarios. The simulation and field test results are presented, validating the effectiveness of the proposed algorithms. These findings have potential applications in both civilian and military domains.

Hybrid Methodology for Rebuilding a Swarm of Drones Based on Local Capabilities and Global Coordination

Victor Sineglazov — 2025-04-09

This work is devoted to solving the problem of restructuring the structure of a drone swarm from one topology to another. A hybrid topology is proposed that combines global centralized assignment of target positions with local potential control of each drone. Attractive and repulsive fields are used for safe maneuvering, while periodic global coordination ensures optimal distribution of roles. A mathematical model, rules for forming control influences, and convergence criteria are presented. The implementation of the proposed hybrid methodology is based on the sequential interaction of a global optimizer that determines the target positions of the swarm and a local potential regulator that ensures safe convergence of drones to these positions. Calculations are performed in discrete time steps with periodic restart of the global planner in case of a task change, the appearance of obstacles, or the loss of individual devices.

Risk Assessment in Emergency Situations Using a Bayesian Network

Victor Sineglazov — 2025-04-08

У статті представлено всебічний аналіз сучасних підходів до оцінки ризиків у надзвичайних ситуаціях, з акцентом на випадки, пов’язані з пожежами. Розглянуто як якісні, так і кількісні методи, зокрема експертні оцінки, моделювання Монте-Карло, дерева рішень, FMEA, FTA та HAZOP. Особливу увагу приділено використанню баєсових мереж як динамічного інструменту ймовірнісного моделювання. Запропонований підхід дозволяє інтегрувати апріорні знання з новими даними та забезпечує оновлення оцінок ризиків у режимі реального часу. Побудовано структуру баєсової мережі для моделювання впливу різних середовищних та експлуатаційних факторів на ключові індикатори ризику, такі як людські втрати, матеріальні збитки та екологічна шкода. Симуляційний сценарій демонструє здатність системи адаптуватися до змінних вхідних даних та підтримувати обґрунтоване прийняття рішень. Результати підтверджують ефективність використання баєсових мереж в аналізі ризиків під час надзвичайних ситуацій, особливо в умовах неповноти даних і потреби у швидкому реагуванні.

Problems of Multispectral Image Processing in Agriculture

Victor Sineglazov — 2025-04-08

This study provides a comprehensive comparative analysis of satellite-based and drone-based imaging platforms for agricultural monitoring, with particular emphasis on multispectral imaging capabilities. Our analysis reveals that while satellite systems offer broad coverage and cost-effectiveness for large-scale monitoring, drone-based platforms provide superior spatial resolution (up to 2.5 cm/pixel) and greater flexibility for targeted data acquisition, making them ideal for medium-sized agricultural plots. The research examines key imaging technologies and platforms, including the Sentinel-2 satellite system and drone-mounted sensors such as the MicaSense RedEdge-MX, evaluating their performance across critical agricultural applications. The paper further explores the implementation of convolutional neural networks for processing multispectral data, demonstrating their exceptional capability in performing crucial agricultural tasks including crop classification, disease detection, and stress assessment. By incorporating spectral indices, thermal indices and biophysical parameters (LAI, chlorophyll content) into neural network training, we develop a robust framework for agricultural monitoring and yield prediction. This research contributes both to the theoretical understanding of remote sensing in agriculture and provides practical guidance for implementing precision agriculture solutions that enhance productivity and sustainability in modern farming systems.

Reverberation Time Estimation Algorithm Accuracy

Arkadiy Prodeus — 2025-04-08

The task of estimating the reverberation time is relevant in the acoustic examination of premises for both civilian purposes (kindergartens, educational institutions, concert halls, etc.) and military purposes (control centers). Reverberation time measurement is usually performed by the method of inverse integration of the room impulse response. However, due to the existence of background noise, the problem of choosing the moment of time (truncation time) from which integration should begin arises. In this paper, the accuracy of the algorithm for calculating the reverberation time, where the truncation time is defined as the moment of approach to zero of the derivative of the “squaring - moving averaging” system output signal, is studied. Estimates of the bias, standard deviation, and total error for the values of the signal-to-noise ratio and reverberation time typical for classrooms are obtained. At a signal-to-noise ratio of 45 dB, for measurements in a wide frequency band of 80 Hz – 11 kHz, the total relative error of the reverberation time estimation does not exceed 6% for reverberation time values of 0.6–1.2 s. When measuring reverberation time in octave frequency bands, the error reaches 20% for the frequency band in the vicinity of 125 Hz, decreases to 10% for the frequency band in the vicinity of 500 Hz, and does not exceed 3% for the frequency band in the vicinity of 8 kHz.

Uasage of Artificial Vortices for Research of Aerodynamic Characteristics of Aircraft

Oleksander Zhdanov — 2024-12-27

This article solves the problem of widening the range of the angle of the attack of safe flight using volumetric vortex generators. The research method is based on experimental tests in the wind tunnel. Analyzing the obtained results allows us to research changes in the integrated aerodynamic characteristics. The features of the experimental test are described. The volumetric vortex generators of three types for the definite blowing model are represented. A comparative analysis of the aerodynamic characteristics of the profile model in the form of graphical dependencies is represented. The graphs show the change in the aerodynamic force coefficients in the speed coordinate system. The visualization of airflow was carried out. Volume vortex generators are described. A description of the experiment in the wind tunnel is given. The analysis of the obtained results has been carried out. Layouts of profile models with lateral screens are represented. The obtained results can be useful for aircraft of a wide class especially for unmanned aerial vehicles. The results can be used in synthesis control laws in disturbed stabilization and control systems.

Automation of Docking of Remotely Сontrolled Refueling Devices in the Air

Мykola Filyashkin — 2024-12-27

The issues of automating the air-to-air refueling of civilian aircraft are considered here. The main attention is paid to the stage of contacting of the "floating up" drogue with of the probe of refueling of the tanker aircraft. The options for implementing this technology are discussed, based on the automatic control loop of the tanker thrust in the mode of change according to the exponential law of approach speed as a function of the range between the refueling devices. To implement an exponential speed change program, it is proposed to use predictive control based on a dynamic process model. An alternative approach to automating the process of approaching the fueling devices involves using remote control of the unwinding of the fuel hose drum of the outboard fueling unit to control of the speed of approach of refueling devices. Such control is less inertial than controlling the flight speed of a multi-ton tanker.

Mathematical Reliability Model of the Aerodrome Power Supply System

Svitlana Deviatkina — 2024-12-27

The article is devoted to the modeling of the reliability of the power supply system at civil aviation aerodromes. ICAO standards and recommended practices establish a specific set of requirements for the power supply systems of modern civil aviation aerodromes. Every new project or modernization/reconstruction of aerodrome equipment, on which flight safety depends, must be accompanied by the determination and evaluation of its reliability indicators. This requirement is explained by the fact that failures of such equipment pose threats and can create risks to flight safety during the technological processes with aircrafts at civil aviation aerodromes. In accordance with ICAO safety management standards, all risks at the aerodrome must be controlled and reduced to an acceptable level. The proposed mathematical model of the reliability of the aerodrome lighting system's power supply allows for the determination of its reliability indicators under various failure criteria, assessing the impact of the power supply system on flight safety, and developing a set of organizational and technical measures to ensure and enhance the reliability of the system as a whole, which will undoubtedly have a positive impact on the safety of aircraft during all phases of visual piloting.

System an Unmanned Aerial Vehicle Trajectory Generation in Real Time

Victor Sineglazov — 2024-12-27

This paper addresses the problem of real-time trajectory generation for unmanned aerial vehicles, emphasizing its importance for various applications such as search and rescue operations, environmental monitoring, and precision agriculture. The challenges associated with dynamic trajectory generation, including obstacle avoidance, adherence to mission constraints, and computational efficiency, are analyzed. A hybrid approach is proposed that integrates advanced path planning algorithms with real-time optimization techniques to ensure safe and efficient unmanned aerial vehicle navigation in complex environments. The system leverages onboard sensors and external data sources, such as GPS and LiDAR, for situational awareness and dynamic obstacle detection. A key feature of the proposed system is the ability to adapt the trajectory in response to real-time changes in the environment, ensuring robustness and reliability during autonomous flight. The implementation utilizes the PX4 autopilot platform, AirSim simulation environment, and QGroundControl software to validate the effectiveness of the proposed approach. The results demonstrate that the system achieves a balance between computational efficiency and trajectory accuracy, enabling its deployment in practical unmanned aerial vehicle applications.

Programmed Micro- and Nanostructures

Oleksandr Melnyk — 2024-12-27

The article examines controversial issues regarding the implementation of specialized, but at the same time universal, large integrated circuits, which appear in the initial stages of automated hierarchical design. To increase the efficiency of automated design systems, universal micro- and nanocircuits with programmable logic have been created. The article offers effective methods of programming multiplexer micro- and nanocircuits with programmable logic for implementing Boolean and majority logic functions. The obtained results are used to configure the functional blocks of the multiplexers. With the use of modern automated design systems, comparative modeling of logical micro- and nanocircuits was carried out, which confirmed the adequacy of their work, as well as the advantages of frequency and temperature characteristics of nanomultiplexer circuits.

Intelligent Medical Image Processing System Using Zero-shot Learning

Victor Sineglazov — 2024-12-27

The work is devoted to the intelligent diagnosis of malignant skin tumors. The classification of malignant skin tumors is presented. The greatest attention was paid to skin melanoma. The modern signs of melanoma were analyzed: Asymmetry, Boundary, Color, and Diameter, and additionally for nodular melanoma: Elevated, Firm, and Growing. A review of works on using artificial intelligence to diagnose malignant skin tumors was performed. A methodology for the intelligent diagnosis of malignant skin tumors was proposed, which is based on the use of preprocessing of dermatoscopic images and solving the segmentation problem based on the use of a hybrid approach, which includes the use of a Segment Anything model based on the combination of the Zero-shot learning model, which consists of an image encoder, prompt encoder, lightweight mask decoder, with YOLOv11. ISIC 2018 was used as the dataset.

BAFUNet: Hybrid U-Net for Segmentation of Spine MR Images

Victor Sineglazov — 2024-12-27

The paper presents the development of a hybrid neural network architecture, BAFUNet, designed for the segmentation of spine MR images in the context of medical diagnostics. The architecture builds upon the classical U-Net, integrating atrous spatial pyramid pooling module in the bottleneck and a two-round fusion module in the skip connections to address challenges such as various object scales and unclear boundaries in medical images. The work describes the design of the proposed BAFUNet architecture, its implementation, and the experimental results. A comparative analysis was performed against classical U-Net and ResUNet++, demonstrating the relationship between the proposed architectural enhancements and segmentation performance. The evaluation was carried out using Dice score and Jaccard score metrics on the SPIDER dataset, a publicly available lumbar spine magnetic resonance imaging dataset. The results indicate that the BAFUNet architecture achieves a slight but consistent improvement in segmentation performance, with an average Dice Score increase of 0.003–0.005 compared to baseline models, highlighting its potential applicability in automated medical diagnostics.

Measurement of Reverberation Time Using a Two-stage Algorithm

Arkadiy Prodeus — 2024-12-27

The use of voice control of unmanned aerial vehicles is relevant due to the ease of practical use and new opportunities. This technology allows one to simplify the interface, making it more intuitive and natural. However, the quality and intelligibility of speech signals indoors can be significantly impaired by noise and reverberation. Therefore, before using voice technologies, it is desirable to take into account the effect of interferences by preliminary assessment of their parameters. In this paper, an algorithm for estimating the boundary (truncation time) between the informative and non-informative parts of the room impulse response, which allows obtaining believable estimates of the reverberation time, is proposed. The proposed algorithm is two-stage. At the first stage, “rough” envelope of the room impulse response is calculated using the detector-integrator, which allows one to find an approximate value of truncation time and construct an approximate envelope of room impulse response using backward integration method to obtain an approximate estimate of the reverberation time. In the second stage, output data of the first stage are used to refine the truncation time and reverberation time estimates. Experimental tests using recordings of real room impulse responses testify to the efficiency of the proposed algorithm.

Research of Aerodynamic Airfoil with Vortex Ceerators in Wind Tunnel

Oleksander Zhdanov — 2024-09-30

This article deals with researching the aerodynamic airfoil model in the wind tunnel using volume vortex generators. The volumetric vortex generators of three types for the definite blowing model are described including their geometrical characteristics. The features of volume vortex generators mounting on the surface of the aerodynamic airfoil model are represented. Features of the experiment in the wind tunnel are described. Comparative changes in aerodynamic characteristics of the aerodynamic airfoil model with and without volume vortex generators in the form of graphical dependencies have been shown. The comparison has been made for different values of Re numbers. The analysis of the obtained dependencies has carried out. The visualization of airflow in an experimental way is illustrated. The results are directed at improving the aerodynamic characteristics of unmanned aerial vehicles. The main significance of the research lies in improving the behavior of unmanned aerial vehicles in the area of critical angles of attack. The obtained results can also be useful for aircraft of the wide class.

Conceptual Aspect of Measuring the Efficiency of Cargo Unmanned Aerial System

Haoyang Li — 2024-09-30

This article examines the conceptual framework for measuring the efficiency of unmanned systems, focusing on a task-oriented and problem-solving approach to evaluating the performance of cargo unmanned aerial vehicles. The efficiency of cargo unmanned aerial vehicles pertains to their ability to complete missions on time and cost-effectively, while maximizing utilization, minimizing resource loss, and maintaining an acceptable level of flight safety. It is important to highlight that the efficiency of cargo unmanned aerial vehicles is multifaceted, encompassing technical, operational, economic, environmental, and regulatory dimensions. Progress in each of these areas contributes to the successful deployment and optimization of cargo drones across various applications, from last-mile deliveries to supply chain operations in remote regions. The article presents two methodologies for assessing the efficiency of cargo drones.

Voice Control System for Robotics in a Noisy Environment

Oleksandr Lavrynenko — 2024-09-30

This paper analyzes the effectiveness of the developed voice control system for robotics based on MFCC and GMM-SVM under the influence of interference in the communication channel. The system allows characterizing individual features of speech signals with their subsequent classification and making a reliable decision on the interpretation and execution of voice commands by robotic equipment. The proposed voice control system for robotics based on MFCC and GMM-SVM is implemented using the following technologies: 1) selection of active speech areas by calculating the short-term energy and the number of zero crossings between adjacent frames of the speech signal; 2) adaptive wavelet filtering of the speech signal, where it is necessary to generate threshold values, which will reduce the impact of additive noise; 3) selection of recognition features, which are used as mel-frequency cepstral coefficients; 4) classification of recognition features based on mixtures of Gaussian distributions and the support vector method using the linear Campbell kernel and the principal component method with a projection on latent structures, which will reduce errors of the 1st and 2nd kind.

Advanced Copula-based Methods for Nonparametric Detection and Characterization of Wideband Radar Signals

Zhanna Bokal — 2024-09-30

This paper introduces advanced copula-based methods for the nonparametric detection and characterization of wideband radar signals. The research focuses on developing signal detection algorithms that are invariant to changes in the probability density function of the sounding or reflected signals, employing multiscale analysis techniques and copula-based statistics. Two primary approaches are explored: multiscale analysis using wavelet transforms and rank-based signal detection with copula-based ambiguity functions. Simulation results confirm the effectiveness of the proposed approaches. The research demonstrates that integrating rank-based methods with copula-based statistics significantly improves the detection and analysis of wideband radar signals, particularly in complex scenarios where signals exhibit intricate dependency structures. This comprehensive detection framework is well-suited for handling high-dimensional radar signal data, enhancing accuracy and reliability under varied conditions. Future work will focus on optimizing copula selection and permutation strategies to further improve performance.

Algorithm for Precise Payload Drop From FPV Drone with Account of Wind Strength and Automatic Position Correction

Victor Sineglazov — 2024-09-30

The work is devoted to the development of an automatic system for dropping cargo from an first-person view drone. An analysis of approaches to the construction of this type of system was performed. It is proven that this problem has not been fully solved, which requires the development of new approaches. In order to increase accuracy, it is proposed to use mathematical models that describe the process of cargo unloading. Mathematical models are given that connect such variables as Height, wind speed, drone speed, cargo weight, cargo surface area, wind direction angle relative to the drone, air density. The use of the obtained mathematical models allows you to calculate the coordinates of the reset point. The developed approach was tested in real conditions. The obtained results showed a significant improvement in the accuracy of dropping the load in the presence of a constantly acting wind.

Comparative Analysis of the Methods of Planning and Coordinating of Manipulator Robot Movement

Victor Sineglazov — 2024-09-30

This paper presents a comparative analysis of two methods for planning and coordinating the movement of robot manipulators in dynamic environments: a neural network-based approach for solving dynamic production scenarios and the rapidly exploring random trees algorithm. The study aims to enhance the trajectory planning of robot manipulators by leveraging the strengths of intelligent systems. The neural network method is designed to perceive the environment, generate accurate control commands, and adapt to changing conditions in real-time. The paper the processes involved in environmental analysis, collision avoidance, and control signal generation for actuators, with an emphasis on the neural network architecture tailored for these tasks. The results demonstrate that the neural network approach offers significant improvements in adaptability and efficiency, providing a robust solution for optimizing automated processes in dynamic production environments.

An Intelligent Mobile Search System

Victor Sineglazov — 2024-09-30

This article is devoted to the development of an intelligent mobile system used for humanitarian demining. At the same time, the problems of detection, localization and storage of the obtained data are solved. The system operation is based on the use of a synthetic aperture ground penetrating radar, which makes it possible to detect mines both on the earth's surface and underground. A quadcopter is used as a carrier. A set of technical means has been developed. The central and graphic processors are used as a processing unit. Intelligent elements for processing the obtained data are convolutional neural networks, for machine learning of which a synthetic dataset was used. The data is organized into S3 segments based on various parameters, such as date, location and sensor type. This organization facilitates data retrieval and management. Data is encrypted both during transmission and at rest using AWS Key Management Service to ensure confidentiality.