Electronics and Control Systems

Analysis of Modern Approaches to Autonomous UAV Navigation

2026-04-20T14:07:07+03:00

The development of autonomous navigation methods for unmanned aerial vehicles (UAVs) is driven by the need to ensure reliable positioning, orientation, and trajectory planning without dependence on external infrastructure. This task is particularly critical in environments with partial or complete uncertainty—such as indoor, underground, or GPS-denied areas. Over the last decade, numerous approaches to autonomous UAV navigation have been proposed and implemented, ranging from traditional geometric and algorithmic techniques to intelligent systems and sensor fusion architectures. The objective is t analyze and systematize modern approaches to autonomous UAV navigation and to highlight the potential of correlation-extreme navigation methods in uncertain environments. The paper presents a structured overview of state-of-the-art navigation methods, their classification by operational principles, and their strengths and limitations in different use cases. Special attention is paid to correlation-extreme navigation methods, which rely on optimizing similarity functions between sensory data and reference maps. These methods demonstrate high potential for fully autonomous navigation in GPS-denied or signal-jammed environments. A comparative matrix highlights the trade-offs between accuracy, computational cost, robustness, and flexibility for each method. Correlation-extreme navigation is a promising direction for achieving robust autonomy in UAV systems operating in uncertain and complex conditions.

Designing Two-axis Gimbaled Control Systems for Observation Units Stabilization

2026-04-20T13:41:20+03:00

This article presents features of designing a two-axis inertially stabilized platform assigned for stabilization of observation units and devices, for example, cameras. The problem of stabilization of images is analysed, including electromechanical, optical, mechanical, and computer techniques. The appropriate structural diagrams are represented. The procedure of the synthesis for the inner and outer tracking loops of the control system is represented. The structure of control laws is proposed. The combined optimization criteria that ensure compromise between the tracking accuracy and resistance to disturbances on angular rate are represented. The structures of the synthesised regulators are described. The results of the synthesized system simulation are shown. The analysis of the simulation results is given. The system’s norms are calculated. The obtained results can be used in the area of stabilization of a wide class of instrumentation assigned for operation on moving objects.

Dynamic Ontology for Semantic-prognostic Self-organization of a UAV Swarm

2026-04-20T11:43:00+03:00

The paper proposes a dynamic ontological model for the semantic-predictive self-organization of an unmanned aerial vehicle (UAV) swarm. A multi-layer ontological model of the UAV swarm is developed, comprising a static core, a dynamic instance layer, an agent layer with local knowledge projections, and a mechanism for semantic synchronization of agents to ensure knowledge consistency in a distributed multi-agent system. A mathematical model is formulated to formalize the processes of knowledge alignment, scenario forecasting, and optimal action selection. The organization of interactions between structural layers is examined, providing knowledge exchange, adaptive role allocation, and support for collective agent behavior. Experimental simulation conducted in the Gazebo environment for forest fire monitoring by a group of 12 UAVs confirms the effectiveness of the proposed approach: semantic consistency reached 91%, the accuracy of critical event prediction increased by 17–20%, and network load decreased by 83.4%. The developed model ensures scalability, robustness to the loss of individual agents, and efficient execution of cooperative tasks, making it suitable for the development of intelligent control systems for autonomous UAV swarms.

Comparative Analysis of Methods of Guiding Aircraft to Maneuvering Targets

2026-04-19T20:30:08+03:00

This article analyzes modern methods for guiding aircraft toward maneuvering targets. The main guidance algorithms are considered, in particular geometric methods, the proportional guidance method, and its modifications. Based on a kinematic model of the relative motion of the interceptor and the target, the principles of generating control accelerations and the specifics of the practical implementation of the corresponding algorithms are analyzed. A comparative analysis of the considered methods is conducted in terms of their effectiveness, implementation complexity, and ability to ensure the interception of maneuvering targets. It is shown that the proportional guidance method is the most widespread in modern systems; however, its effectiveness may decrease in the case of active target maneuvering and the presence of measurement errors. It has been established that modified guidance algorithms, in particular Augmented Proportional Navigation, require estimation of the target’s acceleration, which in real-world conditions is accompanied by measurement uncertainty and noise. This paper identifies the main limitations of existing approaches and justifies the development of adaptive guidance methods capable of accounting for the uncertainty of target motion parameters and the dynamic limitations of control systems.

Impact of Automated Segmentation on Radiomics-based Growth Prediction of Vestibular Schwannoma

2026-04-19T19:59:50+03:00

This paper investigates the impact of nnU-Net-based automatic segmentation on the reproducibility of radiomics features and the quality of vestibular schwannoma growth prediction. The nnU-Net model was trained on 317 T1C image pairs with masks from 155 patients from the Vestibular-Schwannoma-MC-RC2 dataset using 5-fold cross-validation, achieving a mean Dice coefficient of 0.862. ICC analysis showed that 88.8% of wavelet features maintain good or excellent agreement (ICC ≥ 0.75) between manual and automatic masks. Comparison on a subset of 96 patients with growth labels showed: the previously published pipeline (Wavelet + Voting ensemble) with manual masks achieves ROC AUC = 0.742, with automatic masks - 0.639 (−14.0%). Pipeline optimization (ICC filtering, ensemble adaptation to LR + LDA) improves the result to 0.687 (−7.4%). The results determine the cost of automating the radiomics pipeline and provide recommendations for ICC filtering for clinical implementation.

Modal Control of Unmanned Aerial Vehicle Roll Angle

2026-04-19T17:45:25+03:00

The article deals with the problem of automatic control of the roll angle of an unmanned aerial vehicle. Based on the complete mathematical model of the unmanned aerial vehicle and modal control methods, two variants of the automatic control system are considered: an autopilot implemented as a closed-loop feedback system and a system with an observer device. The controllability and observability conditions of the system are analyzed, and the autopilot equation with a structure corresponding to a PID controller is obtained. Numerical simulation results are presented in the form of time dependences of the roll angle, roll rate, and aileron deflection angle for different natural frequencies. Analytical verification confirms the steady-state value of the roll angle obtained by simulation. The parameters of the autopilot for different transient processes are determined. The roots of the characteristic equations for the binomial and Butterworth standard forms are calculated, and analytical expressions for the gain matrix of the Luenberger observer are obtained. The results show that the use of an observer device significantly changes the transient response of the system. The proposed approach based on modal control methods can be applied not only to roll control but also to the design of automatic control systems for pitch and yaw angles of unmanned aerial vehicles and other aircraft.

CAD-based Optimization of the Medium-sized Unmanned Ground Vehicle

2026-04-19T17:26:16+03:00

This paper investigates a multi criteria, computer-aided design system to achieve optimal configurations for unmanned ground vehicles which operate in diverse environments and climate conditions. The core challenge lies in balancing competing design objectives such as cost, payload, reliability and longevity. To address this, a novel design methodology is proposed, integrating an AI-powered CAD system that creates a closed-loop information flow across all development stages, from conceptual design to field testing. This framework was validated through the development of a custom medium-sized unmanned ground vehicle with an integrated motion control system comprised of commercial off-the-shelf components and custom developed parts. Experimental trials in various terrain and climate conditions, including operation in winter, demonstrated high maneuverability, a payload capacity, and a 100 km range. The results confirm the methodology's effectiveness in improving design efficiency and ensuring the seamless integration of mechanical complexity, software, and performance metrics for next-generation unmanned ground vehicles, highlighting the framework's diagnostic strength and its ability to guide targeted R&D.

Nanodevices with Majority Structures

2026-04-19T17:01:43+03:00

The article is devoted to the analysis of topical antagonistic issues regarding the implementation of new computer programming methods that can be used to reproduce heterogeneous binary functions of several arguments based on micro- and nanoelectronic multiplexer modules. The growth of specialization of modern large integrated circuits comes into conflict with their universality, which increases the cost of design work and reduces the production volumes of typonominal devices. The trade-off between specialization and versatility should be minimized by computer-aided design of micro- and nano-sized modules with programmable logic. Programming is not performed to create algorithms for processing discrete information, as implemented by a microprocessor, but by logical settings of multiplexer micro- and nanodevices. The results of the combined simulations obtained in the work confirm the equivalence of their functioning, as well as the advantages of cell-based Coulomb nanomultiplexers in reliability, technological scalability, energy efficiency, speed, and disadvantages in exclusively cryogenic ultra-low temperature applications. Obviously, the evolution of microelectronics will continue only in the context of the widespread introduction of nanoscale modules with new functional principles of operation.

Assessment of QoS Metrics in Aviation Communication Networks

2026-04-19T16:08:10+03:00

An important factor in providing safety in aviation is the reliable operation of radio communication systems. These systems gives possibility to transmit necessary information during flights and the performance of aviation operations, in particular for air traffic control, as well as take-off and landing processes. These systems are aimed at meeting the needs of aviation companies and enterprises. Radio communication facilities have certain parameters that determine their effectiveness in terms of purpose, technical level, economic feasibility, reliability, and operational characteristics. A separate group of these parameters describes the quality of service. This article examines the characteristics of aviation radio communication systems in terms of their ability to meet the needs of users of air navigation services. The authors analyze a generalized list of service quality indicators, and also describe approaches to their calculation and modeling. Particular attention is paid to reliability indicators, analysis of relevant models and the specifics of data processing to determine their quantitative values. The obtained results can be used by aviation industry specialists during the operation of radio communication systems.

Energy Consumption Monitoring and Anomaly Detection Module for a Smart Automatic Irrigation System

2026-04-19T15:33:28+03:00

Modern smart automatic irrigation systems are widely used to improve water efficiency and crop productivity. However, most existing solutions primarily focus on soil moisture control and environmental conditions, while energy consumption behavior of irrigation equipment remains insufficiently analyzed. At the same time, abnormal energy consumption often serves as an early indicator of system malfunctions, water leakage, or inefficient operation of electromechanical components. This paper presents a module for monitoring energy consumption and detecting anomalies in a smart automatic irrigation system. The proposed solution is based on continuous measurement of electrical parameters and adaptive analysis of energy consumption patterns. The module enables early detection of abnormal operating conditions, improves energy efficiency, increases system reliability, and enhances operational autonomy. The developed approach can be integrated into existing irrigation infrastructures without significant hardware modifications and is suitable for private households and scalable smart farming systems.

Quasi-equilibrium Processes of Electronic Excitation Relaxation in the 1.5-Diazidopyrimidine Molecule

2026-04-19T13:54:32+03:00

This work reports combined experimental and theoretical investigations of quasi-equilibrium relaxation of electronic excitation in 1.5-diazidopyrimidine (1.5-DAP) and discusses its practical relevance. Analysis of the energy structure of the molecule shows that the first two singlet excited states (S₁ and S₂) arise from quantum transitions to dissociative pσ* states: S1 is associated with excitation localized on the azido group at position 5, whereas S₂ is localized on the azido group at position 1; the corresponding wave functions are spatially separated. The next two singlet states correspond to quantum transitions to pp*-states that form the absorption spectrum of the molecule. The relaxation of electronic excitation of the molecule is described in terms of a quasi-equilibrium pathway that enables localization of excitation at higher energy levels. Because there is an energy barrier in S₁ prevents N–N bond cleavage along this channel, the process of excitation relaxation proceeds to T₃(pp*) and subsequently to T₂(pσ*), which corresponds to excitation localized on the azido group at position 5. Consequently, the molecule dissociation occurs only via the single dissociative state T₂(pσ*). Relaxation of excitation from T₂ to the lowest triplet excited state T₁(pp*) is found to be forbidden, which provides a high quantum yield of azido-group photodissociation. It is shown that all other channels of electronic excitation relaxation led to the relaxation of the molecule to the T₂ state, i.e. the process is quasi-equilibrium. The consequence of such localization is the dissociation of the molecule with the release of a nitrogen molecule and nitrene. Nitrene, being a highly reactive particle, is widely used in molecular and nanoelectronics to create photoresists, as well as intermediates in nanotechnology.

Detection of Fast Radar Target Trajectories Using the Hough Transform

2026-04-19T13:19:59+03:00

This paper addresses the problem of detecting trajectories of fast-moving radar targets in the presence of noise, missed detections, and K-distributed clutter. A Hough transform-based method is proposed using a physically interpretable parameterization in terms of initial range and velocity. An iterative multi-target detection procedure based on peak extraction and subsequent removal of the found control points, which allows separating even those trajectories that may intersect without data association. A weighted voting scheme based on signal intensity is introduced to improve robustness under low SNR conditions. Performance is evaluated using Monte-Carlo simulations. Results show high track detection probability and robustness to missed detections and clutter. The method is suitable for track initiation in multi-target radar systems.

A Multicriteria Method for Optimizing it Service Management of a Virtual Provider Based on Deep Reinforcement Learning

2026-04-19T12:48:09+03:00

This article addresses the problem of optimizing IT service management for the B2B segment under conditions of dynamic workloads and the probabilistic unreliability of service operators. The architecture of a Virtual Service Provider (VSP) management system is proposed, which automates the service processes for Corporate Customers. The core of the system is a hybrid translation module that mathematically transforms abstract business intents and client context into a deterministic Service-Resource Model with specified technical, financial, and time constraints. To efficiently orchestrate the generated tasks, a multicriteria optimization algorithm, PPO-VSP, based on deep reinforcement learning (Actor-Critic architecture) was developed. The implementation of a reputation assessment module allowed the system to identify unreliable service providers and avoid overloading them. Experimental studies using simulation modeling confirmed the stable mathematical convergence of the algorithm. The trained optimization agent ensured compliance with service level agreements (the Quality of Experience metric) at a level of 95.4% under limited resource conditions. The generation time of the decomposition matrix averaged 18 ms, guaranteeing rapid management decision-making during the operation of high-load infrastructures without system downtime.

Comparative Analysis of LLM-based Graph Represenation Construction for Domain-specific Documents

2026-04-19T12:17:58+03:00

Recent advances in large language models have substantially improved natural language understanding and enabled their application across a wide range of domains. However, highly specialized fields such as law and medicine remain challenging because their documents often contain complex structures, domain-specific terminology, and dense logical dependencies. In such settings, large language models may produce errors when important structural information is not explicitly preserved in the document representation. To address this limitation, we propose a novel approach for document decomposition into graph-based representations that better capture the structural and semantic relationships within complex texts. We develop a method for processing raw legal documents from the Ukrainian domain using an LLM-based decomposition pipeline, transforming them into structured graph representations that can reinforce contextual retrieval and support retrieval-augmented generation. The proposed method improves document understanding by preserving key contextual dependencies and enhancing the representation of legal knowledge in downstream tasks.

Formulating Requirements for Hybrid Distributed Computing Systems: an Infrastructure Perspective

2026-04-18T22:39:50+03:00

The article examines the role of modern computing architectures in enabling the digital transformation of the energy sector, particularly in the context of the development of decentralised electricity markets. The advantages and limitations of on-premises, cloud, and hybrid infrastructure solutions for building distributed, scalable computing systems are analysed. Based on this analysis, requirements for the communication and information system of interaction among participants in a decentralised electricity market are formulated. The key design principles of such a system are identified, including scalability, architectural unification, high availability, and fault tolerance. Particular attention is given to processing large data volumes, ensuring data sovereignty, integrating blockchain technologies and smart contracts, and using consensus mechanisms suitable for industrial systems. Requirements for network infrastructure, geographical distribution of computing nodes, and cyber-resilience are also considered, particularly in the context of cascading risk effects. The study demonstrates that a hybrid computing architecture provides the most balanced approach by combining control over critical infrastructure components with the flexibility and scalability of cloud technologies, thereby creating a technological foundation for a resilient communication and information system supporting a decentralised electricity market.

Model for Integrating Different Types of Data in Multilevel Information Structures

2026-04-18T22:15:59+03:00

This article examines the problem of integrating heterogeneous data within multi-level information structures operating under conditions of heterogeneous data sources and variable dynamics of information flows. The relevance of this research stems from the growing volume of data, the diversity of formats, and the need to establish a harmonised information representation for further analysis. The aim of the work is to develop a mathematical model for the integration of heterogeneous data, which formalises the processes of harmonising information flows of different nature. The paper analyses the characteristics of information flow formation and identifies factors influencing the effectiveness of data integration, in particular varying arrival rates, noise levels and source reliability. A multi-level integration model is proposed, which includes stages of pre-processing, normalisation, harmonisation and the formation of an integrated representation. The results obtained showed that the use of a multi-level approach allows for improved consistency of information flows and a reduction in the impact of noise and uncertainty. The proposed model can be used in data processing and decision support systems within complex information environments.

Information System Functioning Based on Multidimensional Time Series

2026-04-18T21:43:19+03:00

The paper addresses the problem of mathematical modeling of complex information systems characterized by a large number of interrelated parameters and heterogeneous information flows. Such systems demonstrate a dynamic behavior over time, which requires the application of methods capable of analyzing temporal dependencies between system parameters. A mathematical model of complex information system functioning based on multivariate time series is proposed. The developed model allows formalizing the processes of system state evolution and capturing relationships between different information streams. In the proposed approach, the system state is represented as a vector time process that includes multiple parameters describing various aspects of the system operation. The mathematical framework of the model describes the dynamics of system state formation and evolution while taking into account external influences and stochastic disturbances. The proposed approach enables a consistent analysis of multidimensional data and allows investigating patterns in the functioning of information systems in dynamic environments. Simulation experiments demonstrate the applicability of the proposed model for analyzing the behavior of complex information systems and evaluating their states based on temporal relationships between system parameters. The obtained results can be applied in the development of methods for processing multidimensional data and designing decision support systems for managing complex information processes.

Minimum Fidelity for Reliable Architecture Ranking in Bayesian NAS for Object Detection

2026-04-18T20:38:57+03:00

This paper addresses the problem of reducing computational costs in Neural Architecture Search for object detection. The key question in low-fidelity approaches is: after what minimum number of epochs does the early training signal already provide acceptable architecture ranking? This paper presents a methodology for determining minimum fidelity based on out-of-sample rank correlation: trials are split into calibration (70%) and test (30%) sets, a composite proxy is built on the first set and evaluated on the second. An empirical study was conducted on 60 architectures trained for 25 epochs on an object detection dataset (6,772 images, 6 classes). The results show that a rank ensemble of three training metrics (val_loss, train_accuracy, val_accuracy) achieves Spearman ρ = 0.877 out-of-sample at epoch 3, providing 88% computational savings. A more complex 7-component metric underperforms the simple ensemble due to overfitting on a small sample (ρ_test = 0.70 vs. 0.88). The results are locally valid within the studied search space and the specific dataset.

Synthetic CT Generation for Cardiothoracic Injuries with Intracardiac Foreign Bodies

2026-04-18T18:57:38+03:00

This paper proposes novel methods for the generation of synthetic CT images of cardiothoracic injuries with foreign bodies (FB) localized in the cardiac region. These methods are intended to improve the FB segmentation performance by machine learning models through the augmentation of training datasets with synthetic images. A small real-world dataset was collected, consisting of 8 CT scans of combat cardiothoracic injuries with foreign bodies (FB amount ranging from 1 to 3) localized in the heart area, and an additional 4 "clean" CT scans of blast-induced cardiothoracic injuries where foreign bodies are absent in the cardiac region. Through the analysis of the collected dataset and findings from similar studies, the morphology of foreign bodies and the statistics of their localization in the heart during combat cardiothoracic trauma were examined. Two methods for synthesizing artificial CT images of cardiothoracic injuries with heart-localized FBs based on clean CT scans were developed: RealInsFB-CT, based on the insertion of regions of interest extracted from real injury CT scans-containing FB, artifacts, and surrounding damaged tissues – into clean CT scans and MorphGenFB-CT, based on the morphologically and statistically grounded generation of a 3D FB model, filling the model with Hounsfield Unit values corresponding to the selected FB material, inserting the model into one of the heart chambers on a clean CT scan, and the artificial generation of artifacts. Using both methods, CT images were synthesized and compiled into training datasets (19 CT scans + 19 FB masks each). The visual, structural, and statistical similarity between real and synthesized CT scans was proved.

Generating Structured Radiological Reports from Brain MRI Data Based on SigLIP2 Frozen Embeddings

2026-04-18T18:36:55+03:00

Automatic generation of clinical reports from medical images is a relevant task capable of reducing the workload of radiologists and standardizing documentation. In this paper, we investigate an approach to generating structured reports from brain MRI data using a pre-trained multimodal SigLIP2 model as a feature extractor. We propose an architecture in which visual embeddings obtained from a frozen SigLIP2 are projected into the representation space of the GPT-2 language model for subsequent text generation. Experiments were conducted on the open-access BIOSE MRI dataset, containing 34 pairs of "MRI image + clinical report". It is shown that the proposed approach generates semantically meaningful reports, achieving quality comparable to more complex architectures with substantially lower computational costs. Additionally, the influence of pre-training SigLIP2 on a classification task (Brain3-Anomaly-SigLIP2 version) on generation quality is investigated. The results demonstrate the potential of using frozen vision encoders in medical generative tasks under data-scarce conditions.

Intelligent System for Notifying the Driver of Distractions From Driving the Vehicle

2026-04-18T17:53:58+03:00

The article discusses the development of a functional system that, based on the recognition of distracting behaviour by the driver, is capable of issuing warning signals that the driver is distracted from the process of driving a vehicle. The described methodology covers basic methods and procedures necessary for the creation of a precision, resistant to disturbances, and at the same time controllable system for precision stabilization of the equipment assigned for operation on moving vehicles of a wide class. As part of the study, such a system was developed that takes into account the driver's reaction time, age, gender, and duration of the trip. If a critical situation is detected, the system automatically generates a warning signal for the driver. The features of the modern approach to data processing based on neural networks are described. The implementation of such a system is aimed at improving road safety and reducing the number of accidents. The developed emergency warning system not only allows drivers to mitigate the consequences of road accidents, but also helps them to avoid them by drawing their attention to emerging dangers in a timely manner if the driver's response is insufficient or absent.

Data Mining of B2b Transactions Using a High-utility Temporal Approach

2026-04-17T20:46:16+03:00

Modern B2B e-commerce systems generate vast arrays of transactional data, yet classical association rule mining methods face several critical limitations, notably the "financial blindness" of binary models and the "temporal blindness" of static approaches. These constraints hinder the discovery of patterns with genuine economic value and overlook the cyclical nature of wholesale procurement.Methods. This paper proposes a high-utility temporal approach to B2B transaction analysis. The approach is centered on the UP-Growth (Utility Pattern Growth) mathematical model, adapted to operate with a utility function based on the rational transaction context, defined as the product of individual price and wholesale purchase volume. To address the anti-monotonicity problem, the concept of transaction-weighted utility is introduced. The integration of the temporal factor is achieved through the analysis of inter-purchase interval vectors, utilizing the coefficient of variation as a criterion for cycle stability. As a result, a holistic dual-stage pipeline architecture for the intelligent analysis of B2B customer transactional data has been developed. In the first stage, the pipeline performs the extraction of High-Utility Itemsets, while the second stage involves calculating the trigger date to establish a proactive offer window. A comparative analysis with the classical FP-Growth algorithm confirmed the superior efficiency of the proposed approach in identifying economically significant patterns, validated using real-world data from the "Baza Vzuttya".

Stochastic Model for Image Matching Based on Statistical Analysis of Descriptors

2026-04-17T19:57:56+03:00

The paper considers the problem of establishing correspondences between images based on the analysis of local structural features. This problem is one of the key tasks in computer vision systems and arises in image registration, object tracking, scene analysis, and three-dimensional reconstruction. The main difficulty in solving this problem is the influence of noise, illumination variations, geometric distortions, and scale changes, which lead to instability of feature descriptors and reduce the accuracy of correspondence estimation. A stochastic model for establishing correspondences between images is proposed in this study. The model is based on the statistical representation of keypoint descriptors and the use of probabilistic criteria for evaluating their similarity. Within the proposed approach, descriptors are considered as random feature vectors characterized by specific statistical parameters. Their properties are described using a multivariate probabilistic model that takes into account the covariance structure of the features and the correlation between descriptor components. Descriptor comparison is performed using a statistical similarity measure that accounts for the variability and distribution of features. To describe the geometric relationship between corresponding points of the images, a spatial transformation model is introduced. The parameters of this transformation are estimated using a likelihood-based approach. Within the stochastic framework, the alignment errors between corresponding points are treated as random variables, which allows the parameter estimation process to be formulated as a statistical optimization problem. The developed mathematical framework provides a formal representation of the image correspondence estimation process and enables parameter estimation using maximum likelihood or maximum a posteriori criteria. The modeling results demonstrate that the use of a stochastic approach improves the robustness of the correspondence estimation procedure under noise and other destabilizing factors. The obtained results confirm the feasibility of applying stochastic models to image analysis tasks and can be used in computer vision systems, automated video analysis, and other applied information systems.

Multi-agent Deep Reinforcement Learning in the Collision Avoidance Problem

2026-03-13T13:44:38+02:00

Obstacle avoidance is crucial for the succesfull completion of unmanned aerial vehicles missions. This article is devoted to the research of the multi-agent deep reinforcement learning in the collision avoidance problem. It is considered unmanned aerial vehicle swarms encounter diverse obstacles categorized into: static large-scale and small-scale obstacles, dynamic large-scale and small-scale obstacles, complex terrain, thin/low-visibility obstacles, partially-occluded/transparent obstacles. To address the abvove problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each unmanned aerial vehicle independently. It was researched different approaches and multiple 3D simulation environments with help of reinforcement learning for the swarm of unmanned aerial vehicles.

An Integrated Techno-economic Framework for SMS Delivery Optimization in 4G/5G Networks

2026-03-07T22:54:56+02:00

Short Message Service (SMS) remains a critical signaling-layer communication mechanism in 4G and 5G mobile networks, particularly within Application-to-Person (A2P) ecosystems supporting authentication, financial notifications, public services, and IoT fallback signaling. The transition to LTE packet-switched delivery and further to 5G Service-Based Architecture (SBA) introduces dynamic routing, SLA differentiation, multi-channel fallback, and cost variability. Traditional Quality of Service (QoS) indicators are insufficient to assess operational efficiency in such environments. Furthermore, Artificially Inflated Traffic (AIT) distorts economic performance without necessarily degrading technical KPIs. This paper proposes an integrated techno-economic framework for SMS delivery optimization in next-generation networks. The framework introduces the Price Delivery Gap (PDG) as an economic deviation metric and develops the Integrated Gap-Delivery-Performance (IGDP) model combining QoS, Quality of Experience (QoE), and PDG into a unified optimization function. An intelligent architecture incorporating message categorization, AIT detection, adaptive routing, and closed-loop monitoring is presented. Scenario-based evaluation under mixed A2P traffic conditions demonstrates reduced economic deviation and improved integrated efficiency compared to static and partially adaptive approaches.

Determining the Effectiveness Criteria for Intelligent Detectors in Integrated Video Surveillance Systems

2026-03-07T22:26:24+02:00

The paper considers a comprehensive methodology for evaluating the effectiveness of video surveillance systems based on classical motion detection and neural network object recognition algorithms. A system of technical, financial, and operational performance indicators is outlined, including event miss probability, recognition precision, false alarm rate, response time, infrastructure costs, staffing expenses, and operator workload index. The integral detection quality metric based on the F1-score, defined as the harmonic mean of Precision and Recall, is characterized. A weighted aggregation model is proposed that accounts for the criticality level of the protected facility and enables adaptive balancing between technical and economic factors. The mechanism of incident cost impact on financial efficiency through expected losses from missed events is analyzed. Comparative calculations are presented for facilities with different camera counts (10 and 200) and different criticality levels. It is established that under high-criticality conditions neural network algorithms demonstrate significantly higher overall efficiency compared to traditional motion detectors due to lower miss probability and reduced operator workload.

Topology Management of a Swarm of Unmanned Aerial Vehicles

2026-03-07T19:21:32+02:00

The article is devoted to the problem of controlling the topology of a swarm of unmanned aerial vehicles (UAVs). The primary objective of swarm operation is maintaining a dynamic topology, i.e., stable information exchange and structural consistency between swarm elements in a constantly changing environment. It is shown that existing approaches rely on a global navigation satellite system (GPS) for UAV positioning, such as the Global Positioning System (GPS). This approach is unacceptable, as UAVs can suddenly experience loss of GPS signals while performing their missions, potentially resulting in a lack of location information. To support functionality, including the use of geographic routing protocols, maintaining connectivity in dynamic network conditions, and adapting to topological changes in the UAV swarm, this paper utilizes virtual coordinates. This paper develops a virtual coordinate system that forms the basis of the proposed method for UAV swarm topology management. This eliminates the need for global coordinates, a centralized controller, motion model coordination, and pre-calibration of the swarm formation. The system operates solely based on local distance measurements to neighbors, making it universal, scalable, and resilient to the loss of individual UAVs. Algorithms for merging and separating swarms based on the virtual coordinate system have been developed.

Investigation of Arrival Operational Dynamics and Safety in Point Merge System

2026-02-28T05:42:08+02:00

The paper addresses the critical issue of enhancing Air Traffic Management efficiency in terminal manoeuvring areas amidst recovering flight traffic intensity. The study focuses on the operational stability of arrival flows, specifically comparing the effectiveness of the innovative Point Merge System (PM) against traditional Radar Vectoring. The research methodology is based on the analysis of large-scale real-world ADS-B trajectory data acquired from the OpenSky Network for Dublin Airport (EIDW). An ETL (Extract, Transform, Load) approach was applied, and a Python-based software suite was developed to calculate Key Performance Indicators, including arrival headway stability, indicated airspeed variability, and trajectory efficiency. The computational experiment results demonstrated that the geometric structure of PM functions as a "passive controller," transforming the stochastic arrival process into a deterministic closed-loop system. It was established that PM implementation reduced velocity variability in the sequencing zone by an average of 12–15 knots, minimizing the "accordion effect" and flight crew workload. Analysis of the Empirical Cumulative Distribution Function (ECDF) for headways revealed a significant reduction in standard deviation, indicating the dissipation of operational entropy. Although the average flight distance within the PM system increased by 12.4%, this is offset by a 60% reduction in holding time. The study concludes that the PM System provides a superior level of predictability and safety margins during peak loads, effectively trading minor distance extensions for enhanced flow stability.

Docking of UAV for Air-to-Air Refueling Under the Influence of the Bow Wave

2026-02-22T20:18:01+02:00

The issues of automating the air-to-air refueling of unmanned aerial vehicle are considered here. The focus is on the contact phase of the "floating-up" drogue with the refueling probe of the refueling unmanned aerial vehicle. The paper discusses affairs of formation of a rendezvous trajectory using a laser beam from a tanker's gyrostabilized optoelectronic system. In this laser beam must keep the refueling unmanned aerial vehicle and the actively controlled refueling drogue. To eliminate the unpredictability of the drogue's "floating-up" direction during the contact phase, the "offset aiming" strategy and algorithms for countering drogue displacement caused by the bow wave effect are proposed. An optimal contact trajectory in terms of approach speed is proposed. The proposed algorithms and their modifications were investigated using mathematical modeling. Studies have shown that the proposed algorithms for compensation of the drogue's "floating-up" effect are quite workable.

Synthesis of a Regulator for UAV Motion Control System under Uncertainty

2026-02-22T19:47:45+02:00

This article represents the study of uncertainties inherent in motion control systems for unmanned aerial vehicles. Both external and internal disturbances acting on moving objects are considered. Expressions for the turbulent wind are given. The analysis of structured and non-structured uncertainties is presented. Results of studying different types of regulators are given, including PID regulators, LQR regulators, robust regulators, and regulators based on non-linear approaches and artificial intelligence methods. The procedure of H infinity synthesis is described. The block diagram of the conversion a continuous regulator in discrete one is represented. The comparative analysis of application LQR and H infinity regulators in loops of tracking by a given trajectory for conditions of the normal and disturbed atmosphere is given. The appropriate graphical dependencies are shown. The obtained results can be useful for aerial objects of a wide class.

Urban LoRaWAN Resilience under EW Interference: an Operational Model for Municipal Networks

2026-02-22T19:01:06+02:00

This paper reports field experience from a municipal LoRaWAN network that periodically faces electronic-warfare interference. We connect the observed service failures to a simple SINR-based reception model and translate that model into practical actions an operator can take – without expensive instrumentation and without heavy changes on end devices. The result is an operations-oriented playbook: which radio frequency symptoms to watch for in gateway / network-server data, how to recognize likely interference patterns, and what mitigations help keep core city services running under degraded radio conditions.

Prediction of Vestibular Schwannoma Growth Based on Radiomics Features of MRI Images Using Ensemble Machine Learning Methods

2026-02-21T18:46:11+02:00

This paper proposes a method for predicting vestibular schwannoma growth based on the analysis of a single MRI scan using radiomics features and ensemble machine learning methods. A total of 96 patients from the public Vestibular-Schwannoma-MC-RC2 dataset were studied. 744 texture features were extracted using wavelet decomposition. A Voting ensemble combining five classifiers was proposed: SVM, logistic regression, k-NN, Random Forest, and LDA. ROC AUC of 0.742 ± 0.072 was achieved using 5-fold cross-validation. The results confirm the effectiveness of the proposed approach for early prediction of tumor growth.

Methodology of Designing Systems for Spatial Stabilization

2026-02-21T18:08:09+02:00

This article presents a study of the design features of spatial stabilization systems. The described methodology covers basic methods and procedures necessary for the creation of a precision, resistant to disturbances, and at the same time controllable system for precision stabilization of the equipment assigned for operation on moving vehicles of a wide class. The block diagram explaining the interconnection between basic methods and procedures for designing spatial stabilization systems is represented. The algorithms for choosing the controllable system structure and the synthesis of the robust controller are described. The procedures for the choice of the system’s components and modelling of both system components and the system as a whole are presented. The features of the modern approach to data processing based on neural networks are described. The obtained results can be useful for the spatial stabilizing objects assigned for operation on moving objects of a wide class.

Definition of MicroGrid Features for the Construction of an Intelligent Control System

2026-02-21T17:37:03+02:00

The article addresses the problem of identifying the key features of a MicroGrid as a complex energy system, which is a necessary prerequisite for the development of an intelligent control system. The widespread introduction of distributed generation, renewable energy sources, and energy storage systems necessitates adaptive control methods capable of operating under conditions of uncertainty and variable electrical network modes. Existing approaches to MicroGrid control remain limited due to the lack of a unified and scientifically grounded set of structural, operational, dynamic, and informational features that should be taken into account in intelligent systems. The paper analyzes contemporary scientific approaches to the classification, modeling, and real-time control of microgrids, which makes it possible to identify insufficiently studied aspects related to the formation of feature sets and their influence on decision-making processes. The objective of the study is to systematize and substantiate a comprehensive set of microgrid features that can serve as a basis for the development of intelligent control algorithms. A conceptual approach to structuring microgrid features into the following groups is proposed: architectural, energy-technical, operational, features related to the stability of dynamic operating modes, and data-oriented features. Their significance for forecasting, optimization, state estimation, and autonomous operation of local energy systems is demonstrated. The obtained results form a methodological foundation that enhances the effectiveness of developing intelligent microgrid control systems and contributes to improving the flexibility and reliability of local energy networks.

A Model and Method of Transactional-behavioral Data Mining for B2B Content Personalization

2026-02-21T17:00:58+02:00

The growth of financial significance and structural complexity of the B2B e-commerce market segment, alongside the necessity to increase its efficiency, has determined the relevance of developing a model and method for the intelligent analysis of B2B customer transactional and behavioral data for content personalization. This study analyzes data mining methods based on Apriori, FP-Growth, and Eclat algorithms and data structures, identifying ways to improve them for B2B-specific data analysis. A conceptual model for analyzing B2B customer commercial activity has been developed, incorporating the product of item quantity and individual B2B transaction price. Furthermore, the UP-Growth (Utility Pattern Growth) method has been developed, utilizing a weighted node utility calculation within the tree structure instead of the standard frequency counters used in FP-Growth. The paper provides examples of constructed association rules and sequential patterns, accompanied by explanations of their economic significance. The impact of the derived association rules and sequential patterns on the formation of personalized product, information, and recommendation content within B2B e-commerce systems is examined.

A Method for Preparing Convolutional Neural Networks for Edge Deployment

2026-02-03T15:00:36+02:00

This study addresses the critical problem of accuracy loss during the compression of deep neural networks for mobile platforms. The research focuses on optimizing convolutional neural networks for operation under constrained hardware resources and the Memory Wall effect. An innovative Edge-deployment preparation method is proposed, which, unlike traditional sequential approaches, integrates structured pruning, post-training quantization, and a fine-tuning stage into a single iterative cycle. This approach provides a synergistic effect, minimizing accuracy degradation while achieving maximum parameter compression. Comparative analysis results confirm that the developed method meets strict latency and power consumption constraints, which are vital for mobile diagnostics in medical applications. Future research prospects involve adapting this method to other machine learning architectures.

Comparative Analysis of Satellite Images Stitching Methods Based on Local Feature Detection

2025-12-20T23:13:55+02:00

This paper investigates feature-based methods for satellite image stitching under a unified evaluation framework. Four algorithms – SIFT, SURF, ORB and BRISK - are examined with respect to keypoint detection, descriptor formation, correspondence generation and geometric alignment. A standardized MATLAB workflow is employed: grayscale detection and description, nearest-neighbour matching with a ratio test, robust outlier rejection via RANSAC with model escalation and mask-based blending with content cropping. Approximately fifty image sets spanning diverse landforms are processed; a Sahara Desert example illustrates the protocol. The study’s aim is to characterize the accuracy-efficiency trade-offs of vector (SIFT, SURF) and binary (ORB, BRISK) descriptors in realistic orbital conditions and to provide a transparent basis for method selection in remote-sensing workflows.

Definition and Intelligent Extraction of Texture Features of Vestibular Schwannoma Based on MRI Imaging

2025-12-20T22:17:48+02:00

The scientific work is devoted to the development of a method for intelligent extraction of textural features of vestibular schwannomas based on magnetic resonance imaging images for predicting tumor growth. The VS-MC-RC2 dataset was analyzed (421 timepoints, 189 patients, 1990–1999). The ML dataset consists of 211 samples (74 growing, 137 stable, imbalance 1.85:1). Gray Level Co-occurrence Matrix and Gray Level Size Zone Matrix matrices, shape features, wavelet transform, and the PyRadiomics v3.0.1 library were used to extract features from T1C images (priority) and T1 images (fallback) with the following parameters: bins = 32, δ = 1 voxel, 13 3D directions. Model v2 (107 original features) achieved an AUC of 0.618. Model v3 (851 features + 8 wavelet decompositions) achieved an AUC of 0.712 (+15.2%). Validation was performed using 10-fold cross-validation with an 80/20 train/test split. Among the top 15 features, 73% were wavelet features (LHH, LLH, HLH). The best feature, original_glszm_ZoneEntropy (F = 12.67, threshold = 4.51), correlates with the Antoni A/B tissue ratio and the proliferative activity of the tumor.

Reverberation Time Errors From Frequency Domain Filtering

2025-12-20T21:26:11+02:00

Information about the frequency dependence of reverberation time is essential for addressing several tasks, including mitigating the impact of reverberation on speech quality and intelligibility, as well as assessing intelligibility using the indirect modulation method. To obtain this information, the room impulse response must be filtered using a bank of octave or one-third-octave filters. This paper analyzes the influence of frequency bandwidth and the shape of the filter’s amplitude-frequency response on the bias of T₂₀, T₃₀, EDT, and T₁₀ estimates of the T₆₀ reverberation time. The analysis assumes that filtering is implemented in the spectral domain by zeroing the spectral components of the RIR outside the desired passband, and that the filter's amplitude-frequency response has the shape of a Tukey window. The results show that the use of filters with a rectangular amplitude-frequency response (Tukey window with parameter r = 0) is undesirable, as it leads to significant bias in the T₂₀, T₃₀, EDT, and T₁₀ estimates. This bias can reach 60–100% for reverberation times in the range of T₆₀ = 0.4–1.2 s. Using filters with a Tukey window shape and r = 1 reduces the bias to no more than 4% when filtering room impulse responses with octave filters at center frequencies f₀≥ 125 Hz. For one-third-octave filters with f₀≥ 25 Hz, a similar level of bias is observed for T₂₀ and T₃₀ estimates. For EDT and T₁₀ estimates, a bias of no more than 4% is achieved within the T₆₀ = 0.6–1.2 s range.

Influence of Airfoil Layouts on UAV Aerodynamic Characteristics at High Angles of Attack

2025-12-14T21:47:16+02:00

This article represents the study of the influence of airfoil layouts on aerodynamic coefficients at high angles of attack. The review of the previous research on the studied topic is given. The features of experimental equipment are described, including the set of sensors for the aerodynamic balance and the information and measurement system. The features of the described experimental equipment allow to realize the automation of the experimental test. The main features of the experiment technique are given. The main functions of the information and measurement system are listed. The results of the experimental test are represented as graphical dependencies of aerodynamic coefficients on angles of attack. The detailed analysis of the obtained results has been done. The results of the study can be useful for designing unmanned aerial vehicle motion control systems and simulating unmanned aerial vehicle motion, taking into consideration aerodynamic disturbances.

Unmanned Controlled Complex for High-quality Communication in Contemporary Extreme Medicine

2025-12-14T20:39:22+02:00

The general analysis was done of requirements for communication during of medical doctors work in extreme conditions, including locations of military activity. The tasks were determined – it was the need to create reliable stable mobile communication for their work; in the locations where there is no coverage, such communication can be organized using drones, and formation of complex of drones and ground base stations. So, the purpose was stated - to find solutions how to develop technologies to ensure high-quality communication with the use of unmanned aerial vehicles in extreme conditions in Ukraine. Numerous prototypes, including patents, were examined and analyzed. Several different schemes for organizing of mobile communications in cases where conventional wireless networks are vulnerable were observed (due to military operations, natural disasters, extreme situations). We have described some possibilities for the restoring of communications using a number of technological solutions based on the use of drones in extreme conditions. The possibility of software or hardware tools for replacing fixed base stations in wireless network with unmanned aerial vehicles were observed. It was proposed to use such tools in two ways: 1) either temporarily to replace fixed base stations with unmanned aerial vehicles; or 2) on permanent basis, f.e., in geographical locations where the terrain makes it difficult to place fixed base stations (impossible due to dangers of military operations, extreme situations, etc.) Such solutions can help to minimize also great damages of the war to our medicine, agriculture, nature in general.

Features of Mathematical Modelling of Gimballed Inertial Navigation System for Marine Moving Vehicle

2025-12-14T10:22:57+02:00

This article represents the features of creating the mathematical model and carrying out modelling of the gimballed inertial navigation system assigned for operation on marine moving vehicles. To increase the accuracy of the system, some modes of operation are introduced. Features of correction for every mode are described. The characteristic of the integral correction is given. The control moments for levelling and gyrocompassing modes are represented. The expressions for projections of the gyro-stabilized platform angular rates are created. The simulation results of stabilization and navigation processes are represented. The advantages of the integral correction are shown. The obtained results can be useful for the high-precision navigation systems and gyroscopic stabilization systems with payload. The proposed approaches can be applied for moving objects of the wide class.

Feature Extraction for Multispectral Analysis of Cereal Crops Using Optimized Computer Vision Pipelines 44

2025-12-14T09:54:37+02:00

The article presents the results of a study aimed at improving the stability, reproducibility, and structural consistency of computer vision pipelines for multispectral UAV imagery of winter wheat canopies. A new adaptive preprocessing model is introduced, incorporating illumination normalization based on a modified Retinex/MSRCR algorithm, entropy-regulated spatial-spectral filtering for noise suppression, and instability-driven spectral feature fusion to obtain stable multispectral descriptors. The model is formulated as a multi-objective preprocessing framework, jointly optimizing illumination invariance, noise robustness, structural preservation, and information richness. Experiments conducted on the open-access UAV dataset of nine winter-wheat fields (Switzerland) demonstrated a reduction of the coefficient of variation to 0.12 and RMSE to 0.089, together with improvements in structural similarity (SSIM = 0.923) and spectral entropy (H = 5.9), significantly outperforming classical normalization methods. The results confirm the effectiveness of the proposed approach in mitigating illumination heterogeneity and sensor-induced distortions, ensuring stable and phenologically consistent feature extraction. The developed framework can be integrated into computer-integrated and robotic precision-farming systems to enhance the reliability of automated monitoring and decision-support processes in winter-wheat production.

Computer Vision for UAV-based Reconnaissance under Conditions of Modern Warfare

2025-12-13T23:19:19+02:00

This paper considers the application of computer vision and deep learning methods for automated aerial reconnaissance using unmanned aerial vehicles under the conditions of modern warfare. The main classes of reconnaissance objects are analyzed, including military vehicles, fortifications, artillery positions, and groups of personnel. An approach to building an object detection system based on deep neural networks is proposed, in particular using YOLO-type detectors and U-Net segmentation models. The process of data preparation and augmentation with consideration of combat factors (smoke, explosions, low illumination, image shift, and noise) is described. An experimental evaluation of object detection quality under different scenarios is performed. It is shown that the use of specially adapted augmentation significantly increases the robustness of the models to interference. The limitations of the proposed approach and directions for further research are discussed.

Intellectual Diagnostics of Thyroid Tumors

2025-12-13T22:32:22+02:00

The article is devoted to the intelligent diagnosis of thyroid tumors, the diagnosis of papillary thyroid cancer based on general information, ultrasound images, and pathohistological images. It examines modern approaches to the intelligent diagnosis of thyroid tumors using machine learning and artificial intelligence methods. The types of medical intelligent systems, their architecture, accuracy, and the set of tasks they perform for the classification of thyroid cancer are considered. The problems of papillary thyroid cancer are considered, the specifics of the disease and the signs by which it is diagnosed are described. The main tasks of an intelligent system capable of automatically analyzing patient medical data and supporting clinical decision-making by an endocrinologist, segmenting and classifying thyroid tumors are outlined. The equipment used to form the training sample is described, and the process of data collection for building an intelligent medical system is described. The task to be solved is set. The metrics by which the accuracy of the intelligent medical system will be evaluated are characterized. The architecture of the intelligent medical system is presented, its main blocks are characterized, and the functionality of each block is described. A UML diagram is presented, according to which the intelligent medical system will operate. The data that will be used to form the training sample is presented, indicating its type, dimension, how the data is collected, and how this data will be used to train the intelligent medical system. The results of the study are aimed at improving the effectiveness of early detection of thyroid pathologies and reducing the number of false diagnoses, creating a convenient tool that will reduce the time it takes for a doctor to diagnose the disease and increase the accuracy of diagnosing papillary thyroid cancer.

Analysis of Methods for Monitoring the Condition of Building Facades Based on Visual Data

2025-12-13T17:03:11+02:00

The article explores the use of information technology to monitor the condition of building facades based on visual data obtained from unmanned aerial vehicles. The study highlights the growing role of unmanned aerial vehicles in structural inspections, noting their key advantages, including increased safety, efficiency, and accuracy, compared to traditional methods. The study is structured into three main sections. The first section provides an overview of existing approaches to facade monitoring, comparing traditional inspection methods with UAV-based methods. The second section discusses the technological aspects of data collection, processing, and analysis, focusing on artificial intelligence, computer vision, and photogrammetry. The final section presents the practical application of these technologies, an overview of relevant software tools, examples, and economic benefits. The results show that unmanned aerial vehicles, combined with advanced image processing technologies, significantly increase the efficiency and reliability of building facade assessments.

Technology of Objects Recognition by Drones with Humanitarian Medical Cargo

2025-12-11T19:56:28+02:00

The problems of objects recognition by drones (UAVs) were observed, the general analysis of these problems was done. The central purposes of the work were outlined, as well as the set of tasks for these problems solutions. In process of the work the observation of prototypes and analogs – the versions of previously constructed UAV modules with images recognition abilities were done for providing medical assistance in extreme conditions. The aim of present work was to develop mechanisms and possibilities of image recognition by UAVs for medical purposes, and to clarify the possibility of humanitarian cargo transportation by UAVs to programmatically defined object or person. To do this the experience of prototypes creation, and their analysis were taken into account. The algorithms of necessary operations and suitable software were developed to automate processes. Developed software was designed basing on a face recognition technology convolutional neural network. All necessary information concerning all stages of the work was described profoundly. Originally developed humanitarian UAV was constructed with various modules (for diagnosing a person's health state, providing patient with appropriate medical care, others); and module for images recognition in point of destination was one of them. The sample of created algorithm and few fragments of novel program supply were given in present article. Developed UAV can be used repeatedly for different tasks solutions: monitoring of area pollution (chemical or other); medical equipment and first aid means can be parachuted over the point of location of potentially injured person, etc. In both cases images recognition functions are desirably for the drone. Preliminary data concerning successful application of work results, some given practical recommendations were described.

Research on UAV Air Launch in the Jsbsim Software Environment

2025-12-11T19:28:38+02:00

The article considers the scientific and practical aspects of unmanned aerial vehicle air launch, analyzes modern approaches and technologies, and also determines the advantages of this launch method compared to traditional methods. The method of studying unmanned aerial vehicle air launch using simulations in the JSBsim software environment is considered. The database of unmanned aerial vehicle air launch dynamics is presented, confirming the effectiveness of using unmanned aerial vehicle air launch simulation in the JSBsim software environment. The influence of unmanned aerial vehicle weight on the launch process and subsequent unmanned aerial vehicle flight is considered in detail. The scientific novelty lies in the development of a method for high-precision modeling of unmanned aerial vehicle air launch in a circuit with an autopilot, the creation of a high-precision database of unmanned aerial vehicle air launch dynamics.

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

2025-09-30T11:54:50+03:00

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

2025-09-30T11:09:10+03:00

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.