Makoveichuk Oleksandr

Scopus Publications

IMPROVEMENT OF METHODOLOGY FOR ASSESSING THE DYNAMICS OF DEGRADATION AND DIRECT ECONOMIC LOSSES OF THE AGRICULTURAL SECTOR IN THE CONDITIONS OF MODERN CHALLENGES CAUSED BY MILITARY ACTIONS
Mykola Butko, Igor Butko, Oleksandr Makoveichuk, Vladislav Tiutiunnyk, Hennadii Khudov
Technology Audit and Production Reserves, 2026
The object of research is the process of assessing the dynamics of degradation and direct economic losses of the agricultural sector. The hypothesis of research is based on the assumption that the regression model is able to provide a reliable short-term assessment (with a lag of 1 year) of the actual state of land use. Improved methodology for assessing the dynamics of degradation and direct economic losses in the agricultural sector, which, unlike the known ones, involves the following stages: – analysis of the dynamics of the Red and NIR channels within the study area to identify patterns of degradation of agrophytocenoses; – determination of a statistical criterion of landscape structural order (OSI) to differentiate target crops from ruderal vegetation; – conducting a linear regression analysis to determine the areas of active production based on the spectral characteristics of satellite data. Experimental studies have been conducted to assess the volume of direct economic losses in the agricultural sector for the period 2022–2025. Analysis of the dynamics of spectral channels for 2016–2025 showed that starting from 2022, a “scissors” effect has been observed – a steady increase in the average in the Red channel and a decrease in NDVI, which is a sign of land withdrawal from cultivation. In the pre-war period, OSI values were in the range of 0.3–0.7, and starting from 2022 they became negative (about –1.5), which corresponds to the loss of structural integrity of the agricultural landscape. The calculated direct economic losses in the agricultural sector of the Kyiv region (Ukraine) for 2022–2025 are 491.7–548.11 million USD, depending on the calculation method. The gap between official statistics and the calculation method (37.26 million USD) corresponds to the crop that was sown but not harvested due to military threats.
IMPROVING A METHOD FOR FILTERING IMAGES ACQUIRED FROM A SPACE-BASED RADAR OBSERVATION SYSTEM BASED ON THE KUAN ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Serhii Tokarev, Artem Andriushchenko, Oleksandr Pukhovyi, Olexandr Rohulia, Oleh Bilous, Mykola Verovok, Valeriy Samoylenko, Vladyslav Khudov
Eastern European Journal of Enterprise Technologies, 2026
The object of this study is the process of filtering images acquired from a space radar observation system. The task to filter images from a space radar observation system has been solved by applying the Kuan algorithm. The results reported here include the following: – the defined basic stages of the method for filtering images acquired from a space radar observation system based on the Kuan algorithm; – the performed experimental study on filtering images from a space radar observation system based on the Kuan algorithm. A method for filtering images from a space radar observation system based on the Kuan algorithm has been improved. Special features of the improved method, in contrast to those in established ones, are: – selection of a local filtering window; – calculation of local statistical characteristics; – calculation of variation coefficients; – calculation of the Kuan weight coefficient; – sequential filtering of image pixels using the “sliding” window method. A visual analysis of radar image filtering by an improved method based on the Kuan algorithm and known methods based on the Li algorithm and Frost algorithm were carried out. The use of the improved method when filtering an image acquired from a space radar surveillance system made it possible to increase the signal-to-noise ratio. That became possible due to the use of the Kuan algorithm. The choice of the Kuan algorithm has made it possible to achieve a 21% gain in the maximum signal-to-noise ratio in comparison with the known method (based on the Li algorithm). The scope of the improved method application includes filtering images from space radar surveillance systems. Conditions for practical implementation of the results are specialized software in software-hardware systems for processing radar images
DEVELOPMENT OF AN OPTOELECTRONIC IMAGE SEGMENTATION METHOD FROM UNMANNED AERIAL VEHICLES BASED ON THE ANT COLONY OPTIMIZATION ALGORITHM UNDER THE INFLUENCE OF SALT-AND-PEPPER NOISE
Igor Ruban, Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Ihor Butko, Andrii Hryzo, Rostyslav Khudov, Petro Mynko, Oleksii Baranik
Technology Audit and Production Reserves, 2025
The object of research is the process of segmenting an image from an unmanned aerial vehicle based on the ant algorithm under the influence of salt-and-pepper noise. “Salt”-and-“pepper” noise occurs due to data transmission errors, failures of digital camera sensors or malfunctions during recording/reading of information. It is characterized by the random appearance of individual pixels in the image, the value of which is equal to the minimum (“pepper”) or maximum (“salt”) brightness level. Unlike the known ones, the method of segmenting an optoelectronic image based on the ant algorithm provides image segmentation under the influence of salt-and-pepper noise and involves: – initialization of initial parameters; – calculation of the length of the path segment of agents; – calculation of the attractiveness of the route for the agent; – updating the pheromone concentration; – calculation of the probability of transition of agents; – calculation of the objective function; – movement of agents; – determination of the best route of agents. Experimental studies have shown that the segmentation method based on the ant algorithm provides a reduction in segmentation errors of the first kind on average: – in the absence of salt-and-pepper noise – 4%; – at the intensity of salt-and-pepper noise σ = 5–21%; – at the intensity of salt-and-pepper noise σ = 15–10%. The segmentation method based on the ant algorithm provides a reduction in segmentation errors of the second kind on average: – in the absence of salt-and-pepper noise – 3%; – at the intensity of salt-and-pepper noise σ = 5–15%; – at the intensity of salt-and-pepper noise σ = 15–6%. The practical significance of the segmentation method based on the ant algorithm is to ensure high-quality image segmentation under the influence of salt-and-pepper noise.
DEVISING A SEGMENTATION METHOD FOR OPTOELECTRONIC IMAGERY FROM UNMANNED AERIAL VEHICLES BASED ON THE ARTIFICIAL BEE COLONY ALGORITHM
Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Serhii Yarosh, Irina Khizhnyak, Valerii Varvarov, Ihor Butko, Rostyslav Khudov, Yurii Sheviakov, Artem Irkha
Eastern European Journal of Enterprise Technologies, 2025
This paper considers the process of segmenting an optoelectronic image acquired from an unmanned aerial vehicle based on the artificial bee colony algorithm. The principal hypothesis of this study assumes that the use of the artificial bee colony algorithm for segmenting an optoelectronic image acquired from an unmanned aerial vehicle could reduce segmentation errors of the first and second kinds. A method for segmenting an optoelectronic image acquired from an unmanned aerial vehicle based on the artificial bee colony algorithm has been improved, which, unlike known ones, involves the following: – initialization of the population of scout bees; – calculation of the objective function; – determining the best and promising positions; – calculation of the optimal value of the segmentation threshold; – image division into segments; – checking the stopping criterion; – bee migration; – acquisition of a segmented image. Experimental studies have been conducted on the segmentation of an optoelectronic image acquired from an unmanned aerial vehicle using a method based on the artificial bee colony algorithm. The visual quality of the segmented image makes it possible to conclude that segmentation using the artificial bee colony method is possible. Comparative analysis of segmented images (improved and known methods) indicates a clearer separation of the object of interest (car) using the method based on the artificial bee colony algorithm. The results of calculating segmentation errors of the first and second kind indicate a reduction in segmentation errors of the first kind by 9% and errors of the second kind by 7% when segmenting an optoelectronic image using the method based on the artificial bee colony algorithm
DEVELOPMENT OF AN IMAGE SEGMENTATION METHOD FROM UNMANNED AERIAL VEHICLES BASED ON THE ANT COLONY ALGORITHM UNDER THE INFLUENCE OF SPECKLE NOISE
Igor Ruban, Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Nazar Shamrai, Ihor Butko, Rostyslav Khudov, Valerii Varvarov, Oleksandr Kostianets
Technology Audit and Production Reserves, 2025
The object of research is the process of segmenting an image from an unmanned aerial vehicle based on the ant algorithm under the influence of speckle noise. Unlike the known ones, the image segmentation method based on the ant algorithm involves the imitation of the collective behaviour of agents (ants) capable of adapting to local features of the image. In addition, the pheromone marking mechanism contributes to a more distinct delineation of the boundaries between segments, which positively affects the accuracy of dividing the image into segments. Speckle noise is a type of multiplicative noise that occurs in images formed using coherent radiation. Its appearance is due to the interference of reflected waves coming from different points of the same object, but with microscopic differences in phase. This leads to the appearance of a chaotic granular structure that distorts the image and complicates further analysis. Experimental studies have shown that the segmentation method based on the ant algorithm provides a reduction in segmentation errors of the first kind on average from 6% (in the absence of speckle noise) to 30% (at a speckle noise intensity σ = 15). With an increase in the speckle noise intensity, the gain in the value of the segmentation error of the first kind increases. The segmentation method based on the ant algorithm provides a reduction in segmentation errors of the second kind on average from 5% (in the absence of speckle noise) to 32% (at a speckle noise intensity σ = 15). With an increase in the speckle noise intensity, the gain in the value of the segmentation error of the second kind increases. The practical value of the segmentation method based on the ant algorithm lies in the possibility of segmentation under the influence of speckle noise. At the same time, a reduction in segmentation errors of the first and second kind is ensured in comparison with the known method.
EXPERIMENTAL STUDIES OF THE IMAGE SEGMENTATION METHOD QUALITY FROM UNMANNED AERIAL VEHICLES BASED ON THE ANT COLONY OPTIMIZATION ALGORITHM UNDER THE INFLUENCE OF ADDITIVE GAUSSIAN NOISE
Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Valerii Varvarov, Fedir Zots
Advanced Information Systems, 2025
The subject matter of the article is experimental studies of the image segmentation method quality from UAVs based on the Ant Colony Optimization algorithm under the influence of additive Gaussian noise. The goal is to reduce the probability of first and second type errors in image segmentation by applying a segmentation method based on the Ant Colony Optimization algorithm under the influence of additive Gaussian noise. The tasks of the study are to evaluate the robustness and accuracy of the image segmentation method based on the Ant Colony Optimization algorithm under varying levels of additive Gaussian noise, and to compare its performance with the classical Sobel filter–based segmentation approach. The methods used are digital image processing techniques, statistical analysis of segmentation quality, implementation of the Ant Colony Optimization algorithm for image segmentation, modeling of noise-contaminated conditions, and comparison of segmentation errors of the first and second kinds. The following results are obtained: the method based on the Ant Colony Optimization algorithm demonstrates superior noise resistance and maintains higher accuracy than the Sobel filter approach. Specifically, it reduces first-kind segmentation errors by 14–23% and second-kind errors by 9–17%, depending on the level of noise. Visual and quantitative analysis confirms the effectiveness of the proposed method in processing UAV-acquired imagery affected by additive Gaussian noise. Conclusions. The experimental findings confirm that the method based on the Ant Colony Optimization algorithm outperforms conventional edge detection techniques, particularly under noisy conditions, providing improved accuracy and robustness across a range of noise intensities.
DEVELOPMENT OF AN IMAGE SEGMENTATION METHOD FROM UNMANNED AERIAL VEHICLES BASED ON THE PARTICLE SWARM OPTIMIZATION ALGORITHM
Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Illia Hridasov, et al.
Technology Audit and Production Reserves, 2025
The object of research is the process of segmenting images from an unmanned aerial vehicle based on the particle swarm algorithm. One of the most problematic areas in segmenting images from unmanned aerial vehicles is the reduction in the efficiency of known segmentation methods. In addition, most methods do not accurately recognize small objects that occupy a small part of the image. The method of segmenting images from an unmanned aerial vehicle based on the particle swarm algorithm has been improved, in which, unlike the known ones, the following is performed: – the source image is converted to the appropriate color space; – the channel is selected for further analysis; – the particle swarm is initialized on the source image in each channel selected for further analysis; – the objective function is calculated for each particle of the swarm in the image in each selected channel; – the current value of the objective function for each particle of the swarm is compared with the best value of the objective function in the image in each selected channel; – calculating the velocity value and new location for each swarm particle in the image; – moving each swarm particle in the image in each selected channel; – determining the swarm particles with the best value of the objective function in the image in each channel; – combining the channels and forming the resulting image. During the study, it was found that the segmented image by the improved method based on the particle swarm algorithm has better visual quality compared to the known segmentation method. It was found that the improved segmentation method based on the particle swarm algorithm provides an average reduction in segmentation errors of the I kind by 11% and an average reduction in segmentation errors of the II kind by 9%.
Image Segmentation Methods for Kamikaze FPV Drones Targeting to Aid Critical Energy National Infrastructure Assets Protection
Hennadii Khudov, Rostyslav Khudov, Irina Khizhnyak, Oleksandr Makoveichuk, Vladyslav Khudov
Studies in Systems Decision and Control, 2025
Temperature Forecasting with LSTM: A Case Study on Kyiv Weather Data
Ceur Workshop Proceedings, 2025
DEVISING A METHOD OF DISCRETE SEARCH FOR A PLANE THAT CRASHED BY USING THE BLACKWELL-BLACK-KADAN RATIO
Hennadii Khudov, Illia Hridasov, Igor Ruban, Oleksandr Makoveichuk, Ihor Butko, et al.
Eastern European Journal of Enterprise Technologies, 2025
The object of this study is the process of searching for a plane that crashed by using search tools. The main hypothesis of the study assumes that the use of a uniformly optimal search strategy in a discrete search zone taking into account the Blackwell-Black-Kadan relation could minimize the average time for detecting a plane that crashed. An optimal Bayesian rule has been formulated, which involves determining the maximum value of the likelihood ratio in the current discrete search sector and comparing it with the threshold. A class of uniformly optimal search strategies has been introduced. A method of discrete search for a plane that crashed has been improved, according to which, unlike in the known analogs: – the a priori probability of finding the search object in the search sector is taken into account; – the probability that the search object will be detected when viewing the search sector is calculated; – the Blackwell-Black-Kadan relations are determined; – the obtained Blackwell-Black-Kadan values are ranked, and the sequence of the search sectors is examined in accordance with the obtained ranking of the Blackwell-Black-Kadan ratio values. The average time to detect the search object was estimated. It has been established that when optimizing the search for a plane that crashed, the average search time for the search object is reduced by 12%. The limitation of the study is a simplified representation of the search area, which is given by a regular discrete grid without taking into account complex terrain or prohibited areas. In addition, external factors such as weather conditions, wind, etc., which may affect the speed and route of the search vehicle, are not taken into account. The disadvantage of the improved method is its application only for the case of a discrete structure search area
DEVISING A METHOD FOR DETERMINING THE COORDINATES OF AN UNMANNED AERIAL VEHICLE BY A NETWORK OF THREE SOFTWARE-DEFINED-RADIO RECEIVERS USED IN PAIRS
Hennadii Khudov, Andrii Hryzo, Volodymyr Komarov, Kostiantyn Yatsenko, Oleksandr Makoveichuk, Rostyslav Khudov, Petro Mynko, Olena Goncharenko, Oleh Salnyk, Valerii Vlasiuk
Eastern European Journal of Enterprise Technologies, 2025
IMPROVING A METHOD FOR DETERMINING THE COORDINATES OF A RECONNAISSANCE UNMANNED AERIAL VEHICLE BY A SMALL-BASED NETWORK OF TWO SOFTWARE-DEFINED RADIO RECEIVERS
Igor Ruban, Hennadii Khudov, Yelyzaveta Biernik, Oleksandr Makoveichuk, Volodymyr Maliuha, Serhii Yarovyi, Rostyslav Khudov, Vladyslav Khudov, Leonid Poberezhnyi, Olena Goncharenko
Eastern European Journal of Enterprise Technologies, 2025
IMPROVING A METHOD THAT RAPIDLY DETERMINES THE PHANTOMIZATION AREAS IN AN IMAGE ACQUIRED FROM A SPACE-BASED RADAR OBSERVATION SYSTEM
Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Dmytro Huriev, Anatoliy Popov, Serhii Oliynick, Pavlo Malashta, Yaroslav Sydorov, Olexandr Rohulia, Maksym Adamchuk
Eastern European Journal of Enterprise Technologies, 2025
THE METHOD FOR APPROXIMATING THE EDGE DETECTION CONVOLUTIONAL OPERATOR USING A GENETIC ALGORITHM FOR SEGMENTATION OF COMPLEX-STRUCTURED IMAGES
Hennadii Khudov, Oleksandr Makoveichuk, Temir Kalimulin, Vladyslav Khudov, Nazar Shamrai
Advanced Information Systems, 2024
Vegetation zone segmentation in multispectral imagery
I M Butko, O I Golubenko, O M Makoveichuk, I O Zaitsev, V O Kromkach
Iop Conference Series Earth and Environmental Science, 2024
Experimental Studies with Software Defined Radio Receivers of Aerial Objects On-Board Systems Signals
Hennadii Khudov, Oleksandr Kostianets, Kristina Tahyan, Oleksandr Makoveichuk, Yelyzaveta Biernik, Yurii Shevchenko
Conference Proceedings 2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics Telecommunications and Computer Engineering Tcset 2024, 2024
An Oscillatory Model of the Movement of a Flock of Robots in the Convoy Mode
Roman Zinko, Oleksandr Makoveichuk, Oleh Polishchuk, Andrii Polishchuk, Svitlana Lisevych, Adam Mazurkiewicz
2024 IEEE 6th International Conference on Modern Electrical and Energy System Mees 2024, 2024
DEVISING A METHOD FOR DETERMINING THE COORDINATES OF AN UNMANNED AERIAL VECHICLE VIA A NETWORK OF PORTABLE SPECTRUM ANALYZERS
Hennadii Khudov, Oleksandr Makoveichuk, Oleksandr Kostyria, Ihor Butko, Andrii Poliakov, Yaroslav Kozhushko, Serhii Yarovyi, Oleksii Serdiuk, Petro Mynko, Rostyslav Khudov
Eastern European Journal of Enterprise Technologies, 2024
DETERMINING THE NUMBER OF SMALL-SIZED RADARS IN A NETWORK WITH COHERENT SIGNAL PROCESSING FOR THE DETECTION OF STEALTH AERIAL VEHICLES
Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Mykhajlo Murzin, Andrii Zvonko, Anatolii Adamenko, Dmytro Bashynskyi, Oleh Salnyk, Andrii Nyshchuk, Vladyslav Khudov
Eastern European Journal of Enterprise Technologies, 2024
DEVISING A METHOD FOR DETECTING AN AERIAL OBJECT BY RADAR WITH AN ADDITIONAL CHANNEL OF PASSIVE RECEPTION
Hennadii Khudov, Ivan Trofymov, Iurii Repilo, Oleksandr Makoveichuk, Viktor Tkachenko, Denys Kotov, Valya Gridina, Maksym Herda, Vitalii Kryvosheiev, Mikola Shvets
Eastern European Journal of Enterprise Technologies, 2024
Development of the Method for Calculating the Arrival Path of the Special Services Unit to the Sites of Emergency Occurrence
Hennadii Khudov, Irina Khizhnyak, Oleksandr Makoveichuk, Vladyslav Khudov, Rostyslav Khudov
Studies in Systems Decision and Control, 2023
Detection Method of Augmented Reality Systems Mosaic Stochastic Markers for Data-Centric Business and Applications
Hennadii Khudov, Igor Ruban, Oleksandr Makoveichuk, Vladyslav Khudov, Irina Khizhnyak
Studies in Systems Decision and Control, 2023
The Two Stage Method for Detecting Objects of Interest on Optical Satellite RGB Imagery of the Urban Infrastructure
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Vladyslav Khudov, Nazar Shamrai
2023 IEEE 4th Khpi Week on Advanced Technology Khpi Week 2023 Conference Proceedings, 2023
The Development of the Optical-Electronic Images Segmentation Method Based on the Canny Edge Detector
Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Irina Khizhnyak, Temir Kalimulin, Rostyslav Khudov
International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2023
Application of the Particle Swarm Algorithm to the Task of Image Segmentation for Remote Sensing of the Earth
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Igor Butko, Sergey Glukhov, Irina Khizhnyak, Nazar Shamrai, Temir Kalimulin
Lecture Notes in Networks and Systems, 2023
DEVELOPMENT OF A TWO-STAGE METHOD FOR SEGMENTING THE COLOR IMAGES OF URBAN TERRAIN ACQUIRED FROM SPACE OPTIC-ELECTRONIC OBSERVATION SYSTEMS BASED ON THE ANT ALGORITHM AND THE HOUGH ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Irina Khizhnyak, Rostyslav Khudov, Volodymyr Maliuha, Serhii Sukonko, Oleksii Lunov, Mykhailo Buhera, Taras Kravets
Eastern European Journal of Enterprise Technologies, 2023
DETERMINATION OF THE NUMBER OF CLUSTERS ON IMAGES FROM SPACE OPTIC-ELECTRONIC OBSERVATION SYSTEMS USING THE K-MEANS ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Volodymyr Komarov, Vladyslav Khudov, Irina Khizhnyak, Volodymyr Bashynskyi, Stanislav Stetsiv, Yevhen Dudar, Andrii Rudiy, Mykhailo Buhera
Eastern European Journal of Enterprise Technologies, 2023
IMPROVING A METHOD FOR SEGMENTING OPTICAL-ELECTRONIC IMAGES ACQUIRED FROM SPACE OBSERVATION SYSTEMS BASED ON THE FIREFLY ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Irina Khizhnyak, Yurii Dobryshkin, Oleksandr Kondratov, Vitalii Andronov, Ivan Balyk, Tetiana Uvarova, Maksym Kalenyk
Eastern European Journal of Enterprise Technologies, 2023
The Choice of Quality Indicator for the Image Segmentation Evaluation
Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Sergey Glukhov, Nazar Shamrai, Serhii Rudnichenko, Maksym Husak, Rostyslav Khudov
International Journal of Emerging Technology and Advanced Engineering, 2022
The Method for Determining Informative Zones on Images from On-Board Surveillance Systems
Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Bohdan Shamrai, Sergey Glukhov, Oleksii Lunov, Serhii Lohachov, Oleh Chervotoka, Andrey Halosa
International Journal of Emerging Technology and Advanced Engineering, 2022
Application of the Max-Min Ant System to Solve the Problem of Determining the Route of Vehicles in the Presence of Prohibited Areas
Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Irina Khizhnyak, Nazar Shamrai, Dmytro Misiuk
International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2022
The Technique for Detecting Zones of Interest in Satellites and Drones RGB Imagery
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Temit Kalimulin, Nazar Shamrai
2022 IEEE 3rd Khpi Week on Advanced Technology Khpi Week 2022 Conference Proceedings, 2022
THE DEVELOPMENT OF A MANAGEMENT DECISIONMAKING METHOD BASED ON THE ANALYSIS OF INFORMATION FROM SPACE OBSERVATION SYSTEMS
Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Mykola Butko, Veronika Khudolei, Stanislav Kukhtyk
Eastern European Journal of Enterprise Technologies, 2022
An Improved Model for Clarification of Geospatial Information
Khudov Hennadii, Butko Igor, Makoveichuk Oleksandr, Khizhnyak Irina, Khudov Vladyslav, Yuzova Iryna, Solomonenko Yuriy
Lecture Notes in Networks and Systems, 2022
DEVISING A METHOD FOR SEGMENTING COMPLEX STRUCTURED IMAGES ACQUIRED FROM SPACE OBSERVATION SYSTEMS BASED ON THE PARTICLE SWARM ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Oleksandr Oleksenko, Yuriy Khazhanets, Yuriy Solomonenko, Iryna Yuzova, Yevhen Dudar, Stanislav Stetsiv, Vladyslav Khudov
Eastern European Journal of Enterprise Technologies, 2022
METHODS OF UAVS IMAGES SEGMENTATION BASED ON K-MEANS AND A GENETIC ALGORITHM
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Temir Kalimulin, Sergey Glukhov, Pavlo Arkushenko, Taras Kravets, Irina Khizhnyak, Nazar Shamrai
Eastern European Journal of Enterprise Technologies, 2022
DEVISING A METHOD FOR SEGMENTING CAMOUFLAGED MILITARY EQUIPMENT ON IMAGES FROM SPACE SURVEILLANCE SYSTEMS USING A GENETIC ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Igor Gyrenko, Vitalii Stryhun, Oleh Bilous, Nazar Shamrai, Anna Kovalenko, Irina Khizhnyak, Rostyslav Khudov
Eastern European Journal of Enterprise Technologies, 2022
DEVISING A METHOD FOR PROCESSING THE IMAGE OF A VEHICLE'S LICENSE PLATE WHEN SHOOTING WITH A SMARTPHONE CAMERA
Hennadii Khudov, Oleksandr Makoveichuk, Dmytro Misiuk, Hennadii Pievtsov, Irina Khizhnyak, Yuriy Solomonenko, Iryna Yuzova, Volodymyr Cherneha, Valerii Vlasiuk, Vladyslav Khudov
Eastern European Journal of Enterprise Technologies, 2022
DEVISING A METHOD FOR SEGMENTING IMAGES ACQUIRED FROM SPACE OPTICAL AND ELECTRONIC OBSERVATION SYSTEMS BASED ON THE SINECOSINE ALGORITHM
Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Volodymyr Maliuha, Anatolii Andriienko, Yevhen Tertyshnik, Viktor Pashchenko, Dmytro Parashchuk, Irina Khizhnyak, Temir Kalimulin
Eastern European Journal of Enterprise Technologies, 2022
The Method for Identification of Radars Measurements of Nearby Objects Tracking
Ivan Kozhedub Kharkiv National Air Force University, Ukraine, Kharkiv, 61023, Hennadii Khudov, Igor Ruban, Hennadii Pievtsov, Oleksandr Makoveichuk, Oleh Popkov, Danylo Shabanov, Yurii Baranov, Yuriy Solomonenko, Vitalii Kryvosheiev, Rostyslav Khudov
International Journal of Emerging Technology and Advanced Engineering, 2021
Improved imaging model in the presence of multiplicative spatially extended cloaking interference
Ivan Kozhedub Kharkiv National Air Force University, Ukraine, Kharkiv, 61023, Hennadii Khudov, Igor Ruban, Oleksandr Makoveichuk, Yevhen Stepanenko, Irina Khizhnyak, Sergey Glukhov, Olesya Symkanych, Yuriy Solomonenko, Andrii Baranov, Nazar Shamrai, Rostyslav Khudov
International Journal of Emerging Technology and Advanced Engineering, 2021
The Mosaic Sustainable Marker Detection Method for Augmented Reality Systems
Igor Ruban, Olexander Makoveychuk, Vladyslav Khudov, Irina Khizhnyak, Hennadii Khudov, Oleksii Baranik
2020 IEEE International Conference on Problems of Infocommunications Science and Technology Pic S and T 2020 Proceedings, 2021
The visual information structures formation model for the visual information systems processing
Hennadii Khudov, Oleksandr Makoveichuk, Dmytro Misiuk, Igor Butko, Irina Khizhnyak, Nazar Shamrai
2021 IEEE 3rd International Conference on Advanced Trends in Information Theory Atit 2021 Proceedings, 2021
Development of a Method for Improving Stability Method of Applying Digital Watermarks to Digital Images
Oleksandr Makoveichuk, Igor Ruban, Nataliia Bolohova, Andriy Kovalenko, Vitalii Martovytskyi, Tetiana Filimonchuk
Eastern European Journal of Enterprise Technologies, 2021
The Development of a Forecasting Model for the Situation Based on Space Images
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Vladyslav Khudov, Volodymyr Maliuha, Iryna Yuzova, Oleksii Serdiuk
International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2021
The Improved Mathematical Model for Interpretation of Satellite Imagery
Hennadii Khudov, Irina Khizhnyak, Dmytro Misiuk, Nazar Shamrai, Viacheslav Chepurnyi, Igor Ruban, Oleksandr Makoveichuk, Vladyslav Khudov, Igor Butko
2021 IEEE 8th International Conference on Problems of Infocommunications Science and Technology Pic S and T 2021 Proceedings, 2021
The Model and the Method for Forming a Mosaic Sustainable Marker of Augmented Reality
Igor Ruban, Hennadii Khudov, Olexander Makoveychuk, Irina Khizhnyak, Vladyslav Khudov, Vitaliy Lishchenko
Proceedings 15th International Conference on Advanced Trends in Radioelectronics Telecommunications and Computer Engineering Tcset 2020, 2020
Development of methods for determining the contours of objects for a complex structured color image based on the ant colony optimization algorithm
Hennadii Khudov, Igor Ruban, Oleksandr Makoveichuk, Hennady Pevtsov, Vladyslav Khudov, Irina Khizhnyak, Sergii Fryz, Viacheslav Podlipaiev, Yurii Polonskyi, Rostyslav Khudov
Eureka Physics and Engineering, 2020
The method for selecting the urban infrastructure objects contours
Igor Ruban, Oleksandr Makoveichuk, Vladyslav Khudov, Hennadii Khudov, Irina Khizhnyak, Iryna Yuzova, Yevhen Drob
2019 IEEE International Scientific Practical Conference Problems of Infocommunications Science and Technology Pic S and T 2019 Proceedings, 2019
Method for determining elements of urban infrastructure objects based on the results from air monitoring
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Nataliia Lukova-Chuiko, Hennady Pevtsov, Yurii Sheviakov, Iryna Yuzova, Yevhen Drob, Olexander Tytarenko
Eastern European Journal of Enterprise Technologies, 2019
Segmentation of opticalelectronic images from on-board systems of remote sensing of the earth by the artificial bee colony method
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Vladyslav Khudov, Viacheslav Podlipaiev, Viktor Shumeiko, Oleksandr Atrasevych, Anatolii Nikitin, Rostyslav Khudov
Eastern European Journal of Enterprise Technologies, 2019
Construction of methods for determining the contours of objects on tonal aerospace images based on the ant algorithms
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Mykola Chomik, Vladyslav Khudov, Irina Khizhnyak, Viacheslav Podlipaiev, Yurii Sheviakov, Oleksii Baranik, Artem Irkha
Eastern European Journal of Enterprise Technologies, 2019
A Swarm Method for Segmentation of Images Obtained from On-Board Optoelectronic Surveillance Systems
Igor Ruban, Vladyslav Khudov, Oleksandr Makoveichuk, Hennadii Khudov, Irina Khizhnyak
2018 International Scientific Practical Conference on Problems of Infocommunications Science and Technology Pic S and T 2018 Proceedings, 2018
Segmentation of the images obtained from onboard optoelectronic surveillance systems by the evolutionary method
Igor Ruban, Hennadii Khudov, Vladyslav Khudov, Irina Khizhnyak, Oleksandr Makoveichuk
Eastern European Journal of Enterprise Technologies, 2017
Adaptive moving object segmentation algorithms in cluttered environments
Yuriy Ivanov, Dmytro Peleshko, Oleksandr Makoveychuk, Ivan Izonin, Igor Malets, Natalia Lotoshunska, Danylo Batyuk
Proceedings of 13th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics Cadsm 2015, 2015

Makoveichuk Oleksandr

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