On January 29, 2026, a meeting was held in the classroom No. 1 of the educational building No. 5, where 1st-year students of the Faculty of History, Read more
Quotation gallery
Knowledge is rooted in all things – the world is a library.
American Proverb
Faculty News
On January 30, 2026, students of the Faculty of History, Economics, and Law of Manash Kozybayev North Kazakhstan University took part in career guidan Read more
On January 29, 2026, a large-scale environmental fair "Darmarka" was held at the Manash Kozybayev North Kazakhstan University as part of the #StopMuso Read more
23 января 2026 года на факультете истории, экономики и права состоялась рабочая встреча представителей Управления государственных доходов г.Петропавло Read more
In mid-December 2025, the РА "Red Crescent Society of the Republic of Kazakhstan" held a short film competition on the theme "Fundamental Principles o Read more
On December 26, 2025, the Faculty of History, Economics and Law held a final meeting of the Faculty Council dedicated to summarizing the results of th Read more
17.12.2025 на медицинском факультете НАО СКУ им.М.Козыбаева началась экзаменационная сессия. С целью укрепление принципов академической честности, про Read more
12 декабря 2025 года, в рамках реализации плана научных мероприятий медицинского факультета на 2025–2026 годы, был проведён практический семинар с гру Read more
On December 2, 2025, the Department of Justice of the North Kazakhstan region held a lecture for students as part of the implementation of the ideolog Read more
On November 27, 2025, the Faculty of History, Economics and Law hosted a seminar by business consultant and MBA teacher Sh.I. Orazbayeva on the topic Read more
Development of a Neural Network Model for UAV Recognition through an Optical-Electronic Channel Integrated into the Data Fusion System
This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant No. AR19679009).
Project Manager: Kurmashev I.G., Candidate of Technical Sciences.
Project Performers: Kurmashev I.G., Candidate of Technical Sciences, Serbin Vasily Valeryevich, Candidate of Technical Sciences, Arrichiello Filippo, Doctor of Technical Sciences, Semenyuk V.V., Master, Alyoshin D.V., Master, Kryuchkov V.N., Master, Kurmasheva L.B., Master.
Implementation period: 2023 – 2025.
Project goal: To develop a software model for UAV recognition based on neural networks, adapted to the "FMCW radar + video surveillance" platform, performing high-quality and highly accurate recognition, classification, and differentiation of these objects from birds by analyzing the optical channel and micro-Doppler characteristics of the target. Expected outcomes: Development of a software model for UAV recognition based on algorithms of two types of neural networks, adapted to the optical and radar channel of the "FMCW radar + video surveillance" system. Project description: The project idea is to create a neural network software model, one part of which is designed for UAV recognition through radar imaging of micro-Doppler signatures, ensuring more accurate classification of drones and birds. The second segment of the software model defines a neural network application for UAV recognition through video data and photo images of objects in the airspace (quadcopters, "flying wing" UAVs, birds, etc.). The unique feature of this development lies in its adaptation to an Anti-Drone radar system with a software-hardware platform based on "Radar + Optical Channel" as an automation element for UAV recognition using two detection channels. The performance and effectiveness of the developed software model depend on the characteristics of the radar system and optical camera, making the selection and justification of the radar model and surveillance tool a key task. Additionally, the project will present the mathematical characteristics of radar signal reflection from targets with vibration sources, determining Doppler indicators for recognizing flying objects (UAVs and birds). The structural description of the Data Fusion system, into which the developed software model is integrated, along with the characteristics of the neural network algorithms serving as the basis for classification and recognition software within the research framework, will also be provided.