This study investigated a search-free digital method for radio direction-finding that utilizes spatial analytical signal reconstruction. The research focused on optimizing the method's accuracy for estimating the direction of a radio source. The analysis identified the separation distance and number of chosen antenna elements for signal reconstruction as the key parameters influencing accuracy. Analytical optimization and simulations were employed to determine the optimal values for these parameters. The research successfully modeled the impact of antenna element selection on direction-finding errors under specific signal-to-noise conditions. This model can be used to guide further development and optimization of this radio direction-finding method.

The object of the study is the process of radio signal delay and direction estimation using digital spectral-correlation analysis enhanced by machine learning. This process is essential for high-accuracy direction finding in electromagnetic monitoring systems. The problem addressed is the low adaptability and insufficient accuracy of traditional direction finding methods under variable signal conditions, especially due to manual parameter selection and the computational complexity of correlation processing. The essence of the obtained results is a machine learning-based method for predicting radio signal parameters (delay and angle), which reduced the standard deviation of direction finding estimates to 0.08–0.026° and delay estimation error to 1.5–14.8 μs across a signal-to-noise ratio range of 9 to 37 dB. These results are supported by averaging over 1000 realizations using Monte Carlo simulation, confirming their stability under noise. Due to its distinctive features, the proposed solution addressed the problem by enabling automated selection of processing parameters through a trained neural network that adapts to nonlinear signal characteristics, minimizing the need for manual adjustment or exhaustive search. These results are explained by the model’s ability to identify hidden dependencies between signal parameters and processing outcomes, enabling adaptive behavior and reduced deviations. Although no computational complexity assessment is provided, prediction-based parameter estimation is expected to improve processing speed in future implementations. The results can be applied in real-time electromagnetic monitoring, radio surveillance, and defense applications, especially under limited computing resources or varying noise conditions

This research investigates distributed acoustic sensors (DAS) based on fiber optic technologies, focusing on the impact of pressure on signal-to-noise ratio (SNR), noise levels, and dominant frequency shifts. DAS systems are widely used for infrastructure monitoring due to their ability to capture acoustic signals over long distances, making them ideal for seismic and pipeline monitoring. The study examines how fluctuating pressure affects DAS performance, particularly signal quality and noise reduction. In applications like pipeline leak detection and seismic monitoring, pressure changes can degrade signal clarity and complicate anomaly detection. Understanding this relationship is key to optimizing DAS performance and improving system efficiency. The experiment varied pressure from 0.1 atm to 5 atm, showing that increased pressure raised SNR from 10 dB to 48 dB, reduced noise from 10 dB to 7 dB, and shifted the dominant frequency from 0.5 Hz to 3 Hz. Fourier analysis provided insights into these frequency spectrum changes. Higher pressure compresses the medium, enhancing signal isolation and improving SNR while reducing noise. The frequency shift results from changes in acoustic wave propagation speed under higher pressure, highlighting its role in signal processing. The key finding is that higher pressure significantly improves signal quality and reduces noise, enhancing DAS performance. The frequency shift improves environmental detection capabilities. These results are valuable for DAS applications in environments with pressure variations, like pipeline monitoring, where high signal quality is crucial. Improved signal fidelity and frequency shifts make DAS systems more reliable for long-term monitoring and contribute to accurate anomaly detection

In recent decades, the reliability and safety of large engineering structures have become a critical issue due to failures caused by undetected micro-level deformations. Fiber-optic strain sensors, especially Fiber Bragg Grating (FBG) and interferometric systems, are widely used in structural health monitoring (SHM); however, their standard sensitivity is often insufficient for early detection of nano-strain level damage. This paper presents a comparative analysis and system-level optimization of the main sensitivity enhancement methods, including mechanical amplification, functional coatings and composite embedding, interferometric schemes, and advanced spectral signal processing. Analytical modeling and numerical simulations were performed. It is shown that flexure-beam amplifiers provide a stable sensitivity gain of 2.1–4.8, whereas lever-type mechanisms achieve higher amplification (5.6–9.3) at the cost of dynamic degradation. Functional coatings increase the strain transfer coefficient from 0.62 to 0.68 to 0.91–0.97, but introduce temperature-induced errors up to 1.5–2.0 µε. Interferometric systems can detect strains at the 10⁻⁸ level but exhibit high temperature cross-sensitivity. Advanced spectral processing reduces the Bragg wavelength estimation error by 8–15 times, improving the equivalent strain resolution to (2–5) × 10⁻⁸. Based on these results, an optimized integrated approach combining moderate mechanical amplification (2.5–3.5), improved strain transfer (η ≈ 0.85–0.92), and efficient spectral processing is proposed. This improves the equivalent strain resolution from 1 × 10⁻⁶ to (1.5–3.0) × 10⁻⁸ while keeping temperature-induced errors within 15–25% and limiting the computational load increase to 2–3 times. The proposed solution is suitable for long-term monitoring of large engineering structures.

The object of research is the application of multimedia technologies to support literacy learning in the Kazakh language through interactive tools based on the integration of IoT and Web technologies. The problem addressed is the lack of effective, interactive tools for learning correct spelling and pronunciation in the Kazakh language adaptable to different user roles and levels. This study focuses on the development of a multimedia, interactive educational system aimed at enhancing literacy and promoting active language acquisition. To design aimed system in hardware side an ESP8266 microcontroller was used. 74HC165 shift registers, pull-up resistors are also installed on each button to ensure signal stability. A server side software solution built on the Laravel framework. Communication between the microcontroller and the server is established using the WebSocket protocol, a secure connection between the hardware device and the server is provided through the MAC address. The server runs in a Docker container using the Nginx and supports user roles such administrator, teacher, and student. The results obtained include a working prototype of learning system that allows users to complete tasks in five interactive modes. The results show that the integration of real-time hardware input with a flexible server-side architecture improves user engagement and learning personalization. The features of the solution are its real-time two-way communication via Websocket, role-based access, modular design in Docker, and support for Kazakh language content. The solution is intended at supporting educational institutions, kindergartens, and remote learning environments for learning letters and words, advancing digital education practices, and improving adaptable methods and technologies for the development of IoT based educational tools

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·10⁶ V/m, the ion concentration stabilizes in the range of 10¹³–10¹⁵ m⁻³, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators.