Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances.

The object of research is the processes of automation of the technical and operational assessment of railway stations (RWS). Research solves the problem of developing models, methods and information technologies to automate the process of technical and operational assessment of the work of RWS. As a result of the study, the following results were obtained: Analysis of scientific papers on the problem of automating the work of RWS. Development of new method and mathematical models to automate the task. Development of new descriptions for the technological processes (TechP) of the RWS based on visual programming methods. Development of simulation models for the automation of railway infrastructure management task. The UML diagrams of state and activity have been adapted in order to represent the RWS operation technology. When formalizing the description of the RWS, the state diagrams are submitted taking into account the specifics of the description of the change in the phases of servicing objects in the process of TechP of individual objects maintenance. It is shown that the state diagram for the RWS is a state machine (SM) that models the sequence of changing the states of an object. The detalization of the behavior of objects serviced at the RWS has been completed. Detalization is performed using diagrams of activity. The diagrams of activity are used to formally describe the technical support with objects and executors of work on the railway. The scientific results obtained in the article, as well as new and improved models and methods, can be used in the development, improvement and formalization of the TechP of the RWS, research methods for informatization and automation objects on the railways of the Republic of Kazakhstan.

This article presents a detailed analytical review of distributed acoustic sensing (DAS) systems for seismic monitoring, with emphasis on their optical infrastructure, signal processing methodologies, and integration with machine learning (ML) approaches. DAS leverages existing fiber-optic cables by utilizing Rayleigh backscattering to convert them into high-resolution seismic sensing networks. These networks offer spatial resolution down to one meter and can operate effectively over distances up to 250 kilometers in real time. The system’s responsiveness to pressure and environmental fluctuations has been captured through mathematical modeling. To enhance signal quality, seismic data is subjected to advanced processing techniques, including Fourier analysis, wavelet transformation, and adaptive noise filtering, yielding signal-to-noise improvements of up to 15 dB. In terms of data interpretation, machine learning models such as support vector machines (SVM), long short-term memory networks (LSTM), and gradient boosting classifiers have achieved high performance, often surpassing 90% accuracy in seismic event detection. Furthermore, scalable insights are supported through unsupervised and semi-supervised learning strategies. To address challenges related to model transparency, explainability tools like SHAP and LIME are applied to aid in the interpretation of predictive outputs. Field deployments of DAS systems, combined with intelligent analytics, demonstrate significant promise for large-scale seismic detection across both terrestrial and marine environments.

In the 21st century-the century of Information Technology, it is difficult to imagine life without any gadgets: phones, tablets, computers, laptops, and so on. Today, smartphones and tablets are becoming popular, and thus their operating systems become popular too. Android is designed for low-power devices that run on battery power at full capacity, using all of its services, such as cameras, lights, GPS navigation, Wi-Fi, etc. In Kazakhstani universities, the process of checking students' attendance is one of the important issues, because final grade evaluation of students is based on their total number of appearances and their grades during the whole semester. This raises the question of having some tool to control students' attendance. There are many possible ways of controlling attendance: there are many examples when universities prefer to control attendance by the use of paper sheet, and some universities prefer to use two-stage way of controlling attendance: firstly, teachers and professors use paper sheet for checking students' attendance and after this, they fill out these information into a system manually. However, this is not an efficient way since there will be spent much of time for calling students names and putting marks like "presence" or "absence" if the class is a lecture class, and in this class at least 5 groups are presented. Furthermore, some students may call his/her friend as "presence" nevertheless to the fact that this student is currently absent. After taking into consideration all these issues and the fact that many gadgets use Android platform, authors of the following research paper decided to create a mobile system that makes easier to check students' attendance automatically, and this system is implemented in Almaty Management University, Kazakhstan. The system is based on Android platform, and in this paper, details of this system are presented.

Real-time monitoring of structural deformation in space infrastructure is a critical factor in ensuring mission reliability and structural integrity. FBG sensors represent an effective solution due to their advantages, such as high sensitivity, low weight, resistance to electromagnetic interference, and multiplexing capability. This review article examines the scientific foundations of using FBG sensors in space environments to accurately monitor structural deformation under conditions including high temperature, radiation, vacuum, and micrometeoroid impacts. Additionally, it analyzes modern research focused on methods of integrating FBG sensors into small spacecraft structures, interrogation techniques, sensor network architectures, and their ability to operate in real time.

The unconventional proliferation of unmanned aerial vehicles (UAVs) has led to an urgent demand for advanced counter-unmanned aerial system (C-UAS) technologies capable of accurately detecting, classifying, and mitigating these threats. This paper offers a comprehensive overview of current detection methodologies, including radio frequency (RF) signal analysis, deep learning-based visual recognition (e.g., YOLOv5), thermal imaging, acoustic pattern classification using convolutional neural networks (CNNs), and integrated sensor systems utilizing attention mechanisms. A comparative analysis is conducted based on key performance indicators such as precision rates, mean average precision (mAP), operational range, response time, and robustness to environmental noise. These performance metrics are organized into a summarizing table for clarity. Additionally, several real-world C-UAS platforms such as DedroneTracker, AUDS, and Fortem DroneHunter are examined to illustrate approaches to full system integration. The discussion also encompasses the legal and ethical considerations, the implications of autonomous UAV swarms, and emerging trends in C-UAS strategies, especially those leveraging edge computing and cognitive modeling. The findings support the effectiveness of adaptable, modular, and interpretable counter-drone frameworks suited for dynamic and high-threat environments.