Early detection of earthquakes is essential for minimizing potential damage and ensuring public safety. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), provide a promising alternative for analyzing seismic waves. In contrast, traditional methods such as the short-term average/long-term average (STA/LTA) algorithm and the Akaike information criterion (AIC) have limitations in detecting primary (P) waves in high-noise conditions, caused by industrial and anthropogenic disturbances. This study presents a CNN-based automatic P-wave detection model tailored for the Almaty city region. The seismic dataset used in this research was obtained from the IRIS database and includes data collected from seven stations within a 333 km radius of Almaty, Kazakhstan. The proposed model achieves a recall rate of 89.1% and an accuracy of 94.1% compared to other deep learning-based models. Experimental results demonstrate that this method enhances the reliability of automatic early earthquake warning systems and improves the accuracy of P-wave detection. The research outputs presented for the local region are unique. Applying CNNs in seismic monitoring facilitates the development of efficient automated systems that minimize risks and improve response measures for natural disasters.

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network’s topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks.

This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications.

Unmanned Aerial Vehicles (UAVs) are increasingly employed for real-time object detection in critical appli cations such as security surveillance, disaster response, and environmental monitoring. However, accurate detection in UAV imagery remains challenging due to small target sizes, cluttered backgrounds, and varying environmental conditions. This study evaluates the performance of YOLOv8n/v8s and YOLOv11n/11s models for human detection in UAV-captured imagery across diverse natural landscapes. To ensure practi cal deployment in resource-constrained environments, the models were implemented on a Raspberry Pi 5 using the OpenVINO framework. Experimental results show that both YOLO series achieve comparable detection accuracy in the range of 80–82%, with YOLOv8n and YOLOv11n demonstrating the highest processing speeds of 10.50 and 11.04 frames per second (FPS), respectively. These findings confirm the feasibility of using lightweight YOLO models for real-time human detection on embedded systems. The re sults highlight the potential of integrating edge AI and UAVs for autonomous, on-site monitoring in remote or complex terrains, offering scalable solutions for intelligent aerial surveillance. Key words: UAV, object detection, accuracy, YOLO models.

Division remains one of the most computationally demanding operations in digital arithmetic. Traditional algorithms, such as restoring, non-restoring, and SRT (Sweeney–Robertson–Tocher) division, are limited by sequential dependencies that reduce throughput in hardware implementations. To overcome these constraints, this work proposes a pipelined integer divider architecture that employs precomputed divisor multiples and comparator-based logic to eliminate the need for full binary adders in the quotient selection stages. The proposed design consists of a three-stage pipeline, where each stage compares the shifted partial remainder with stored multiples of the divisor (B, 2B, 3B) to generate two quotient bits per clock cycle. This approach achieves a 2× reduction in the number of computation stages compared with conventional radix-2 dividers and ensures continuous operation after an initial pipeline latency. The architecture was described in Verilog hardware description language (HDL) and implemented on a Xilinx Artix-7 (XC7A100T-1CSG324C) field-programmable gate array (FPGA) using the Xilinx ISE Design Suite 14.4. Post-synthesis simulation confirmed correct quotient and remainder generation with a maximum operating frequency of 208 MHz. The implementation occupied less than 0.3% the look-up table (LUT) resources, achieving over a twofold performance improvement compared with a non-pipelined baseline. These results demonstrate that the proposed divider provides an efficient trade-off between speed and hardware cost, making it suitable for digital signal processing and embedded computation systems.

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels ($SNR\ge 2$). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at $SNR\ge 2$, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems.