P. N. Huu

Navigation Methods for UAVs in GNSS-Denied Environments Using Artificial Intelligence

With the rapid advancement of autonomous flight technology, there is an increasing demand for higher precision and advanced navigation techniques, with permissible distance errors often restricted to a few meters. Furthermore, the ubiquitous deployment of Unmanned Aerial Vehicles (UAVs) necessitates the integration of novel technologies to ensure operational continuity under adverse conditions, such as environmental signal interference, hostile attacks, or traversal through zones of complete signal loss. This study presents a methodology to address these challenges, enabling themaintenance of coordinates and navigation for UAVs to traverse jammed or completely out-of-coverage zones, thereby avoiding the need for emergency landings or Return-to-Home (RTH) protocols common in current UAV systems. The proposed approach leverages the TransGAN model, a framework typically employed for data analysis comprising a Generator and a Discriminator. In this context, the model processes sequential real-world coordinate data. Under normal GNSS operation, TransGAN is trained as a high-precision prediction model utilizing velocity and coordinate data as inputs. Conversely, during GNSS outages or interference, the trained TransGAN model is utilized to generate coordinates, thereby maintaining navigation capabilities for the UAV.

A multivariate LSTM-based framework for accurate short-term gold price forecasting in Vietnam

Design and Implementation of an IoT-Based Smart Home System Using the Matter Protocol

The rapid growth of the Internet of Things (IoT) has significantly accelerated the development of smart home systems, enabling automation, energy efficiency, and enhanced user experience. However, the lack of interoperability among heterogeneous devices and platforms remains a major challenge, resulting in fragmented ecosystems and limited scalability. To address these issues, the Matter protocol has emerged as a unified, IP-based connectivity standard for smart home environments. This paper presents a comprehensive study of the Matter protocol, including its architecture, communication mechanisms, and security model. A practical IoT-based smart home system is designed and implemented using ESP32 platforms and the Matter SDK. The system integrates multiple devices such as smart lighting, switches, smart plugs, and environmental sensors, supporting crossplatform interaction across different ecosystems. Experimental evaluation is conducted under real-world conditions, focusing on interoperability, latency, system stability, and security. The results show that the proposed system achieves reliable crossplatform compatibility, stable network performance, low communication latency, and secure device authentication. Additionally, the system maintains core functionality even under limited network conditions, demonstrating strong robustness. These findings confirm that the Matter protocol is a promising solution for building scalable, secure, and interoperable next-generation smart home systems.