Indoor positioning technology plays an important role in improving efficiency and automating industrial processes such as warehouse management, production lines, and mobile robot navigation. However, existing RFID-based and odometry-only localization methods still suffer from limited accuracy, drift, and dependence on predefined infrastructure. To address these challenges, this work proposes a lightweight sensor fusion framework that combines wheel encoder data and phase-based RFID signals using an Extended Kalman Filter (EKF) for accurate indoor localization of mobile robots. The proposed method does not require prior knowledge of the tag map and enables convergence even when the robot starts outside the reader's range. Simulation results demonstrate that the fused method achieves an average positioning error of 5.4 cm and a final error of less than 8 cm. An ablation study comparing odometry-only, RFID-only, and fusion scenarios confirms the superiority of the integrated approach in terms of accuracy and robustness. The system is suitable for real-time implementation in cost-effective embedded platforms and has potential for deployment in smart warehouses and logistics environments.

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.

In many wireless sensor network (WSN) applications, nodes are randomly deployed and self-organize into a wireless network to perform tasks. In practice, recharging the batteries of network nodes after deployment is often difficult. Network nodes often operate autonomously, so the main focus is on increasing the node lifetime. Data redundancy is another limitation that makes nodes inefficient. In most cases, densely deployed nodes in a monitoring area will have redundant data from neighboring nodes. Therefore, we propose a clustering technique to select the Cluster Head (CH) node in small-scale WSNs. Since transmission consumes more energy than data collection, this protocol enables reactive routing, where transmission occurs only when a certain threshold is reached. In addition, based on their heterogeneous energy levels, nodes can be grouped into three categories: Normal, Intermediate, and Advanced. Simulation results in MATLAB/Simulink show that, after approximately 3000 rounds, the proposed method successfully transmitted about 3.1 × 104 packets to the base station, compared to 2.3 × 104 packets for the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In addition, the time when the last node died was approximately 3,500 rounds, whereas the LEACH protocol only maintained about 1,500 rounds. The results have shown the effectiveness of this technique in reducing the dead node rate and increasing packet transmission efficiency.

This paper presents a vehicle detection, tracking, and counting system for urban traffic videos based on integrating YOLOv9s and DeepSORT. The proposed method aims to address challenges such as occlusions, high vehicle density, and identity consistency in surveillance video analysis. A private dataset consisting of annotated traffic videos recorded in Thai Nguyen, Vietnam, was used for evaluation. The YOLOv9s model was selected for its balance between speed and accuracy, while DeepSORT provides robust multi-object tracking using appearance features and Kalman filtering. Experimental results demonstrate that the system achieves a mean Average Precision (mAP at 0.5) of 91.4%, an mAP at 0.5-0.95 of 82.7%, and operates at an average speed of 12.9 frames per second (FPS). Vehicle counting was validated against manually annotated ground truth with an average error rate of less than 4%. These results indicate that the proposed approach is both accurate and efficient for real-time traffic monitoring applications.

Object detection for sensing images is one of the promising research directions in computer vision. Applications for object detection from remote sensing images play an important role in analyzing aerial or satellite imagery. Benefits include applications in monitoring buildings and infrastructure, transportation, supporting search and rescue or responding to natural disasters, and environmental research. However, detecting objects in remote sensing images is difficult due to the diversity of shapes and sizes, viewing angles of objects, and complex background environments. In this paper, the authors present a Deep Learning (DL)-based object detection process from remotely sensed images, the main goal of which is to improve the ability to detect small objects in high-resolution aerial images. Implement and evaluate the super-slicing inference technique in the YOLOv11 model to improve the ability to detect very small and extremely small objects. Many simulation results are tested experimentally in the problem of detecting and tracking vehicles in Vietnam (Thai Nguyen). The results show that the system can accurately detect small objects such as pedestrians, motorbikes, and cars at a distance, with confidence ranging from 0.31 to 0.90. Some detection situations are successful even when the object is located at the edge of the slice. Finally, the authors discuss potential future research directions and unaddressed formulations.

This paper presents recent research in the field of human face recognition and detection. Technology development has marked a clear transformation in the implementation of recognition tasks. It is a transition from classical methods to methods with AI and deep learning applications. We have reviewed the latest works in famous journals to evaluate this field. This paper provides a comprehensive taxonomy across detection, identification, and tracking. It offers a comparative analysis of state-of-the-art AI approaches and highlights their strengths, limitations, and practical considerations. We have evaluated the level of compliance, completeness, and methodology of related studies. According to the results of our evaluation, significant improvements and developments have occurred in this field. However, some challenges still need to be focused on, such as improving the efficiency of training models in different environmental conditions. The negative effects of lighting conditions, occlusion of faces, or distortions due to different shooting angles are overcome.

Indoor positioning plays an important role in a wide range of applications, such as autonomous mobile robots (AMRs), healthcare smart homes, and industrial automation. Popular positioning methods include received signal strength indicator (RSSI)-based positioning, proximity-based positioning, angle-of-arrival (AoA)-based positioning, and fingerprinting. Each method has specific trade-offs regarding accuracy, cost, and deployment complexity. Recent research has focused on solutions to improve accuracy. These include noise filtering techniques, such as Kalman and particle filters, as well as the use of machine learning to optimize the positioning model. In particular, graph-based positioning methods have demonstrated the ability to improve accuracy by optimizing the spatial relationship between landmarks. In addition, regression-based error estimation helps predict and correct positioning errors, improving system reliability. This paper presents a focused review of state-of-the-art techniques to enhance indoor positioning systems' accuracy. They consist of filtering, hybrid methods, and Artificial Intelligence (AI)-based optimization, offering a practical reference for enhancing system performance in real-world applications. By reinforcing recent developments, this review serves as a foundation for researchers and engineers seeking effective positioning solutions for AMR and smart environments. Looking ahead, hybrid positioning systems integrate multiple technologies. AI and machine learning enhance them. Hybrid positioning systems are expected to offer greater accuracy, flexibility, and scalability. These advancements will be essential in optimizing operations and enhancing user experiences in future automation applications and the Internet of Things(IoT).