In recent years, regional bus operations have faced critical challenges including declining ridership and driver short-ages. To address these issues and develop efficient operational plans, collecting Origin-Destination (OD) data is essential. However, conventional OD data collection methods are constrained by labor costs and time limitations, while IC card-based approaches miss flat-rate users and cash-paying passengers. Additionally, the introduction of facial recognition cameras raises social acceptability concerns due to surveillance implications. To address these challenges, this study focuses on using Bluetooth Low Energy (BLE) advertising packets emitted by passengers' mobile devices. However, modern devices periodically and randomly change their BLE MAC addresses, making continuous tracking difficult throughout a trip. To overcome this, we propose a bus OD data estimation method that applies MAC address carry-over technique. This approach exploits the asynchronous nature of MAC address changes and the embedded information in packets to enable continuous tracking. To evaluate the effectiveness of the proposed method, we conducted field experiments on two different operational bus routes. The results demonstrate that the proposed method can estimate OD data with an average precision of $\text{7 0 \%}(\text{m a x}: \text{1 0 0 \%}, \text{m i n}: \text{5 3 \%})$ and an average recall of $\text{3 4 \%}($ max: $\text{5 9 \%}, \min: \text{1 5 \%})$.
This study proposes a system in which a tail-shaped device responds to smartphone message notifications by conveying emotions intuitively. Messages are analyzed for sentiment and classified into three polarities: positive, neutral, and negative. Corresponding tail movements are generated based on the results. A user study confirmed that participants could intuitively interpret the emotions from the tail's movement, demonstrating the effectiveness of this non-verbal notification method.
Many studies have investigated remote robotic arm operation systems using virtual reality environments. However, conventional VR teleoperation systems suffer from communication latency, which makes it difficult for operators to receive immediate visual feedback of the robot's posture. This mismatch between input and visual feedback may increase cognitive and psychological load and degrade task performance. We propose a teleoperation system that overlays a virtual robotic arm to provide immediate visual feedback of user operation prior to receiving the delayed video from the physical robotic arm. By superimposing a virtual robotic arm on the stereo video, the system enables operators to simultaneously maintain immersion in the remote environment and perceive the input posture of the robotic arm. We evaluated information presentation methods through remote robotic arm task experiments in VR and user feedback. The results confirmed that, under high-latency conditions, the virtual robotic arm display reduced operator anxiety and helped maintain confidence during teleoperation.
Reconfigurable Intelligent Surfaces (RIS) have recently emerged as a key enabling technology for future 6 G networks, offering the capability to manipulate the wireless propagation environment intelligently. Here, we investigate the use of multiple RIS panels to establish Line-of-Sight (LOS) connectivity between a transmitter and a receiver via successive signal reflections. A core challenge lies in selecting the optimal RIS-assisted reflection path that maximizes end-to-end channel quality. To address this, we formulate the path selection problem as a Mixed-Integer Programming (MIP) optimization task, where the cost of each link depends on the selection of preceding links, introducing significant computational complexity. Simulation results demonstrate that the proposed scheme achieves near-optimal performance with minimal loss, validating its practical feasibility. Our results indicate minimal performance loss using Follow-The-Leader (FTL) and Follow-the-Perturbed-Leader (FTPL) schemes.
This paper presents an adaptive user state estimation/detection system for indoor occupants using off-the-shelf Bluetooth (BT) and Bluetooth Low Energy (BLE) devices. The proposed system classifies the user states into occupancy, distance, and activity level, and estimates each by processing Received Signal Strength (RSS) from nearby devices. In this study, a two-stage channel adaptation approach is proposed: a generalized base model is applied initially, followed by automatic adaptation to an environment-specific model after accumulating real-time data. User presence is inferred using a signal continuity based method that tracks both connection status and interruption patterns. For distance estimation, we propose a Hybrid Nakagami Fading model that integrates path loss, shadowing, and multipath effects into a unified formulation. Activity recognition is performed using RSS based pattern modeling, further enhanced by clustering with Dynamic Time Warping (DTW) to improve robustness across user behavior patterns. Experiments conducted across seminar rooms, offices, and residential apartment demonstrate strong performance: 100 % presence detection accuracy with graceperiod handling, 0.41 m average error in distance estimation, and 81.25% activity recognition accuracy, showing that the proposed system can estimate the user state with high accuracy.
