This paper presents a novel system for simultaneous underwater acoustic (UWA) positioning and communication aimed at enhancing underwater drone operations. The proposed method utilizes one of the communication schemes, orthogonal signal division multiplexing for UWA positioning. An experimental study was conducted in a coastal area to validate the system’s performance in actual sea conditions. The results demonstrated that the proposed system achieved a mean positioning error of 1.38 meters and error-free communication in more than 95% of cases. This system is anticipated to serve as a foundational technology for enhancing the capabilities of underwater drones.
This research investigates the influence of UI components on decision-making in E-commerce services, with a focus on decision fatigue. Through depth interviews and experiments within the apparel E-commerce genre, we analyzed the effects of different decision-making experience models featuring varying levels of button component differentiation. Emotional influences were assessed using the Profile of Mood States (POMS). The results revealed significant changes in "vigor" and "confusion," suggesting that differentiating UI components can positively influence decision-making experiences. The research provides guidelines for UI design to mitigate decision fatigue and improve user experiences.
In recent years, the development of autonomous driving technology has increased the need to acquire information about the surrounding area. Accordingly, LiDAR sensors are used to acquire a 3D point cloud of the surrounding environment and detect vehicle areas and other object as 3D bounding boxes. However, high-precision LiDAR sensors are very expensive, and it is difficult to install them in all vehicles. A method to generate 3D point clouds from images has been proposed to improve this problem, but the generated point clouds are subject to noise and missing parts, resulting in low detection accuracy of 3D bounding boxes. Therefore, this method improves the accuracy of 3D bounding box detection by applying appropriate noise reduction and completion processing to point clouds obtained from stereo images.
A direction finding algorithm based on point cloud data analysis is described for a planar antenna array. Cumulative phases are calculated by sequentially adding inter-element phase differences to free from wrapping between -π and π. The cumulative phases are transformed into propagation delays as the point cloud data. The norm vector, which is perpendicular to an approximated plane of the point cloud data, is obtained as the eigenvector with the minimum eigenvalue through eigenvalue decomposition of a 3 × 3 covariance matrix. Simulation results show that the angle of arrival is successfully estimated in environments with a single wave of arrival.
This paper explores the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Human-Computer Interaction (HCI) to enhance data analysis and decision-making in healthcare, particularly during the COVID-19 pandemic. Through a mixed-methods approach, including a literature review and case study analysis, the study highlights how AI-driven analytics and IoT data streams, exemplified by the COVID-19 Data Lake and WHO Health Alert platform, provide real-time insights and improve decision-making efficiency. The success of these systems relies on effective HCI integration, robust data validation, and comprehensive user training. Challenges such as data accuracy and system adaptability are identified, with future research directions focusing on advanced AI techniques and the development of sophisticated HCI frameworks. By addressing these challenges, the integration of AI, IoT, and HCI can further transform healthcare and enhance public health outcomes.
