Physics education applications using augmented reality technology, which has been developed extensively in recent years, have a lot of restrictions in terms of performance and accuracy. The purpose of our research is to develop a real-time simulation system for physics education that is based on parallel processing. In this paper, we present a video see-through AR (Augmented Reality) system that includes an environment recognizer using a depth image of Microsoft’s Kinect V2 and a real-time soft body simulator based on parallel processing using GPU (Graphic Processing Unit). Soft body simulation can provide more realistic simulation results than rigid body simulation, so it can be more effective in systems for physics education. We have designed and implemented a system that provides the physical deformation and movement of 3D volumetric objects, and uses them in education. To verify the usefulness of the proposed system, we conducted a questionnaire survey of 10 students majoring in physics education. As a result of the questionnaire survey, 93% of respondents answered that they would like to use it for education. We plan to use the stand-alone AR device including one or more cameras to improve the system in the future.

Augmented and Virtual Reality-based surgical simulations have become some of the fastest-developing areas, due to the recent technological advances and changes, in surgical education. Cutting simulation is a crucial part of the virtual surgery simulation in which an incision operation is performed. It is a complex process that includes three main tasks: soft body simulation, collision detection and handling, and topological deformation of the soft body. In this paper, considering the content developer’s convenience, the deformable object simulation, using position-based dynamics (PBD), was applied in the Unity 3D environment. The proposed algorithm for fast collision detection and handling between the cutting tool and the deformable object uses a sweep surface. In case of incision, the algorithm updates the mesh topology by deleting intersected triangles, re-triangulation, and refinement. In the refinement part, the boundary edges threshold was used to match the resolution of new triangles to the existing mesh triangles. Additionally, current research is focused on triangle surface meshes, which help to reduce the computational costs of the topology modifications. It was found that the algorithm can successfully handle arbitrary cuts, keeping the framerate within interactive and, in some cases, in the real-time.

Numerous studies are currently being carried out on personalized services based on data analysis to find and provide valuable information about information overload. Furthermore, the number of studies on data analysis of teaching-learning activities for personalized services in the field of teaching-learning is increasing, too. This paper proposes a learning style recency-frequency-durability (LS-RFD) model for quantified analysis on the level of activities of learners, to provide the elements of teaching-learning activities according to the learning style of the learner among various parameters for personalized service. This is to measure preferences as to teaching-learning activity according to recency, frequency and durability of such activities. Based on the results, user characteristics can be classified into groups for teaching-learning activity by categorizing the level of preference and activity of the learner.

Mean-shift tracking has gained more interests, nowadays, aided by its feasibility of real-time and reliable tracker implementation. In order to reduce background clutter interference to mean-shift object tracking, this paper proposes a novel indicator function generation method. The proposed method takes advantage of two ‘a priori’ knowledge elements, which are inherent to a kernel support for initializing a target model. Based on the assured background labels, a gradient-based label propagation is performed, resulting in a number of objects differentiated from the background. Then the proposed region growing scheme picks up one largest target object near the center of the kernel support. The grown object region constitutes the proposed indicator function and this allows an exact target model construction for robust mean-shift tracking. Simulation results demonstrate the proposed exact target model could significantly enhance the robustness as well as the accuracy of mean-shift object tracking.

Real-time physics simulations are vital for creating immersive and interactive experiences in extended reality (XR) applications. Balancing computational efficiency and simulation accuracy is challenging, especially in environments with multiple deformable objects that require complex interactions. In this study, we introduce a GPU-based parallel processing framework combined with a position-based dynamics (PBD) solver to tackle these challenges. The system is deployed within the Unity engine and enhances real-time performance through the use of sophisticated collision detection and response algorithms. Our method employs an AABB-based bounding volume hierarchy (BVH) structure to efficiently detect collisions, and incorporates the Möller–Trumbore algorithm for precise triangle-level interactions. We also boost computational efficiency by storing collision data in GPU-accessible 2D textures. Experimental assessments show performance improvements of up to 1705% in GPU simulations over CPU counterparts, achieving stable real-time frame rates for complex models such as the Stanford Bunny and Armadillo. Furthermore, utilizing 2D texture storage improves the FPS by up to 117%, confirming its efficacy for XR applications. This study offers a robust, scalable framework for real-time physics simulations, facilitating more natural and immersive XR experiences.