Hierarchical Bounding Sphere FFD-AABB Algorithm for Fast Collision Handing of 3D Deformable Objects on Smart Devices

Interacting with the Web of Things

The present study examined whether learners with low English proficiency pay more attention when learning vocabulary using smart devices and learn vocabulary better. The experiment was conducted in a middle school. Four lower level classes each of which has 10 students participated in the study. Ten target words were selected from the textbook based on the vocabulary pre-test. The researcher observed the students` attitude using a classroom observation scheme to see whether students showed more interest and were more motivated when using smart devices. Both experimental and control groups studied the vocabulary for four lessons, the former using a tablet PC and the latter only with the teacher in the traditional classroom. After each lesson, both groups took a post-test immediately. Delayed post-tests were administered two weeks later for each lesson. The result showed that the experimental group was more motivated and gained better grades in both the post-tests and the delayed-post tests. However, when comparing the post-test and the delayed post-test grades for each group, there was not a significant difference. This may mean that learners with low English proficiency learn vocabulary better when using smart devices but still need support to put their vocabulary knowledge into long term memory.

We perform collision detection between static rigid objects using a bounding volume hierarchy which consists of an oriented bounding box (OBB) tree enhanced with bounding spheres. This approach combines the compactness of OBBs and the simplicity of spheres. The majority of distant objects are separated using the simpler sphere tests. The remaining objects are in close proximity, where some separation axes are significantly more effective than others. We select 5 from among the 15 potential separating axes for OBBs. Experimental results show that our algorithm achieved favorable speed up with respect to the existing OBB algorithms.

As the live-fire training in the many shortcomings, in order to effectively improve the athlete's level of actual shooting, the rules to adapt training, technical difficulties and other changes, development of real-time interactive, real-time sensing and interactive simulation training platform is today's hot spots. The virtual scene development of virtual reality training system for CTS (Clay Target Shooter) includes real-time virtual scene generation, clay target and shot collision detection and display, and realization of large field scene with mosaic. In this paper training real-time virtual scene for CTS is generated by adopting Vega, Creator, Visual C++ development platform, modeling grapeshot and clay target, and simulating the real flight path of them. By using classical AABB bounding box algorithm and Los technique in Vega software, the collision of clay target and shots is detected and the special effect of the collision displayed.

This study aims to develop arts therapy program to lower the career related stress of the university students using virtual reality (VR). Among total 12 sessions of arts therapy program, 4 sessions were developed using VR. Arts therapy program was designed using VR in application with exposure therapy in wearable devices and smart devices for arts therapeutic approach of counselling for career, getting job, and psychology in the university students based on the studies related to exposure approach. VR was incorporated in view of cognitive-behaviorism to apply mirror type and imaginal exposure therapy for self-reflection. It is anticipated that this study would be helpful to apply arts therapy program to lower career related stress of the university students in the future. Policies to support new discoveries of the potential of VR are much needed. This is because application of VR to arts therapy thus far has been practically non-existent, but it holds great potential as an alternative therapy method and a means to develop new markets when appropriate content is developed and applied. Therefore, studies on VR not only in the arts field but also in healthcare require government level support in terms of policies.

Collision detection in Vega Prime is based on the simple line segment, and collision detection based on bounding box is not realized. By studying the composition of three-dimensional object, we define a collision detection class, which inherits from vpIsector. After finding out all vertices of geometry in object, we connect these vertices one by one and constitute a trend line. The trend line constitutes our bounding box. Now, our collision detection is based on three-dimensional objects, but not the simple line segment. Additionally, we can expand x, y, z in all vertices in proportion and achieve early warning mechanism in virtual reality

For many people, especially young people, a smartphone is a constant companion. Mobile apps which allow individuals to use a smart device to enhance their learning have the potential to be very useful for mastering basic educational material. In order to evaluate and enhance the effectiveness of such applications when deployed at large scale, an infrastructure designed specifically for the collection of educational analytics data from such mobile apps is required. We detail here a set of applications and their associated infrastructure which was developed to allow students in courses using digital textbooks to enhance their knowledge of the basic course content anywhere and anytime by using their smart device to do spaced practice of the knowledge components of a course. The power of current smart devices allows the entire application, including content and adaptive algorithm to be hosted and run locally on the user’s smart device, so it functions fully even when no network connection is available. The infrastructure for the collection and analysis of the educational analytics data is entirely cloud-based, using AWS S3 for data collection and storage, and the Apache Spark parallel computing framework for data analysis. Thus, the entire system requires only laptop computers for the mobile developers who create the applications and this is also sufficient for the learning scientists who analyze the data. Both the data collection system and the data analysis system can scale to handle the data from many millions of users with no modification to their architecture. Similar architectures are now used for the Internet of Things (IOT) but have not yet been widely used for educational applications. These applications have currently been deployed to thousands of users’ smart devices and analytics data is being received from these users’ smart devices from a wide range of locations on several continents. In our highly connected world, this type of application will become much more common. We describe here the type of infrastructure, security, and analytic methods needed to use these apps to advance learning and learning science.

본 연구는 PC·모바일 중심의 디지털 환경이 사물인 터넷 환경으로 변화됨에 따라 사용자와 디지털 시스템 사이에서 이루어지는 상호작용이 어떠한 형태로 변화하는지에 대한 체계적 자료를 마련하는데 목적이 있다. 이를 위하여 사물인터넷 및 HCI 관점에서의 상호작용에 관한 서적, 선행연구물, 보고서, 인터넷 검색을 통한 문헌 연구와 국 내외에 공개된 사물인터넷 관련 제품과 서비스, 시스템을 대상으로 한 사례 연구로 나누어 연구를 진행하였으며, 상호작용의 과정, 상호작용의 연결 대상, 상호작용 주체, 상호작용의 연결 수준으로 나누어 사례를 분석, 결론을 도출하였다. 첫째, 기존 디지털 환경에서의 상호작용은 사용자와 디지털 시스템 양자(兩者)간의 1:1 물리적 작용이었으나 사물인터넷 환경에서의 상호작용은 사용자와 사물, 그리고 제 3, 제 4 의 사용자·사물이 하나의 연결고리 안에서 물리 적·심리적으로 상호작용하는 다층적 구조를 보이고 있다. 둘째, 기존 디지털 환경에서는 사용자의 명령과 디지털 시스템의 반응이 반복되며 이루어지는 상호작용형태를 보였으나 사물인터넷 환경에서는 기존의 디지털 환경에서보다 복잡하고 복합적이며, 확장된 상호작용 양상을 보인다. 뿐만 아니라 사용자의 행동 패턴을 분석, 사용자의 행동 및 습관 개선뿐 아니라 사회문제를 변화시키기 위한 긍정적 원동력이 된다. 셋째, 기존 디지털 환경에서 디지털 시스템은 사용자의 명령에 의해 작동하는 수동적 존재였으나 사물인터넷 환경에서 사물은 인간의 개입 없이도 스스로필요한 데이터를 수집, 정보를 생산, 이를 통한 능동적 반응을 보임으로써 상호작용의 주체가 되기도 한다. 반면, 사용자는 사물인터넷 환경에서 상호작용의 객체가 될 수도 있다. 넷째, 기존 디지털 환경에서 사용자와 디지털 시스템 간의 상호작용은 정보를 주고받는 단순한 ‘통신’의 수준이었으나 사물인터넷 환경에서는 사물을 의식을 가진 살아있는 존재로 인식하여 심리적 상호작용을 이어나갈 수 있다. 사물인터넷이 개인과 사회의 문제를 개선할 수 있는 긍정적 가치를 지닌다는 점에서 사물인터넷은 반드시 필요한 시스템이다. 앞으로 인터랙션 디자인, 경험 디자인, 정보디자인 및 인터페이스 디자인 등 사용자와 사물간의 효과적인 상호작용을 위한 종합적 관점의 디자인 방향에 대해 고찰하고 연구해야 할 것이다. 더불어 사용자의 개인 정보에 대한 빅데이터를 수집하고 활용하는 사물인터넷 체계에서 높은 수준의 보안 시스템 구축이 선행되어야 할 것이다.

최근의 3D게임이나 가상현실을 위한 지형 시각화 응용에서는 사실적인 장면을 렌더링 하기 위해 고화질 영상을 실시간에 제공하는 GPU기반의 광선투사법을 이용한다. 이 방법은 지형데이터의 크기가 증가할수록 샘플링 해야 하는 텍셀의 개수가 증가하기 때문에 렌더링 속도가 저하된다. 이러한 문제점을 해결하기 위해서 본 논문에서는 GPU에서 사진트리를 기반으로 수행되는 바운딩 박스 세분화를 이용하여 빈 공간이 제거된 바운딩 박스를 생성하고 이를 이용하여 광선투사법을 가속화하는 방법을 제안한다. 이 방법은 각 광선마다 빈 공간 도약을 위해 트리를 탐색하여 중복된 탐색연산을 수행해야 했던 기존의 방법과 달리 바운딩 박스를 이용하여 탐색 연산을 단 1번만 수행하도록 하여 수행속도를 가속화 하였다.

We present an algorithm for collision detection between multiple deformable objects translating in a large environment. We use Spatial Partitioning to subdivide the scene and a Bounding Volume Hierarchy to decompose the objects, using octrees in both cases. The algorithm is divided in two parts, the Broad and Narrow Phases, with objects that can be rigid or deformable. In the Broad Phase, an octree is used to partition the scene and cull away the object’s Bounding Volumes that are distant. In the Narrow Phase, a hierarchical decomposition of Axis Aligned Bounding Boxes or spheres is employed to reduce the number of primitives in the pairwise comparisons. In summary this work is a general-purpose collision detection technique for performing real time collision detection of deformable bodies in interactive 3D applications.

We present a hybrid approach to generate plausible motions for high-DOF human-like articulated figures in constrained environment with multiple obstacles. The method combines motion planning and motion blending techniques to efficiently produce natural and collision-free human motion for different tasks.

Virtual Reality applications run in a lot of different environments using several different input devices. This can lead to incoherent input metaphors across different environments, even using the same application. Mobile smart devices, such as smart phones and tablets can be used as alternative input devices. They can provide an uniform way of interaction, independent of the actual setting of the VR environment. Additionally, their use does scale well to the number of users in multi-user environments. This chapter presents the current State-of-the-Art in using smart devices as input devices and assesses their applicability in the field of Virtual Reality.

The use of gesture based interaction with devices has been a significant area of research in the field of computer science since many years. The main idea of these kind of interactions is to ease the user experience by providing high degree of freedom and provide more interactive way of communication with the technology in a natural way. The significant areas of applications of gesture recognition are in video gaming, human computer interaction, virtual reality, smart home appliances, medical systems, robotics and several others. With the availability of the devices such as Kinect, Leap Motion and Intel RealSense cameras accessing the depth as well as color information has become available to the public with affordable costs. The Intel RealSense camera is a USB powered controller that can be supported with few hardware requirements such as Windows 8 and above. This is one such camera that can be used to track the human body information similar to the Kinect and Leap Motion. It was designed specifically to provide more minute information about the different parts of the human body such as face, hand etc. This camera was designed to give users more natural and intuitive interactions with the smart devices by providing some features such as creating 3D avatars, high quality 3D prints, high-quality graphic gaming visuals, virtual reality and others. The main aim of this study is to try to analyze hand tracking information and build a training model in order to decide if this camera is suitable for sign language. In this study, we have extracted the joint information of 22 joint labels per single hand .We trained the model to identify the Indian Sign Language(ISL) numbers from 0-9. Through this study we analyzed that multi-class SVM model showed higher accuracy of 93.5% when compared to the decision tree and KNN models.

With the advancement in electronics & communication industry internet becomes part of our life. In this scenario it is required to connect each & every device using internet which gives the concept of “Internet of Things”. It is a novel paradigm which is based on artificial intelligence and enhances the concept of Machine to Machine interaction. As millions of devices are connected together so there is a need of a platform that is used to handle the processing of dozens of data generated by these connected devices. Cloud remote centers are not suitable for this purpose because it takes a lot of time to transfer a large amount of data to cloud centers which increases the processing time. On the other hand cloud is not suitable for applications that need low delay. To solve this problem extension of cloud “Fog Computing” is used. In this paper we focus our attention on the integration of Fog and IoT. Many works in literature have surveyed Fog and IoT separately: their main properties, features, underlying technologies, and open issues. But these works lack a detailed analysis of the Fog and IoT paradigm. We start analyzing and discussing the need for integrating them, application areas that are benefitted from this integration the challenges deriving from such integration, and implementation of this integrated environment in the field. Keywords: Fog, IoT, Smart device

Mother's Perception on the Effect of Using Smart Devices by Young Childen