Earticle

Ground speed calculation is basic work for trajectory prediction, conflict detection and air traffic flow management. This paper proposes a novel algorithm based on Vincenty’s formulas for ground speed calculation. Our experiments used simulations with wind components and our experimental results show that our ground speed calculation exhibits much better accuracy performance.

Natural scenes from the real world are highly complex, such that the modeling and rendering of natural shapes, like mountains, trees and clouds, are very difficult and time consuming and require a huge amount of memory. Intuitively, the critical characteristics of natural scenes are their selfsimilarity properties. Motivated by the self-similarity feature of the natural scenes that surround us, we present a hardware accelerated fractal based rendering method for natural environments. To illustrate the problem that classical geometry has in dealing with natural objects, we considered the basic fractal example as the Mandelbrot set which is a 2D structure. We examined the serial algorithm of this set and devised a parallel algorithm for implementation on a massive parallel graphics processing unit (GPU) using the computer unified device architecture (CUDA) programming model. We also considered the modeling of 3D fractals such as terrains and evaluated its performance both in terms of execution time and hardware acceleration. Performance is evaluated in terms of execution time and it was observed that a parallel implementation of the method on a GeForce GTX 650 GPU is on average 2X times faster than its sequential implementation. The running behavior of the system at various system states is also evaluated to strongly support our approach.

Video storytelling has become a popular technology to let users design and plan their films. As the development of multimedia devices and network, more and more people will share their productions on the Internet. This approach is not only used in movie industry, but e-learning, digital archive and art. With the development of cloud computing, it can provide various computing and storage services over the Internet. Instead of using 3-D model generation system, we proposed a system composed of several video technologies to let teachers plan materials with existing avatars and scene from the cloud data base in this paper. Users can design the background by cloud panorama generation at first. Then, drug a trajectory line on the scene. The authoring tool will select an avatar from the database and make it with several kinds of behaviors. Users can select several behaviors during the motion trajectory; our tool will search the most suitable avatar and insert it into the panorama. This tool equips with special functions to integrate different motion tracks for the generation of video narratives is also presented in this paper.

Moving object detection in a video sequence is one of the most challenging research topics due to its various applications and complexity. Sometimes, it is also required to identify a moving object without any previous information about its shape or motion. In this paper, we introduce a global mesh generation technique which can construct a hierarchical mesh on moving object(s) inside a video frame. This technique is efficient enough to solve the problem to a great extent and can identify a moving object in a video frame without any previous knowledge. The generated mesh is triangular and probabilistic in nature. The granularity of the mesh depends upon the level of hierarchy, neighborhood triangle decomposition, gradient and temporal information of the video sequence. Experiments show that the proposed method can identify the moving object(s) with good accuracy without any pre or post-processing steps.

The reason for the trend in high-level large-area photomasks is that mask size is becoming larger and the line size and gap design is becoming thinner. In addition, the uniformity requirements for producing photomasks are becoming stricter. Consequently, the difficulties faced in manufacturing photomasks have substantially increased and a particular graphic process is required to assist with improving the yield of photomask production. During the etching process of mask production, a non-uniform etching rate produces an inferior uniformity of the critical dimensions. The reasons for the inferior etching rate results include the uniformity of the photomask pattern, the spin speed of the etching machine, and the nozzle of the etching arms. This research focuses on the photomask pattern line-width compensation for improving uniformity and precision. The research combines logical operations to select the patterns that are necessary for special compensation, adding the appropriate compensation value to the pattern and combining it with the original pattern to generate a complete photomask pattern to compensate for the inferior etching rate that produces inferior line-width uniformity.

E-learning is a useful method which can support effective learning processes by utilizing various learning materials developed by teachers or developers. Moreover, recently, cloud computing technology has been applied to learning systems. Despite technical advances, most learning systems focus on high learning performance. For this purpose, the AEHS (adaptive education hypermedia system) has been introduced. AEHS analyzes learners’ learning patterns and their learning processes and, according to the analyzed learning performance, the system identifies the most appropriate process for each learner. But, in AEHS, it is difficult for learners to choose learning content fitted for their preference or learning ability. It still lacks the potential for the learner to select learning content. In this paper, we propose cloud-computing-based AEHS that enables a learner's preference. The preference can be decided by the learner's study level, learning unit difficulty and learning performance. In order to incorporate existing technology, the proposed system was connected to an LMS (learning management system) and an LCMS (learning contents management system). An LMS performs the interaction between learner/instructor and learning system, and administers the data of the learning course, documentation, tracking, and the reporting of training programs. An LCMS focuses on the management and publishing of the content that will typically be delivered via an LMS.

With the advent of the age of the Knowledge Economy, the desire for knowledge has been growing gradually. E-learning not only escapes from the limitations of time and space but allows researchers to develop related services via an open and powerful environment (i.e. cloud computing) through the development of information communication techniques and the popularity of portable devices. In this paper, we will introduce the two core mechanisms, asynchronous caching and adaptive delivery that integrate in the cloud environment. The first mechanism takes advantage of background scheduling to allow partial content processing and downloading in parallel. The second pays more attention to personalization service provision in accordance with situations such as location, time, and external information. The system optimizes the pervasive learning environment through the processing of the mechanisms mentioned above in the cloudbased environment.