Developing E-learning Courses for Mobile Devices

The recent and rapid development of mobile devices and the increasing popularity of e learning have created a demand for mobile learning packages and environments. We have analyzed the possibilities of adapting the existing content for mobile devices, and have implemented two fundamentally different systems to satisfy the demand that has arisen. One of the systems creates e learning courses from existing materials and adapts them to the specified platform (this system realizes the functionalities of the Content Management System). The other system is a modified version of the Moodle Learning Management System, which can adapt existing courses right before displaying them. This paper discuses the fundamentals of e learning, the design considerations and investigates various methods of scalable video coding. Finally the realization details of the two systems are presented.

A Study on Efficient Design of A Multimedia Conversion Module in PESMS for Social Media Services

The main contribution of this paper is to present the Platform-as-a-Service(PaaS) Environment for Social Multimedia Service (PESMS), derived fromthe Social Media Cloud Computing Service Environment. The main role ofour PESMS is to support the development of social networking services thatinclude audio, image, and video formats. In this paper, we focus in particular on the design and implementation of PESMS, including the transcoding function for processing large amounts of social media in a parallel and distributed manner. PESMS is designed to improve the quality and speed of multimedia conversions by incorporating a multimedia conversion module based on Hadoop, consisting of Hadoop Distributed File System for storing large quantities of social data and MapReduce for distributed parallel processing of these data. In this way, our PESMS has the prospect of exponentially reducing the encoding time for transcoding large numbers of image files into specific formats. To test system performance for the transcoding function, we measured the image transcoding time under a variety of experimental conditions. Based on experiments performed on a 28-node cluster, we found that our system delivered excellent performance in the image transcoding function

A New Compressive Video Sensing Framework for Mobile Broadcast

A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise discrete cosine transform coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast