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Design of a New Mobile-Optimized Remote Laboratory Application Architecture for M-Learning

发布时间: 2017-06-24

主题:    Design of a New Mobile-Optimized Remote Laboratory  Application Architecture for M-Learning主讲人:   陈学敏地点:   松江校区二号学院楼226室时间:   2017-07-11 10:30:00组织单位:   信息科学与技术学院 数字化纺织服装技术教育部工程研究中心

主讲人简介: Dr.Xuemin Chen is the founding Director of Virtual and Remote  Laboratory (VR-Lab)and an Associate Professor of Electricaland Computer  Engineering at the Texas Southern University (TSU). He receivedhis BS, MS and  Ph.D. degrees in Electrical Engineering from the Nanjing University of Science  and Technology (NJUST), China, in 1985, 1988 and 1991 respectively. He joined  the faculty of TSU in the Department of EngineeringTechnology in September 2006.  Prior to that, he had fifteen years workingexperience in academia with six years  at NJUST and another nine years atUniversity of Houston. He was the recipient of  the Top Research Innovations andFindings Award from Texas Department of  Transportation (TxDOT) for hiscontribution in the “Thickness Measurement of  Reinforced Concrete Pavement byUsing Ground Penetrating Radar” in 2004. Upon  joining the TSU, he activelyengaged in the conception and implementation of  next-generation remotelaboratory. He initiated the Virtual and Remote Laboratory  at TSU in 2008. Withthe support of NSF HBCU-UP, CCLI and IEECI programs, and  Qatar NPRP award, hehas established a state of the art VR-Lab at TSU. His other  interests includewireless sensor networks. He is an investigator of NSF Center  for Research onComplex Networks at TSU.

内容摘要:As mobile learning (M-Learning) has demonstrated increasing impacts on  onlineeducation, more and more mobile applications are designed and developed  for theM-Learning. In this presentation, a new mobile-optimized application  architectureusing Ionic framework is proposed to integrate the remote laboratory  intomobile environment for the M-Learning. With this mobile-optimized  applicationarchitecture, remote experiment applications can use a common  codebase todeploy native-like applications on many different mobile platforms  such as iOS,Android, Windows Mobile, and Blackberry. To demonstrate the  effectiveness ofthe proposed new architecture for M-Learning, an innovative  remote networkedproportional–integral–derivative control experiment has been  successfullyimplemented based on this new application architecture. The  performance isvalidated by the Baidu mobile cloud testing bed.



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