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Lin Zhang Yu Liu Xin Zhan Xiaomin Yang Xiaoqiang Chi Shizhen Zhao 《Wireless Personal Communications》2012,66(3):493-509
Mobile devices have steadily gained acceptance as a multimedia platform, not only that, one of the most valuable pieces of contextual information for an intelligent mobile application is the user??s location. The university campus is an interesting application environment for location-based and context-aware mobile services: wherever the students are, they are eager to announce to others and want to know what happens in the university campus, even alumni always care about the latest in his or her campus. The Campus View, what is described in this article, is a smart-phone based context and location-aware video sharing mobile application implemented on Android 2.1. It first gives a full panorama to one Chinese university campus, which will provide a campus overview and variations to alumni and potential students. Different from Street-view of Google which was only created once and demands not to drive anywhere more than once, our campus panorama is timely updated by our DIYed Street-view camera. What??s more, the application users inside the campus can use it to recode the latest happenstance and then upload the video short with Geo-information to the server. The recoded video short will be further shared as a H. 264 stream attached to a specific panorama. This paper presents the development framework of Campus View application and an evaluation of key framework components to assess their consumption of mobile device??s resources. 相似文献
83.
A switched reluctance motor (SRM) drive generally requires a rotor position sensor for commutation and current control. However, the use of this position sensor increases both cost and size of the motor drive and causes limitations for industrial applications. In this paper, a novel indirect position sensing technique, namely, the sliding-mode observer, is proposed for SRM drives. The corresponding design approach and operating performance are provided to illustrate the fast convergence and high robustness of the observer against disturbances and variations 相似文献
84.
Bin Zheng Yaochun Liu Bin Zhan Yuanhua Lin Jinle Lan Xiaoping Yang 《Journal of Electronic Materials》2014,43(9):3695-3700
BiCuSeO/polyaniline (BCSO/PANI) bulk composites have been successfully fabricated by a ball-milling and hot-pressing method. Microstructure analysis shows that BCSO particles are well mixed and dispersed in the PANI matrix. Our results indicate that the Seebeck coefficient can be increased substantially by adding BCSO filler to the PANI matrix, especially for 40 wt.% BCSO (5–87 μV K?1). Electrical conductivity and thermal conductivity both change slightly with the increasing filler content. The highest figure of merit, ZT, among these bulk composites is 0.004 at 341 K, which is almost 500 times greater than that of pure PANI. 相似文献
85.
Yingchun Wang Jingyi Wang Weizhan Zhang Yufeng Zhan Song Guo Qinghua Zheng Xuanyu Wang 《Digital Communications & Networks》2022,8(1):1-17
With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas. 相似文献
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NRZ伪随机码序列同步时钟提取 总被引:2,自引:0,他引:2
在光纤数字通信系统中,NRZ非归零码是一种最常见的基带信号。它本身不含有位同步时钟分量。只有对其进行非线性处理,转换成RZ归零码后,方可提取出同步时钟,本文通过对NRZ,RZ伪随机码序列进行频谱分析,得知当NRZ码变换成码元占空比为1/2的RZ码时,所提取的同步时钟功率最强。 相似文献
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