刘向阳教授关于用WiFi信号识别人类行为活动的讲座

日期:2016-05-27 | 学术活动 - 学术会议

时间:27/05/2016

地点718会议室

演讲人:刘向阳

主题Human Acticity Recognition Using WiFi Signals


人物简介:

  刘向阳,2006年博士毕业于美国德州大学奥斯丁分校计算机科学系, 主要从事网络与安全的研究工作。曾获美国国家自然科学基金颁发的杰出青年教授奖(Career Award),美国电气和电子工程师协会(IEEE)和国际信息处理联合会(IFIP)联合颁发的 William C. Carter奖,美国密西根州立大学颁发的杰出学者奖(Withrow Distinguished Scholar Award),国际一流会议ICNP最佳论文奖,美国德州大学奥斯丁分校颁发的George H. Mitchell卓越研究生科研奖,美国德州大学奥斯丁分校计算机科学系James C. Browne杰出研究生奖,以及中国教育部颁发的国家自费留学生奖学金。作为第一或者唯一项目负责人承担了六项美国国家自然科学基金项目。担任国际顶级期刊ACM/IEEE Transactions on Networking (ToN)编委,国际SCI期刊 Journal of Computer Communications (COMCOM)专题主编。多次在国际一流会议(诸如SIGMETRICSINFOCOM ICNPICDCS等)担任程序委员会委员与主席等职务。获得五项关于防火墙的美国专利。出版两本关于防火墙的英文学术专著: 防火墙设计与分析(Firewall Design and Analysis ), 基于硬件的快速数据包分类(Hardware Based Packet Classification for High Speed Internet Routers )。在国际一流会议(例如MOBICOM, INFOCOM, SIGMETRICS, PODC, ICSE, USENIX Security, ICNP, ICDCS, NDSS, DSN, VLDB, ICDE), 已发表论文百余篇。在ACM/IEEE Transactions等著名SCI期刊上已发表论文几十篇。多篇论文已经被美国一流大学的研究生课程所选用。例如, 三篇论文被美国著名学府普度(Purdue)大学CS590N Logical Methods for Information Security”选为教学内容。一些科研成果已经被学术界和工业界广泛采用。例如, 提出的FDD数据结构已经被美国著名的威斯康星大学麦迪逊分校的Suman Banerjee教授作为他的多篇SIGMETRICS论文的核心数据结构; 提出的Web安全方案(secure cookie scheme)已经被著名的开源系统Wordpress所采用。


内容摘要: 

Human activity recognition is the core technology that enables a wide variety of applications such as health care, smart homes, fitness tracking, and building surveillance. Traditional approaches use cameras, radars, or wearable sensors. However,camera based approaches have the fundamental limitations of requiring line of sight with enough lighting and breaching human privacy potentially. Low cost 60 GHz radar solutions have limited operation range of just tens of centimeters. Wearable sensors based approaches are inconvenient sometimes because ofthe sensors that users have to wear. Recently, WiFi signal based human activity recognition systems, such as WiSee, E-eyes,and WiHear, have been proposed based on the observation that different human activities introduce different multi-path distortion sin WiFi signals.The key advantages of WiFi based method over cameraand sensor based approaches are that they do not require lighting,provide better coverage as they can operate through walls, preserve user privacy, and do not require users to carry any devices as they rely on the WiFi signals reflected by humans.




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