报 告 人：Xuemin Chen, Ph.D.
报告时间：2019年5月21日上午9:00-11:00
报告地点：东校区11教学术报告厅
参加人员：城市建设学院相关专业的师生
欢迎广大师生参加！
城市建设学院
2019年5月17日
报告简介：
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. An HMM is a probabilistic sequence model: given a sequence of units (words, letters, morphemes, sentences, whatever), they compute a probability distribution over possible sequences of labels and choose the best label sequence. The HMM has been most successfully applied to the automatic speech recognition (ASR) problem. In recent years, the HMM has attracted lot of attention in different areas. In this talk, an introduction of HMM and its application will be given.
报告人简介：
Dr. Xuemin Chen is the founding Director of Virtual and Remote Laboratory (VR-Lab) and a Professor of Electrical and Computer Engineering at the Texas Southern University (TSU). He received his 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 Engineering Technology in September 2006. Prior to that, he had fifteen years working experience in academia with six years at NJUST and another nine years at University of Houston. He was the recipient of the Top Research Innovations and Findings Award from Texas Department of Transportation (TxDOT) for his contribution in the “Thickness Measurement of Reinforced Concrete Pavement by Using Ground Penetrating Radar” in 2004. Upon joining the TSU, he actively engaged in the conception and implementation of next-generation remote laboratory. He initiated the Virtual and Remote Laboratory at TSU in 2008. With the support of NSF HBCU-UP, CCLI and IEECI programs, and Qatar NPRP award, he has established a state of the art VR-Lab at TSU. His research interests are in wireless sensor networks, virtual and remote laboratory, and structural health monitoring. His research is currently supported by NSF Centers of Research Excellence in Science and Technology (CREST), NSF Platforms for Advanced Wireless Research (PAWR), NSF Networking Technology and Systems (NeTS) and NSF Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) programs at TSU.
