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  为了让广大师生更好地了解互联网技术的发展现状,提高广大师生的网络知识水平,工学部网络工程教研室将连续举办三场专题讲座,欢迎广大师生参加。

  时间:7月12日(星期三)13:30

  地点:19号楼301会议室

  主办:工学部计算机与通信工程学院网络工程教研室


  讲座一:Minimum Cost Deployment of Heterogeneous Directional Sensor Networks for Differentiated Target Coverage

  主讲人:陈学敏,副教授、美国德州南方大学工程系虚拟与远程实验室主任

  

  主讲人简介:

  Dr. Xuemin Chen is the founding Director ofVirtual and Remote Laboratory(VR-Lab)and an Associate Professor of Electrical and Computer Engineering at the Texas Southern University(TSU). Hereceived 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, networked control system and structural health monitoring. He is an investigator of NSF Center for Research on Complex Networks at TSU.

  讲座简介:

  A basic deployment goal of a directional sensor network (DSN) is to satisfy the coverage quality requirements of the targets. The limited sensing angle of directional sensors makes the deployment of such a network more complicated than that of an omnidirectional sensor network. In some emerging DSN applications, the targets may have differentiated coverage quality requirements due to their differentiated importance or priorities. Meanwhile, directional sensors of multiple types with different sensing ranges and prices are available for optimally organizing a network. To deploy such a DSN with satisfied network connectivity under the minimum cost constraint is a significant problem to be solved. In the presentation, we formulate and address the problem for the first time. First, we prove its NP-completeness. Then, this problem is formulated as an integer linear programming model. To solve it approximately, we propose three algorithms, i.e., a greedy heuristic, local search, and particle swarm optimization. Extensive numerical experiments are conducted to validate the proposed algorithms. The results suggest that the proposed algorithms can effectively solve the problem, and the last one can achieve both the highest success rate and the best solution quality.


  讲座二:Several Challenging Problems in Wireless Networks Research

  主讲人:李伟,教授、美国德州南方大学计算机科学系NSF以及复合网络研究中心主任

  

  主讲人简介:

  Dr. Wei Li is a Professor in the Department of Computer Science at Texas Southern University (TSU), Houston, USA. He is also the founding Director/PI of NSF Center for Research on Complex Networks at TSU, one of 27 active NSF Centers of Research Excellence in Science and Technology (CREST) in 2017. Before joining TSU, Dr. Li was an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science at the University of Toledo, USA, and was once also an Associate Professor in the Department of Operations Research at the Chinese Academy of Sciences, Beijing, China. His research interests are in wireless sensor networks and mobile ad hoc networks; adaptation, design, and implementation of dynamic models for wireless and mobile networks; radio resource allocations in wireless multimedia networks; and mobile and high-performance computing etc. He is the author/co-author of 5 books and over 150 peer-reviewed papers in professional journals and the proceedings of conferences, including IEEE TC, IEEE TWC, IEEE TVT, IEEE/ACM TN, IEEE JSAC, and INFOCOM et al. During last ten years, his research activities have been supported as Principal Investigator (PI) for over $6 million by five NSF Research Grants, two AFOSR Grants and one Industry Project. He is currently serving as an Editor for three professional journals, is serving or has served as a Steering Committee Member/ General Co-Chair/ TPC Co-Chair/ Publicity Chair/ Session Chair/ TPC members respectively for a number of professional conferences, such as INFOCOM, Globecom, ICC, and WCNC et al.

  讲座简介:

  In this presentation, the speaker will present three recent research results in wireless networks. One is in bandwidth adaptation of wireless networks by using network reversible process property. This research verifies the product-form solution of whole wireless network and further reveals the importance of single-node research in wireless networks. Another research is in call admission control based billing strategies by using the method of Markov decision process. This research verifies the optimal admission threshold policy for new generated data and then provides a novel method in resolving dynamic optimal problems in wireless networks. The third research is on the investigation of joining strategies of Second Users (SUs) in a cognitive radio system with a single Primary User (PU) band that enables SUs to utilize the unused spectrum band originally allocated to a PU opportunistically. This research derives the Nash Equilibriums for non-cooperative joining strategy and cooperative joining strategies, respectively. By observing that an individually optimal strategy does not yield the socially optimal strategy, this research furthermore proposes an appropriate admission fee strategy imposed on the SUs and then successfully bridge the gap between the individually and socially optimal strategies. As a potential collaboration with scholars who are interested in these areas, the speaker will discuss a few challenging problems in the areas which may need to be paid attention in the near future. Finally, the speaker will present on how the current research be linked to the short range wireless communications for connected vehicle to vehicle (V2V) networks.


  讲座三:Robotic Exoskeletons for Human Rehabilitation

  主讲:余文,教授、墨西哥国立理工学院系主任

  

  主讲人简介:

  Wen Yu received the B.S. degree from Tsinghua University, Beijing, China in 1990 and the M.S. and Ph.D. degrees, both in Electrical Engineering, from Northeastern University, Shenyang, China, in 1992 and 1995, respectively. From 1995 to 1996, he served as a Lecturer in the Department of Automatic Control at Northeastern University, Shenyang, China. Since 1996, he has been with CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico, where he is currently a Professor with the Departamento de Control Automatico. From 2002 to 2003, he held research positions with the InstitutoMexicanodelPetroleo. He was a Senior Visiting Research Fellow with Queen’s University Belfast, Belfast, U.K., from 2006 to 2007, and a Visiting Associate Professor with the University of California, Santa Cruz, from 2009 to 2010. He also holds a visiting professorship at Northeastern University in China from 2006. Dr.Wen Yu serves as associate editors of IEEE Transactions on Cybernetics, Neurocomputing, and Journal of Intelligent and Fuzzy Systems. He is a member of the Mexican Academy of Sciences.

  讲座简介:

  Exoskeletons could be regarded as wearable robots, which are worn by the human operators as orthotic devices. The adoption of a purely positional control strategy may lead to the build up of large forces (both external and internal). Hence, an impedance control strategy is devised aimed at limiting both internal and contact forces. The desired trajectory is trained in joint space without the dynamic time warping. We use two techniques, Lloyd's algorithm and modified hidden Markov model, to solve the problems in joint space learning. Since the desired trajectories are the joint angles, they can be applied directly without inverse kinematics. Experimental results show that the proposed algorithm works well for human behavior learning.

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