【Talk】Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
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Topic:Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
Time:December 22, 2017 ( Friday, 11:00AM~12:00PM)
Venue:R210, Engineering Building 4, NCTU
交通大學工程四館210室
Speaker:Prof. Yik-Chung Wu / The University of Hong Kong
Language:Lectured in English
Abstract: In this talk, we will review the advances of clock synchronization in wireless sensor network in the past few years. We will begin with the optimal clock synchronization algorithms in pairwise setting, in which maximum likelihood (ML) estimator from traditional estimation theory is the major tool. Then, we will discuss the more challenging networkwide synchronization, in which every node in the network needs to synchronize with each other. In this case, more powerful distributed signal processing techniques are required. In particular, we will illustrate how Belief Propagation (BP), distributed Kalman Filter (KF) and Alternating Direction Method of Multipliers (ADMM) method help in solving networkwide synchronization.
Bio: Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU). He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005. From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff. Since September 2006, he has been with HKU, currently as an Associate Professor. He has been a visiting scholar at Princeton University for the summers of 2011 and 2015. His research interests are in general area of signal processing, machine learning, and communication systems, and in particular distributed signal processing and robust optimization theories with applications to communication systems and smart grid. Dr. Wu served as an Editor for IEEE Communications Letters, is currently an Editor for IEEE Transactions on Communications and Journal of Communications and Networks.
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【Talk】Prof. Zhengya Zhang (U Michigan): Neuromorphic Computing Using Sparse Codes: From Algorithm to Hardware (July 16, 2015(Thursday, 10:30am-12pm)
Invite you all to join it. 歡迎踴躍參加 !
Title: Neuromorphic Computing Using Sparse Codes: From Algorithm to Hardware
Date: July 16, 2015 ( Thursday, 10:30 am ~ 12:00 pm)
Place: ED528, 5F, Engineering Building 4, NCTU
交通大學(光復校區)工程四館5樓528室
Speaker: Prof. Zhengya Zhang (University of Michigan, Ann Arbor)
Abstract:
Some of the latest advances in computer vision have been built upon the understanding of the mammalian primary visual cortex (V1). The receptive fields of V1 neurons can be compared to the basis functions underlying natural images. Learning the receptive fields allows us to carry out complex vision processing, including efficient image encoding, feature detection, and classification. Sparse coding is one development in unsupervised machine learning for training a network of neurons using natural images to extract the receptive fields that resemble the V1 receptive fields. We explore the dynamics of the sparse coding algorithm for an efficient mapping onto practical hardware. Design considerations involving tuning network and neuron responses have a significant impact on the neuron spiking pattern that determines the fidelity of image processing and the efficiency of resource utilization. The spiking pattern can be further exploited to improve the performance and scalability of the hardware architecture. The soft neural computation is intrinsically error tolerant and many opportunities exist in approximating the neuron communication and computation in designing high-performance and energy-efficient image processing hardware.
Biography:
Zhengya Zhang received the B.Sc. degree from the University of Waterloo in Canada in 2003, and the M.S. and Ph.D. degrees from the University of California, Berkeley, in 2005 and 2009, respectively. Since 2009, he has been with the Department of Electrical Engineeringand Computer Science at the University of Michigan, Ann Arbor, where he is currently an Associate Professor. His research is in the area of low-power and high-performance VLSI circuits and systems for computing, communications and signal processing. Dr. Zhang received the Intel Early Career Faculty Award in 2013, the National Science Foundation CAREER Award in 2011, the David J. Sakrison Memorial Prize from UC Berkeley in 2009, and the Best Student Paper Award at the Symposium on VLSI Circuits in 2009. He is an Associate Editor of the IEEE Transactions on Circuits and Systems-I, II, and the IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
Host: 交大電子系楊家驤教授 Email: chy@nctu.edu.tw
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