Prof. Kwang-Cheng Chen
IEEE Fellow
University of South Florida, USA
Kwang-Cheng Chen has been a Professor at
the Department of Electrical Engineering, University of
South Florida, since 2016. From 1987 to 2016, Dr. Chen
worked with SSE, Communications Satellite Corp., IBM Thomas
J. Watson Research Center, National Tsing Hua University, HP
Labs., and National Taiwan University in mobile
communications and networks. He visited TU Delft (1998),
Aalborg University (2008), Sungkyunkwan University (2013),
and Massachusetts Institute of Technology (2012-2013,
2015-2016). He founded a wireless IC design company in 2001,
which was acquired by MediaTek Inc. in 2004. He has been
actively involving in the organization of various IEEE
conferences and serving editorships with a few IEEE
journals, together with various IEEE volunteer services to
the IEEE, Communications Society, Vehicular Technology
Society, and Signal Processing Society, such as founding the
Technical Committee on Social Networks in the IEEE
Communications Society. Dr. Chen also has contributed
essential technology to various international standards,
namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A,
5G-NR, and ITU-T FG ML5G. He has authored and co-authored
over 300 IEEE publications, 4 books published by Wiley and
River (most recently, Artificial Intelligence in Wireless
Robotics, 2020), and more than 24 granted US patents. Dr.
Chen is an IEEE Fellow and has received a number of awards
including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE
Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP
Outstanding Paper Award. Dr. Chen’s current research
interests include wireless networks, artificial intelligence
and machine learning, quantum communications and computing,
IoT/CPS, social networks and data analytics, and
cybersecurity.
Speech Title: Wireless Networked Multi-Robot Systems in a
Smart Factory
Abstract: Industry 4.0 based on artificial
intelligence and information communication technology
emerges as a primary contributor to the digital economy and
Internet of Things. In order to execute flexible production
in real-time, smart manufacturing must holistically
integrate 6G wireless networking, AI computing, sensing, and
automatic control technologies. This talk explores the
challenges of this complex systems engineering scenario, by
modeling the operation of a smart factory as a wireless
networked multi-robot system of production robots and
transportation robots. The real-time multi-robot task
assignment for flexible production has been innovated in a
computationally effective way. Furthermore, the special
sequential decision-making of a multi-robot manufacturing
system is examined toward a multi-robot system of high
yield. Applying social learning extends the resilience of
precision operation in a networked multi-robot system while
taking network topology into consideration. Wireless
networked multi-robot system integrating AI and wireless
comtrol reveals the new landscape of smart factories and
Industry 4.0.
Prof. Inkyu Lee
IEEE Fellow
Korea University, South Korea
Inkyu Lee received the B.S. degree
(Hons.) in control and instrumentation engineering from
Seoul National University, Seoul, South Korea, in 1990, and
the M.S. and Ph.D. degrees in electrical engineering from
Stanford University, Stanford, CA, USA, in 1992 and 1995,
respectively. From 1995 to 2002, he was a member of the
Technical Staff with Bell Laboratories, Lucent Technologies,
Murray Hill, NJ, USA, where he studied high-speed wireless
system designs. Since 2002, he has been with Korea
University, Seoul, where he is currently a Professor with
the School of Electrical Engineering. He has also served as
the Department Head for the School of Electrical
Engineering, Korea University, from 2019 to 2021. In 2009,
he was a Visiting Professor with the University of Southern
California, Los Angeles, CA, USA. He has authored or
coauthored more than 190 journal articles in IEEE
publications and holds 30 U.S. patents granted or pending.
His research interests include digital communications,
signal processing, and coding techniques applied for
next-generation wireless systems. Dr. Lee was a recipient of
the IT Young Engineer Award at the IEEE/IEEK Joint Award in
2006, the Best Paper Award at the Asia–Pacific Conference on
Communications in 2006, the IEEE Vehicular Technology
Conference in 2009, the IEEE International Symposium on
Intelligent Signal Processing and Communication Systems in
2013, the Best Research Award from the Korean Institute of
Communications and Information Sciences in 2011, the Best
Young Engineer Award from the National Academy of
Engineering of Korea in 2013, and the Korea Engineering
Award from the National Research Foundation of Korea in
2017. He has served as an Associate Editor for the IEEE
TRANSACTIONS ON COMMUNICATIONS from 2001 to 2011 and the
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS from 2007 to
2011. In addition, he was a Chief Guest Editor for the IEEE
JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (Special Issue
on 4G Wireless Systems) in 2006. He currently serves as the
Co-Editor-in-Chief for the Journal of Communications and
Networks. He has been elected as a member of the National
Academy of Engineering of Korea in 2015, and is currently a
Distinguished Lecturer of IEEE, and is an IEEE Fellow.
Speech Title: Deep Learning Approaches for Multiuser
Communication Systems
Abstract: In conventional multi-user multiple-input
multiple-output (MU-MIMO) systems with frequency division
duplexing (FDD), channel acquisition and precoder
optimization processes have been designed separately
although they are highly coupled. In this talk, I will
present an end-to-end design of downlink MU-MIMO systems
which include pilot sequences, limited feedback, and
precoding. To address this problem, a novel deep learning
(DL) framework is proposed which jointly optimizes the
feedback information generation at users and the precoder
design at a base station (BS). Each procedure in the MU-MIMO
systems is replaced by intelligently designed multiple deep
neural networks (DNN) units. At the BS, a neural network
generates pilot sequences and helps the users obtain
accurate channel state information.
At each user, the channel feedback operation is carried out
in a distributed manner by an individual user DNN. Then,
another BS DNN collects feedback information from the users
and determines the MIMO precoding matrices. A joint training
algorithm is proposed to optimize all DNN units in an
end-to-end manner. Numerical results demonstrate the
effectiveness of the proposed DL framework compared to
classical optimization techniques and other conventional DNN
schemes.
Prof.
James Xiaojiang Du
IEEE Fellow
Stevens Institute of Technology, USA
Xiaojiang (James) Du is the Anson Wood
Burchard Endowed-Chair Professor in the Department of
Electrical and Computer Engineering at Stevens Institute of
Technology. He was a professor at Temple University. Dr. Du
received his B.S. from Tsinghua University, Beijing, China
in 1996. He received his M.S. and Ph.D. degree in Electrical
Engineering from the University of Maryland, College Park in
2002 and 2003, respectively. His research interests are
security, wireless networks, and systems. He has authored
over 500 journal and conference papers in these areas, as
well as a book published by Springer. Dr. Du has been
awarded more than 8 million US Dollars research grants from
the US National Science Foundation (NSF), Army Research
Office, Air Force Research Lab, the State of Pennsylvania,
and Amazon. He won the best paper award at IEEE ICC 2020,
IEEE GLOBECOM 2014 and the best poster runner-up award at
the ACM MobiHoc 2014. He serves on the editorial boards of
three IEEE journals. Dr. Du is an IEEE Fellow and a Life
Member of ACM.
Speech Title: HAWatcher: Semantics-Aware Anomaly
Detection for Appified Smart Homes
Abstract: As IoT devices are integrated via
automation and coupled with the physical environment,
anomalies in an appified smart home, whether due to attacks
or device malfunctions, may lead to severe consequences.
Prior works that utilize data mining techniques to detect
anomalies suffer from high false alarm rates and missing
many real anomalies. Our observation is that data
mining-based approaches miss a large chunk of information
about automation programs (also called smart apps) and
devices. We propose Home Automation Watcher (HAWatcher), a
semantics-aware anomaly detection system for appified smart
homes. HAWatcher models a smart home’s normal behaviors
based on both event logs and semantics. Given a home,
HAWatcher generates hypothetical correlations according to
semantic information, such as apps, device types, relations
and installation locations, and verifies them with event
logs. The mined correlations are refined using correlations
extracted from the installed smart apps. The refined
correlations are used by a Shadow Execution engine to
simulate the smart home’s normal behaviors. During runtime,
inconsistencies between devices’ real-world states and
simulated states are reported as anomalies. We evaluate our
prototype on the SmartThings platform in four real-world
testbeds and test it against totally 62 different anomaly
cases. The results show that HAWatcher achieves high
accuracy, significantly outperforming prior approaches. This
work has been accepted by one of the top four security
conferences - USENIX Security 2021 (acceptance rate = 17%).