WOCC2021-Speakers

Keynote Speakers


	Gong-Ru Lin Distinguished Professor National Taiwan University Title: Optical Wireless Communication based on Visible Laser Diode Lighting Optical Wireless Communication based on Visible Laser Diode Lighting Optical wireless communication (OWC) based on visible laser-diode white-lighting module has emerged as an intriguing technology with supplementary data transmission functionality to fulfill with the need of wired and wireless access network coverage in the near future. In this talk, different kinds of the multi-color visible laser diodes and light-emitting-diodes with modularized package will be introduced to enable both white lighting and visible wavelength division multiplexing (VWDM) functions. The significant developing progress on the VWDM using visible laser diode sources is overviewed and expected for compact free-space white-lighting communication toward 40-100 Gbit/s toward VWDM wireless access network for indoor, outdoor, and vehicle communications.
Gong-Ru Lin (IEEE S’93-M’96-SM’04-F’20) received the B.S. degree in physics from Soochow University, Taipei, Taiwan, R.O.C., in 1988, the M.S. and the PhD degree in Electro-Optical Engineering from National Chiao Tung University, Hsinchu, Taiwan, in 1990 and 1996, respectively. He was the faculty member of several universities in Taiwan from 1997 to 2006. He joined the Graduate Institute of Photonics and Optoelectronics (GIPO) and the Department of Electrical Engineering in National Taiwan University (NTU) a full professor since 2006. He is currently the director of the GIPO at NTU. his covers broad research interests in the Laboratory of Fiber Laser Communications and Si Nano-Photonics, including the visible-light, millimeter-wave-over-fiber, and fiber-optic communications, the all-optical data processing, the femtosecond fiber lasers, the Si quantum-dot photonics, the ultrafast photoconductors and the optoelectronic phase-locked loops. He is the Fellow of IEEE, OSA, SPIE, IET, and IOP. He used to serve as the Chair of IEEE/Photonics Taipei Chapter. He has co-authored more than 300 SCI-ranked journal papers, 500 international conference papers, 10 book chapters and 30 invited talks during his research career. He is the lifetime distinguished professor of NTU (2015-), the traveling lecturer of the OSA (2012-), the visiting lecturer of the SPIE (2012-), the steering committee of the CLEO-PR conference (2018-), and the current President of the Taiwan Photonics Society (2017-2020).
	Edward Coyle J.B. Peatman Distinguished Professor And GRA Eminent Scholar School of Electrical and Computer Engineering Georgia Institute of Technology Title: Cognitive Wireless Networks: Designing Applications and Developing Testbeds Cognitive Wireless Networks: Designing Applications and Developing Testbeds As wireless applications become more widespread and sophisticated, optimal management of their access to the finite RF-spectrum becomes essential. This is a very ambitious task that will require, among other things: applications that can adapt rapidly to policy decisions, software defined radios and networks capable of rapidly implementing these decisions, and management and scheduling algorithms that can sense and adapt quickly to a changing environment. Before such systems can be widely adopted they must be prototyped and thoroughly tested. These are the goals of our projects at Georgia Tech, where we have been developing a large-scale testbed for the wireless networks of the future. In this talk, we describe this testbed – our university’s football stadium – and the work that we have underway on the design of both future wireless applications and the systems for monitoring spectrum usage for fixed and mobile devices. Two Vertically Integrated Project (VIP) teams are involved in this effort and are crucial to achieving the scale and reliability necessary for this work to be successful.
Edward J. Coyle is the John B. Peatman Distinguished Professor of ECE at the Georgia Institute of Technology and a Georgia Research Alliance Eminent Scholar. His research interests include: signal and information processing; wireless and sensor networks; and systemic reform of higher education. In 1998, Dr. Coyle was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his contributions to the theory of nonlinear signal processing and he received the 1986 Paper Award from IEEE Signal Processing Society. He is the Founder of the Vertically Integrated Projects (VIP) Program, which integrates research and education by embedding large-scale, long-term, multidisciplinary teams of undergraduates into the research efforts of faculty and their graduate students. The VIP Consortium, which consists of 44 universities around the world that have VIP Program received ABET’s 2019 Innovation Award. Dr. Coyle was a co-recipient of the U.S. National Academy of Engineering’s 2005 Bernard M. Gordon Prize for Innovation in Engineering and Technology Education.
	Chee Ching President Far EasTone Telecommunications Co., Ltd. Title: 5G Innovations & Applications 5G Innovations & Applications
Dr. Chee Ching joined Far EasTone in Sep. 2018 as the Chief Transformation Officer and became the President in Jan. 2019. Chee brought to Far EasTone 23 years of rich experience in network operations and IT management in AT&T. Chee was awarded Outstanding 50 Asian Americans in Business in 2018 for her distinguished performance. Chee leads FET team to transform from a traditional Mobile Operator to a Service Company, exploiting Big data, AI, and IoT capabilities to provide Consumer digital services and Enterprise ICT solutions; and has also greatly improved FET employees’ engagement by emphasizing on open communication, transparency and caring. Under Chee’s leadership, FET has advanced to the #2 Mobile Service Provider in Taiwan since Nov 2019; successfully launched 5G in July 2020; enabled the first 5G Telemedicine services and the first 5G Smart Factory in Taiwan; launched Mobile Circle to provide a suite of digital services and customer rewards; and awarded #1 Fastest 5G network in Taiwan by both OpenSignal & Ookla SpeedTest. Chee was named The Best CEO in Taiwan by FinanceAsia in April 2021.
	Han-Chieh Chao President National Dong Hwa University Title: Deep Learning Platform for B5G Mobile Network Deep Learning Platform for B5G Mobile Network The 3G and 4G mobile communications had been developed for many years. The 5G mobile communication is scheduled to be launched in 2020. In the future, a wireless network is of various size of cells and different type of communication technologies, forming a special architecture of Heterogeneous Networks (HetNet). Under the complex network architecture, interference and handover problems are critical challenges in access network. How to efficiently manage small cells and to choose an adequate access mechanism for the better quality of service is a vital research issue. Traditional network architecture can no longer support existing network requirements. It is necessary to develop a novel network architecture. Therefore, this keynote speech will share a solution of deep learning-based B5G mobile network which can enhance and improve communication performance through combing some specific technologies. e.g., deep learning, fog computing, cloud compu ting, cloud radio access network (C-RAN) and fog radio access network (F-RAN).
Han-Chieh Chao received his M.S. and Ph.D. degrees in Electrical Engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1993, respectively. He is currently a professor with the Department of Electrical Engineering, National Dong Hwa University, where he also serves as president. He is also with the Department of Computer Science and Information Engineering, National Ilan University, Taiwan. He was the Director of the Computer Center for Ministry of Education Taiwan from September 2008 to July 2010. His research interests include IPv6, Cross-Layer Design, Cloud Computing, IoT, and 5G Mobile Networks. He has authored or co-authored 4 books and has published about 400 refereed professional research papers. He has completed more than 150 MSEE thesis students and 11 Ph.D. students. He serves as the Editor-in-Chief for the Institution of Engineering and Technology Networks, the Journal of Internet Technology, the International Journal of Internet Protocol Technology, and the International Journal of Ad Hoc and Ubiquitous Computing. He is a Fellow of IET (IEE) and a Chartered Fellow of the British Computer Society. Dr. Chao has been ranked as the top 10 Computer Scientists in Taiwan for 2020 by Guide2Research. Due to Dr. Chao’s contribution of suburban ICT education, he has been awarded the US President's Lifetime Achievement Award and International Albert Schweitzer Foundation Human Contribution Award in 2016, and South East Asia Regional Computer Confederation, SEARCC in 2017.

Invited Speakers

	Changyuan YU Professor The Hong Kong Polytechnic University Title: Design and spectral reconstruction assisted by intelligent algorithms for high-resolution Fourier transform spectrometer Design and spectral reconstruction assisted by intelligent algorithms for high-resolution Fourier transform spectrometer A miniature Fourier transform spectrometer is designed with tailored optical path differences for visible spectral reconstruction. The combination of intelligent algorithms, the convex optimization algorithm (CVX) and the one-dimension modified convolutional neural network (Unet), is used to retrieve spectra under measurement with noise. This scheme recovers four kinds of spectra accurately with the mean-square error of 8.32E-4.
Prof. Changyuan YU received his Ph.D. in Electrical Engineering from the University of Southern California, USA in 2005. He was a visiting researcher at NEC Labs America in Princeton, USA in 2005. He then joined the faculty of National University of Singapore (NUS) in 12/2005, where he served as the founding leader of Photonic System Research Group in Department of Electrical and Computer Engineering till 12/2015. He was also a joint senior scientist with Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) in Singapore in this period. In 12/2015, he joined the faculty of The Hong Kong Polytechnic University, where he is now a full professor in Department of Electronic and Information Engineering, while he also continues as an adjunct faculty member of NUS. His research focuses on photonic devices, subsystems, optical fiber communication and sensor systems, and biomedical instruments. He has been the PI/co-PI/co-I of 40+ research projects and supervised 10+ postdocs and 20+ PhD students. He has authored/co-authored 6 book chapters and 500+ journal/conference papers (100+ keynote/invited, including OFC2012 in USA). He served in technical program committee (TPC) or organizing committee for 80+ international conferences, and Telecommunications Standards Advisory Committee for Singapore government. His group won 7 best paper awards in conferences and the national championship (biomedical area) in the 3rd China Innovation and Entrepreneurship Competition in 2014.
	Ming-Fang Huang Senior Reacher NEC Laboratories America Title: Employing Telecom Fiber Cables as Sensing Media for Road Traffic Applications Employing Telecom Fiber Cables as Sensing Media for Road Traffic Applications Distributed fiber optic sensing systems (DFOS) allow deployed fiber cables to be sensing media, not only dedicated function of data transmission. The fiber cable can monitor the ambient environment over wide area for many applications. We review recent field trial results, and show how artificial intelligence (AI) can help on the application of road traffic monitoring. The results show that fiber sensing can monitor the periodic traffic changes in hourly, daily, weekly and seasonal
Ming-Fang Huang is a senior researcher of Optical Networking & Sensing Group, NEC Laboratories America, Princeton, NJ, USA. She did a one-year postdoc at Georgia Institute of Technology and then joined NEC. She was working in optical communication such as package switching technologies, mobile backhaul and radio over fiber systems, optical passive networks, and long-haul & high capacity transmission. Recently, her research interests include machine learning, optical gas sensing, optical fiber sensing and applications.
	Nan Chi Professor Fudan University Title: Visible light communication towards 6G: advances, challenges and prospects. Visible light communication towards 6G: advances, challenges and prospects. In the next-generation (6G) wireless communication networks, visible light communication (VLC) has been expected as a promising candidate technology. In this report, a summary of the latest progress of VLC system will be introduced, which includes novel devices, advanced modulation, high-speed system, and Artificial intelligence (AI) related signal processing algorithm. We will also discuss the challenges and the prospects of high-speed VLC system in 6G.
Professor Nan Chi is with School of Information Science and Engineering, Fudan University, China. She received the BS degree and PhD degree in electrical engineering from Beijing University of Posts and Telecommunications, China. She is the author or co-author of more than 300 papers and has been cited more than 8900 times. She has been awarded as The National Science Fund for Distinguished Young Scholars, the New Century Excellent Talents Awards from the Education Ministry of China, Shanghai Shu Guang scholarship. Her current research interests include advanced modulation format, optical packet/label switching, optical fiber communication and visible light communication. She is a fellow of the OSA.
	Simon Pun Associate Professor The Chinese University of Hong Kong, Shenzhen Title: Transformer empowered CSI feedback for massive MIMO systems Transformer empowered CSI feedback for massive MIMO systems In this talk, we discuss the problem of CSI feedback in massive multiple-input multiple-output (MIMO) systems. It is well-known that accurate CSI plays a crucial role in realizing the beamforming gain promised by the MIMO technology. However, CSI feedback often incurs excessive feedback overhead. To cope with this problem, this work proposes an effective and robust CSI feedback scheme called CsiTransformer by leveraging a recently developed machine learning (ML) model named transformer in its encoder and decoder. In contrast to the existing ML approaches, CsiTransformer can more effectively exploit the correlation among elements in the channel matrix, which leads to improved CSI reconstruction quality. Furthermore, motivated by the fact that cellphones are power-limited, we propose a further reduced-complexity scheme called MixedCsiNet by using the less computationally expensive conventional convolutional neural networks (CNNs) as the encoder while the transformer model as the decoder. Extensive computer simulation is performed to confirm that CsiTransformer and MixedCsiNet are able to achieve substantially better CSI feedback at different compression rates as compared to existing CSI feedback schemes.
Dr. Simon Pun received his BEng. from the Chinese University of Hong Kong (CUHK) in 1996, MEng. from University of Tsukuba, Japan in 1999 and Ph.D. from University of Southern California (USC) in Los Angeles, U.S.A in 2006, respectively. He was a post-doctoral research associate at Princeton University (USA) from 2006 to 2008. Prior to joining CUHK(SZ) in 2015, he held research positions at Huawei (USA), Mitsubishi (Boston) and Sony (Tokyo). Prof. Pun’s research interests include AI Internet of Things (AIoT) and applications of machine learning in communications and satellite remote sensing. He is the director for the CUHK(SZ) - CAS-NOVA Joint Laboratory of Satellite Communication and Remote Sensing Technology (2020-2025) and the vice-director for the Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology (2018-2020). Dr. Pun served as associate editor in the channel modeling track for the IEEE Transactions on Wireless Communications in 2010 - 2014. Dr. Pun has published over 80 technical publications with 2700 citations. He also holds 12 granted patents including LTE essential patents. Among his publications, his book entitled “Multi-Carrier Techniques for Broadband Wireless Communications” is collected by 1200 libraries worldwide including the National Library of China and the M.I.T. library. Dr. Pun has received three prestigious best paper awards including IEEE VTC'06 Fall, IEEE ICC'08 and IEEE Infocom'09. Dr. Pun also serves as the associate college master for Harmonia College and the director of the Nobel Class at CUHKSZ. He was the assistant Dean (Student Affairs) at the School of Science and Engineering from 2016 to 2019.
	Yan-Ann Chen Assistant Professor Dept. of Computer Science and Engineering Asia University Title: A Similarity-based Framework of Participant Selections for Federated Learning in Edge Computing A Similarity-based Framework of Participant Selections for Federated Learning in Edge Computing In federated learning, each edge device executes the training locally and uploads local parameters onto the server for the further model aggregation. The major difference between federated learning and distributed learning is that client devices generate and process their data locally without exposing their original data. But, it increases the communication cost between the server and clients because of the iterative training. In this work, we formulate the problem of participant selections and propose a framework for reducing the communication cost of federated learning by considering internal and external similarities. We also introduce some potential methods for computing these similarities.
Dr. Yan-Ann Chen is currently an assistant professor in Department of Computer Science and Engineering, Yuan Ze University, Taiwan. He received his Ph.D. in Computer Science from National Chiao Tung University, Taiwan, in 2016. His research interests include Internet of Things, Cyber-Physical System, Mobile/Wearable Sensing, and Edge Artificial Intelligence. He has published almost 20 papers in international journals and conferences.
	Yang Yue Professor Nankai University Title: Beyond Octave-Spanning Supercontinuum Generation of Optical Vortices in Ring-Core Photonic Crystal Fiber Beyond Octave-Spanning Supercontinuum Generation of Optical Vortices in Ring-Core Photonic Crystal Fiber Recent advances on supercontinuum generation of optical vortices in ring-core photonic crystal fiber is reviewed. Beyond octave-spanning supercontinuum generation of TE01, TM01, OAM1,1 modes in the As2S3 photonic crystal fiber will be discussed.
Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the Institute of Modern Optics, Nankai University, Tianjin, China. Dr. Yue’s current research interests include intelligent photonics, optical communications and networking, optical interconnect, detection, imaging and display technology, integrated photonics, free-space and fiber optics. He has published over 200 peer-reviewed journal papers (including Science) and conference proceedings with >8,000 citations, four edited books, >50 issued or pending patents, >100 invited presentations. Dr. Yue is a Senior Member of the Institute of Electronic and Electrical Engineers (IEEE). He is an Associate Editor for IEEE Access, and an Editor Board Member for three other scientific journals. He also served as Guest Editor for eight journal special issues, Chair and Committee Member for >50 international conferences, Reviewer for >60 prestigious journals.
	Yao-Chung Chang Professor & Chair National Taitung University Title: Resource Allocation based on Genetic Algorithm for Cloud Computing Resource Allocation based on Genetic Algorithm for Cloud Computing With the fast development of Cloud computing, many applications and services have become convenient. When resource allocation is unbalanced in Cloud, the processing time becomes longer. Hence, the resource allocation strategy for the virtual machine is a critical problem for the cloud computing environment. In this paper, we propose two phases to overcome the problem with resource allocation. First, we predict the workload with each virtual machine to estimate the resource is significant than the threshold. Then, the genetic algorithm allocates the task to solve the optimal solution for resource allocation. The experiment results show that using the proposed method achieves the load balance and reduces Cloud’s processing time for cloud computing.
Dr. Yao-Chung Chang received a B.S. degree (1992-1996) in Computer Science and Information Engineering from Tamkang University (TKU), Taipei, Taiwan. After that, Dr. Chang completed his M.S. degree (1996-1998) and Ph.D. degree (2002-2006) in Computer Science and Information Engineering from National Dong Hwa University (NDHU), Hualien, Taiwan. He served as the Director of Computer Center and the Director of Taitung Regional Network Center, National Taitung University from Aug. 2009 to Jul. 2011. Dr. Chang was promoted to Professor in 2018. He has been the session chair of 33 international conferences, the program committee of 100 international conferences, and the conference reviewer of 71 international conferences. He has published more than 60 papers in international journals and conferences. Also, he received the Outstanding Teaching Award from Nation Taitung University. His main research interests include AIoT & Machine Learning, Big Data Analysis, Cloud & 5G Mobile Computing.
	Zhaoyu Zhang Professor The Chinese University of Hong Kong, Shenzhen Title: Various microcavity lasers monolithically grown on planar on-axis Si (001) substrates Various microcavity lasers monolithically grown on planar on-axis Si (001) substrates With exponential growth in data traffic and limitations of Moore’s Law, electronic integrated circuits have reached a bottleneck period. Photonic Integrated Circuits (PICs) are seemed as candidate for solving the problem of data transmission. Moreover, robust laser sources are significant building block for PICs design. Here, we demonstrate several InAs/GaAs quantum dot microcavity lasers monolithically grown on planar on-axis Si (001) substrates, including microdisk cavity, 2D Photonic Crystal with L3 defects cavity, 1D Photonic Crystal nanobeam cavity, bandedge Photonic Crystal cavity, bound states in the continuum (BIC) cavity, etc.
Prof. Zhaoyu Zhang is an Associate Professor in School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen. He is also the Director of Shenzhen Key Lab for semiconductor Lasers. He received his Ph.D. degree in Electrical Engineering from California Institute of technology in 2007. He worked as a postdoc associate in Chemistry at UC Berkeley and Molecular Foundry at Lawrence Berkeley National Lab from 2008 to 2011. After that, he worked as an Associate Professor at Peking University from 2001 to 2015. He moved to the Chinese University of Hong Kong, Shenzhen in June 2015. Currently, his research interests include compact semiconductor laser monolithic integration with silicon photonics for telecommunication, organic light emitting diodes and laser for display and illumination applications. He is a member of OSA, IEEE photonics, and SPIE.
	Yun-Wei Lin Assistant Professor College of Artificial Intelligence, National Yang Ming Chiao Tung University Title: AgriTalk‒A Smart Agriculture System based on IoTtalk AgriTalk‒A Smart Agriculture System based on IoTtalk AgriTalk is an economical Internet of Things platform used for precision farming and soil cultivation. We demonstrated how to use the agricultural intelligence to intuitively configure the connection between sensors and actuators and effectively maintain AgriTalk to achieve precision agriculture. We measure and analyze the experiments to study AgriTalk's IoT message latency. Our result shows that the delay of long-distance intelligent control is very short, and AgriTalk can easily respond to the rapid dynamic changes of field environmental conditions in the soil. Also, AgriTalk is capable of disease prediction. Rice blast is a serious plant diseases in Taiwan during spring. Many rice blast management methods require the expertise of experienced farmers. Monitoring farms for disease detection is a labor-intensive task. By using AgriTalk, we can predict or detect plant diseases more effectively. To detect rice blast, existing research uses image or non-image hyperspectral data to detect plant diseases. However, image detection is usually too late, because the spores of rice blast may already spread to infect the other plants. AgriTalk uses multiple non-image sensors to predict and detect rice blast. These non-image sensor data can be automatically trained and analyzed using the AI mechanism to significantly reduce man-power for disease prediction and detection.
Yun-Wei Lin received Ph.D. degrees in computer science and information engineering from National Chung Cheng University, Chiayi, Taiwan, in 2011, respectively. He has been an Assistant Professor in the College of Artificial Intelligence, National Chiao Tung University, Taiwan since 2019. His current research interests include mobile ad hoc network, wireless sensor network, vehicular ad hoc networks, and IoT/M2M communications.