WOCC2018-Speakers

Plenary Speakers
(Speakers are listed in alphabetical order by last names.)


	Dr. Cheng-Shang Chang Tsing Hua Distinguished Chair Professor , IEEE Fellow Dept. of Electrical Engineering National Tsing Hua University Title: Exponentially Twisted Sampling for Structural Network Analysis in Attributed Networks Exponentially Twisted Sampling for Structural Network Analysis in Attributed Networks In this talk, we provide a unified framework for structural network analysis in attributed networks, including centrality analysis and community detection. An attributed network, as a generalization of a graph, has node attributes and edge attributes that represent the "features" of nodes and edges. Traditionally, centrality analysis and community detection of a graph are done by providing a sampling method, such as a random walk, for the graph. To take node attributes and edge attributes into account, the sampling method in an attributed network needs to be twisted from the original sampling method in the underlining graph. For this, we consider the family of exponentially twisted sampling methods and propose using path measures to specify how the sampling method should be twisted. For signed networks, we define the influence centralities by using a path measure from opinions dynamics and the trust centralities by using a path measure from a chain of trust. For attributed networks with node attributes, we also define advertisement-specific influence centralities by using a specific path measure that models influence cascades in such networks. For networks with a distance measure, we define the path measure as the total distance along a path. By specifying the desired average distance between two randomly sampled nodes, we are able to detect communities with various resolution parameters. Various experiments are conducted to further illustrate these exponentially twisted sampling methods by using three real datasets: the political blogs, the MemeTracker dataset, and the WonderNetwork.
Cheng-Shang Chang received the B.S. degree from the National Taiwan University", Taipei, Taiwan, in 1983, and the M.S. and Ph.D. degrees from Columbia University", New York, NY, in 1986 and 1989, respectively, all in Electrical Engineering. From 1989 to 1993 he was employed as a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, N.Y. In 1993, he joined the Department of Electrical Engineering at National Tsing Hua University, Taiwan, R.O.C., where he is a Tsing Hua Distinguished Chair Professor. He is the author of the book Performance Guarantees in Communication Networks (Springer, 2000) and the coauthor of the book Principles, Architectures and Mathematical Theory of High Performance Packet Switches (Ministry of Education, R.O.C., 2006). His current research interests are concerned with network science, high-speed switching, communication network theory, and mathematical modeling of the Internet. Dr. Chang served as an Editor for Operations Research from 1992 to 1999, an Editor for the IEEE/ACM TRANSACTIONS ON NETWORKING from 2007 to 2009, and an Editor for the IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING from 2014 to 2017. He is currently serving as an Editor-at-Large for the IEEE/ACM TRANSACTIONS ON NETWORKING. He is a member of IFIP Working Group 7.3. He received an IBM Outstanding Innovation Award in 1992, an IBM Faculty Partnership Award in 2001, and Outstanding Research Awards from the National Science Council, Taiwan, in 1998, 2000, and 2002, respectively. He also received Outstanding Teaching Awards from both the College of EECS and the university itself in 2003. He was appointed as the first Y. Z. Hsu Scientific Chair Professor in 2002. He received the Merit NSC Research Fellow Award from the National Science Council, R.O.C. in 2011. He also received the Academic Award in 2011 and the National Chair Professorship in 2017 from the Ministry of Education, R.O.C. He is the recipient of the 2017 IEEE INFOCOM Achievement Award.
	Dr. Tzi-Cker Chiueh Vice President and General Director Information and Communications Research Laboratories Industrial Technology Research Institute (ITRI) Title: PCIe Express Networking for Disaggregated Rack Architecture PCIe Express Networking for Disaggregated Rack Architecture PCI Express (PCIe) was originally designed as a local bus interconnect technology for connecting CPUs, GPUs and I/O devices inside a machine, and has since been enhanced to be a full-blown switched network that features point-to-point links, hop-by-hop flow control, end-to-end re-transmission, etc. Recently, researchers have further extended PCIe into an intra-rack interconnect designed to connect multiple servers within the same rack, and to enable these servers to share memory and I/O resources. In this talk, I will go over the system architecture and design of ITRI's PCIe network, and show how it supports the vision of disaggregated rack architecture with more flexible rack-wide resource sharing.
Dr. Tzi-cker Chiueh is the Secretary General of Cloud Computing Association in Taiwan (CCAT), the Generatl Director of Cloud Computing Center for Mobile Applications (CCMA) of Industrial Technology Research Institute (ITRI), and Professor of Computer Science Department of Stony Brook University. He is the main architect of the ITRI Cloud OS, an end-to-end data center software stack for cloud computing that is recently ported to the Essex distribution of OpenStack. Previously, he served as the Director of Core Research at Symantec Research Labs. Dr. Chiueh received his Master's and Ph.D. degree from Stanford and Berkeley, respectively, and has published over 180 refereed journal and conference papers in the areas of storage system, network architecture, virtualization and software security.
	Dr. James Hwang Professor , IEEE Life Fellow Dept. of Electrical And Computer Engineering Lehigh University Title: How Can RF MEMS be as Successful as Other MEMS? How Can RF MEMS be as Successful as Other MEMS? This talk will explore the major challenges for radio-frequency micro-electromechanical systems (RF MEMS) and ways to overcome them. MEMS is ubiquitous. For example, a smartphone can have many MEMS components including microphones/speakers, camera focus/vibration controls, micro-projectors, silicon clocks, position/motion sensors, pressure/humidity/temperature sensors, etc. In comparison, RF MEMS have not been widely used despite their promising performance. In fact, like many new technologies, RF MEMS followed the Gartner hype cycle to reach the peak of inflated expectations around 1995, only to crash to the trough of disillusionment around 2010. However, this also implies that they are now on the slope of enlightenment and may soon reach the plateau of productivity if major challenges can be overcome in a timely manner. The initial hype of RF MEMS originates from their inherent advantages in terms of lower loss, higher isolation, wider bandwidth, better linearity, and lower power consumption compared to their semiconductor counterparts. However, the hype quickly turned into disillusionment mainly due to the reliability issue. Over the following decade, the reliability issue was mostly overcome by careful choice of design, material and bias conditions, more for capacitive switches than ohmic switches. The main challenge then turned into a yield issue, which was largely overcome by fabrication through standard CMOS foundries instead of dedicated MEMS foundries. Now, the main issue of RF MEMS is insufficient performance advantage to overcome the entry barrier faced by any new technology in competition with the entrenched CMOS technology, such as in existing 3G/4G smartphone sockets. In this case, the savior appears to be the necessity for 5G wireless systems to expand above 6 GHz and to deploy phased arrays, which can only enlarge the inherent advantage of RF MEMS, especially capacitive MEMS. However, RF MEMS need to quickly increase their integration level with CMOS circuits. Otherwise, RF MEMS will risk inheriting the curse previously placed on compound semiconductors as “the technology of the future and it will always be.”
Dr. James Hwang is Professor of Electrical Engineering at Lehigh University. He graduated with a B.S. degree in Physics from National Taiwan University, and M.S. and Ph.D. degrees in Materials Science from Cornell University. After twelve years of industrial experience at IBM, Bell Labs, GE, and GAIN, he joined Lehigh in 1988. He cofounded GAIN and QED; the latter became a public company (IQE). He has been a visiting professor at Shanghai Jiao Tong University, East China Normal University, and University of Science and Technology in China, Nanyang Technological University in Singapore, and Marche Polytechnic University in Italy. Most recently, he was a Program Officer for GHz-THz Electronics at the US Air Force Office of Scientific Research. He is a Life Fellow of the Institute of Electrical and Electronic Engineers. He has published approximately 350 refereed technical papers with the impact factor h > 40 according to Google Scholar. He has been granted eight U. S. patents. He has researched on the design, modeling and characterization of microwave, optical and micro-electromechanical devices and integrated circuits. His current research interest focuses on RF MEMS, electromagnetic sensors for individual biological cells, 2D atomic-layered materials and devices, and scanning microwave microscopy.
	Dr. Jia-Ming Liu Distinguished Professor , IEEE Fellow Dept. of Electrical and Computer Engineering University of California, Los Angeles Title: Graphene photonics Graphene photonics Being a truly two-dimensional semiconductor that has a high electron mobility, graphene also has unique optical, optoelectronic, and plasmonic properties. Its properties vary significantly across the electromagnetic spectrum. In particular, it has very interesting optoelectronic properties in the terahertz spectral region. These special features imply opportunities for scientific research and novel photonic devices applications. In this talk, I will present give an overview on graphene photonics. I will present our theoretical and experimental studies on graphene plasmonics and will discuss our work on terahertz plasmonic waveguides and modulators that are enabled by graphene.
Jia-Ming Liu is Professor of Electrical Engineering at UCLA. He is also Chair Professor of Photonics at National Chiao Tung University, Taiwan. He received the B.S. in Electrophysics from National Chiao Tung University in 1975 and became a Licensed Professional Electrical Engineer in 1977. He earned his M.S. and Ph.D. in Applied Physics from Harvard University, in 1979 and 1982, respectively. He was an Assistant Professor with the Department of Electrical and Computer Engineering, State University of New York at Buffalo from 1982 to 1983 and was a senior member of the technical staff with GTE Laboratories from 1983 to 1986. He joined the faculty of the UCLA Electrical Engineering Department in 1986. Professor Liu's research interests cover nonlinear optics, ultrafast optics, semiconductor lasers, photonic devices, optical wave propagation, nonlinear laser dynamics, chaotic communications, chaotic radar, optical imaging, biophotonics, and graphene photonics. He is a fellow of the Optical Society of America, the American Physical Society, the IEEE, and the Guggenheim foundation.
	Dr. Ness B. Shroff Ohio Eminent Scholar in Networking and Communications Chaired Professor , IEEE Fellow Dept. of Electrical and Computer Engineering The Ohio State University Title: A Fresh Look at an Old Problem: Network Utility Maximization — Convergence, Delay, and Complexity A Fresh Look at an Old Problem: Network Utility Maximization — Convergence, Delay, and Complexity Network Utility Maximization has been studied for resource allocation problems in communication networks for nearly two decades. Nonetheless, a major weakness of previous solutions is that they do not converge quickly. This implies that in dynamic networks the algorithms are often operating in sub-optimal conditions, since the network configurations may change over time-scales that are faster than the time-scales of convergence. This could result in unacceptably large delays and buffer overflows in the network. Thus, a major challenge that continues to remain open is how to develop a distributed congestion control, routing and scheduling algorithms that can simultaneously provide utility optimality, fast convergence speed, and low delay. To address this challenge we take a fresh perspective on this old problem and develop a new algorithm that offers the fastest known convergence speed, vanishing utility optimality gap with finite queue length, and low routing complexity. Our key contributions in this work are: i) the design of a new joint congestion control and routing algorithm based on a type of inexact Uzawa method in the Alternating Directional Method of Multiplier; ii) a new theoretical path to prove global and linear convergence rate without requiring the full rank assumption of the constraint matrix; and iii) a clear path for implementing the proposed method in a fully distributed fashion.
Ness B. Shroff received his Ph.D. degree from Columbia University, NY in 1994 and joined Purdue university immediately thereafter as an Assistant Professor. At Purdue, he became Professor of the school of Electrical and Computer Engineering in 2003 and director of CWSA in 2004, a university-wide center on wireless systems and applications. In July 2007, he joined the ECE and CSE departments at The Ohio State University, where he holds the Ohio Eminent Scholar Chaired Professorship of Networking and Communications. From 2009-2012, he also served as a Guest Chaired professor of Wireless Communications at Tsinghua University, Beijing, China, and currently holds an honorary Guest professor at Shanghai Jiaotong University in China and visiting position at the Indian Institute of Technology, Bombay. Dr. Shroff's research interests span the areas of communication, networking, storage, cloud, recommender, social, and cyberphysical systems. He is especially interested in fundamental problems in learning, design, control, performance, pricing, and security of these complex systems. He currently serves as editor-at-large in the IEEE/ACM Trans. on Networking, and as senior editor of the IEEE Transactions on Control of Networked Systems. He also serves on the editorial boards of the IEEE Network Magazine, and the Network Science journal. He has served on the technical and executive committees of several major conferences and workshops. For example, he was the technical program co-chair of IEEE INFOCOM'03, the premier conference in communication networking, the technical program co-chair of ACM Mobihoc 2008, the General co-chair of WICON'08, and the conference chair of IEEE CCW'99. He has served as a keynote speaker and panelist on several major conferences in these fields. Dr. Shroff was also a co-organizer of the NSF workshop on Fundamental Research in Networking in 2003, and the NSF workshop on the Future of Wireless Networks in 2009. Dr. Shroff is a Fellow of the IEEE, and a National Science Foundation CAREER awardee. His papers have received numerous awards at top-tier venues. For example, he received the best paper award at IEEE INFOCOM 2006, IEEE INFOCOM 2008, and IEEE INFOCOM 2016, the best paper of the year in the journal of Communication and Networking (2005) and in Computer Networks (2003). He also also received runner-up awards at IEEE INFOCOM 2005 and IEEE INFOCOM 2013. In addition, his papers have received the best student paper award (from all papers whose first author is a student) at ACM Sigmetrics 2017, IEEE WiOPT 2013, IEEE WiOPT 2012, and IEEE IWQoS 2006. Dr. Shroff is on the list of highly cited researchers from Thomson Reuters ISI (previously ISI web of Science) in 2014 and 2015, and in Thomson Reuters Book on The World's Most Influential Scientific Minds in 2014. He received the IEEE INFOCOM achievement award for seminal contributions to scheduling and resource allocation in wireless networks, in 2014.
	Dr. Yu-Dong Yao Professor and Department Chair , IEEE Fellow Dept. of Electrical and Computer Engineering Stevens Institute of Technology Title: Machine Learning and Deep Learning in Wireless Communications Engineering Machine Learning and Deep Learning in Wireless Communications Engineering We will present AI research results of Machine Learning and Deep Learning as applied to wireless communications and cognitive radio networks. Specific topics include modulation classification, MAC protocol classification, and RF fingerprinting for radio terminal identification. Jamming and communications reliability issues will also be addressed.
Yu-Dong Yao received the B.Eng. and M.Eng. degrees from Nanjing University of Posts and Telecommunications, Nanjing, China, in 1982 and 1985, respectively, and the Ph.D. degree from Southeast University, Nanjing, in 1988, all in electrical engineering. From 1987 to 1988, he was a visiting student at Carleton University, Ottawa, Canada. From 1989 to 2000, he was with Carleton University, Spar Aerospace Ltd., Montreal, Canada, and Qualcomm Inc., San Diego, USA. Since 2000, he has been with Stevens Institute of Technology, Hoboken, USA, where he is currently a Professor and Chair of the Department of Electrical and Computer Engineering. He holds one Chinese patent and 13 U.S. patents. His research interests include wireless communications, cognitive radio, and machine learning and deep learning. He served as an Associate Editor for the IEEE COMMUNICATIONS LETTERS (2000 to 2008) and the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2001 to 2006) and as an Editor for the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2001 to 2005). For his contributions to wireless communications systems, he was elected a Fellow of IEEE (2011), National Academy of Inventors (2015), and Canadian Academy of Engineering (2017).
	Dr. Yongxing Zhou Vice President Wireless Radio Access Technology Department Huawei Title: 5G: Challenges and Beyond 5G: Challenges and Beyond In addition to provision of much improved mobile Internet user experience, 5G is also expected to be the key driving force behind the shift of digitalization of all industries and a more intelligent, connected world. This has brought unprecedented challenges to radio access and mobile core networks. Technology and spectrum innovations have to meet divergent requirements within an integrated/unified 5G standard framework. This talk will introduce progress and roadmap of 5G standard and commercial developments and further discuss evolution of 5G with opportunities and challenges. New evaluation metrics, spectrum and technology innovations will be analyzed and evaluated to demonstrate the potentials of accelerating business success of 5G and IoT.
Yongxing Zhou is Vice President of Huawei Wireless Radio Access Technology Department and Head of MIMO and Spectrum Research Competency Center. He is currently working on 3GPP LTE-Pro and 5G technologies. Prior to 2014, he headed Huawei 3GPP LTE Standardization Team and particularly led development of LTE and LTE-Advanced technologies such as MIMO, FD-MIMO, CoMP, 3D channel modeling, ePDCCH and FDD/TDD Carrier Aggregation etc. Dr. Yongxing Zhou has more than 100 issued patents. Before joining Huawei, he was with Samsung from 2002 to 2009 working on IEEE 802.22, IEEE 802.11n standard and implementations as well as TDD related research. He received his Ph.D degree from Tsinghua University, China.

Invited Speakers
(Speakers are listed in alphabetical order by last names.)


	Dr. Jen-Jee Chen Assistant Professor Dept. of Electrical Engineering National University of Tainan Title: Integration of Open Source Platform Duckietown and Gesture Recognition as an Interactive Interface for the Museum Robotic Guide Integration of Open Source Platform Duckietown and Gesture Recognition as an Interactive Interface for the Museum Robotic Guide In recent years, population aging becomes a serious problem. To decrease the demand for labor when navigating visitors in museums, exhibitions, or libraries, this research designs an automatic museum robotic guide which integrates image and gesture recognition technologies to enhance the guided tour quality of visitors. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving based on the function of lanefollowing via image recognition. This enables the robot to lead guests to visit artworks following the preplanned route. In conjunction with the vocal service about each artwork, the robot can convey the detailed description of the artwork to the guest. We also design a simple wearable device to perform gesture recognition. As a human machine interface, the guest is allowed to interact with the robot by his or her hand gestures. To improve the accuracy of gesture recognition, we design a two phase hybrid machine learning-based framework. In the first phase (or training phase), k-means algorithm is used to train historical data and filter outlier samples to prevent future interference in the recognition phase. Then, in the second phase (or recognition phase), we apply KNN (k-nearest neighboring) algorithm to recognize the hand gesture of users in real time. Experiments show that our method can work in real time and get better accuracy than other methods.
Jen-Jee Chen received the B.S. and M.S. degrees in computer science and information engineering from National Chiao Tung University, Hsinchu, Taiwan, in 2001 and 2003, respectively, and the Ph.D. degree in computer science from National Chiao Tung University, Hsinchu, Taiwan, in 2009. He was a Visiting Scholar at the University of Illinois, Urbana-Champaign, IL, USA, during the 2007–2008 academic year and a Postdoctoral Research Fellow at the Department of Electrical Engineering, National Chiao Tung University, Taiwan, during 2010–2011. Since 2011, he joined the Department of Electrical Engineering, National University of Tainan, Taiwan, where he is currently an Associate Professor. His research interests include wireless communications and networks, mobile computing, cross-layer design, autonomous driving applications, and Internet of Things. Dr. Chen is a member of IEEE and Phi Tau Phi Society.
	Dr. Chong-Yung Chi Professor Dept. of Electrical Engineering National Tsing Hua University Title: Blind Deconvolution Based Super-resolution Imaging with ROSIS/HYDICE/AVIRIS Sensors via Big Data Convex Optimization Blind Deconvolution Based Super-resolution Imaging with ROSIS/HYDICE/AVIRIS Sensors via Big Data Convex Optimization Direct acquisition of hyperspectral and high-spatial-resolution (HSR) image data (target images) with remote-sensing sensors is expensive, while such images play an essential role in accurate identification/classification of the underlying materials in the scene of interest. In this talk, we present a newly invented convex optimization based coupled non-negative matrix factorization (CO-CNMF) algorithm to fuse two different datasets, one captured by HSR multispectral sensor and the other by low-spatial-resolution (LSR) hyperspectral sensor. With sparsity-promoting l1-norm regularization and simplex (of materials’ spectral signatures) volume-demoting regularization, this algorithm solves a large-scale bi-convex problem, by the alternating direction method of multipliers (ADMM), where two non-negative matrix factorizations are intrinsically coupled by a low-rank model of the target image, one for extracting the spectral information from the LSR hyperspectral image and the other for extracting the spatial information from the HSR multispectral image. The CO-CNMF algorithm can be shown to converge to a stationary-point solution together with a carefully designed ADMM that are practically applicable to fusion of million-scale datasets. Finally, experiments designed based on the Wald’s protocol are presented to demonstrate that the CO-CNMF algorithm yields much superior fusion performance over six benchmark super-resolution methods on ROSIS, HYDICE and AVIRIS datasets, in terms of several performance measures including peak signal-to-noise ratio (PSNR) and root mean square error (RMSE), with lower or comparable computational load.
Chong-Yung Chi received the Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, California, in 1983. From 1983 to 1988, he was with the Jet Propulsion Laboratory, Pasadena, California. He has been a Professor with the Department of Electrical Engineering since 1989 and the Institute of Communications Engineering (ICE) since 1999 (also the Chairman of ICE during 2002-2005), National Tsing Hua University, Hsinchu, Taiwan. He has published more than 220 technical papers (with citations more than 3900 times by Google-Scholar), including more than 80 journal papers (mostly in IEEE Trans. Signal Processing), more than 130 peer-reviewed conference papers, 4 book chapters, and 2 books, including a new textbook (432 pages), Convex Optimization for Signal Processing and Communications, CRC Press, 2017 (which has been popularly used in an invited 2-week intensive short course 15 times in 8 major universities in China since 2010 before its publication). His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis. Dr. Chi is a senior member of IEEE. He has been a Technical Program Committee member for many IEEE sponsored and co-sponsored workshops, symposiums and conferences on signal processing and wireless communications, including Co-organizer and General Co-chairman of 2001 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), and Co-Chair of Signal Processing for Communications (SPC) Symposium, ChinaCOM 2008 & Lead Co-Chair of SPC Symposium, ChinaCOM 2009. He was an Associate Editor (AE) of IEEE Trans. Signal Processing (5/2001~4/2006, 1/2012~12/2015), IEEE Trans. Circuits and Systems II (1/2006-12/2007), IEEE Trans. Circuits and Systems I (1/2008-12/2009), AE of IEEE Signal Processing Letters (6/2006~5/2010), and a member of Editorial Board of Signal Processing (6/2005~5/2008), and an editor (7/2003~12/2005) as well as a Guest Editor (2006) of EURASIP Journal on Applied Signal Processing. He was a member of Signal Processing Theory and Methods Technical Committee (SPTM-TC) (2005-2010), IEEE Signal Processing Society, a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) (2011-2016). Currently, he is a member of Sensor Array and Multichannel Technical Committee (SAM-TC), IEEE Signal Processing Society.
	Dr. San-Liang Lee Distinguished Professor and Vice President Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Title: Development of 400 Gb/s optical transceivers for SMF based datacenter optical interconnect Development of 400 Gb/s optical transceivers for SMF based datacenter optical interconnect We reported the progress of developing 400-Gb/s optical transceivers with 1.3-µm electro-absorption modulators for optical interconnect applications in data centers. The key technologies include high-speed light sources, silicon photonics based WDM multiplexers/de-multiplexers, driving and receiving circuits, signal processing and monitoring schemes.
San-Liang Lee received the B.S. degree in electronics engineering from the National Chiao Tung University, Hsinchu, Taiwan, in 1984, the M.S. degree in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1986, and the Ph.D. degree in electrical and computer engineering from the University of California, Santa Barbara (UCSB), in 1995. Dr. Lee joined the faculty of the Department of Electronic Engineering, National Taiwan University of Science and Technology (NTUST), Taipei, in 1988. He became an Associate Professor in 1995 and a Professor in 2002. He is currently the Chairman of the Department. He is also the Director of the program office for the National Innovative Education Program on Image Display Technology, sponsored by the Ministry of Education, Taiwan. He was the Director of the Center for Optoelectronic Science and Technology, College of Electrical and Computer Engineering, NTUST, from 2002 to 2005. He organized the Optic and Photonics Taiwan conference with more than 900 attendee in 2002. He served as the Technical Program Committee members for IEEE Globecom 2004 and OECC 2005. His research interests include semiconductor optoelectronic components, photonic integrated circuits, nanophotonics, and optical switching technologies. He has published more than 70 referred papers in international journals and conferences and holds 14 patents.
	Dr. Chia-Wen Lin Professor Dept. of Electrical Engineering National Tsing Hua University Title: Introduction to CNN and Its Applications to Large-Scale Image Clustering Introduction to CNN and Its Applications to Large-Scale Image Clustering In the this talk, we will first briefly introduce the concept and applications of convolutional neural networks (CNNs) which are among the most successful deep neural networks. CNNs have powerful representation learning ability making them very successful in many supervised learning problems with a sufficiently large amount of labelled training data. Nevertheless, unsupervised learning applications have no labelled training data, making it difficult to learn CNN models from the unlabeled data. We will give an example to show how to apply CNNs to unsupervised learning problems such as large-scale image clustering. To this end, we propose a clustering CNN to jointly solve clustering and representation learning in an iterative manner. In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pre-trained from the ImageNet dataset. Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images have been processed. Subsequently, the proposed CNN simultaneously updates the parameters of the proposed CNN and the centroids of image clusters iteratively based on stochastic gradient descent. We also propose a feature drift compensation scheme to mitigate the drift error caused by feature mismatch in representation learning. Experimental results demonstrate the proposed method outperforms start-of-the-art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.
Prof. Chia-Wen Lin received his PhD degree in Electrical Engineering from National Tsing Hua University (NTHU), Hsinchu, Taiwan in 2000. He is currently a Professor with the Department of Electrical Engineering, National Tsing Hua University, Taiwan. He is Deputy Director of the AI Research Center of NTHU and Director of the Multimedia Technology Research Center of the EECS College, NTHU. His research interests include image/video processing and video networking. Dr. Lin is an IEEE Fellow. He is a Distinguished Lecturer of IEEE Circuits and System Society during 2018-2019. He has served as Associate Editor of IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Multimedia, and Journal of Visual Communication and Image Representation. He also served as a Steering Committee member of the IEEE Transactions on Multimedia during 2013-2015. He was Chair of the Multimedia Systems and Applications Technical Committee of the IEEE Circuits and Systems Society. He served as Technical Program Co-Chair of the IEEE ICME in 2010 and will be the TPC Chair of IEEE ICIP 2019 in Taipei. His papers won the Best Paper Award of IEEE VCIP 2015, and the Young Investigator Award of SPIE VCIP 2005.
	Dr. Gong-Ru Lin Distinguished Professor and Chairman Graduate Institute of Photonics and Optoelectronics National Taiwan University Title: SiC Waveguide Based All-Optical Data Processing SiC Waveguide Based All-Optical Data Processing The SiC waveguide employed to the all-optical data processing is demonstrated by using two-photon absorption induced free-carrier absorption and nonlinear Kerr effect induced refractive index switching. The composition ratio and structure of the SiC material can be detuned to change its nonlinear absorption as well as refractive index. The free-carrier absorption provides a transient pump-to-probe uniform transformation with its data rate limited by the free carrier lifetime. By using the pure nonlinear refractive index switching, the SiC waveguide can demonstrate up to 12-Gbps wavelength conversion of pulsed return-to-zero on-off keying data stream with the signal to noise ratio of 8 dB and a bit-error-ratio of 2.85×10-10.
Prof. Gong-Ru Lin received his B. S. degree of Physics from Soochow University in 1988, M. S. and Ph. D degrees of electro-optical engineering from National Chiao Tung University (NCTU) in 1990 and 1996, respectively. He joined National Lien Ho College of Technology in 1997 and Tatung University in 1998 as assistant professor, and became an associate professor with National Taipei University of Technology in 2002. He has promoted as a professor in 2004 with the Institute of Electro-Optical Engineering at National Chiao Tung University. Since 2006, He is the full professor with the Graduate Institute of Electro-Optical Engineering and Department of Electrical Engineering, National Taiwan University. His research interests include ultrafast fiber lasers, silicon nanophotonics, fiber-optic communications, silicon carbide, graphene and carbon nanostructures. He has authored 3 invited papers, 10 book chapters, more than 300 SCI-ranked papers, over 500 international conference papers, and 25 invited talks. His publications has been cited up to 4400 times with a H-index of 32. He has served in SPIE as Award Commitee (since 2003), Secretary of Taiwan Chapter (since 2004), and Vice Chair of Taiwan Chapter (since 2006). He is also the treasurer (since 2004), Vice Chair (since 2006), and Chair (since 2008) of IEEE/LEOS Taipei Chapter. In 2009-2012, he was the Member-at-Large and Committee of the Membership and Education Services Council of OSA.
	Dr. Hai-Han Lu Lifetime Distinguished Professor Dept. of Electro-Optical Engineering National Taipei University of Technology Title: A 25-Gbps UWOC System with a Two-Stage Injection-Locked VCSEL Transmitter and an Afocal Scheme A 25-Gbps UWOC System with a Two-Stage Injection-Locked VCSEL Transmitter and an Afocal Scheme A 25-Gbps underwater wireless optical communication (UWOC) system with a two-stage injectionlocked 680-nm vertical-cavity surface-emitting laser (VCSEL) transmitter to enhance the frequency response and an afocal scheme to expand the collimated beam diameter over a 5-m highly turbid harbor water link is proposed and practically demonstrated. Given that the overall attenuation coefficient at 680 nm is smaller than that at 520 nm and 450 nm, a 680-nm redlight VCSEL transmitter is adopted in this proposed 5 m/25 Gbps UWOC system rather than a 450-nm blue-light laser diode (LD) transmitter or a 520-nm green-light LD transmitter. Satisfactory bit error rate performance (3×10-9) and clear eye diagram are acquired in real-time. This proposed UWOC system with a twostage injection-locked 680 nm VCSEL transmitter and an afocal scheme brings important improvements in the scenario characterized by high turbidity.
Hai-Han Lu received the B.S. in the Electro-Physics Department of the National Chiao-Tung University, Taiwan, in 1987. He received the M.S. and Ph.D. degree in the Institute of Optical Sciences of the National Central University, Taiwan, in 1991 and 2000, respectively. He has joined the Institute of Electro-Optical Engineering, National Taipei University of Technology as an Associate Professor since 2001. In 2003, he became a full professor. His current research interests include the applications of the lightwave communication and hybrid DWDM systems. Professor Hai-Han Lu has contributed significantly to fiber optical CATV systems, visible light communication (VLC) systems, and hybrid lightwave subcarrier transmission systems in both academia and in industry. He is an Associate Editor for the Optical Engineering journal in the area of lightwave communication systems. In addition Prof. Lu has devoted himself to community services: he is a member of the CATV Deliberation Committee, which defines the national CATV standard in Taiwan, and is a member of the Review Board of the Ministry of Science and Technology and the Bureau of Industrial Development for the Ministry of Economic Affairs in Taiwan. He has extensive research collaboration in the US, Canada, China, Taiwan, Japan and Australia. It will be clear from the brief summary below and his CV, that Prof. Hai-Han Lu has played a pivotal role in achieving outstanding advances in fiber optical CATV, VLC, and hybrid carrier subcarrier systems. These excellent achievements in research have stimulated and continue to widen horizons in newer, faster and integrated ways for high-speed communications, using the available infrastructure to its fullest. In teaching, and service to the community at NTUT and TNU, Prof. Lu has also demonstrated leadership, mentoring, and an unmatched management capability. Currently, he is a Distinguished Professor at NTUT, was the Vice President at TNU, and the Chair of the Department of Electro-Optical Engineering at NTUT. He has 7 granted patent and 6 trchnologies transfer applications. Prof. Lu’s recognized research in optics and photonics in creating optimised cost-effective hybrid systems for high-speed communications of the future, his outstanding leadership in academia and in engineering, make his nomination most deserving of the prestegious election to Fellow of the Optical Society of America.
	Dr. Hsuan-Jung Su Professor Dept. of Electrical Engineering National Taiwan University Title: Probabilistic Channel Feedback for Multiuser MIMO Broadcast Channels with Orthogonal Beamforming Probabilistic Channel Feedback for Multiuser MIMO Broadcast Channels with Orthogonal Beamforming In multiuser multi-input multi-output (MIMO) cellular systems operating in the frequency division duplexing (FDD) mode, the channel state information (CSI) feedback from users are required in order to schedule users effectively and achieve system capacity. When the number of transmitting antennas or the number of users increases, the tremendous amount of feedback load makes it unrealistic to have perfect CSI at the transmitter. Threshold based feedback reduction is a potential approach to reduce the feedback load. However, the previous works on threshold based feedback reduction only considered users with independent channels. In this paper, we prove that, while the threshold based feedback is optimal when the channels of the users are independent, it is not if there is correlation among users’ channels. The main reason is that, when the users experience similarly poor channels, there is a higher probability that none of them feeds back, which results in insufficient information for the base station (BS) to perform scheduling, thus possible rate loss; while when the users’ channels are similarly good, too many of them feedback and waste the feedback bandwidth. This observation motivates us to propose and investigate a more general probabilistic feedback strategy which can avoid the aforementioned problems and achieve higher system sum rate in correlated channels. In addition to the theoretical proof, numerical evaluation was conducted to validate the claim.
Hsuan-Jung Su received the B.S. degree in Electronics Engineering from the National Chiao Tung University, Taiwan, in 1992, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland, College Park, in 1996 and 1999, respectively. From 1999 to 2000, he was a Postdoctoral Research Associate with the Institute for Systems Research, University of Maryland. From 2000 to 2003, he was with the Bell Laboratories, Lucent Technologies, Holmdel, New Jersey, where he received the Central Bell Labs Teamwork Award in 2002 and the Bell Labs President's Gold Award in 2003 for his contribution to the 3G wireless network design and standardization. In 2003, Dr. Su joined the Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, where he is currently a Professor. From 2014 to 2015, Dr. Su was a Visiting Fellow at Princeton University. Dr. Su is an Area Editor of the Physical Communication (PHYCOM) journal (Elsevier), and has guest edited special issues for journals such as IEEE Access. He has also served on the organizing committees and TPCs of many international conferences, including serving as the Finance Chair of IEEE ICASSP 2009, the Finance Co-Chair and a TPC Track Chair of IEEE VTC 2010 Spring, a TPC Co-Chair of WPMC 2012, a TPC Co-Chair of IEEE GreenCom 2014, and the TPC Chair of WOCC 2015. Su was the Chair of IEEE Information Theory Society, Taipei Chapter (2013-2015), the Secretary and Treasurer (2014-2015) and the Technical Affairs Committee Vice Chair (2016-2017) of the IEEE Communications Society Asia-Pacific Board . His research interests cover coding, modulation, signal processing, interference management, resource allocation, and MAC protocols of wireless communication, cognitive, M2M (IoT) and D2D networks.
	Dr. Chia-Chien Wei Associate Professor Dept. of Photonics National Sun Yat-sen University Title: APD-based Delay-Division-Multiplexing OFDM-PONs APD-based Delay-Division-Multiplexing OFDM-PONs Based on pre-allocated relative sampling delays among optical network users (ONUs) in an orthogonal frequencydivision multiplexing (OFDM) passive optical network (PON), the delay-division-multiplexing (DDM) technique employs signal pre-processing to enable demand data to be received by subNyquist analog-to-digital sampling. This work employed an avalanche photodiode (APD) in a DDM-OFDM-PON to achieve higher loss budget, compared to a p-i-n photodiode (PIN). To successfully enable the pre-processing, however, the knowledge of channel response is required; thus, it is critical to estimate complete channel response in DDM-OFDM systems under the constraint of sub-Nyquist sampling. To avoid spectral overlapping in channel estimation, the subcarriers of training symbols in different frequency zones are allocated to different time slots. Unfortunately, this scheme may sustain inaccurate channel estimation due to different degrees of APD saturation for training subcarriers at different frequency zones, leading to significant penalty in performance. Thus, careful control of electrical driving power and optical received power for the training symbols will be important to reduce the impact of saturation on channel estimation, thereby improving transmission performance. We experimentally examined the impact of the accuracy of channel estimation in a 25-Gbps APD-based OFDM-PON. Compared to a PIN, we successfully demonstrated the DDM scheme based on an APD and 1/32 of Nyquist rate with 5-dB improvement in sensitivity after 25-km fiber transmission.
Chia-Chien Wei received his Ph.D. degrees in Electro-Optical Engineering from National Chiao Tung University, Taiwan, and Electrical Engineering from University of Maryland, Baltimore County, US, in 2008. In 2011 he joined National Sun Yat-sen University, Taiwan, where he is currently an associate professor of Department of Photonics. His current research interests include optical and electrical signal processing, advanced modulation formats, optical access networks, and radio-over-fiber systems.
	Dr. Changyuan Yu Associate Professor Dept. of Electronic and Information Engineering The Hong Kong Polytechnic University Title: Non-invasive vital signs monitoring system based on smart sensor mat embedded with optical fiber interferometer Non-invasive vital signs monitoring system based on smart sensor mat embedded with optical fiber interferometer A non-invasive vital signs monitoring system based on fiber-optic interferometer is presented, where human being’s heart beat and respiration signals can be precisely extracted and monitored in an unobtrusive way. The fiber sensing system is wholly embedded in a thin mat placed on the mattress.
Dr. Changyuan YU received his B.S. in Applied Physics and B. Economics in Management from Tsinghua University, China in 1997, M.S. in Electrical and Computer Engineering from University of Miami, USA in 1999, and Ph.D. in Electrical Engineering from Optical Communications Lab at University of Southern California, USA in 2005. And he was a visiting researcher at NEC Labs America in Princeton, USA in 2005. He then joined Dept. of Electrical and Computer Engineering, National University of Singapore (NUS) in 12/2005 and served as the founding leader of Photonic System Research Group in NUS till 12/2015. He was also a joint senior scientist with A*STAR Institute for Infocomm Research in this period. As the PI/co-PI, he secured over 4 million US dollars grants, and supervised 8 postdocs and 16 PhD students in Singapore. In 12/2015, he joined Department of Electronic and Information Engineering, The Hong Kong Polytechnic University as an associate professor. His research focuses on photonic devices, subsystems, and optical fiber communication and sensor systems. Till 12/2015, he has authored/co-authored 1 US patent, 6 book chapters and over 260 journal and conference papers (46 invited, including OFC2012 in USA). He has served in technical program committee (TPC) or organizing committee for 50+ international conferences (including TPC member of OFC2014-2016, general chair of SPPCom2015, and TPC chair of SPPCom2014 in USA). His group won 6 best paper awards in conferences, and also the championship in biomedical area in the 3rd China Innovation and Entrepreneurship Competition (out of 10,000+ competitors) in 2014.