Welcome to Workshop on Advances in Wireless Communications 2017  (WAWC 2017).

Date: 14:00 - 16:00 Thursday, May 18 and 13:30 - 18:00 Tuesday, July 18, 2017.

Location: Conference hall, Yifu Science and Technology Museum, Southeast University, Nanjing, China.

There is a recent surge in research activities around the idea of the so-calleddeep neural networks (DNN). As a technical item, DNN without a doubt is an important classroom topic and several tutorial articles and related learning resources are available. Nevertheless, streams of questions about DNN never subside from students or researchers and there appears to be a frustrating tendency among the learners to treat DNN simply as a black box. In this talk, a pedagogy is attempted with the aim to present DNN in the well-established traditional pattern recognition framework so that a deeper understanding of DNN can be reached through proper contrast to conventional techniques. In particular, we review the developmental aspect of DNN, in terms of how advances in connectionist models have evolved into this powerful technique. Time permitting, we'll discuss the application of DNN in the area of automatic speech recognition so as to ascertain its efficacy, as compared to traditional statistical modeling, and to bring to surface possibly unrealized potentials of DNN and conventional techniques.

Biing-Hwang (Fred) Juang is the Motorola Foundation Chair Professor and a Georgia Research Alliance Eminent Scholar at Georgia Institute of Technology. He is also enlisted as Honorary Chair Professor at several renowned universities. He received a Ph.D. degree from University of California, Santa Barbara. He had conducted research work at Speech Communications Research Laboratory (SCRL) and Signal Technology, Inc. (STI) in the late 1970s on a number of Government-sponsored research projects and at Bell Labs during the 80s and 90s until he joined Georgia Tech in 2002. Prof. Juang's notable accomplishments include development of vector quantization for voice applications, voice coders at extremely low bit rates (800 bps and ~300 bps), robust vocoders for satellite communications, fundamental algorithms in signal modeling for automatic speech recognition, mixture hidden Markov models, discriminative methods in pattern recognition and machine learning, stereo- and multi-phonic teleconferencing, and a number of voice-enabled interactive communication services. He was Director of Acoustics and Speech Research at Bell Labs (1996-2001). Prof. Juang has published extensively, including the bookFundamentals of Speech Recognition, co-authored with L.R. Rabiner, and holds nearly two dozen patents. He received the Technical Achievement Award from the IEEE Signal Processing Society in 1998 for contributions to the field of speech processing and communications and the Third Millennium Medal from the IEEE in 2000. He also received two Best Senior Paper Awards, in 1993 and 1994 respectively, and a Best Paper Awards in 1994, from the IEEE Signal Processing Society. He served as the Editor-in- Chief of the IEEE Transactions on Speech and Audio Processing from 1996 to 2002. He was elected an IEEE Fellow (1991), a Bell Labs Fellow (1999), a member of the US National Academy of Engineering (2004), and an Academician of the Academia Sinica (2006). He was named recipient of the IEEE Field Award in Audio, Speech and Acoustics, the J.L. Flanagan Medal, and a Charter Fellow of the National Academy of Inventors (NAI), in 2014.

The last decade has seen the digitization of our everyday life and pursuits, from physical signals collected by IoT everywhere devices to our social, business, health, marketing you name it lives, leading to the generation, storage, transmission of tremendous amounts of data-the era of Big Data. It is incumbent upon us Signal Processors to develop the analytics to make sense out of this data. Beyond all the seven V that characterize Big Data (volume, velocity, variety, variability, veracity, visualization, value) there is an intrinsic unstructured nature to the data; it just does not fit the traditional framework of time series signals, images, or video. To cope with this new nature of data, we introduced recently a new analytics framework for the new data, Signal Processing on Graphs, also referred to as Graph Signal Processing. In this talk, I will introduce the theory and recent developments that make it an exciting field and illustrate the concepts with datasets drawn from physical to social networks.

Jose M. F. Moura is the Philip L. and Marsha Dowd University Professor at Carnegie Mellon University, with the Electrical and Computer Engineering and, by courtesy, the BioMedical Engineering. He is a member of the US National Academy of Engineers, see IEEE Technical SPOTLIGHT announcement, Fellow of the US National Academy of Inventors, a corresponding member of the Portugal Academy of Science, an IEEE Fellow, and a Fellow of the AAAS. He holds a D. Sc. in Electrical Engineering and Computer Science, M.Sc., and EE degrees all from MIT and an EE degree from Instituto Superior Tecnico (IST, Portugal). He was a visiting Professor at the Center for Urban Science and Progress (CUSP) and at NYU in 2013-2014, a visiting Professor at MIT (2006-2007, 1999-2000, and 1984-86), a visiting scholar at USC (Summers of 79-81), and was on the faculty of IST (Portugal). Moura's research interests are in Data Science and Signal Processing on Graphs, also referred to as Graph Signal Processing, including distributed decision and inference in networked systems. Besides signal processing on graphs and analytics for Big Data, recent projects included distributed detection in sensor networks, robust detection and imaging by time reversal, bioimaging, SPIRAL, DSP on Graphs, SMART, and image/video processing. His work has been sponsored by several Darpa, NIH, ONR, ARO, AFOSR, and NSF grants, and several industrial grants. Moura received the IEEE Signal Processing Society Society Award for outstanding technical contributions and leadership in signal processing, the IEEE Signal Processing Society Technical Achievement Award for fundamental contributions to statistical signal processing. In 2016 he was IEEE Vice President for Technical Activities and IEEE Board Member and Director.

Coming soon.

Prof. Liao was an Associate Editor of IEEE Transactions on Wireless Communications and IEEE Transactions on Multimedia, and served on the organizing committees of many international conferences. She was an IEEE Communications Society (ComSoc) Distinguished Lecturer, an IEEE Fellow Committee member, and the IEEE ComSoc Asia Pacific Board (APB) Director. She is on IEEE Award Board Award Review Committee and IEEE Transactions on Mobile Computing Steering Committe. She received many research awards and recognition from different government and professional organizations. She was a recipient of Outstanding Teaching Award at NTU in 2000, Outstanding EE Professor Award of Chinese IEE in 2006, Outstanding Research Award of National Science Council (NSC) in 2006, 2009, and 2012, K. T. Li Research Breakthrough Award in 2009, Outstanding Engineering Professor Award of Chinese Institute of Engineer in 2010, Teco Award in 2014, and received the Distinguished Alumni Award from National Chiao-Tung University in 2012. She is a Fellow of the IEEE.

Coming soon.

Prof. Xin Yao is a Professor of Computer Science in the School of Computer Science at the University of Birmingham and the Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA). I'm also a Fellow of IEEE, a Distinguished Lecturer, and a Past (2014-15) President of the IEEE Computational Intelligence Society. I was a Distinguished Visiting Professor at the Nature Inspired Computation and Applications Laboratory (NICAL), USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications, University of Science and Technology of China, Hefei, China. His research interests include evolutionary computation (evolutionary optimization, evolutionary learning, evolutionary design), neural network ensembles and multiple classifiers (especially on the diversity issue), meta-heuristic algorithms, data mining, computational complexity of evolutionary algorithms, and various real-world applications. According to Thomson Reuters, He is one of the Highly Cited Researchers 2016 (http://hcr.stateofinnovation.thomsonreuters.com/), one of the seven from the University of Birmingham.

Coming soon.

Abbas Jamalipour received a Ph.D. degree from Nagoya University, Nagoya, Japan. He is Professor of Ubiquitous Mobile Networking with the School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for contributions to next generation networks for traffic control; a Fellow of the Institute of Electrical, Information, and Communication Engineers (IEICE) for contributions to design and development of mobile wireless communication networks; a Fellow of the Institute of Engineers Australia; an IEEE Distinguished Lecturer; and a Technical Editor of several scholarly journals. He has been an organizer or the chair of several international conferences, including the IEEE International Conference on Communications and the IEEE Global Communications Conference and the IEEE Wireless Communications and Networking Conference. He is the Vice President - Conferences and a member of Board of Governors of the IEEE Communications Society (ComSoc). He is the recipient of several awards, including the 2010 IEEE ComSoc Harold Sobol Award for Exemplary Service to Meetings and Conferences, the 2006 IEEE ComSoc Satellite Technical Committee Award - Distinguished Contribution to Satellite Communications, and the 2006 IEEE ComSoc Best Tutorial Paper Award.

Coming soon.

Prof. Fang received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002, and is the recipient of the Best Paper Award in IEEE Globecom (2011), IEEE International Conference on Network Protocols (ICNP, 2006) and the recipient of the IEEE TCGN Best Paper Award in the IEEE High-Speed Networks Symposium, IEEE Globecom (2002). He has also received a 2010-2011 UF Doctoral Dissertation Advisor/Mentoring Award, 2011 Florida Blue Key/UF Homecoming Distinguished Faculty Award and the 2009 UF College of Engineering Faculty Mentoring Award. Prof. Fang is also active in professional activities. He is a Fellow of IEEE and a member of ACM. He has been serving as the Editor-in-Chief of IEEE Transactions on Vehicular Technology since April 2013. He served as the Editor-in-Chief for IEEE Wireless Communications (2009-2012) and serves/served on several editorial boards of technical journals including IEEE Transactions on Mobile Computing (2003-2008, 2011-present), IEEE Network (2012-present), IEEE Transactions on Communications (2000-2011), IEEE Transactions on Wireless Communications (2002-2009), IEEE Journal on Selected Areas in Communications (1999-2001), IEEE Wireless Communications Magazine (2003-2009) and ACM Wireless Networks (2001-present). He served on the Steering Committee for IEEE Transactions on Mobile Computing (2008-2010). He has been actively participating in professional conference organizations such as serving as the Technical Program Co-Chair for IEEE INOFOCOM'2014, the Steering Committee Co-Chair for QShine (2004-2008), the Technical Program Vice-Chair for IEEE INFOCOM'2005, the Technical Program Area Chair for IEEE INFOCOM (2009-2013), Technical Program Symposium Co-Chair for IEEE Globecom'2004, and a member of Technical Program Committee for IEEE INFOCOM (1998, 2000, 2003-2008).