张青富教授为IEEE fellow，是IEEE Transactions on Evolutionary Computation、IEEE Transactions on Cybernetics等计算智能领域权威期刊的副主编，同时是其他三个计算智能领域国际期刊的编委会成员，计算智能领域顶级国际会议的多届分会主席等。并且获得了2010年IEEE Transactions on Evolutionary Computation的优秀论文奖（Outstanding Paper Award）；2015年入选国家“千人计划”；2016年入选Web of Science公布的计算科学领域的高引学者。
Xiaoming Xiong received the B.S. degree in electrical engineering from South China University of Technology, Guangzhou, China in 1982 and the Ph.D. degree in electrical engineering and computer science from University of California, Berkeley, California, USA in 1998. He is currently a professor with the school of automation, Guangdong University of Technology, Guangzhou, China. His current research interests include intelligent control, integrated circuit (IC) and System-on-Chip (SoC) design, computer aided design (CAD) and electronic design automation (EDA), computer algorithms, graph theory and computational geometry.
Steven Guan received his M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a professor in the computer science and software engineering department at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor.
Prof. Guan's research interests include: machine learning, modeling, security, networking, and pseudorandom number generation. He has published extensively in these areas, with 130 journal papers and 170+ book chapters or conference papers. He has chaired and delivered keynote speech for 40+ international conferences and served in 130+ international conference committees and 20+ editorial boards
Jenq-Shiou Leu received his B.S. in mathematics and his M.S. in computer science and information engineering from National Taiwan University, Taipei, Taiwan, respectively, and his Ph.D. on a part-time basis in computer science from National Tsing Hua University, HsingChu, Taiwan. He was with Rising Star Technology, Taiwan, as an R&D Engineer from 1995 to 1997, and worked in the telecommunication industry (Mobitai Communications and Taiwan Mobile) from 1997 to 2007 as an Assistant Manager. In February 2007, he joined the Department of Electronic and Computer Engineering at National Taiwan University of Science and Technology (NTUST) as an Assistant Professor. From February 2011 to January 2014, he was an Associate Professor. Since February 2014, he is a Professor. Dr. Leu’s research interests include: Heterogeneous Network Integration, Mobile Service and Platform Design, Cybersecurity, Distributed Computing, Green and Orange Technology Integration. He has published extensively in these areas, with 55 SCI indexed journal papers, 53 conference papers or book chapters, and led 13 MOST project, 13 industry-academia projects, and 5 cross-university projects after he joined NTUST. He is a senior member of IEEE.
Title: Energy Efficient Streaming for Smartphone by Video Adaptation and Backlight Control
Abstract: Smartphone becomes an indispensable gadget in our daily life. Prolonging battery life on smartphone can extend the usability of the phone without being recharged, especially for accessing streaming video, which is a paramount service in the mobile Internet era. Many researchers have proposed energy efficient streaming methods, including bandwidth control and packet scheduling. Such studies have focused on reducing the energy consumption of central processing units (CPUs) and wireless networks; however, screens may drain substantial energy on smartphone. In this study, an energy efficient streaming sys- tem is proposed, combining adaptive coding and a backlight control mechanism. A non-parametric signal prediction is used to predict the network condition and then some adaptive encoding parameters are subsequently adjusted to fit the network capability. A histogram equalization is applied to compensate for the loss of image contrast after reducing the backlight. To validate the proposed concept, some experiments were conducted and the corresponding results show that the proposed streaming system can effectively reduce energy consumption on smartphone, while accessing the video streaming service.
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