Blind Source Separation

Yong Xiang received the B.E. and M.E. degrees from the University of Electronic Science and Technology of China, Chengdu, China, in 1983 and 1989, respectively. In 2003, he received the Ph.D. degree from The University of Melbourne, Melbourne, Australia. He was with the Southwest Institute of Electronic Equipment of China, Chengdu, from 1983 to 1986. In 1989, he joined the University of Electronic Science and Technology of China, where he was a Lecturer from 1989 to 1992 and an Associate Professor from 1992 to 1997. He was a Senior Communications Engineer with Bandspeed Inc., Melbourne, Australia, from 2000 to 2002. He is currently an Associate Professor with the School of Information Technology at Deakin University, Australia. His research interests include blind signal/system estimation, communication signal processing, information and network security, speech and image processing, and pattern recognition.Dezhong Peng received the B.S. degree in applied mathematics, the M.S. and Ph.D. degrees in computer software and theory from the University of Electronic Science and Technology of China, Chengdu, China, in 1998, 2001 and 2006, respectively. He was a Postdoctoral Research Fellow at the School of Engineering, Deakin University, Australia from 2007 to 2009. Currently, he is a Professor at the Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China. His current research interests include blind signal processing and neural networks.Zuyuan Yang received the B.E. degree from Hunan University of Science and Technology, Xiangtan, China, and the Ph.D. degree from South China University of Technology, Guangzhou, China, in 2003 and 2010, respectively. He was a recipient of the Excellent Ph.D. Thesis Award of Guangdong Province in 2010. Dr Yang is currently a Researcher with the Faculty of Automation, Guangdong University of Technology, Guangzhou. His current research interests include blind source separation, compressed sensing, nonnegative matrix factorization, and image processing.

• Introduction.- DCA Exploiting Nonnegativity.- DCA Utilizing Time-Frequency Analysis (TFA).- DCA Employing Precoding.- Summary and Future Work.