Time: 15:00, June 12th (Monday)
Venue: A104, Building of Electrics
Topic: Big Data Processing Based on Compressed Sensing
Lecturer: Prof. Lixiang Li, Beijing University of Posts and Telecommunications
About the Lecturer: Li, a professor and doctoral dissertation supervisor, is a winner of the National 100 Excellent Doctoral Dissertations Award, New Century Excellent Talents (Ministry of Education), Fok Ying-Tong Education Foundation, Excellent Young Talents of Chinese Association for Cryptologic Research, and Beijing Higher Education Young Elite Teacher Project. She was a visiting scholar at Potsdam Institute for Climate Impact Research and Hong Kong Scholar of The Chinese University of Hong Kong, and now she is now the key researcher of National Engineering Laboratory for Disaster Backup and Recovery and National Key Laboratory of Networking and Switching Technology, and key teacher of School of Cyberspace Security, Beijing University of Posts and Telecommunications.
Engaged in the fields of swarm intelligence, complex network, compressed sensing, and cyberspace security, she is also an editorial board member of journals like PLOS ONE and Journal of Applied Mathematics. She is the author of more than 100 academic papers published on international SCI journals like PNAS and Scientific Reports; 22 of the papers have her as the first author and 47 as the corresponding author. Her papers have been cited more than 1000 times on SCI and 3200 times on Google. Besides, several of them were selected into ESI high cited papers. She also has 1 published academic book.
Her paper Chaos-order Transition in Foraging Behavior of Ants on swarm intelligence was published on PNAS, the proceedings of American Academy of Sciences (one of the 4 international well-known journals together with Nature, Science, and CELL), and has drawn broad attentions. More than 200 media both in China and abroad published long reports about it within 1 month after the publication, including Time, Science Daily, Christian Science Monitor in the US, Daily Mail in the UK, and Science and Technology Daily, People’s Daily Online and Xinhuanet in China. Besides, well-known media AOL and NEWSY of the US also had video reports of it.
Currently, she is in charge of 1 National Key R&D Program and 1 National General Program. What’s more, she was in charge of 8 projects of national, ministerial and provincial scientific programs, including: National Natural Science Foundation of China, A Foundation for the Author of National Excellent Doctoral Dissertation of PR China, Fok Ying-Tong Foundation. Besides, as the main researcher, she also participated in 11 projects of national, ministerial and provincial programs, including: sub-projects of 973 Program, National Natural Science Foundation of China, Asia 3 Foresight Program, and NSFC-RGC Joint Foundation.
About the lecture: compressed sensing is popular both in China and abroad. One of the key bases of modern signal processing is the sampling theory of Shannon-Nyquist: the number of discrete samples for a signal’s undistorted reconstruction is determined by its bandwidth. The Compressing Theory, a new sampling theory, helps to get the discrete samples of signals randomly within a sampling rate much less than the Shannon-Nyquist one by developing the sparseness of the signals, and then to reconstruct the signal perfectly with the non-liner reconstruction algorithms.
The Compressing Theory was proposed in 2004 by Zhexuan Tao, IEEE Fellow E. J. Candes, the winners ofFields Medal; and D.L. Donoho, Academician of American Academy of Sciences. The theory has drawn broad attentions from the academic circles and industrial circles right after it was proposed. It was broadly applied in many fields, including: information theory, picture processing, geosciences, optics, microwave imaging, pattern recognition, wireless communication, aerology and geology. Therefore, it was named one of the Top 10 Scientific and Technological Progresses by Technology Review of the USA in 2007.
This lecture will focus on the latest progress of compressed sensing and its application in the field of information security. Specifically, this lecture will systematically illustrate the basic theory of compressed sensing based on the effective data collection, focused on the compressed sensing theory with semi-tensor product recently proposed. The theory breaks through the limit of traditional compressed sensing theory when measuring matrix dimensions. It can reduce significantly the number of matrix dimensions to be processed when sampling and recovering the data, thus getting a lower sampling rate.
The lecture will also introduce the compressing sensing theory with chaos semi-tensor product and its application in image encryption, which saves the memory space of secret key while keeping high-quality encryption effect. Meanwhile, this lecture will also introduce the potential application prospects of compressing sensing theory with semi-tensor product in the fields of high speed communication network, internet of things, space and ground based network, meteorological satellite, big data processing, information theory and coding, and controlling.