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Structured Sensing Matrices in Compressive Sensing
Date: 2015/1/6             Browse: 880

Speaker:Kezhi Li

Time: Jan. 6, 3:15-4:15pm

Location: Room 220, Building 8, Yueyang Road Campus


The compressive/compressed sensing (CS) theory, which was proposed in around 2006, is a framework for estimating sparse signals based on incomplete set of noiseless or noisy measurements. In CS, there are two fundamental problems: compression and reconstruction. In this talk, after reviewing the basic concepts of CS, we focus on the compression part, in particular structured sensing matrices as compression operators. Due to the special structures, these compression operators are easier to generate, require less storage space and have theoretical recovery guarantees. Finally, some applications of CS in different areas are introduced and analyzed. This talk is suitable for senior bachelor, graduate and PhD students and researchers who are interested in signal processing and compressed sensing.


Kezhi Li is currently a research scientist at Imperial College London, United Kingdom. He graduated from University of Science and Technology of China (USTC) and Imperial College London with Bachelor and PhD degree, respectively. After that he was a postdoctoral fellow at Royal Institute of Technology (KTH) and then a researcher at University of Cambridge, from 2013 to 2014. His research interests include statistical signal processing and machine learning, particularly compressive sensing and its applications in data/image processing.                                                                                                

                      SIST-Seminar 14048