Presented by: 
Padraig Ó Catháin, UQ
Tue 9 Sep, 3:00 pm - 4:00 pm
Room 442, Building 67
Compressed sensing is a technique used in signal processing to reconstruct under-sampled data, subject to some assumptions. It has been intensively studied in the past fifteen years or so, and lies at the interface of mathematics,  statistics and electrical engineering. One of the main challenges is the construction of good matrices for use in compressed sensing. In this talk, we will give an introduction to the area, emphasizing the relation with well-known concepts in linear algebra. We then describe a new construction for compressed sensing matrices using pairwise balanced designs and Hadamard matrices. We conclude with a theoretical and computational comparison of our construction with other known constructions.