Justin Romberg
Title: Architectures for Compressive Sampling
Abstract: Several recent results in Compressive Sampling show that a sparse
signal (i.e. one which can be compressed in a known orthobasis) can be
efficiently acquired by taking linear measurements against random test
functions. In practice, however, it is difficult to build sensing
devices which take these types of measurements. In this presentation,
we will show how to extend some of the results in Compressive Sampling
to measurement systems which are more amenable to real-world
implementation. We will discuss applications to radar imaging,
coherent optical imaging, and next-generation analog-to-digital
converters.