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.