This poster presents a simple adaptive sensing and group testing algorithm
termed Compressive Adaptive Sense and Search (CASS).  The algorithm is shown
to
be near-optimal in that it succeeds at the lowest possible SNR levels.  Like
compressed sensing, the CASS algorithm requires only k log n measurements to
recover a k-sparse signal of dimension n.  However, CASS succeeds at SNR
levels
that are a factor of log(n) less than required by standard compressed sensing.

CASS is also demonstrated to perform better in simulation.  To the best of our
knowledge, this is the first demonstration of an adaptive sensing algorithm
with
near-optimal theoretical guarantees and excellent practical performance.