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SUMMARY:Are there needles in a (moving) haystack? Adaptive sensing for det
 ection and estimation of static and dynamically evolving signals - Rui Cas
 tro (Technische Universiteit Eindhoven)
DTSTART:20180628T134500Z
DTEND:20180628T143000Z
UID:TALK107494@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:In this work we investigate the problem of testing for the pre
 sence of dynamically evolving sparse signals.  This is motivated by settin
 gs were the signal of interest changes rapidly while measurements are bein
 g collected (e.g.\, monitoring of the electromagnetic spectrum\, or detect
 ion of hot spots of a rapidly spreading disease).  We model the signal as 
 an n dimensional vector that has s non-zero components\, and at each time 
 a fraction of the non-zero components may change their location.  The stat
 istical problem is to decide whether the signal is a zero vector or in fac
 t it has non-zero components.  This decision is based on m noisy observati
 ons of individual signal components.  We consider two different sensing pa
 radigms\, namely adaptive and non-adaptive sensing.  For non-adaptive sens
 ing the choice of components to measure has to be decided before the data 
 collection process started\, while for adaptive sensing one can adjust the
  sensing process based on observations collected earlier.  We characterize
  the difficulty of this detection problem in both sensing paradigms\, with
  special interest to the speed of change of the active components.  In add
 ition we provide an adaptive sensing algorithm for this problem and contra
 st its performance to that of best non-adaptive detection algorithms.  Int
 erestingly\, when the number of measurements is small (on the order n/s)\,
  there is a significant difference in performance between the two sensing 
 paradigms\, as non-adaptive sensing is unable to exploit the dynamic natur
 e of the signal (based on joint works with Ervin T&aacute\;nczos).
LOCATION:Seminar Room 1\, Newton Institute
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