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SUMMARY:Gain from Multiple Measurements: Measurement Diversity and Resourc
 e Allocation - Dr Wei Dai\, Imperial College London
DTSTART:20151112T140000Z
DTEND:20151112T150000Z
UID:TALK61339@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:Multiple measurements are involved in many applications includ
 ing radar/sonar processing\,  hyper-spectral imaging\, sensor networks\, w
 ireless communications\, etc. In the simplest scenario where the underlyin
 g signals remain the same for all the measurement instances\, multiple mea
 surements help in averaging out the noise and hence improving the signal-t
 o-noise performance. In the more general setting\, the signals in differen
 t measurement instances can be different but somewhat dependent\; the meas
 urement processes can be designed so that the measurement operators can be
  distinct (Measurement Diversity)\; the sampling rates and consumed energy
  can be optimized across measurement instances (Resource Allocation). Our 
 contribution is to exactly quantify the performance gains from measurement
  diversity and resource allocation in an asymptotic region. The asymptotic
  results provide important insights on the system design. \n\n*BIO*: Dr. W
 ei Dai is currently a Lecturer in the Electrical and Electronic Engineerin
 g Department at Imperial College London. He received his Ph.D. degree in E
 lectrical and Computer Engineering from the University of Colorado at Boul
 der in 2007. From 2007 to 2010\, he was a Postdoctoral Research Associate 
 in the Department of Electrical and Computer Engineering\, University of I
 llinois at Urbana-Champaign. His research interests include sparse signal 
 processing\, wireless communications\, and more recently topics in big dat
 a processing. In theoretical study\, one of his works on compressive sensi
 ng reconstruction has been cited around 1000 times. On the more practical 
 side\, he was involved in the development of the first compressive sensing
  DNA microarray prototype in the world\, and was the PI of the first hardw
 are implementation of compressed sampling in the UK.\n
LOCATION:LR5\, Department of Engineering
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