The  Weizmann  Institute  of  Science
                  Faculty of Mathematics and Computer Science
 
                          Vision and Robotics Seminar
 
                     Lecture Hall, Room 1, Ziskind Building
                           on Monday, April 30, 2012
                                    at 13:30
 
                         Note the unusual day and time
 
                                  Yonina Eldar
                                    Technion
 
                                 will speak on
 
                Defying Nyquist in Analog to Digital Conversion
 
Abstract:
The famous Shannon-Nyquist theorem has become a landmark in the development of
digital signal processing. However, in many modern applications, the signal
bandwidths have increased tremendously, while the acquisition capabilities have
not scaled sufficiently fast.  Consequently, conversion to digital has become a
serious bottleneck.  Furthermore, the resulting high rate digital data requires
storage, communication and processing at very high rates which is
computationally expensive and requires large amounts of power.
 
In this talk a new framework for sampling wideband analog signals at rates far
below that dictated by the Nyquist rate will be presented.  We refer to this
methodology as Xampling: A combination of compression and sampling, performed
simultaneously. The focus will be both on the theoretical developments, as well
as on actual hardware implementations and considerations that allow realization
of sub-Nyquist samplers in practice. Applications to a variety of different
problems in communications, bioimaging, and signal processing will also be
described. In particular, we consider an application to ultrasound imaging and
demonstrate recovery of noisy ultrasound images from sub-Nyquist samples while
performing beamforming in the compressed domain.  Motivated by problems in
optics, we extend these principles to nonlinear problems leading to quadratic
and more general nonlinear compressed sensing techniques, enabling phase
recovery from magnitude measurements and super-resolution imaging.
 
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Technion Math Net-2 (TECHMATH2)
Editor: Michael Cwikel   <techm@math.technion.ac.il> 
Announcement from: Yaeli Malka   <yaeli.malka@weizmann.ac.il>