Distributed processing in frames for sparse approximation (bibtex)
by
Abstract:
Beyond signal processing applications, frames are also powerful tools for modeling the sensing and information processing of many biological and man-made systems that exhibit inherent redundancy. In many cases, these systems are required to use distributed computational strategies to analyze and process the sensory information. In this talk, I will review the use of frames to model distributed sensing systems with a particular focus on sensory neural systems. In light of the evidence that many of these systems employ sparse codes, I will describe our Locally Competitive Algorithms (LCAs) that use a dynamical system to solve many sparse approximation problems. These LCAs employ a parallel computational architecture with simple analog components. I will show numerical simulation results for these systems and describe their relationship to the many recently-proposed iterative thresholding algorithms. Our LCA approach also demonstrates potential advantages in coding time-varying signals (e.g., video) by reflecting the smooth signal changes in smooth coefficient variations. Finally, I will highlight some future directions where we hope to impact areas such as efficient analog signal processing devices, fast discrete approximation algorithms, and video processing and computer vision in complex temporal environments.
Reference:
Distributed processing in frames for sparse approximationC.J. Rozell. In Proceedings of the Conference on Information Sciences and Systems (CISS), March 2008. Invited paper
Bibtex Entry:
@InProceedings{rozell.08b,
  author = 	 {Rozell, C.J.},
  title = 	 {Distributed processing in frames for sparse approximation},
  booktitle =	 {Proceedings of the Conference on Information Sciences and Systems (CISS)},
  year =	 2008,
  address =	 {Princeton, NJ},
  month =	 {March},
  note= {Invited paper},
abstract = {Beyond signal processing applications, frames are also powerful tools
for modeling the sensing and information processing of many biological
and man-made systems that exhibit inherent redundancy.  In many cases,
these systems are required to use distributed computational strategies
to analyze and process the sensory information.  In this talk, I will
review the use of frames to model distributed sensing systems with a
particular focus on sensory neural systems.  In light of the evidence
that many of these systems employ sparse codes, I will describe our
Locally Competitive Algorithms (LCAs) that use a dynamical system to
solve many sparse approximation problems.  These LCAs employ a
parallel computational architecture with simple analog components.  I
will show numerical simulation results for these systems and describe
their relationship to the many recently-proposed iterative
thresholding algorithms.  Our LCA approach also demonstrates potential
advantages in coding time-varying signals (e.g., video) by reflecting
the smooth signal changes in smooth coefficient variations. Finally, I
will highlight some future directions where we hope to impact areas
such as efficient analog signal processing devices, fast discrete
approximation algorithms, and video processing and computer vision in
complex temporal environments.},
url =          {http://siplab.gatech.edu/pubs/rozellCISS2008.pdf}
}
Powered by bibtexbrowser