Analog Sparse Approximation for Compressed Sensing Recovery (bibtex)
by and
Abstract:
Non-smooth convex optimization programs such as L1 minimization produce state-of-the-art results in many signal and image processing applications. Despite the progress in algorithms to solve these programs, they are still too computationally expensive for many real-time applications. Using recent results describing dynamical systems that efficiently solve these types of programs, we demonstrate through simulation that custom analog ICs implementations of this system could potentially perform compressed sensing recovery for real time applications approaching 500 KHz. Furthermore, we show that this architecture can implement several other optimization programs of recent interest, including reweighted L1 and group L1 minimization.
Reference:
Analog Sparse Approximation for Compressed Sensing RecoveryC.J. Rozell and P. Garrigues. In Proceedings of the Asilomar Conference on Signals, Systems, and Computers, November 2010.
Bibtex Entry:
@InProceedings{rozell.10a,
  author = 	 {Rozell, C.J. and Garrigues, P.},
  title = 	 {Analog Sparse Approximation for Compressed Sensing Recovery},
  booktitle =	 {{Proceedings of the Asilomar Conference on Signals, Systems, and Computers}},
  year =	 2010,
  month = {November},
  address =	 {Pacific Grove, CA},
abstract = {Non-smooth convex optimization programs such as L1 minimization produce state-of-the-art results in many signal and image processing applications.  Despite the progress in algorithms to solve these programs, they are still too computationally expensive for many real-time applications.   Using recent results describing dynamical systems that efficiently solve these types of programs, we demonstrate through simulation that custom analog ICs implementations of this system could potentially perform compressed sensing recovery for real time applications approaching 500 KHz.  Furthermore, we show that this architecture can implement several other optimization programs of recent interest, including reweighted L1 and group L1 minimization.}
}
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