Petros T. Boufounos
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors.
Tue, 04/19/2011 - 15:01 — SomNambulAuthors: Jason N. Laska, Richard G. Baraniuk, Petros T. Boufounos, Laurent Jacques
Subjects: Information Theory
AbstractThe Compressive Sensing (CS) framework aims to ease the burden on
analog-to-digital converters (ADCs) by reducing the sampling rate required to
acquire and stably recover sparse signals. Practical ADCs not only sample but
also quantize each measurement to a finite number of bits; moreover, there is
an inverse relationship between the achievable sampling rate and the bit depth.
In this paper, we investigate an alternative CS approach that shifts the
emphasis from the sampling rate to the number of bits per measurement.Universal Rate-Efficient Scalar Quantization.
Fri, 09/17/2010 - 15:02 — SomNambulAuthors: Petros T. Boufounos
Subjects: Information Theory
AbstractScalar quantization is the most practical and straightforward approach to
signal quantization. However, it has been shown that scalar quantization of
oversampled or Compressively Sensed signals can be inefficient in terms of the
rate-distortion trade-off, especially as the oversampling rate or the sparsity
of the signal increases. In this paper, we modify the scalar quantizer to have
discontinuous quantization regions.Sparse Recovery from Combined Fusion Frame Measurements.
Thu, 12/31/2009 - 16:01 — SomNambulAuthors: Gitta Kutyniok, Petros T. Boufounos, Holger Rauhut
Subjects: Information Theory
AbstractSparse representations have emerged as a powerful tool in signal and
information processing, culminated by the success of new acquisition and
processing techniques such as Compressed Sensing (CS). Fusion frames are very
rich new signal representation methods that use collections of subspaces
instead of vectors to represent signals. This work combines these exciting
fields to introduce a new sparsity model for fusion frames. Signals that are
sparse under the new model can be compressively sampled and uniquely
reconstructed in ways similar to sparse signals using standard CS.A simple proof that random matrices are democratic.
Thu, 11/05/2009 - 16:01 — SomNambulAuthors: Mark A. Davenport, Jason N. Laska, Richard G. Baraniuk, Petros T. Boufounos
Subjects: Numerical Analysis
AbstractThe recently introduced theory of compressive sensing (CS) enables the
reconstruction of sparse or compressible signals from a small set of
nonadaptive, linear measurements. If properly chosen, the number of
measurements can be significantly smaller than the ambient dimension of the
signal and yet preserve the significant signal information. Interestingly, it
can be shown that random measurement schemes provide a near-optimal encoding in
terms of the required number of measurements.
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