21 research outputs found
Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons
In this letter, we propose a sparsity promoting feedback acquisition and
reconstruction scheme for sensing, encoding and subsequent reconstruction of
spectrally sparse signals. In the proposed scheme, the spectral components are
estimated utilizing a sparsity-promoting, sliding-window algorithm in a
feedback loop. Utilizing the estimated spectral components, a level signal is
predicted and sign measurements of the prediction error are acquired. The
sparsity promoting algorithm can then estimate the spectral components
iteratively from the sign measurements. Unlike many batch-based Compressive
Sensing (CS) algorithms, our proposed algorithm gradually estimates and follows
slow changes in the sparse components utilizing a sliding-window technique. We
also consider the scenario in which possible flipping errors in the sign bits
propagate along iterations (due to the feedback loop) during reconstruction. We
propose an iterative error correction algorithm to cope with this error
propagation phenomenon considering a binary-sparse occurrence model on the
error sequence. Simulation results show effective performance of the proposed
scheme in comparison with the literature
Channel-Optimized Vector Quantizer Design for Compressed Sensing Measurements
We consider vector-quantized (VQ) transmission of compressed sensing (CS)
measurements over noisy channels. Adopting mean-square error (MSE) criterion to
measure the distortion between a sparse vector and its reconstruction, we
derive channel-optimized quantization principles for encoding CS measurement
vector and reconstructing sparse source vector. The resulting necessary optimal
conditions are used to develop an algorithm for training channel-optimized
vector quantization (COVQ) of CS measurements by taking the end-to-end
distortion measure into account.Comment: Published in ICASSP 201
Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements
We consider a resource-constrained scenario where a compressed sensing- (CS)
based sensor has a low number of measurements which are quantized at a low rate
followed by transmission or storage. Applying this scenario, we develop a new
quantizer design which aims to attain a high-quality reconstruction performance
of a sparse source signal based on analysis-by-synthesis framework. Through
simulations, we compare the performance of the proposed quantization algorithm
vis-a-vis existing quantization methods.Comment: 5 pages, Published in ICASSP 201
Distributed Quantization for Compressed Sensing
We study distributed coding of compressed sensing (CS) measurements using
vector quantizer (VQ). We develop a distributed framework for realizing
optimized quantizer that enables encoding CS measurements of correlated sparse
sources followed by joint decoding at a fusion center. The optimality of VQ
encoder-decoder pairs is addressed by minimizing the sum of mean-square errors
between the sparse sources and their reconstruction vectors at the fusion
center. We derive a lower-bound on the end-to-end performance of the studied
distributed system, and propose a practical encoder-decoder design through an
iterative algorithm.Comment: 5 Pages, Accepted for presentation in ICASSP 201
One-Bit ExpanderSketch for One-Bit Compressed Sensing
Is it possible to obliviously construct a set of hyperplanes H such that you
can approximate a unit vector x when you are given the side on which the vector
lies with respect to every h in H? In the sparse recovery literature, where x
is approximately k-sparse, this problem is called one-bit compressed sensing
and has received a fair amount of attention the last decade. In this paper we
obtain the first scheme that achieves almost optimal measurements and sublinear
decoding time for one-bit compressed sensing in the non-uniform case. For a
large range of parameters, we improve the state of the art in both the number
of measurements and the decoding time
One-bit compressed sensing by linear programming
We give the first computationally tractable and almost optimal solution to
the problem of one-bit compressed sensing, showing how to accurately recover an
s-sparse vector x in R^n from the signs of O(s log^2(n/s)) random linear
measurements of x. The recovery is achieved by a simple linear program. This
result extends to approximately sparse vectors x. Our result is universal in
the sense that with high probability, one measurement scheme will successfully
recover all sparse vectors simultaneously. The argument is based on solving an
equivalent geometric problem on random hyperplane tessellations.Comment: 15 pages, 1 figure, to appear in CPAM. Small changes based on referee
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