Publications related to Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Methods and systems for noise resilient, pin-efficient and low power communications with sparse signaling codes

Mohammad Amin Shokrollahi, Seyed Armin Tajalli, Harm Cronie

In bus communications methods and apparatus, a first set of physical signals representing the information to be conveyed over the bus is provided, and mapped to a codeword of a sparse signaling code, wherein a codeword is representable as a vector of a plu ...

2012

-Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measurements. Our reconstruction approach is based on a convex minimization which penalizes both the nuclear norm and the l(2,1) mixed-norm of the dat ...

Ieee2012

Bearing estimation via spatial sparsity using compressive sensing

Volkan Cevher

Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, we propose a method to specifically exploit this spatial sparsity property. The method us ...

Institute of Electrical and Electronics Engineers2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Structured Sparse Coding for Microphone Array Location Calibration

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We address the problem of microphone location cali- bration where the sensor positions have a sparse spatial approximation on a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-channel signals ...

2012

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Anastasios Kyrillidis

For linear models, compressed sensing theory and methods enable recovery of sparse signals of interest from few measurements-in the order of the number of nonzero entries as opposed to the length of the signal of interest. Results of similar flavor have mo ...

Institute of Electrical and Electronics Engineers2012

Structured Sparse Coding for Microphone Array Location Calibration

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We address the problem of microphone location calibration from a sparse coding perspective where the sensor positions are approximated over a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-c ...

2012

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Michael Christoph Gastpar

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the high-dimensional setting, it is known that recovery with a vanishing fraction ...

Institute of Electrical and Electronics Engineers2012

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

David Atienza Alonso, Andreas Peter Burg, Pascal Andreas Meinerzhagen, Jeremy Hugues-Felix Constantin, Ahmed Yasir Dogan, Oskar Andersson

Compressed sensing (CS) is a universal technique for the compression of sparse signals. CS has been widely used in sensing platforms where portable, autonomous devices have to operate for long periods of time with limited energy resources. Therefore, an ul ...

2012

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

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Methods and systems for noise resilient, pin-efficient and low power communications with sparse signaling codes

-Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Bearing estimation via spatial sparsity using compressive sensing

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Structured Sparse Coding for Microphone Array Location Calibration

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Structured Sparse Coding for Microphone Array Location Calibration

Sparse diffusion LMS for distributed adaptive estimation

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Methods and systems for noise resilient, pin-efficient and low power communications with sparse signaling codes

-Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Bearing estimation via spatial sparsity using compressive sensing

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Structured Sparse Coding for Microphone Array Location Calibration

Structured Sparse Coding for Microphone Array Location Calibration

Sparse diffusion LMS for distributed adaptive estimation

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing