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- Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, Kenneth E. Barner
- 2011 IEEE International Conference on Acoustics…
- 2011

Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals that enables sampling rates significantly below the classical Nyquist rate. Based on the fact that electrocardiogram (ECG) signals can be approximated by a linear combination of a few coefficients taken from a Wavelet basis, we propose a compressed sensing-based… (More)

- Rafael E. Carrillo, Luisa F. Polania, Kenneth E. Barner
- 2010 IEEE International Conference on Acoustics…
- 2010

Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS. In this paper, we extend the ideas of these works to modify three iterative algorithms to incorporate the known support in the recovery process. The performance and effect of the… (More)

- Rafael E. Carrillo, Kenneth E. Barner, Tuncer C. Aysal
- IEEE Journal of Selected Topics in Signal…
- 2010

Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Since noise is always present in practical data acquisition systems, sensing, and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal… (More)

We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsity over representations in multiple wavelet bases. The algorithm, defined in the versatile framework of… (More)

- Rafael E. Carrillo, Kenneth E. Barner
- 2009 43rd Annual Conference on Information…
- 2009

Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L<inf>0</inf> norm and the… (More)

- Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, Kenneth E. Barner
- IEEE Journal of Biomedical and Health Informatics
- 2015

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by… (More)

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a minimization problem for image reconstruction. This approach was shown, in theory and through… (More)

- Rafael E. Carrillo, Jason D. McEwen, Dimitri Van De Ville, Jean-Philippe Thiran, Yves Wiaux
- IEEE Signal Processing Letters
- 2013

We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted formulation, through… (More)

- Rafael E. Carrillo, Kenneth E. Barner
- 2011 IEEE International Conference on Acoustics…
- 2011

In this paper we propose a robust iterative hard thresholding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use a Lorentzian cost function instead of the L<inf>2</inf> cost function employed by the traditional IHT algorithm. The derived algorithm is comparable in computational load to the… (More)

- Rafael E. Carrillo, Tuncer C. Aysal, Kenneth E. Barner
- EURASIP J. Adv. Sig. Proc.
- 2010

Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution (GCD) family… (More)