Spectral analysis of nonuniformly sampled data–a review
In this paper, we present a comprehensive review of methods for spectral analysis of
nonuniformly sampled data. For a given finite set of nonuniformly sampled data, a …
nonuniformly sampled data. For a given finite set of nonuniformly sampled data, a …
Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation
We examine the recovery of block sparse signals and extend the recovery framework in two
important directions; one by exploiting the signals' intra-block correlation and the other by …
important directions; one by exploiting the signals' intra-block correlation and the other by …
Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a
telemonitoring system via a wireless body area network with low energy consumption for …
telemonitoring system via a wireless body area network with low energy consumption for …
Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals
We consider the problem of recovering block-sparse signals whose cluster patterns are
unknown a priori. Block-sparse signals with nonzero coefficients occurring in clusters arise …
unknown a priori. Block-sparse signals with nonzero coefficients occurring in clusters arise …
A review of sparsity-based clustering methods
In case of high dimensionality, a class of data clustering methods has been proposed as a
solution that includes suitable subspace search to find inherent clusters. Sparsity-based …
solution that includes suitable subspace search to find inherent clusters. Sparsity-based …
Compressed sensing based on dictionary learning for extracting impulse components
It is essential to extract impulse components embedded in heavy background noise in
engineering applications. The methods based on wavelet have obtained huge success in …
engineering applications. The methods based on wavelet have obtained huge success in …
SPICE and LIKES: Two hyperparameter-free methods for sparse-parameter estimation
SPICE (SParse Iterative Covariance-based Estimation) is a recently introduced method for
sparse-parameter estimation in linear models using a robust covariance fitting criterion that …
sparse-parameter estimation in linear models using a robust covariance fitting criterion that …
Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov …
With the ever-growing computational power of personal computers over the past few
decades, stochastic simulation of spatially varying three-dimensional (3D) quantities has …
decades, stochastic simulation of spatially varying three-dimensional (3D) quantities has …
Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations
In this paper, we address the problem of spectrum estimation of multiple frequency-hopping
(FH) signals in the presence of random missing observations. The signals are analyzed …
(FH) signals in the presence of random missing observations. The signals are analyzed …
Enhanced ISAR imaging by exploiting the continuity of the target scene
This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by
exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced …
exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced …