Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation

Zhang, Zhilin; Rao, Bhaskar D.

doi:10.1109/tsp.2013.2241055

Cited by 476 publications

(449 citation statements)

References 19 publications

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“…Block sparse Bayesian learning-bound optimization (BSBL-BO) [11] was applied to restore x ො from the compressed EPSPs signal y. The compression ratio (CR) was calculated using…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing of Excitatory Postsynaptic Potential Bio-Signals

Shin

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Block sparse Bayesian learning-bound optimization (BSBL-BO) [11] was applied to restore x ො from the compressed EPSPs signal y. The compression ratio (CR) was calculated using…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing of Excitatory Postsynaptic Potential Bio-Signals

Shin

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…For the first constraint, although many algorithms have been proposed to solve it, bSBL [17] is the only algorithm that investigated the intra-block correlation, which is based on sparse Bayesian learning to obtain the sparsest solution of a sparse reconstruction problem. Sparse Bayesian learning was proposed by Wipf and Rao [23] to discover the correlation of sparse coefficients.…”

Section: Structural Sparsity Of Remote Sensing Imagesmentioning

confidence: 99%

“…Sparse Bayesian learning was proposed by Wipf and Rao [23] to discover the correlation of sparse coefficients. Then, Zhang et al [17,18] continued their work and proposed the blocked version-bSBL, which will be introduced here. After learning the dictionary for image patches, the over-complete dictionary and corresponding coefficients are obtained.…”

Section: Structural Sparsity Of Remote Sensing Imagesmentioning

confidence: 99%

“…Some researchers [16][17][18] have investigated the support mode of sparse coefficients and found that hidden structures of nonzero entries existed. As spatiotemporal fusion is partly linked to dictionary learning based remote sensing reconstruction, two kinds of structures are very possibly available: cluster-structured sparsity, and joint-structured sparsity.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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“…A type-II maximum likelihood approximation employs a point estimate for α to maximize the approximate log marginal likelihood [21], [29], [30], [31], [32],…”

Section: Sparse Bayesian Classificationmentioning

confidence: 99%

Radar‐based fall detection based on Doppler time–frequency signatures for assisted living

Zhang

Tao

et al. 2015

IET Radar, Sonar & Navigation

148

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Falls are a major public health concern and main causes of accidental death in the senior U.S.population. Timely and accurate detection permits immediate assistance after a fall and, thereby, reduces complications of fall risk. Radar technology provides an effective means for this purpose because it is non-invasive, insensitive to lighting conditions as well as obstructions, and has less privacy concerns. In this paper, we develop an effective fall detection scheme for the application in continuous-wave radar systems. The proposed scheme exploits time-frequency characteristics of the radar Doppler signatures, and the motion events are classified using the joint statistics of three different features, including the extreme frequency, extreme frequency ratio, and the length of event period. Sparse Bayesian classifier based on the relevance vector machine is used to perform the classification. Laboratory experiments are performed to collect radar data corresponding to different motion patterns to verify the effectiveness of the proposed algorithm.

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Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation

Cited by 476 publications

References 19 publications

Compressed Sensing of Excitatory Postsynaptic Potential Bio-Signals

Compressed Sensing of Excitatory Postsynaptic Potential Bio-Signals

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

Radar‐based fall detection based on Doppler time–frequency signatures for assisted living

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