592 research outputs found
Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
Iterative reweighted algorithms, as a class of algorithms for sparse signal
recovery, have been found to have better performance than their non-reweighted
counterparts. However, for solving the problem of multiple measurement vectors
(MMVs), all the existing reweighted algorithms do not account for temporal
correlation among source vectors and thus their performance degrades
significantly in the presence of correlation. In this work we propose an
iterative reweighted sparse Bayesian learning (SBL) algorithm exploiting the
temporal correlation, and motivated by it, we propose a strategy to improve
existing reweighted algorithms for the MMV problem, i.e. replacing
their row norms with Mahalanobis distance measure. Simulations show that the
proposed reweighted SBL algorithm has superior performance, and the proposed
improvement strategy is effective for existing reweighted algorithms.Comment: Accepted by ICASSP 201
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 framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One family, directly derived from the BSBL framework, requires knowledge of the
block structure. Another family, derived from an expanded BSBL framework, is
based on a weaker assumption on the block structure, and can be used when the
block structure is completely unknown. Using these algorithms we show that
exploiting intra-block correlation is very helpful in improving recovery
performance. These algorithms also shed light on how to modify existing
algorithms or design new ones to exploit such correlation and improve
performance.Comment: Matlab codes can be downloaded at:
https://sites.google.com/site/researchbyzhang/bsbl, or
http://dsp.ucsd.edu/~zhilin/BSBL.htm
Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals
Telehealth and wearable equipment can deliver personal healthcare and
necessary treatment remotely. One major challenge is transmitting large amount
of biosignals through wireless networks. The limited battery life calls for
low-power data compressors. Compressive Sensing (CS) has proved to be a
low-power compressor. In this study, we apply CS on the compression of
multichannel biosignals. We firstly develop an efficient CS algorithm from the
Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination
of the block sparse model and multiple measurement vector model. Experiments on
real-life Fetal ECGs showed that the proposed algorithm has high fidelity and
efficiency. Implemented in hardware, the proposed algorithm was compared to a
Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one
has low power consumption and occupies less computational resources.Comment: 2013 International Workshop on Biomedical and Health Informatic
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
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