3,496 research outputs found
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
Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing
In this work the dynamic compressive sensing (CS) problem of recovering
sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear
measurements is explored from a Bayesian perspective. While there has been a
handful of previously proposed Bayesian dynamic CS algorithms in the
literature, the ability to perform inference on high-dimensional problems in a
computationally efficient manner remains elusive. In response, we propose a
probabilistic dynamic CS signal model that captures both amplitude and support
correlation structure, and describe an approximate message passing algorithm
that performs soft signal estimation and support detection with a computational
complexity that is linear in all problem dimensions. The algorithm, DCS-AMP,
can perform either causal filtering or non-causal smoothing, and is capable of
learning model parameters adaptively from the data through an
expectation-maximization learning procedure. We provide numerical evidence that
DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety
of operating conditions. We further describe the result of applying DCS-AMP to
two real dynamic CS datasets, as well as a frequency estimation task, to
bolster our claim that DCS-AMP is capable of offering state-of-the-art
performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Compressive sensing has been successfully used for optimized operations in
wireless sensor networks. However, raw data collected by sensors may be neither
originally sparse nor easily transformed into a sparse data representation.
This paper addresses the problem of transforming source data collected by
sensor nodes into a sparse representation with a few nonzero elements. Our
contributions that address three major issues include: 1) an effective method
that extracts population sparsity of the data, 2) a sparsity ratio guarantee
scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
We introduce an unsupervised neural network to extract an intrinsic sparse
coding of the data. The sparse codes are generated at the activation of the
hidden layer using a sparsity nomination constraint and a shrinking mechanism.
Our analysis using real data samples shows that the proposed method outperforms
conventional sparsity-inducing methods.Comment: 8 page
Dynamic Iterative Pursuit
For compressive sensing of dynamic sparse signals, we develop an iterative
pursuit algorithm. A dynamic sparse signal process is characterized by varying
sparsity patterns over time/space. For such signals, the developed algorithm is
able to incorporate sequential predictions, thereby providing better
compressive sensing recovery performance, but not at the cost of high
complexity. Through experimental evaluations, we observe that the new algorithm
exhibits a graceful degradation at deteriorating signal conditions while
capable of yielding substantial performance gains as conditions improve.Comment: 6 pages, 7 figures. Accepted for publication in IEEE Transactions on
Signal Processin
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