1,082 research outputs found
Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems
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 state-of-the-art
wavelet based algorithms. In this paper, we propose to exploit the structure of
the wavelet representation of the ECG signal to boost the performance of
CS-based methods for compression and reconstruction of ECG signals. More
precisely, we incorporate prior information about the wavelet dependencies
across scales into the reconstruction algorithms and exploit the high fraction
of common support of the wavelet coefficients of consecutive ECG segments.
Experimental results utilizing the MIT-BIH Arrhythmia Database show that
significant performance gains, in terms of compression rate and reconstruction
quality, can be obtained by the proposed algorithms compared to current
CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health
Informatic
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the goal
is to estimate an unknown signal from compressive measurements that are
corrupted by additive pre-measurement noise (interference, or clutter) as well
as post-measurement noise, in the specific setting where some (perhaps limited)
prior knowledge on the signal, interference, and noise is available. The
specific aim here is to devise a strategy for incorporating this prior
information into the design of an appropriate compressive measurement strategy.
Here, the prior information is interpreted as statistics of a prior
distribution on the relevant quantities, and an approach based on Bayesian
Experimental Design is proposed. Experimental results on synthetic data
demonstrate that the proposed approach outperforms traditional random
compressive measurement designs, which are agnostic to the prior information,
as well as several other knowledge-enhanced sensing matrix designs based on
more heuristic notions.Comment: 5 pages, 4 figures. Accepted for publication at The Asilomar
Conference on Signals, Systems, and Computers 201
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