Networked and Embedded Systems Laboratory

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Abstract

Although many real-world signals are
known to follow standard models, these signals are usually first
sampled, rather wastefully, at the Nyquist rate and only then
parametrized and compressed for efficient transport and analysis.
Compressed sensing (CS) is a new technique that promises to
directly produce a compressed version of a signal by projecting
it to a lower dimensional but information preserving domain
before the sampling process. Designing hardware to accomplish
this projection, however, has remained problematic and while
some hardware architectures do exist, they are either limited
in signal model or scale poorly for low power implementations.
In this paper, we design, implement and evaluate CapMux, a
scalable hardware architecture for a compressed sensing analog
front end. CapMux is low power and can handle arbitrary
sparse and compressible signals, i.e. it is universal. The key
idea behind CapMux’s scalability is time multiplexed access to
a single shared signal processing chain that projects the signal
onto a set of pseudo-random sparse binary basis functions. We
demonstrate the performance of a proof-of-concept 16-channel
CapMux implementation for signals sparse in the time, frequency
and wavelet domains. This circuit consumes 20µA on average
while providing over 30dB SNR recovery in most instances.

<hr>
<a href=http://nesl.ee.ucla.edu/fw/cs/HC_schem.pdf>Schematic</a>
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