Abstract:
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A methodology to calculate analytical figures of merit is not well established for detection
systems that are based on sensor arrays with low sensor selectivity. In this work, we
present a practical approach to estimate the Resolving Power of a sensory system,
considering non-linear sensors and heteroscedastic sensor noise. We use the definition
introduced by Shannon in the field of communication theory to quantify the number
of symbols in a noisy environment, and its version adapted by Gardner and Barlett
for chemical sensor systems. Our method combines dimensionality reduction and the
use of algorithms to compute the convex hull of the empirical data to estimate the data
volume in the sensor response space. We validate our methodology with synthetic data
and with actual data captured with temperature-modulated MOX gas sensors. Unlike
other methodologies, our method does not require the intrinsic dimensionality of the
sensor response to be smaller than the dimensionality of the input space. Moreover,
our method circumvents the problem to obtain the sensitivity matrix, which usually is
not known. Hence, our method is able to successfully compute the Resolving Power of
actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology
to calculate it, that was missing in the literature to benchmark broad-response gas sensor
arrays. |