Sensors 2013, 13(8), 9604-9623; doi:10.3390/s130809604
Article

Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification

1 Department of Electrical Engineering, University of Nebraska–Lincoln, Lincoln, NE 68588 0511, USA 2 Phillips Healthcare, 1 Echo Drive, Reedsville, PA 17084, USA
* Author to whom correspondence should be addressed.
Received: 17 April 2013; in revised form: 17 July 2013 / Accepted: 22 July 2013 / Published: 25 July 2013
(This article belongs to the Section Physical Sensors)
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Abstract: A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouri´er Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to existing studies with the same data sets. Results from the military vehicle classification and identification of machine-bearing fault applications shows that the front-end suits a wide range of harmonic signal sources. Analog-related errors are modeled to evaluate the feasibility of and to set design parameters for an IC implementation to maintain good system-level performance. Design of a preliminary transistor-level integrator circuit in a 0:µm complementary metal-oxide-silicon (CMOS) integrated circuit process showed the ability to use online self-calibration to reduce fabrication errors to a sufficiently low level. Estimated power dissipation is about three orders of magnitude less than similar vehicle classification systems that use commercially available FFT spectral extraction.
Keywords: Analog Harmonic Transform; classification; mixed-signal; low-power

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MDPI and ACS Style

White, D.J.; William, P.E.; Hoffman, M.W.; Balkir, S. Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification. Sensors 2013, 13, 9604-9623.

AMA Style

White DJ, William PE, Hoffman MW, Balkir S. Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification. Sensors. 2013; 13(8):9604-9623.

Chicago/Turabian Style

White, Daniel J.; William, Peter E.; Hoffman, Michael W.; Balkir, Sina. 2013. "Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification." Sensors 13, no. 8: 9604-9623.

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