title: Low-power analog processing for sensing applications: low-frequency harmonic signal classification.
abstract: A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér 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 0.13 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.
authors.affiliation: Department of Electrical Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0511,USA. d.j.white@ieee.org
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: White
authors.forename: Peter E
authors.initials: PE
authors.lastname: William
authors.forename: Michael W
authors.initials: MW
authors.lastname: Hoffman
authors.forename: Sina
authors.initials: S
authors.lastname: Balkir
mesh_terms: D000465:Algorithms; D000703:Analog-Digital Conversion; D011211:Electric Power Supplies; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005240:Feasibility Studies; D005583:Fourier Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D014159:Transducers
references.citation: Neural Comput. 1998 Oct 1;10(7):1601-38
references.pmid: 9744889