Abstract
Analog-to-probability conversion is introduced as a new concept for efficient parameter extraction from analog signals that can be described by nonlinear models. The current state of information about these parameters is represented by a multivariate probability distribution. Only a digital-to-analog converter and a comparator are required as acquisition hardware. The introduced approach reduces the number of comparisons to be done by the hardware and therefore the total energy consumption. As a proof of concept the algorithm is implemented on a system-on-chip and compared to a nonlinear least squares approach.
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Adam, C., Teyfel, M.H., Schroeder, D. (2019). Analog-to-Probability Conversion— Efficient Extraction of Information Based on Stochastic Signal Models. In: Faragó, I., Izsák, F., Simon, P. (eds) Progress in Industrial Mathematics at ECMI 2018. Mathematics in Industry(), vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-27550-1_74
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DOI: https://doi.org/10.1007/978-3-030-27550-1_74
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