Analog-to-Probability Conversion— Efficient Extraction of Information Based on Stochastic Signal Models

  • Christian AdamEmail author
  • Michael H. Teyfel
  • Dietmar Schroeder
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 30)


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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christian Adam
    • 1
    Email author
  • Michael H. Teyfel
    • 1
  • Dietmar Schroeder
    • 1
  1. 1.Institute of Nano and Medical ElectronicsHamburg University of TechnologyHamburgGermany

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