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

Start Page
583
End Page
587
Citation
20th European Conference on Mathematics for Industry, 2018
Contribution to Conference
20th European Conference on Mathematics for Industry  
Publisher DOI
10.1007/978-3-030-27550-1_74
Publisher
Springer
ISBN
978-3-030-27550-1
978-3-030-27549-5
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.
DDC Class
600: Technology