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

Publikationstyp
Conference Paper
Date Issued
2019
Sprache
English
Author(s)
Adam, Christian  orcid-logo
Teyfel, Michael H.  
Schröder, Dietmar 
Herausgeber*innen
Faragó, István  
Izsák F.  
Simon, P.  
Institut
Integrierte Schaltungen E-9  
TORE-URI
http://hdl.handle.net/11420/5529
Start Page
583
End Page
587
Citation
Progress in Industrial Mathematics at ECMI (2019)
Contribution to Conference
20th European Conference on Mathematics for Industry  
Publisher DOI
10.1007/978-3-030-27550-1_74
Publisher
Springer International Publishing
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.