Publication Details

Category Text Publication
Reference Category Book chapters
DOI 10.1109/IEEECONF67917.2025.11443854
Title (Primary) Pollen quantification using bayesian probability mass function estimation with automatic rank detection
Title (Secondary) Proceedings; 2025 59th International Conference on Signals, Systems, and Computers, Pacific Grove, California, 26-20 October 2025
Author Manina, A; Chege, J.K.; Hornick, T. ORCID logo ; Dunker, S. ORCID logo ; Yeredor, A.; Haardt, M.
Year 2026
Department iDiv; PHYDIV
Page From 624
Page To 628
Language englisch
Topic T5 Future Landscapes
Keywords Dielectric measurements, Soil measurements, Electromagnetic scattering inverse problems
Abstract

A broadband soil dielectric spectra retrieval approach (1 MHz to 2 GHz) has been implemented for a layered half space. The inversion kernel consists of a two port transmission line forward model in the frequency domain and a constitutive material equation based of a power law soil mixture rule (Complex Refractive Index Model - CRIM) considering (i) the volume fractions of the soil phases, (ii) dielectric relaxation of the aqueous pore solution, (iii) electrical losses and (iv) low frequency dispersion due to the interactions between the pore solution and solid particles. The spatially distributed reconstruction of broadband dielectric spectra is achieved with a global optimization approach based on a Shuffled Complex Evolution (SCE) algorithm using the full set of the scattering parameter. The possibilities and limitations of the inverse parameter estimation were numerically analyzed.

Manina, A, Chege, J.K., Hornick, T., Dunker, S., Yeredor, A., Haardt, M. (2026):
Pollen quantification using bayesian probability mass function estimation with automatic rank detection
Proceedings; 2025 59th International Conference on Signals, Systems, and Computers, Pacific Grove, California, 26-20 October 2025
Institute of Electrical and Electronics Engineers (IEEE), New York, NY, p. 624 - 628
10.1109/IEEECONF67917.2025.11443854