Blind deconvolution for distributed parameter systems with unbounded input and output and determining blood alcohol concentration from transdermal biosensor data

DOI10.1016/j.amc.2013.12.099zbMath1410.93062OpenAlexW2166926179WikidataQ30794999 ScholiaQ30794999MaRDI QIDQ1644545

I. G. Rosen, Susan E. Luczak, Jordan Weiss

Publication date: 21 June 2018

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3972634

zbMATH Keywords

distributed parameter systemsblind deconvolutionblood alcohol concentrationtransdermal alcohol biosensorunbounded input and output

Mathematics Subject Classification ID

Control/observation systems governed by partial differential equations (93C20) Control/observation systems in abstract spaces (93C25)

Related Items (9)

Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors ⋮ Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* ⋮ A population model‐based linear‐quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing ⋮ Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data ⋮ Comparing a distributed parameter model-based system identification technique with more conventional methods for inverse problems ⋮ Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor ⋮ Nonparametric estimation of blood alcohol concentration from transdermal alcohol measurements using alcohol biosensor devices ⋮ Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach ⋮ Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: a population model-based LQG approach in Hilbert space

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