Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: a population model-based LQG approach in Hilbert space
DOI10.1016/j.automatica.2022.110699zbMath1505.93112OpenAlexW4309633808MaRDI QIDQ2103676
I. G. Rosen, Susan E. Luczak, Mengsha Yao
Publication date: 9 December 2022
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2022.110699
deconvolutiontransdermal alcohol biosensoralcohol withdrawal syndromeLQG trackingrandom abstract parabolic systems
Nonlinear systems in control theory (93C10) Control/observation systems in abstract spaces (93C25) Control/observation systems governed by functional relations other than differential equations (such as hybrid and switching systems) (93C30) Biosensors (not for medical applications) (92C47)
Cites Work
- Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors
- Semigroups of linear operators and applications to partial differential equations
- Approximation of discrete-time LQG compensators for distributed systems with boundary input and unbounded measurement
- Blind deconvolution for distributed parameter systems with unbounded input and output and determining blood alcohol concentration from transdermal biosensor data
- Optimality of adaptive Galerkin methods for random parabolic partial differential equations
- Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor
- Sparse tensor discretizations of high-dimensional parametric and stochastic PDEs
- The Linear Regulator Problem for Parabolic Systems
- Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: a population model-based LQG approach in Hilbert space
