Attributes | Values |
---|
rdf:type
| |
Description
| - This toolbox contains implementation of square-root Cubature Kalman Filter and square-root Rauch-Tang-Striebel smoother (SCKF-SCKS). These algorithms perform joint estimation of the states, input and parameters of stochastic continuous-discrete state-space models. The state equations must have a form of ordinary differential equations, where their discretization is performed through an efficient local-linearization scheme. Additionally, the parameter noise covariance is estimated dynamicaly via stochastic Robbins-Monro approximation method, and the measurement noise covariance is estimated online as well, using combination of varitional Bayesian (VB) approach with nonlinear filter/smoother. In particular, this method was designed to perform the nonlinear blind deconvolution of hemodynamic responses from fMRI data to estimate the underlying neuronal signal.
- This toolbox contains implementation of square-root Cubature Kalman Filter and square-root Rauch-Tang-Striebel smoother (SCKF-SCKS). These algorithms perform joint estimation of the states, input and parameters of stochastic continuous-discrete state-space models. The state equations must have a form of ordinary differential equations, where their discretization is performed through an efficient local-linearization scheme. Additionally, the parameter noise covariance is estimated dynamicaly via stochastic Robbins-Monro approximation method, and the measurement noise covariance is estimated online as well, using combination of varitional Bayesian (VB) approach with nonlinear filter/smoother. In particular, this method was designed to perform the nonlinear blind deconvolution of hemodynamic responses from fMRI data to estimate the underlying neuronal signal. (en)
|
Title
| - SCKS toolbox: Blind deconvolution of hemodynamic response
- SCKS toolbox: Blind deconvolution of hemodynamic response (en)
|
skos:prefLabel
| - SCKS toolbox: Blind deconvolution of hemodynamic response
- SCKS toolbox: Blind deconvolution of hemodynamic response (en)
|
skos:notation
| - RIV/00216305:26220/10:PR24892!RIV11-MSM-26220___
|
http://linked.open...avai/riv/aktivita
| |
http://linked.open...avai/riv/aktivity
| - P(1M0572), S, Z(MSM0021630513)
|
http://linked.open...vai/riv/dodaniDat
| |
http://linked.open...aciTvurceVysledku
| |
http://linked.open.../riv/druhVysledku
| |
http://linked.open...iv/duvernostUdaju
| |
http://linked.open...titaPredkladatele
| |
http://linked.open...dnocenehoVysledku
| |
http://linked.open...ai/riv/idVysledku
| - RIV/00216305:26220/10:PR24892
|
http://linked.open...terniIdentifikace
| |
http://linked.open...riv/jazykVysledku
| |
http://linked.open.../riv/klicovaSlova
| - Blind deconvolution, fMRI, neuronal signal, cubature Kalman filter, smoother, nonlinear (en)
|
http://linked.open.../riv/klicoveSlovo
| |
http://linked.open...ontrolniKodProRIV
| |
http://linked.open...okalizaceVysledku
| - http:/icatb.sourceforge.net/scks/scks.htm Ústav biomedicínského inženýrství, Vysoké učení technické v Brně, Kolejní 2906/4, 612 00 Brno, Česká republika
|
http://linked.open...in/vavai/riv/obor
| |
http://linked.open...ichTvurcuVysledku
| |
http://linked.open...cetTvurcuVysledku
| |
http://linked.open...vavai/riv/projekt
| |
http://linked.open...UplatneniVysledku
| |
http://linked.open...echnickeParametry
| - Software může být spuštěn v programovém prostředí Matlab na standardních PC.
|
http://linked.open...iv/tvurceVysledku
| - Jan, Jiří
- Brázdil, Milan
- Calhoun, V. D.
- Friston, Karl J.
- Havlíček, Martin
|
http://linked.open...avai/riv/vlastnik
| |
http://linked.open...itiJinymSubjektem
| |
http://linked.open...n/vavai/riv/zamer
| |
http://localhost/t...ganizacniJednotka
| |