Journal: Sensors (Basel, Switzerland)

Article Title: “Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues

doi: 10.3390/s100706751

Figure Lengend Snippet: Application of the Kalman filtering method of Facchinetti et al. to two FreeStyle Navigator® time-series exhibiting a different SNR (the green line is the filter output). Top panel: same time-series as in the top panel of and . Bottom panel: same time-series as in the top panel of .

Article Snippet: A first comprehensive description of the CGM measurement process is due to Knobbe and Buckingham [ ]: BG-to-IG kinetics model was explicitly taken into account in order to reconstruct BG levels at continuous time from CGM measurements and an Extended Kalman Filter (EKF) approach was used to estimate the unknown variables.

Techniques:

Journal: PLoS ONE

Article Title: The mechanism of monomer transfer between two structurally distinct PrP oligomers

doi: 10.1371/journal.pone.0180538

Figure Lengend Snippet: A priori k on 0 = 0 . 031 min - 1 μ M - 1 , k dep 0 = 0 . 09 min - 1 , k dis 0 = 0 . 07 min - 1 , θ 0 = 0.2, observation covariance W = 10, predicted standard deviation of initial a priori estimations as in . A: One curve every ten iterations of the Extended Kalman method, number of the iteration in colorscale. B: Trajectories of the final estimators (red solid lines), the final estimations (red dashed lines) and 95% prediction uncertainty intervals (red areas). From the left to the right estimators of k on , k dep , k dis and θ .

Article Snippet: To estimate the four parameters k on , k dep , k dis and θ = o i a ( 0 ) o i a ( 0 ) + o i b ( 0 ) , we rely on an Extended Kalman Filter (EKF) approach [ ] implemented in MATLAB.

Techniques: Standard Deviation

Journal: PLoS ONE

Article Title: The mechanism of monomer transfer between two structurally distinct PrP oligomers

doi: 10.1371/journal.pone.0180538

Figure Lengend Snippet: A priori k on 0 = 0 . 031 min - 1 μ M - 1 , k dep 0 = 0 . 09 min - 1 , k dis 0 = 0 . 07 min - 1 , θ 0 = 0.2, observation covariance W = 8, predicted standard deviation of initial a priori estimations as in . A: 19 iterations of the Extended Kalman method, number of the iteration in colorscale. B: Trajectories of the final estimators (red lines), the final estimations (red dashed lines) and 95% prediction uncertainty intervals (red areas). From the left to the right estimators of k on , k dep , k dis and θ .

Article Snippet: To estimate the four parameters k on , k dep , k dis and θ = o i a ( 0 ) o i a ( 0 ) + o i b ( 0 ) , we rely on an Extended Kalman Filter (EKF) approach [ ] implemented in MATLAB.

Techniques: Standard Deviation

Journal: PLoS ONE

Article Title: The mechanism of monomer transfer between two structurally distinct PrP oligomers

doi: 10.1371/journal.pone.0180538

Figure Lengend Snippet: A priori k on 0 = 0 . 1 min - 1 μ M - 1 , k dep 0 = 0 . 1 min - 1 , k dis 0 = 0 . 1 min - 1 , θ 0 = 0.1, observation covariance W = 10, predicted standard deviation of initial a priori estimations as in . A: 9 iterations of the Extended Kalman method black, number of the iteration in colorscale. B: Trajectories of the final estimators (red lines), the final estimations (red dashed lines) and 95% prediction uncertainty intervals (red areas). From the left to the right estimators of k on , k dep , k dis and θ .

Article Snippet: To estimate the four parameters k on , k dep , k dis and θ = o i a ( 0 ) o i a ( 0 ) + o i b ( 0 ) , we rely on an Extended Kalman Filter (EKF) approach [ ] implemented in MATLAB.

Techniques: Standard Deviation