PURPOSE A computationally efficient algorithm (linear iterative type) based on singular value decomposition (SVD) of the Jacobian has been developed that can be used in rapid dynamic near-infrared (NIR) diffuse optical tomography. METHODS Numerical and experimental studies have been conducted to prove the computational efficacy of this SVD-based algorithm over conventional optical image recon...

BACKGROUND Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. AIMS The purpose of the current study was to evaluate the instructional benefits of an iterative lesson sequence compa...

We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and...

This paper presents an identiication-based mechanism for introducing guaranteed stability when using a data-driven model-free iterative control design method known as Iterative Feedback Tuning. Also, the use of unbiased estimates of the Hessian is shown to signiicantly improve the user control over the tuning procedure.

This paper presents a new iterative procedure for solving finite noncooperative games, the reasoning-based expected utility procedure (RBEU), and compares this with existing iterative procedures. RBEU deletes more strategies than iterated deletion of strictly dominated strategies, while avoiding the conceptual problems associated with iterated deletion of weakly dominated strategies. It uses a ...

This work presents a novel technique to perform audience privacy protection in video lectures. The main contribution of this work is a heuristic based iterative clustering procedure that isolates the lecturer from audience members. This iterative process provides the labelling required to identify and blur audience members.

We show that the derivatives of Sinkhorn–Knopp algorithm, or iterative proportional fitting procedure, converge towards entropic regularization optimal transport problem with a locally uniform linear convergence rate.