When dealing with ill-posed problems such as fluorescence diffuse optical tomography (fDOT) the choice of the regularization parameter is extremely important for computing a reliable reconstruction. Several automatic methods for the selection of the regularization parameter have been introduced over the years and their performance depends on the particular inverse problem. Herein a U-curve-base...

Abstract: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy ...

A long-standing problem in classification is the determination of the regularization parameter. Nearly every classification algorithm uses a parameter (or set of parameters) to control classifier complexity. Crossvalidation on the training set is usually done to determine the regularization parameter(s). [1] proved a leave-one-out cross-validation (LOOCV) bound for a class of kernel classifiers...

natural signals are continues, therefore, digitizing is an essential task enabling us to use computing tools to process them. according to the nyquist/shannon sampling theory, the sampling frequency must be at least twice the maximum frequency contained in the signal which is being sampled; otherwise, some high frequencies may be aliased and result in a bad reconstruction. the nyquist sampling ...

The electrocardiographic imaging (ECGI) inverse problem is highly ill-posed and regularization is needed to stabilize the problem and to provide a unique solution. When Tikhonov regularization is used, choosing the regularization parameter is a challenging problem. Mathematically, a suitable value for this parameter needs to fulfill the Discrete Picard Condition (DPC). In this study, we propose...