This article describes a simple computational method for obtaining the maximum likelihood estimates (MLE) in nonlinear mixed-effects models when the random effects are assumed to have a nonnormal distribution. Many computer programs for fitting nonlinear mixed-effects models, such as PROC NLMIXED in SAS, require that the random effects have a normal distribution. However, there is often interes...

visual servoing is generally contained of control and feature tracking. study of previous methods shows that no attempt has been made to optimize these two parts together. in kernel based visual servoing method, the main objective is to combine and optimize these two parts together and to make an entire control loop. this main target is accomplished by using lyapanov theory. a lyapanov candidat...

This article describes useful theory and techniques for evaluating the dynamic performance of A/D converters. Four techniques are discussed: (1) beat frequency, (2) histogram analysis, (3) sine wave curve fitting, and (4) discrete finite Fourier transform.

We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot ...

The development of a method to analyze molecular tagging velocimetry data based on the Hough transform is presented. This method, based on line fitting, parameterizes the grid lines "written" into a flowfield. Initial proof-of-principle illustration of this method was performed to obtain two-component velocity measurements in the wake of a cylinder in a Mach 4.6 flow, using a data set derived f...

We have developed a new, robust, curvature estimator. It is based on ”fitting” circular segments to a curve using a generalised Radon transform. The key step in the approach is that the fitting operation is not done in the original image, but in a transformed version, called Orientation Space. The original curve is transformed into another curve in Orientation Space. Apart from the original spa...

We introduce a parallelization of the maximumlikelihood cosine transform. This transform consists of a computationally intensive iterative fitting process, but is readily decomposed for parallel processing. The parallel implementation is not only scalable, but has also brought the execution time of this previously intractable problem to feasible levels using contemporary and cost-efficient high...