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In this paper we provide a hands-on discussion of the use of the HyperStar phase-type fitting tool in common application scenarios. HyperStar allows fitting Hyper-Erlang distributions to empirical data, using a variety of algorithms and operation modes. We describe simple cluster-based fitting, a new graphical method for refining the density approximation, a new command-line interface, and the integration of HyperStar with a Mathematica implementation of a fitting algorithm. Furthermore, we describe the use of Hyper-Erlang distributions in simulation. Throughout our discussion we illustrate the concepts on a data set which has been shown to be difficult to fit with a PH distribution.

In this chapter we examine the nature of the electronic excitations stimulated during collision cascades. We find that these excitations are well-characterized by an elevated electronic pseudo-temperature and develop a simple temperature fitting algorithm. We explain the emergence of a pseudo-temperature despite the absence of thermalizing electron–electron interactions or a correct treatment of the electron–phonon interaction in our Ehrenfest dynamics by considering the spectrum of frequencies in the ionic motion in a typical cascade. Identifying an inconsistency between a rising electronic temperature and the fixed entropy of the electronic density matrix, we briefly consider an alternative definition of the electronic entropy.

This paper describes an approach for detecting overlapped ellipses by combining region and edge data. The Principle Component Analysis method is used to give the shape and position of an ellipse. A region based EM iterative algorithm is proposed to calculate the number of ellipses and their initial shapes in the overlapped region. As a result, every edge point is assigned to a certain ellipse by statistics decision. Then an edge fitting algorithm is employed to efine the ellipses' geometric parameters based on the edge data. Above coarse-to-fine algorithm is applied to detect the overlapped fruits and the moving targets. The result is stable and accurate.

It is now well established that 3D facial shape can be effectively and conveniently reconstructed using spherical harmonics. This paper extends the state-of-the-art by showing how to recover a 3D facial shape reconstruction using a spherical parameterization and minimizing a Harmonic Energy based on spherical medians. The solution is formulated as a linear system and we propose an iterative residual fitting algorithm (LSQR-IRF) to solve it. We demonstrate the effectivenss of the method on range-data.

The functional-structural GreenLab model uses a set of recurrence equations to simulate plant development and eco-physiological processes. The parameters of the source and sink functions are calibrated by fitting the model to plant architectural data using the nonlinear least squares method. Starting from good initial values is a key issue for the iterative fitting algorithm to converge to the global optimal solution. For the case of single-stemmed cotton (Gossypium hirsutum L.) plant, we present a method to retrieve plant functional parameters directly from interpolated measurement data. The advantage of this method is that not only the calculations of source parameters are independent of the sink parameters, but the sink parameters are also separately estimated with respect to organ type. The method is tested to be practical on two years of experimental data of cotton plants.

A fully implantable middle ear hearing device (F-IMEHD) has been investigated in Korea. Generally, F-IMEHD is implanted under the skin at human temporal bone and it uses implantable microphone and vibrating transducer. Therefore, F-IMEHD must require a different fitting process from conventional hearing aids. In this paper, a fitting program for the F-IMEHD has been developed using Lab VIEW. We calculate proper fitting parameters based on FIG6 fitting rule. And also, it takes account of frequency characteristics of F-IMEHD’s components. Through experimental results, it can be confirmed that the proposed fitting program is suitable to F-IMEHD.

A new algorithm able to efficiently detect a large number of overlapping ellipses with a reduced number of false positives is described. The algorithm estimates the number of candidate ellipse centers in an image with the help of a 2-dimensional accumulator and determines the five ellipse parameters with an ellipse fitting algorithm. The proposed ellipse detection algorithm uses a heuristic to select, among all image points, those with greater probabilities of belonging to an ellipse. This leads to an increase in classification efficiency, even in the presence of noise. Testing has shown that the proposed algorithm detected 97.4% of the ellipses in 100 images. Each image contained ten overlapping ellipses surrounded by noise. The ellipse parameters were determined with great accuracy.

In this paper, we propose an Active Appearance Models (AAMs) fitting algorithm, adaptive fitting algorithm, to localize an object in an image containing occlusion. The adaptive fitting algorithm conducts the fitting problem of AAMs containing object occlusion in a statistical framework. We assume that the residual errors can be treated as mixture statistical model of Gaussian and uniform model. We then reformulated the basic fitting algorithm and maximum a-posteriori (MAP) estimation algorithm of model parameter for AAMs to make the adaptive fitting algorithm. Extensive experiments are provided to demonstrate our algorithm.

Although some of the linear curve fitting techniques provide improvements over the classical least squares fit algorithm, most of them cannot globally stabilize majority of data sets, and are not robust enough to handle moderate levels of noise or missing data. In this paper, we apply “ridge regression regularization” to strengthen the stability and robustness of a linear fitting method, 3L fitting algorithm, while maintaining its Euclidean invariance.

We present a fitting method applied to high resolution two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). The goal of this technique is to improve protein quantification by 2D-PAGE modeling. Two-dimensional gaussian functions (2D-Gaussian) have been used to model protein patterns contained in a 2D-PAGE. The patterns are sequentially approximated by a 2D-Gaussian. This is performed, first, by a least square fitting algorithm (linear method); then, by a conjugate gradient minimization algorithm (non-linear method). Results of this method are discussed.