Abstract: In testing digital waveform recorders, an important part is to fit a sinusoidal model to recorded data, and calculate the parameters that result in the best fit. Methods are already standardized; however, they demand high computational power. In this article a new, quick and accurate sinefitting algorithm will be shown based on Levenberg-Marquardt (LM) method. The constraints of conve...

Incorporating higher-order optimization functions, such as Levenberg–Marquardt (LM) have revealed better generalizable solutions for deep learning problems. However, these functions suffer from very large processing time and training complexity especially datasets become large, in multi-view classification problems, where finding global optima is a costly problem. To solve this issue, we develo...

Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems w...

In this paper, the problem of constructing the whole view of a scene background from an image sequence is considered. First, point or block correspondence between each pair of successive frames is determined. Three parametric motion models are used: 2-D translation with scale change, affine, and projective. Motion parameters are estimated using either robust criteria and the Levenberg–Marquardt...

The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its uppe...

Introduction: Energy resolving X-ray photon counting detectors are able to assign each detected photon to energy bins [1] (Figure 1). This allows the decomposition of an X-ray image into the materials of the acquired object [1, 2], which has potential benefits in angiography. Contrast agent can be extracted without acquiring a mask image, possibly saving dose and avoiding problems with patient ...