On the representation of an inclined Gaussian beam

In most contemporary optics courses, Gaussian beams are demonstrated in the form of propagation along one coordinate axis. This is referred to as the conventional representation and is in fact a special form. In this paper, we derive the general representation of a Gaussian beam propagating obliquely to the coordinate axis, by performing a coordinate rotation transformation on the conventional representation. When doing so on the beam parameters, a restrictive condition has to be taken into account. Without this condition, the expressions for the beam parameters after the rotation are not consistent with the conventional ones.

Transverse trapping forces of focused Gaussian beam on ellipsoidal particles

A modified T-matrix method is presented to compute the scattered fields of various realistically shaped particles; then the radiation forces on the particles can be calculated via the Maxwell stress tenser integral. Numerical results of transverse trapping efficiencies of a focused Gaussian beam on ellipsoidal and spherical particles with the same volume are compared, which show that the shape and orientation of particles affect the maximal transverse trapping force and the displacement corresponding to the maximum. The effect of the polarization direction of the incident beam on the transverse trapping forces is also revealed. (c) 2007 Optical Society of America.

Accurate description of a radially polarized Gaussian beam

An accurate description of a radially polarized fundamental Gaussian beam is presented on the basis of complex-source-point spherical waves (CSPSWs). In contrast to other descriptions based on the perturbative Lax series, the expressions for the electromagnetic field components of this description have explicit and simple mathematical forms. Numerical calculations show that both paraxial and fifth-order corrected beam descriptions have large relative error when the diffraction angle is large, while the accurate description based on the CSPSW approach proposed here can give field expressions which satisfy Maxwell's equations with great accuracy.

Gaussian diffraction model for Sb thin films in super-resolution near-field structure

SPIE

Characteristics of beam profile of Gaussian beam passing through an axicon

Bessel beam can overcome the limitation of the Rayleigh range of Gaussian beam with the same spot size propagation without any spreading due to diffraction, which is considered as an useful function in guiding particles in the next generation of optical tweezers. The mathematical description of the Bessel beam generated by an axicon is usually based on the Fresnel diffraction integral theory. In this paper, we deduce another type of analytic expression suitable for describing the beam profile generated from the axicon illuminated by the Gaussian beam based on the interferential theory. Compared with the Fresnel diffraction integral theory, this theory does not use much approximation in the process of mathematical analysis. According to the derived expression, the beam intensity profiles at any positions behind the axicon can be calculated not just restricted inside the cross region as the Fresnel diffraction integral theory gives. The experiments prove that the theoretical results fit the experimental results very well. (C) 2004 Elsevier B.V. All rights reserved.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Deconvolution of overlapped peaks based on the exponentially modified Gaussian model in comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography (GC x GC) has attracted much attention for the analys is of complex samples. Even with a large peak capacity in GC x GC, peak overlapping is often met. In this paper, a new method was developed to resolve overlapped peaks based on the mass conservation and the exponentially modified Gaussian (EMG) model. Linear relationships between the calculated sigma, tau of primary peaks with the corresponding retention time (t(R)) were obtained, and the correlation coefficients were over 0.99. Based on such relationships, the elution profile of each compound in overlapped peaks could be simulated, even for the peak never separated on the second-dimension. The proposed method has proven to offer more accurate peak area than the general data processing method. (c) 2005 Elsevier B.V. All rights reserved.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.