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.