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.

Crossing the phantom divide in brane cosmology with curvature corrections and brane-bulk energy transfer

We consider the Randall-Sundrum brane-world model with bulk-brane energy transfer where the Einstein-Hilbert action is modified by curvature correction terms: a four-dimensional scalar curvature from induced gravity on the brane, and a five-dimensional Gauss-Bonnet curvature term. It is remarkable that these curvature terms will not change the dynamics of the brane universe at low energy. Parameterizing the energy transfer and taking the dark radiation term into account, we find that the phantom divide of the equation of state of effective dark energy could be crossed, without the need of any new dark energy components. Fitting the two most reliable and robust SNIa datasets, the 182 Gold dataset and the Supernova Legacy Survey (SNLS), our model indeed has a small tendency of phantom divide crossing for the Gold dataset, but not for the SNLS dataset. Furthermore, combining the recent detection of the SDSS baryon acoustic oscillations peak (BAO) with lower matter density parameter prior, we find that the SNLS dataset also mildly favors phantom divide crossing.

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.

高斯束偏移的方法及应用

Gaussian beam is the asymptotic solution of wave equation concentred at the central ray. The Gaussian beam ray tracing method has many advantages over ray tracing method. Because of the prevalence of multipath and caustics in complex media, Kirchhoff migration usually can not get satisfactory images, but Gaussian beam migration can get better results.The Runge-Kutta method is used to carry out the raytracing, and the wavefront construction method is used to calculate the multipath wavefield. In this thesis, a new method to determine the starting point and initial direction of a new ray is proposed take advantage of the radius of curvature calculated by dynamic ray tracing method.The propagation characters of Gaussian beam in complex media are investigated. When Gaussian beam is used to calculate the Green function, the wave field near the source was decomposed in Gaussian beam in different direction, then the wave field at a point is the superposition of individual Gaussian beams.Migration aperture is the key factor for Kirchhoff migration. In this thesis, the criterion for the choice of optimum aperture is discussed taking advantage of stationary phase analysis. Two equivalent methods are proposed, but the second is more preferable.Gaussian beam migration based on dip scanning and its procedure are developed. Take advantage of the travel time, amplitude, and takeoff angle calculated by Gaussian beam method, the migration is accomplished.Using the proposed migration method, I carry out the numerical calculation of simple theoretical model, Marmousi model and field data, and compare the results with that of Kirchhoff migration. The comparison shows that the new Gaussian beam migration method can get a better result over Kirchhoff migration, with fewer migration noise and clearer image at complex structures.

Parallel Computation of Vernier Offsets, Curvature and Chevrons in Humans

A vernier offset is detected at once among straight lines, and reaction times are almost independent of the number of simultaneously presented stimuli (distractors), indicating parallel processing of vernier offsets. Reaction times for identifying a vernier offset to one side among verniers offset to the opposite side increase with the number of distractors, indicating serial processing. Even deviations below a photoreceptor diameter can be detected at once. The visual system thus attains positional accuracy below the photoreceptor diameter simultaneously at different positions. I conclude that deviation from straightness, or change of orientation, is detected in parallel over the visual field. Discontinuities or gradients in orientation may represent an elementary feature of vision.

The Alignment of Objects With Smooth Surfaces: Error Analysis of the Curvature Method

The recognition of objects with smooth bounding surfaces from their contour images is considerably more complicated than that of objects with sharp edges, since in the former case the set of object points that generates the silhouette contours changes from one view to another. The "curvature method", developed by Basri and Ullman [1988], provides a method to approximate the appearance of such objects from different viewpoints. In this paper we analyze the curvature method. We apply the method to ellipsoidal objects and compute analytically the error obtained for different rotations of the objects. The error depends on the exact shape of the ellipsoid (namely, the relative lengths of its axes), and it increases a sthe ellipsoid becomes "deep" (elongated in the Z-direction). We show that the errors are usually small, and that, in general, a small number of models is required to predict the appearance of an ellipsoid from all possible views. Finally, we show experimentally that the curvature method applies as well to objects with hyperbolic surface patches.

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.

Accommodating curvature in a highly ordered functionalized metal oxide nanofiber: synthesis, characterization and multi-scale modeling of layered nanosheets

A key element in the rational design of hybrid organic-inorganic nanostructures, is control of surfactant packing and adsorption onto the inorganic phase in crystal growth and assembly. In layered single crystal nanofibers and bilayered 2D nanosheets of vanadium oxide, we show how the chemisorption of preferred densities of surfactant molecules can direct formation of ordered, curved layers. The atom-scale features of the structures are described using molecular dynamics simulations that quantify surfactant packing effects and confirm the preference for a density of 5 dodecanethiol molecules per 8 vanadium attachment sites in the synthesised structures. This assembly maintains a remarkably well ordered interlayer spacing, even when curved. The assemblies of interdigitated organic bilayers on V2O5 are shown to be sufficiently flexible to tolerate curvature while maintaining a constant interlayer distance without rupture, delamination or cleavage. The accommodation of curvature and invariant structural integrity points to a beneficial role for oxide-directed organic film packing effects in layered architectures such as stacked nanofibers and hybrid 2D nanosheet systems.

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.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.

Sticky central limit theorems on open books

Given a probability distribution on an open book (a metric space obtained by gluing a disjoint union of copies of a half-space along their boundary hyperplanes), we define a precise concept of when the Fréchet mean (barycenter) is sticky. This nonclassical phenomenon is quantified by a law of large numbers (LLN) stating that the empirical mean eventually almost surely lies on the (codimension 1 and hence measure 0) spine that is the glued hyperplane, and a central limit theorem (CLT) stating that the limiting distribution is Gaussian and supported on the spine.We also state versions of the LLN and CLT for the cases where the mean is nonsticky (i.e., not lying on the spine) and partly sticky (i.e., is, on the spine but not sticky). © Institute of Mathematical Statistics, 2013.