Ultra-broadband optical parametric chirped-pulse amplification by matching of both group-velocity and pulse-front

Ultra-broadband optical parametric chirped-pulse amplification is analyzed based the compensation of phase-mismatch, which is achieved by matching of both group-velocity and pulse-front between signal and idler by the combination of the noncollinear-phase-match and pulse-front-tilt. The results show exactly matching of both group-velocity and pulse-front is the important criterion for constructing an UBOPCPA. Its general model is developed, in which the group velocities, noncollinear angles. spatial walk-off angles, linear angular spectral dispersion coefficients and pulse-front tilted angles are suitably linked to each other. Finally, specific numerical calculations and simulations are presented for beta-barium borate OPCPA with type-1 noncollinear angularly dispersed geometry. (C) 2005 Elsevier B.V. All rights reserved.

Beam matching between SFC and SSC

The beam matching status between the two isochronous cyclotrons in the Heavy Ion Research Facility at the Lanzhou-Cooling Storage Ring (HIRFL-CSR) is described. Several methods which can be used to accomplish 100% matching are proposed. By comparing of them, the best method is determined. The advantage due to this method is discussed.

CHARMED BARYON IN A STRING MODEL

Charmed baryon spectroscopy has been studied under a string model. In this model, charmed baryons are composed of a diquark and a charm quark which are connected by a constant tension. In this diquark picture, the quantum numbers J(P) of confirmed baryons have been well assigned. Energies of the first and second orbital excitations have been predicted and compared with the experimental data. Meanwhile, diquark masses have been extracted in the background of charm quark which satisfy a splitting relation based on spin-spin interaction.

Refinement of edge-to-edge matching model and its application in the Mg17Al12/alpha-Mg and alpha-Y/alpha-Mg systems

A refined version of the edge-to-edge matching model is described here. In the original model, the matching directions were obtained from the planes with all the atomic centers that were exactly in the plane, or the distance from the atomic center to the plane which was less than the atomic radius. The direction-matching pairs were the match of straight rows-straight rows and zigzag rows-zigzag rows. In the refined model, the matching directions were obtained from the planes with all the atomic centers that were exactly in the plane.

Extended application of edge-to-edge matching model to HCP/HCP (alpha-Mg/MgZn2) system in magnesium alloys

In the present work, the edge-to-edge matching model has been introduced to predict the orientation relationships (OR) between the MgZn2 phase which has hexagonal close packed (HCP) structure and the HCP a-Mg matrix. Based on the crystal structures and lattice parameters only, the model has predicted the two most preferred ORs and they are: (1) [1 1 2 3](alpha-Mg) vertical bar vertical bar]1 1 2 3](alpha-Mg), (0 0 0 1)(alpha-Mg) 0.27 degrees from (0 0 0 1)(MgZn2), (1 0 1 1)(alpha-Mg) 26.18 degrees from (1 1 2 2)(MgZn2), (2) [1 0 1 0](alpha-Mg),vertical bar vertical bar[1 1 2 0](MgZn2), (0 0 0 1)(alpha-Mg) vertical bar vertical bar(0 0 0 1)(MgZn2), (1 0 1 1)(alpha-Mg) 3.28 degrees from ( 1 1 2 2)(MgZn2). Four experimental ORs have been reported in the alpha-Mg/MgZn2 system, and the most frequently reported one is ideally the OR (2). The other three experimental ORs are near versions of the OR (2). The habit plane of the OR (2) has been predicted and it agrees well with the experimental results.

Uso de imagens históricas no aplicativo Google Earth para avaliação de mapas temáticos.

2010

Analysis of Differential and Matching Methods for Optical Flow

Several algorithms for optical flow are studied theoretically and experimentally. Differential and matching methods are examined; these two methods have differing domains of application- differential methods are best when displacements in the image are small (<2 pixels) while matching methods work well for moderate displacements but do not handle sub-pixel motions. Both types of optical flow algorithm can use either local or global constraints, such as spatial smoothness. Local matching and differential techniques and global differential techniques will be examined. Most algorithms for optical flow utilize weak assumptions on the local variation of the flow and on the variation of image brightness. Strengthening these assumptions improves the flow computation. The computational consequence of this is a need for larger spatial and temporal support. Global differential approaches can be extended to local (patchwise) differential methods and local differential methods using higher derivatives. Using larger support is valid when constraint on the local shape of the flow are satisfied. We show that a simple constraint on the local shape of the optical flow, that there is slow spatial variation in the image plane, is often satisfied. We show how local differential methods imply the constraints for related methods using higher derivatives. Experiments show the behavior of these optical flow methods on velocity fields which so not obey the assumptions. Implementation of these methods highlights the importance of numerical differentiation. Numerical approximation of derivatives require care, in two respects: first, it is important that the temporal and spatial derivatives be matched, because of the significant scale differences in space and time, and, second, the derivative estimates improve with larger support.

Feature Matching for Object Localization in the Presence of Uncertainty

We consider the problem of matching model and sensory data features in the presence of geometric uncertainty, for the purpose of object localization and identification. The problem is to construct sets of model feature and sensory data feature pairs that are geometrically consistent given that there is uncertainty in the geometry of the sensory data features. If there is no geometric uncertainty, polynomial-time algorithms are possible for feature matching, yet these approaches can fail when there is uncertainty in the geometry of data features. Existing matching and recognition techniques which account for the geometric uncertainty in features either cannot guarantee finding a correct solution, or can construct geometrically consistent sets of feature pairs yet have worst case exponential complexity in terms of the number of features. The major new contribution of this work is to demonstrate a polynomial-time algorithm for constructing sets of geometrically consistent feature pairs given uncertainty in the geometry of the data features. We show that under a certain model of geometric uncertainty the feature matching problem in the presence of uncertainty is of polynomial complexity. This has important theoretical implications by demonstrating an upper bound on the complexity of the matching problem, an by offering insight into the nature of the matching problem itself. These insights prove useful in the solution to the matching problem in higher dimensional cases as well, such as matching three-dimensional models to either two or three-dimensional sensory data. The approach is based on an analysis of the space of feasible transformation parameters. This paper outlines the mathematical basis for the method, and describes the implementation of an algorithm for the procedure. Experiments demonstrating the method are reported.

Affine Matching with Bounded Sensor Error: A Study of Geometric Hashing and Alignment

Affine transformations are often used in recognition systems, to approximate the effects of perspective projection. The underlying mathematics is for exact feature data, with no positional uncertainty. In practice, heuristics are added to handle uncertainty. We provide a precise analysis of affine point matching, obtaining an expression for the range of affine-invariant values consistent with bounded uncertainty. This analysis reveals that the range of affine-invariant values depends on the actual $x$-$y$-positions of the features, i.e. with uncertainty, affine representations are not invariant with respect to the Cartesian coordinate system. We analyze the effect of this on geometric hashing and alignment recognition methods.