CHARACTERIZATION OF A LASER-PRODUCED PLASMA USING THE TECHNIQUE OF POINT-PROJECTION ABSORPTION-SPECTROSCOPY

The technique of point-projection spectroscopy has been shown to be applicable to the study of expanding aluminum plasmas generated by approximately 80 ps laser pulses incident on massive, aluminum stripe targets of approximately 125-mu-m width. Targets were irradiated at an intensity of 2.5 +/- 0.5 x 10(13) W/cm2 in a line focus geometry and under conditions similar to those of interest in x-ray laser schemes. Hydrogenic and heliumlike aluminum resonance lines were observed in absorption using a quasicontinuous uranium back-lighter plasma. Using a pentaerythrital Bragg crystal as the dispersive element, a resolving power of approximately 3500 was achieved with spatial resolution at the 5-mu-m level in frame times of the order of 100 ps. Reduction of the data for times up to 150 ps after the peak of the incident laser pulse produced estimates of the temperature and ion densities present, as a function of space and time. The one-dimensional Lagrangian hydrodynamic code MEDUSA coupled to the atomic physics non-local-thermodynamic-equilibrium ionized material package was used to simulate the experiment in planar geometry and has been shown to be consistent with the measurements.

A unified view on the effect of fibers and loading on sfrc creep through linear projection to latent structures

Creep of Steel Fiber Reinforced Concrete (SFRC) under flexural loads in the cracked state and to what extent different factors determine creep behaviour are quite understudied topics within the general field of SFRC mechanical properties. A series of prismatic specimens have been produced and subjected to sustained flexural loads. The effect of a number of variables (fiber length and slenderness, fiber content, and concrete compressive strength) has been studied in a comprehensive fashion. Twelve response variables (creep parameters measured at different times) have been retained as descriptive of flexural creep behaviour. Multivariate techniques have been used: the experimental results have been projected to their latent structure by means of Principal Components Analysis (PCA), so that all the information has been reduced to a set of three latent variables. They have been related to the variables considered and statistical significance of their effects on creep behaviour has been assessed. The result is a unified view on the effects of the different variables considered upon creep behaviour: fiber content and fiber slenderness have been detected to clearly modify the effect that load ratio has on flexural creep behaviour.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.