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.

DESIGNING VLSI SIGNAL PROCESSING CHIPS USING DATA DEPENDENCE GRAPHS.

The highly structured nature of many digital signal processing operations allows these to be directly implemented as regular VLSI circuits. This feature has been successfully exploited in the design of a number of commercial chips, some examples of which are described. While many of the architectures on which such chips are based were originally derived on heuristic basis, there is an increasing interest in the development of systematic design techniques for the direct mapping of computations onto regular VLSI arrays. The purpose of this paper is to show how the the technique proposed by Kung can be readily extended to the design of VLSI signal processing chips where the organisation of computations at the level of individual data bits is of paramount importance. The technique in question allows architectures to be derived using the projection and retiming of data dependence graphs.

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.