Small-angle approximation to the transfer of narrow laser beams in anisotropic scattering media

The broadening and the signal power detected of a laser beam traversing an anisotropic scattering medium were examined using the small-angle approximation to the radiative transfer equation in which photons suffering large-angle deflections are neglected. To obtain tractable answers, simple Gaussian and non-Gaussian functions for the scattering phase functions are assumed. Two other approximate approaches employed in the field to further simplify the small-angle approximation solutions are described, and the results obtained by one of them are compared with those obtained using small-angle approximation. An exact method for obtaining the contribution of each higher order scattering to the radiance field is examined but no results are presented

The Hunting of the Bump: On Maximizing Statistical Discrepancy

Anomaly detection has important applications in biosurveilance and environmental monitoring. When comparing measured data to data drawn from a baseline distribution, merely, finding clusters in the measured data may not actually represent true anomalies. These clusters may likely be the clusters of the baseline distribution. Hence, a discrepancy function is often used to examine how different measured data is to baseline data within a region. An anomalous region is thus defined to be one with high discrepancy. In this paper, we present algorithms for maximizing statistical discrepancy functions over the space of axis-parallel rectangles. We give provable approximation guarantees, both additive and relative, and our methods apply to any convex discrepancy function. Our algorithms work by connecting statistical discrepancy to combinatorial discrepancy; roughly speaking, we show that in order to maximize a convex discrepancy function over a class of shapes, one needs only maximize a linear discrepancy function over the same set of shapes. We derive general discrepancy functions for data generated from a one- parameter exponential family. This generalizes the widely-used Kulldorff scan statistic for data from a Poisson distribution. We present an algorithm running in $O(\smash[tb]{\frac{1}{\epsilon} n^2 \log^2 n})$ that computes the maximum discrepancy rectangle to within additive error $\epsilon$, for the Kulldorff scan statistic. Similar results hold for relative error and for discrepancy functions for data coming from Gaussian, Bernoulli, and gamma distributions. Prior to our work, the best known algorithms were exact and ran in time $\smash[t]{O(n^4)}$.Comment: 11 pages. A short version of this paper will appear in SODA06. This full version contains an additional short appendi

Energy Efficiency Analysis of the Discharge Circuit of Caltech Spheromak Experiment

The Caltech spheromak experiment uses a size A ignitron in switching a 59-μF capacitor bank (charged up to 8 kV) across an inductive plasma load. Typical power levels in the discharge circuit are ~200 MW for a duration of ~10 μs. This paper describes the setup of the circuit and the measurements of various impedances in the circuit. The combined impedance of the size A ignitron and the cables was found to be significantly larger than the plasma impedance. This causes the circuit to behave like a current source with low energy transfer efficiency. This behavior is expected to be common with other pulsed plasma experiments of similar size that employ an ignitron switch