An electron momentum spectrometer has been constructed which measures electron binding energies and momenta by fully determining the kinematics of the incident, scattered, and ejected electrons resulting from (e,2e) ionizing collisions in a thin solid foil. The spectrometer operates with incident beam energies of 20–30 keV in an asymmetric, non-coplanar scattering geometry. Bethe ridge kinematics are used which for 20 keV incident energy has scattered electron energies of 18.8 keV at a polar angle of θs=14°and azimuthal angles φs in the range from −18° to +18° and ejected electrons of 1.2 keV and θe=76°with φe=π±6°. The technique uses transmission through the target foil, but it is most sensitive to the surface from which the 1.2 keV electrons emerge, to a depth of about 2 nm. Scattered and ejected electron energies and azimuthal angles are detected in parallel using position sensitive detection, yielding true coincidence count rates of 6 Hz from a 5.5 nm thick evaporated carbon target and an incident beam current of around 100 nA. The energy resolution is approximately 1.3 eV and momentum resolution approximately 0.15 a0−1. The energy resolution could readily be improved by monochromating the incident electron beam.

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Stochastic background models incorporating spatial correlations can be used to enhance the detection of targets in natural terrain imagery, but are generally difficult to apply when the statistics are non-Gaussian. Chapple and Bertilone (see Opt. Commun., vol.150, p.71-76, 1998) proposed a simple stochastic model for images of natural backgrounds based on the pointwise nonlinear transformation of Gaussian random fields, and demonstrated its effectiveness and computational efficiency in modeling the textures found in natural terrain imagery acquired from airborne IR sensors. In this paper, we show how this model can be used to design algorithms that detect small targets (up to several pixels in size) in natural imagery by identifying anomalous regions of the image where the statistics differ significantly from the rest of the background. All of the model-based algorithms described here involve nonlinear spatial processing prior to the final decision threshold. Monte Carlo testing reveals that the model-based algorithms generally perform better than both the adaptive threshold filter and the generalized matched filter for detecting low-contrast targets, despite the fact that they do not require the target statistics needed for constructing the matched filter.

This design note presents magnetic field uniformity design data for several alternative current loop systems. Universal field symmetry properties of the class of current loop systems that is being considered are elucidated. A common property of the five loop systems that are investigated in detail is that they are all in a sense optimal. This 'Nth-order' optimality criterion is defined and discussed.Parameters of selected Nth-order current loop systems are quoted. Computations of the field uniformity of these loop systems are presented in graphical form, as 'isogauss' contours, and in tabular form, as the 'normalized volumes' enclosed by the isogauss contours. Information is provided about a current loop system that was actually constructed on the basis of the design data presented here.

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