The National Ignition Facility, a center for the study of high energy density plasma physics and fusion energy ignition, is currently under construction at the Lawrence Livermore National Laboratory. The heart of the NIF is a frequency tripled, flashlamp-pumped Nd:glass laser system comprised of 192 independent laser beams. The laser system is capable of gen-erating output energies of 1.8MJ at 351nm and at peak powers of 500 TW in a flexible temporal pulse format. A descrip-tion of the NIF laser system and its major components is presented. We also discuss the manufacture of nearly 7500 pre-cision large optics required by the NIF including data on the manufactured optical quality vs. specification. In addition, we present results from an on-going program to improve the operational lifetime of optics exposed to high fluence in the 351-nm section of the laser.
The polymerase chain reaction (PCR) is an enzyme-based chemical reaction that manufactures copies of one or more identifying regions of double-stranded DNA sequences (target sequences). These copies of target DNA are known as "amplicons". By creating millions of these copies of the identifying sequences (when they are present!), PCR allows researchers to detect by them, and hence the presence of the relevant organism, with techniques such as electrophoresis, flow cytometry, or spectrometry. Although there are numerous commercial PCR instruments that are designed for bench-top use in a laboratory, the challenges of building a battery-powered instrument that could perform such assays in the field.
Adaptive optics enables high resolution imaging through the atmospheric by correcting for the turbulent air's aberrations to the light waves passing through it. The Lawrence Livermore National Labratory for a number of years has been at the forefront of applying adaptive optics technology to astronomy on the world's largest astronomical telescopes, in particular at the Keck 10-meter telescope on Mauna Kea, Hawaii. The technology includes the development of high-speed electrically driven deformable mirrors, high-speed low-noise CCD sensors, and real-time wavefront reconstruction and control hardware. Adaptive optics finds applications in many other areas where light beams pass through aberrating media and must be corrected to maintain diffraction-limited performance. We describe systems and results in astronomy, medicine (vision science), and horizontal path imaging, all active programs in our group.
Astronomical applications of adaptive optics at Lawrence Livermore National Laboratory (LLNL) has a history that extends from 1984. The program started with the Lick Observatory Adaptive Optics system and has progressed through the years to lever-larger telescopes: Keck, and now the proposed CELT (California Extremely Large Telescope) 30m telescope. LLNL AO continues to be at the forefront of AO development and science.
Adaptive optics can be used to correct the aberrations in the human eye caused by imperfections in the cornea and the lens and thereby, improve image quality both looking into and out of the eye. Under the auspices of the NSF Center for Adaptive Optics and the DOE Biomedical Engineering Program, Lawrence Livermore National Laboratory has joined together with leading vision science researchers around the country to develop and test new ophthalmic imaging systems using novel wavefront corrector technologies. Results of preliminary comparative evaluations of these technologies in initial system tests show promise for future clinical utility.
The difficulty in terrestrial imaging over long horizontal or slant paths is that atmospheric aberrations and distortions reduce the resolution and contrast in images recorded at high resolution. This paper will describe the problem of horizontal-path imaging, briefly cover various methods for imaging over horizontal paths and then describe the speckle imaging method actively being pursued at LLNL.
We will review some closer range (1-3 km range) imagery of people we have already published, as well as show new results of vehicles we have obtained over longer slant-range paths greater than 20 km.
Image segmentation transforms pixel-level information from raw images to a higher level of abstraction in which related pixels are grouped into disjoint spatial regions. Such regions typically correspond to natural or man-made objects or structures, natural variations in land
cover, etc. For many image interpretation tasks (such as land use assessment, automatic target cueing, defining relationships between objects, etc.), segmentation can be an important early step.
Remotely sensed images (e.g., multi-spectral and hyperspectral images) often contain many spectral bands (i.e., multiple layers of 2D images). Multi-band images are important because they contain more information than single -band images. Objects or natural variations
that are readily apparent in certain spectral bands may be invisible in 2D broadband images. In this paper, the classical region growing approach to image segmentation is generalized from single to multi-band images. While it is widely recognized that the quality of image segmentation is affected by which segmentation algor ithm is used, this paper shows that algorithm parameter values can have an even more profound effect. A novel self-calibration framework is developed
for automatically selecting parameter values that produce segmentations that most closely resemble a calibration edge map (derived separately using a simple edge detector). Although the
framework is generic in the sense that it can imbed any core segmentation algorithm, this paper only demonstrates self-calibration with multi-band region growing. The framework is applied to
a variety of AVIRIS image blocks at different spectral resolutions, in an effort to assess the impact of spectral resolution on segmentation quality. The image segmentations are assessed
quantitatively, and it is shown that segmentation quality does not generally appear to be highly correlated with spectral resolution.
The automated production of maps of human settlement from recent satellite images is essential to detailed studies of urbanization, population movement, and the like. Commercial satellite imagery is becoming available with sufficient spectral and spatial resolution to apply computer vision techniques previously considered only for laboratory (high resolution, low noise) images. In this paper we attempt to extract human settlement from IKONOS 4-band and panchromatic images using spectral segmentation together with a form of generalized second-order statistics and detection of edges, corners, and other candidate human-made features in the imagery.
This paper presents various architectural options for implementing a K-Means Re-Clustering algorithm suitable for unsupervised segmentation of hyperspectral images. Performance metrics are developed based upon quantitative comparisons of convergence rates and segmentation quality. A methodology for making these comparisons is developed and used to establish K values that produce the best segmentations with minimal processing requirements. Convergence rates depend on the initial choice of cluster centers. Consequently, this same methodology may be used to evaluate the effectiveness of different initialization techniques.
Identification of materials in hyperspectral imagery is a fundamental
analysis task. Materials are often identified by building pixel
models using a library of reference spectra along with a
regression technique. This paper describes several regression
techniques that are useful in modeling hyperspectral pixels,
demonstrates the characteristics of the algorithms on simulated
data, and compares the strengths and weaknesses of the
Optical design capabilities continue to play the same strong role at Lawrence Livermore National Laboratory (LLNL) that they have in the past. From defense applications to the solid-state laser programs to the Atomic Vapor Laser Isotope Separation (AVLIS), members of the optical design group played critical roles in producing effective system designs and are actively continuing this tradition. This talk will explain the role of optical design at LLNL, outline current capabilities and summarize a few activities in which the optical design team has been recently participating.