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The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory is the world's most energetic laser, providing a scientific research center to study inertial confinement fusion and matter at extreme energy densities and pressures. A target shot involves over 30 specialized diagnostics measuring critical x-ray, optical and nuclear phenomena to quantify ignition results for comparison with computational models. The Shot Analysis and Visualization System (SAVI) acquires and analyzes target diagnostic data for display within a time-budget of 30 min. Laser and target diagnostic data are automatically loaded into the NIF archive database through clustered software data collection agents. The SAVI Analysis Engine distributes signal and image processing tasks to a Linux cluster where computation is performed. Intermediate results are archived at each step of the analysis pipeline. Data is archived with metadata and pedigree. Experiment results are visualized through a web-based user interface in interactive dashboards tailored to single or multiple shot perspectives. The SAVI system integrates open-source software, commercial workflow tools, relational database and messaging technologies into a service-oriented and distributed software architecture that is highly parallel, scalable, and flexible. The architecture and functionality of the SAVI system will be presented along with examples.

Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple data channels with redundant time samples and missing data points. The data channels can have different, time-varying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.

An efficient method for accurately calculating the center-of-rotation, or projection center, for parallel computed tomography projection data, or sinograms, is described. This method uses all the data in the sinogram to estimate the center by a least-squares technique and requires no previous calibration scan. The method also finds the object's center-of-mass without reconstructing its image. Since the method uses the measured data, it is sensitive to noise in the measurements, but that sensitivity is relatively small compared to other techniques. Examples of its use on simulated and actual data are included. For fan-beam data over 360 degrees , two related methods are described to find the center in the presence or absence of a midline offset. >

A high-speed Radon transform computer (RTC) has been designed and simulated that operates on the principles of the parallel projection pipeline engine (PPPE) processor suggested by J.L.C. Sanz, et al. (1988). The computer consists of an array of identical processing elements with fast communication links to each of its neighbors and uses forward- and back-projection formulas tests linear interpolation. Simulation and experimental timing tests have been performed on both a systolic array of 64 transputer processors and on arrays of commercial DSP (digital signal processor) chips. A custom VLSI implementation is currently under design which is expected to perform forward and inverse transforms of 512 by 512 images at or near video rates. >

A class of hybrid domain-iterative algorithms for 2-D image reconstruction is described. The consistent iterative reconstruction-reprojection (CIRR) algorithm was designed and implemented along with other classical domain-iterative methods. The algorithms are tested on the hollow projections problem, where simple object models are assumed known. Results show that the CIRR algorithm is robust and accurate, and converges quickly. >

Current ground penetrating radars (GPR) have been tested for land mine detection, but they have generally been costly and have poor performance. Comprehensive modeling and experimentation must be done to predict the electromagnetic (EM) signatures of mines to access the effect of clutter on the EM signature of the mine, and to understand the merit and limitations of using radar for various mine detection scenarios. This modeling can provide a basis for advanced radar design and detection techniques leading to superior performance. Lawrence Livermore National Laboratory (LLNL) has developed a radar technology that when combined with comprehensive modeling and detection methodologies could be the basis of an advanced mine detection system. Micropower Impulse Radar (MIR) technology exhibits a combination of properties, including wideband operation, extremely low power consumption, extremely small size and low cost, array configurability, and noise encoded pulse generation. LLNL is in the process of developing an 'optimal' processing algorithm to use with the MIR sensor. In this paper, we use classical numerical models to obtain the signature of mine-like targets and examine the effect of surface roughness on the reconstructed signals. These results are then qualitatively compared to experimental data.

The image reconstruction problem, also known as the inverse Radon transform, for x-ray computed tomography (CT) is found in numerous applications in medicine and industry. The most common algorithm used in these cases is filtered backprojection (FBP), which, while a simple procedure, is time-consuming for large images on any type of computational engine. Specially designed, dedicated parallel processors are commonly used in medical CT scanners, whose results are then passed to a graphics workstation for rendering and analysis. However, a fast direct FBP algorithm can be implemented on modern texture-mapping hardware in current high-end workstation platforms. This is done by casting the FBP algorithm as an image warping operation with summing. Texture- mapping hardware, such as that on the silicon Graphics Reality Engine, shows around 600 times speedup of backprojection over a CPU-based implementation (a 100 Mhz R4400 in our case). This technique has the further advantages of flexibility and rapid programming. In addition, the same hardware can be used for both image reconstruction and for volumetric rendering. Our technique can also be used to accelerate iterative reconstruction algorithms. The hardware architecture also allows more complex operations than straight-ray backprojection if they are required, including fan-beam, cone-beam, and curved ray paths, with little or no speed penalties.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The Systolic Processor with a Reconfigurable Interconnection Network of Transputers (SPRINT) [1] is a sixty-four-element multiprocessor developed at Lawrence Livermore National Laboratory to evaluate systolic algorithms and architectures experimentally. The processors are interconnected in a reconfigurable network which can emulate networks such as the two-dimensional mesh, the triangular mesh, the tree, and the shuffle-exchange network. New systolic algorithms and architectures are described which perform the Radon transform [8] and inverse Radon transform with efficiency arbitrarily close to 100%. High efficiency is possible with any connected network topology, even with low communication bandwidth. The results of the algorithms executed on the SPRINT compare closely with theory.