Publications

Abstract not provided.

In this report we discuss a new maritime surveillance and detection concept based on Raman scattering of water molecules. Using a scanning lidar that detects Raman scattered photons from water, the absence or change of signal indicates the p resence of a non - water object. With sufficient spatial resolution a negative two dimensional imag e of the object can be generate d by the scanning lidar . Because Raman scatt er ing is an inelastic process with a relatively large wavelength shift for water , this concept completely avoids the problematic elastic sc attering for objects at or very close to the water surface . Elastic scattering makes it difficult to disc riminate between water and dark objects at or near the water surface especially when automated detection is required . It is also difficult to deal wit h elastic scattering from the bottom surface for shallow waters. The maximum detection depth for this concept is limited by the attenuation of the excitation and return Raman light in water. If excitation in the UV is used, fluorescence can be used for dis crimination between organic and non - organic objects. Range gating can be used for this concept for detection of objects below a specified depth. In this report we develop a lidar model for this concept to estimate the number of detected Raman photons fo r variable lidar parameters and depths in the presence of the solar background . We also report on the results of proof - of - concept measurements using the Sandia Ares lidar with excitation at 355 nm. The measurements show good agreement with the lidar mode l predictions. The detected number of photons for typical lidar parameter shows the concept is viable and applicable to a variety of day and nighttime detection scenarios. This concept has many potential applications including ne ar - surface mine detection, swimmer detection for security purposes, wide area search, as well as other civilian applications.

Suppression of the ambient gamma background radiation by traffic structure and cargo is a well-understood and studied effect for deployed radiation portal monitors (RPM). For effective analysis of measured RPM profiles with the objective of inferring the spatial characteristics of radiation sources, it is important to account for the effects of background suppression. In this report we analyze background suppression for a test dataset from vehicle RPMs at a sample port and estimate the distributions of suppression amplitudes and shapes. Cluster analysis of standardized and normalized profiles is used to obtain the dominant suppression shapes in the sample field data. We determine that a large fraction of non-alarm RPM occupancies are represented by a small number of suppression shapes. This fraction increases when the signal-to-noise ratio of an occupancy profile is improved by the addition of signals for multiple RPM detectors located at the same height. The calculated suppression shapes from RPM data can be used along with source models in the process of spatial profile analysis both in the field or offline. This background suppression analysis is an important step in improving the effectiveness of the RPM profile analysis methodology which is currently being investigated and may lead to methods that reduce the number of secondary inspections as well as to decision support tools that aid operators in evaluating RPM data that do not contain spectral information.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Conventional full spectrum gamma spectroscopic analysis has the objective of quantitative identification of all the radionuclides present in a measurement. For low-energy resolution detectors such as NaI, when photopeaks alone are not sufficient for complete isotopic identification, such analysis requires template spectra for all the radionuclides present in the measurement. When many radionuclides are present it is difficult to make the correct identification and this process often requires many attempts to obtain a statistically valid solution by highly skilled spectroscopists. A previous report investigated using the targeted principal component analysis method (TPCA) for detection of embedded sources for RPM applications. This method uses spatial/temporal information from multiple spectral measurements to test the hypothesis of the presence of a target spectrum of interest in these measurements without the need to identify all the other radionuclides present. The previous analysis showed that the TPCA method has significant potential for automated detection of target radionuclides of interest, but did not include the effects of shielding. This report complements the previous analysis by including the effects of spectral distortion due to shielding effects for the same problem of detection of embedded sources. Two examples, one with one target radionuclide and the other with two, show that the TPCA method can successfully detect shielded targets in the presence of many other radionuclides. The shielding parameters are determined as part of the optimization process using interpolation of library spectra that are defined on a 2D grid of atomic numbers and areal densities.

Conventional full spectrum gamma spectroscopic analysis has the objective of quantitative identification of all the isotopes present in a measurement. For low energy resolution detectors, when photopeaks alone are not sufficient for complete isotopic identification, such analysis requires template spectra for all the isotopes present in the measurement. When many isotopes are present it is difficult to make the correct identification and this process often requires many trial solutions by highly skilled spectroscopists. This report investigates the potential of a new analysis method which uses spatial/temporal information from multiple low energy resolution measurements to test the hypothesis of the presence of a target spectrum of interest in these measurements without the need to identify all the other isotopes present. This method is referred to as targeted principal component analysis (TPCA). For radiation portal monitor applications, multiple measurements of gamma spectra are taken at equally spaced time increments as a vehicle passes through the portal and the TPCA method is directly applicable to this type of measurement. In this report we describe the method and investigate its application to the problem of detection of a radioactive localized source that is embedded in a distributed source in the presence of an ambient background. Examples using simulated spectral measurements indicate that this method works very well and has the potential for automated analysis for RPM applications. This method is also expected to work well for isotopic detection in the presence of spectrally and spatially varying backgrounds as a result of vehicle-induced background suppression. Further work is needed to include effects of shielding, to understand detection limits, setting of thresholds, and to estimate false positive probability.

Abstract not provided.

In a multiyear research agreement with Tenix Investments Pty. Ltd., Sandia has been developing field deployable technologies for detection of biotoxins in water supply systems. The unattended water sensor or UWS employs microfluidic chip based gel electrophoresis for monitoring biological analytes in a small integrated sensor platform. This instrument collects, prepares, and analyzes water samples in an automated manner. Sample analysis is done using the {mu}ChemLab{trademark} analysis module. This report uses analysis results of two datasets collected using the UWS to estimate performance of the device. The first dataset is made up of samples containing ricin at varying concentrations and is used for assessing instrument response and detection probability. The second dataset is comprised of analyses of water samples collected at a water utility which are used to assess the false positive probability. The analyses of the two sets are used to estimate the Receiver Operating Characteristic or ROC curves for the device at one set of operational and detection algorithm parameters. For these parameters and based on a statistical estimate, the ricin probability of detection is about 0.9 at a concentration of 5 nM for a false positive probability of 1 x 10{sup -6}.

We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of high-copy number proteins to generate unique protein profiles. Viral proteins are solubilized by heating at 95°C in borate buffer containing detergent (5 min), then labeled with fluorescamine dye (10 s), and analyzed using the μChemLab CGE system (5 min). Analyses of closely related T2 and T4 bacteriophage demonstrate sufficient assay sensitivity and peak resolution to distinguish the two phage. CGE analyses of four additional viruses - MS2 bacteriophage, Epstein - Barr, respiratory syncytial, and vaccinia viruses - demonstrate reproducible and visually distinct protein profiles. To evaluate the suitability of the method for unique identification of viruses, we employed a Bayesian classification approach. Using a subset of 126 replicate electropherograms of the six viruses and phage for training purposes, successful classification with non-training data was 66/69 or 95% with no false positives. The classification method is based on a single attribute (elution time), although other attributes such as peak width, peak amplitude, or peak shape could be incorporated and may improve performance further. The encouraging results suggest a rapid and simple way to identify viruses without requiring specialty reagents such as PCR probes and antibodies. © 2008 American Chemical Society.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.