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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Statistical Assessment of Proton Treatment Plans Under Setup and Range Uncertainties

Purpose: To evaluate a method for quantifying the effect of setup errors and range uncertainties on dose distribution and dose–volume histogram using statistical parameters; and to assess existing planning practice in selected treatment sites under setup and range uncertainties. Methods and Materials: Twenty passively scattered proton lung cancer plans, 10 prostate, and 1 brain cancer scanning-beam proton plan(s) were analyzed. To account for the dose under uncertainties, we performed a comprehensive simulation in which the dose was recalculated 600 times per given plan under the influence of random and systematic setup errors and proton range errors. On the basis of simulation results, we determined the probability of dose variations and calculated the expected values and standard deviations of dose–volume histograms. The uncertainties in dose were spatially visualized on the planning CT as a probability map of failure to target coverage or overdose of critical structures. Results: The expected value of target coverage under the uncertainties was consistently lower than that of the nominal value determined from the clinical target volume coverage without setup error or range uncertainty, with a mean difference of −1.1% (−0.9% for breath-hold), −0.3%, and −2.2% for lung, prostate, and a brain cases, respectively. The organs with most sensitive dose under uncertainties were esophagus and spinal cord for lung, rectum for prostate, and brain stem for brain cancer. Conclusions: A clinically feasible robustness plan analysis tool based on direct dose calculation and statistical simulation has been developed. Both the expectation value and standard deviation are useful to evaluate the impact of uncertainties. The existing proton beam planning method used in this institution seems to be adequate in terms of target coverage. However, structures that are small in volume or located near the target area showed greater sensitivity to uncertainties.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

The Surface-Topography Challenge: A Multi-Laboratory Benchmark Study to Advance the Characterization of Topography

Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is Ra, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking.

42 ENGINEERING↗
DOE OSTI2016

Unexpected series of regular frequency spacing of δ Scuti stars in the non-asymptotic regime - I. The methodology

In this study, a sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequences (echelle ridges) were found in the 5–21 d –1 region where the pairs of the sequences are shifted (between 0.5 and 0.59 d –1 ) by twice the value of the estimated rotational splitting frequency (0.269 d –1 ). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d –1 ) are in better agreement with the sum of a possible 1.710 d –1 large separation and two or one times, respectively, the value of the rotational frequency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Thermal Conductivity of AM Fabricated Stainless Steel 316L Part

Thermal properties are an integral part of many diverse engineering applications, and additive manufacturing (AM), particularly laser powder bed fusion (LPBF) is shown to affect thermal properties due to the laser processing parameters. For 316L stainless steel, there is little prior research to determine the effects of the process on thermal properties. In this work, the temperature gradient is shown to create uniform, chess board-like distributions of grains in the build direction. These zones create dislocations which were visualized with EBSD techniques. Processing parameters cause hierarchal grain size distribution, with localized concentrations of small grains and large grains. Thermomechanical stresses in the rapid solidification increases dislocation density during grain formation. Previous research shows a higher density of dislocations decreases local thermal conductivity. Local and bulk thermal conductivity are shown in this work to have statistically lowered values to an average of 10-12 W/m-K compared to 14 W/m-K for conventional 316L.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Neuroanatomical and cognitive correlates of visual hallucinations in Parkinson’s disease and dementia with Lewy bodies: Voxel-based morphometry and neuropsychological meta-analysis

Visual hallucinations (VH) are common in Parkinson's disease and dementia with Lewy bodies, two forms of Lewy body disease (LBD), but the neural substrates and mechanisms involved are still unclear. We conducted meta-analyses of voxel-based morphometry (VBM) and neuropsychological studies investigating the neuroanatomical and cognitive correlates of VH in LBD. For VBM (12 studies), we used Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), including statistical parametric maps for 50% of the studies. For neuropsychology (35 studies), we used MetaNSUE to consider non-statistically significant unreported effects. VH were associated with smaller grey matter volume in occipital, frontal, occipitotemporal, and parietal areas (peak Hedges' g -0.34 to -0.49). In patients with Parkinson's disease without dementia, VH were associated with lower verbal immediate memory performance (Hedges' g -0.52). Both results survived correction for multiple comparisons. Here, abnormalities in these brain regions might reflect dysfunctions in brain networks sustaining visuoperceptive, attention, and executive abilities, with the latter also being at the basis of poor immediate memory performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-18A-10: Comparison of Patient Dose and Vessel Visibility Between Antiscatter Grid Removal and Lower Angiographic Radiation Dose Settings for Pediatric Imaging: A Preclinical Investigation

Purpose: To define an alternative to antiscatter grid (ASG) removal in angiographic systems which achieves similar patient dose reduction as ASG removal without degrading image quality during pediatric imaging. Methods: This study was approved by the local institution animal care and use committee (IACUC). Six different digital subtraction angiography settings were evaluated that altered the mAs, (100, 70, 50, 35, 25, 17.5% of reference mAs) with and without ASG. Three pigs of 5, 15, and 20 kg (9, 15, and 17 cm abdominal thickness; smaller than a newborn, average 3 yr old, and average 10 year old human abdomen respectively) were imaged using the six dose settings with and without ASG. Image quality was defined as the order of vessel branch that is visible relative to the injected vessel. Five interventional radiologists evaluated all images. Image quality and patient dose were statistically compared using analysis of variance and receiver operating curve (ROC) analysis to define the preferred dose level and use of ASG for a minimum visibility of 2nd or 3rd order branches of vessel visibility. Results: ASG grid removal reduces dose by 26% with reduced image quality. Only with the ASG present can 3rd order branches be visualized; 100% mAs is required for 9 cm pig while 70% mAs is adequate for the larger pigs. 2nd order branches can be visualized with ASG at 17.5% mAs for all three pig sizes. Without the ASG, 50%, 35% and 35% mAs is required for smallest to largest pig. Conclusion: Removing ASG reduces patient dose and image quality. Image quality can be improved with the ASG present while further reducing patient dose if an optimized radiographic technique is used. Rami Nachabe is an employee of Philips Health Care; Keith Strauss is a paid consultant of Philips Health Care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks

Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Addressing Two-Phase Flow Maldistribution in Microchannel Heat and Mass Exchangers

Multiphase flow phenomena in single micro and minichannels have been widely studied. Characteristics of two-phase flow through a large array of microchannels are investigated here. An air–water mixture is used to represent the two phases flowing through a microchannel array representative of those employed in practical applications. Flow distribution of the air and water flow across 52 parallel microchannels of 0.4 mm hydraulic diameter is visually investigated using high-speed photography. Two microchannel configurations are studied and compared, with mixing features incorporated into the second configuration. Slug and annular flow regimes are observed in the channels. Void fractions and interfacial areas are calculated for each channel from these observations. The flow distribution is tracked at various lengths along the microchannel array sheets. Statistical distributions of void fraction and interfacial area along the microchannel array are measured. The design with mixing features yields improved flow distribution. Void fraction and interfacial area change along the length of the second configuration, indicating a change in fluid distribution among the channels. The void fraction and interfacial area results are used to predict the performance of different microchannel array configurations for heat and mass transfer applications. Results from this study can help inform the design of compact thermal-fluid energy systems.

Engineering↗
DOE OSTI2019

Visualizing feedstock siting in biomass production: Tradeoffs between economic and water quality objectives

New domestic, renewable energy resources must be considered to increase energy security in the U.S. Ethanol production through second-generation (cellulosic) feedstocks will help the U.S. meet the legislative Renewable Fuel Standard, which mandates 36 billion gallons of renewable fuels by 2022. Yet, conversion of cropland to meet the cellulosic feedstock production goals may have unforeseen environmental consequences. Here, using Soil Water Assessment Tool (SWAT) outputs and National Agricultural Statistics Service (USDA NASS) economic data, we conducted a spatial optimization of bioenergy feedstock introduction into the Arkansas White-Red River Basin based on water quality and economic objectives, subject to constraints on total land conversion. Results displayed tradeoffs between bioenergy yield for three crops (switchgrass, sorghum and poplar) and land rent objectives. Optimal solutions tended to prioritize conversion of land in eastern AWR subbasins where yield and water quality objective improvements were greatest. A small number of subbasins contributed to basin-wide water quality improvements, whereas subbasins contributing to economic benefits were more spatially dispersed, indicating that water quality responses are more likely to constrain feedstock placement. Biomass production targets can be met vianumerous spatial arrangements, whereas marginal improvement in water quality objectives can best be achieved by selectively siting perennial feedstocks in the eastern half of the region.

09 BIOMASS FUELS↗
DOE OSTI2025

Q -score as a reliability measure for protein, nucleic acid and small-molecule atomic coordinate models derived from 3DEM maps

Atomic coordinate models are important for the interpretation of 3D maps produced with cryoEM and cryoET (3D electron microscopy; 3DEM). In addition to visual inspection of such maps and models, quantitative metrics can inform about the reliability of the atomic coordinates, in particular how well the model is supported by the experimentally determined 3DEM map. A recently introduced metric, Q-score, was shown to correlate well with the reported resolution of the map for well fitted models. Here, we present new statistical analyses of Q-score based on its application to ∼10 000 maps and models archived in the EMDB (Electron Microscopy Data Bank) and PDB (Protein Data Bank). Further, we introduce two new metrics based on Q-score to represent each map and model relative to all entries in the EMDB and those with similar resolution. We explore through illustrative examples of proteins, nucleic acids and small molecules how Q-scores can indicate whether the atomic coordinates are well fitted to 3DEM maps and also whether some parts of a map may be poorly resolved due to factors such as molecular flexibility, radiation damage and/or conformational heterogeneity. These examples and statistical analyses provide a basis for how Q-scores can be interpreted effectively in order to evaluate 3DEM maps and atomic coordinate models prior to publication and archiving.

B factors↗
DOE OSTI2013

Quantitative Monte Carlo-based holmium-166 SPECT reconstruction

Purpose: Quantitative imaging of the radionuclide distribution is of increasing interest for microsphere radioembolization (RE) of liver malignancies, to aid treatment planning and dosimetry. For this purpose, holmium-166 ({sup 166}Ho) microspheres have been developed, which can be visualized with a gamma camera. The objective of this work is to develop and evaluate a new reconstruction method for quantitative {sup 166}Ho SPECT, including Monte Carlo-based modeling of photon contributions from the full energy spectrum.Methods: A fast Monte Carlo (MC) simulator was developed for simulation of {sup 166}Ho projection images and incorporated in a statistical reconstruction algorithm (SPECT-fMC). Photon scatter and attenuation for all photons sampled from the full {sup 166}Ho energy spectrum were modeled during reconstruction by Monte Carlo simulations. The energy- and distance-dependent collimator-detector response was modeled using precalculated convolution kernels. Phantom experiments were performed to quantitatively evaluate image contrast, image noise, count errors, and activity recovery coefficients (ARCs) of SPECT-fMC in comparison with those of an energy window-based method for correction of down-scattered high-energy photons (SPECT-DSW) and a previously presented hybrid method that combines MC simulation of photopeak scatter with energy window-based estimation of down-scattered high-energy contributions (SPECT-ppMC+DSW). Additionally, the impact of SPECT-fMC on whole-body recovered activities (A{sup est}) and estimated radiation absorbed doses was evaluated using clinical SPECT data of six {sup 166}Ho RE patients.Results: At the same noise level, SPECT-fMC images showed substantially higher contrast than SPECT-DSW and SPECT-ppMC+DSW in spheres ≥17 mm in diameter. The count error was reduced from 29% (SPECT-DSW) and 25% (SPECT-ppMC+DSW) to 12% (SPECT-fMC). ARCs in five spherical volumes of 1.96–106.21 ml were improved from 32%–63% (SPECT-DSW) and 50%–80% (SPECT-ppMC+DSW) to 76%–103% (SPECT-fMC). Furthermore, SPECT-fMC recovered whole-body activities were most accurate (A{sup est}= 1.06 × A − 5.90 MBq, R{sup 2}= 0.97) and SPECT-fMC tumor absorbed doses were significantly higher than with SPECT-DSW (p = 0.031) and SPECT-ppMC+DSW (p = 0.031).Conclusions: The quantitative accuracy of {sup 166}Ho SPECT is improved by Monte Carlo-based modeling of the image degrading factors. Consequently, the proposed reconstruction method enables accurate estimation of the radiation absorbed dose in clinical practice.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Deactivation and decommissioning web log analysis using big data technology - 15710

The D and D Knowledge Management Information Tool (D and D KM- IT) is a web-based knowledge management information tool built for the D and D user community. Big data is a massive volume of structured and unstructured datasets which is so large that it's difficult to process using traditional database techniques. Web logs are the repository of files which are generated automatically for any operation on the web site. Web log files generated from the D and D KM-IT will be processed using the Apache Hadoop Framework to extract meaningful data. Hadoop is an open-source software framework for storing and processing big data in a distributed environment on large clusters of commodity hardware. Hadoop framework consists of two main layers: Hadoop distributed file system (HDFS) and the Execution engine (MapReduce). Automatic parallelization and distribution; clean and simple programming abstraction. Implementation: Web log files from the D and D KM-IT server are fetched using Apache Flume and loaded into the Hadoop distributed file system. Using the Apache Hcatalog tool, the web log data is parsed and converted into a structured table format. A Pig program is developed to query the tables to retrieve and store the significant information onto the HDFS. The extracted data is fed to business visualization tools such as Microsoft Excel for analysis and reporting. Results The reports generated through the web log data processing will be analyzed to improve the usability and performance of the D and D KM-IT. Conclusion/Future Work: User keyword information can be explored to add new trending topics to the D and D KM-IT application. Statistics generated can be compared to analysis reports from existing analytical tools.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Effect of low tube voltage and low iodine concentration abdominal CT on image quality and radiation dose in children: preliminary study

Purpose: To evaluate the image quality of a double-low protocol (low tube voltage and low iodine concentration) for abdominal CT in children. Materials and methods: The double-low protocol was compared to the conventional protocol in pediatric patients weighing less than 40 kg from May 2016 to December 2016. Double-low protocol (Group A, n = 18): tube voltage, 70 kVp; and iodine concentration,: 250 mgI/mL versus Conventional protocol (Group B, n = 13): tube voltage, 80–100 kVp; and iodine concentration, 350 mgI/mL. Mean attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between the two groups. Image contrast, noise, beam-hardening artifacts, and overall image quality were subjectively scored. Reader performance for correctly differentiating two groups by visual assessment was evaluated. Radiation dose and total iodine load were recorded. Results: The mean attenuations of the portal vein and liver and the mean image noise in Group A were higher than in Group B (p = 0.04, 0.03, 0.004, respectively). The mean SNR and CNR of the main portal vein and liver were lower in Group A without any statistically significant difference. There were no statistically significant differences between the two groups in qualitative analysis (image contrast, image noise, and overall image quality) with substantial agreement between the reviewers (weighted kappa values; 0.59–0.76). Significantly diminished radiation dose and iodine load were observed in Group A compared with Group B (25.0%, 36.8% reduction; p = 0.007, 0.006, respectively). Conclusion: The double-low protocol was feasible for pediatric abdominal CT and reduced both radiation dose and iodine load, while maintaining image quality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images

Purpose: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. Methods: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework. Quantitative evaluation of the reconstructed images was based on the signal-to-noise ratio (SNR), local binary pattern (LBP) and histogram of oriented gradients (HOG) metrics. Results: It was observed that lung and outside tissue regions have different MRF patterns predicted from the NdCT. Visual inspection showed that our method obviously outperformed the traditional FBP method. Comparing with the region-smoothing PWLS method, our method has, in average, 13% increase in SNR, 15% decrease in LBP difference, and 12% decrease in HOG difference from reference standard for all regions of interest, which indicated the superior performance of the proposed method in terms of image resolution and texture preservation. Conclusion: We proposed a novel LdCT image reconstruction method by learning similar image characteristics from a set of NdCT images, and the to-be-learnt NdCT image does not need to be scans from the same subject. This approach is particularly important for enhancing image quality in radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Visualizing red blood cell sickling and the effects of inhibition of sphingosine kinase 1 using soft X-ray tomography

ABSTRACT Sickle cell disease is a destructive genetic disorder characterized by the formation of fibrils of deoxygenated hemoglobin, leading to the red blood cell (RBC) morphology changes that underlie the clinical manifestations of this disease. Using cryogenic soft X-ray tomography (SXT), we characterized the morphology of sickled RBCs in terms of volume and the number of protrusions per cell. We were able to identify statistically a relationship between the number of protrusions and the volume of the cell, which is known to correlate to the severity of sickling. This structural polymorphism allows for the classification of the stages of the sickling process. Recent studies have shown that elevated sphingosine kinase 1 (Sphk1)-mediated sphingosine 1-phosphate production contributes to sickling. Here, we further demonstrate that compound 5C, an inhibitor of Sphk1, has anti-sickling properties. Additionally, the variation in cellular morphology upon treatment suggests that this drug acts to delay the sickling process. SXT is an effective tool that can be used to identify the morphology of the sickling process and assess the effectiveness of potential therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗