Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene

Multimodal Representation Learning and Set Attention for LWIR In-Scene Atmospheric Compensation

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this paper to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors (T;H2O;O3) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared to Fast Line-of-Sight Atmospheric Analysis of Hypercubes - Infrared (FLAASH-IR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate 8 times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real-world, time sensitive tasks

Carbon Nanotube Growth Rate Regression using Support Vector Machines and Artificial Neural Networks

Control of carbon nanotube growth rates is a challenging problem, thus limiting their use in a wide variety of applications. Carbon nanotubes demonstrating metallic or semiconducting properties allow for high strength materials and high current densities in smaller wires. Due to their simplicity and desirable properties, SWNTs are considered for chiral-selective growth experiments. A machine learning based approach for chiral selective growth of SWNTs using a laser-induced chemical vapor deposition growth system is introduced. Determination of SWNT growth rates is performed through in-situ Raman spectroscopy using a 532 nm excitation laser. A total of 450 experiments are performed and a subset of 121 experiments are used to train a SWNT vs. MWNT SVM classifier. The SVM classifier determines parameter values for 99% probability or greater of SWNT growth with an accuracy of 95.04%. This subset of synthesis parameters are evaluated using an ANN to predict SWNT growth rates and growth lengths. Analysis of the ANN growth rate model showed a peak in growth rate as a function of water concentration and growth temperature. The growth length model was trained using the same growth experiments as the growth rate model and showed a 80% reduction in validation errors. The growth length model also identified an optimal water/ethylene ratio for maximizing SWNT length

Dielectric Breakdown in Silica–Amorphous Polymer Nanocomposite Films: The Role of the Polymer Matrix

The ultimate energy storage performance of an electrostatic capacitor is determined by the dielectric characteristics of the material separating its conductive electrodes. Polymers are commonly employed due to their processability and high breakdown strength; however, demands for higher energy storage have encouraged investigations of ceramic–polymer composites. Maintaining dielectric strength, and thus minimizing flaw size and heterogeneities, has focused development toward nanocomposite (NC) films; but results lack consistency, potentially due to variations in polymer purity, nanoparticle surface treatments, nanoparticle size, and film morphology. To experimentally establish the dominant factors in broad structure–performance relationships, we compare the dielectric properties for four high-purity amorphous polymer films (polymethyl methacrylate, polystyrene, polyimide, and poly-4-vinylpyridine) incorporating uniformly dispersed silica colloids (up to 45% v/v). Factors known to contribute to premature breakdownfield exclusion and agglomerationhave been mitigated in this experiment to focus on what impact the polymer and polymer–nanoparticle interactions have on breakdown. Our findings indicate that adding colloidal silica to higher breakdown strength amorphous polymers (polymethyl methacrylate and polyimide) causes a reduction in dielectric strength as compared to the neat polymer. Alternatively, low breakdown strength amorphous polymers (poly-4-vinylpyridine and especially polystyrene) with comparable silica dispersion show similar or even improved breakdown strength for 7.5–15% v/v silica. At ∼15% v/v or greater silica content, all the polymer NC films exhibit breakdown at similar electric fields, implying that at these loadings failure becomes independent of polymer matrix and is dominated by silica

Association of omega 3 polyunsaturated fatty acids with incident chronic kidney disease : pooled analysis of 19 cohorts

Peer reviewe

Role of Polyunsaturated Fat in Modifying Cardiovascular Risk Associated With Family History of Cardiovascular Disease: Pooled De Novo Results From 15 Observational Studies.

BACKGROUND: It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium. METHODS: Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction. RESULTS: After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions. CONCLUSIONS: A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD