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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A validation of the fibre orientation and fibre length attrition prediction for long fibre-reinforced thermoplastics

To improve the mechanical performance of polymeric parts, fibre reinforcement has established in industrial applications during the last decades. Next to the widely used Short Fibre-reinforced Thermoplastics (SFT) the use of Long Fibre-reinforced Thermoplastics (LFT) is increasingly growing. Especially for non-polar polymeric matrices like polypropylene (PP), longer fibres can significantly improve the mechanical performance. As with every kind of discontinuous fibre reinforcement the fibre orientations (FO) show a high impact on the mechanical properties. On the contrary to SFT where the local fibre length distribution (FLD) can be often neglected, for LFT the FLD show a high impact on the material’s properties and has to be taken into account in equal measure to the FOD. Recently numerical models are available in commercial filling simulation software and allow predicting both the local FOD and FLD in LFT parts. The aim of this paper is to compare i.) the FOD results and ii) the FLD results from available orientation- and fibre length attrition-models to those obtained from experimental data. The investigations are conducted by the use of different injection moulded specimens made from long glass fibre reinforced PP. In order to determine the FOD, selected part sections are examined by means of Computed Tomographic (CT) analyses. The fully three dimensional measurement of the FOD is then performed by digital image processing using grey scale correlation. The FLD results are also obtained by using digital image processing after a thermal pyrolytic separation of the polymeric matrix from the fibres. Further the FOD and the FLD are predicted by using a reduced strain closure (RSC) as well as an anisotropic rotary diffusion - reduced strain closure model (ARD-RSC) and Phelps-Tucker fibre length attrition model implemented in the commercial filling software Moldflow, Autodesk Inc., San Rafael, CA, USA.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Processing of X-ray snapshots from crystals in random orientations

A new method for the treatment of partial reflections from X-ray snapshots is implemented in the program package nXDS, which yields intensity data of almost the same quality as those obtained by the classical rotation method. A functional expression is introduced that relates scattered X-ray intensities from a still or a rotation snapshot to the corresponding structure-factor amplitudes. The new approach was implemented in the program nXDS for processing monochromatic diffraction images recorded by a multi-segment detector where each exposure could come from a different crystal. For images containing indexable spots, the intensities of the expected reflections and their variances are obtained by profile fitting after mapping the contributing pixel contents to the Ewald sphere. The varying intensity decline owing to the angular distance of the reflection from the surface of the Ewald sphere is estimated using a Gaussian rocking curve. This decline is dubbed ‘Ewald offset correction’, which is well defined even for still images. Together with an image-scaling factor and other corrections, an explicit expression is defined that predicts each recorded intensity from its corresponding structure-factor amplitude. All diffraction parameters, scaling and correction factors are improved by post-refinement. The ambiguous case of a lower point group than the lattice symmetry is resolved by a method reminiscent of the technique of ‘selective breeding’. It selects the indexing alternative for each image that yields, on average, the highest correlation with intensities from all other images. Processing a test set of rotation images by XDS and treating the same images by nXDS as snapshots of crystals in random orientations yields data of comparable quality, clearly indicating an anomalous signal from Se atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Texture analysis improves level set segmentation of the anterior abdominal wall

Purpose: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention.Methods: In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors’ approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.Results: Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2–5 mm) for methods that did not use the texture.Conclusions: The authors’ approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Super-resolution for scanning light stimulation systems

Super-resolution (SR) is a technique used in digital image processing to overcome the resolution limitation of imaging systems. In this process, a single high resolution image is reconstructed from multiple low resolution images. SR is commonly used for CCD and CMOS (Complementary Metal-Oxide-Semiconductor) sensor images, as well as for medical applications, e.g., magnetic resonance imaging. Here, we demonstrate that super-resolution can be applied with scanning light stimulation (LS) systems, which are common to obtain space-resolved electro-optical parameters of a sample. For our purposes, the Projection Onto Convex Sets (POCS) was chosen and modified to suit the needs of LS systems. To demonstrate the SR adaption, an Optical Beam Induced Current (OBIC) LS system was used. The POCS algorithm was optimized by means of OBIC short circuit current measurements on a multicrystalline solar cell, resulting in a mean square error reduction of up to 61% and improved image quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

TU-F-CAMPUS-I-02: Contrast Enhanced Cone Beam CT Imaging with Dual- Gantry Image Acquisition and Constrained Iterative Reconstruction-a Simulation Study for Liver Imaging Application

Purpose: Contrast time-density curves may help differentiate malignant tumors from normal tissues or benign tumors. Repetitive scans using conventional CT or cone beam CT techniques, which Result in unacceptably high dose, may not achieve the desired temporal resolution. In this study we describe and demonstrate a 4D imaging technique for imaging and quantifying contrast flows requiring only one or two 360° scans. Methods: A dual-gantry system is used to simultaneously acquire two projection images at orthogonal orientations. Following the scan, each or both of the two 360° projection sets are used to reconstruct an average contrast enhanced image set which is then segmented to form a 3D contrast map. Alternatively, a pre-injection scan may be made and used to reconstruct a pre-injection image set which is subtracted from the post-injection image set to form the 3D contrast map. Each of the two 360° projection sets is divided into 12 subsets, thus creating 12 pairs of 30° limited angle projection sets, each corresponding to a time spanning over 1/12 of the scanning time. Each pair of the projection sets are reconstructed as a time specific 3D image set with the maximum likelihood estimation iterative algorithm using the contrast map as the constraint. As a demonstration, a 4D abdominal phantom was constructed from clinical CT images with blood flow through the normal tissue and a tumor modeled and imaging process simulated. Results: We have successfully generated a 4D image phantom, and calculated the projection images. The time density curves derived from the reconstructed image set matched well with the flow model used to generate the phantom. Conclusion: Dual-gantry image acquisition and constrained iterative reconstruction algorithm may help to obtain time-density curves of contrast agents in blood flows, which may help differentiate malignant tumors from normal tissues or benign tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

The Gemini NICI planet-finding campaign: The companion detection pipeline

We present high-contrast image processing techniques used by the Gemini NICI Planet-Finding Campaign to detect faint companions to bright stars. The Near-Infrared Coronographic Imager (NICI) is an adaptive optics instrument installed on the 8 m Gemini South telescope, capable of angular and spectral difference imaging and specifically designed to image exoplanets. The Campaign data pipeline achieves median contrasts of 12.6 mag at 0.''5 and 14.4 mag at 1'' separation, for a sample of 45 stars (V = 4.3-13.9 mag) from the early phase of the campaign. We also present a novel approach to calculating contrast curves for companion detection based on 95% completeness in the recovery of artificial companions injected into the raw data, while accounting for the false-positive rate. We use this technique to select the image processing algorithms that are more successful at recovering faint simulated point sources. We compare our pipeline to the performance of the Locally Optimized Combination of Images (LOCI) algorithm for NICI data and do not find significant improvement with LOCI.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Focus enhancement in long schlieren imaging systems using corrector lenses

Long schlieren imaging systems, where the parallel light test section is longer than the focal length of the focusing schlieren optics, have a limited region in the test section in which any occluding object, such as a wind tunnel model, is in focus in the schlieren image. Corrector lenses are introduced here to alter the location in the test section where image focus is achieved. Corrector lenses with focal lengths varying from −1000 to −100mm were studied. Lens-type inline and z -type mirror schlieren systems were experimentally tested with multiple collecting optic focal lengths to characterize the changes in focal position. An automated image processing method was used to determine the plane of best focus from image sequences. The introduction of the corrector lens was observed to move the location of best focus within the schlieren system test section while also causing a decrease in the focal sharpness of the images and altering the magnification. The thin lens equation was found to provide a good estimate of the focal location change in the schlieren imaging systems with the addition of the corrector lens.

Torres, Sivana M.↗
DOE OSTI2019

Opportunistic bone density screening for the abdominal radiologist using colored CT images: a pilot retrospective study

Purpose: The purpose of the study was to develop an accurate and reproducible method for detecting low spinal bone density on abdominal CT images. Methods: For this IRB-approved HIPAA-compliant single-center retrospective study, nonenhanced CT images of the lower abdomen were obtained in 631 African-American participants. Mean attenuation of L3/L4 was associated with quantitative CT bone density (QCT) in a randomly selected training cohort (N = 511), and receiver operating characteristics analysis was used to identify the optimal mean attenuation threshold for differentiating normal from low bone density. Custom image processing software was used to generate grayscale and colored CT images of the midline spine, with green for normal and red for low bone density. Five radiologists independently assessed bone density at L3/L4 in a validation cohort (N = 120) using various methods: QCT, visual assessment of sagittal grayscale images (Grayscale), quantitative measurement of mean attenuation on a midline sagittal image (Attenuation), and visual assessment of a midline sagittal colored image (Color). Accuracy was calculated using the average QCT bone density as a reference standard. Inter-observer agreement was assessed using intraclass correlation coefficient (ICC). Results: The optimal mean attenuation threshold for differentiating normal from low bone density at L3/L4 was 145 Hounsfield Units. The average accuracy of Grayscale, Attenuation, and Color methods was 58, 87, and 91% (p < 0.001), respectively. Inter-observer agreement was poor for Grayscale (ICC: 0.20; 95% CI 0.12, 0.28) and excellent for both Attenuation (ICC: 0.85; 95% CI 0.73, 0.91) and Color methods (ICC: 0.87; 95% CI 0.83, 0.90). Conclusion: Detection of low spinal bone density using colored abdominal CT images was highly accurate and reproducible.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Perona Malik anisotropic diffusion model using Peaceman Rachford scheme on digital radiographic image

In image processing, it is important to remove noise without affecting the image structure as well as preserving all the edges. Perona Malik Anisotropic Diffusion (PMAD) is a PDE-based model which is suitable for image denoising and edge detection problems. In this paper, the Peaceman Rachford scheme is applied on PMAD to remove unwanted noise as the scheme is efficient and unconditionally stable. The capability of the scheme to remove noise is evaluated on several digital radiography weld defect images computed using MATLAB R2009a. Experimental results obtained show that the Peaceman Rachford scheme improves the image quality substantially well based on the Peak Signal to Noise Ratio (PSNR). The Peaceman Rachford scheme used in solving the PMAD model successfully removes unwanted noise in digital radiographic image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Digital image analysis to quantify carbide networks in ultrahigh carbon steels

A method has been developed and demonstrated to quantify the degree of carbide network connectivity in ultrahigh carbon steels through digital image processing and analysis of experimental micrographs. It was shown that the network connectivity and carbon content can be correlated to toughness for various ultrahigh carbon steel specimens. The image analysis approach first involved segmenting the carbide network and pearlite matrix into binary contrast representations via a grayscale intensity thresholding operation. Next, the carbide network pixels were skeletonized and parceled into braches and nodes, allowing the determination of a connectivity index for the carbide network. Intermediate image processing steps to remove noise and fill voids in the network are also detailed. The connectivity indexes of scanning electron micrographs were consistent in both secondary and backscattered electron imaging modes, as well as across two different (50 × and 100 ×) magnifications. Results from ultrahigh carbon steels reported here along with other results from the literature generally showed lower connectivity indexes correlated with higher Charpy impact energy (toughness). A deviation from this trend was observed at higher connectivity indexes, consistent with a percolation threshold for crack propagation across the carbide network. - Highlights: • A method for carbide network analysis in steels is proposed and demonstrated. • ImageJ method extracts a network connectivity index from micrographs. • Connectivity index consistent in different imaging conditions and magnifications. • Impact energy may plateau when a critical network connectivity is exceeded.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials

In this work, we present a description of an electroluminescence (EL) apparatus, easily sourced from commercially available components, with a quantitative image processing platform that demonstrates feasibility for the widespread utility of EL imaging as a characterization tool. We validated our system using a Gage R&R analysis to fnd a variance contribution by the measurement system of 80.56%, which is typically unacceptable, but through quantitative image processing and development of correction factors a variance contribution by the measurement system of 2.41% was obtained. We further validated the system by quantifying the signal-to-noise ratio (SNR) and found values consistent with other systems published in the literature, at SNR values of 10-100, albeit at exposure times of greater than 1 s compared to 10 ms for other systems. Finally, this SNR value range is acceptable for image feature recognition, providing the opportunity for widespread data acquisition and large scale data analytics of photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2024

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

A Streamlined and Standardized Benthic Habitat Mapping Approach for Marine and Hydrokinetic Site Environmental Assessments

We have combined two seafloor mapping techniques to efficiently map benthic habitat conditions. Multibeam echosounder (MBES) surveys provide detailed bathymetry and backscatter mosaics. Sediment profile and plan view imaging (SPI/PV) rapidly provides dense ground truth data on seabed physical and biological features. A key innovation of this project has been the development of a computer-assisted image processing system that standardizes the SPI data generation process. A unique combination of machine- and deep-learning techniques for grain-size classification, sediment–water interface delineation, and object detection (e.g., infauna and feeding voids) is described. An automated SPI image analysis system provides a standardized and cost-effective approach for image processing. Combining MBES with SPI/PV camera technology provides a first tier approach to rapidly documenting seabed physical and biological conditions in high resolution at MHK sites. When coupled with computer automated data extraction from the imagery, this approach provides a standardized and cost-effective method to establish and monitor benthic habitat conditions over time.

Benthic Habitat Maping, Sediment-Profile Imaging, ↗
DOE OSTI2015

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗
DOE OSTI2015

Developing stereo image based robot control system

Application of image processing is developed in various field and purposes. In the last decade, image based system increase rapidly with the increasing of hardware and microprocessor performance. Many fields of science and technology were used this methods especially in medicine and instrumentation. New technique on stereovision to give a 3-dimension image or movie is very interesting, but not many applications in control system. Stereo image has pixel disparity information that is not existed in single image. In this research, we proposed a new method in wheel robot control system using stereovision. The result shows robot automatically moves based on stereovision captures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗