Results for “Image Processing” — Page 87

NASA NTRSDate not supplied

Milli-Arcsecond (MAS) Imaging with SmallSat Enabled Super-resolution

The objective of this project is to demonstrate a new imaging technology with unprecedented capabilities for scientific exploration. This technology innovation will enable extremely high-resolution imaging capability, otherwise unattainable with conventional approaches. It is based on a unique combination of novel diffractive optical sensing (photon sieve), advanced image processing, and small satellite formation flying. The spatial resolution of current solar instruments are more than 100 times coarser than the size of energy dissipation features in coronal heating (<100 km) since conventional optics cannot be figured to near diffraction-limited accuracy at extreme ultraviolet (EUV) wavelengths. This project will demonstrate the feasibility of imaging of a few milli-arcseconds (MAS) to study the structure and development of these dissipation regions.

Farzad Kamalabadi↗

NASA NTRS1996

A Noncontact Measurement Technique for the Density and Thermal Expansion Coefficient of Solid and Liquid Materials

A noncontact measurement technique for the density and the thermal expansion refractory materials in their molten as well as solid phases is presented. This technique is based on the video image processing of a levitated sample. Experiments were performed using the high-temperature electrostatic levitator (HTESL) at the Jet Propulsion Laboratory in which 2-3 mm diameter samples can be levitated, melted, and radiatively cooled in a vacuum. Due to the axisymmetric nature of the molten samples when levitated in the HTESL, a rather simple digital image analysis can be employed to accurately measure the volumetric change as a function of temperature. Density and the thermal expansion coefficient measurements were made on a pure nickel sample to test the accuracy of the technique in the temperature range of 1045-1565 C. The result for the liquid phase density can be expressed by p = 8.848 + (6.730 x 10(exp -4)) x T (degC) g/cu cm within 0.8% accuracy, and the corresponding thermal expansion coefficient can be expressed by Beta=(9.419 x 10(exp -5)) - (7.165 x 10(exp -9) x T (degC)/K within 0.2% accuracy.

Chung, Sang K.↗

NASA NTRS1997

Improvements for Image Compression Using Adaptive Principal Component Extraction (APEX)

The issues of image compression and pattern classification have been a primary focus of researchers among a variety of fields including signal and image processing, pattern recognition, data classification, etc. These issues depend on finding an efficient representation of the source data. In this paper we collate our earlier results where we introduced the application of the. Hilbe.rt scan to a principal component algorithm (PCA) with Adaptive Principal Component Extraction (APEX) neural network model. We apply these technique to medical imaging, particularly image representation and compression. We apply the Hilbert scan to the APEX algorithm to improve results

Ziyad, Nigel A.↗

NASA NTRS2011

Low Latency DESDynI Data Products for Disaster Response, Resource Management and Other Applications

We are developing onboard processor technology targeted at the L-band SAR instrument onboard the planned DESDynI mission to enable formation of SAR images onboard opening possibilities for near-real-time data products to augment full data streams. Several image processing and/or interpretation techniques are being explored as possible direct-broadcast products for use by agencies in need of low-latency data, responsible for disaster mitigation and assessment, resource management, agricultural development, shipping, etc. Data collected through UAVSAR (L-band) serves as surrogate to the future DESDynI instrument. We have explored surface water extent as a tool for flooding response, and disturbance images on polarimetric backscatter of repeat pass imagery potentially useful for structural collapse (earthquake), mud/land/debris-slides etc. We have also explored building vegetation and snow/ice classifiers, via support vector machines utilizing quad-pol backscatter, cross-pol phase, and a number of derivatives (radar vegetation index, dielectric estimates, etc.). We share our qualitative and quantitative results thus far.

applications↗

DOE OSTI2016

Bubble-Size Evolution during Polyurethane Foam Expansion

We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane (PMDI) foam used to encapsulate electronic components or to produce lightweight structural parts. The polyurethane of interest is a chemically blown foam, where carbon dioxide is produced via the reaction of water, a blowing agent, and isocyanate. Here, we take a careful look at the evolution of the bubble sizes during blowing. This information will help the development of subgrid models to predict bubble formation, growth, coalescence and collapse, drainage, and, hence, eventually the development of engineering models to predict foam expansion into a mold. Close-up views of bubbles at a transparent wall of a narrow, temperature-controlled channel are recorded during the foaming reaction and analyzed with image processing. Because these bubbles are pressed against the wall, the bubble sizes in the last frames after the expansion has stopped are compared to scanning electron microscope (SEM) images of the interior of some of the cured samples to determine if the presence of the wall significantly changes the bubble sizes. In addition, diffusing wave spectroscopy (DWS) is used to determine the average bubble sizes across the width of a similar channel as the bubbles change with time. DWS also gives information about microstructural changes as bubbles rearrange upon bubble collapse or coalescence. In this paper we conclude qualitatively that the bubble size distribution is heavily dependent on the formulation of foam being tested, temperature, the height in the foam bar, the proximity to a wall, and the degree of over-packing.

36 MATERIALS SCIENCE↗

DOE OSTI2016

MO-B-201-00: Motion Management in Current Stereotactic Body Radiation Therapy (SBRT) Practice

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-B-201-01: Overcoming the Challenges of Motion Management in Current Lung SBRT Practice

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-B-201-02: Motion Management for Proton Lung SBR

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-B-201-03: MRI-Guided Tracking and Gating

The motion management in stereotactic body radiation therapy (SBRT) is a key to success for a SBRT program, and still an on-going challenging task. A major factor is that moving structures behave differently than standing structures when examined by imaging modalities, and thus require special considerations and employments. Understanding the motion effects to these different imaging processes is a prerequisite for a decent motion management program. The commonly used motion control techniques to physically restrict tumor motion, if adopted correctly, effectively increase the conformity and accuracy of hypofractionated treatment. The effective application of such requires one to understand the mechanics of the application and the related physiology especially related to respiration. The image-guided radiation beam control, or tumor tracking, further realized the endeavor for precision-targeting. During tumor tracking, the respiratory motion is often constantly monitored by non-ionizing beam sources using the body surface as its surrogate. This then has to synchronize with the actual internal tumor motion. The latter is often accomplished by stereo X-ray imaging or similar techniques. With these advanced technologies, one may drastically reduce the treated volume and increase the clinicians’ confidence for a high fractional ablative radiation dose. However, the challenges in implementing the motion management may not be trivial and is dependent on each clinic case. This session of presentations is intended to provide an overview of the current techniques used in managing the tumor motion in SBRT, specifically for routine lung SBRT, proton based treatments, and newly-developed MR guided RT. Learning Objectives: Through this presentation, the audience will understand basic roles of commonly used imaging modalities for lung cancer studies; familiarize the major advantages and limitations of each discussed motion control methods; familiarize the major advantages and limitations of each discussed radiation beam control methodology and tumor tacking method; understand the key points in motion management for a high quality SBRT program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Design optimization of MAPS-based detectors using a data-driven fast simulation approach

A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180 nm complementary metal-oxide-semiconductor imaging process. Modifications to the sensor’s periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65 nm Tower Partners Semiconductor Co. process.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Temperature Effects on Droplet Oscillation Decay with Application to Fuel Property Measurement

Observation of oscillation decay in droplets has been shown to be an effective approach in determining physical properties such as viscosity and surface tension for emerging biofuels using μl quantities. Herein this work extends the approach to higher temperatures relevant to fuel injection conditions for internal combustion engines. Experiments are conducted that use high-resolution strobed imaging of moving, heated, μm-sized, fuel droplets to capture shape oscillation decay through image processing and analysis. Two fuels are investigated, iso-butanol and a primary reference fuel (PRF 84), which is a mixture of iso-octane and n-heptane. A piezoelectric droplet generator is used to generate a continuous train of single droplets, which are given an initial perturbation and subsequently undergo damped oscillations, captured using strobed imaging. Surface tension and viscosity calculated at four different temperatures ranging from 30°C-55°C using the frequency and decay time associated with the fundamental mode are found to be within ~ 10% of reference values obtained from the literature. Complementary numerical simulations are performed that utilize a volume-of-fluid approach to track transient droplet oscillation phenomena along with heat and mass transfer in a 2D axisymmetric domain. Simulations, where droplet size and temperature can be independently varied, capture an expected decrease in oscillation frequency and increase in decay time, with increase in fuel temperature. The simulations are further used to study the relative contribution to deviations in surface tension and viscosity predictions due to heat and mass transfer from the droplet, as well as viscous effects violating the inviscid flow assumption in the droplet oscillation theory. Mass loss effects are found to be negligible. Temperature change due to heat transfer is found to have the next highest sensitivity, particularly for the more volatile PRF 84, which has a lower viscosity and associated Ohnesorge number. For isobutanol, which has a higher viscosity and Ohnesorge number, viscous effects contribute the most to deviation in fuel property predictions.

09 BIOMASS FUELS↗

DOE OSTI2016

Sky camera geometric calibration using solar observations

A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. The performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. In conclusion, calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Adapting the Reconfigurable SpaceCube Processing System for Multiple Mission Applications

This paper will detail the use of SpaceCube in multiple space flight applications including the Hubble Space Telescope Servicing Mission 4 (HST-SM4), an International Space Station (ISS) radiation test bed experiment, and the main avionics subsystem for two separate ISS attached payloads. Each mission has had varying degrees of data processing complexities, performance requirements, and external interfaces. We will show the methodology used to minimize the changes required to the physical hardware, FPGA designs, embedded software interfaces, and testing.This paper will summarize significant results as they apply to each mission application. In the HST-SM4 application we utilized the FPGA resources to accelerate portions of the image processing algorithms more than 25 times faster than a standard space processor in order to meet computational speed requirements. For the ISS radiation on-orbit demonstration, the main goal is to show that we can rely on the commercial FPGAs and processors in a space environment. We describe our FPGA and processor radiation mitigation strategies that have resulted in our eight PowerPCs being available and error free for more than 99.99 of the time over the period of four years. This positive data and proven reliability of the SpaceCube on ISS resulted in the Department of Defense (DoD) selecting SpaceCube, which is replacing an older and slower computer currently used on ISS, as the main avionics for two upcoming ISS experiment campaigns. This paper will show how we quickly reconfigured the SpaceCube system to meet the more stringent reliability requirements

SpaceCube↗

NASA NTRS1992

A neural network prototyping package within IRAF

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Bazell, D.↗

DOE OSTI2021

High-speed phase contrast imaging of spray breakup of jet fuels under combusting conditions

X-ray phase contrast imaging is conducted at 90,517 Hz on a fuel spray to characterize the breakup of the bulk fuel into ligaments and individual droplets in a single-sector, swirl-stabilized combustor under reacting conditions. Phase contrast imaging is used to qualitatively assess spray breakup for two fuels: Jet-A, which represents a normal viscosity fuel, and a blend of JP-5 (64%) and farnesane (36% by volume), which represents a high-viscosity fuel, at three fuel-flow rates and two air inlet temperatures. Time-series images of the fuels show that atomization occurs much more rapidly at higher fuel pressure with thinner ligaments being rapidly shredded. The high-viscosity fuel is qualitatively observed to have markedly longer, thicker ligaments and larger droplet diameters after the primary breakup than the normal viscosity fuel. Using an image processing and droplet quantification strategy, the mean droplet velocity magnitudes were found to decrease with decreasing fuel pressure and fuel-flow rate. The increase in preheat temperature from 323 to 370 K results in an increase in the mean droplet velocity magnitude and a decrease in numerical average and Sauter mean diameter, indicating improved atomization. The high-viscosity fuel was found to have larger average droplet diameters at each condition than the normal viscosity fuel. The increased preheat is found to enhance the combustion efficiency, which is hypothesized to couple with the improved atomization due to the higher combustion temperatures. This indicates the importance of examining spray atomization and breakup under realistic combusting conditions for relevant fuels as the heat release directly couples with the atomization process.

42 ENGINEERING↗

DOE OSTI2024

Random projection using random quantum circuits

The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024

Kumaran, Keerthi (ORCID:0009000949125721)↗

NASA NTRS1984

Two-dimensional optical processing using one-dimensional input devices

Two-dimensional optical processing architectures that are implemented with one-dimensional input spatial light modulators are reviewed. The advanced state of the art of available one-dimensional devices and the flexibility that exists in the design of two-dimensional architectures with one-dimensional transducers leads to the implementation of the most powerful and versatile optical processors. Signal and image processing architectures of this type are discussed.

Psaltis, D.↗

DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗