Results for “Imaging & optical processing” — Page 13

NASA NTRS2018

Evaluation of X-Ray Reflectors by Optical Diffraction Patterns

Performance of X-ray reflectors affects that of X-ray mirrors. Modern X-ray mirrors have thousands of reflectors to gain large effective area. Evaluation of the reflectors is an important process in production of the mirrors. A diffraction pattern dominates reflector image when the parallel optical beam illuminates the reflector along its optical axis because the reflectors are used at grazing incident angles of around 1 deg and their effective width are 1–10 mm. A diffraction pattern from the entire reflector surface can be acquired at once with the aid of a lens. The diffraction pattern holds information of the surface profiles of the reflectors. To quantitatively evaluate the reflectors with the diffraction pattern, we created a diffraction pattern model by the wave optics with the ideal surface profile and fitted it to data. As a result, a correlation between fitting residual and the normal vector distribution of the surface profile was found. With our method, the reflectors can be evaluated and sorted out more efficiently.

Hayashi, Takayuki↗

NASA NTRS2017

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and record flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds and by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts.

SERVIR↗

NASA NTRS2012

Visual perception system and method for a humanoid robot

A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image.

Wells, James W.↗

NASA NTRS2007

Hybrid-dual-fourier tomographic algorithm for a fast three-dimensionial optical image reconstruction in turbid media

A reconstruction technique for reducing computation burden in the 3D image processes, wherein the reconstruction procedure comprises an inverse and a forward model. The inverse model uses a hybrid dual Fourier algorithm that combines a 2D Fourier inversion with a 1D matrix inversion to thereby provide high-speed inverse computations. The inverse algorithm uses a hybrid transfer to provide fast Fourier inversion for data of multiple sources and multiple detectors. The forward model is based on an analytical cumulant solution of a radiative transfer equation. The accurate analytical form of the solution to the radiative transfer equation provides an efficient formalism for fast computation of the forward model.

Alfano, Robert R.↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface. In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha-Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC for Lunar Exploration: Differential Dynamic Microscopy to Spectroscopy to Computer Vision

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRSDate not supplied

Recent Advances in Soft Matter Characterization Capabilities Developed at NASA GRC

In 1991, famous French scientist Pierre-Gilles de Genes was awarded Nobel prize for his impactful research in soft matter, more specifically polymers. He is defined as the founding father of soft matter. In his Nobel lecture (https://www.nobelprize.org/uploads/2018/06/gennes-lecture.pdf ) he described soft matter aka complex fluids as materials with two primary features – (a) complexity and (b) flexibility. The sub-categories of soft matter (e.g.- granular materials, polymers, foams, colloids etc.) are defined on the basis of Pierre-Gilles de Gennes’ definition. At NASA GRC, we are pushing the boundaries for fundamental study of soft matter on Lunar Surface. With regard to Lunar surface science, we are focusing on developing capabilities pertaining to granular materials and bio-soft/active matter to facilitate future efforts in ISRU and bio-ISRU capabilities. In order to achieve fundamental goals of soft matter research within the limitations of Lunar environment, the scientific capabilities need to be small, flexible, modular, off the shelf and the focus needs to be more on developing an interdisciplinary capability that leverages the recent growth in AI/ML and Computer Vision to augment our understanding of fundamental science. This strategy would allow us to reduce our resource requirement during launch, installation, and occupied real estate footprint on Lunar surface In this talk, we will go over 3 different capabilities that we have developed in house and in close collaboration – (a) Differential Dynamic Microscopy (DDM), (b) Portable In-situ Chemical Spectroscopy (PICS) and (c) Computer Vision Enabled Observation. At very high level, Differential Dynamic Microscopy (DDM) allows us to study the structure-property-process relation (microrheology) of bio-soft/active matter using optical microscope and improved image analysis capabilities. PICS uses AI/ML-based advanced signal deconvolution and analysis technique that can work with existing portable spectroscopy tools to perform materials analysis (e.g.- granular materials and bio-soft/active matter) inspection on the go. Finally, computer vision enabled analysis allows us to use simple camera images for 3D reconstruction of experimental process and tracking of objects of interest in an experiment. We expect that this detailed process will allow us reach a thorough understanding of soft matter in Lunar environment. The capabilities developed by us will help to validate and establish fundamental understanding in Lunar environment. This will, in turn, allow us to guide future space exploration missions and expand the knowledge base of the scientific and engineering communities.

Suman Sinha Ray↗

NASA NTRS2013

Phoenix Telemetry Processor

Phxtelemproc is a C/C++ based telemetry processing program that processes SFDU telemetry packets from the Telemetry Data System (TDS). It generates Experiment Data Records (EDRs) for several instruments including surface stereo imager (SSI); robotic arm camera (RAC); robotic arm (RA); microscopy, electrochemistry, and conductivity analyzer (MECA); and the optical microscope (OM). It processes both uncompressed and compressed telemetry, and incorporates unique subroutines for the following compression algorithms: JPEG Arithmetic, JPEG Huffman, Rice, LUT3, RA, and SX4. This program was in the critical path for the daily command cycle of the Phoenix mission. The products generated by this program were part of the RA commanding process, as well as the SSI, RAC, OM, and MECA image and science analysis process. Its output products were used to advance science of the near polar regions of Mars, and were used to prove that water is found in abundance there. Phxtelemproc is part of the MIPL (Multi-mission Image Processing Laboratory) system. This software produced Level 1 products used to analyze images returned by in situ spacecraft. It ultimately assisted in operations, planning, commanding, science, and outreach.

Stanboli, Alice↗

NASA NTRS1988

Image-gathering system design for information and fidelity

Image gathering and processing are assessed in terms of information and fidelity, and the relationship between these two figures of merit is examined. It is assumed that the system is linear and isoplanatic and that the signal and noise amplitudes are Gaussian, wide-sense stationary, and statistically independent. Within these constraints, it is found that the combined process of image gathering and reconstruction (which is intended to reproduce the output of the image-gathering system) behaves as optical, or photographic, image formation in that the informationally optimized design of the image-gathering system ordinarily does not maximize the fidelity of the reconstructed image. The combined process of image gathering and restoration (which is intended to reproduce the input of the image-gathering system) behaves more as a communication channel in that the informationally optimized design of the image-gathering system tends to maximize the fidelity of optimally restored representations of the input.

Huck, Friedrich O.↗

NASA NTRS1979

High-resolution X-ray observations of the Cassiopeia A supernova remnant with the Einstein Observatory

Cas A was observed with the imaging detectors on the Einstein X-ray Observatory. The mass of the expanding shell due to the interaction of the blast wave and the ambient medium is determined. This indicates that Cas A is probably not in the free expansion phase, but has not yet reached the adiabatic phase of expansion. Detailed comparison of X-ray, optical, and radio images shows various degrees of correlation, indicating that several processes for emission are present. An upper limit of 1,500,000 K is established for any stellar remnant that is below the expected temperature of a neutron star by a factor of about 5. The total mass of the SNR is found to be 10-30 solar masses

Murray, S. S.↗

NASA NTRS1982

Evaluation of ERIM optically processed SEASAT SAR data

The results of three studies on the radiometric and geometric properties of optically processed SEASAT SAR imagery are summarized. The accuracy with which the image scale can be predicted based upon a knowledge of the SAR platform and recording system parameters and the processor characteristics was evaluated. The considerations involved in making radiometric measurements from image films, the use of point targets for calibrating the effects of Doppler spectrum shifts on the radiometric calibration of the SAR image data over extended swath lengths was evaluated

Shuchman, R. A.↗

NASA NTRS1989

Optical Processing With Photorefractive Semiconductors

Experimental phase-conjugate four-wave-mixing apparatus used to demonstrate capabilities of GaAs (and potentially of other photorefractive semiconductors like InP and CdTe) for optical processing of information. With modifications, performs any of three basic image-processing functions: transfer to different light beam, enhancement of edges, and autocorrelation. Includes crystal of GaAs of 5 by 9 by 9 mm with cubic crystalline axes. Advantages include high speed and compatibilty with other semiconductor devices.

Cheng, Li-Jen↗

NASA NTRS1989

Optical Matrix.Matrix Multiplier

Proposed apparatus for optical multiplication of two matrices based on Stanford optical vector.matrix multiplier. Does not require redundant representation of one of matrices. Because apparatus performs multiplication in fully parallel manner, incorporated as subsystem into large optical-processing system. Optical matrix.matrix multiplier processes inputs in fully parallel fashion, without redundant matrix images or ancillary intermediate electronic processing.

Gheen, Gregory↗

NASA NTRS1988

An albedo map of P/Halley on 13 March 1986

An albedo map is presented of comet Halley from a 10 micrometer image taken from the Wyoming Infrared Observatory on 13 Mar. 1986, and a 7311 A CCD image taken from the Anglo-Australian Telescope about an hour later. To construct this map, the CCD image was first converted from 0.49 arcsec/pixel to 1 arcsec/pixel to match the scale of the 10 micron image, then both were calibrated in lambda F sub lambda units. According to the albedo map, most of the inner coma of Halley lies between gamma = 0.04 and 0.08. The overall smoothness of the map is remarkable considering the large dynamic range of the optical and IR maps, and the differences in spatial resolution and image processing.

Hayward, Thomas L.↗

NASA NTRS1991

Design of an automated imaging system for use in a space experiment

An experiment, occurring in an orbiting platform, examines the mass transfer across gas-liquid and liquid-liquid interfaces. It employs an imaging system with real time image analysis. The design includes optical design, imager selection and integration, positioner control, image recording, software development for processing and interfaces to telemetry. It addresses the constraints of weight, volume, and electric power associated with placing the experiment in the Space Shuttle cargo bay. Challenging elements of the design are: imaging and recording of a 200-micron-diameter bubble with a resolution of 2 microns to serve a primary source of data; varying frame rates from 500 per second to 1 frame per second, depending on the experiment phase; and providing three-dimensional information to determine the shape of the bubble.

Hartz, William G.↗

NASA NTRS2000

The Fundamental Physical Processes Producing and Controlling Stellar Coronal/Transition Region/Chromospheric Activity and Structure

Our LTSA (Long Term Space Astrophysics) research has utilized current NASA and ESA spacecraft, supporting ground-based IR, radio, and sub-mm telescopes, and the extensive archives of HST (Hubble Space Telescope), IUE (International Ultraviolet Explorer), ROSAT, EUVE (Extreme Ultraviolet Explorer), and other missions. Our research effort has included observational work (with a nonnegligible groundbased component), specialized processing techniques for imaging and spectral data, and semiempirical modelling, ranging from optically thin emission measure studies to simulations of optically thick resonance lines. In our previous LTSA efforts, we have had a number of major successes, including most recently: organizing and carrying out an extensive cool star UV survey in HST cycle eight; obtaining observing time with new instruments, such as Chandra and XMM (X-ray Multi-Mirror) in their first cycles; collaborating with the Chandra GTO program and participating with the Chandra Emission Line Project on multi-wavelength observations of HR 1099 and Capella. These are the main broad-brush themes of our previous investigation: a) Where do Coronae Occur in the Hertzsprung-Russell Diagram? b) Winds of Coronal and Noncoronal Stars; c) Activity, Age, Rotation Relations; d) Atmospheric Inhomogeneities; e) Heating Mechanisms, Subcoronal Flows, and Flares; f) Development of Analysis and Modelling Tools.

Ayres, T. R.↗

NASA NTRS2002

System Model for MEMS based Laser Ultrasonic Receiver

A need has been identified for more advanced nondestructive Evaluation technologies for assuring the integrity of airframe structures, wiring, etc. Laser ultrasonic inspection instruments have been shown to detect flaws in structures. However, these instruments are generally too bulky to be used in the confined spaces that are typical of aerospace vehicles. Microsystems technology is one key to reducing the size of current instruments and enabling increased inspection coverage in areas that were previously inaccessible due to instrument size and weight. This paper investigates the system modeling of a Micro OptoElectroMechanical System (MOEMS) based laser ultrasonic receiver. The system model is constructed in software using MATLAB s dynamical simulator, Simulink. The optical components are modeled using geometrical matrix methods and include some image processing. The system model includes a test bench which simulates input stimuli and models the behavior of the material under test.

Wilson, William C.↗