Results for “3D imaging” — Page 11

NASA NTRS1998

Three-dimensional echocardiographic planimetry of maximal regurgitant orifice area in myxomatous mitral regurgitation: intraoperative comparison with proximal flow convergence

OBJECTIVES: We sought to validate direct planimetry of mitral regurgitant orifice area from three-dimensional echocardiographic reconstructions. BACKGROUND: Regurgitant orifice area (ROA) is an important measure of the severity of mitral regurgitation (MR) that up to now has been calculated from hemodynamic data rather than measured directly. We hypothesized that improved spatial resolution of the mitral valve (MV) with three-dimensional (3D) echo might allow accurate planimetry of ROA. METHODS: We reconstructed the MV using 3D echo with 3 degrees rotational acquisitions (TomTec) using a transesophageal (TEE) multiplane probe in 15 patients undergoing MV repair (age 59 +/- 11 years). One observer reconstructed the prolapsing mitral leaflet in a left atrial plane parallel to the ROA and planimetered the two-dimensional (2D) projection of the maximal ROA. A second observer, blinded to the results of the first, calculated maximal ROA using the proximal convergence method defined as maximal flow rate (2pi(r2)va, where r is the radius of a color alias contour with velocity va) divided by regurgitant peak velocity (obtained by continuous wave [CW] Doppler) and corrected as necessary for proximal flow constraint. RESULTS: Maximal ROA was 0.79 +/- 0.39 (mean +/- SD) cm2 by 3D and 0.86 +/- 0.42 cm2 by proximal convergence (p = NS). Maximal ROA by 3D echo (y) was highly correlated with the corresponding flow measurement (x) (y = 0.87x + 0.03, r = 0.95, p < 0.001) with close agreement seen (AROA (y - x) = 0.07 +/- 0.12 cm2). CONCLUSIONS: 3D echo imaging of the MV allows direct visualization and planimetry of the ROA in patients with severe MR with good agreement to flow-based proximal convergence measurements.

NASA Discipline Cardiopulmonary↗

NASA NTRS2005

The Effect of Projectile Density and Disruption on the Crater Excavation Flow-Field

The ejection parameters of material excavated by a growing crater directly relate to the subsurface excavation flow-field. The ejection angles and speeds define the end of subsurface material streamlines at the target surface. Differences in the subsurface flow-fields can be inferred by comparing observed ejection parameters of various impacts obtained using three-dimensional particle image velocimetry (3D PIV). The work presented here investigates the observed ejection speeds and angles of material ejected during vertical (90 impact angle) experimental impacts for a range of different projectile types. The subsurface flow-fields produced during vertical impacts are simple when compared with that of oblique impacts, affected primarily by the depth of the energy and momentum deposition of the projectile. This depth is highly controlled by the projectile/target density ratio and the disruption of the projectile (brittle vs. ductile deformation). Previous studies indicated that cratering efficiency and the crater diameter/depth ratio were affected by projectile disruption, velocity, and the projectile/target density ratio. The effect of these projectile properties on the excavation flow-field are examined by comparing different projectile materials.

Anderson, Jennifer L. B.↗

NASA NTRS2005

Laser Incident in the NASA Unitary Plan Wind Tunnel

A viewgraph presentation describing the laser incident in the NASA Unitary Plan Wind Tunnel (UPWT) is shown. The topics include: 1) Unitary Plan Wind Tunnel (UPWT); 2) UPWT 11' x 11' Transonic Wind Tunnel; 3) UPWT 9' x 7' Supersonic Wind Tunnel; 4) 3D Particle Image Velocimetry (PIV); 5) PIV System in 11' x 11' Transonic Test Section; 6) PIV installation in 11' x 11' TWT; 7) 3% Space Shuttle Ascent Configuration Test in UPWT 9' x 7' Test Section; 8) PIV installation in 9' x 7' SWT; 9) PIV Laser System in 9' x 7' SWT; 10) PIV Laser System Access Window; 11) Cooling Water Leak at Rear of Power Supply; 12) PIV Laser Safety Interlock System; 13) Safety Interlock Cable; 14) Q-Switch located on top of 9' x 7' Test Section; 15) Investigation Findings and Recommendations; and 16) PIV Results for 3% Space Shuttle Model in UPWT 9' x 7'.

George, Michael W.↗

NASA NTRS2007

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

Sun, Guoqing↗

NASA NTRS2012

In-Space Inspection Technologies Vision

Purpose: Assess In-Space NDE technologies and needs - current & future spacecraft. Discover & build on needs, R&D & NDE products in other industries and agencies. Stimulate partnerships in & outside NASA to move technologies forward cooperatively. Facilitate group discussion on challenges and opportunities of mutual benefit. Focus Areas: Miniaturized 3D Penetrating Imagers Controllable Snake-arm Inspection systems Miniature Free-flying Micro-satellite Inspectors

Studor, George↗

NASA NTRS2015

3D Endoscope to Boost Safety, Cut Cost of Surgery

Researchers at the Jet Propulsion Laboratory worked with the brain surgeon who directs the Skull Base Institute in Los Angeles to create the first endoscope fit for brain surgery and capable of producing 3D video images. It is also the first to be able to steer its lens back and forth. These improvements to visibility are expected to improve safety, speeding patient recovery and reducing medical costs.

Source record↗

NASA NTRS2015

Characterization of Apollo Regolith by X-Ray and Electron Microbeam Techniques: An Analog for Future Sample Return Missions

The Apollo missions collected 382 kg of rock and regolith from the Moon; approximately 1/3 of the sample mass collected was regolith. Lunar regolith consists of well mixed rocks, minerals, and glasses less than 1-centimeter n size. The majority of most surface regolith samples were sieved into less than 1, 1-2, 2-4, and 4-10- millimiter size fractions; a portion of most samples was re-served unsieved. The initial characterization and classification of most Apollo regolith particles was done primarily by binocular microscopy. Optical classification of regolith is difficult because (1) the finest fraction of the regolith coats and obscures the textures of the larger particles, and (b) not all lithologies or minerals are uniquely identifiable optically. In recent years, we have begun to use more modern x-ray beam techniques [1-3], coupled with high resolution 3D optical imaging techniques [4] to characterize Apollo and meteorite samples as part of the curation process. These techniques, particularly in concert with SEM imaging of less than 1-millimeter regolith grain mounts, allow for the rapid characterization of the components within a regolith.

Zeigler, Ryan A.↗

NASA NTRS2017

Weathering the Storm: Unmanned Aircraft Systems in the Maritime, Atmospheric and Polar Environments

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs.

data collection↗

NASA NTRS2016

Using Remotely Piloted Aircraft and Onboard Processing to Optimize and Expand Data Collection

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASA’s Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs.

Fladeland, Matthew M.↗

NASA NTRSDate not supplied

Generation of 3D Microstructures From 2D Surface Images Using Markov Random Fields

Explore the source record for details and available documents.

Iman Javaheri↗

NASA NTRSDate not supplied

Science Café April 2021: Fetching Samples from Mars

Perseverance rover recently landed in an ancient lakebed on Mars. Its mission is to collect rock and soil samples, and place these in strategic locations for a future mission to return them to Earth. This talk provides an overview of the combined effort between NASA and the European Space Agency to bring those samples home. This involves the use of a small, but highly mobile rover, nicknamed ‘Fetch’. Fetch rover will be the first Mars rover to use flexible tires known as Spring Tires. These tires were invented in the Cleveland area by NASA and Goodyear. The benefits and challenges of this technology will be discussed in the context of this Mars mission, future missions, as well as on Earth. Note: To view full presentations with embedded video please download and permit 3D video imaging for this document.

Mars Sample Return↗

NASA NTRSDate not supplied

Analysis of "Stone" Samples from C-Type Asteroid Ryugu

As a part of activities of initial analysis, we analyzed eighteen Ryugu “stone” samples of 1 ~ 8mm in size: seven stones from the 1st touch down site and eleven from the 2nd touch down site. Ryugu stones were analyzed first using X-rays, UV, visible, near-infrared, mid-infrared, and muon probes for surface reflectance spectra, internal 3D structure and element distribution, crystal structures, and bulk composition of light elements such as C, N, and O. Measurements for magnetic, thermal, and physical properties using some stones were also performed. TOF-SIMS analysis of fluid inclusions as being continued. Small particles separated from some stones were analyzed by IR-CT. Based on 3D-CT images of individual stones, objects of interests were identified, separated by pFIB cutting, and exposed by polishing on the surface of epoxy disks. FE-EPMA/FE-SEM analysis was made on ~40 polished epoxy disks for chemical composition and textural observation. FIB sections and small (~15 μm in size) fragments were separated from polished surface and analyzed by TEM, STXM,XRF, and nano-CT.

T. Nakamura↗

NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

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 NTRS2024

Pull-off Behavior of Stitched Composite T-Joints

T-joints are key structural elements that connect opposing surfaces, thereby providing the load path between flat or curved panels (i.e., upper and lower wing skins) and transverse components (i.e., stiffeners). Due to the low interlaminar strength of polymer matrix composites and their geometrical discontinuities, these joints are vulnerable to pull-off loads. To address these issues, through-thickness reinforcements can be employed to enhance the interlaminar capability of these type of joints. In this study, T-joints were manufactured using through-thickness stitching in dry carbon preforms and cured using the vacuum-assisted resin transfer molding (VARTM) process. Stitched and unstitched T-joints were tested under pull-off loading conditions, and surface strain fields were obtained using a 3D digital image correlation system. The ultimate load, displacement, and absorbed energy of the stitched T-joints were greater than their unstitched counterparts by approximately 16%, 34%, and 58%, respectively. Failure mechanisms were identified by examining fracture surfaces using optical microscopy. Results demonstrate that through-thickness stitching significantly improves the damage tolerance of T-joints, which highlights the effectiveness of stitching to enhance the structural integrity of large aerospace components.

VARTM↗

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↗