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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 271 records · Page 15

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NASA NTRS2004

DSP Implementation of the Multiscale Retinex Image Enhancement Algorithm

The Retinex is a general-purpose image enhancement algorithm that is used to produce good visual representations of scenes. It performs a non-linear spatial/ spectral transform that synthesizes strong local contrast enhancement and color constancy. A real-time, video frame rate implementation of the Retinex is required to meet the needs of various potential users. Retinex processing contains a relatively large number of complex computations, thus to achieve real-time performance using current technologies requires specialized hardware and software. In this paper we discuss the design and development of a digital signal processor (DSP) implementation of the Retinex. The target processor is a Texas Instruments TMS320C6711 floating point DSP. NTSC video is captured using a dedicated frame grabber card, Retinex processed, and displayed on a standard monitor. We discuss the optimizations used to achieve real-time performance of the Retinex and also describe our future plans on using alternative architectures.

Hines, Glenn D.↗

NASA NTRS2004

NASA Tech Briefs, August 2004

Topics covered include: Data Relay Board with Protocol for High-Speed, Free-Space Optical Communications; Software and Algorithms for Biomedical Image Data Processing and Visualization; Rapid Chemometric Filtering of Spectral Data; Prioritizing Scientific Data for Transmission; Determining Sizes of Particles in a Flow from DPIV Data; Faster Processing for Inverting GPS Occultation Data; FPGA-Based, Self-Checking, Fault-Tolerant Computers; Ultralow-Power Digital Correlator for Microwave Polarimetry; Grounding Headphones for Protection Against ESD; Lightweight Stacks of Direct Methanol Fuel Cells; Highly Efficient Vector-Inversion Pulse Generators; Estimating Basic Preliminary Design Performances of Aerospace Vehicles; Framework for Development of Object-Oriented Software; Analyzing Spacecraft Telecommunication Systems; Collaborative Planning of Robotic Exploration; Tools for Administration of a UNIX-Based Network; Preparing and Analyzing Iced Airfoils; Evaluating Performance of Components; Fuels Containing Methane of Natural Gas in Solution; Direct Electrolytic Deposition of Mats of MnxOy Nanowires; Bubble Eliminator Based on Centrifugal Flow; Inflatable Emergency Atmospheric-Entry Vehicles; Lightweight Deployable Mirrors with Tensegrity Supports; Centrifugal Adsorption Cartridge System; Ultrasonic Apparatus for Pulverizing Brittle Material; Transplanting Retinal Cells using Bucky Paper for Support; Using an Ultrasonic Instrument to Size Extravascular Bubbles; Coronagraphic Notch Filter for Raman Spectroscopy; On-the-Fly Mapping for Calibrating Directional Antennas; Working Fluids for Increasing Capacities of Heat Pipes; Computationally-Efficient Minimum-Time Aircraft Routes in the Presence of Winds; Liquid-Metal-Fed Pulsed Plasma Thrusters; Personal Radiation Protection System; and Attitude Control for a Solar-Sail Spacecraft.

Source record↗

NASA NTRS2019

Rapid Development of the Seeker Free-Flying Inspector Guidance, Navigation, and Control System

Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a streamlined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to pro-ceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule, development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor downselection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measurements for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.

Sullivan, Jacob↗

NASA NTRS2019

Rapid Development of the Seeker Free-Flying Inspector Guidance, Navigation, and Control System

Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a stream-lined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to proceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule. Development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor down-selection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measure-ments for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.

Sullivan, Jake↗

NASA NTRSDate not supplied

Implementation of Field-Oriented Control in Joint Actuator Electronics for Satellite-Servicing Robotics

NASA’s OSAM-1 (On-orbit Servicing, Assembly, and Manufacturing 1) is a robotic spacecraft designed to extend the life of a satellite. Launching in 2026, the OSAM-1 servicer will use a robotic arm to grasp, refuel, and relocate Landsat 7. In order to accomplish the various servicing operations, including the critical auto-grapple capturing operation, the robot arm uses active compliance and visual servo control loops. These sophisticated control algorithms are enabled by the high performance Joint Control Boards (JCBs). At the core of the robot arm control architecture, the JCBs employ field-oriented control (FOC) to accurately commutate and efficiently control joint actuator torque. FOC offers numerous benefits over other current control methods, allowing the JCBs to achieve the high bandwidth and performance required by the outer control loops. As an added benefit, a torque feed-forward term allows the FOC loop to simulate microgravity actuator performance during ground testing. In this paper, a FOC implementation for space robotics applications and an end-to-end actuator calibration process are discussed.

Sam Zhao↗

NASA NTRS1994

Visualizing Flow Over Parametric Surfaces Using Line Integral Convolution

Line Integral Convolution (LIC) is a powerful technique for imaging and animating vector fields. We extend the LIC paradigm in three ways: (1) The existing technique is limited to vector fields over a regular Cartesian grid. We extend it to vector fields over parametric surfaces, such as those found in curvilinear grids, used in computational fluid dynamics simulations; (2) Periodic motion filters can be used to animate the flow visualization. When the flow lies on a parametric surface, however, the motion appears misleading. We explain why this problem arises and show how to adjust the LIC algorithm to handle it; (3) We introduce a technique to visualize vector magnitudes as well as vector direction. Cabral and Leedom have suggested a method for variable-speed animation, which is based on varying the frequency of the filter function. We develop a different technique based on kernel phase shifts which we have found to show substantially better results. Our implementation of these algorithms utilizes texture-mapping hardware to run in real time, which allows them to be included in interactive applications.

Forssell, Lisa↗

DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2016

SU-F-J-72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy

Purpose: To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all-in-one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques. Methods: ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model-View-ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness. Results: ACE can take patient’s CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases. Conclusion: ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗

NASA NTRS2016

Stall Recovery Guidance Algorithms Based on Constrained Control Approaches

Aircraft loss-of-control, in particular approach to stall or fully developed stall, is a major factor contributing to aircraft safety risks, which emphasizes the need to develop algorithms that are capable of assisting the pilots to identify the problem and providing guidance to recover the aircraft. In this paper we present several stall recovery guidance algorithms, which are implemented in the background without interfering with flight control system and altering the pilot's actions. They are using input and state constrained control methods to generate guidance signals, which are provided to the pilot in the form of visual cues. It is the pilot's decision to follow these signals. The algorithms are validated in the pilot-in-the loop medium fidelity simulation experiment.

Aircraft stall↗

DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Improving Property Graph Layouts by Leveraging Attribute Similarity for Structurally Equivalent Nodes

Many real-world networks contain structurally-equivalent nodes. These are defined as vertices that share the same set of neighboring nodes, making them interchangeable with a traditional graph layout approach. However, many real-world graphs also have properties associated with nodes, adding additional meaning to them. We present an approach for swapping locations of structurally-equivalent nodes in graph layout so that those with more similar properties have closer proximity to each other. This improves the usefulness of the visualization from an attribute perspective without negatively impacting the visualization from a structural perspective. We include an algorithm for finding these sets of nodes in linear time, as well as methodologies for ordering nodes based on their attribute similarity, which works for scalar, ordinal, multidimensional, and categorical data.

graph drawing, network visualization, property gra↗

DOE OSTI2024

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. Finally, we evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Individual pore and interconnection size analysis of macroporous ceramic scaffolds using high-resolution X-ray tomography

The pore interconnection size of β-tricalcium phosphate scaffolds plays an essential role in the bone repair process. Although, the μCT technique is widely used in the biomaterial community, it is rarely used to measure the interconnection size because of the lack of algorithms. In addition, discrete nature of the μCT introduces large systematic errors due to the convex geometry of interconnections. We proposed, verified and validated a novel pore-level algorithm to accurately characterize the individual pores and interconnections. Specifically, pores and interconnections were isolated, labeled, and individually analyzed with high accuracy. The technique was verified thoroughly by visually inspecting and verifying over 3474 properties of randomly selected pores. This extensive verification process has passed a one-percent accuracy criterion. Scanning errors inherent in the discretization, which lead to both dummy and significantly overestimated interconnections, have been examined using computer-based simulations and additional high-resolution scanning. Then accurate correction charts were developed and used to reduce the scanning errors. Only after the corrections, both the μCT and SEM-based results converged, and the novel algorithm was validated. Material scientists with access to all geometrical properties of individual pores and interconnections, using the novel algorithm, will have a more-detailed and accurate description of the substitute architecture and a potentially deeper understanding of the link between the geometric and biological interaction. - Highlights: •An algorithm is developed to analyze individually all pores and interconnections. •After pore isolating, the discretization errors in interconnections were corrected. •Dummy interconnections and overestimated sizes were due to thin material walls. •The isolating algorithm was verified through visual inspection (99% accurate). •After correcting for the systematic errors, algorithm was validated successfully.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗

DOE OSTI2026

Rasterization with Data-Parallel Primitives

Parallel rasterization can suffer from race conditions during fragment generation, which is traditionally addressed by using specialized hardware accessible via vendor graphics APIs. Unfortunately, graphics APIs are increasingly problematic on high-performance computers, either because they are not provided or because of concerns about dependencies with in situ visualization. In response, we present a hardware-agnostic rasterization algorithm that handles race conditions using only data-parallel primitives (DPPs), enabling efficient rendering on HPC systems without graphics API dependencies and aligning with recent efforts to deliver visualization software with DPPs. Our evaluation consists of three phases: (1) evaluating portability across different CPU and GPU architectures, (2) evaluating competitiveness with a community standard, and (3) evaluating performance across varying workloads and available parallelism. The supporting experiments run on both AMD and NVIDIA GPUs, considering data sets as large as 460 million triangles and 160 million pixels. While performance generally falls short of graphics API baselines, it achieves interactive frame rates on most workloads. As a result, we conclude our approach is a viable solution for rasterization on high-performance computers since our approach is portably performant across different architectures without the need for specialized vendor support.

Buckley, Makani [University of Oregon] (ORCID:0009↗

NASA NTRS1997

Advanced Image Processing for Defect Visualization in Infrared Thermography

Results of a defect visualization process based on pulse infrared thermography are presented. Algorithms have been developed to reduce the amount of operator participation required in the process of interpreting thermographic images. The algorithms determine the defect's depth and size from the temporal and spatial thermal distributions that exist on the surface of the investigated object following thermal excitation. A comparison of the results from thermal contrast, time derivative, and phase analysis methods for defect visualization are presented. These comparisons are based on three dimensional simulations of a test case representing a plate with multiple delaminations. Comparisons are also based on experimental data obtained from a specimen with flat bottom holes and a composite panel with delaminations.

Plotnikov, Yuri A.↗

NASA NTRS2014

Comparing MODIS C6 'Deep Blue' and 'Dark Target' Aerosol Data

The MODIS Collection 6 Atmospheres product suite includes refined versions of both 'Deep Blue' (DB) and 'Dark Target' (DT) aerosol algorithms, with the DB dataset now expanded to include coverage over vegetated land surfaces. This means that, over much of the global land surface, users will have both DB and DT data to choose from. A 'merged' dataset is also provided, primarily for visualization purposes, which takes retrievals from either or both algorithms based on regional and seasonal climatologies of normalized difference vegetation index (NDVI). This poster present some comparisons of these two C6 aerosol algorithms, focusing on AOD at 550 nm derived from MODIS Aqua measurements, with each other and with Aerosol Robotic Network (AERONET) data, with the intent to facilitate user decisions about the suitability of the two datasets for their desired applications.

MODIS Collection 6↗