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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Integration of airborne Thematic Mapper Simulator (TMS) data and digitized aerial photography via an ISH transformation

A simple method for enhancing the spatial and spectral resolution of disparate data sets is presented. Two data sets, digitized aerial photography at a nominal spatial resolution 3,7 meters and TMS digital data at 24.6 meters, were coregistered through a bilinear interpolation to solve the problem of blocky pixel groups resulting from rectification expansion. The two data sets were then subjected to intensity-saturation-hue (ISH) transformations in order to 'blend' the high-spatial-resolution (3.7 m) digitized RC-10 photography with the high spectral (12-bands) and lower spatial (24.6 m) resolution TMS digital data. The resultant merged products make it possible to perform large-scale mapping, ease photointerpretation, and can be derived for any of the 12 available TMS spectral bands.

Ambrosia, Vincent G.↗
NASA NTRS1988

Image processing in optical astronomy

Successful efforts to enhance optical-astronomy images through digital processing often exploit such 'weaknesses' of the image as the objects' near-symmetry, their preferred directionality, or a differentiation in spatial frequency between the object or objects and superimposed clutter. Attention is presently given to the calibration of a camera prior to astronomical data-acquisition, methods for the enhancement of faint surface brightness features, automated target detection and extraction techniques, the importance of the geometric transformations of digital imagery, the preparation of two-dimensional histograms, and the application of polarization.

Lorre, Jean J.↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT - Poster

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRS1973

Automated thematic mapping and change detection of ERTS-A images

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

Gramenopoulos, N.↗
NASA NTRS1974

Techniques for the processing of remotely sensed imagery

The following techniques are considered for classifying low resolution satellite imagery: (1) Gradient operations; (2) histogram methods; (3) gray level detection; (4) frequency domain operations; (5) Hadamard transform in digital image matching; and (6) edge and line detection schemes.

Deutsch, E. S.↗
NASA NTRS1976

Ballistic Signature Identification System Study

The first phase of a research project directed toward development of a high speed automatic process to be used to match gun barrel signatures imparted to fired bullets was documented. An optical projection technique has been devised to produce and photograph a planar image of the entire signature, and the phototransparency produced is subjected to analysis using digital Fourier transform techniques. The success of this approach appears to be limited primarily by the accuracy of the photographic step since no significant processing limitations have been encountered.

Source record↗
NASA NTRS1976

Stereo mapping with the Viking Lander camera imagery

Stereomapping techniques applicable to the processing of imagery from facsimile cameras on board the Viking Mars landing vehicles are discussed. Two methods of stereomapping are considered: use of a gnomonic projection to transform the digital imagery to an equivalent of a perspective projection by computerized rectification through image processing; and interfacing a high-speed digital computer to an analytical plotter so that rectification and corrections proceed in real time. Contour maps produced during tests of the Viking Lander cameras, as well as maps of the area surrounding the Viking Lander on the Martian surface, are presented.

Wu, S. S. C.↗
NASA NTRS1977

Viking image processing

The paper discusses the camera systems capable of recording black and white and color imagery developed for the Viking Lander imaging experiment. Each Viking Lander image consisted of a matrix of numbers with 512 rows and an arbitrary number of columns up to a maximum of about 9,000. Various techniques were used in the processing of the Viking Lander images, including: (1) digital geometric transformation, (2) the processing of stereo imagery to produce three-dimensional terrain maps, and (3) computer mosaicking of distinct processed images. A series of Viking Lander images is included.

Green, W. B.↗
NASA NTRS1985

Visualization of computer-generated flow fields

Flow field variables are visualized using color representations described on surfaces that are interpolated from computational grids and transformed to digital images. The color at a point on a surface represents the magnitude of a variable, and several surfaces can be included in a single digital image. Typically the surfaces are a boundary surface, a windward surface, and a crossflow surface. Sequences of images, in pictorial form, are presented to describe an entire flow field or a time history of a flow field. Several examples are presented.

Smith, R.↗
NASA NTRS1988

Signal-to-noise limitations in white light holography

A simple derivation is given for the SNR in images reconstructed from incoherent holograms. Dependence is shown to be on the hologram SNR, object complexity, and the number of pixels in the detector. Reconstruction of involved objects becomes possible with high-dynamic-range detectors such as CCDs. White-light holograms have been produced by means of a rotational shear interferometer combined with a chromatic corrector. A digital inverse transform recreated the object.

Ribak, Erez↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRS1988

Applications of Digital Image Processing 11

A new technique, digital image velocimetry, is proposed for the measurement of instantaneous velocity fields of time dependent flows. A time sequence of single-exposure images of seed particles are captured with a high-speed camera, and a finite number of the single-exposure images are sampled within a prescribed period in time. The sampled images are then digitized on an image processor, enhanced, and superimposed to construct an image which is equivalent to a multiple exposure image used in both laser speckle velocimetry and particle image velocimetry. The superimposed image and a single-exposure Image are digitally Fourier transformed for extraction of information on the velocity field. A great enhancement of the dynamic range of the velocity measurement is accomplished through the new technique by manipulating the Fourier transform of both the single-exposure image and the superimposed image. Also the direction of the velocity vector is unequivocally determined. With the use of a high-speed video camera, the whole process from image acquisition to velocity determination can be carried out electronically; thus this technique can be developed into a real-time capability.

Cho, Y. -C.↗