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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Radar transponder antenna pattern analysis for the space shuttle

In order to improve tracking capability, radar transponder antennas will soon be mounted on the Shuttle solid rocket boosters (SRB). These four antennas, each being identical cavity-backed helices operating at 5.765 GHz, will be mounted near the top of the SRB's, adjacent to the intertank portion of the external tank. The purpose is to calculate the roll-plane pattern (the plane perpendicular to the SRB axes and containing the antennas) in the presence of this complex electromagnetic environment. The large electrical size of this problem mandates an optical (asymptotic) approach. Development of a specific code for this application is beyond the scope of a summer fellowship; thus a general purpose code, the Numerical Electromagnetics Code - Basic Scattering Code, was chosen as the computational tool. This code is based on the modern Geometrical Theory of Diffraction, and allows computation of scattering of bodies composed of canonical problems such as plates and elliptic cylinders. Apertures mounted on a curved surface (the SRB) cannot be accomplished by the code, so an antenna model consisting of wires excited by a method of moments current input was devised that approximated the actual performance of the antennas. The improvised antenna model matched well with measurements taken at the MSFC range. The SRB's, the external tank, and the shuttle nose were modeled as circular cylinders, and the code was able to produce what is thought to be a reasonable roll-plane pattern.

Radcliff, Roger↗
NASA NTRS2018

GC11H _ 0998: A spatial pattern analysis of forest loss in the Madre de Dios region, Peru

Previous studies have quantified the extension of gold mining activities and associated it with forest loss in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map forest loss within and outside key land tenure areas in this region, Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2017 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗
NASA NTRS2019

A Spatial Pattern Analysis of Forest Loss in the Madre de Dios Region, Peru

Previous studies have quantified the expansion of gold mining-related forest loss (Espejo et al., 2018; Asner et al., 2017; Swenson et al., 2011) in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map general forest loss within and outside key land tenure areas in this region. Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2018 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗
DOE OSTI2023

Usage Pattern Analysis for the Summit Login Nodes

High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit’s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.

Eiffert, Brett↗
DOE OSTI2025

Respiration Signal Pattern Analysis for Doppler Radar Sensor with Passive Node and Its Application in Occupancy Sensing of a Stationary Subject

Doppler radar node occupancy sensors are promising for applications in smart buildings due to their simple circuits and price advantage compared to quadrature radar sensors. However, single-channel sensitivity limitations may result in low sensitivity and misinterpreted motion rates if the detected subject is at or close to “null” points. We designed and tested a novel method to eliminate such limits, demonstrating that passive nodes can be used to detect a sedentary person regardless of position. This method is based on characteristics of chest motion due to respiration, found via both simulations and experiments based on a sinusoidal model and a more realistic model of cardiorespiratory motion. In addition, respiratory rate variability is considered to distinguish a true human presence from a mechanical target. Sensor node data were collected simultaneously with an infrared camera system, which provided a respiration signal reference, to test the algorithm with 19 human subjects and a mechanical target. The results indicate that a human presence was detected with 100% accuracy and successfully differentiated from a mechanical target in a controlled environment. The developed method can greatly improve the occupancy detection accuracy of single-channel radar-based occupancy sensors and facilitate their adoption in smart building applications.

Song, Chenyan↗
NASA NTRS1972

Ground pattern analysis in the Great Plains

The author has identified the following significant results. During preliminary analysis of the ERTS-1 RBV band-2 images it was possible to detect many geologic features and to delineate several geologic formation boundaries. Also, the spatial frequency characteristics of some different geologic formations are distinguishable from each other. At the present time there is not enough data to provide any statistical evaluation of these differences, but it is hoped that more quantitative results will be produced within the next few months. Multispectral analysis will begin when the retrospective order for the other spectral bands of the first images is received. Since many geologic formation boundaries are visible on the single band that has been analyzed so far, even greater visibility is expected with the spectral information available in the additional bands.

Egbert, D. D.↗
NASA NTRS1986

Radiation pattern analysis of the tapered slot antenna, appendix 1

A theoretical model for the tapered slot antenna is presented. The model is valid for any smooth taper of the slot. The problem is solved by modeling the slot electric field and using the half plane Green's function to compute the far fields. The aperture field is obtained by affecting a stepped approximation to the continuous taper and utilizing the uniform wide slot line data. The uniform wide slot line is solved by the spectral Galerkin's technique and closed form experssions are developed for the slot wave length and the slot characteristic impedance. Numerous comparisons with measurement are made to demonstrate the versatility of the model in treating an arbitrary slot taper.

Janaswamy, Ramakrishna↗
NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗
NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗
NASA NTRS1992

Pattern recognition analysis of polar clouds during summer and winter

A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

Ebert, Elizabeth E.↗