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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 685 records · Page 38

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NASA NTRS1979

Radar image processing of real aperture SLAR data for the detection and identification of iceberg and ship targets

An iceberg detection and identification system consisting of a moderate resolution Side Looking Airborne Radar (SLAR) interfaced with a Radar Image Processor (RIP) based on a ROLM 1664 computer with a 32K core memory updatable to 64K is described. The system can be operated in high- or low-resolution sampling modes. Specifically designed algorithms are applied to digitized signal returns to provide automatic target detection and location, geometrically correct video image display and data recording. The real aperture Motorola AN/APS-94D SLAR operates in the X-band and is tunable between 9.10 and 9.40 GHz; its output power is 45 kW peak with a pulse repetition rate of 750 pulses per hour. Schematic diagrams of the system are provided, together with preliminary test data.

Marthaler, J. G.↗

DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗

NASA NTRS1981

Texture measurements from Seasat - SAR images for urban land use interpretation

Different grey tones in Seasat Synthetic Aperture Radar (SAR) images for the same type of urban land cover considerably impair the possibilities of establishing automatic classification procedures for these types of data. Since the orientation of the main features like street patterns and buildings with respect to the azimuth angle of the radar antenna is the crucial factor for the observed differences in grey tone, prior information on these elements and special processing of the data would be required to eliminate this effect. Another approach suggested in this paper is to make use of the textural information in the image rather than of its grey tone. For different study sites within the Los Angeles urbanized area texture measures could be derived which result in characteristic values for specific types of land cover and are largely independent of the azimuth angle effect. At the same time the results for the study area indicate an improvement of the overall separability for the different land cover types included in the analysis.

Fasler, F.↗

NASA NTRS1981

The Dynamics Explorer Langmuir probe instrument

The Dynamics Explorer Langmuir probe instrument (DE-LANG), a spare unit from the Pioneer Venus mission that has been modified to work with the DE spacecraft, is described. Two independent sensors are connected to individual adaptive sweep voltage circuits that continuously track the changing electron temperature and spacecraft potential while autoranging electrometers adjust their gain in response to the changing plasma density. The control signals used in achieving this automatic tracking provide a continuous monitor of the ionospheric parameters without telemetering each volt-ampere curve. In addition, internal data storage circuits permit high-resolution, high-data-rate sampling of selected volt-ampere curves for transmission to the ground to verify or correct the inflight processed data.

Krehbiel, J. P.↗

NASA NTRS1999

Optical Properties of Aerosols from Long Term Ground-Based Aeronet Measurements

AERONET is an optical ground-based aerosol monitoring network and data archive supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions including AEROCAN (AERONET CANada) and PHOTON (PHOtometrie pour le Traiteinent Operatonnel de Normalisation Satellitaire). The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities purchased for their own monitoring and research objectives. Data are transmitted hourly through the data collection system (DCS) on board the geostationary meteorological satellites GMS, GOES and METEOSAT and received in a common archive for daily processing utilizing a peer reviewed series of algorithms thus imposing a standardization and quality control of the product data base. Data from this collaboration provides globally distributed near real time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. Access to the AERONET data base has shifted from the interactive program 'demonstrat' (reserved for PI's) to the AERONET homepage allowing faster access and greater development for GIS object oriented retrievals and analysis with companion geocoded data sets from satellites, LIDAR and solar flux measurements for example. We feel that a significant yet under utilized component of the AERONET data base are inversion products made from hourly principal plane and almucanter measurements. The current inversions have been shown to retrieve aerosol volume size distributions. A significant enhancement to the inversion code has been developed and is presented in these proceedings.

Holben, B. N.↗

NASA NTRS2016

Combining Stereo SECCHI COR2 and HI1 Images for Automatic CME Front Edge Tracking

COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 215 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 1590 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 andHI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvement scan greatly help in making the quantitative CME analysis more accurate and have the potential to assist in space weather forecasting.

Kirnosov, Vladimir↗

NASA NTRS1991

Automatic fringe analysis

To satisfy the requirement for fast, accurate interferometric analytical tools, the Fringe Analysis Workstation (FAW) has been developed to analyze complex fringe image data easily and rapidly. FAW is employed for flow studies in hydrodynamics and aerodynamics experiments, and for target shell characterization in inertial confinement fusion research. Three major components of the FAW system: fringe analysis/image processing, input/output, and visualization/graphical user interface are described.

Chiu, Arnold↗

NASA NTRS1993

Automatic Detection of Seizures with Applications

There are an estimated two million people with epilepsy in the United States. Many of these people do not respond to anti-epileptic drug therapy. Two devices can be developed to assist in the treatment of epilepsy. The first is a microcomputer-based system designed to process massive amounts of electroencephalogram (EEG) data collected during long-term monitoring of patients for the purpose of diagnosing seizures, assessing the effectiveness of medical therapy, or selecting patients for epilepsy surgery. Such a device would select and display important EEG events. Currently many such events are missed. A second device could be implanted and would detect seizures and initiate therapy. Both of these devices require a reliable seizure detection algorithm. A new algorithm is described. It is believed to represent an improvement over existing seizure detection algorithms because better signal features were selected and better standardization methods were used.

Olsen, Dale E.↗

NASA NTRS1994

Universal signal conditioning amplifier

A state-of-the-art instrumentation amplifier capable of being used with most types of transducers has been developed at the Kennedy Space Center. This Universal Signal Conditioning Amplifier (USCA) can eliminate costly measurement setup item and troubleshooting, improve system reliability and provide more accurate data than conventional amplifiers. The USCA can configure itself for maximum resolution and accuracy based on information read from a RAM chip attached to each transducer. Excitation voltages or current are also automatically configured. The amplifier uses both analog and digital state-of-the-art technology with analog-to-digital conversion performed in the early stages in order to minimize errors introduced by offset and gain drifts in the analog components. A dynamic temperature compensation scheme has been designed to achieve and maintain 12-bit accuracy of the amplifier from 0 to 70 C. The digital signal processing section allows the implementation of digital filters up to 511th order. The amplifier can also perform real-time linearizations up to fourth order while processing data at a rate of 23.438 kS/s. Both digital and analog outputs are available from the amplifier.

Medelius, Pedro J.↗

NASA NTRS1975

Summary of Skylab S-193 altimeter altitude results

The SKYLAB S-193 altimeter altitude results are presented in a concise format for further use and analysis by the scientific community. The altimeter mission and instrumentation is described along with the altimeter processing techniques and values of parameters used for processing. The determination of reference orbits is discussed, and the tracking systems utilized are tabulated. Techniques for determining satellite pointing are presented and a tabulation of pointing for each data mission included. The geographical location, the ocean bottom topography, the altimeter-determined ocean surface topography, and the altimeter automatic gain control history is presented. Some typical applications of this data are suggested.

Mcgoogan, J. T.↗

NASA NTRS1988

Frequency domain laser velocimeter signal processor

A laser velocimeter signal processor for measuring the signal frequency within a signal burst. The input signal is converted to digital by an ADC 16 and then shifted into shift registers 30 and 31. An automatic gain circuit 15 controls the gain of the input signal. A signal integration circuit 32 determines when a signal burst has been captured by the shift registers and then transfers the contents of the registers to data latches 33 and 34. The data in data latches 33 and 34 is processed by digital bandpass filters 57-63, square law detectors 64-70, burst counters 71-77 and signal processor 78 to determine the frequency of the signal within the captured signal burst.

James F Meyers↗

NASA NTRS1991

An intelligent advisory system for pre-launch processing

The shuttle system of interest in this paper is the shuttle's data processing system (DPS). The DPS is composed of the following: (1) general purpose computers (GPC); (2) a multifunction CRT display system (MCDS); (3) mass memory units (MMU); and (4) a multiplexer/demultiplexer (MDM) and related software. In order to ensure the correct functioning of shuttle systems, some level of automatic error detection has been incorporated into all shuttle systems. For the DPS, error detection equipment has been incorporated into all of its subsystems. The automated diagnostic system, (MCDS) diagnostic tool, that aids in a more efficient processing of the DPS is described.

Engrand, Peter A.↗

NASA NTRS1983

Space Derived Air Monitor

COPAMS, Commonwealth of Pennsylvania Air Monitoring System, derives from technology involved in building unmanned spacecraft. The Nimbus spacecraft carried experimental sensors to measure temperature, pressure, ozone, and water vapor, and instruments for studying solar radiation and telemetry. The process which relayed these findings to Earth formed the basis for COPAMS. The COPAMS system consists of data acquisition units which measure and record pollution level, and sense wind speed and direction, etc. The findings are relayed to a central station where the information is computerized. The system is automatic and supplemented by PAQSS, PA Air Quality Surveillance System.

Source record↗

NASA NTRS2003

Relatively Inexpensive Rapid Prototyping of Small Parts

Parts with complex three-dimensional shapes and with dimensions up to 8 by 8 by 10 in. (20.3 by 20.3 by 25.4 cm) can be made as unitary pieces of a room-temperature-curing polymer, with relatively little investment in time and money, by a process now in use at Johnson Space Center. The process is one of a growing number of processes and techniques that are known collectively as the art of rapid prototyping. The main advantages of this process over other rapid-prototyping processes are greater speed and lower cost: There is no need to make paper drawings and take them to a shop for fabrication, and thus no need for the attendant paperwork and organizational delays. Instead, molds for desired parts are made automatically on a machine that is guided by data from a computer-aided design (CAD) system and can reside in an engineering office.

Swan, Scott A.↗

NASA NTRS2021

Noise Exposure Maps of Urban Air Mobility

A noise exposure map is “a scaled geographic depiction of an airport, its noise exposure contours, noise-sensitive facilities, and land uses in the airport surrounding area” developed in accordance with the FAA’s 14 Code of Federal Regulation Part 150. This paper is the first to explore the applicability of airport noise exposure maps to Urban Air Mobility (UAM). The FAA’s airport noise compatibility planning program is first described. Then the applicability of the noise exposure map to Urban Air Mobility (UAM) is explored. Finally, new airspace infrastructure, including vertiport locations and UAM routes from NASA’s UAM engineering simulations, and local noise-sensitive facility locations and land use information were collected and processed to develop the noise exposure maps of UAM near the Dallas-Fort Worth area. The DNL noise contours resulting from a six-passenger electric quadrotor prototype vehicle are predicted using NASA’s AIRNOISEUAM software. The noise exposure maps of UAM are generated automatically using Python’s data analysis and visualization libraries. The results have applications for UAM’s noise compatibility planning, noise-reducing route planning, and vertiport location selection

Urban Air Mobility↗

NASA NTRS2021

Noise Exposure Map of Urban Air Mobility

Urban Air Mobility↗

NASA NTRSDate not supplied

Transcribing Air Traffic Control System Command Center Planning Telecons Using Cloud-Based Automatic Speech Recognition

This paper addresses the challenge of using Automatic Speech Recognition (ASR) technology to transcribe regular teleconferences that happen between FAA Air Traffic Control System Command Center (ATCSCC) planners, stakeholders and air users. These planning teleconferences (aka telecons or planning webinars) are an integral part of managing air traffic in the U.S. National Airspace System (NAS). In particular, the meetings facilitate the creation and modification of various traffic management initiatives (TMIs), that are used to regulate the flow of air traffic. This is typically a human intensive process, requiring specialists to listen to the entire meeting audio (10-20 minutes duration) and inferring the state of the NAS (e.g., weather phenomenon) that was discussed. It would be advantageous to have digital transcripts of the audio and have useful information (e.g., related to TMIs) automatically extracted from the transcripts. In this regard, we are exploring the adoption of state-of-the-art speech to text and Natural Language Processing (NLP) tools that will achieve our objective of digitizing the webinar audio. Unfortunately, the highly technical phraseology present in the audio and limited data availability for model building make ASR difficult. To overcome this challenge, we have taken the critical first step in creating a human transcription dataset from ~20 hours of speech in the ATCSCC audio with the help of subject matter experts. A novelty of our work is the creation of a ground truth transcription dataset for ATCSCC teleconference webinars, which is particularly important for Aviation domain-specific NLP tasks. Using Microsoft Speech Studio, a cloud-based ASR platform, we have fine-tuned the English pre-trained ASR models (available in speech studio) and achieved an average word error rate (WER) of 6.81%. The baseline ASR also provides a digital version of each planning webinar, making it accessible and text-searchable for future references. Additionally, the transcriptions can serve as a bridge between raw audio data and a range of text-based NLP tasks, such as named entity recognition (NER) and intent classification, potentially enhancing the digital footprint of the webinars and other connected data sources. Our work has several potential applications. Firstly, the transcriptions can be analyzed to understand the complex decision process of creating, implementing and modifying TMIs and may also contribute to TMI prediction services. Secondly, our dataset and model can be used to develop more accurate ASR systems for aviation-specific language, which can bring about digital communication in the aviation industry (and aid current “voice only” communications, which are inherently error-prone). Lastly, the transcriptions themselves can be used as a valuable resource for training other NLP models.

Stephen S. B. Clarke↗

NASA NTRS2007

Real-Time Wireless Data Acquisition System

Current and future aerospace requirements demand the creation of a new breed of sensing devices, with emphasis on reduced weight, power consumption, and physical size. This new generation of sensors must possess a high degree of intelligence to provide critical data efficiently and in real-time. Intelligence will include self-calibration, self-health assessment, and pre-processing of raw data at the sensor level. Most of these features are already incorporated in the Wireless Sensors Network (SensorNet(TradeMark)), developed by the Instrumentation Group at Kennedy Space Center (KSC). A system based on the SensorNet(TradeMark) architecture consists of data collection point(s) called Central Stations (CS) and intelligent sensors called Remote Stations (RS) where one or more CSs can be accommodated depending on the specific application. The CS's major function is to establish communications with the Remote Stations and to poll each RS for data and health information. The CS also collects, stores and distributes these data to the appropriate systems requiring the information. The system has the ability to perform point-to-point, multi-point and relay mode communications with an autonomous self-diagnosis of each communications link. Upon detection of a communication failure, the system automatically reconfigures to establish new communication paths. These communication paths are automatically and autonomously selected as the best paths by the system based on the existing operating environment. The data acquisition system currently under development at KSC consists of the SensorNet(TradeMark) wireless sensors as the remote stations and the central station called the Radio Frequency Health Node (RFHN). The RFF1N is the central station which remotely communicates with the SensorNet(TradeMark) sensors to control them and to receive data. The system's salient feature is the ability to provide deterministic sensor data with accurate time stamps for both time critical and non-time critical applications. Current wireless standards such as Zigbee(TradeMark) and Bluetooth(Registered TradeMark) do not have these capabilities and can not meet the needs that are provided by the SensorNet technology. Additionally, the system has the ability to automatically reconfigure the wireless communication link to a secondary frequency if interference is encountered and can autonomously search for a sensor that was perceived to be lost using the relay capabilities of the sensors and the secondary frequency. The RFHN and the SensorNet designs are based on modular architectures that allow for future increases in capability and the ability to expand or upgrade with relative ease. The RFHN and SensorNet sensors .can also perform data processing which forms a distributed processing architecture allowing the system to pass along information rather than just sending "raw data points" to the next higher level system. With a relatively small size, weight and power consumption, this system has the potential for both spacecraft and aircraft applications as well as ground applications that require time critical data.

Valencia, Emilio J.↗