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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Enabling Smart Urban Airspaces through Distributed Sensing Technologies

Distributed sensing systems offer potential for enhancing safety and efficiency in urban airspaces and low-altitude metropolitan flight corridors. These systems leverage sensor networks to provide real-time airspace monitoring, situational awareness, and data for decision-making. This paper examines application of distributed sensing and smart airspace concepts to enable advanced urban flight, focusing on critical need applications such as airspace monitoring, corridor surveillance, precision navigation, and hazard avoidance. Benefits such as improved monitoring and increased situational awareness of activity within the urban airspace are considered alongside challenges in system design and implementation. This paper summarizes an ongoing effort to study the feasibility and effectiveness of distributed sensing systems for enabling urban airspace operations. Current research and design considerations are summarized, and a proposed roadmap for future work is presented.

Corey A Ippolito↗
NASA NTRS2010

Autonomous System for Monitoring the Integrity of Composite Fan Housings

A low-cost and reliable system assesses the integrity of composite fan-containment structures. The system utilizes a network of miniature sensors integrated with the structure to scan the entire structural area for any impact events and resulting structural damage, and to monitor degradation due to usage. This system can be used to monitor all types of composite structures on aircraft and spacecraft, as well as automatically monitor in real time the location and extent of damage in the containment structures. This diagnostic information is passed to prognostic modeling that is being developed to utilize the information and provide input on the residual strength of the structure, and maintain a history of structural degradation during usage. The structural health-monitoring system would consist of three major components: (1) sensors and a sensor network, which is permanently bonded onto the structure being monitored; (2) integrated hardware; and (3) software to monitor in-situ the health condition of in-service structures.

Qing, Xinlin P.↗
NASA NTRS1991

Real-time earthquake monitoring: Early warning and rapid response

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

Source record↗
NASA NTRS2006

Valve Health Monitoring System Utilizing Smart Instrumentation

The valve monitoring system is a stand alone unit with network capabilities for integration into a higher level health management system. The system is designed for aiding in failure predictions of high-geared ball valves and linearly actuated valves. It performs data tracking and archiving for identifying degraded performance. The data collection types are cryogenic cycles, total cycles, inlet temperature, body temperature torsional strain, linear bonnet strain, preload position, total travel and total directional changes. Events are recorded and time stamped in accordance with the IRIG B True Time. The monitoring system is designed for use in a Class 1 Division II explosive environment. The basic configuration consists of several instrumentation sensor units and a base station. The sensor units are self contained microprocessor controlled and remotely mountable in three by three by two inches. Each unit is potted in a fire retardant substance without any cavities and limited to low operating power for maintaining safe operation in a hydrogen environment. The units are temperature monitored to safeguard against operation outside temperature limitations. Each contains 902-928 MHz band digital transmitters which meet Federal Communication Commission's requirements and are limited to a 35 foot transmission radius for preserving data security. The base-station controller correlates data from the sensor units and generates data event logs on a compact flash memory module for database uploading. The entries are also broadcast over an Ethernet network. Nitrogen purged National Electrical Manufactures Association (NEMA) Class 4 enclosures are used to house the base-station

Jensen, Scott L.↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
NASA NTRS2008

Data Optical Networking Architecture Using Wavelength-Division Multiplexing Method for Optical Sensors

Recently there has been a growth in the number of fiber optical sensors used for health monitoring in the hostile environment of commercial aircraft. Health monitoring to detect the onset of failure in structural systems from such causes as corrosion, stress corrosion cracking, and fatigue is a critical factor in safety as well in aircraft maintenance costs. This report presents an assessment of an analysis model of optical data networking architectures used for monitoring data signals among these optical sensors. Our model is focused on the design concept of the wavelength-division multiplexing (WDM) method since most of the optical sensors deployed in the aircraft for health monitoring typically operate in a wide spectrum of optical wavelengths from 710 to 1550 nm.

Nguyen, Hung D.↗
NASA NTRS1994

Hidden Markov models and neural networks for fault detection in dynamic systems

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Smyth, Padhraic↗
NASA NTRS2011

The Development of the Puerto Rico Lightning Detection Network for Meteorological Research

A land-based Puerto Rico Lightning Detection Network (PR-LDN) dedicated to the academic research of meteorological phenomena has being developed. Five Boltek StormTracker PCI-Receivers with LTS-2 Timestamp Cards with GPS and lightning detectors were integrated to Pentium III PC-workstations running the CentOS linux operating system. The Boltek detector linux driver was compiled under CentOS, modified, and thoroughly tested. These PC-workstations with integrated lightning detectors were installed at five of the University of Puerto Rico (UPR) campuses distributed around the island of PR. The PC-workstations are left on permanently in order to monitor lightning activity at all times. Each is networked to their campus network-backbone permitting quasi-instantaneous data transfer to a central server at the UPR-Bayam n campus. Information generated by each lightning detector is managed by a C-program developed by us called the LDN-client. The LDN-client maintains an open connection to the central server operating the LDN-server program where data is sent real-time for analysis and archival. The LDN-client also manages the storing of data on the PC-workstation hard disk. The LDN-server software (also an in-house effort) analyses the data from each client and performs event triangulations. Time-of-arrival (TOA) and related hybrid algorithms, lightning-type and event discriminating routines are also implemented in the LDN-server software. We also have developed software to visually monitor lightning events in real-time from all clients and the triangulated events. We are currently monitoring and studying the spatial, temporal, and type distribution of lightning strikes associated with electrical storms and tropical cyclones in the vicinity of Puerto Rico.

Legault, Marc D.↗
NASA NTRS2020

GGOS Bureau of Networks and Observations: Network Infrastructure and Related Activities

The GGOS Bureau of Networks and Observations works with the IAG Services (IVS, ILRS, IGS, IDS, IGFS, and PSMSL) to advocate for the expansion and upgrade of space geodesy networks for the maintenance and improvement of the reference frame and other applications, as well as for the integration with other techniques, including absolute gravity and sea level measurements from tide gauges. New sites are being established following the GGOS concept of “core” and co-location sites, and new technologies are being implemented to enhance performance in data yield as well as accuracy. The Bureau continues to meet with organizations to discuss possibilities, including partnerships, for new and expanded participation. The GGOS Network continues to grow as new stations join every year. The Bureau holds meetings frequently, providing the opportunity for representatives from the services to meet and share progress and plans, and to discuss issues of common interest. It also monitors the status and projects the evolution of the network based on information from the current and expected future participants. Of particular interest at the moment is the integration of gravity and tide gauge networks and the forthcoming establishment of the new absolute gravity reference frame. The IAG Committees and Joint Working Groups play an essential role in the Bureau activity. The Standing Committee on Performance Simulations and Architectural Trade-offs (PLATO) uses simulation and analysis techniques to project future network capability and to examine trade-off options. The Committee on Data and Information is working on a strategy for a GGOS metadata system for data products and a more comprehensive long-term plan for an all-inclusive system. The Committee on Satellite Missions is working to enhance communication with the space missions, to advocate for missions that support GGOS goals and to enhance ground systems support. The IERS Working Group on Site Survey and Co-location (also participating in the Bureau) is working to enhance standardization in procedures, outreach and to encourage new survey groups to participate and improve procedures to determine systems’ reference points, a crucial aid in the detection of technique-specific systematic errors. We will give a brief update on the status and projection of the network infrastructure of the next several years, and the progress and plans of the Committees/Working Group in their critical role in enhancing data product quality and accessibility to the users.

Carey Noll↗
NASA NTRS2004

Oh! I Slipped the Surly Bounds of Earth....and Ran into Space Weather!

Over the past decade the concept of space weather has been introduced and matured in both the scientific community and popular press. Likewise the concept of space climatology recently also is being advanced. Closely linked to these concepts are their impacts on ground- and space-based technological systems; one such system commonly mentioned is manned space flight exemplified by the Space Shuttle and International Space Station (ISS). From a manned space flight perspective, space weather and space climatology have significant effects on the amount of radiation exposure received by humans in space from the ambient high-energy charged particles present in interplanetary space and trapped in the geomagnetosphere. Whereas the impact of space weather for most technological systems is usually discrete and well correlated in time, the principle impact of space weather and space climatology is to increase the probability of latent cancer formation in thetraveler cohort. In this regard, while space weather may be the dominating factor for a given mission, over the life of a long-term program such as the Space Shuttle or ISS space climatology is the controlling factor of latent cancer risk. Human radiation exposure enhancements associated with space weather disturbances has been a concern among scientist and mission controllers since the inception of manned spaceflight nearly forty years ago. This led NASA to develop, in conjunction with the Environmental Science Services Administration s Space Disturbance Forecast Center and the USAF/AWS, the Solar Particle Alert Network (SPAN)-the foundation of an initial U.S. space weather monitoring and forecasting service. Since Apollo, routine space flight operations have evolved to include the use of space weather and climatology data provided through a world-wide network of operational space weather data services to predict and recommend actions to minimize astronaut radiation exposures. NASA Space Radiation Analysis Group (SRAG) flight controllers use real-time space weather data to detect and assess the impact of solar particle events, outer electron belt enhancements, the formation of pseudo-stable additional trapped radiation belts, and the solar cycle modulation of trapped radiation belts and galactic cosmic rays. Energetic particle data from GOES spacecraft are automatically ingested from NOAA Space Environment Center data servers and used to drive a model for the estimating the exposure to astronauts from solar particle events. While adequate for current manned space flight support, the existing operational space weather support system requires improvements to address the anticipated evolution in both the character of manned missions as well as space flight operations management. Necessary space weather data improvements include: reliably available (near) real-time space weather data on a fixed schedule via redundant access methods that support autonomous data acquisition by computer systems behind enterprise firewalls; and rapid transition of promising research sensors into operational systems.

Golightly, Michael J.↗
NASA NTRS2021

Coastal California Water Resources: Assessing Estuarine Ecosystems in California for Improved Wetland Monitoring and Management

Estuaries are vital ecosystems that serve important ecological functions. The Marine Life Protection Act aims to protect these ecosystems by establishing a network of marine protected areas (MPAs), in part by requiring regulatory agencies to monitor estuary extent and health. However, California has 23 estuarine MPAs (EMPAs) and approximately 440,000 total acres of estuarine habitat and, therefore, ground-based data collection can be time and resource intensive. This project used remotely sensed data to examine the health of California EMPAs in an effort to supplement ground-based field measurements. Specifically using Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, Chlorophyll-a, and colored dissolved organic matter (CDOM) for estuaries observable with these sensors. The Normalized Water Difference Index (NDWI) from Sentinel-2 MSI was capable of capturing estuary mouth state and inundation extent. Meanwhile, Landsat 8 OLI and Sentinel-2 MSI indicated a capacity to capture differences in water quality metrics coinciding with changes to estuary mouth state using algorithms applied in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tools will allow project partners to better monitor and understand estuarine dynamics and health.

Karina Alvarez↗
NASA NTRS2001

Validation of Satellite Aerosol Retrievals from AERONET Ground-Based Measurements

Accurate and comprehensive assessment of the parameters that control key atmospheric and biospheric processes including assessment of anthropogenic effects on climate change is a fundamental measurement objective of NASA's EOS program (King and Greenstone, 1999). Satellite assessment programs and associated global climate models require validation and additional parameterization with frequent reliable ground-based observations. A critical and highly uncertain element of the measurement program is characterization of tropospheric aerosols requiring basic observations of aerosols optical and microphysical properties. Unfortunately as yet we do not know the aerosol burden man is contributing to the atmosphere and thus we will have no definitive measure of change for the future. This lack of aerosol assessment is the impetus for some of the EOS measurement activities (Kaufman et al., 1997; King et al., 1999) and the formation of the AERONET program (Holben et al., 1998). The goals of the AERONET program are to develop long term monitoring at globally distributed sites providing critical data for multiannual trend changes in aerosol loading and optical properties with the specific goal of providing a data base for validation of satellite derived aerosol optical properties. The AERONET program has evolved into an international federated network of approximately 100 ground-based remote sensing monitoring stations to characterize the optical and microphysical properties of aerosols.

Holben, Brent↗
NASA NTRS1982

Field interface module for antenna control assembly

The hardware aspects and capabilities of the field interface module (FIM) developed for monitor and control functions in the antenna mechanical subsystems of the deep space network and in the technical facilities controllers for the various complexes are described. The FIM is capable of monitoring and responding to a range of anaog and digital inputs and controlling external elements. The flexibility of the design makes it applicable to other control needs, using software developed for those specific applications.

Baher, F.↗
NASA NTRS1991

Engineering monitoring expert system's developer

This research project is designed to apply artificial intelligence technology including expert systems, dynamic interface of neural networks, and hypertext to construct an expert system developer. The developer environment is specifically suited to building expert systems which monitor the performance of ground support equipment for propulsion systems and testing facilities. The expert system developer, through the use of a graphics interface and a rule network, will be transparent to the user during rule constructing and data scanning of the knowledge base. The project will result in a software system that allows its user to build specific monitoring type expert systems which monitor various equipments used for propulsion systems or ground testing facilities and accrues system performance information in a dynamic knowledge base.

Lo, Ching F.↗
NASA NTRS1993

Small Business Innovations

Under an Army Small Business Innovation Research (SBIR) grant, Symbiotics, Inc. developed a software system that permits users to upgrade products from standalone applications so they can communicate in a distributed computing environment. Under a subsequent NASA SBIR grant, Symbiotics added additional tools to the SOCIAL product to enable NASA to coordinate conventional systems for planning Shuttle launch support operations. Using SOCIAL, data may be shared among applications in a computer network even when the applications are written in different programming languages. The product was introduced to the commercial market in 1993 and is used to monitor and control equipment for operation support and to integrate financial networks. The SBIR program was established to increase small business participation in federal R&D activities and to transfer government research to industry. InQuisiX is a reuse library providing high performance classification, cataloging, searching, browsing, retrieval and synthesis capabilities. These form the foundation for software reuse, producing higher quality software at lower cost and in less time. Software Productivity Solutions, Inc. developed the technology under Small Business Innovation Research (SBIR) projects funded by NASA and the Army and is marketing InQuisiX in conjunction with Science Applications International Corporation (SAIC). The SBIR program was established to increase small business participation in federal R&D activities and to transfer government research to industry.

Source record↗
NASA NTRS2013

Automated Meteor Fluxes with a Wide-Field Meteor Camera Network

Within NASA, the Meteoroid Environment Office (MEO) is charged to monitor the meteoroid environment in near ]earth space for the protection of satellites and spacecraft. The MEO has recently established a two ]station system to calculate automated meteor fluxes in the millimeter ]size ]range. The cameras each consist of a 17 mm focal length Schneider lens on a Watec 902H2 Ultimate CCD video camera, producing a 21.7 x 16.3 degree field of view. This configuration has a red ]sensitive limiting meteor magnitude of about +5. The stations are located in the South Eastern USA, 31.8 kilometers apart, and are aimed at a location 90 km above a point 50 km equidistant from each station, which optimizes the common volume. Both single station and double station fluxes are found, each having benefits; more meteors will be detected in a single camera than will be seen in both cameras, producing a better determined flux, but double station detections allow for non ]ambiguous shower associations and permit speed/orbit determinations. Video from the cameras are fed into Linux computers running the ASGARD (All Sky and Guided Automatic Real ]time Detection) software, created by Rob Weryk of the University of Western Ontario Meteor Physics Group. ASGARD performs the meteor detection/photometry, and invokes the MILIG and MORB codes to determine the trajectory, speed, and orbit of the meteor. A subroutine in ASGARD allows for the approximate shower identification in single station meteors. The ASGARD output is used in routines to calculate the flux in units of #/sq km/hour. The flux algorithm employed here differs from others currently in use in that it does not assume a single height for all meteors observed in the common camera volume. In the MEO system, the volume is broken up into a set of height intervals, with the collecting areas determined by the radiant of active shower or sporadic source. The flux per height interval is summed to obtain the total meteor flux. As ASGARD also computes the meteor mass from the photometry, a mass flux can be also calculated. Weather conditions in the southeastern United States are seldom ideal, which introduces the difficulty of a variable sky background. First a weather algorithm indicates if sky conditions are clear enough to calculate fluxes, at which point a limiting magnitude algorithm is employed. The limiting magnitude algorithm performs a fit of stellar magnitudes vs camera intensities. The stellar limiting magnitude is derived from this and easily converted to a limiting meteor magnitude for the active shower or sporadic source.

Blaauw, R. C.↗
NASA NTRS1991

Automated implementation of rule-based expert systems with neural networks for time-critical applications

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Ramamoorthy, P. A.↗