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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 NTRS2012

Detection of Coccolithophore Blooms in Ocean Color Satellite Imagery: a Generalized Approach for Use with Multiple Sensors

A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cellsmL and 43,000 and 78,000 lithsmL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 19972010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemi- sphere with values of 2.00 106 km2 and 0.75 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75106 km2 is dominated by contributions from the Southern Ocean.

chlorophyll fluorescence↗
NASA NTRS2018

Intelligent Data Transfer for Multiple Sensor Networks over a Broad Temperature Range

A sensor network may be configured to operate in extreme temperature environments. A sensor may be configured to generate a frequency carrier, and transmit the frequency carrier to a node. The node may be configured to amplitude modulate the frequency carrier, and transmit the amplitude modulated frequency carrier to a receiver.

Krasowski, Michael↗
NASA NTRS1994

Passive fetal heart rate monitoring apparatus and method with enhanced fetal heart beat discrimination

An apparatus for acquiring signals emitted by a fetus, identifying fetal heart beats and determining a fetal heart rate is presented. Multiple sensor signals are outputted by a passive fetal heart rate monitoring sensor. Multiple parallel nonlinear filters filter these multiple sensor signals to identify fetal heart beats in the signal data. A processor determines a fetal heart rate based on these identified fetal heart beats. The processor includes the use of a figure of merit weighting of heart rate estimates based on the identified heart beats from each filter for each signal. The fetal heart rate thus determined is outputted to a display, storage, or communications channel. A method for enhanced fetal heart beat discrimination includes acquiring signals from a fetus, identifying fetal heart beats from the signals by multiple parallel nonlinear filtering, and determining a fetal heart rate based on the identified fetal heart beats. A figure of merit operation in this method provides for weighting a plurality of fetal heart rate estimates based on the identified fetal heart beats and selecting the highest ranking fetal heart rate estimate.

Zahorian, Stephen A.↗
NASA NTRS1996

Passive fetal heart rate monitoring apparatus and method with enhanced fetal heart beat discrimination

An apparatus for acquiring signals emitted by a fetus, identifying fetal heart beats and determining a fetal heart rate. Multiple sensor signals are outputted by a passive fetal heart rate monitoring sensor. Multiple parallel nonlinear filters filter these multiple sensor signals to identify fetal heart beats in the signal data. A processor determines a fetal heart rate based on these identified fetal heart beats. The processor includes the use of a figure of merit weighting of heart rate estimates based on the identified heart beats from each filter for each signal. The fetal heart rate thus determined is outputted to a display, storage, or communications channel. A method for enhanced fetal heart beat discrimination includes acquiring signals from a fetus, identifying fetal heart beats from the signals by multiple parallel nonlinear filtering, and determining a fetal heart rate based on the identified fetal heart beats. A figure of merit operation in this method provides for weighting a plurality of fetal heart rate estimates based on the identified fetal heart beats and selecting the highest ranking fetal heart rate estimate.

Zahorian, Stephen A.↗
NASA NTRS1985

The earth's surface studied from space; Proceedings of Workshop II of the COSPAR 25th Plenary Meeting, Graz, Austria, June 25-July 7, 1984

Consideration is given to: Landsat image data quality studies; a preliminary evaluation of Landsat-4 Thematic Mapper (TM) data for mineral exploration; and the early evaluation of TM data for mapping forest, agricultural and soil resources. Among other topics discussed are: shortwave infrared detection of vegetation; SPOT image quality and post-launch assessment; an evaluation of SPOT HRV simulation data for Corps of Engineers applications; and the application potential of SPOT imagery for topographic mapping. Consideration is also given to: verification studies of MOS-1 sensors; multiple sensor geocoded data; and the utility of proposed sensors for coastal engineering studies.

Ungar, S. G.↗
NASA NTRS2013

Coherent Uncertainty Analysis of Aerosol Measurements from Multiple Satellite Sensors

Aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS altogether, a total of 11 different aerosol products were comparatively analyzed using data collocated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations within the Multi-sensor Aerosol Products Sampling System (MAPSS, http://giovanni.gsfc.nasa.gov/mapss/ and http://giovanni.gsfc.nasa.gov/aerostat/). The analysis was performed by comparing quality-screened satellite aerosol optical depth or thickness (AOD or AOT) retrievals during 2006-2010 to available collocated AERONET measurements globally, regionally, and seasonally, and deriving a number of statistical measures of accuracy. We used a robust statistical approach to detect and remove possible outliers in the collocated data that can bias the results of the analysis. Overall, the proportion of outliers in each of the quality-screened AOD products was within 12%. Squared correlation coefficient (R2) values of the satellite AOD retrievals relative to AERONET exceeded 0.6, with R2 for most of the products exceeding 0.7 over land and 0.8 over ocean. Root mean square error (RMSE) values for most of the AOD products were within 0.15 over land and 0.09 over ocean. We have been able to generate global maps showing regions where the different products present advantages over the others, as well as the relative performance of each product over different landcover types. It was observed that while MODIS, MISR, and SeaWiFS provide accurate retrievals over most of the landcover types, multi-angle capabilities make MISR the only sensor to retrieve reliable AOD over barren and snow / ice surfaces. Likewise, active sensing enables CALIOP to retrieve aerosol properties over bright-surface shrublands more accurately than the other sensors, while POLDER, which is the only one of the sensors capable of measuring polarized aerosols, outperforms other sensors in certain smoke-dominated regions, including broadleaf evergreens in Brazil and South-East Asia.

remote sensing↗
NASA NTRS2010

Cross-Characterization of Aerosol Properties from Multiple Spaceborne Sensors Facilitated by Regional Ground-Based Observations

Aerosol observations from space have become a standard source for retrieval of aerosol properties on both regional and global scales. Indeed, the large number of currently operational spaceborne sensors provides for unprecedented access to the most complete set of complimentary aerosol measurements ever to be available. Nonetheless, this resource remains under-utilized, largely due to the discrepancies and differences existing between the sensors and their aerosol products. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have designed and implemented an online Multi-sensor Aerosol Products Sampling System (MAPSS) that facilitates the joint sampling of aerosol data from multiple sensors. MAPSS consistently samples aerosol products from multiple spaceborne sensors using a unified spatial and temporal resolution, where each dataset is sampled over Aerosol Robotic Network (AERONET) locations together with coincident AERONET data samples. In this way, MAPSS enables a direct cross-characterization and data integration between aerosol products from multiple sensors. Moreover, the well-characterized co-located ground-based AERONET data provides the basis for the integrated validation of these products.

Petrenko, Maksym↗
NASA NTRS1993

Phase discriminating capacitive array sensor system

A phase discriminating capacitive sensor array system which provides multiple sensor elements which are maintained at a phase and amplitude based on a frequency reference provided by a single frequency stabilized oscillator. Sensor signals provided by the multiple sensor elements are controlled by multiple phase control units, which correspond to the multiple sensor elements, to adjust the sensor signals from the multiple sensor elements based on the frequency reference. The adjustment made to the sensor signals is indicated by output signals which indicate the proximity of the object. The output signals may also indicate the closing speed of the object based on the rate of change of the adjustment made, and the edges of the object based on a sudden decrease in the adjustment made.

Vranish, John M.↗
NASA NTRS2012

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

Petrenko, Maksym↗
NASA NTRS2011

Coherent Evaluation of Aerosol Data Products from Multiple Satellite Sensors

Aerosol retrieval from satellite has practically become routine, especially during the last decade. However, there is often disagreement between similar aerosol parameters retrieved from different sensors, thereby leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus, and the inconsistencies are not well characterized and understood, there will be no way of developing reliable model inputs and climate data records from satellite aerosol measurements. Fortunately, the Aerosol Robotic Network (AERONET) is providing well-calibrated globally representative ground-based aerosol measurements corresponding to the satellite-retrieved products. Through a recently developed web-based Multi-sensor Aerosol Products Sampling System (MAPSS), we are utilizing the advantages offered by collocated AERONET and satellite products to characterize and evaluate aerosol retrieval from multiple sensors. Indeed, MAPSS and its companion statistical tool AeroStat are facilitating detailed comparative uncertainty analysis of satellite aerosol measurements from Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP. In this presentation, we will describe the strategy of the MAPSS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.

Ichoku, Charles↗
NASA NTRS2015

DRAGONS-A Micrometeoroid and Orbital Debris Impact Sensor on the ISS

The Debris Resistive/Acoustic Grid Orbital Navy-NASA Sensor (DRAGONS) is intended to be a large area impact sensor for in situ measurements of micrometeoroids and orbital debris (MMOD) in the sub-millimeter to millimeter size regime in the near Earth space environment. These MMOD particles are too small to be detected by ground-based radars and optical telescopes, but still large enough to be a serious threat to human space activities and robotic missions in the low Earth orbit (LEO) region. The nominal detection area of DRAGONS is 1 sq m, consisting of four 0.5 m × 0.5 m independent panels, but the dimensions of the panels can easily be modified to accommodate different payload constraints. The approach of the DRAGONS design is to combine three particle impact detection concepts to maximize information that can be extracted from each detected impact. The first is a resistive grid consisting of 75-micrometer-wide resistive lines, coated in parallel and separated by 75 micrometer gaps on a 25-micrometer thin film. When a particle a few hundred micrometers or larger strikes the grid, it would penetrate the film and sever some resistive lines. The size of the damage area can be estimated from the increased resistance. The second concept is based on polyvinylidene fluoride (PVDF) acoustic impact sensors. Multiple PVDF sensors are attached to the thin film to provide the impact timing information. From the different signal arrival times at different acoustic sensors, the impact location can be calculated via triangulation algorithms. The third concept employs a dual-layer film system where a second 25-micrometer film is placed 15 cm behind the resistive-grid film. Multiple PVDF acoustic sensors are also attached to the second film. The combination of impact timing and location information from the two films allows for direct measurements of the impact direction and speed. The DRAGONS technology development has been funded by several NASA organizations since 2002, first by the NASA Science Mission Directorate and the NASA Exploration Systems Mission Directorate, then by the NASA JSC Innovative Research and Development Program and the NASA Orbital Debris Program Office. The NASA Orbital Debris Program Office leads the effort with collaboration from the U.S. Naval Academy, Naval Research Laboratory, University of Kent at Canterbury in Great Britain, and Virginia Tech. The project recently reached a major milestone when DRAGONS was approved for a technology demonstration mission by the International Space Station (ISS) Program in October 2014. The plan is to deploy a 1 sq m DRAGONS on the ISS with the detection surface facing the ram-direction for 2 to 3 years. The tentative launch schedule is in early 2017. This mission will collect data on orbital debris in the sub-millimeter size regime to better define the small orbital debris environment at the ISS altitude. The mission will also advance the DRAGONS Technology Readiness Level to 9 and greatly enhance the opportunities to deploy DRAGONS on other spacecraft to high LEO orbits in the future.

Liou, J.-C.↗
DOE OSTI2023

Anomalous behavior detection by an artificial intelligence-enabled system with multiple correlated sensors

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

Serebryakov, Sergey↗
NASA NTRS2020

Field Evaluation of Portable Soil Water Content Sensors in a Sandy Loam

Ground observations are critical in the validation of soil water content (SWC) estimates from both satellites and land surface models. Portable SWC sensors provide useful information to determine the amount of SWC in the topsoil layer for various applications; however, these probes are not accurate without site‐specific correction. In the present study, we examined and compared six different types of portable electromagnetic (EM) SWC sensors, including multiple sensors made by the same manufacturers, for a total of 16 EM‐based SWC probes equipped with portable data loggers. All SWC probes met the target accuracy after onsite correction—the RMSD was <0.025 m(exp 3) m(exp -3). Using the two‐sample t-tests, we observed that SWC data obtained from similar electrode lengths and from different manufacturers showed similar distributions over time with the same mean. Furthermore, using the maximize R method to combine SWC data from two different types of sensors increased the accuracy of the results. When datasets from two different types of sensors were combined, the Pearson's correlation coefficient (R value) and RMSD values were improved. The average R value improved from .930 to .945, and the RMSD decreased from 0.036 m(exp 3) m(exp -3) to 0.018 m(exp 3) m(exp −3). These results indicate that, along with site‐specific correction, synergetic use of multiple manufacturers’ EM‐based SWC probes can improve the R value and reduce systematic bias.

soil moisture data↗
DOE OSTI2022

Coastal Acoustic Buoy for Offshore Wind: Project Synthesis

The population of North Atlantic right whales is critically endangered and their habitat overlaps with offshore windfarm leases. It is therefore imperative that effective mitigation strategies be used to avoid impacts on right whales during the construction of offshore windfarms. The Department of Energy issued FOA Number DE-FOA-0001924 to encourage the development of technology that could monitor large exclusion zones for right whales in order to mitigate potential impact of construction noise on right whales. This report summarizes the past two years of the development and evaluation of the Coastal Acoustic Buoy for Offshore Wind (CABOW) project which aimed to develop technology to monitor large exclusion zones for North Atlantic right whales. Over the course of the project SMRU Consulting have implemented a rigorous design process including comparison of different approaches (e.g., single sensor vs multiple sensors), as well as consideration of placement and timing of acoustic monitoring. We have evaluated critical components of the CABOW system including, reliability, detection range, and bearing accuracy in areas adjacent to offshore windfarm leases in Maryland by conducting 3,536 playbacks of simulated right whale upcalls. The maximum call detection range was 7.5 km when noise was 99 dB re 1µPa rms (50-225 Hz), but this reduced to < 1 km when ambient noise levels were high. Our detection probability in the field was measured as a function of range as well as the source-to-noise level ratio allowing us to build a model to predict the probability of detection under various scenarios (sample size: 3,536 calls x 5 buoys = 17,680). The median bearing error was -0.25° but this is likely an underestimate of error due to experimental design. Using the published recall and precision of the two detectors we implemented in the CABOW system, we estimate that at a recall of 80%, our precision was > 80%, within the range of what we were aiming for in this project. To estimate our exclusion zone false negative and false positive rates, we built a simulation model using the empirical data from our field trial. We modelled three to nine CABOW units placed on the 10 km exclusion zone and estimated our false negative rate to be 1% or less (which was our project goal) and our false positive rate to be between seven and nine percent, slightly above our goal of 5%. However, we also modelled an equivalent PAM system that does not have bearing capabilities and found the false positive rates for that system to be six to eight times higher than the CABOW rate. This higher false positive rate of PAM systems with low spatial information could have significant cost repercussions for offshore wind developers by adding work shutdowns or delays without providing additional protection for right whales. The model we built allows us to explore the placement of PAM systems under various scenarios and will thus help facilitate planning of PAM mitigation systems to meet NOAA Incidental Harassment Authorizations for specific windfarms. We achieved an average system uptime of 98.3%, just below our goal of 99%. The issues that caused these short losses of data have been identified and fixed. Right whale detections and audio clips were typically transferred via radio from the buoys to the base station in two to four seconds. We therefore believe we have developed a highly robust real-time PAM system. Based on the above, we feel we have achieved the stated funding goal of developing a cost-effective and robust real-time PAM system that enables the monitoring of large exclusion zones for right whales during the constructions of offshore windfarms. This should lead to decreased costs and risks for the offshore wind sector while providing robust mitigation for right whales. It is important to state that PAM mitigation will need to be implemented with other mitigation strategies (e.g., visual observers) to provide a complete mitigation strategy to ensure that any effects on right whales from offshore windfarm construction is minimized.

17 WIND ENERGY↗
NASA NTRS2005

FPGA development for high altitude subsonic parachute testing

This paper describes a rapid, top down requirements-driven design of an FPGA used in an Earth qualification test program for a new Mars subsonic parachute. The FPGA is used to process and store data from multiple sensors at multiple rates during launch, ascent, deployment and descent phases of the subsonic parachute test.

telemetry↗
NASA NTRS2007

North American Vegetation Dynamics Observed with Multi-Resolution Satellite Data

North American vegetation has been discovered to be a net carbon sink, with atypical behavior of drawing down more carbon from the atmosphere during the past century. It has been suggested that the Northern Hemisphere will respond favorably to climate warming by enhancing productivity and reducing the impact of fossil fuel emissions into the atmosphere. Many investigations are currently underway to understand and identify mechanisms of storage so they might be actively managed to offset carbon emissions which have detrimental consequences to the functioning of ecosystems and human well being. This paper used a time series of satellite data from multiple sensors at multiple resolutions over the past thlrty years to identify and understand mechanisms of change to vegetation productivity throughout North America. We found that humans had a marked impact to vegetation growth in half of the six selected study regions which cover greater than two million km2. We found climatic influences of increasing temperatures, and longer growing seasons with reduced snow cover in the northern regions of North America with forest fire recovery in the Northern coniferous forests of Canada. The Mid-latitudes had more direct land cover changes induced by humans coupled with climatic influences such as severe drought and altered production strategies of rain-fed agriculture in the upper Midwest, expansion of irrigated agriculture in the lower Midwest, and insect outbreaks followed by subsequent logging in the upper Northeast. Vegetation growth over long time periods (20+ years) in North America appears to be associated with long term climate change but most of the marked changes appear to be associated with climate variability on decadal and shorter time scales along with direct human land cover conversions. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.

Neigh, Christopher S. R.↗
NASA NTRS2012

DRAGONS - A Micrometeoroid and Orbital Debris Impact Sensor

The Debris Resistive/Acoustic Grid Orbital Navy Sensor (DRAGONS) is intended to be a large area impact sensor for in-situ measurements of micrometeoroids and orbital debris (MMOD) in the approx.0.2 to 1 mm size regime. These MMOD particles are too small to be detected by groundbased radars and optical telescopes, but still large enough to be a safety concern for human space activities and robotic missions in the low Earth orbit (LEO) region. The nominal detection area of DRAGONS is 1 sq m, consisting of four 0.5 m x 0.5 m independently operated panels. The concept of the DRAGONS design is to combine three different detection technologies to maximize information extracted from each detected impact. The first technology is a resistive grid consisting of 62.5-microns-wide resistive lines, coated in parallel and separated by 62.5 micron gaps on a Kapton film. When a particle a few hundred micrometers or larger strikes the grid, it world penetrate the film and sever some resistive lines. The size of the damage area can be estimated from the increased resistance. The second technology employs a dual-layer, 25-microns-thick Kapton film with a 10 cm separation. By measuring the time difference between impacts on the two films, the impact speed can be calculated. The third technology is based on polyvinylidene fluoride (PVDF) acoustic impact sensors. Multiple PVDF sensors are attached to the backside of both Kapton films to provide impact timing measurements. The impact location on each film can be identified from the triangulation of signals received at different PVDF sensors and provides an estimate of the impact direction. The development of DRAGONS is supported by the NASA Orbital Debris Program Office. The project is led by the U.S. Naval Academy (USNA), with additional collaboration from the U.S. Naval Research Laboratory (NRL), the University of Kent at Canterbury in Great Britain, and Virginia Tech (VT). The short-term goal of DRAGONS is to advance its Technology Readiness Level to 9 and to demonstrate the system capabilities of detecting and characterizing submillimeter MMOD impacts. The long-term goal is to deploy a large detection area (>1 sq m) DRAGONS to 700-1000 km altitude and collect sufficient data for better environment definition of MMOD in the 0.2- to 1-mm size regime. The Preliminary Design Review (PRD) of DRAGONS was held at the USNA in June 2012. The Critical Design Review (CDR) is scheduled for early 2013. A flight-ready unit with a 0.25 sq m detection area will be completed and tested by the end of September 2013. The biggest challenge for the project, however, is to identify a demonstration opportunity on the International Space Station in the coming years.

Liou, J. C.↗