The Distribution of Carbon Monoxide in the GOCART Model

NASA Technical Reports Server (NTRS)

Fan, Xiaobiao; Chin, Mian; Einaudi, Franco (Technical Monitor)

2000-01-01

Carbon monoxide (CO) is an important trace gas because it is a significant source of tropospheric Ozone (O3) as well as a major sink for atmospheric hydroxyl radical (OH). The distribution of CO is set by a balance between the emissions, transport, and chemical processes in the atmosphere. The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the atmospheric distribution of CO. The GOCART model is driven by the assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS DAS) in an off-line mode. We study the distribution of CO on three time scales: (1) day to day fluctuation produced by the synoptic waves; (2) seasonal changes due to the annual cycle of CO sources and sinks; and (3) interannual variability induced by dynamics. Comparison of model results with ground based and remote sensing measurements will also be presented.

The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape.

PubMed

Chambers, Jeffrey Q; Negron-Juarez, Robinson I; Marra, Daniel Magnabosco; Di Vittorio, Alan; Tews, Joerg; Roberts, Dar; Ribeiro, Gabriel H P M; Trumbore, Susan E; Higuchi, Niro

2013-03-05

Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling. Results demonstrate that a steady state of patches of varying successional age occurs over a relatively large spatial scale, with important implications for detecting temporal trends on plots that sample a small fraction of the landscape. Long highly significant stochastic runs averaging 1.0 Mg biomass⋅ha(-1)⋅y(-1) were often punctuated by episodic disturbance events, resulting in a sawtooth time series of hectare-scale tree biomass. To maximize the detection of temporal trends for this Central Amazon site (e.g., driven by CO2 fertilization), plots larger than 10 ha would provide the greatest sensitivity. A model-based analysis of fractional mortality across all gap sizes demonstrated that 9.1-16.9% of tree mortality was missing from plot-based approaches, underscoring the need to combine plot and remote-sensing methods for estimating net landscape carbon balance. Old-growth tropical forests can exhibit complex large-scale structure driven by disturbance and recovery cycles, with ecosystem and community attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition.

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations

PubMed Central

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability. PMID:27010692

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations.

PubMed

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998-2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002-2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Assumption-versus data-based approaches to summarizing species' ranges.

PubMed

Peterson, A Townsend; Navarro-Sigüenza, Adolfo G; Gordillo, Alejandro

2018-06-01

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications. © 2016 Society for Conservation Biology.

The phytoplankton bloom in the northwestern Arabian Sea during the southwest monsoon of 1979

NASA Technical Reports Server (NTRS)

Brock, John C.; Mcclain, Charles R.; Luther, Mark E.; Hay, William W.

1991-01-01

The present study investigates the biological variability of the northwestern Arabian Sea during the 1979 southwest monsoon by the synthesis of satellite ocean color remote sensing with an analysis of in situ hydrographic and meteorological data sets and the results of wind-driven modeling of upper-ocean circulation. The phytoplankton bloom peaked during August-September, extended from the Oman coast to about 65 deg E, and lagged behind the development of open-sea upwelling by at least 1 mo. The pigment distributions, hydrographic data, and model results all suggest that the boom was driven by spatially distinct upward nutrient fluxes to the euphotic zone forced by the physical processes of coastal upwelling and offshore Ekman pumping. Coastal upwelling was evident from May through September, yielded the most extreme concentrations of phytoplankton biomass, and, along the Arabian coast, was limited to the continental shelf in the promotion of high concentrations of phytoplankton.

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

Artan, G.; Gadain, Hussein; Smith, Jodie; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

2007-01-01

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

Probability distributions for Markov chain based quantum walks

NASA Astrophysics Data System (ADS)

Balu, Radhakrishnan; Liu, Chaobin; Venegas-Andraca, Salvador E.

2018-01-01

We analyze the probability distributions of the quantum walks induced from Markov chains by Szegedy (2004). The first part of this paper is devoted to the quantum walks induced from finite state Markov chains. It is shown that the probability distribution on the states of the underlying Markov chain is always convergent in the Cesaro sense. In particular, we deduce that the limiting distribution is uniform if the transition matrix is symmetric. In the case of a non-symmetric Markov chain, we exemplify that the limiting distribution of the quantum walk is not necessarily identical with the stationary distribution of the underlying irreducible Markov chain. The Szegedy scheme can be extended to infinite state Markov chains (random walks). In the second part, we formulate the quantum walk induced from a lazy random walk on the line. We then obtain the weak limit of the quantum walk. It is noted that the current quantum walk appears to spread faster than its counterpart-quantum walk on the line driven by the Grover coin discussed in literature. The paper closes with an outlook on possible future directions.

Emergent dynamic structures and statistical law in spherical lattice gas automata.

PubMed

Yao, Zhenwei

2017-12-01

Various lattice gas automata have been proposed in the past decades to simulate physics and address a host of problems on collective dynamics arising in diverse fields. In this work, we employ the lattice gas model defined on the sphere to investigate the curvature-driven dynamic structures and analyze the statistical behaviors in equilibrium. Under the simple propagation and collision rules, we show that the uniform collective movement of the particles on the sphere is geometrically frustrated, leading to several nonequilibrium dynamic structures not found in the planar lattice, such as the emergent bubble and vortex structures. With the accumulation of the collision effect, the system ultimately reaches equilibrium in the sense that the distribution of the coarse-grained speed approaches the two-dimensional Maxwell-Boltzmann distribution despite the population fluctuations in the coarse-grained cells. The emergent regularity in the statistical behavior of the system is rationalized by mapping our system to a generalized random walk model. This work demonstrates the capability of the spherical lattice gas automaton in revealing the lattice-guided dynamic structures and simulating the equilibrium physics. It suggests the promising possibility of using lattice gas automata defined on various curved surfaces to explore geometrically driven nonequilibrium physics.

Emergent dynamic structures and statistical law in spherical lattice gas automata

NASA Astrophysics Data System (ADS)

Yao, Zhenwei

2017-12-01

Various lattice gas automata have been proposed in the past decades to simulate physics and address a host of problems on collective dynamics arising in diverse fields. In this work, we employ the lattice gas model defined on the sphere to investigate the curvature-driven dynamic structures and analyze the statistical behaviors in equilibrium. Under the simple propagation and collision rules, we show that the uniform collective movement of the particles on the sphere is geometrically frustrated, leading to several nonequilibrium dynamic structures not found in the planar lattice, such as the emergent bubble and vortex structures. With the accumulation of the collision effect, the system ultimately reaches equilibrium in the sense that the distribution of the coarse-grained speed approaches the two-dimensional Maxwell-Boltzmann distribution despite the population fluctuations in the coarse-grained cells. The emergent regularity in the statistical behavior of the system is rationalized by mapping our system to a generalized random walk model. This work demonstrates the capability of the spherical lattice gas automaton in revealing the lattice-guided dynamic structures and simulating the equilibrium physics. It suggests the promising possibility of using lattice gas automata defined on various curved surfaces to explore geometrically driven nonequilibrium physics.

Pulse-driven micro gas sensor fitted with clustered Pd/SnO2 nanoparticles.

PubMed

Suematsu, Koichi; Shin, Yuka; Ma, Nan; Oyama, Tokiharu; Sasaki, Miyuki; Yuasa, Masayoshi; Kida, Tetsuya; Shimanoe, Kengo

2015-08-18

Real-time monitoring of specific gas concentrations with a compact and portable gas sensing device is required to sense potential health risk and danger from toxic gases. For such purposes, we developed an ultrasmall gas sensor device, where a micro sensing film was deposited on a micro heater integrated with electrodes fabricated by the microelectromechanical system (MEMS) technology. The developed device was operated in a pulse-heating mode to significantly reduce the heater power consumption and make the device battery-driven and portable. Using clustered Pd/SnO2 nanoparticles, we succeeded in introducing mesopores ranging from 10 to 30 nm in the micro gas sensing film (area: ϕ 150 μm) to detect large volatile organic compounds (VOCs). The micro sensor showed quick, stable, and high sensor responses to toluene at ppm (parts per million) concentrations at 300 °C even by operating the micro heater in a pulse-heating mode where switch-on and -off cycles were repeated at one-second intervals. The high performance of the micro sensor should result from the creation of efficient diffusion paths decorated with Pd sensitizers by using the clustered Pd/SnO2 nanoparticles. Hence we demonstrate that our pulse-driven micro sensor using nanostructured oxide materials holds promise as a battery-operable, portable gas sensing device.

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-01-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology. It also presents, for the first time, a method to manually calibrate temperatures along the optical fiber.

From heavy-tailed to exponential distribution of interevent time in cellphone top-up behavior

NASA Astrophysics Data System (ADS)

Wang, Peng; Ma, Qiang

2017-05-01

Cellphone top-up is a kind of activities, to a great extent, driven by individual consumption rather than personal interest and this behavior should be stable in common sense. However, our researches find there are heavy-tails both in interevent time distribution and purchase frequency distribution at the global level. Moreover, we find both memories of interevent time and unit price series are negative, which is different from previous bursty activities. We divide individuals into five groups according to the purchase frequency and the average unit price respectively. Then, the group analysis shows some significant heterogeneity in this behavior. On one hand, we obtain only the individuals with high purchase frequency have the heavy-tailed nature in interevent time distribution. On the contrary, the negative memory is only caused by low purchase-frequency individuals without burstiness. On the other hand, the individuals with different preferential price also have different power-law exponents at the group level and there is no data collapse after rescaling between these distributions. Our findings produce the evidence for the significant heterogeneity of human activity in many aspects.

Goddard Atmospheric Composition Data Center: Aura Data and Services in One Place

NASA Technical Reports Server (NTRS)

Leptoukh, G.; Kempler, S.; Gerasimov, I.; Ahmad, S.; Johnson, J.

2005-01-01

The Goddard Atmospheric Composition Data and Information Services Center (AC-DISC) is a portal to the Atmospheric Composition specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques for the better science data usage. It provides convenient access to Atmospheric Composition data and information from various remote-sensing missions, from TOMS, UARS, MODIS, and AIRS, to the most recent data from Aura OMI, MLS, HIRDLS (once these datasets are released to the public), as well as Atmospheric Composition datasets residing at other remote archive site.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.

PubMed

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-02-23

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

PubMed Central

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-01-01

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies. PMID:26907297

Rainy Day: A Remote Sensing-Driven Extreme Rainfall Simulation Approach for Hazard Assessment

NASA Astrophysics Data System (ADS)

Wright, Daniel; Yatheendradas, Soni; Peters-Lidard, Christa; Kirschbaum, Dalia; Ayalew, Tibebu; Mantilla, Ricardo; Krajewski, Witold

2015-04-01

Progress on the assessment of rainfall-driven hazards such as floods and landslides has been hampered by the challenge of characterizing the frequency, intensity, and structure of extreme rainfall at the watershed or hillslope scale. Conventional approaches rely on simplifying assumptions and are strongly dependent on the location, the availability of long-term rain gage measurements, and the subjectivity of the analyst. Regional and global-scale rainfall remote sensing products provide an alternative, but are limited by relatively short (~15-year) observational records. To overcome this, we have coupled these remote sensing products with a space-time resampling framework known as stochastic storm transposition (SST). SST "lengthens" the rainfall record by resampling from a catalog of observed storms from a user-defined region, effectively recreating the regional extreme rainfall hydroclimate. This coupling has been codified in Rainy Day, a Python-based platform for quickly generating large numbers of probabilistic extreme rainfall "scenarios" at any point on the globe. Rainy Day is readily compatible with any gridded rainfall dataset. The user can optionally incorporate regional rain gage or weather radar measurements for bias correction using the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework. Results from Rainy Day using the CMORPH satellite precipitation product are compared with local observations in two examples. The first example is peak discharge estimation in a medium-sized (~4000 square km) watershed in the central United States performed using CUENCAS, a parsimonious physically-based distributed hydrologic model. The second example is rainfall frequency analysis for Saint Lucia, a small volcanic island in the eastern Caribbean that is prone to landslides and flash floods. The distinct rainfall hydroclimates of the two example sites illustrate the flexibility of the approach and its usefulness for hazard analysis in data-poor regions.

Intertwined electron-nuclear motion in frustrated double ionization in driven heteronuclear molecules

NASA Astrophysics Data System (ADS)

Vilà, A.; Zhu, J.; Scrinzi, A.; Emmanouilidou, A.

2018-03-01

We study frustrated double ionization (FDI) in a strongly-driven heteronuclear molecule HeH+ and compare with H2. We compute the probability distribution of the sum of the final kinetic energies of the nuclei for strongly-driven HeH+. We find that this distribution has more than one peak for strongly-driven HeH+, a feature we do not find to be present for strongly-driven H2. Moreover, we compute the probability distribution of the principal quantum number n of FDI. We find that this distribution has several peaks for strongly-driven HeH+, while the respective distribution has one main peak and a ‘shoulder’ at lower principal quantum numbers n for strongly-driven H2. Surprisingly, we find this feature to be a clear signature of the intertwined electron-nuclear motion.

A study using a Monte Carlo method of the optimal configuration of a distribution network in terms of power loss sensing.

PubMed

Moon, Hyun Ho; Lee, Jong Joo; Choi, Sang Yule; Cha, Jae Sang; Kang, Jang Mook; Kim, Jong Tae; Shin, Myong Chul

2011-01-01

Recently there have been many studies of power systems with a focus on "New and Renewable Energy" as part of "New Growth Engine Industry" promoted by the Korean government. "New And Renewable Energy"-especially focused on wind energy, solar energy and fuel cells that will replace conventional fossil fuels-is a part of the Power-IT Sector which is the basis of the SmartGrid. A SmartGrid is a form of highly-efficient intelligent electricity network that allows interactivity (two-way communications) between suppliers and consumers by utilizing information technology in electricity production, transmission, distribution and consumption. The New and Renewable Energy Program has been driven with a goal to develop and spread through intensive studies, by public or private institutions, new and renewable energy which, unlike conventional systems, have been operated through connections with various kinds of distributed power generation systems. Considerable research on smart grids has been pursued in the United States and Europe. In the United States, a variety of research activities on the smart power grid have been conducted within EPRI's IntelliGrid research program. The European Union (EU), which represents Europe's Smart Grid policy, has focused on an expansion of distributed generation (decentralized generation) and power trade between countries with improved environmental protection. Thus, there is current emphasis on a need for studies that assesses the economic efficiency of such distributed generation systems. In this paper, based on the cost of distributed power generation capacity, calculations of the best profits obtainable were made by a Monte Carlo simulation. Monte Carlo simulations that rely on repeated random sampling to compute their results take into account the cost of electricity production, daily loads and the cost of sales and generate a result faster than mathematical computations. In addition, we have suggested the optimal design, which considers the distribution loss associated with power distribution systems focus on sensing aspect and distributed power generation.

Electronic circuit provides accurate sensing and control of dc voltage

NASA Technical Reports Server (NTRS)

Loftus, W. D.

1966-01-01

Electronic circuit used relay coil to sense and control dc voltage. The control relay is driven by a switching transistor that is biased to cutoff for all input up to slightly less than the threshold level.

Optical image cryptosystem using chaotic phase-amplitude masks encoding and least-data-driven decryption by compressive sensing

NASA Astrophysics Data System (ADS)

Lang, Jun; Zhang, Jing

2015-03-01

In our proposed optical image cryptosystem, two pairs of phase-amplitude masks are generated from the chaotic web map for image encryption in the 4f double random phase-amplitude encoding (DRPAE) system. Instead of transmitting the real keys and the enormous masks codes, only a few observed measurements intermittently chosen from the masks are delivered. Based on compressive sensing paradigm, we suitably refine the series expansions of web map equations to better reconstruct the underlying system. The parameters of the chaotic equations can be successfully calculated from observed measurements and then can be used to regenerate the correct random phase-amplitude masks for decrypting the encoded information. Numerical simulations have been performed to verify the proposed optical image cryptosystem. This cryptosystem can provide a new key management and distribution method. It has the advantages of sufficiently low occupation of the transmitted key codes and security improvement of information transmission without sending the real keys.

Compressed digital holography: from micro towards macro

NASA Astrophysics Data System (ADS)

Schretter, Colas; Bettens, Stijn; Blinder, David; Pesquet-Popescu, Béatrice; Cagnazzo, Marco; Dufaux, Frédéric; Schelkens, Peter

2016-09-01

signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the scene as well. This overview paper discusses contributions in the field of compressed digital holography at the micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale where much higher resolution holograms must be acquired and processed on the computer.

Space-based Remote Sensing: A Tool for Studying Bird Migration Across Multiple Scales

NASA Technical Reports Server (NTRS)

Smith, James A.

2005-01-01

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Further, these models provide an ecological forecasting tool for science and application users to visualize the possible consequences of loss of wetlands, flooding, or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Pan-Arctic distributions of continental runoff in the Arctic Ocean.

PubMed

Fichot, Cédric G; Kaiser, Karl; Hooker, Stanford B; Amon, Rainer M W; Babin, Marcel; Bélanger, Simon; Walker, Sally A; Benner, Ronald

2013-01-01

Continental runoff is a major source of freshwater, nutrients and terrigenous material to the Arctic Ocean. As such, it influences water column stratification, light attenuation, surface heating, gas exchange, biological productivity and carbon sequestration. Increasing river discharge and thawing permafrost suggest that the impacts of continental runoff on these processes are changing. Here, a new optical proxy was developed and implemented with remote sensing to determine the first pan-Arctic distribution of terrigenous dissolved organic matter (tDOM) and continental runoff in the surface Arctic Ocean. Retrospective analyses revealed connections between the routing of North American runoff and the recent freshening of the Canada Basin, and indicated a correspondence between climate-driven changes in river discharge and tDOM inventories in the Kara Sea. By facilitating the real-time, synoptic monitoring of tDOM and freshwater runoff in surface polar waters, this novel approach will help understand the manifestations of climate change in this remote region.

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-03-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology, with a focus on vertical high-resolution to measure temperatures in shallow thermohaline environments. It also presents a new method to manually calibrate temperatures along the optical fiber achieving significant improved resolution. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. The vertical high-resolution DTS system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals.

Moving code - Sharing geoprocessing logic on the Web

NASA Astrophysics Data System (ADS)

Müller, Matthias; Bernard, Lars; Kadner, Daniel

2013-09-01

Efficient data processing is a long-standing challenge in remote sensing. Effective and efficient algorithms are required for product generation in ground processing systems, event-based or on-demand analysis, environmental monitoring, and data mining. Furthermore, the increasing number of survey missions and the exponentially growing data volume in recent years have created demand for better software reuse as well as an efficient use of scalable processing infrastructures. Solutions that address both demands simultaneously have begun to slowly appear, but they seldom consider the possibility to coordinate development and maintenance efforts across different institutions, community projects, and software vendors. This paper presents a new approach to share, reuse, and possibly standardise geoprocessing logic in the field of remote sensing. Drawing from the principles of service-oriented design and distributed processing, this paper introduces moving-code packages as self-describing software components that contain algorithmic code and machine-readable descriptions of the provided functionality, platform, and infrastructure, as well as basic information about exploitation rights. Furthermore, the paper presents a lean publishing mechanism by which to distribute these packages on the Web and to integrate them in different processing environments ranging from monolithic workstations to elastic computational environments or "clouds". The paper concludes with an outlook toward community repositories for reusable geoprocessing logic and their possible impact on data-driven science in general.

Nonlinear Internal Waves on the Inner Shelf: Observations Using a Distributed Temperature Sensing (DTS) System.

NASA Astrophysics Data System (ADS)

Davis, K. A.; Reid, E. C.; Cohen, A. L.

2016-02-01

Internal waves propagating across the continental slope and shelf are transformed by the competing effects of nonlinear steepening and dispersive spreading, forming nonlinear internal waves (NLIWs) that can penetrate onto the shallow inner shelf, often appearing in the form of bottom-propagating nonlinear internal bores or boluses. NLIWs play a significant role in nearshore dynamics with baroclinic current amplitudes on the order of that of wind- and surface wave-driven flows and rapid temperature changes on the order of annual ranges. In June 2014 we used a Distributed Temperature Sensing (DTS) system to give a continuous cross-shelf view of nonlinear internal wave dynamics on the forereef of Dongsha Atoll, a coral reef in the northern South China Sea. A DTS system measures temperature continuously along the length of an optical fiber, resolving meter-to-kilometer spatial scales. This unique view of cross-shelf temperature structure made it possible to observe internal wave reflection, variable propagation speed across the shelf, bolus formation and dissipation. Additionally, we used the DTS data to track internal waves across the shallow fore reef and onto the reef flat and to quantify spatial patterns in temperature variability. Shoaling internal waves are an important process affecting physical variability and water properties on the reef.

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Chemical Sensing for Buried Landmines - Fundamental Processes Influencing Trace Chemical Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

PHELAN, JAMES M.

2002-05-01

Mine detection dogs have a demonstrated capability to locate hidden objects by trace chemical detection. Because of this capability, demining activities frequently employ mine detection dogs to locate individual buried landmines or for area reduction. The conditions appropriate for use of mine detection dogs are only beginning to emerge through diligent research that combines dog selection/training, the environmental conditions that impact landmine signature chemical vapors, and vapor sensing performance capability and reliability. This report seeks to address the fundamental soil-chemical interactions, driven by local weather history, that influence the availability of chemical for trace chemical detection. The processes evaluated include:more » landmine chemical emissions to the soil, chemical distribution in soils, chemical degradation in soils, and weather and chemical transport in soils. Simulation modeling is presented as a method to evaluate the complex interdependencies among these various processes and to establish conditions appropriate for trace chemical detection. Results from chemical analyses on soil samples obtained adjacent to landmines are presented and demonstrate the ultra-trace nature of these residues. Lastly, initial measurements of the vapor sensing performance of mine detection dogs demonstrates the extreme sensitivity of dogs in sensing landmine signature chemicals; however, reliability at these ultra-trace vapor concentrations still needs to be determined. Through this compilation, additional work is suggested that will fill in data gaps to improve the utility of trace chemical detection.« less

A Reduced Order Model of the Linearized Incompressible Navier-Strokes Equations for the Sensor/Actuator Placement Problem

NASA Technical Reports Server (NTRS)

Allan, Brian G.

2000-01-01

A reduced order modeling approach of the Navier-Stokes equations is presented for the design of a distributed optimal feedback kernel. This approach is based oil a Krylov subspace method where significant modes of the flow are captured in the model This model is then used in all optimal feedback control design where sensing and actuation is performed oil tile entire flow field. This control design approach yields all optimal feedback kernel which provides insight into the placement of sensors and actuators in the flow field. As all evaluation of this approach, a two-dimensional shear layer and driven cavity flow are investigated.

US Army Research Laboratory and University of Notre Dame Distributed Sensing: Hardware Overview

DTIC Science & Technology

2017-11-01

ARL-TR-8199 ● NOV 2017 US Army Research Laboratory US Army Research Laboratory and University of Notre Dame Distributed Sensing...US Army Research Laboratory US Army Research Laboratory and University of Notre Dame Distributed Sensing: Hardware Overview by Roger P...TITLE AND SUBTITLE US Army Research Laboratory and University of Notre Dame Distributed Sensing: Hardware Overview 5a. CONTRACT NUMBER 5b. GRANT

Transfer functions of double- and multiple-cavity Fabry-Perot filters driven by Lorentzian sources.

PubMed

Marti, J; Capmany, J

1996-12-20

We derive expressions for the transfer functions of double- and multiple-cavity Fabry-Perot filters driven by laser sources with Lorentzian spectrum. These are of interest because of their applications in sensing and channel filtering in optical frequency-division multiplexing networks.

Transfer functions of double- and multiple-cavity Fabry Perot filters driven by Lorentzian sources

NASA Astrophysics Data System (ADS)

Marti, Javier; Capmany, Jose

1996-12-01

We derive expressions for the transfer functions of double- and multiple-cavity Fabry Perot filters driven by laser sources with Lorentzian spectrum. These are of interest because of their applications in sensing and channel filtering in optical frequency-division multiplexing networks.

Design and modeling of magnetically driven electric-field sensor for non-contact DC voltage measurement in electric power systems.

PubMed

Wang, Decai; Li, Ping; Wen, Yumei

2016-10-01

In this paper, the design and modeling of a magnetically driven electric-field sensor for non-contact DC voltage measurement are presented. The magnetic drive structure of the sensor is composed of a small solenoid and a cantilever beam with a cylindrical magnet mounted on it. The interaction of the magnet and the solenoid provides the magnetic driving force for the sensor. Employing magnetic drive structure brings the benefits of low driving voltage and large vibrating displacement, which consequently results in less interference from the drive signal. In the theoretical analyses, the capacitance calculation model between the wire and the sensing electrode is built. The expression of the magnetic driving force is derived by the method of linear fitting. The dynamical model of the magnetic-driven cantilever beam actuator is built by using Euler-Bernoulli theory and distributed parameter method. Taking advantage of the theoretical model, the output voltage of proposed sensor can be predicted. The experimental results are in good agreement with the theoretical results. The proposed sensor shows a favorable linear response characteristic. The proposed sensor has a measuring sensitivity of 9.87 μV/(V/m) at an excitation current of 37.5 mA. The electric field intensity resolution can reach 10.13 V/m.

Retaking the Test

ERIC Educational Resources Information Center

Backer, David Isaac; Lewis, Tyson Edward

2015-01-01

"Data-driven" teaching and learning is common sense in education today, and it is common sense that these data should come from standardized tests. Critiques of standardization either make no constructive suggestions for what to use in place of the tests or they call for better, more scientifically rigorous, reliable, and…

Online catalog access and distribution of remotely sensed information

NASA Astrophysics Data System (ADS)

Lutton, Stephen M.

1997-09-01

Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

NASA Astrophysics Data System (ADS)

Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Reconstruction of atmospheric pollutant concentrations from remote sensing data - An application of distributed parameter observer theory

NASA Technical Reports Server (NTRS)

Koda, M.; Seinfeld, J. H.

1982-01-01

The reconstruction of a concentration distribution from spatially averaged and noise-corrupted data is a central problem in processing atmospheric remote sensing data. Distributed parameter observer theory is used to develop reconstructibility conditions for distributed parameter systems having measurements typical of those in remote sensing. The relation of the reconstructibility condition to the stability of the distributed parameter observer is demonstrated. The theory is applied to a variety of remote sensing situations, and it is found that those in which concentrations are measured as a function of altitude satisfy the conditions of distributed state reconstructibility.

Newton's method for nonlinear stochastic wave equations driven by one-dimensional Brownian motion.

PubMed

Leszczynski, Henryk; Wrzosek, Monika

2017-02-01

We consider nonlinear stochastic wave equations driven by one-dimensional white noise with respect to time. The existence of solutions is proved by means of Picard iterations. Next we apply Newton's method. Moreover, a second-order convergence in a probabilistic sense is demonstrated.

Choice-Driven Peer Play, Self-Regulation and Number Sense

ERIC Educational Resources Information Center

Ivrendi, Asiye

2016-01-01

Peer play provides ample opportunities for the use and development of self-regulatory and mathematical skills. This study aimed at examining whether children's engagement in solitary low-level play, interactive play and competent play influences their self-regulatory and number sense skills. The effect of demographic variables and children's…

Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.

DOT National Transportation Integrated Search

2017-05-31

The overarching goal of this project was to integrate data from commercial remote sensing and spatial information (CRS&SI) technologies to create a novel data-driven decision making framework that empowers the railroad industry to monitor, assess, an...

Soil organic carbon - a large scale paired catchment assessment

NASA Astrophysics Data System (ADS)

Kunkel, V.; Hancock, G. R.; Wells, T.

2016-12-01

Soil organic carbon (SOC) concentration can vary both spatially and temporally driven by differences in soil properties, topography and climate. However most studies have focused on point scale data sets with a paucity of studies examining larger scale catchments. Here we examine the spatial and temporal distribution of SOC for two large catchments. The Krui (575 km2) and Merriwa River (675km2) catchments (New South Wales, Australia). Both have similar shape, soils, topography and orientation. We show that SOC distribution is very similar for both catchments and that elevation (and associated increase in soil moisture) is a major influence on SOC. We also show that there is little change in SOC from the initial assessment in 2006 to 2015 despite a major drought from 2003 to 2010 and extreme rainfall events in 2007 and 2010 -therefore SOC concentration appears robust. However, we found significant relationships between erosion and deposition patterns (as quantified using 137Cs) and SOC for both catchments again demonstrating a strong geomorphic relationship. Vegetation across the catchments was assessed using remote sensing (Landsat and MODIS). Vegetation patterns were temporally consistent with above ground biomass increasing with elevation. SOC could be predicted using both these low and high resolution remote sensing platforms. Results indicate that, although moderate resolution (250 m) allows for reasonable prediction of the spatial distribution of SOC, the higher resolution (30 m) improved the strength of the SOC-NDVI relationship. The relationship between SOC and 137Cs, as a surrogate for the erosion and deposition of SOC, suggested that sediment transport and deposition influences the distribution of SOC within the catchment. The findings demonstrate that over the large catchment scale and at the decadal time scale that SOC is relatively constant and can largely be predicted by topography.

Investigating the differences of cirrus cloud properties in nucleation, growth and sublimation regions based on airborne water vapor lidar measurements

NASA Astrophysics Data System (ADS)

Urbanek, Benedikt; Groß, Silke; Wirth, Martin

2017-04-01

Cirrus clouds impose high uncertainties on weather and climate prediction, as knowledge on important processes is still incomplete. For instance it remains unclear how cloud optical, microphysical, and radiative properties change as the cirrus evolves. To gain better understanding of cirrus clouds, their optical and microphysical properties and their changes with cirrus cloud evolution the ML-CIRRUS campaign was conducted in March and April 2014. Measurements with a combined in-situ and remote sensing payload were performed with the German research aircraft HALO based in Oberpfaffenhofen. 16 research flights with altogether 88 flight hours were performed over the North-Atlantic, western and central Europe to probe different cirrus cloud regimes and cirrus clouds at different stages of evolution. One of the key remotes sensing instruments during ML-CIRRUS was the airborne differential absorption and high spectral lidar system WALES. It measures the 2-dimensional distribution of water vapor inside and outside of cirrus clouds as well as the optical properties of the clouds. Bases on these airborne lidar measurements a novel classification scheme to derive the stage of cirrus cloud evolution was developed. It identifies regions of ice nucleation, particle growth by deposition of water vapor, and ice sublimation. This method is used to investigate differences in the distribution and value of optical properties as well as in the distribution of water vapor and relative humidity depending on the stage of evolution of the cloud. We will present the lidar based classification scheme and its application on a wave driven cirrus cloud case, and we will show first results of the dependence of optical cloud properties and relative humidity distributions on the determined stage of evolution.

Combining hydrological modeling and remote sensing observations to enable data-driven decision making for Devils Lake flood mitigation in a changing climate

NASA Astrophysics Data System (ADS)

Zhang, X.; Lim, Y. H.; Teng, W. L.; Kirilenko, A.

2010-12-01

The water level of Devils Lake in North Dakota has been rising since 1993, reaching record highs in each of the past three years. Nearly $1 billion have already been spent in mitigating the flooding impacts. If the current wet cycle continues, Devils Lake, a terminal lake currently at 1452 ft, will likely overflow at 1458 ft and cause extensive downstream flooding, with devastating environmental and economic impacts at local, regional, and international levels. We have implemented a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model. The entire watershed with an area of about 10,000 km2 was delineated into six sub-basins using 30 m DEM, with each sub-basin having several hundred thousand hydrological cells. We generated a fine-resolution weather data set, based on a combination of ground observations and remote sensing data, to drive the hydrological simulations. Compared with a very limited number of data series available from five meteorological stations located within the watershed (none belonging to the US Historical Climate Network), NASA data offer a uniform coverage and dense distribution. The satellite and ground observations of precipitation and temperature agreed well with each other. However, if only weather station data were used, the observed runoff was underestimated by at least 30%, regardless of the value of the snow melt-rate coefficient used. The inclusion of NASA data, on the other hand, greatly improved the accuracy of runoff estimates, to within 2% of observations. Better runoff estimates will enable better predictions of water levels. The watershed hydrological model is coupled with a reservoir model, HEC-ResSim. The calibration against the observed lake elevation and monthly evaporation estimates from 2001 to 2004 showed a lake seepage varying between 500 - 1300 cfs. The coupled models can reproduce water level of the lake at sub-feet accuracy, and will be driven by the downscaled CMIP-3 projections of future climate, to provide decision support for mitigation measures in response to the potential flooding.

Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

PubMed

Wang, Yuhao; Li, Xin; Xu, Kai; Ren, Fengbo; Yu, Hao

2017-04-01

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

Time-resolved Sensing of Meso-scale Shock Compression with Multilayer Photonic Crystal Structures

NASA Astrophysics Data System (ADS)

Scripka, David; Lee, Gyuhyon; Summers, Christopher J.; Thadhani, Naresh

2017-06-01

Multilayer Photonic Crystal structures can provide spatially and temporally resolved data needed to validate theoretical and computational models relevant for understanding shock compression in heterogeneous materials. Two classes of 1-D photonic crystal multilayer structures were studied: optical microcavities (OMC) and distributed Bragg reflectors (DBR). These 0.5 to 5 micron thick structures were composed of SiO2, Al2O3, Ag, and PMMA layers fabricated primarily via e-beam evaporation. The multilayers have unique spectral signatures inherently linked to their time-resolved physical states. By observing shock-induced changes in these signatures, an optically-based pressure sensor was developed. Results to date indicate that both OMCs and DBRs exhibit nanosecond-resolved spectral shifts of several to 10s of nanometers under laser-driven shock compression loads of 0-10 GPa, with the magnitude of the shift strongly correlating to the shock load magnitude. Additionally, spatially and temporally resolved spectral shifts under heterogeneous laser-driven shock compression created by partial beam blocking have been successfully demonstrated. These results illustrate the potential for multilayer structures to serve as meso-scale sensors, capturing temporal and spatial pressure profile evolutions in shock-compressed heterogeneous materials, and revealing meso-scale pressure distributions across a shocked surface. Supported by DTRA Grant HDTRA1-12-1-005 and DoD, AFOSR, National Defense Science and Eng. Graduate Fellowship, 32 CFR 168a.

A Study Using a Monte Carlo Method of the Optimal Configuration of a Distribution Network in Terms of Power Loss Sensing

PubMed Central

Moon, Hyun Ho; Lee, Jong Joo; Choi, Sang Yule; Cha, Jae Sang; Kang, Jang Mook; Kim, Jong Tae; Shin, Myong Chul

2011-01-01

Recently there have been many studies of power systems with a focus on “New and Renewable Energy” as part of “New Growth Engine Industry” promoted by the Korean government. “New And Renewable Energy”—especially focused on wind energy, solar energy and fuel cells that will replace conventional fossil fuels—is a part of the Power-IT Sector which is the basis of the SmartGrid. A SmartGrid is a form of highly-efficient intelligent electricity network that allows interactivity (two-way communications) between suppliers and consumers by utilizing information technology in electricity production, transmission, distribution and consumption. The New and Renewable Energy Program has been driven with a goal to develop and spread through intensive studies, by public or private institutions, new and renewable energy which, unlike conventional systems, have been operated through connections with various kinds of distributed power generation systems. Considerable research on smart grids has been pursued in the United States and Europe. In the United States, a variety of research activities on the smart power grid have been conducted within EPRI’s IntelliGrid research program. The European Union (EU), which represents Europe’s Smart Grid policy, has focused on an expansion of distributed generation (decentralized generation) and power trade between countries with improved environmental protection. Thus, there is current emphasis on a need for studies that assesses the economic efficiency of such distributed generation systems. In this paper, based on the cost of distributed power generation capacity, calculations of the best profits obtainable were made by a Monte Carlo simulation. Monte Carlo simulations that rely on repeated random sampling to compute their results take into account the cost of electricity production, daily loads and the cost of sales and generate a result faster than mathematical computations. In addition, we have suggested the optimal design, which considers the distribution loss associated with power distribution systems focus on sensing aspect and distributed power generation. PMID:22164047

Fire forbids fifty-fifty forest

PubMed Central

Staal, Arie; Hantson, Stijn; Holmgren, Milena; Pueyo, Salvador; Bernardi, Rafael E.; Flores, Bernardo M.; Xu, Chi; Scheffer, Marten

2018-01-01

Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30‒60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse. PMID:29351323

Fire forbids fifty-fifty forest.

PubMed

van Nes, Egbert H; Staal, Arie; Hantson, Stijn; Holmgren, Milena; Pueyo, Salvador; Bernardi, Rafael E; Flores, Bernardo M; Xu, Chi; Scheffer, Marten

2018-01-01

Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30‒60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse.

Dual-mass vibratory rate gyroscope with suppressed translational acceleration response and quadrature-error correction capability

NASA Technical Reports Server (NTRS)

Clark, William A. (Inventor); Juneau, Thor N. (Inventor); Lemkin, Mark A. (Inventor); Roessig, Allen W. (Inventor)

2001-01-01

A microfabricated vibratory rate gyroscope to measure rotation includes two proof-masses mounted in a suspension system anchored to a substrate. The suspension has two principal modes of compliance, one of which is driven into oscillation. The driven oscillation combined with rotation of the substrate about an axis perpendicular to the substrate results in Coriolis acceleration along the other mode of compliance, the sense-mode. The sense-mode is designed to respond to Coriolis accelerationwhile suppressing the response to translational acceleration. This is accomplished using one or more rigid levers connecting the two proof-masses. The lever allows the proof-masses to move in opposite directions in response to Coriolis acceleration. The invention includes a means for canceling errors, termed quadrature error, due to imperfections in implementation of the sensor. Quadrature-error cancellation utilizes electrostatic forces to cancel out undesired sense-axis motion in phase with drive-mode position.

CMOS Active-Pixel Image Sensor With Intensity-Driven Readout

NASA Technical Reports Server (NTRS)

Langenbacher, Harry T.; Fossum, Eric R.; Kemeny, Sabrina

1996-01-01

Proposed complementary metal oxide/semiconductor (CMOS) integrated-circuit image sensor automatically provides readouts from pixels in order of decreasing illumination intensity. Sensor operated in integration mode. Particularly useful in number of image-sensing tasks, including diffractive laser range-finding, three-dimensional imaging, event-driven readout of sparse sensor arrays, and star tracking.

Mapping geomorphic process domains to predict hillslope sediment size distribution using remotely-sensed data and field sampling, Inyo Creek, California

NASA Astrophysics Data System (ADS)

Leclere, S.; Sklar, L. S.; Genetti, J. R.

2014-12-01

The size distribution of sediments produced on hillslopes and supplied to channels depends on the geomorphic processes that weather, detach and transport rock fragments down slopes. Little in the way of theory or data is available to predict patterns in hillslope size distributions at the catchment scale from topographic and geologic maps. Here we use aerial imagery and a variety of remote sensing techniques to map and categorize geomorphic landscape units (GLUs) by inferred sediment production process regime, across the steep mountain catchment of Inyo Creek, eastern Sierra Nevada, California. We also use field measurements of particle size and local geomorphic attributes to test and refine GLU determinations. Across the 2 km of relief in this catchment, landcover varies from bare bedrock cliffs at higher elevations to vegetated, regolith-covered convex slopes at lower elevations. Hillslope gradient could provide a simple index of sediment production process, from rock spallation and landsliding at highest slopes, to tree-throw and other disturbance-driven soil production processes at lowest slopes. However, many other attributes are needed for a more robust predictive model, including elevation, curvature, aspect, drainage area, and color. We combine tools from ArcGIS, ERDAS Imagine and Envi with groundtruthing field work to find an optimal combination of attributes for defining sediment production GLUs. Key challenges include distinguishing: weathered from freshly eroded bedrock, boulders from intact bedrock, and landslide deposits from talus slopes. We take advantage of emerging technologies that provide new ways of conducting fieldwork and comparing field data to mapping solutions. In particular, cellphone GPS is approaching the accuracy of dedicated GPS systems and the ability to geo-reference photos simplifies field notes and increases accuracy of later map creation. However, the predictive power of the GLU mapping approach is limited by inherent uncertainty in remotely sensed data and aerial imagery. This work is a contribution toward the long-term goal of reliable and automated mapping of hillslope sediment size distributions for use in sediment budgets and hazard delineation, and for understanding the feedbacks between climate, erosion and topography that drive sediment production.

LAI inversion algorithm based on directional reflectance kernels.

PubMed

Tang, S; Chen, J M; Zhu, Q; Li, X; Chen, M; Sun, R; Zhou, Y; Deng, F; Xie, D

2007-11-01

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.

Causes and consequences of a tropical forest gold rush in the Guiana Shield, South America.

PubMed

Hammond, David S; Gond, Valéry; de Thoisy, Benoit; Forget, Pierre-Michel; DeDijn, Bart P E

2007-12-01

Statistical and spatial analyses of both historical time series and remotely sensed data show a link between the spatial distribution and growth of gold production across the Guiana Shield in northeast Amazonia. Results indicate that an exponential rise in production across an expanding area is primarily a delayed response to the 1971-1978 market flotation of international gold prices. The subsequent 10-fold (2-fold) average nominal (real) price increase has provided a compelling economic incentive to mass exploitation of lower-grade gold deposits. The ground-based and remotely sensed distributions of mining activity are strongly attached to these deposits that dominate the region's gold geology. The presence of these gold-bearing formations in conservation and sustainable timber zones has sparked social conflict and environmental degradation across the region. Left unmanaged, more than a quarter-million square-kilometer area of tropical forest zoned for protection and sustainable management could ultimately be compromised by the price-driven boom in gold mining through poorly integrated resource use planning, lack of reclamation effort, and control of illegal operations. Serious public health issues propagated through the unregulated mining environment further erode the financial benefits achieved through gold extraction. This study demonstrates in part how international economic policies successfully stabilizing more conspicuous centers of the global economy can have unintended but profound environmental and social impacts on remote commodity frontiers.

Bio-hybrid cell-based actuators for microsystems.

PubMed

Carlsen, Rika Wright; Sitti, Metin

2014-10-15

As we move towards the miniaturization of devices to perform tasks at the nano and microscale, it has become increasingly important to develop new methods for actuation, sensing, and control. Over the past decade, bio-hybrid methods have been investigated as a promising new approach to overcome the challenges of scaling down robotic and other functional devices. These methods integrate biological cells with artificial components and therefore, can take advantage of the intrinsic actuation and sensing functionalities of biological cells. Here, the recent advancements in bio-hybrid actuation are reviewed, and the challenges associated with the design, fabrication, and control of bio-hybrid microsystems are discussed. As a case study, focus is put on the development of bacteria-driven microswimmers, which has been investigated as a targeted drug delivery carrier. Finally, a future outlook for the development of these systems is provided. The continued integration of biological and artificial components is envisioned to enable the performance of tasks at a smaller and smaller scale in the future, leading to the parallel and distributed operation of functional systems at the microscale. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Beyond "Perezhivanie," Sense, and Language: An Empirical Study of Early Childhood Teachers' Subjective Senses

ERIC Educational Resources Information Center

Madeira Coelho, Cristina M.

2016-01-01

Based on the Theory of Subjectivity in a cultural-historical perspective, this paper presents an empirical study of two infant teachers and their subjective dynamics of being "enchanted" with singular aspects of their professional lives. Through the constructive-interpretative principle driven from the Qualitative Epistemology, the study…

Avian influenza, domestic ducks and rice agriculture in Thailand

PubMed Central

Gilbert, Marius; Xiao, Xiangming; Chaitaweesub, Prasit; Kalpravidh, Wantanee; Premashthira, Sith; Boles, Stephen; Slingenbergh, Jan

2008-01-01

Highly pathogenic avian influenza (HPAI) caused by H5N1 viruses has become a global scale problem which first emerged in southern China and from there spread to other countries in Southeast and East Asia, where it was first confirmed in end 2003. In previous work, geospatial analyses demonstrated that free grazing ducks played critical role in the epidemiology of the disease in Thailand in the winter 2004/2005, both in terms of HPAI emergence and spread. This study explored the geographic association between free grazing duck census counts and current statistics on the spatial distribution of rice crops in Thailand, in particular the crop calendar of rice production. The analysis was carried out using both district level rice statistics and rice distribution data predicted with the aid of remote sensing, using a rice-detection algorithm. The results indicated a strong association between the number of free grazing ducks and the number of months during which second-crop rice harvest takes place, as well as with the rice crop intensity as predicted by remote sensing. These results confirmed that free grazing duck husbandry was strongly driven by agricultural land use and rice crop intensity, and that this later variable can be readily predicted using remote sensing. Analysis of rice cropping patterns may provide an indication of the location of populations of free grazing ducks in other countries with similar mixed duck and rice production systems and less detailed duck census data. Apart from free ranging ducks and rice cropping, the role of hydrology and seasonality of wetlands and water bodies in the HPAI risk analysis is also discussed in relation to the presumed dry season aggregation of wild waterfowl and aquatic poultry offering much scope for virus transmission. PMID:18418464

Energy-efficient sensing in wireless sensor networks using compressed sensing.

PubMed

Razzaque, Mohammad Abdur; Dobson, Simon

2014-02-12

Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support.

PubMed

Mirel, Barbara; Görg, Carsten

2014-04-26

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.

Scientists’ sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support

PubMed Central

2014-01-01

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design. PMID:24766796

Pan-Arctic distributions of continental runoff in the Arctic Ocean

PubMed Central

Fichot, Cédric G.; Kaiser, Karl; Hooker, Stanford B.; Amon, Rainer M. W.; Babin, Marcel; Bélanger, Simon; Walker, Sally A.; Benner, Ronald

2013-01-01

Continental runoff is a major source of freshwater, nutrients and terrigenous material to the Arctic Ocean. As such, it influences water column stratification, light attenuation, surface heating, gas exchange, biological productivity and carbon sequestration. Increasing river discharge and thawing permafrost suggest that the impacts of continental runoff on these processes are changing. Here, a new optical proxy was developed and implemented with remote sensing to determine the first pan-Arctic distribution of terrigenous dissolved organic matter (tDOM) and continental runoff in the surface Arctic Ocean. Retrospective analyses revealed connections between the routing of North American runoff and the recent freshening of the Canada Basin, and indicated a correspondence between climate-driven changes in river discharge and tDOM inventories in the Kara Sea. By facilitating the real-time, synoptic monitoring of tDOM and freshwater runoff in surface polar waters, this novel approach will help understand the manifestations of climate change in this remote region. PMID:23316278

Fluoride-driven 'turn on' ESPT in the binding with a novel benzimidazole-based sensor.

PubMed

Liu, Kai; Zhao, Xiaojun; Liu, Qingxiang; Huo, Jianzhong; Zhu, Bolin; Diao, Shihua

2015-01-01

A novel fluorescence sensor (BIP) bearing NH and OH subunits displayed a highly selective and sensitive recognition property for fluoride over other anions. Fluoride-driven ESPT, poorly used in anion recognition and sensing, was suggested to be responsible for the fluorescence enhancement with a blue shift of 35 nm in the emission spectrum.

Chip-Scale Trace-Gas Spectrometers for Methane Leak Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, William

Panelists from industry, academia, government laboratories and venture capital community will discuss opportunities driven by miniaturization of spectroscopic sensing systems and optical sensor applications. The speakers will discuss industry needs for wide-range commercial, security and defense applications. Listen and interact with leaders in the spectroscopy and sensing community, and come to share your ideas.

Satellite detection of land-use change and effects on regional forest aboveground biomass estimates

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey

2008-01-01

We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg·ha−1, dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992-2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data...

Evaluation of two typical distributed energy systems

NASA Astrophysics Data System (ADS)

Han, Miaomiao; Tan, Xiu

2018-03-01

According to the two-natural gas distributed energy system driven by gas engine driven and gas turbine, in this paper, the first and second laws of thermodynamics are used to measure the distributed energy system from the two parties of “quantity” and “quality”. The calculation results show that the internal combustion engine driven distributed energy station has a higher energy efficiency, but the energy efficiency is low; the gas turbine driven distributed energy station energy efficiency is high, but the primary energy utilization rate is relatively low. When configuring the system, we should determine the applicable natural gas distributed energy system technology plan and unit configuration plan according to the actual load factors of the project and the actual factors such as the location, background and environmental requirements of the project. “quality” measure, the utilization of waste heat energy efficiency index is proposed.

Incorporating remotely sensed tree canopy cover data into broad scale assessments of wildlife habitat distribution and conservation

Treesearch

Sebastian Martinuzzi; Lee A. Vierling; William A. Gould; Kerri T. Vierling; Andrew T. Hudak

2009-01-01

Remote sensing provides critical information for broad scale assessments of wildlife habitat distribution and conservation. However, such efforts have been typically unable to incorporate information about vegetation structure, a variable important for explaining the distribution of many wildlife species. We evaluated the consequences of incorporating remotely sensed...

Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing

PubMed Central

Medina, Esunly; Lopez, David; Meseguer, Roc; Ochoa, Sergio F.; Royo, Dolors; Santos, Rodrigo

2016-01-01

Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios. PMID:27409617

Bi-Directional Brillouin Optical Time Domain Analyzer System for Long Range Distributed Sensing.

PubMed

Guo, Nan; Wang, Liang; Wang, Jie; Jin, Chao; Tam, Hwa-Yaw; Zhang, A Ping; Lu, Chao

2016-12-16

We propose and experimentally demonstrate a novel scheme of bi-directional Brillouin time domain analyzer (BD-BOTDA) to extend the sensing range. By deploying two pump-probe pairs at two different wavelengths, the Brillouin frequency shift (BFS) distribution over each half of the whole fiber can be obtained with the simultaneous detection of Brillouin signals in both channels. Compared to the conventional unidirectional BOTDA system of the same sensing range, the proposed BD-BOTDA scheme enables distributed sensing with a performance level comparable to the conventional one with half of the sensing range and a spatial resolution of 2 m, while maintaining the Brillouin signal-to-noise ratio (SNR) and the BFS uncertainty. Based on this technique, we have achieved distributed temperature sensing with a measurement range of 81.9 km fiber at a spatial resolution of 2 m and BFS uncertainty of ~0.44 MHz without introducing any complicated components or schemes.

Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction.

PubMed

Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

2017-08-08

Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

PubMed Central

Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

2017-01-01

Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors. PMID:28786957

Resistive pulse sensing of magnetic beads and supraparticle structures using tunable pores

PubMed Central

Willmott, Geoff R.; Platt, Mark; Lee, Gil U.

2012-01-01

Tunable pores (TPs) have been used for resistive pulse sensing of 1 μm superparamagnetic beads, both dispersed and within a magnetic field. Upon application of this field, magnetic supraparticle structures (SPSs) were observed. Onset of aggregation was most effectively indicated by an increase in the mean event magnitude, with data collected using an automated thresholding method. Simulations enabled discrimination between resistive pulses caused by dimers and individual particles. Distinct but time-correlated peaks were often observed, suggesting that SPSs became separated in pressure-driven flow focused at the pore constriction. The distinct properties of magnetophoretic and pressure-driven transport mechanisms can explain variations in the event rate when particles move through an asymmetric pore in either direction, with or without a magnetic field applied. Use of TPs for resistive pulse sensing holds potential for efficient, versatile analysis and measurement of nano- and microparticles, while magnetic beads and particle aggregation play important roles in many prospective biosensing applications. PMID:22662090

Secure distribution for high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Liu, Jin; Sun, Jing; Xu, Zheng Q.

2010-09-01

The use of remote sensing images collected by space platforms is becoming more and more widespread. The increasing value of space data and its use in critical scenarios call for adoption of proper security measures to protect these data against unauthorized access and fraudulent use. In this paper, based on the characteristics of remote sensing image data and application requirements on secure distribution, a secure distribution method is proposed, including users and regions classification, hierarchical control and keys generation, and multi-level encryption based on regions. The combination of the three parts can make that the same remote sensing images after multi-level encryption processing are distributed to different permission users through multicast, but different permission users can obtain different degree information after decryption through their own decryption keys. It well meets user access control and security needs in the process of high resolution remote sensing image distribution. The experimental results prove the effectiveness of the proposed method which is suitable for practical use in the secure transmission of remote sensing images including confidential information over internet.

Global retrieval of soil moisture and vegetation properties using data-driven methods

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre for Medium-Range Weather Forecasts (ECMWF). Finally, retrievals using radiative transfer models can also be used as a reference SM dataset for the training phase. This approach was used to retrieve soil moisture from ASMR-E, as mentioned above, and also to implement the official European Space Agency (ESA) SMOS soil moisture product in Near-Real-Time. We will finish with a discussion of the retrieval of vegetation parameters from SMOS observations using data-driven methods.

Polar cap potential distributions during periods of positive IMF B(sub y) and B(sub z)

NASA Technical Reports Server (NTRS)

Burke, William J.; Basinska, Ewa M.; Maynard, Nelson C.; Hanson, William B.; Slavin, James A.; Winningham, J. David

1994-01-01

We compare the DE-2 electric field measurements used by HEPPNER and MAYNARD (1987) to illustrate strongly distorted, BC convection patterns for interplanetary magnetic field (IMF) B(sub z) greater than 0 and large absolute value of B(sub y), with simultaneous detections of particle spectra, plasma drifts and magnetic perturbations. Measured potentials greater than 50 keV, driven by the solar wind speeds exceeding 500 km/s, are greater than published correlation analysis predictions by up to 27%. The potential distributions show only two extrema and thus support the basic conclusion that under these conditions the solar wind/IMF drives two-rather than four-cell convection patterns. However, several aspects of the distorted two-cell convection pattern must be revised. In addition to the strong east-west convection in the vicinity of the cusp, indicated by Heppner and Maynard, we also detect comparable components of sunward (equatorward) plasma flow. Combined equipotential and particle precipitation distributions indicate the presence of a lobe cell embedded within the larger, afternoon reconnection cell. Both types rotate in the same sense, with the lobe cell carrying 20-40% of the total afternoon cell potential. We detected no lobe cell within morning convection cell.

Focusing patterns of seismicity with relocation and collapsing

NASA Astrophysics Data System (ADS)

Li, Ka Lok; Gudmundsson, Ólafur; Tryggvason, Ari; Bödvarsson, Reynir; Brandsdóttir, Bryndís

2016-07-01

Seismicity is generally concentrated on faults or in fault zones of varying, sometimes complex geometry. An earthquake catalog, compiled over time, contains useful information about this geometry, which can help understanding the tectonics of a region. Interpreting the geometrical distribution of events in a catalog is often complicated by the diffuseness of the earthquake locations. Here, we explore a number of strategies to reduce this diffuseness and hence simplify the seismicity pattern of an earthquake catalog. These strategies utilize information about event locations contained in their overall catalog distribution. They apply this distribution as an a priori constraint on relocations of the events, or as an attractor for each individual event in a collapsing scheme, and thereby focus the locations. The latter strategy is not a relocation strategy in a strict sense, although event foci are moved, because the movements are not driven by data misfit. Both strategies simplify the seismicity pattern of the catalog and may help to interpret it. A synthetic example and a real-data example from an aftershock sequence in south west Iceland are presented to demonstrate application of the strategies. Entropy is used to quantify their effect.

To the National Map and beyond

USGS Publications Warehouse

Kelmelis, J.

2003-01-01

Scientific understanding, technology, and social, economic, and environmental conditions have driven a rapidly changing demand for geographic information, both digital and analog. For more than a decade, the U.S. Geological Survey (USGS) has been developing innovative partnerships with other government agencies and private industry to produce and distribute geographic information efficiently; increase activities in remote sensing to ensure ongoing monitoring of the land surface; and develop new understanding of the causes and consequences of land surface change. These activities are now contributing to a more robust set of geographic information called The National Map (TNM). The National Map is designed to provide an up-to-date, seamless, horizontally and vertically integrated set of basic digital geographic data, a frequent monitoring of changes on the land surface, and an understanding of the condition of the Earth's surface and many of the processes that shape it. The USGS has reorganized its National Mapping Program into three programs to address the continuum of scientific activities-describing (mapping), monitoring, understanding, modeling, and predicting. The Cooperative Topographic Mapping Program focuses primarily on the mapping and revision aspects of TNM. The National Map also includes results from the Land Remote Sensing and Geographic Analysis and Monitoring Programs that provide continual updates, new insights, and analytical tools. The National Map is valuable as a framework for current research, management, and operational activities. It also provides a critical framework for the development of distributed, spatially enabled decision support systems.

[Application of remote sensing and GIS in study of suitability distribution of Swertia mussotii, a Tibetan medicine in Sichuan province].

PubMed

Dong, Yong-Bo; Luo, Yao; Zhu, Cong; Peng, Wen-Fu; Xu, Xin-Liang; Fang, Qing-Mao

2017-11-01

Swertia mussotii is a kind of rare medicinal materials, the relevant researches are mainly concentrated on its medicinal efficacy and medicinal value till now, researches of adaptive distribution by applying remote sensing and GIS are relatively less. This study is to analyze the adaptive distribution of S.mussotii in Sichuan province by applying remote sensing and GIS technology, and provide scientific basis for the protection and development of wild resources, artificial cultivation and adjustment of Chinese medicine industrial distribution in Sichuan province. Based on literature review and ecological factors such as altitude, annual precipitation and annual average temperature, this study extracted ecological factors, overlay analysis in GIS, as well as combining GPS field validation data by means of remote sensing and GIS, discusses the adaptive distribution of SMF sin Sichuan province. ①The area of adaptive distribution of S. mussotii in Sichuan province is 1 543.749 km², mainly in Dege county, Ganzi county, Daofu county, Kangding county, Barkam, Jinchuan county, Xiaojin county, Danba county, Daocheng county, Xiangcheng county, Xinlong county, Aba county, Muli county and other counties and cities, accounts for about 7.25% in total area. ② Combining statistical information and field validation, this study found that S. mussotii adaptive distribution gained by remote sensing and GIS is in conformity with its actual distribution. The study shows that remote sensing and GIS technology are feasible to obtain the S. mussotii adaptive distribution, they can further be applied to studies on adaptive distributions of other rare Chinese medicinal herb. Copyright© by the Chinese Pharmaceutical Association.

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Mechanical instability driven self-assembly and architecturing of 2D materials

NASA Astrophysics Data System (ADS)

Cai Wang, Michael; Leem, Juyoung; Kang, Pilgyu; Choi, Jonghyun; Knapp, Peter; Yong, Keong; Nam, SungWoo

2017-06-01

Two-dimensional (2D) materials have been well studied for their diverse and impressive properties and superlative mechanical strength. Their atomic thinness and weak van der Waals interaction, while fascinating and unique, dictate their tendency to exhibit out of plane morphologies such as bending, buckling, folding, rippling, scrolling, and wrinkling, etc. In this review, we discuss the mechanisms behind these instability driven morphologies and the resultant phenomena that arise. We then survey methods to manipulate them especially in a scalable manner, and elucidate some interesting applications uniquely enabled by these structures. Contrary to conventional wisdom, the deterministic control of these features has great implications for the local and overall material properties due to heterogeneous distribution of stresses and strains. The introduction of deformable and shape memory substrates especially allow for facile and large scale synthesis of various types of out of plane morphologies. We show that a variety of exciting phenomena and applications arise, including tunable surfaces and coatings, robust devices and electronics, adaptive optoelectronics, material toughening, energy storage, and chemical sensing. This new perspective on these otherwise nuisance thin-film phenomena enable new tools for future materials discovery, design, and synthesis with the ever growing library of 2D atomically thin materials.

Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution

NASA Astrophysics Data System (ADS)

Yang, Han-Xin; Wang, Bing-Hong

2016-04-01

We study the traffic-driven epidemic spreading on scale-free networks with tunable degree distribution. The heterogeneity of networks is controlled by the exponent γ of power-law degree distribution. It is found that the epidemic threshold is minimized at about γ=2.2. Moreover, we find that nodes with larger algorithmic betweenness are more likely to be infected. We expect our work to provide new insights in to the effect of network structures on traffic-driven epidemic spreading.

Bi-Directional Brillouin Optical Time Domain Analyzer System for Long Range Distributed Sensing

PubMed Central

Guo, Nan; Wang, Liang; Wang, Jie; Jin, Chao; Tam, Hwa-Yaw; Zhang, A. Ping; Lu, Chao

2016-01-01

We propose and experimentally demonstrate a novel scheme of bi-directional Brillouin time domain analyzer (BD-BOTDA) to extend the sensing range. By deploying two pump-probe pairs at two different wavelengths, the Brillouin frequency shift (BFS) distribution over each half of the whole fiber can be obtained with the simultaneous detection of Brillouin signals in both channels. Compared to the conventional unidirectional BOTDA system of the same sensing range, the proposed BD-BOTDA scheme enables distributed sensing with a performance level comparable to the conventional one with half of the sensing range and a spatial resolution of 2 m, while maintaining the Brillouin signal-to-noise ratio (SNR) and the BFS uncertainty. Based on this technique, we have achieved distributed temperature sensing with a measurement range of 81.9 km fiber at a spatial resolution of 2 m and BFS uncertainty of ~0.44 MHz without introducing any complicated components or schemes. PMID:27999250

Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Bo; Wu, Wenting; Yang, Zhaoqing

Driven by rapid economic development, population growth, and urbanization, China has experienced severe coastal land reclamation over the last decades, which resulted in significant loss of coastal wetland and wildlife habitat, and degradation of marine ecosystems. This study used advanced remote-sensing techniques to analyze the spatial and temporal distributions of coastal reclamation in China and investigated the relationships between coastal land reclamation and coastal economy, population growth, and urbanization. Analysis of long-term Landsat images time series from 1985 to 2010 in 5-year intervals, in combination with remotely sensed image techniques, indicated a sharp increasing trend of land reclamation after 2005,more » which accounted for over 35% of China’s total reclamation during the 25-year period since 1985. High-intensity coastal reclamation in China was mainly driven by the booming economy associated with urbanization and industrial development in the coastal region. Analysis indicated that coastal land reclamation is closely correlated with the GDP per capita in China. Study results of Landsat images showed that 754,697 ha of coastal wetlands have been reclaimed across all coastal provinces and metropolises from 1985 to 2010, at an annual rate of 5.9%. Coastal areas within the three major economic zones (Bohai Bay, Yangtze River Delta, and Pearl River Delta) were found to generally have higher reclamation rates. For example, the built-up area in Shanghai, which is located in the Yangtze River Delta, increased more than five times from 1985 to 2010. Approximately 35% of the reclamation occurred in Bohai Bay, in which the CRI between 2005 and 2010 was three times higher than the average CRI over the 25-year period.« less

Analysis-Driven Design of Representations For Sensing-Action Systems

DTIC Science & Technology

2017-10-01

from the Defense Technical Information Center (DTIC) (http://www.dtic.mil). AFRL-RY-WP-TR-2017-0196 HAS BEEN REVIEWED AND IS APPROVED FOR...Layered Sensing Exploitation Division This report is published in the interest of scientific and technical information exchange, and its...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

A landscape-scale wildland fire study using coupled weather-wildland fire model and airborne remote sensing

Treesearch

J.L. Coen; Philip Riggan

2011-01-01

We examine the Esperanza fire, a Santa Ana-driven wildland fire that occurred in complex terrain in spatially heterogeneous chaparral fuels, using airborne remote sensing imagery from the FireMapper thermal-imaging radiometer and a coupled weather-wildland fire model. The radiometer data maps fire intensity and is used to evaluate the error in the extent of the...

Floodplains as an Achilles’ heel of Amazonian forest resilience

PubMed Central

Flores, Bernardo M.; Holmgren, Milena; van Nes, Egbert H.; Jakovac, Catarina C.; Mesquita, Rita C. G.; Scheffer, Marten

2017-01-01

The massive forests of central Amazonia are often considered relatively resilient against climatic variation, but this view is challenged by the wildfires invoked by recent droughts. The impact of such fires that spread from pervasive sources of ignition may reveal where forests are less likely to persist in a drier future. Here we combine field observations with remotely sensed information for the whole Amazon to show that the annually inundated lowland forests that run through the heart of the system may be trapped relatively easily into a fire-dominated savanna state. This lower forest resilience on floodplains is suggested by patterns of tree cover distribution across the basin, and supported by our field and remote sensing studies showing that floodplain fires have a stronger and longer-lasting impact on forest structure as well as soil fertility. Although floodplains cover only 14% of the Amazon basin, their fires can have substantial cascading effects because forests and peatlands may release large amounts of carbon, and wildfires can spread to adjacent uplands. Floodplains are thus an Achilles’ heel of the Amazon system when it comes to the risk of large-scale climate-driven transitions. PMID:28396440

Task-based data-acquisition optimization for sparse image reconstruction systems

NASA Astrophysics Data System (ADS)

Chen, Yujia; Lou, Yang; Kupinski, Matthew A.; Anastasio, Mark A.

2017-03-01

Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.

Taylor dispersion in wind-driven current

NASA Astrophysics Data System (ADS)

Li, Gang; Wang, Ping; Jiang, Wei-Quan; Zeng, Li; Li, Zhi; Chen, G. Q.

2017-12-01

Taylor dispersion associated with wind-driven currents in channels, shallow lakes and estuaries is essential to hydrological environmental management. For solute dispersion in a wind-driven current, presented in this paper is an analytical study of the evolution of concentration distribution. The concentration moments are intensively derived for an accurate presentation of the mean concentration distribution, up to the effect of kurtosis. The vertical divergence of concentration is then deduced by Gill's method of series expansion up to the fourth order. Based on the temporal evolution of the vertical concentration distribution, the dispersion process in the wind-driven current is concretely characterized. The uniform shear leads to a special symmetrical distribution of mean concentration free of skewness. The non-uniformity of vertical concentration is caused by convection and smeared out gradually by the effect of diffusion, but fails to disappear even at large times.

On the imprint of surfactant-driven stabilization of laboratory breaking wave foam with comparison to oceanic whitecaps

NASA Astrophysics Data System (ADS)

Callaghan, A. H.; Deane, G. B.; Stokes, M. D.

2017-08-01

Surfactants are ubiquitous in the global oceans: they help form the materially-distinct sea surface microlayer (SML) across which global ocean-atmosphere exchanges take place, and they reside on the surfaces of bubbles and whitecap foam cells prolonging their lifetime thus altering ocean albedo. Despite their importance, the occurrence, spatial distribution, and composition of surfactants within the upper ocean and the SML remains under-characterized during conditions of vigorous wave breaking when in-situ sampling methods are difficult to implement. Additionally, no quantitative framework exists to evaluate the importance of surfactant activity on ocean whitecap foam coverage estimates. Here we use individual laboratory breaking waves generated in filtered seawater and seawater with added soluble surfactant to identify the imprint of surfactant activity in whitecap foam evolution. The data show a distinct surfactant imprint in the decay phase of foam evolution. The area-time-integral of foam evolution is used to develop a time-varying stabilization function, ϕ>(t>) and a stabilization factor, Θ, which can be used to identify and quantify the extent of this surfactant imprint for individual breaking waves. The approach is then applied to wind-driven oceanic whitecaps, and the laboratory and ocean Θ distributions overlap. It is proposed that whitecap foam evolution may be used to determine the occurrence and extent of oceanic surfactant activity to complement traditional in-situ techniques and extend measurement capabilities to more severe sea states occurring at wind speeds in excess of about 10 m/s. The analysis procedure also provides a framework to assess surfactant-driven variability within and between whitecap coverage data sets.