Статті в журналах: "Fine-resolution"

1

Green, David G., and Gary S. Dolman. "Fine Resolution Pollen Analysis." Journal of Biogeography 15, no. 4 (July 1988): 685. http://dx.doi.org/10.2307/2845445.

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2

Fredman, Gabrielle, Sudeshna Sadhu, and Nicholas Rymut. "Fine-tuning inflammation-resolution programs." Current Opinion in Clinical Nutrition & Metabolic Care 20, no. 2 (March 2017): 117–23. http://dx.doi.org/10.1097/mco.0000000000000351.

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3

Cai, Dingding, Ke Chen, Yanlin Qian, and Joni-Kristian Kämäräinen. "Convolutional low-resolution fine-grained classification." Pattern Recognition Letters 119 (March 2019): 166–71. http://dx.doi.org/10.1016/j.patrec.2017.10.020.

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4

ENVIRONMENTALEQUIPMENTSNORTHER. "Fine resolution plotting at high speed." Displays 8, no. 1 (January 1987): 44. http://dx.doi.org/10.1016/0141-9382(87)90012-6.

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5

Brown, W. M., J. L. Walker, and W. R. Boario. "Sighted automation and fine resolution imaging." IEEE Transactions on Aerospace and Electronic Systems 40, no. 4 (October 2004): 1426–45. http://dx.doi.org/10.1109/taes.2004.1386898.

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6

Baker, T. C., H. Y. Fadamiro, and A. A. Cosse. "Moth uses fine tuning for odour resolution." Nature 393, no. 6685 (June 1998): 530. http://dx.doi.org/10.1038/31131.

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7

Kaczmarska, Jo, Valerie Isham, and Christian Onof. "Point process models for fine-resolution rainfall." Hydrological Sciences Journal 59, no. 11 (September 26, 2014): 1972–91. http://dx.doi.org/10.1080/02626667.2014.925558.

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8

Sobotka, M., J. A. Bonet, and M. Vazquez. "High Resolution Observations of Umbral Fine Structure." International Astronomical Union Colloquium 141 (1993): 20–23. http://dx.doi.org/10.1017/s0252921100028694.

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9

Mitome, M., Y. Ohshima, M. Itoh, and K. Takayanagi. "High-resolution electron microscopy of fine particles." Microscopy Microanalysis Microstructures 4, no. 2-3 (1993): 297–304. http://dx.doi.org/10.1051/mmm:0199300402-3029700.

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10

Bindhu, V. M., and B. Narasimhan. "A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (December 23, 2014): 1397–401. http://dx.doi.org/10.5194/isprsarchives-xl-8-1397-2014.

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Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

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11

Brown, Rodger A., Bradley A. Flickinger, Eddie Forren, David M. Schultz, Dale Sirmans, Phillip L. Spencer, Vincent T. Wood, and Conrad L. Ziegler. "Improved Detection of Severe Storms Using Experimental Fine-Resolution WSR-88D Measurements." Weather and Forecasting 20, no. 1 (February 1, 2005): 3–14. http://dx.doi.org/10.1175/waf-832.1.

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Abstract Doppler velocity and reflectivity measurements from Weather Surveillance Radar-1988 Doppler (WSR-88D) radars provide important input to forecasters as they prepare to issue short-term severe storm and tornado warnings. Current-resolution data collected by the radars have an azimuthal spacing of 1.0° and range spacing of 1.0 km for reflectivity and 0.25 km for Doppler velocity and spectrum width. To test the feasibility of improving data resolution, National Severe Storms Laboratory’s test bed WSR-88D (KOUN) collected data in severe thunderstorms using 0.5°-azimuthal spacing and 0.25-km-range spacing, resulting in eight times the resolution for reflectivity and twice the resolution for Doppler velocity and spectrum width. Displays of current-resolution WSR-88D Doppler velocity and reflectivity signatures in severe storms were compared with displays showing finer-resolution signatures. At all ranges, fine-resolution data provided better depiction of severe storm characteristics. Eighty-five percent of mean rotational velocities derived from fine-resolution mesocyclone signatures were stronger than velocities derived from current-resolution signatures. Likewise, about 85% of Doppler velocity differences across tornado and tornadic vortex signatures were stronger than values derived from current-resolution data. In addition, low-altitude boundaries were more readily detected using fine-resolution reflectivity data. At ranges greater than 100 km, fine-resolution reflectivity displays revealed severe storm signatures, such as bounded weak echo regions and hook echoes, which were not readily apparent on current-resolution displays. Thus, the primary advantage of fine-resolution measurements over current-resolution measurements is the ability to detect stronger reflectivity and Doppler velocity signatures at greater ranges from a WSR-88D.

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12

Zhang, Jingyu, Jindi Wang, Rui Sun, Hongmin Zhou, and Helin Zhang. "A Model-Downscaling Method for Fine-Resolution LAI Estimation." Remote Sensing 12, no. 24 (December 18, 2020): 4147. http://dx.doi.org/10.3390/rs12244147.

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The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the precise monitoring of cropland growth. LAI downscaling methods are efficient at improving the spatial resolution of LAI products but often ignore the scaling effect of the model. In this study, a novel model-downscaling method is proposed for fine-resolution LAI estimation. It uses scaling equations of model parameters (SEMPs) to describe the scaling relations of models at different spatial resolutions and construct a downscaled model from a coarse-resolution model. Landsat Normalized Difference Vegetation Index (NDVI) at 30 m and Global LAnd Surface Satellite (GLASS) LAI at 1 km spatial resolutions are used because they are readily available. The downscaled model is evaluated by a fine-resolution model directly constructed with fine-resolution data. The fine-resolution LAI values estimated by this model-downscaling method are evaluated with field LAI measurements. The validation results show that the proposed method can generate highly accurate LAIs, with an RMSE of 0.821 at the Pshenichne cropland site in Ukraine and an RMSE of 0.515 at the Camerons forest site in Australia when compared with field LAI measurements. The results are also better than those of Ovakoglou’s downscaling method. These results demonstrate that the model-downscaling method for fine-resolution LAI estimation is viable and referable for related studies.

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13

Lee, Dong Seok, Jong Ki Lee, and Ji Whoa Kim. "Resolution of Hypoechoic Mass by Fine-Needle Aspiration." Journal of Korean Breast Cancer Society 7, no. 1 (2004): 37. http://dx.doi.org/10.4048/jkbcs.2004.7.1.37.

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14

van Sebille, Erik, Matthew H. England, Jan D. Zika, and Bernadette M. Sloyan. "Tasman leakage in a fine-resolution ocean model." Geophysical Research Letters 39, no. 6 (March 24, 2012): n/a. http://dx.doi.org/10.1029/2012gl051004.

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15

Dell'Acqua, F., P. Gamba, and G. Lisini. "Coregistration of Multiangle Fine Spatial Resolution SAR Images." IEEE Geoscience and Remote Sensing Letters 1, no. 4 (October 2004): 237–41. http://dx.doi.org/10.1109/lgrs.2004.832698.

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16

JACOBI, WALTER HOWER STEPHAN. "Fine-grained conflict resolution in constraint satisfaction problems." Journal of Experimental & Theoretical Artificial Intelligence 10, no. 1 (January 1998): 37–47. http://dx.doi.org/10.1080/095281398146897.

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17

Sun, Yue, and Hua Zhang. "A Two-Stage Spatiotemporal Fusion Method for Remote Sensing Images." Photogrammetric Engineering & Remote Sensing 85, no. 12 (December 1, 2019): 907–14. http://dx.doi.org/10.14358/pers.85.12.907.

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This paper presents a two-stage spatiotemporal fusion method for obtaining dense remote sensing images with both high spatial and temporal resolution. Considering the large resolution differences between fine- and coarse-resolution images, the proposed method is implemented in two stages. In the first stage, the input fine- and coarse-resolution images are preprocessed to the same intermediate resolution images, respectively. Then, a linear interpolation model is introduced to fuse these resampled images for predicting preliminary fusion results. In the second stage, a residual dense network is used to learn the nonlinear mapping between the preliminary fusion results and the real fine-resolution data to reconstruct the final fine-resolution data. Two data sets with different land surface types are employed to test the performance of the proposed method. Experimental results show that the proposed method is advantageous in such areas with phenological changes, and even for the data sets with land cover changes being the main type, it still has a good ability to predict spatial structure information of images.

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18

Wang, Liguo, Xiaoyi Wang, and Qunming Wang. "Using 250-m MODIS Data for Enhancing Spatiotemporal Fusion by Sparse Representation." Photogrammetric Engineering & Remote Sensing 86, no. 6 (June 1, 2020): 383–92. http://dx.doi.org/10.14358/pers.86.6.383.

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Spatiotemporal fusion is an important technique to solve the problem of incompatibility between the temporal and spatial resolution of remote sensing data. In this article, we studied the fusion of Landsat data with fine spatial resolution but coarse temporal resolution and Moderate Resolution Imaging Spectroradiometer (MODIS) data with coarse spatial resolution but fine temporal resolution. The goal of fusion is to produce time-series data with the fine spatial resolution of Landsat and the fine temporal resolution of MODIS. In recent years, learning-based spatiotemporal fusion methods, in particular the sparse representation-based spatiotemporal reflectance fusion model (SPSTFM), have gained increasing attention because of their great restoration ability for heterogeneous landscapes. However, remote sensing data from different sensors differ greatly on spatial resolution, which limits the performance of the spatiotemporal fusion methods (including SPSTFM) to some extent. In order to increase the accuracy of spatiotemporal fusion, in this article we used existing 250-m MODISbands (i.e., red and near-infrared bands) to downscale the observed 500-m MODIS bands to 250 m before SPTSFM-based fusion of MODIS and Landsat data. The experimental results show that the fusion accuracy of SPTSFM is increased when using 250-m MODIS data, and the accuracy of SPSTFM coupled with 250-m MODIS data is greater than the compared benchmark methods.

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19

Bisht, G., and W. J. Riley. "Topographic controls on soil moisture scaling properties in polygonal ground using idealized high-resolution surface–subsurface simulations." Hydrology and Earth System Sciences Discussions 11, no. 11 (November 18, 2014): 12833–82. http://dx.doi.org/10.5194/hessd-11-12833-2014.

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Abstract. Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. Here, we analyze the hypothesis that microtopography is a dominant controller of soil moisture in polygonal landscapes. We perform multi-year surface–subsurface isothermal flow simulations using the PFLOTRAN model for summer months at six spatial resolutions (0.25–8 m, in increments of a factor of 2). Simulations are performed for four study sites near Barrow, Alaska that are part of the NGEE-Arctic project. Results indicate a non-linear scaling relationship for statistical moments of soil moisture. Mean soil moisture for all study sites is accurately captured in coarser resolution simulations, but soil moisture variance is significantly under-estimated in coarser resolution simulations. The decrease in soil moisture variance in coarser resolution simulations is greater than the decrease in soil moisture variance obtained by coarsening out the fine resolution simulations. We also develop relationships to estimate the fine-resolution soil moisture probability distribution function (PDF) using coarse resolution simulations and topography. Although the estimated soil moisture PDF is underestimated during very wet conditions, the moments computed from the inferred soil moisture PDF had good agreement with the full model solutions (bias < ± 4 % and correlation > 0.99) for all four sites. Lastly, we develop two spatially-explicit methods to downscale coarse-resolution simulations of soil moisture. The first downscaling method requires simulation of soil moisture at fine and coarse resolution, while the second downscaling approach uses only topographical information at the two resolutions. Both downscaling approaches are able to accurately estimate fine-resolution soil moisture spatial patterns when compared to fine-resolution simulations (mean error for all study sites are < ± 1 %), but the first downscaling method more accurately estimates soil moisture variance.

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20

Li, Xiaodong, Feng Ling, Giles M. Foody, Yong Ge, Yihang Zhang, and Yun Du. "Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps." Remote Sensing of Environment 196 (July 2017): 293–311. http://dx.doi.org/10.1016/j.rse.2017.05.011.

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21

Gomez, Rocio, Mohan Sridharan, and Heather Riley. "What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration." Annals of Mathematics and Artificial Intelligence 89, no. 1-2 (March 24, 2020): 179–208. http://dx.doi.org/10.1007/s10472-019-09672-4.

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AbstractThe architecture described in this paper encodes a theory of intentions based on the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. For any given goal, nonmonotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture’s capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.

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22

Chu, Hone-Jay, Muhammad Zeeshan Ali, and Thomas J. Burbey. "Spatio-temporal data fusion for fine-resolution subsidence estimation." Environmental Modelling & Software 137 (March 2021): 104975. http://dx.doi.org/10.1016/j.envsoft.2021.104975.

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23

Marron, Joseph C., and Timothy J. Schulz. "Three-dimensional, fine-resolution imaging using laser frequency diversity." Optics Letters 17, no. 4 (February 15, 1992): 285. http://dx.doi.org/10.1364/ol.17.000285.

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24

Metcalf, A. J., V. Torres-Company, V. R. Supradeepa, D. E. Leaird, and A. M. Weiner. "Programmable broadband ultra-fine resolution 2-D pulse shaping." EPJ Web of Conferences 41 (2013): 11002. http://dx.doi.org/10.1051/epjconf/20134111002.

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25

Williams, W., and T. M. Wright. "High-resolution micromagnetic models of fine grains of magnetite." Journal of Geophysical Research: Solid Earth 103, B12 (December 10, 1998): 30537–50. http://dx.doi.org/10.1029/98jb01120.

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26

Bakillah, Mohamed, Steve Liang, Amin Mobasheri, Jamal Jokar Arsanjani, and Alexander Zipf. "Fine-resolution population mapping using OpenStreetMap points-of-interest." International Journal of Geographical Information Science 28, no. 9 (April 24, 2014): 1940–63. http://dx.doi.org/10.1080/13658816.2014.909045.

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27

Shah, Payal, Mindy L. Mallory, Amy W. Ando, and Glenn R. Guntenspergen. "Fine-resolution conservation planning with limited climate-change information." Conservation Biology 31, no. 2 (November 14, 2016): 278–89. http://dx.doi.org/10.1111/cobi.12793.

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28

Naka, T., T. Asada, T. Katsuragawa, K. Hakamata, M. Yoshimoto, K. Kuwabara, M. Nakamura, et al. "Fine grained nuclear emulsion for higher resolution tracking detector." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 718 (August 2013): 519–21. http://dx.doi.org/10.1016/j.nima.2012.11.106.

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29

Aplin, P., P. M. Atkinson, and P. J. Curran. "Fine spatial resolution satellite sensors for the next decade." International Journal of Remote Sensing 18, no. 18 (December 1997): 3873–81. http://dx.doi.org/10.1080/014311697216694.

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30

Granroth, G. E., D. H. Vandergriff, and S. E. Nagler. "SEQUOIA: A fine resolution chopper spectrometer at the SNS." Physica B: Condensed Matter 385-386 (November 2006): 1104–6. http://dx.doi.org/10.1016/j.physb.2006.05.379.

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31

Kwon, S. M., S. S. Hong, J. L. Weinberg, and N. Y. Misconi. "Fine Resolution Brightness Distribution of the Visible Zodiacal Light." International Astronomical Union Colloquium 126 (1991): 183–86. http://dx.doi.org/10.1017/s0252921100066732.

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AbstractApplying time-dependent corrections of the atmospheric diffuse light to the observed night sky brightness, we have determined brightness of the zodiacal light over the region 40° ≤λ − λ⊙ ≤;320ΰand − 20°≤β≤20ΰ. The resulting map of equal brightness contours has an angular resolution of two degrees, and exhibits east-west and north-south asymmetries.

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32

Hering, Susanne V., and Mark R. Stolzenburg. "Automated, high-time resolution measurement of fine particle nitrate." Journal of Aerosol Science 29 (September 1998): S1189—S1190. http://dx.doi.org/10.1016/s0021-8502(98)90777-7.

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33

Stevens, D. P., and S. R. Thompson. "The South Atlantic in the Fine-Resolution Antarctic Model." Annales Geophysicae 12, no. 9 (August 31, 1994): 826–39. http://dx.doi.org/10.1007/s00585-994-0826-5.

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Abstract. The geographical area covered by the Fine-Resolution Antarctic Model (FRAM) includes that part of the South Atlantic south of 24°S. A description of the dynamics and thermodynamics of this region of the model is presented. Both the mean and eddy fields in the model are in good agreement with reality, although the magnitude of the transients is somewhat reduced. The heat flux is northward and in broad agreement with many other estimates. Agulhas eddies are formed by the model and propagate westward into the Atlantic providing a mechanism for fluxing heat from the Indian Ocean. The confluence of the Brazil and Falkland currents produces a strong front and a large amount of mesoscale activity. In the less stratified regions to the south, topographic steering of the Antarctic circumpolar current is important.

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34

Zhang, Kaibing, Dacheng Tao, Xinbo Gao, Xuelong Li, and Jie Li. "Coarse-to-Fine Learning for Single-Image Super-Resolution." IEEE Transactions on Neural Networks and Learning Systems 28, no. 5 (May 2017): 1109–22. http://dx.doi.org/10.1109/tnnls.2015.2511069.

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35

Sluzky, Esther, Teresa Avalos, and Kenneth R. Hesse. "Evaluating the resolution of ultra-fine-grain phosphor screens." Journal of the Society for Information Display 1, no. 1 (1993): 3. http://dx.doi.org/10.1889/1.1984829.

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36

Schlichenmaier, R., O. von der Lühe, S. Hoch, D. Soltau, T. Berkefeld, D. Schmidt, W. Schmidt, et al. "Active region fine structure observed at 0.08 arcsec resolution." Astronomy & Astrophysics 596 (November 30, 2016): A7. http://dx.doi.org/10.1051/0004-6361/201628561.

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37

Gutin, Jenia, Ronen Sadeh, Nitzan Bodenheimer, Daphna Joseph-Strauss, Avital Klein-Brill, Adi Alajem, Oren Ram, and Nir Friedman. "Fine-Resolution Mapping of TF Binding and Chromatin Interactions." Cell Reports 22, no. 10 (March 2018): 2797–807. http://dx.doi.org/10.1016/j.celrep.2018.02.052.

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38

Li, Xiao Gen, Zhi Quan Huang, Tong Jiang, and An Ming Wang. "The Multi-Resolution Watershed Model Based on DEM." Advanced Materials Research 250-253 (May 2011): 3059–63. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.3059.

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The multi-resolution flooding routing model advanced firstly is composed of the extensive flooding routing model and the fine flooding routing model. In the article to put up research and discuss detailedly the 3D visualization of the mul-resolution flooding routing model which is as the non-engineering measure about the water conservancy management. In the article to analysis the constructing method of the mul-resolution flooding routing model mainly ,which is the theory and method about the extensive flooding routing model and the fine flooding routing model constructed and to work out material the extensive flooding routing model and the fine flooding routing model. Finally to applying the statistic analysis method to get the below conclusion: ①Applying the visualization system about the multi-resolution flooding routing model is to act the non-engineering measure about the water conservancy management and it provides a new thought and trusty scientific decision-making for the water conservancy management department; ② The results of the storage capacity and the submergence area worked out by the the fine flooding routing model are more precision than the result of the general method. The above conclusion proves the feasibility and science about the construction of the multi-resolution flooding routing model adequately.

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39

Zhou, Yihui, Yi Zhang, Jian Li, Rucong Yu, and Zhuang Liu. "Configuration and evaluation of a global unstructured mesh atmospheric model (GRIST-A20.9) based on the variable-resolution approach." Geoscientific Model Development 13, no. 12 (December 14, 2020): 6325–48. http://dx.doi.org/10.5194/gmd-13-6325-2020.

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Abstract. Targeting a long-term effort towards a variable-resolution (VR) global weather and climate model, this study systematically configures and evaluates an unstructured mesh atmospheric model based on the multiresolution approach. The model performance is examined from dry dynamics to simple physics and full physics scenarios. In the dry baroclinic wave test, the VR model reproduces comparable fine-scale structures in the refined regions as a fine-resolution quasi-uniform (QU) mesh model. The mesh transition zone does not adversely affect the wave pattern. Regional kinetic energy spectra show that the fine-scale resolving ability improves as the fine resolution increases. Compared to a QU counterpart that has equivalent degrees of freedom, the VR model tends to increase the global errors, but the errors can be reduced when the resolution of the coarse region is increased. The performance over the coarse region is generally close to that of a low-resolution QU counterpart. Two multi-region refinement approaches, the hierarchical and polycentric refinement modes, further validate the model performance under the multiresolution refinement. Activating hyperdiffusion for horizontal velocity is helpful with respect to VR modeling. An idealized tropical cyclone test is further used to examine its ability to resolve fine-scale structures. In the simple physics environment, the VR model can have the tropical cyclone stably pass the transition zone in various configurations. A series of sensitivity tests examines the model performance in a hierarchical refinement mode. The simulations exhibit consistency even when the VR mesh is slightly perturbed by one of the three parameters that control the density function. The tropical cyclone, starting from the second refinement region and passing through the inner transition zone, gets intensified and covers a smaller area in the refined regions. Such variations are consistent with the behavior that one may observe when uniformly refining the QU mesh. In the full physics environment with a highly variable mesh that reaches sub-10 km resolution, the VR model also produces a reasonable evolution for the tropical cyclone. The explicit diffusion shows its usefulness in terms of suppressing some unrealistic isolated-scale structures that are far away from the initial vortex and does not adversely affect the physically important object. The fine-scale structure is determined mainly by the fine-resolution area, although the systems may have larger differences before they move into the fine-resolution area. Altogether, this work demonstrates that the multiresolution configuration is a reliable and economic alternative to high-resolution global modeling. The adverse impact due to mesh transition and the coarse region can be controlled well.

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40

Perry, Matthew, and Alberto Troccoli. "An approach for generating synthetic fine temporal resolution solar radiation time series from hourly gridded datasets." Meteorologische Zeitschrift 26, no. 3 (June 14, 2017): 265–76. http://dx.doi.org/10.1127/metz/2016/0746.

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41

Alpak, Faruk O. "Quasiglobal Multiphase Upscaling of Reservoir Models With Nonlocal Stratigraphic Heterogeneities." SPE Journal 20, no. 02 (August 6, 2014): 277–93. http://dx.doi.org/10.2118/170245-pa.

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Summary Representing the complete spectrum of fine-scale stratigraphic details in full-field dynamic models of geologically complex clastic reservoirs is beyond the limits of existing computational capabilities. A quasiglobal multiphase upscaling method—the regional-scale multiphase upscaling (RMU) method—is developed, in which the dynamic effects of subgrid-scale (typically subseismic) nonlocal stratigraphic reservoir elements (e.g., channels, lobes, sand bars, and shale drapes) are captured by means of pseudofunctions for flow simulation. Unlike conventional dynamic multiphase upscaling methods, the RMU method does not require fine-resolution reservoir-scale simulations. Rather, it relies on intermediate-scale sector-model simulations for pseudoization. The intermediate scale, also referred to as the regional scale, is defined as the spatial scale at which the global multiphase flow effects of nonlocal stratigraphic elements can be approximated by fine-resolution flow simulations with reasonable accuracy. During the pseudoization process, dynamic multiphase flow responses of coarse regional-scale sector models are calibrated against those stemming from their corresponding fine-resolution parent models. Each regional-scale sector model is simulated only once at the fine geologic resolution. The process involves automatic determination and subsequent modification of the parameters that describe rock relative permeability and capillary pressure functions. Coarse regional-scale models are simulated a few times until a reasonable match between their coarse- and fine-resolution dynamic responses can be attained. The parameter-estimation step of the pseudoization process is performed by use of a very efficient constrained nonlinear optimization algorithm. The RMU method is evaluated in two proof-of-concept numerical examples involving a plethora of turbidite stratigraphic architectures. The method yields simulation results that are always more accurate than conventionally upscaled coarse-resolution model predictions. Incorporating geologically based pseudofunctions into otherwise simple coarse-resolution full-field reservoir models reduces the simulation cycle time significantly and improves the accuracy of production forecasts. The RMU method typically delivers two to three orders of magnitude in speed up of flow simulations.

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42

Mager, Mary. "Improving the Resolution of Sputter-coated Films." Microscopy Today 8, no. 2 (March 2000): 16–17. http://dx.doi.org/10.1017/s1551929500057448.

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After an inquiry from the Microscopy Listserver, I went back to my 1980 copy of Scanning Electron Microscopy, volume I. Several authors had investigated the structure of thin metal films by depositing the films onto carbon-film-covered TEM grids and imaging the films at high magnification. There were several proposals for new devices that have since become standards for high-resolution coaters, but the Listserver inquiry was for a fine conducting film suitabie for high-resolution SEM from an existing sputter coater.There were several factors studied that influenced the fine structure of the films. The first was the materials sputtered: for a given set of conditions of voltage, current and time, platinum gave the finest film, 60% gold-40% palladium (Au/Pd) the next finest and pure gold the least fine.

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43

William, Jino Hans, N. Venkateswaran, Srinath Narayanan, and Sandeep Ramachandran. "An Example-Based Super-Resolution Algorithm for Selfie Images." Scientific World Journal 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8306342.

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A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details.

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Fung, Wong, and Chan. "Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network." Remote Sensing 11, no. 18 (September 4, 2019): 2077. http://dx.doi.org/10.3390/rs11182077.

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Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.

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45

Muad, Anuar M., and Giles M. Foody. "Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution." International Journal of Applied Earth Observation and Geoinformation 15 (April 2012): 79–91. http://dx.doi.org/10.1016/j.jag.2011.06.002.

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46

Yang, Xiaohong, Zhong Xie, Feng Ling, Xiaodong Li, Yihang Zhang, and Ming Zhong. "Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering." Remote Sensing 10, no. 8 (August 2, 2018): 1212. http://dx.doi.org/10.3390/rs10081212.

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Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

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Van Nieuwenhuizen, Nigel, John B. Lindsay, and Ben DeVries. "Automated Mapping of Transportation Embankments in Fine-Resolution LiDAR DEMs." Remote Sensing 13, no. 7 (March 30, 2021): 1308. http://dx.doi.org/10.3390/rs13071308.

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Fine-resolution LiDAR DEMs can represent surface features such as road and railway embankments with high fidelity. However, transportation embankments are problematic for several environmental modelling applications, and particularly hydrological modelling. Currently, there are no automated techniques for the identification and removal of embankments from LiDAR DEMs. This paper presents a novel algorithm for identifying embankments in LiDAR DEMs. The algorithm utilizes repositioned transportation network cells as seed points in a region-growing operation. The embankment region grows based on derived morphometric parameters, including road surface width, embankment width, embankment height, and absolute slope. The technique was tested on eight LiDAR DEMs representing subsections of four watersheds in southwestern Ontario, Canada, ranging in size from 16 million cells to 134 million cells. The algorithm achieved a recall greater than or equal to 90% for seven of the eight DEMs, while achieving a Pearson’s phi correlation coefficient greater than 80% for five of the eight DEMs. Therefore, the method has moderate to high accuracy for identifying embankments. The processing times associated with applying the technique to the eight study site DEMs ranged from 1.4 s to 20.3 s, which demonstrates the practicality of using the embankment mapping tool in applications with data set sizes commonly encountered in practice.

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Green, David, Gurdip Singh, Henry Polach, Daphne Moss, John Banks, and Elizabeth A. Geissler. "A Fine-Resolution Palaeoecology and Palaeoclimatology from South-Eastern Australia." Journal of Ecology 76, no. 3 (September 1988): 790. http://dx.doi.org/10.2307/2260574.

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Huang, Hailiang, Ming Fang, Luke Jostins, Maša Umićević Mirkov, Gabrielle Boucher, Carl A. Anderson, Vibeke Andersen, et al. "Fine-mapping inflammatory bowel disease loci to single-variant resolution." Nature 547, no. 7662 (June 28, 2017): 173–78. http://dx.doi.org/10.1038/nature22969.

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50

Witter, Jonathan D., John G. Lyon, and Barry M. Puskas. "Differential GPS Geometric Rectification of Fine-Resolution Aircraft Scanner Data." Journal of Surveying Engineering 127, no. 2 (May 2001): 52–58. http://dx.doi.org/10.1061/(asce)0733-9453(2001)127:2(52).

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