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Journal articles on the topic 'CloudMe'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Teing, Yee-Yang, Ali Dehghantanha, and Kim-Kwang Raymond Choo. "CloudMe forensics: A case of big data forensic investigation." Concurrency and Computation: Practice and Experience 30, no. 5 (2017): e4277. http://dx.doi.org/10.1002/cpe.4277.

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2

Ferry, Nicolas, Franck Chauvel, Hui Song, Alessandro Rossini, Maksym Lushpenko, and Arnor Solberg. "CloudMF." ACM Transactions on Internet Technology 18, no. 2 (2018): 1–24. http://dx.doi.org/10.1145/3125621.

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3

Wang, P., and P. Stammes. "Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements." Atmospheric Measurement Techniques 7, no. 5 (2014): 1331–50. http://dx.doi.org/10.5194/amt-7-1331-2014.

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Abstract. Two SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) O2 A band cloud height products are evaluated using ground-based radar/lidar measurements between January 2003 and December 2011. The products are the ESA (European Space Agency) Level 2 (L2) version 5.02 cloud top height and the FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) version 6 cloud height. The radar/lidar profiles are obtained at the Cloudnet sites of Cabauw and Lindenberg, and are averaged for 1 h centered at the SCIAMACHY overpass time. In total we have 217 cases of sin

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4

Wang, P., and P. Stammes. "Validation of SCIAMACHY O<sub>2</sub> A band cloud heights using Cloudnet radar/lidar measurements." Atmospheric Measurement Techniques Discussions 6, no. 5 (2013): 8603–45. http://dx.doi.org/10.5194/amtd-6-8603-2013.

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Abstract. For the first time two SCIAMACHY O2 A band cloud height products are validated using ground-based radar/lidar measurements between January 2003 and December 2011. The products are the ESA Level 2 (L2) version 5.02 cloud top height and the FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) version 6 cloud height. The radar/lidar profiles are obtained at the Cloudnet sites of Cabauw and Lindenberg, and are averaged for one hour centered at the SCIAMACHY overpass time to achieve an optimal temporal and spatial match. In total we have about 220 cases of single layer clouds

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5

Baciu, George, Chenhui Li, Yunzhe Wang, and Xiujun Zhang. "Cloudet." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 1 (2016): 12–31. http://dx.doi.org/10.4018/ijcini.2016010102.

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Streaming data cognition has become a dominant problem in interactive visual analytics for event detection, meteorology, cosmology, security, and smart city applications. In order to interact with streaming data patterns in an elastic cloud environment, we present a new elastic framework for big data visual analytics in the cloud, the Cloudet. The Cloudet is a self-adaptive cloud-based platform that treats both data and compute nodes as elastic objects. The main objective is to readily achieve the scalability and elasticity of cloud computing platforms in order to process large streaming data

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6

Zou, Tao, Ronan Le Bras, Marcos Vaz Salles, Alan Demers, and Johannes Gehrke. "ClouDiA." Proceedings of the VLDB Endowment 6, no. 2 (2012): 121–32. http://dx.doi.org/10.14778/2535568.2448945.

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7

Wood, Timothy, K. K. Ramakrishnan, Prashant Shenoy, and Jacobus van der Merwe. "CloudNet." ACM SIGPLAN Notices 46, no. 7 (2011): 121–32. http://dx.doi.org/10.1145/2007477.1952699.

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8

Vestin, Jonathan, Peter Dely, Andreas Kassler, Nico Bayer, Hans Einsiedler, and Christoph Peylo. "CloudMAC." ACM SIGMOBILE Mobile Computing and Communications Review 16, no. 4 (2013): 42–45. http://dx.doi.org/10.1145/2436196.2436217.

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9

Illingworth, A. J., R. J. Hogan, E. J. O'Connor, et al. "Cloudnet." Bulletin of the American Meteorological Society 88, no. 6 (2007): 883–98. http://dx.doi.org/10.1175/bams-88-6-883.

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The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there ar

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10

Compernolle, Steven, Athina Argyrouli, Ronny Lutz, et al. "Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O<sub>2</sub>–O<sub>2</sub>, MODIS, and Suomi-NPP VIIRS." Atmospheric Measurement Techniques 14, no. 3 (2021): 2451–76. http://dx.doi.org/10.5194/amt-14-2451-2021.

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Abstract. Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud

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11

Martin, Steve. "Coral clouded." New Scientist 206, no. 2754 (2010): 23. http://dx.doi.org/10.1016/s0262-4079(10)60788-9.

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12

Schiermeier, Quirin. "Clouded vision." Nature 422, no. 6929 (2003): 356–57. http://dx.doi.org/10.1038/nj6929-356a.

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13

Bahtovski, Aleksandar, and Marjan Gusev. "Cloudlet Challenges." Procedia Engineering 69 (2014): 704–11. http://dx.doi.org/10.1016/j.proeng.2014.03.045.

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14

Pounds, J. Alan, and Robert Puschendorf. "Clouded futures." Nature 427, no. 6970 (2004): 107–9. http://dx.doi.org/10.1038/427107a.

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15

Steirer, Gregory. "Clouded Visions." Television & New Media 16, no. 2 (2014): 180–95. http://dx.doi.org/10.1177/1527476414524842.

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16

Callaway, Karen Simpson. "Clouded Horizons." Psychotherapy Patient 7, no. 1-2 (1991): 67–75. http://dx.doi.org/10.1300/j358v07n01_08.

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17

Hayes, J. "Cloud's caveats." Engineering & Technology 5, no. 12 (2010): 46–48. http://dx.doi.org/10.1049/et.2010.1209.

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18

Koren, I., L. Oreopoulos, G. Feingold, L. A. Remer, and O. Altaratz. "How small is a small cloud?" Atmospheric Chemistry and Physics Discussions 8, no. 2 (2008): 6379–407. http://dx.doi.org/10.5194/acpd-8-6379-2008.

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Abstract. The interplay between clouds and aerosols and their contribution to the radiation budget is one of the largest uncertainties of climate change. Most work to date has separated cloudy and cloud-free areas in order to evaluate the individual radiative forcing of aerosols, clouds, and aerosol effects on clouds. Here we examine the size distribution and the optical properties of small, sparse cumulus clouds and the associated optical properties of what is considered a cloud-free atmosphere within the cloud field. We show that any separation between clouds and cloud free atmosphere will i

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19

Sanchez, Adriana, Nicole M. Hughes, and William K. Smith. "Importance of natural cloud regimes to ecophysiology in the alpine species, Caltha leptosepala and Arnica parryi, Snowy Range Mountains, southeast Wyoming, USA." Functional Plant Biology 42, no. 2 (2015): 186. http://dx.doi.org/10.1071/fp14096.

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The south-central Rocky Mountains, USA, are characterised by a dry, continental mesoclimate with typical convective cloud formation during the afternoon. Little is known about the specific influence of such predictable cloud patterns on the microclimate and ecophysiology of associated species. During the summer of 2012, days with afternoon clouds were most common (50% of all days) compared with completely clear (24%) or cloudy days (6.5%). In two representative alpine species, Caltha leptosepala DC. and Arnica parryi A. Gray, fully overcast days reduced mean daily photosynthesis (A) by nearly

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20

Koren, I., L. Oreopoulos, G. Feingold, L. A. Remer, and O. Altaratz. "How small is a small cloud?" Atmospheric Chemistry and Physics 8, no. 14 (2008): 3855–64. http://dx.doi.org/10.5194/acp-8-3855-2008.

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Abstract. The interplay between clouds and aerosols and their contribution to the radiation budget is one of the largest uncertainties of climate change. Most work to date has separated cloudy and cloud-free areas in order to evaluate the individual radiative forcing of aerosols, clouds, and aerosol effects on clouds. Here we examine the size distribution and the optical properties of small, sparse cumulus clouds and the associated optical properties of what is considered a cloud-free atmosphere within the cloud field. We show that any separation between clouds and cloud free atmosphere will i

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21

D’Cruze, Neil, and David W. Macdonald. "Clouded in mystery: the global trade in clouded leopards." Biodiversity and Conservation 24, no. 14 (2015): 3505–26. http://dx.doi.org/10.1007/s10531-015-1010-9.

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22

Smaglik, Paul. "Chemistry's clouded view." Nature 421, no. 6920 (2003): 295. http://dx.doi.org/10.1038/nj6920-295a.

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23

BAUM, RUDY M. "Employment Outlook: Clouded." Chemical & Engineering News Archive 89, no. 45 (2011): 5. http://dx.doi.org/10.1021/cen-v089n045.p005.

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24

Beardsley, Tim. "Clouded Crystal Ball." Scientific American 259, no. 3 (1988): 32–35. http://dx.doi.org/10.1038/scientificamerican0988-32b.

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25

Hunter, P. "Cloud's broadcast benefits." Engineering & Technology 7, no. 5 (2012): 72–75. http://dx.doi.org/10.1049/et.2012.0519.

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26

Shapiro, Daniel. "A Clouded Illumination." American Book Review 34, no. 5 (2013): 26–27. http://dx.doi.org/10.1353/abr.2013.0089.

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27

Mehrabi, Abbas, Matti Siekkinen, Teemu Kämäräinen, and Antti yl¨-J¨¨ski. "Multi-Tier CloudVR." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3429441.

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The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency:

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28

Lu, Shiming, Mingjun He, Shuangyan He, et al. "An Improved Cloud Masking Method for GOCI Data over Turbid Coastal Waters." Remote Sensing 13, no. 14 (2021): 2722. http://dx.doi.org/10.3390/rs13142722.

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Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrat

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29

Zhao, Xiaoyi, Kristof Bognar, Vitali Fioletov, et al. "Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic." Atmospheric Measurement Techniques 12, no. 4 (2019): 2463–83. http://dx.doi.org/10.5194/amt-12-2463-2019.

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Abstract. Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to investigate whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop

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30

Li, J., Z. Wu, Z. Hu, Y. Zhang, and M. Molinier. "AUTOMATIC CLOUD DETECTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORKS IN REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 885–92. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-885-2020.

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Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the differ

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31

Dinh, Tra, and Stephan Fueglistaler. "Cirrus, Transport, and Mixing in the Tropical Upper Troposphere." Journal of the Atmospheric Sciences 71, no. 4 (2014): 1339–52. http://dx.doi.org/10.1175/jas-d-13-0147.1.

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Abstract The impact of cloud radiative heating on transport time scales from the tropical upper troposphere to the stratosphere is studied in two-dimensional numerical simulations. Clouds are idealized as sources of radiative heating and are stochastically distributed in space and time. A spatial probability function constrains clouds to occur in only part of the domain to depict heterogeneously distributed clouds in the atmosphere. The transport time from the lower to upper boundaries (age of air) is evaluated with trajectories. The spectra of age of air obtained in the simulations are bimoda

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32

Wall, Casey J., Tsubasa Kohyama, and Dennis L. Hartmann. "Low-Cloud, Boundary Layer, and Sea Ice Interactions over the Southern Ocean during Winter." Journal of Climate 30, no. 13 (2017): 4857–71. http://dx.doi.org/10.1175/jcli-d-16-0483.1.

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During austral winter, a sharp contrast in low-cloud fraction and boundary layer structure across the Antarctic sea ice edge is seen in ship-based measurements and in active satellite retrievals from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO), which provide an unprecedented view of polar clouds during winter. Sea ice inhibits heat and moisture transport from the ocean to the atmosphere, and, as a result, the boundary layer is cold, stable, and clear over sea ice and warm, moist, well mixed, and cloudy over open water. The mean low-cloud fraction observed by C

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33

Roy, Deepsubhra Guha, Debashis De, Anwesha Mukherjee, and Rajkumar Buyya. "Application-aware cloudlet selection for computation offloading in multi-cloudlet environment." Journal of Supercomputing 73, no. 4 (2016): 1672–90. http://dx.doi.org/10.1007/s11227-016-1872-y.

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34

B., Santhosh Kumar. "A 3-tier Cloud-Cloudlet-Device Hierarchical Trust-based Service Management for Mobile Cloud IoT Systems." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (2020): 249–53. http://dx.doi.org/10.5373/jardcs/v12i1/20201036.

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35

Hutchison, Keith D., Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson. "Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms." Journal of Atmospheric and Oceanic Technology 25, no. 4 (2008): 501–18. http://dx.doi.org/10.1175/2007jtecha1004.1.

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Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust,

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36

Zuidema, P., B. Baker, Y. Han, et al. "An Arctic Springtime Mixed-Phase Cloudy Boundary Layer Observed during SHEBA." Journal of the Atmospheric Sciences 62, no. 1 (2005): 160–76. http://dx.doi.org/10.1175/jas-3368.1.

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Abstract The microphysical characteristics, radiative impact, and life cycle of a long-lived, surface-based mixed-layer, mixed-phase cloud with an average temperature of approximately −20°C are presented and discussed. The cloud was observed during the Surface Heat Budget of the Arctic experiment (SHEBA) from 1 to 10 May 1998. Vertically resolved properties of the liquid and ice phases are retrieved using surface-based remote sensors, utilize the adiabatic assumption for the liquid component, and are aided by and validated with aircraft measurements from 4 and 7 May. The cloud radar ice microp

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37

Pomozi, István, Gábor Horváth, and Rüdiger Wehner. "How the clear-sky angle of polarization pattern continues underneath clouds: full-sky measurements and implications for animal orientation." Journal of Experimental Biology 204, no. 17 (2001): 2933–42. http://dx.doi.org/10.1242/jeb.204.17.2933.

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SUMMARY One of the biologically most important parameters of the cloudy sky is the proportion P of the celestial polarization pattern available for use in animal navigation. We evaluated this parameter by measuring the polarization patterns of clear and cloudy skies using 180° (full-sky) imaging polarimetry in the red (650nm), green (550nm) and blue (450nm) ranges of the spectrum under clear and partly cloudy conditions. The resulting data were compared with the corresponding celestial polarization patterns calculated using the single-scattering Rayleigh model. We show convincingly that the pa

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38

Yang, S., and X. Zou. "Temperature Profiles and Lapse Rate Climatology in Altostratus and Nimbostratus Clouds Derived from GPS RO Data." Journal of Climate 26, no. 16 (2013): 6000–6014. http://dx.doi.org/10.1175/jcli-d-12-00646.1.

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Abstract Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) refractivity profiles in altostratus and nimbostratus clouds from 2007 to 2010 are first identified based on collocated CloudSat data. Vertical temperature profiles in these clouds are then retrieved from cloudy refractivity profiles. Contributions of cloud liquid water content and ice water content are also included in the retrieval algorithm. The temperature profiles and their lapse rates are compared with those from a standard GPS RO wet retrieval without including cloud effects.

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39

Chou, Ming-Dah, Kyu-Tae Lee, Si-Chee Tsay, and Qiang Fu. "Parameterization for Cloud Longwave Scattering for Use in Atmospheric Models." Journal of Climate 12, no. 1 (1999): 159–69. http://dx.doi.org/10.1175/1520-0442-12.1.159.

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Abstract A parameterization for the scattering of thermal infrared (longwave) radiation by clouds has been developed based on discrete-ordinate multiple-scattering calculations. The effect of backscattering is folded into the emission of an atmospheric layer and the absorption between levels by scaling the cloud optical thickness. The scaling is a function of the single-scattering albedo and asymmetry factor. For wide ranges of cloud particle size, optical thickness, height, and atmospheric conditions, flux errors induced by the parameterization are small. They are &lt;4 W m−2 (2%) in the

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40

Sand, Philipp. "Clouded Issues for PHACTR1." International Journal of Molecular Sciences 16, no. 12 (2015): 9770–71. http://dx.doi.org/10.3390/ijms16059770.

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41

Đekić, Milica. "The cloud's computing security." Tehnika 73, no. 2 (2018): 300–304. http://dx.doi.org/10.5937/tehnika1802300d.

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42

Porto, A. J. M., and A. F. Galvão. "Choice with Clouded Concepts." Economic Analysis of Law Review 5, no. 1 (2014): 46–68. http://dx.doi.org/10.18836/2178-0587/ealr.v5n1p46-68.

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43

Royar, Robert. "New horizons, clouded vistas." Computers and Composition 11, no. 2 (1994): 93–105. http://dx.doi.org/10.1016/s8755-4615(06)80002-2.

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44

Carroll, Michael E. "Clouded Judgment or Greed?" Journal of Cataract & Refractive Surgery 15, no. 4 (1989): 465. http://dx.doi.org/10.1016/s0886-3350(89)80077-x.

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45

Kastenhofer, Julia, and Shadrack Katuu. "Declassification: a clouded environment." Archives and Records 37, no. 2 (2016): 198–224. http://dx.doi.org/10.1080/23257962.2016.1194814.

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46

Nicklason, F. "Croce, Clouded Land (Videotape)." Oral History Review 16, no. 1 (1988): 200–202. http://dx.doi.org/10.1093/ohr/16.1.200.

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47

Liao, Lingxia, Meikang Qiu, and Victor C. M. Leung. "Software Defined Mobile Cloudlet." Mobile Networks and Applications 20, no. 3 (2015): 337–47. http://dx.doi.org/10.1007/s11036-015-0616-1.

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48

Somula, Ramasubbareddy, and Sasikala R. "A Load and Distance Aware Cloudlet Selection Strategy in Multi-Cloudlet Environment." International Journal of Grid and High Performance Computing 11, no. 2 (2019): 85–102. http://dx.doi.org/10.4018/ijghpc.2019040105.

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Day to day the usage of mobile devices (MD) is growing in people's lives. But still the MD is limited in terms of memory, battery life time, processing capacity. In order to overcome these issues, the new emerging technology named mobile cloud computing (MCC) has been introduced. The offloading mechanism execute the resource intensive application on the remote cloud to save both the battery utilization and execution time. But still the high latency challenges in MCC need to be addressed by executing resource intensive task at nearby resource cloud server. The key challenge is to find optimal c

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49

Yao, Hong, Changmin Bai, Muzhou Xiong, Deze Zeng, and Zhangjie Fu. "Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing." Concurrency and Computation: Practice and Experience 29, no. 16 (2016): e3975. http://dx.doi.org/10.1002/cpe.3975.

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Mukherjee, Anwesha, Debashis De, and Deepsubhra Guha Roy. "A Power and Latency Aware Cloudlet Selection Strategy for Multi-Cloudlet Environment." IEEE Transactions on Cloud Computing 7, no. 1 (2019): 141–54. http://dx.doi.org/10.1109/tcc.2016.2586061.

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