Relevant bibliographies by topics / CLOUDLET CLASSIFICATION

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'CLOUDLET CLASSIFICATION'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "CLOUDLET CLASSIFICATION"

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Ashima, Ashima, and Vikramjit Singh. "A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (2018): 7103–10. http://dx.doi.org/10.24297/ijct.v17i1.7004.

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Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and dat
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Deng, Minjie, Yong Han, Yan Liu, et al. "Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet)." Remote Sensing 17, no. 3 (2025): 519. https://doi.org/10.3390/rs17030519.

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Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex terrains. Machine learning approaches based on two-dimensional images face challenges such as data scarcity and high annotation costs, which hinder accurate pixel-level cloud identification. Additionally, single-pixel classification methods fail to effecti
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Kalesse-Los, Heike, Willi Schimmel, Edward Luke, and Patric Seifert. "Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network." Atmospheric Measurement Techniques 15, no. 2 (2022): 279–95. http://dx.doi.org/10.5194/amt-15-279-2022.

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Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to
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Kalesse-Los, Heike, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn. "The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations." Atmospheric Measurement Techniques 16, no. 6 (2023): 1683–704. http://dx.doi.org/10.5194/amt-16-1683-2023.

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Abstract. Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer, which was developed within the framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 2020 in the tropical western Atlantic. The Virga-Sniffer Python package is highly
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Roschke, Johanna, Jonas Witthuhn, Marcus Klingebiel, et al. "Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory." Atmospheric Measurement Techniques 18, no. 2 (2025): 487–508. https://doi.org/10.5194/amt-18-487-2025.

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Abstract. The highly sensitive Ka-band cloud radar at the Barbados Cloud Observatory (BCO) frequently reveals radar reflectivity signals below −50 dBZ within the convective sub-cloud layer. These so-called haze echoes are signals from hygroscopically grown sea salt aerosols. Within the Cloudnet target classification scheme, haze echoes are generally misclassified as precipitation (target class: “drizzle or rain”). We present a technique to discriminate between “drizzle or rain” and sea salt aerosols in Cloudnet that is applicable to marine Cloudnet sites. The method is based on deriving heuris
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Pîrloagă, Răzvan, Dragoş Ene, Mihai Boldeanu, Bogdan Antonescu, Ewan J. O’Connor, and Sabina Ştefan. "Ground-Based Measurements of Cloud Properties at the Bucharest–Măgurele Cloudnet Station: First Results." Atmosphere 13, no. 9 (2022): 1445. http://dx.doi.org/10.3390/atmos13091445.

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Data collected over a period of 18 months (December 2019–May 2021) at the Bucharest–Măgurele Cloudnet station were analysed for the first time to determine the macrophysical and microphysical cloud properties over this site. A total number of 1,327,680 vertical profiles containing the target classification based on the Cloudnet algorithm were analysed, of which 1,077,858 profiles contained hydrometeors. The highest number of profiles with hydrometeors (>60%) was recorded in December 2020, with hydrometeors being observed mainly below 5 km. Above 5 km, the frequency of occurrence of hydromet
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Kulie, Mark S., Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer. "A Shallow Cumuliform Snowfall Census Using Spaceborne Radar." Journal of Hydrometeorology 17, no. 4 (2016): 1261–79. http://dx.doi.org/10.1175/jhm-d-15-0123.1.

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Abstract The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall e
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Liu, Cheng-Chien, Yu-Cheng Zhang, Pei-Yin Chen, et al. "Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation." Remote Sensing 11, no. 2 (2019): 119. http://dx.doi.org/10.3390/rs11020119.

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Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive
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Zhang, Jinglin, Pu Liu, Feng Zhang, and Qianqian Song. "CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network." Geophysical Research Letters 45, no. 16 (2018): 8665–72. http://dx.doi.org/10.1029/2018gl077787.

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Behrangi, Ali, Terry Kubar, and Bjorn Lambrigtsen. "Phenomenological Description of Tropical Clouds Using CloudSat Cloud Classification." Monthly Weather Review 140, no. 10 (2012): 3235–49. http://dx.doi.org/10.1175/mwr-d-11-00247.1.

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Abstract Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from sha
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Dissertations / Theses on the topic "CLOUDLET CLASSIFICATION"

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Nam, Christine C. W., and Johannes Quaas. "Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data." American Meteorological Society, 2012. https://ul.qucosa.de/id/qucosa%3A13468.

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Observations from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellites are used to evaluate clouds and precipitation in the ECHAM5 general circulation model. Active lidar and radar instruments on board CALIPSO and CloudSat allow the vertical distribution of clouds and their optical properties to be studied on a global scale. To evaluate the clouds modeled by ECHAM5 with CALIPSO and CloudSat, the lidar and radar satellite simulators of the Cloud Feedback Model Intercomparison Project’s Observation Simulator Package are used. Comparison of ECHAM5 w
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Nam, Christine C. W., Johannes Quaas, Roel Neggers, Drian Colombe Siegenthaler-Le, and Francesco Isotta. "Evaluation of boundary layer cloud parameterizations in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data." American Geophysical Union (AGU), 2014. https://ul.qucosa.de/id/qucosa%3A13458.

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Three different boundary layer cloud models are incorporated into the ECHAM5 general circulation model (GCM) and compared to CloudSat and CALIPSO satellite observations. The first boundary layer model builds upon the standard Tiedtke (1989) parameterization for shallow convection with an adapted convective trigger; the second is a bulk parameterization of the effects of transient shallow cumulus clouds; and lastly the Dual Mass Flux (DMF) scheme adjusted to better represent shallow convection. The three schemes improved (Sub)Tropical oceanic low-level cloud cover, however, the fraction of low-
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Simmel, Martin, Robert Reilein, Gudula Rünger, and Gerd Tetzlaff. "Parallele Strategien für ein spektrales Wolkenmodul in einem 3-dimensionalen Mesoskalenmodell." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 12 = Meteorologische Arbeiten aus Leipzig ; 4 (1999), S. 217-224, 1999. https://ul.qucosa.de/id/qucosa%3A15135.

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A spectral cloud model is developed for a 3-dimensional mesoscale model considering only the microphysical conversion processes of the warm cloud. Because of the expected computation requirements, which are strongly increased in relation to the bulk-parameterization, we develop concepts for the parallelization of the module, explain their applicability and present first results.<br>Für ein 3-dimensionales Mesoskalenmodell wird ein spektrales Wolkenmodul entwickelt, das zunächst nur die mikrophysikalischen Umwandlungsprozesse der warmen Wolke berücksichtigt. Aufgrund des zu erwartenden, im Verg
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CHAURASIA, DEEPAK. "COMPUTATIONAL OPTIMIZATION IN CLOUD COMPUTING." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15111.

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The arrival of cloud computing in recent years has attracted an interest from various users, organizations and institutes to take benefits of services and applications. Cloud computing offers on-demand availability of resources with high scalability. But as the number of internet users is increasing day by day, it gets quite difficult to handle the requests coming from millions of user. In such cases it is very common that the performance and availability of system goes down. This presented work discusses the design and implementation of system which does neural network based cloudlet cl
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Book chapters on the topic "CLOUDLET CLASSIFICATION"

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Benabbes, S., and S. M. Hemam. "An Approach Based on (Tasks-VMs) Classification and MCDA for Dynamic Load Balancing in the CloudIoT." In Lecture Notes in Networks and Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37207-1_41.

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Nasrollahi, Nasrin. "Reducing False Rain in Satellite Precipitation Products Using Cloudsat Cloud Classification Maps and Modis Multi-spectral Images." In Springer Theses. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12081-2_4.

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Conference papers on the topic "CLOUDLET CLASSIFICATION"

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Coelho, Willen Borges, Giovanni Comarela, and Rodolfo S. Villaça. "Early Detection and Classification of Malicious Activities in Network and Cloud Services." In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet). IEEE, 2024. https://doi.org/10.1109/cloudnet62863.2024.10815800.

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Shaikh, Shahzaib, and Manar Jammal. "A Multi-Stage Framework for Failure Prediction and Classification in Cloud Native Applications." In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet). IEEE, 2024. https://doi.org/10.1109/cloudnet62863.2024.10815849.

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Serra, Wendel, Warley Junior, Isaac Barros, Hugo Kuribayashi, and João Carmona. "Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment." In Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbcup.2021.16008.

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Due to the limited computing resources of drones, it is difficult to handle computation-intensive tasks locally, hence, fog-based computation offloading has been widely adopted. The effectiveness of an offloading operation, however, is determined by its ability to infer where the execution of code/data represents less computational effort for the drone, so that, by deciding where to offload correctly, the device benefits. Thus, this paper proposes MonDroneFog, a novel fog-based architecture that supports image offloading, as well as monitoring and storing the performance metrics related to the
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Piraisoody, Gajen, Changcheng Huang, Biswajit Nandy, and Nabil Seddigh. "Classification of applications in HTTP tunnels." In 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet). IEEE, 2013. http://dx.doi.org/10.1109/cloudnet.2013.6710559.

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ElKenawy, Ahmed S., and Sherif G. Aly. "An Enhanced Cloud-Native Deep Learning Pipeline for Network Traffic Classification." In 2022 IEEE 11th International Conference on Cloud Networking (CloudNet). IEEE, 2022. http://dx.doi.org/10.1109/cloudnet55617.2022.9978774.

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Kurapov, Anton, Danil Shamsimukhametov, Mikhail Liubogoshchev, and Evgeny Khorov. "CloudETC: a Privacy-Preserving Encrypted Traffic Classification Platform for QoS in Wi-Fi." In 2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE, 2023. http://dx.doi.org/10.1109/blackseacom58138.2023.10299740.

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Ding, Leah, Roberto Corizzo, Colin Bellinger, et al. "Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020783.

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Xu, Yi, Jie Hu, Zhiqiao Gao, and Jinpeng Chen. "UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification." In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2022. http://dx.doi.org/10.1109/ictai56018.2022.00210.

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Xie, Minghui, Qi Zhang, Shengbo Chen, and Fengli Zha. "A lithological classification method from fully polarimetric SAR data using Cloude-Pottier decomposition and SVM." In Applied Optics and Photonics China (AOPC2015), edited by Haimei Gong, Nanjian Wu, Yang Ni, Weibiao Chen, and Jin Lu. SPIE, 2015. http://dx.doi.org/10.1117/12.2196856.

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Cowan, Matthew, Joseph Lieberman, Jacob Cimbalista, and Bryan Schlake. "Electronic Freight Car Inspection Recording and Application of Internet-of-Things (IoT) and Machine-to-Machine (M2M) Frameworks." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6192.

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Freight railroad classification yards have been compared to large-scale manufacturing plants, with inbound trains as the inputs and outbound trains as the outputs. Railcars often take up to 24 hours to be processed through a railyard due to the need for manual inbound inspection, car classification, manual outbound inspection, and other intermediate processes. Much of the inspection and repair process has historically been completed manually with handwritten documents. Until recently, car inspections were rarely documented unless repairs were required. Currently, when a defect is detected in t
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Journal articles
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