Relevant bibliographies by topics / Mesh data

Academic literature on the topic 'Mesh data'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Mesh data"

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Hyeon, Dae-Hwan, and Taeg-Keun WhangBo. "Noise reduction for mesh smoothing of 3D mesh data." International Journal of Contents 5, no. 4 (December 28, 2009): 1–6. http://dx.doi.org/10.5392/ijoc.2009.5.4.001.

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Journeaux, Antoine Alexandre, Frederic Bouillault, and Jean Yves Roger. "Reducing the Cost of Mesh-to-Mesh Data Transfer." IEEE Transactions on Magnetics 50, no. 2 (February 2014): 437–40. http://dx.doi.org/10.1109/tmag.2013.2281461.

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Garimella, Rao V. "Mesh data structure selection for mesh generation and FEA applications." International Journal for Numerical Methods in Engineering 55, no. 4 (2002): 451–78. http://dx.doi.org/10.1002/nme.509.

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Kim, D., A. Bolat, and K. J. Li. "INDOOR SPATIAL DATA CONSTRUCTION FROM TRIANGLE MESH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 101–8. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-101-2018.

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<p><strong>Abstract.</strong> The 3D triangle mesh is widely used to represent indoor space. One of widely used methods of generating 3D triangle mesh data of indoor space is the construction from the point cloud collected using LIDAR. However, there are many problems in using generated triangle mesh data as a geometric representation of the indoor space. First, the number of triangles forming the triangle mesh is very large, which results in a bottleneck of the performance for storage and management. Second, no consideration on the properties of indoor space has been done by the previous work on mesh simplification for indoor geometric representation. Third, there is no research to construct indoor spatial standard data from triangle mesh data. For resolving these problems, we propose the a method for generating triangular mesh data for indoor geometric representation based in the observations mentioned above. First this method removes unnecessary objects and reduces the number of surfaces from the original fine-grained triangular mesh data using the properties of indoor space. Second, it also produces indoor geometric data in IndoorGML &amp;ndash; an OGC standard for indoor spatial data model. In experimental studies, we present a case study of indoor triangle mesh data from real world and compare results with raw data.</p>
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Bronsart, Robert. "Automatic Pre-Mesh CAD Data Repairing." International Journal of Mechanical Engineering and Applications 1, no. 1 (2013): 1. http://dx.doi.org/10.11648/j.ijmea.20130101.11.

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Young, P. G., D. Raymont, and V. Bui Xuan. "Mesh generation from biomedical scan data." Computer Methods in Biomechanics and Biomedical Engineering 12, sup1 (August 2009): 261. http://dx.doi.org/10.1080/10255840903097947.

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Wang, Jun, Jin Huang, Fu Lee Wang, Mingqiang Wei, Haoran Xie, and Jing Qin. "Data-driven Geometry-recovering Mesh Denoising." Computer-Aided Design 114 (September 2019): 133–42. http://dx.doi.org/10.1016/j.cad.2019.05.027.

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Gao, Yicong, Zixian Zhang, Yixiong Feng, Maria Savchenko, Ichiro Hagiwara, and Hao Zheng. "Flexible mesh morphing in sustainable design using data mining and mesh subdivision." Future Generation Computer Systems 108 (July 2020): 987–94. http://dx.doi.org/10.1016/j.future.2017.11.031.

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Meyer, Quirin, Benjamin Keinert, Gerd Sußner, and Marc Stamminger. "Data-Parallel Decompression of Triangle Mesh Topology." Computer Graphics Forum 31, no. 8 (October 4, 2012): 2541–53. http://dx.doi.org/10.1111/j.1467-8659.2012.03221.x.

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Fang, F., M. D. Piggott, C. C. Pain, G. J. Gorman, and A. J. H. Goddard. "An adaptive mesh adjoint data assimilation method." Ocean Modelling 15, no. 1-2 (January 2006): 39–55. http://dx.doi.org/10.1016/j.ocemod.2006.02.002.

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Dissertations / Theses on the topic "Mesh data"

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Karlsson, Simon. "A Data Collection Framework for Bluetooth Mesh Networks." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157660.

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This thesis presents a framework for collecting network traffic data usable in performance evaluations of Bluetooth Mesh networks. The framework is designed to be adaptive, effective, and efficient. These design goals are intended to minimize resource usage and thereby take constraints in Bluetooth Mesh into account. An implementation of the framework, based on the Bluetooth Mesh model concept, is also presented. The implementation is then validated and evaluated to analyse to what degree it fulfills the requirements of adaptive, effective, and efficient data collection. The evaluation demonstrates the importance of minimizing the size of the reports sent in the framework since larger messages sent with short intervals have a noticeable effect on both the packet delivery ratio of user traffic and the reporting latency. It is also shown that the adaptive reporting feature, that aims to reduce the effect of the framework on user traffic by postponing reporting during high traffic loads, has a positive effect on neighboring nodes overall packet delivery ratio.
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Lee, Kai-wah, and 李啟華. "Mesh denoising and feature extraction from point cloud data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664330.

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Lee, Kai-wah. "Mesh denoising and feature extraction from point cloud data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664330.

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Al, Shbat Sherin [Verfasser]. "Decoupling Mesh and Data Representations for Geo-spatial Data Visualization / Sherin Al Shbat." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2012. http://d-nb.info/1035265885/34.

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Cheung, Steven. "Packet routing on mesh-connected computers /." [Hong Kong] : University of Hong Kong, 1992. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13209607.

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Kruskall, Peter S. (Peter Stephen). "Collaborative internet and voice data transfer using bluetooth mesh networking." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/63013.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 61-62).
We present a new networking protocol, AirRAID, intended for wireless devices that, using the collective power of multiple devices within short-range communication sight, extends the availability of a secondary medium over an ad-hoc mesh network, resilient to the erratic movements of the mobile nodes from which it is comprised. We suggest improvements to the Bluetooth discovery algorithm, making use of a quantized hop velocity space to lower the probability of two devices missing each other completely during discovery, and introduce the concept of redundant backup paths to the wireless mesh, allowing for improved reliability in dynamic mesh network situations.
by Peter S. Kruskall.
M.Eng.
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Xiao, Fei. "Hexahedral Mesh Generation from Volumetric Data by Dual Interval Volume." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1532003347814656.

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Maglo, Adrien Enam. "Progressive and Random Accessible Mesh Compression." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00966180.

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Previous work on progressive mesh compression focused on triangle meshes but meshes containing other types of faces are commonly used. Therefore, we propose a new progressive mesh compression method that can efficiently compress meshes with arbitrary face degrees. Its compression performance is competitive with approaches dedicated to progressive triangle mesh compression. Progressive mesh compression is linked to mesh decimation because both applications generate levels of detail. Consequently, we propose a new simple volume metric to drive the polygon mesh decimation. We apply this metric to the progressive compression and the simplification of polygon meshes. We then show that the features offered by progressive mesh compression algorithms can be exploited for 3D adaptation by the proposition of a new framework for remote scientific visualization. Progressive random accessible mesh compression schemes can better adapt 3D mesh data to the various constraints by taking into account regions of interest. So, we propose two new progressive random-accessible algorithms. The first one is based on the initial segmentation of the input model. Each generated cluster is compressed independently with a progressive algorithm. The second one is based on the hierarchical grouping of vertices obtained by the decimation. The advantage of this second method is that it offers a high random accessibility granularity and generates one-piece decompressed meshes with smooth transitions between parts decompressed at low and high levels of detail. Experimental results demonstrate the compression and adaptation efficiency of both approaches.
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張治昌 and Steven Cheung. "Packet routing on mesh-connected computers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1992. http://hub.hku.hk/bib/B3121020X.

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Olsson, Rasmus, and Jens Egeland. "Reinforcement Learning Routing Algorithm for Bluetooth Mesh Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234287.

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Today’s office and home environments are moving towards more connected dig- ital infrastructures, meaning there are multiple heterogeneous devices that uses short-range communication to stay connected. Mobile phones, tablets, lap- tops, sensors, printers are examples of devices in such environments. From this, the Internet of Things (IoT) paradigm arises, and to enable it, energy efficient machine-to-machine (M2M) communications are needed. Our study will use Bluetooth Low Energy (BLE) technology for communication between devices, and it demonstrates the impact of routing algorithms in such networks. With the goal to increase the network lifetime, a distributed and dynamic Reinforce- ment Learning (RL) routing algorithm is proposed. The algorithm is based on a RL technique called Q-learning. Performance analysis is performed in different scenarios comparing the proposed algorithm against two static and centralized reference routing algorithms. The results show that our proposed RL routing algorithm performs better as the node degree of the topology increases. Com- pared to the reference algorithms the proposed algorithm can handle a higher load on the network with significant performance improvement, due to the dy- namic change of routes. The increase in network lifetime with 75 devices is 124% and 100 devices is 349%, because of the ability to change routes as time passes which is emphasized when the node degree increases. For 35, 55 and 75 devices the average node degrees are 2.21, 2.39 and 2.54. On a lower number of devices our RL routing algorithm performs nearly as good as the best refer- ence algorithm, the Energy Aware Routing (EAR) algorithm, with a decrease in network lifetime around 19% on 35 devices and 10% on 55 devices. A decrease in the network lifetime on lower number of devices is because of the cost for learning new paths is higher than the gain from exploring multiple paths.
Dagens kontors- och hemmiljöer rör sig mot mer sammankopplad digital in-frastruktur, vilket innebär att det finns många heterogena enheter som behöver kommunicera med varandra på korta avstånd. Mobiltelefoner, tablets, bärbara datorer, sensorer, skrivare är exempel på enheter i sådana miljöer. Utifrån detta uppkommer IoT, och för att möjliggöra det, behövs energieffektiva M2M kom-munikationslösningar. Vår studie kommer att anvanda BLE teknik för kommu-nikation mellan enheter, och den kommer att demonstrera effekterna av routing algoritmer i sådana nätverk. Med målet att öka livstiden för nätverket föreslås en distribuerad och dynamisk RL routing algoritm baserad på Q-learning. En jämförelse mellan den föreslagna algoritmen och de två statiska och centraliser-ade referensalgoritmerna görs i olika simulerings scenarier. Resultaten visar att vår föreslagna RL routing algoritm fungerar bättre när nod graden i topologin ökar. Jämfört med referensalgoritmerna kan den föreslagna algoritmen hantera en högre belastning på nätverket med betydande prestandaförbättring, tack vare den dynamiska förändringen av rutter som leder till en bättre belastningsbal-ans. Ökningen i nätverkslivstiden med 75 enheter är 124% och med 100 enheter är ökningen 349%, på grund av förmågan att byta rutter vilket syns tydligare när nodgraden ökar. För 35, 55 och 75 enheter är nodgraderna 2.21, 2.39 och 2.54. Vid ett lägre antal enheter presterar vår RL routing algoritm nästan lika bra som den bästa referensalgoritmen, EAR, med en minskning av nätverks livstiden på runt 19% med 35 enheter och 10% med 55 enheter. En minskning av nätverks livstiden på lägre antal enheter beror på att kostnaden för att lära sig nya vägar är högre än vinsten från att utforska flera vägar.
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Books on the topic "Mesh data"

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Pentz, Mike. Handling experimental data. Milton Keynes, [England]: Open University Press, 1988.

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Gilsinn, David. Constructing Sibson elements for a rectangular mesh. [Gaithersburg, Md.]: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Upton, Graham J. G. The analysis of cross-tabulated data. Ann Arbor, Mich: University Microfilms International, 1991.

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Automatic mesh generation: Application to finite element methods. Chichester: J. Wiley, 1991.

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Özturan, Can. Worst case complexity of parallel triangular mesh refinement by longest edge bisection. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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Goldsmith, Michael John. Data structures and other techniques for improving the performance of an advancing front mesh generator. Manchester: University of Manchester, 1996.

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McNally & co. [from old catalog] Rand. The 1988 Data Protection Act. 2nd ed. London: Stationery Office, 2000.

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Mavriplis, Dimitri. Parallel performance investigations of an unstructured mesh Navier-Stokes solver. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2000.

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Mavriplis, Dimitri. Large-scale parallel unstructured mesh computations for 3D high-lift analysis. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

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Mavriplis, Dimitri. An assessment of linear versus non-linear multigrid methods for unstructured mesh solver. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2001.

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Book chapters on the topic "Mesh data"

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Ern, Alexandre, and Jean-Luc Guermond. "Data Structuring and Mesh Generation." In Theory and Practice of Finite Elements, 337–56. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4355-5_7.

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Osborne, John D., Lihua (Julie) Zhu, Simon M. Lin, and Warren A. Kibbe. "Interpreting Microarray Results With Gene Ontology and MeSH." In Microarray Data Analysis, 223–41. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_14.

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Głut, Barbara, and Tomasz Jurczyk. "Mesh Adaptation Based on Discrete Data." In Parallel Processing and Applied Mathematics, 559–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_67.

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De Floriani, Leila, and Paola Magillo. "Multiresolution Mesh Representation: Models and Data Structures." In Mathematics and Visualization, 363–417. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04388-2_13.

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Qian, Guiping, Ruofeng Tong, Wen Peng, and Jinxing Dong. "Bayesian Mesh Reconstruction from Noisy Point Data." In Advances in Artificial Reality and Tele-Existence, 819–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941354_85.

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Ivanov, Anton, and Anastasia Perepelkina. "Zipped Data Structure for Adaptive Mesh Refinement." In Lecture Notes in Computer Science, 245–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86359-3_19.

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Mehta, Satya, and Rahul Taware. "Smart Traffic Control Using Bluetooth Mesh Technology." In Lecture Notes on Data Engineering and Communications Technologies, 373–85. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1002-1_39.

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Hasan, Ragib, Sumittra Chakraborti, Md Zonieed Hossain, Taukir Ahamed, Md Abdul Hamid, and M. F. Mridha. "Character and Mesh Optimization of Modern 3D Video Games." In Advances in Data and Information Sciences, 655–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0694-9_60.

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Mohamed, Fatma, Rasha M. Ismail, Nagwa L. Badr, and M. F. Tolba. "Optimized Elastic Query Mesh for Cloud Data Streams." In Computational Science and Its Applications -- ICCSA 2015, 367–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21404-7_27.

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Cirio, Gabriel, Guillaume Lavoué, and Florent Dupont. "Data-Driven Progressive Mesh Compression Using Associated Properties." In Communications in Computer and Information Science, 203–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25382-9_14.

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Conference papers on the topic "Mesh data"

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Garcia, Oscar, Jordi Sorribas, Enric Trullols, Joaquin del Rio, Joan Olive, Dulce Afonso, Eduardo Arilla, et al. "Data Acquisition in Oceanographic Research Vessels." In 2010 Third International Conference on Advances in Mesh Networks (MESH 2010). IEEE, 2010. http://dx.doi.org/10.1109/mesh.2010.25.

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Geibig, Joanna. "Availability of Data in Locality-Aware Unreliable Networks." In 2009 Second International Conference on Advances in Mesh Networks (MESH). IEEE, 2009. http://dx.doi.org/10.1109/mesh.2009.24.

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Raptis, P., V. Vitsas, P. Chatzimisios, and K. Paparrizos. "Voice and Data Traffic Analysis in IEEE 802.11 DCF Infrastructure WLANs." In 2009 Second International Conference on Advances in Mesh Networks (MESH). IEEE, 2009. http://dx.doi.org/10.1109/mesh.2009.14.

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Akleman, Ergun, Shenyao Ke, and You Wu. "Physical mesh data structures." In SIGGRAPH '16: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897839.2927462.

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Singhi, Mayank, Ying Ding, and Yuyin Sun. "Muzk Mesh: Interlinking Semantic Music Data." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.162.

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Weber, Gunther H., Hans Hagen, Bernd Hamann, Kenneth I. Joy, Terry J. Ligocki, Kwan-Liu Ma, and John M. Shalf. "Visualization of adaptive mesh refinement data." In Photonics West 2001 - Electronic Imaging, edited by Robert F. Erbacher, Philip C. Chen, Jonathan C. Roberts, Craig M. Wittenbrink, and Matti Grohn. SPIE, 2001. http://dx.doi.org/10.1117/12.424922.

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Kim, Tae Hyun, Jian Ni, and Nitin H. Vaidya. "A Distributed Throughput-Optimal CSMA with Data Packet Collisions." In 2010 Fifth IEEE Workshop On Wireless Mesh Networks. IEEE, 2010. http://dx.doi.org/10.1109/wimesh.2010.5507899.

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Jurgens, Martin, Dennis Meis, Dominik Mollers, Felix Nolte, Etienne Stork, Gottfried Vossen, Christian Werner, and Hendrik Winkelmann. "Bluetooth Mesh Networks for Indoor Localization." In 2019 20th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2019. http://dx.doi.org/10.1109/mdm.2019.00-13.

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Sharif, A. M., and A. N. Barrett. "Utilising knowledge for optimum mesh design." In IEE Two-day Colloquium on Knowledge Discovery and Data Mining. IEE, 1998. http://dx.doi.org/10.1049/ic:19980547.

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Shibalabala, Jeremiah, and Theo G. Swart. "Performance Analysis of Wireless Mesh Networks for Underground Mines." In 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2020. http://dx.doi.org/10.1109/icabcd49160.2020.9183849.

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Reports on the topic "Mesh data"

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Kim, J. J. Adaptive Mesh in Visualization of Terrain Elevation Data. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255481.

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Schaaf, Crystal B., Joan M. Ward, H. S. Muench, Robert P. d'Entremont, and Michael K. Griffin. The Hemispheric Eighth Mesh Terrain Elevation and Geography Data Sets. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada231592.

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Ajuha, S. A Full Mesh ATCA-based General Purpose Data Processing Board (Pulsar II). Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1431570.

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Sandstrom, Mary M., Geoffrey W. Brown, Daniel N. Preston, Colin J. Pollard, Kirstin F. Warner, Daniel N. Sorenson, Daniel L. Remmers, et al. Integrated Data Collection Analysis (IDCA) Program — KClO3/Icing Sugar (-100 mesh) Mixture. Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1305846.

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Zhu, Yue, Timothy C. Johnson, and Zhuanfang Zhang. A New Capability of E4D For 3D Parallel Joint Inversion of DC Resistivity And Traveltime Data on Unstructured Mesh. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1633415.

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Jackie M. Williams, Jay A. Sampson, and Brian D. Rodriguez. Magnetotelluric Data, Rainier Mesa/Shoshone Mountain, Nevada Test Site, Nevada. Office of Scientific and Technical Information (OSTI), November 2006. http://dx.doi.org/10.2172/895290.

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Jasoni, Richard L., Jessica D. Larsen, Brad F. Lyles, John M. Healey, Clay A. Cooper, Ronald L. Hershey, and Karen J. Lefebre. Data Report: Meteorological and Evapotranspiration Data from Sagebrush and Pinyon Pine/Juniper Communities at Pahute Mesa, Nevada National Security Site, 2011-2012. Office of Scientific and Technical Information (OSTI), April 2013. http://dx.doi.org/10.2172/1078148.

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Guy A. DeMeo, Alan L. Flint, Randell J. Laczniak, and Walter E. Nylund. Micrometeorological and Soil Data for Calculating Evapotranspiration for Rainier Mesa, Nevada Test Site, Nevada 2002-05. US: United States Geological Survey - Nevada, Henderson, Nevada, December 2006. http://dx.doi.org/10.2172/896768.

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Job, Jacob. Mesa Verde National Park: Acoustic monitoring report. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286703.

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In 2015, the Natural Sounds and Night Skies Division (NSNSD) received a request to collect baseline acoustical data at Mesa Verde National Park (MEVE). Between July and August 2015, as well as February and March 2016, three acoustical monitoring systems were deployed throughout the park, however one site (MEVE002) stopped recording after a couple days during the summer due to wildlife interference. The goal of the study was to establish a baseline soundscape inventory of backcountry and frontcountry sites within the park. This inventory will be used to establish indicators and thresholds of soundscape quality that will support the park and NSNSD in developing a comprehensive approach to protecting the acoustic environment through soundscape management planning. Additionally, results of this study will help the park identify major sources of noise within the park, as well as provide a baseline understanding of the acoustical environment as a whole for use in potential future comparative studies. In this deployment, sound pressure level (SPL) was measured continuously every second by a calibrated sound level meter. Other equipment included an anemometer to collect wind speed and a digital audio recorder collecting continuous recordings to document sound sources. In this document, “sound pressure level” refers to broadband (12.5 Hz–20 kHz), A-weighted, 1-second time averaged sound level (LAeq, 1s), and hereafter referred to as “sound level.” Sound levels are measured on a logarithmic scale relative to the reference sound pressure for atmospheric sources, 20 μPa. The logarithmic scale is a useful way to express the wide range of sound pressures perceived by the human ear. Sound levels are reported in decibels (dB). A-weighting is applied to sound levels in order to account for the response of the human ear (Harris, 1998). To approximate human hearing sensitivity, A-weighting discounts sounds below 1 kHz and above 6 kHz. Trained technicians calculated time audible metrics after monitoring was complete. See Methods section for protocol details, equipment specifications, and metric calculations. Median existing (LA50) and natural ambient (LAnat) metrics are also reported for daytime (7:00–19:00) and nighttime (19:00–7:00). Prominent noise sources at the two backcountry sites (MEVE001 and MEVE002) included vehicles and aircraft, while building and vehicle predominated at the frontcountry site (MEVE003). Table 1 displays time audible values for each of these noise sources during the monitoring period, as well as ambient sound levels. In determining the current conditions of an acoustical environment, it is informative to examine how often sound levels exceed certain values. Table 2 reports the percent of time that measured levels at the three monitoring locations were above four key values.
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10

Acred, Aleksander, Milena Devineni, and Lindsey Blake. Opioid Free Anesthesia to Prevent Post Operative Nausea/Vomiting. University of Tennessee Health Science Center, July 2021. http://dx.doi.org/10.21007/con.dnp.2021.0006.

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Purpose The purpose of this study is to compare the incidence of post-operative nausea and vomiting (PONV) in opioid-utilizing and opioid-free general anesthesia. Background PONV is an extremely common, potentially dangerous side effect of general anesthesia. PONV is caused by a collection of anesthetic and surgical interventions. Current practice to prevent PONV is to use 1-2 antiemetics during surgery, identify high risk patients and utilize tracheal intubation over laryngeal airways when indicated. Current research suggests minimizing the use of volatile anesthetics and opioids can reduce the incidence of PONV, but this does not reflect current practice. Methods In this scoping review, the MeSH search terms used to collect data were “anesthesia”, “postoperative nausea and vomiting”, “morbidity”, “retrospective studies”, “anesthesia, general”, “analgesics, opioid”, “pain postoperative”, “pain management” and “anesthesia, intravenous”. The Discovery Search engine, AccessMedicine and UpToDate were the search engines used to research this data. Filters were applied to these searches to ensure all the literature was peer-reviewed, full-text and preferably from academic journals. Results Opioid free anesthesia was found to decrease PONV by 69%. PONV incidence was overwhelming decreased with opioid free anesthesia in every study that was reviewed. Implications The future direction of opioid-free anesthesia and PONV prevention are broad topics to discuss, due to the nature of anesthesia. Administration of TIVA, esmolol and ketamine, as well as the decision to withhold opioids, are solely up to the anesthesia provider’s discretion. Increasing research and education in the importance of opioid-free anesthesia to decrease the incidence of PONV will be necessary to ensure anesthesia providers choose this protocol in their practice.
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