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Статті в журналах з теми "3D Networks"
Liang, Long, Christopher Jones, Shaohua Chen, Bo Sun, and Yang Jiao. "Heterogeneous force network in 3D cellularized collagen networks." Physical Biology 13, no. 6 (October 25, 2016): 066001. http://dx.doi.org/10.1088/1478-3975/13/6/066001.
Повний текст джерелаGou, Pingzhang, Baoyong Guo, Miao Guo, and Shun Mao. "VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm." Sensors 23, no. 2 (January 4, 2023): 573. http://dx.doi.org/10.3390/s23020573.
Повний текст джерелаLeng, Biao, Yu Liu, Kai Yu, Xiangyang Zhang, and Zhang Xiong. "3D object understanding with 3D Convolutional Neural Networks." Information Sciences 366 (October 2016): 188–201. http://dx.doi.org/10.1016/j.ins.2015.08.007.
Повний текст джерелаChaaban, Fadi, Hanan Darwishe, and Jamal El Khattabi. "A Semi-Automatic Approach in GIS for 3D Modeling and Visualization of Utility Networks: Application for Sewer & Stormwater networks." MATEC Web of Conferences 295 (2019): 02003. http://dx.doi.org/10.1051/matecconf/201929502003.
Повний текст джерелаWang, Shaohua, Yeran Sun, Yinle Sun, Yong Guan, Zhenhua Feng, Hao Lu, Wenwen Cai, and Liang Long. "A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks." ISPRS International Journal of Geo-Information 8, no. 10 (October 8, 2019): 441. http://dx.doi.org/10.3390/ijgi8100441.
Повний текст джерелаHUANG, MING, JINGJING YANG, ZHE XIAO, JUN SUN, and JINHUI PENG. "MODELING THE DIELECTRIC RESPONSE IN HETEROGENEOUS MATERIALS USING 3D RC NETWORKS." Modern Physics Letters B 23, no. 25 (October 10, 2009): 3023–33. http://dx.doi.org/10.1142/s0217984909021090.
Повний текст джерелаFries, David, and Geran Barton. "3D MICROSENSOR IMAGING ARRAYS NETWORKS." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2015, DPC (January 1, 2015): 000348–78. http://dx.doi.org/10.4071/2015dpc-ta33.
Повний текст джерелаJeong, Cheol, and Won-Yong Shin. "Capacity of 3D Erasure Networks." IEEE Transactions on Communications 64, no. 7 (July 2016): 2900–2912. http://dx.doi.org/10.1109/tcomm.2016.2569580.
Повний текст джерелаBerber, Mustafa, Petr Vaníček, and Peter Dare. "Robustness analysis of 3D networks." Journal of Geodynamics 47, no. 1 (January 2009): 1–8. http://dx.doi.org/10.1016/j.jog.2008.02.001.
Повний текст джерелаThomas, Edwin L. "Nanoscale 3D ordered polymer networks." Science China Chemistry 61, no. 1 (December 13, 2017): 25–32. http://dx.doi.org/10.1007/s11426-017-9138-5.
Повний текст джерелаДисертації з теми "3D Networks"
Stigeborn, Patrik. "Generating 3D-objects using neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230668.
Повний текст джерелаCosta, Breno Jacinto Duarte da. "3D Routing with Context Awareness." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/1771.
Повний текст джерелаConselho Nacional de Desenvolvimento Científico e Tecnológico
O surgimento de interfaces de rede sem-fio de baixo custo no mercado e o crescimento na demanda por dispositivos móveis (como Smartphones, PDAs, Internet Tablets e Laptops) permitiram a criação de cenários onde serviços de rede para usuários móveis possam existir sem nenhuma infra-estrutrutura pré-configurada. No entanto, a interoperabilidade entre tais redes, que são dinâmicas e heterogêneas, é atualmente objeto de pesquisa. Várias pesquisas na área de redes ad hoc sem-fio tem focado em uma única tecnologia sem-fio, baseada no padrão IEEE 802.11, onde os nós da rede são vistos de maneira plana (2D), ou seja, como elementos homogêneos, identificados apenas por endereços IP, não levando em consideração seus perfis de hardware e tecnologias de rede. Desta forma, pesquisas envolvendo mais de uma tecnologia de rede encontram-se em estágios iniciais. Novas propostas são necessárias para estes cenários, que são cada vez mais comuns, envolvendo múltiplos dispositivos com múltiplas interfaces de rede (multi-homed). Este trabalho propõe o protocolo de roteamento 3D, direcionado a cenários onde há heterogeneidade de dispositivos e tecnologias de rede. O objetivo do protocolo de roteamento proposto é prover mecanismos para a interoperabilidade de redes ad hoc heterogêneas, considerando outra dimensão de informações, aqui denominada de terceira dimensão (3D), que consiste em agregar mais informações, como informações de contexto, recursos dos dispositivos e interfaces de rede, ao processo de roteamento. Para isto, o protocolo considera os seguintes aspectos fundamentais: o processo de bootstrapping da rede heterogênea e dos nós, a construção e disseminação de informações de ciência de contexto entre os nós, e a atribuição de papéis específicos para determinados nós da rede. A avaliação do protocolo é feita através de experimentos em um test-bed real, utilizando um protótipo da implementação do protocolo, num cenário composto de dispositivos móveis como Smartphones OpenMoko, Internet Tablets N810 da Nokia e Laptops, possuindo tecnologias Bluetooth e 802.11, executando versões embarcadas do sistema operacional Linux
Zhao, Yao. "Autonomous Localization in 3D Surface Wireless Sensor Networks." Thesis, University of Louisiana at Lafayette, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3622968.
Повний текст джерелаLocation awareness is imperative for a variety of sensing applications and network operations. Although a diversity of GPS-less and GPS-free solutions have been developed recently for autonomous localization in wireless sensor networks, they primarily target at 2D planar or 3D volumetric settings. There exists unique and fundamental hardness to extend them to 3D surfaces.
The contributions of this work are twofold. First, it proposes a theoretically-proven algorithm for the 3D surface localization problem. Seeing the challenges to localize general 3D surface networks and the solvability of the localization problem on single-value (SV) surface, this work proposes the cut-and-sew algorithm that takes a divide-and-conquer approach by partitioning a general 3D surface network into SV patches, which are localized individually and then merged into a unified coordinates system. The algorithm is optimized by discovering the minimum SV partition, an optimal partition that creates a minimum set of SV patches.
Second, it develops practically-viable solutions for real-world sensor network settings where the inputs are often noisy. The proposed algorithm is implemented and evaluated via simulations and experiments in an indoor testbed. The results demonstrate that the proposed cut-and-sew algorithm achieves perfect 100% localization rate and the desired robustness against measurement errors.
Cronje, Frans. "Human action recognition with 3D convolutional neural networks." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15482.
Повний текст джерелаNihlén, Ramström Max. "Sketch to 3D Model using Generative Query Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251507.
Повний текст джерелаFör digitala artister och animatörer är processen att gå ifrån en idé i form av en sketch till en färdig 3D-modell tidskrävande och sträcker sig över en mängd olika mjukvaror. Detta arbete presenterar en Generativ Modell som direkt kan generera bilder av en 3D-modell ifrån sketchade bilder i 2D. Modellen är baserad på Generative Query Networks och två olika Generativa Modeller testades för att generera nya bilder, den första en Variational Auto Encoder och den andra en Generative Adversarial Network. Modellen lär sig att skapa nya bilder ifrån godtyckliga synvinklar vilket tillåter den att utföra så kallad mental rotation av ett objekt på samma sätt som om en 3D-modell hade genererats. För att kunna träna modellen skapades ett dataset där bilder sparades både i ursprungs- samt i sketchform tillsammans med synvinklarna där bilderna tagits ifrån. Modellen som använde sig av en Variational Auto Encoder visade sig kunna generera trovärdiga bilder efter att endast ha observerat en sketch medan modellen som använde ett Generative Adversarial Network misslyckades med att betinga de genererade bilderna på de sketcher den observerat.
Mohib, Hamdullah. "End-to-end 3D video communication over heterogeneous networks." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8293.
Повний текст джерелаBirgersson, Anna, and Klara Hellgren. "Texture Enhancement in 3D Maps using Generative Adversarial Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162446.
Повний текст джерелаNguyen, Thu Duc. "System support for distributed 3D real-time rendering on commodity clusters /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/7018.
Повний текст джерелаAhmad, Waqar. "Core Switching Noise for On-Chip 3D Power Distribution Networks." Doctoral thesis, KTH, Elektroniksystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103566.
Повний текст джерелаQC 20121015
Nordhus, Lars Espen Strand. "Ray Tracing for Simulation of Wireless Networks in 3D Scenes." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23002.
Повний текст джерелаКниги з теми "3D Networks"
1966-, O'Driscoll Tony, ed. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Pfeiffer, 2010.
Знайти повний текст джерелаKapp, Karl M. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Pfeiffer, 2010.
Знайти повний текст джерелаKapp, Karl M. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Jossey-Bass, 2010.
Знайти повний текст джерелаBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
Знайти повний текст джерелаInternational, Workshop on Laser and Fiber-optical Networks Modeling (3rd 2001 Kharkiv Ukraine). LFNM'2001: Proceedings of 3d International Workshop on Laser and Fiber-optical Networks Modeling : Kharkiv State University of Radio Electronics : Ukraine, May 23, 2000. Piscataway, New Jersey: IEEE, 2001.
Знайти повний текст джерелаCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2013 Florence. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-372-4.
Повний текст джерелаKim, Moon S. Defense and security 2008: Special sessions on food safety, visual analytics, resource restricted embedded and sensor networks, and 3D imaging and display : 17-18 March 2008, Orlando, Florida, USA. Edited by Society of Photo-optical Instrumentation Engineers. Bellingham, Wash: SPIE, 2008.
Знайти повний текст джерелаTatas, Konstantinos, Kostas Siozios, Dimitrios Soudris, and Axel Jantsch. Designing 2D and 3D Network-on-Chip Architectures. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-4274-5.
Повний текст джерелаCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2014 Florence. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-573-5.
Повний текст джерелаFrega, Monica. Neuronal Network Dynamics in 2D and 3D in vitro Neuroengineered Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30237-9.
Повний текст джерелаЧастини книг з теми "3D Networks"
Bellocchio, Francesco, N. Alberto Borghese, Stefano Ferrari, and Vincenzo Piuri. "Hierarchical Radial Basis Functions Networks." In 3D Surface Reconstruction, 77–110. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5632-2_5.
Повний текст джерелаJagtap, Yash, Hitesh Shewale, Dinesh Bhadane, and M. V. Rao. "3D Smart Map." In Lecture Notes in Networks and Systems, 527–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3812-9_54.
Повний текст джерелаJiang, Hao, and Guan Gui. "3D Scattering Channel Modeling for Microcell Communication Environments." In Wireless Networks, 41–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32869-6_3.
Повний текст джерелаAkdere, Mert, Uğur Çetintemel, Daniel Crispell, John Jannotti, Jie Mao, and Gabriel Taubin. "Data-Centric Visual Sensor Networks for 3D Sensing." In GeoSensor Networks, 131–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79996-2_8.
Повний текст джерелаBorzemski, Leszek, and Anna Kamińska-Chuchmała. "3D Web Performance Forecasting Using Turning Bands Method." In Computer Networks, 102–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21771-5_12.
Повний текст джерелаGardikis, Georgios, Evangelos Pallis, and Michael Grafl. "Media-Aware Networks in Future Internet Media." In 3D Future Internet Media, 105–12. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8373-1_7.
Повний текст джерелаMensing, Glennys, and David J. Beebe. "Liquid Phase 3D Channel Networks." In Micro Total Analysis Systems 2002, 410–12. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0295-0_137.
Повний текст джерелаTigadoli, Rishabh, Ramesh Ashok Tabib, Adarsh Jamadandi, and Uma Mudenagudi. "3D-GCNN - 3D Object Classification Using 3D Grid Convolutional Neural Networks." In Lecture Notes in Computer Science, 269–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34869-4_30.
Повний текст джерелаPiorkowski, Adam, Lukasz Jajesnica, and Kamil Szostek. "Creating 3D Web-Based Viewing Services for DICOM Images." In Computer Networks, 218–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_26.
Повний текст джерелаSkabek, Krzysztof, and Łukasz Ząbik. "Network Transmission of 3D Mesh Data Using Progressive Representation." In Computer Networks, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_38.
Повний текст джерелаТези доповідей конференцій з теми "3D Networks"
Marquez, Alejandra, and Alex Cuadros. "3D Medical Image Segmentation based on 3D Convolutional Neural Networks." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812031.
Повний текст джерелаAn, Dong, Tianxu Xu, Yiwen Zhang, and Yang Yue. "Hand Gesture Recognition Using ToF Camera and 3D Point Cloud Networks." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.jw4b.56.
Повний текст джерелаDulikravich, George S., and Thomas J. Martin. "Optimization of 3D Branching Networks of Microchannels for Microelectronic Device Cooling." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22719.
Повний текст джерелаBerhan, L., C. W. Wang, and A. M. Sastry. "Damage Initiation in Bonded Particulate Networks: 3D Simulations." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/ad-25304.
Повний текст джерелаZhao, Yongheng, Tolga Birdal, Haowen Deng, and Federico Tombari. "3D Point Capsule Networks." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00110.
Повний текст джерелаCha, Geonho, Minsik Lee, and Songhwai Oh. "Unsupervised 3D Reconstruction Networks." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00395.
Повний текст джерелаQamar, Isabel P. S., and Richard S. Trask. "Development of Multi-Dimensional 3D Printed Vascular Networks for Self-Healing Materials." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3829.
Повний текст джерелаGöhnert, Tilman, Sabrina Ziebarth, Henrik Detjen, Tobias Hecking, and H. Ulrich Hoppe. "3D DynNetVis." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2808798.
Повний текст джерелаGraham, Ben. "Sparse 3D convolutional neural networks." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.150.
Повний текст джерелаJin, Shengmin, and Reza Zafarani. "Representing Networks with 3D Shapes." In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00033.
Повний текст джерелаЗвіти організацій з теми "3D Networks"
Santoyo, C., M. R. Ceron, and M. M. Biener. Integration of Fullerenes as Electron-Acceptors in 3D Graphene Networks. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1567989.
Повний текст джерелаDe Crescenzi, Maurizio. 3D Carbon Nanotube Networks as Mechanical, Electrical and Photovoltaic Transducer and Superhydrophobic Filter. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada621229.
Повний текст джерелаWang, Shiren. Proof-of-Concept: Assembling Carbon Nanocrystals for Ordered 3D Network. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada566278.
Повний текст джерелаHuang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-017.
Повний текст джерелаKHVOENKOVA, Nina, and Matthieu DELORME. An Optimal Method to Model Transient Flows in 3D Discrete Fracture Network. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0088.
Повний текст джерелаKompaniets, Alla, Hanna Chemerys, and Iryna Krasheninnik. Using 3D modelling in design training simulator with augmented reality. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3740.
Повний текст джерелаMuto, Kazuo. Trend of 3D CAD/CAE/CAM/CAT/Network Systems and PLM System in Advance Technology for Manufacturing Engineering Development. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0137.
Повний текст джерелаHabib, Ayman, Darcy M. Bullock, Yi-Chun Lin, and Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
Повний текст джерелаKirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
Повний текст джерела