Academic literature on the topic '3D Networks'
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Journal articles on the topic "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.
Full textGou, 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.
Full textLeng, 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.
Full textChaaban, 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.
Full textWang, 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.
Full textHUANG, 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.
Full textFries, 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.
Full textJeong, 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.
Full textBerber, 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.
Full textThomas, 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.
Full textDissertations / Theses on the topic "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.
Full textCosta, Breno Jacinto Duarte da. "3D Routing with Context Awareness." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/1771.
Full textConselho 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.
Full textLocation 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.
Full textNihlén, 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.
Full textFör 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.
Full textBirgersson, 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.
Full textNguyen, 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.
Full textAhmad, 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.
Full textQC 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.
Full textBooks on the topic "3D Networks"
1966-, O'Driscoll Tony, ed. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Pfeiffer, 2010.
Find full textKapp, Karl M. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Pfeiffer, 2010.
Find full textKapp, Karl M. Learning in 3D: Adding a new dimension to enterprise learning and collaboration. San Francisco, CA: Jossey-Bass, 2010.
Find full textBrath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
Find full textInternational, 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.
Find full textCappellini, 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.
Full textKim, 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.
Find full textTatas, 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.
Full textCappellini, 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.
Full textFrega, 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.
Full textBook chapters on the topic "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.
Full textJagtap, 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.
Full textJiang, 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.
Full textAkdere, 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.
Full textBorzemski, 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.
Full textGardikis, 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.
Full textMensing, 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.
Full textTigadoli, 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.
Full textPiorkowski, 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.
Full textSkabek, 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.
Full textConference papers on the topic "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.
Full textAn, 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.
Full textDulikravich, 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.
Full textBerhan, 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.
Full textZhao, 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.
Full textCha, 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.
Full textQamar, 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.
Full textGöhnert, 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.
Full textGraham, 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.
Full textJin, 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.
Full textReports on the topic "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.
Full textDe 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.
Full textWang, 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.
Full textHuang, 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.
Full textKHVOENKOVA, 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.
Full textKompaniets, 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.
Full textMuto, 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.
Full textHabib, 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.
Full textKirchhoff, 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.
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