Literatura académica sobre el tema "Spatio temporal networks"
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Artículos de revistas sobre el tema "Spatio temporal networks"
Williams, Matthew J. y Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness". Royal Society Open Science 3, n.º 6 (junio de 2016): 160196. http://dx.doi.org/10.1098/rsos.160196.
Texto completoQIN, Chao y Xiaoguang GAO. "Spatio-Temporal Generative Adversarial Networks". Chinese Journal of Electronics 29, n.º 4 (1 de julio de 2020): 623–31. http://dx.doi.org/10.1049/cje.2020.04.001.
Texto completoChao, Qin y Gao Xiaoguang. "Distributed spatio-temporal generative adversarial networks". Journal of Systems Engineering and Electronics 31, n.º 3 (junio de 2020): 578–92. http://dx.doi.org/10.23919/jsee.2020.000026.
Texto completoPichardo-Corpus, J. A., H. A. Solano Lamphar, R. Lopez-Farias y O. Delgadillo Ruiz. "Spatio-temporal networks of light pollution". Journal of Quantitative Spectroscopy and Radiative Transfer 253 (septiembre de 2020): 107068. http://dx.doi.org/10.1016/j.jqsrt.2020.107068.
Texto completoGao, Nan, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman y Flora D. Salim. "Generative Adversarial Networks for Spatio-temporal Data: A Survey". ACM Transactions on Intelligent Systems and Technology 13, n.º 2 (30 de abril de 2022): 1–25. http://dx.doi.org/10.1145/3474838.
Texto completoYang, Zhaoqilin, Gaoyun An y Ruichen Zhang. "STSM: Spatio-Temporal Shift Module for Efficient Action Recognition". Mathematics 10, n.º 18 (10 de septiembre de 2022): 3290. http://dx.doi.org/10.3390/math10183290.
Texto completoSchutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut y Markus Reischl. "Strategies for supplementing recurrent neural network training for spatio-temporal prediction". at - Automatisierungstechnik 67, n.º 7 (26 de julio de 2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.
Texto completoTempelmeier, Nicolas, Udo Feuerhake, Oskar Wage y Elena Demidova. "Mining Topological Dependencies of Recurrent Congestion in Road Networks". ISPRS International Journal of Geo-Information 10, n.º 4 (8 de abril de 2021): 248. http://dx.doi.org/10.3390/ijgi10040248.
Texto completoZhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng y Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.
Texto completoLi, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang y Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction". ACM Transactions on Intelligent Systems and Technology 13, n.º 2 (30 de abril de 2022): 1–21. http://dx.doi.org/10.1145/3510829.
Texto completoTesis sobre el tema "Spatio temporal networks"
Moradi, Mohammad Mehdi. "Spatial and spatio-temporal point patterns on linear networks". Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/664140.
Texto completoThe last decade witnessed an extraordinary increase in interest in the analysis of network related data and trajectories. This pervasive interest is partly caused by a strongly expanded availability of such datasets. In the spatial statistics field, there are numerous real examples such as the locations of traffic accidents and geo-coded locations of crimes in the streets of cities that need to restrict the support of the underlying process over such linear networks to set and define a more realistic scenario. Examples of trajectories are the path taken by moving objects such as taxis, human beings, animals, etc. Intensity estimation on a network of lines, such as a road network, seems to be a surprisingly complicated task. Several techniques published in the literature, in geography and computer science, have turned out to be erroneous. We propose several adaptive and non-adaptive intensity estimators, based on kernel smoothing and Voronoi tessellation. Theoretical properties such as bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation, and adaptive smoothing are discussed. Moreover, their statistical performance is studied through simulation studies and is compared with existing methods. Adding the temporal component, we also consider spatio-temporal point patterns with spatial locations restricted to a linear network. We present a nonparametric kernel-based intensity estimator and develop second-order characteristics of spatio-temporal point processes on linear networks such as K-function and pair correlation function to analyse the type of interaction between points. In terms of trajectories, we introduce the R package trajectories that contains different classes and methods to handle, summarise and analyse trajectory data. Simulation and model fitting, intensity estimation, distance analysis, movement smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago (US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın (Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).
O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks". Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.
Texto completoSutherland, Connie. "Spatio-temporal feedback in stochastic neural networks". Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.
Texto completoMitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks". Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.
Texto completoAkbarzadeh, Vahab. "Spatio-temporal coverage optimization of sensor networks". Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/27065.
Texto completoSensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks.
Dondo, C. "Bayesian networks for spatio-temporal integrated catchment assessment". Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10327.
Texto completoIncludes bibliographical references (leaves 181-203).
In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.
YEGHIKYAN, Gevorg. "Urban Structure and Mobility as Spatio-temporal complex Networks". Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/94477.
Texto completoSu, Jionglong. "Online predictions for spatio-temporal systems using time-varying RBF networks". Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578701.
Texto completoSturrock, Marc. "Spatio-temporal modelling of gene regulatory networks containing negative feedback loops". Thesis, University of Dundee, 2013. https://discovery.dundee.ac.uk/en/studentTheses/b824506e-d515-442a-b9dc-ff82568f3c09.
Texto completoHolm, Noah y Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks". Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.
Texto completoLibros sobre el tema "Spatio temporal networks"
George, Betsy y Sangho Kim. Spatio-temporal Networks. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4918-8.
Texto completoGeorge, Betsy. Spatio-temporal Networks: Modeling and Algorithms. New York, NY: Springer New York, 2013.
Buscar texto completoSpatio-temporal narratives: Historical GIS and the study of global trading networks (1500-1800). Newcastle upon Tyne: Cambridge Scholars Publishing, 2014.
Buscar texto completoZhongguo chan ye ji qun shi kong fa zhan yan jiu: Spatio-temporal development study on industrial clusters in China. Beijing Shi: Jing ji guan li chu ban she, 2011.
Buscar texto completoSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Buscar texto completoSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Buscar texto completoSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Buscar texto completoBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Buscar texto completoBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Buscar texto completoBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Buscar texto completoCapítulos de libros sobre el tema "Spatio temporal networks"
Manganaro, Gabriele, Paolo Arena y Luigi Fortuna. "Spatio-temporal Phenomena". En Cellular Neural Networks, 105–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60044-9_5.
Texto completoHolan, Scott H. y Christopher K. Wikle. "Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data". En Spatio-Temporal Design, 269–84. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118441862.ch12.
Texto completoGeorge, Betsy y Sangho Kim. "Spatio-temporal Networks: An Introduction". En SpringerBriefs in Computer Science, 1–6. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4918-8_1.
Texto completoHuaien, Luo y Sadasivan Puthusserypady. "Neural Networks for fMRI Spatio-temporal Analysis". En Neural Information Processing, 1292–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_201.
Texto completoBabloyantz, A. y J. A. Sepulchre. "Information Processing by Spatio-temporal Chaotic Networks". En ICANN ’93, 670–75. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_183.
Texto completoCozza, Vittoria, Antonio Messina, Danilo Montesi, Luca Arietta y Matteo Magnani. "Spatio-Temporal Keyword Queries in Social Networks". En Advances in Databases and Information Systems, 70–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40683-6_6.
Texto completoGunturi, Viswanath, Shashi Shekhar y Arnab Bhattacharya. "Minimum Spanning Tree on Spatio-Temporal Networks". En Lecture Notes in Computer Science, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15251-1_11.
Texto completoSteer, Kelly y Joseph G. Vella. "Link Prediction Based on Spatio-Temporal Networks". En Information Systems and Management Science, 228–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86223-7_20.
Texto completoCho, Sangwoo y Hassan Foroosh. "Spatio-Temporal Fusion Networks for Action Recognition". En Computer Vision – ACCV 2018, 347–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_22.
Texto completoMartirosyan, Anahit y Azzedine Boukerche. "Spatio-temporal Context in Wireless Sensor Networks". En Monographs in Theoretical Computer Science, 293–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14849-1_10.
Texto completoActas de conferencias sobre el tema "Spatio temporal networks"
Khan, Shujaat, Jawwad Ahmad, Alishba Sadiq, Imran Naseem y Muhammad Moinuddin. "Spatio-Temporal RBF Neural Networks". En 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE, 2018. http://dx.doi.org/10.1109/iceest.2018.8643322.
Texto completoRao, Aniruddha Rajendra, Qiyao Wang, Haiyan Wang, Hamed Khorasgani y Chetan Gupta. "Spatio-Temporal Functional Neural Networks". En 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00020.
Texto completoHerzig, Roei, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson y Trevor Darrell. "Spatio-Temporal Action Graph Networks". En 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00288.
Texto completoMaiti, Rajib, Arobinda Gupta y Niloy Ganguly. "Delay tolerant networks as spatio-temporal networks". En 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2013. http://dx.doi.org/10.1109/infcomw.2013.6970737.
Texto completoChanhyun Kang, A. Pugliese, J. Grant y V. S. Subrahmanian. "STUN: Spatio-Temporal Uncertain (Social) Networks". En 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.93.
Texto completoLe Van Quyen, M., J. Martinerie y F. J. Varela. "SPATIO-TEMPORAL DYNAMICS OF EPILEPTOGENIC NETWORKS". En Proceedings of the Workshop. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812793782_0008.
Texto completoCheng, Zida, Siheng Chen y Ya Zhang. "Spatio-Temporal Graph Complementary Scattering Networks". En ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747790.
Texto completoKim, Joongheon, Seunghoon Park, Soyi Jung y Seehwan Yoo. "Spatio-Temporal Split Learning". En 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2021. http://dx.doi.org/10.1109/dsn-s52858.2021.00016.
Texto completoMachireddy, Amrutha, Prayag Gowgi y Shayan Srinivasa Garani. "Extracting Temporal Correlations Using Hierarchical Spatio-Temporal Feature Maps". En 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534337.
Texto completoWu, Haoze, Jiawei Liu, Zheng-Jun Zha, Zhenzhong Chen y Xiaoyan Sun. "Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/136.
Texto completoInformes sobre el tema "Spatio temporal networks"
Shastri, Lokendra. Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1995. http://dx.doi.org/10.21236/ada299746.
Texto completoBurns, Robert T., Nir Keren, Ross Muhlbauer, Hongwei Xin, Steven J. Hoff y Randy Swestka. Development of a Wireless Sensor Network to Quantify Spatial and Temporal H2S Concentrations in Swine Houses (A Progress Report). Ames (Iowa): Iowa State University, enero de 2009. http://dx.doi.org/10.31274/ans_air-180814-279.
Texto completoLeis, Sherry. Network scale fire atlas supports land management in national parks. Editado por Tani Hubbard. National Park Service, noviembre de 2022. http://dx.doi.org/10.36967/2295133.
Texto completoLeis, Sherry, Mike DeBacker, Lloyd Morrison, Gareth Rowell y Jennifer Haack. Vegetation community monitoring protocol for the Heartland Inventory and Monitoring Network: Narrative, Version 4.0. Editado por Tani Hubbard. National Park Service, noviembre de 2022. http://dx.doi.org/10.36967/2294948.
Texto completoPstuty, Norbert, Mark Duffy, Dennis Skidds, Tanya Silveira, Andrea Habeck, Katherine Ames y Glenn Liu. Northeast Coastal and Barrier Network Geomorphological Monitoring Protocol: Part I—Ocean Shoreline Position, Version 2. National Park Service, junio de 2022. http://dx.doi.org/10.36967/2293713.
Texto completoFait, Aaron, Grant Cramer y Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, mayo de 2014. http://dx.doi.org/10.32747/2014.7594398.bard.
Texto completoSpatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. US Geological Survey, 1994. http://dx.doi.org/10.3133/wri944076.
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