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Статті в журналах з теми "Time-varying graph signals"
Stanković, Ljubiša, Jonatan Lerga, Danilo Mandic, Miloš Brajović, Cédric Richard, and Miloš Daković. "From Time–Frequency to Vertex–Frequency and Back." Mathematics 9, no. 12 (June 17, 2021): 1407. http://dx.doi.org/10.3390/math9121407.
Повний текст джерелаGiraldo, Jhony H., Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, and Thierry Bouwmans. "Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness." IEEE Transactions on Signal and Information Processing over Networks 8 (2022): 201–14. http://dx.doi.org/10.1109/tsipn.2022.3156886.
Повний текст джерелаWang, Wenyuan, and Qiang Sun. "Robust Adaptive Estimation of Graph Signals Based on Welsch Loss." Symmetry 14, no. 2 (February 21, 2022): 426. http://dx.doi.org/10.3390/sym14020426.
Повний текст джерелаJiang, Junzheng, David B. Tay, Qiyu Sun, and Shan Ouyang. "Recovery of Time-Varying Graph Signals via Distributed Algorithms on Regularized Problems." IEEE Transactions on Signal and Information Processing over Networks 6 (2020): 540–55. http://dx.doi.org/10.1109/tsipn.2020.3010613.
Повний текст джерелаLewenfus, Gabriela, Wallace A. Martins, Symeon Chatzinotas, and Bjorn Ottersten. "Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning." IEEE Transactions on Signal and Information Processing over Networks 6 (2020): 761–73. http://dx.doi.org/10.1109/tsipn.2020.3040042.
Повний текст джерелаShafipour, Rasoul, and Gonzalo Mateos. "Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information." Algorithms 13, no. 9 (September 9, 2020): 228. http://dx.doi.org/10.3390/a13090228.
Повний текст джерелаPodusenko, Albert, Wouter M. Kouw, and Bert de Vries. "Message Passing-Based Inference for Time-Varying Autoregressive Models." Entropy 23, no. 6 (May 28, 2021): 683. http://dx.doi.org/10.3390/e23060683.
Повний текст джерелаJiang, Bo, Yuming Huang, Ashkan Panahi, Yiyi Yu, Hamid Krim, and Spencer L. Smith. "Dynamic Graph Learning: A Structure-Driven Approach." Mathematics 9, no. 2 (January 15, 2021): 168. http://dx.doi.org/10.3390/math9020168.
Повний текст джерелаLan, Jie, and Tongyu Xu. "Adaptive Fuzzy Consensus Tracking Control for Nonlinear Multiagent Systems with Time-Varying Delays and Constraints." Complexity 2021 (June 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/9940257.
Повний текст джерелаLi, Pinwei, Jiyang Dai, Jin Ying, Zhe Zhang, and Cheng He. "Distributed Adaptive Fixed-Time Tracking Consensus Control for Multiple Uncertain Nonlinear Strict-Feedback Systems under a Directed Graph." Complexity 2020 (August 26, 2020): 1–21. http://dx.doi.org/10.1155/2020/4130945.
Повний текст джерелаДисертації з теми "Time-varying graph signals"
Giraldo, Zuluaga Jhony Heriberto. "Graph-based Algorithms in Computer Vision, Machine Learning, and Signal Processing." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS037.
Повний текст джерелаGraph representation learning and its applications have gained significant attention in recent years. Notably, Graph Neural Networks (GNNs) and Graph Signal Processing (GSP) have been extensively studied. GNNs extend the concepts of convolutional neural networks to non-Euclidean data modeled as graphs. Similarly, GSP extends the concepts of classical digital signal processing to signals supported on graphs. GNNs and GSP have numerous applications such as semi-supervised learning, point cloud semantic segmentation, prediction of individual relations in social networks, modeling proteins for drug discovery, image, and video processing. In this thesis, we propose novel approaches in video and image processing, GNNs, and recovery of time-varying graph signals. Our main motivation is to use the geometrical information that we can capture from the data to avoid data hungry methods, i.e., learning with minimal supervision. All our contributions rely heavily on the developments of GSP and spectral graph theory. In particular, the sampling and reconstruction theory of graph signals play a central role in this thesis. The main contributions of this thesis are summarized as follows: 1) we propose new algorithms for moving object segmentation using concepts of GSP and GNNs, 2) we propose a new algorithm for weakly-supervised semantic segmentation using hypergraph neural networks, 3) we propose and analyze GNNs using concepts from GSP and spectral graph theory, and 4) we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples
Частини книг з теми "Time-varying graph signals"
Mao, Xianghui, and Yuantao Gu. "Time-Varying Graph Signals Reconstruction." In Signals and Communication Technology, 293–316. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_8.
Повний текст джерелаBohannon, Addison W., Brian M. Sadler, and Radu V. Balan. "A Filtering Framework for Time-Varying Graph Signals." In Signals and Communication Technology, 341–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_10.
Повний текст джерелаТези доповідей конференцій з теми "Time-varying graph signals"
Gama, Fernando, Elvin Isufi, Geert Leus, and Alejandro Ribeiro. "Control of Graph Signals Over Random Time-Varying Graphs." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462381.
Повний текст джерелаLiu, Yuhao, Chen Cui, Marzieh Ajirak, and Petar M. Djurić. "Estimation of Time-Varying Graph Topologies from Graph Signals." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10094731.
Повний текст джерелаLoukas, Andreas, and Damien Foucard. "Frequency analysis of time-varying graph signals." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905861.
Повний текст джерелаAcar, Abdullah Burak, and Elif Vural. "Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries." In 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022. http://dx.doi.org/10.1109/siu55565.2022.9864704.
Повний текст джерелаQiu, Kai, Xiaohan Wang, Tiejian Li, and Yuantao Gu. "Graph-based reconstruction of time-varying spatial signals." In 2016 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2016. http://dx.doi.org/10.1109/icdsp.2016.7868578.
Повний текст джерелаAcar, Abdullah Burak, and Elif Vural. "Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals." In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2022. http://dx.doi.org/10.1109/mlsp55214.2022.9943416.
Повний текст джерелаQi, Zefeng, Guobing Li, Shiyu Zhai, and Guomei Zhang. "Incremental Data-Driven Topology Learning for Time-Varying Graph Signals." In GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE, 2020. http://dx.doi.org/10.1109/globecom42002.2020.9322448.
Повний текст джерелаKojima, Hayate, Hikari Noguchi, Koki Yamada, and Yuichi Tanaka. "Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10094838.
Повний текст джерелаNatali, Alberto, Elvin Isufi, Mario Coutino, and Geert Leus. "Online Graph Learning From Time-Varying Structural Equation Models." In 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf53345.2021.9723163.
Повний текст джерелаSaad, Leila Ben, and Baltasar Beferull-Lozano. "Graph Filtering of Time-Varying Signals over Asymmetric Wireless Sensor Networks." In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019. http://dx.doi.org/10.1109/spawc.2019.8815521.
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