Relevant bibliographies by topics / Time-varying graph signals

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  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Time-varying graph signals'

Author: Grafiati
Published: 14 June 2023

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Journal articles on the topic "Time-varying graph signals"

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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.

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The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.
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Giraldo, 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.

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Wang, 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.

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This paper considers the problem of adaptive estimation of graph signals under the impulsive noise environment. The existing least mean squares (LMS) approach suffers from severe performance degradation under an impulsive environment that widely occurs in various practical applications. We present a novel adaptive estimation over graphs based on Welsch loss (WL-G) to handle the problems related to impulsive interference. The proposed WL-G algorithm can efficiently reconstruct graph signals from the observations with impulsive noises by formulating the reconstruction problem as an optimization based on Welsch loss. An analysis on the performance of the WL-G is presented to develop effective sampling strategies for graph signals. A novel graph sampling approach is also proposed and used in conjunction with the WL-G to tackle the time-varying case. The performance advantages of the proposed WL-G over the existing LMS regarding graph signal reconstruction under impulsive noise environment are demonstrated.
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Jiang, 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.

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Lewenfus, 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.

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Shafipour, 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.

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We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations’ covariance matrix and the so-called graph shift operator (GSO—a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations’ empirical covariance matrix. For streaming data, said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.
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Podusenko, 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.

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Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message passing-based inference for states and parameters. We derive structured variational update rules for a composite “AR node” with probabilistic observations that can be used as a plug-in module in hierarchical models, for example, to model the time-varying behavior of the hyper-parameters of a time-varying AR model. Our method includes tracking of variational free energy (FE) as a Bayesian measure of TVAR model performance. The proposed methods are verified on a synthetic data set and validated on real-world data from temperature modeling and speech enhancement tasks.
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Jiang, 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.

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The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality across all regions of the brain, and possibly at individual neurons. We formulate it as an optimization problem, a quadratic objective functional and tensor information of observed node signals over short time intervals. The proper regularization constraints reflect the graph smoothness and other dynamics involving the underlying graph’s Laplacian, as well as the time evolution smoothness of the underlying graph. The resulting joint optimization is solved by a continuous relaxation of the weight parameters and an introduced novel gradient-projection scheme. While the work may be applicable to any time-evolving data set (e.g., fMRI), we apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to be not only viable but also efficiently computable.
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Lan, 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.

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This paper proposes an adaptive fuzzy distributed consensus tracking control scheme for a class of uncertain nonlinear dynamic multiagent systems (MASs) with state time-varying delays and state time-varying constraints. The existing controllers with Lyapunov–Krasovskii functions (LKFs) were not suitable to address time-varying delays and time-varying constraints in nonlinear MASs simultaneously. State constraints further increase the difficulty of controller design and stability analysis, especially for nonstrict feedback systems. Fuzzy logic systems (FLSs) tackle the approximation of unknown dynamics functions and parameters. Especially when the distributed consensus tracking error is infinitely close to the origin, although there is no singular value, it would lead to the rapid growth of control rate or uncontrollability. Constructing appropriate piecewise functions can effectively avoid the above occurrence and accelerate convergence. Based on Lyapunov stability theory and algebraic graph theory, the constructed tracking control can ensure states within defined time-varying constraint bounds and eliminate the influence of time delays. All signals in closed-loop systems can be guaranteed semiglobally uniformly ultimately bounded (SUUB). Finally, the validity of the theoretical method is verified by the simulation.
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Li, 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.

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In this brief, we study the distributed adaptive fixed-time tracking consensus control problem for multiple strict-feedback systems with uncertain nonlinearities under a directed graph topology. It is assumed that the leader’s output is time varying and has been accessed by only a small fraction of followers in a group. The distributed fixed-time tracking consensus control is proposed to design local consensus controllers in order to guarantee the consensus tracking between the followers and the leader and ensure the error convergence time is independent of the systems’ initial state. The function approximation technique using radial basis function neural networks (RBFNNs) is employed to compensate for unknown nonlinear terms induced from the controller design procedure. From the Lyapunov stability theorem and graph theory, it is shown that, by using the proposed fixed-time control strategy, all signals in the closed-loop system and the consensus tracking errors are cooperatively semiglobally uniformly bounded and the errors converge to a neighborhood of the origin within a fixed time. Finally, the effectiveness of the proposed control strategy has been proved by rigorous stability analysis and two simulation examples.
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Dissertations / Theses on the topic "Time-varying graph signals"

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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.

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L'apprentissage de la représentation graphique et ses applications ont suscité une attention considérable ces dernières années. En particulier, les Réseaux Neuronaux Graphiques (RNG) et le Traitement du Signal Graphique (TSG) ont été largement étudiés. Les RNGs étendent les concepts des réseaux neuronaux convolutionnels aux données non euclidiennes modélisées sous forme de graphes. De même, le TSG étend les concepts du traitement classique des signaux numériques aux signaux supportés par des graphes. Les RNGs et TSG ont de nombreuses applications telles que l'apprentissage semi-supervisé, la segmentation sémantique de nuages de points, la prédiction de relations individuelles dans les réseaux sociaux, la modélisation de protéines pour la découverte de médicaments, le traitement d'images et de vidéos. Dans cette thèse, nous proposons de nouvelles approches pour le traitement des images et des vidéos, les RNGs, et la récupération des signaux de graphes variant dans le temps. Notre principale motivation est d'utiliser l'information géométrique que nous pouvons capturer à partir des données pour éviter les méthodes avides de données, c'est-à-dire l'apprentissage avec une supervision minimale. Toutes nos contributions s'appuient fortement sur les développements de la TSG et de la théorie spectrale des graphes. En particulier, la théorie de l'échantillonnage et de la reconstruction des signaux de graphes joue un rôle central dans cette thèse. Les principales contributions de cette thèse sont résumées comme suit : 1) nous proposons de nouveaux algorithmes pour la segmentation d'objets en mouvement en utilisant les concepts de la TSG et des RNGs, 2) nous proposons un nouvel algorithme pour la segmentation sémantique faiblement supervisée en utilisant des réseaux de neurones hypergraphiques, 3) nous proposons et analysons les RNGs en utilisant les concepts de la TSG et de la théorie des graphes spectraux, et 4) nous introduisons un nouvel algorithme basé sur l'extension d'une fonction de lissage de Sobolev pour la reconstruction de signaux graphiques variant dans le temps à partir d'échantillons discrets
Graph 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
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Book chapters on the topic "Time-varying graph signals"

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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.

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Bohannon, 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.

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Conference papers on the topic "Time-varying graph signals"

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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.

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Liu, 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.

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Loukas, 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.

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Acar, 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.

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Qiu, 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.

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Acar, 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.

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Qi, 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.

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Kojima, 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.

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Natali, 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.

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Saad, 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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