Relevant bibliographies by topics / Time-vertex signal processing

Academic literature on the topic 'Time-vertex signal processing'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Time-vertex signal processing"

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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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Grassi, Francesco, Andreas Loukas, Nathanael Perraudin, and Benjamin Ricaud. "A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs." IEEE Transactions on Signal Processing 66, no. 3 (February 1, 2018): 817–29. http://dx.doi.org/10.1109/tsp.2017.2775589.

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Jiang, Junzheng, Hairong Feng, David B. Tay, and Shuwen Xu. "Theory and Design of Joint Time-Vertex Nonsubsampled Filter Banks." IEEE Transactions on Signal Processing 69 (2021): 1968–82. http://dx.doi.org/10.1109/tsp.2021.3064984.

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Skulrattanakulchai, San. "Δ-List vertex coloring in linear time." Information Processing Letters 98, no. 3 (May 2006): 101–6. http://dx.doi.org/10.1016/j.ipl.2005.12.007.

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Fan, Tiffany, David I. Shuman, Shashanka Ubaru, and Yousef Saad. "Spectrum-Adapted Polynomial Approximation for Matrix Functions with Applications in Graph Signal Processing." Algorithms 13, no. 11 (November 13, 2020): 295. http://dx.doi.org/10.3390/a13110295.

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We propose and investigate two new methods to approximate f(A)b for large, sparse, Hermitian matrices A. Computations of this form play an important role in numerous signal processing and machine learning tasks. The main idea behind both methods is to first estimate the spectral density of A, and then find polynomials of a fixed order that better approximate the function f on areas of the spectrum with a higher density of eigenvalues. Compared to state-of-the-art methods such as the Lanczos method and truncated Chebyshev expansion, the proposed methods tend to provide more accurate approximations of f(A)b at lower polynomial orders, and for matrices A with a large number of distinct interior eigenvalues and a small spectral width. We also explore the application of these techniques to (i) fast estimation of the norms of localized graph spectral filter dictionary atoms, and (ii) fast filtering of time-vertex signals.
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Dereniowski, Dariusz. "Maximum vertex occupation time and inert fugitive: Recontamination does help." Information Processing Letters 109, no. 9 (April 2009): 422–26. http://dx.doi.org/10.1016/j.ipl.2008.12.022.

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Carrabs, F., R. Cerulli, M. Gentili, and G. Parlato. "A linear time algorithm for the minimum Weighted Feedback Vertex Set on diamonds." Information Processing Letters 94, no. 1 (April 2005): 29–35. http://dx.doi.org/10.1016/j.ipl.2004.12.008.

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Il’ev, A. V., and V. P. Il’ev. "ALGORITHMS FOR SOLVING SYSTEMS OF EQUATIONS OVER VARIOUS CLASSES OF FINITE GRAPHS." Prikladnaya Diskretnaya Matematika, no. 53 (2021): 89–102. http://dx.doi.org/10.17223/20710410/53/6.

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The aim of the paper is to study and to solve finite systems of equations over finite undirected graphs. Equations over graphs are atomic formulas of the language L consisting of the set of constants (graph vertices), the binary vertex adjacency predicate and the equality predicate. It is proved that the problem of checking compatibility of a system of equations S with k variables over an arbitrary simple n-vertex graph Γ is N P-complete. The computational complexity of the procedure for checking compatibility of a system of equations S over a simple graph Γ and the procedure for finding a general solution of this system is calculated. The computational complexity of the algorithm for solving a system of equations S with k variables over an arbitrary simple n-vertex graph Γ involving these procedures is O(k 2n k/2+1(k + n) 2 ) for n > 3. Polynomially solvable cases are distinguished: systems of equations over trees, forests, bipartite and complete bipartite graphs. Polynomial time algorithms for solving these systems with running time O(k 2n(k + n) 2 ) are proposed.
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Lin, Min-Sheng, and Yung-Jui Chen. "Linear time algorithms for counting the number of minimal vertex covers with minimum/maximum size in an interval graph." Information Processing Letters 107, no. 6 (August 2008): 257–64. http://dx.doi.org/10.1016/j.ipl.2008.03.008.

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FUKUOKA, T. "A Linear Time Algorithm for Bi-Connectivity Augmentation of Graphs with Upper Bounds on Vertex-Degree Increase." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 4 (April 1, 2005): 954–63. http://dx.doi.org/10.1093/ietfec/e88-a.4.954.

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Dissertations / Theses on the topic "Time-vertex signal processing"

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GRASSI, FRANCESCO. "Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710580.

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The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals.
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Conference papers on the topic "Time-vertex signal processing"

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Perraudin, Nathanael, Andreas Loukas, Francesco Grassi, and Pierre Vandergheynst. "Towards stationary time-vertex signal processing." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952890.

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Humbert, Pierre, Laurent Oudre, and Nicolas Vayatis. "Subsampling of Multivariate Time-Vertex Graph Signals." In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902836.

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Yu, Junhao, Xuan Xie, Hui Feng, and Bo Hu. "On Critical Sampling of Time-Vertex Graph Signals." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969108.

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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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Grassi, Francesco, Nathanael Perraudin, and Benjamin Ricaud. "Tracking time-vertex propagation using dynamic graph wavelets." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905862.

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Guneyi, Eylem Tugce, Abdullah Canbolat, and Elif Vural. "Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations." In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2021. http://dx.doi.org/10.1109/mlsp52302.2021.9596563.

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Song, Linghao, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel Perdue, and Thomas E. Potok. "Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683736.

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