Distributed reconstruction of time-varying graph signals via a modified Newton’s method

Journal of the Franklin Institute - Tập 359 - Trang 9401-9421 - 2022
Fang Zhou1, Junzheng Jiang1,2, David B. Tay3
1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2National and Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service, Guilin 541004, China
3School of Information Technology, Deakin University, Waurn Ponds, Victoria 3216, Australia

Tài liệu tham khảo

Shuman, 2013, The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Process. Mag., 30, 83, 10.1109/MSP.2012.2235192

Sandryhaila, 2013, Discrete signal processing on graphs, IEEE Trans. Signal Process., 61, 1644, 10.1109/TSP.2013.2238935

Sandryhaila, 2014, Big data analysis with signal processing on graphs: representation and processing of massive data sets with irregular structure, IEEE Signal Process. Mag., 31, 80, 10.1109/MSP.2014.2329213

Ortega, 2018, Graph signal processing: overview, challenges, and applications, Proc. IEEE, 106, 808, 10.1109/JPROC.2018.2820126

Jabłoński, 2017, Graph signal processing in applications to sensor networks, smart grids, and smart cities, IEEE Sens. J., 17, 7659, 10.1109/JSEN.2017.2733767

R. Ramakrishna, A. Scaglione, Grid-graph signal processing (grid-GSP): a graph signal processing framework for the power grid, IEEE Trans. Signal Process.10.1109/TSP.2021.3075145

Sandryhaila, 2014, Discrete signal processing on graphs: frequency analysis, IEEE Trans. Signal Process., 62, 3042, 10.1109/TSP.2014.2321121

Sadreazami, 2018, Iterative graph-based filtering for image abstraction and stylization, IEEE Trans. Circuits Syst. - II, 65, 251255, 10.1109/TCSII.2017.2669866

Tay, 2021, Time-varying graph signal denoising via median filters, IEEE Trans. Circuits Syst. II, 68, 1053, 10.1109/TCSII.2020.3017800

Shuman, 2020, Localized spectral graph filter frames: a unifying framework, survey of design considerations, and numerical comparison, IEEE Signal Process. Mag., 37, 43, 10.1109/MSP.2020.3015024

Narang, 2012, Perfect reconstruction two-channel wavelet filter banks for graph structured data, IEEE Trans. Signal Process., 60, 2786, 10.1109/TSP.2012.2188718

Tanaka, 2014, M-channel oversampled graph filter banks, IEEE Trans. Signal Process., 62, 3578, 10.1109/TSP.2014.2328983

Jiang, 2019, Nonsubsampled graph filter banks: theory and distributed implementation, IEEE Trans. Signal Process., 67, 3938, 10.1109/TSP.2019.2922160

Wang, 2015, Local-set-based graph signal reconstruction, IEEE Trans. Signal Process., 63, 2432, 10.1109/TSP.2015.2411217

Brugnoli, 2020, Iterative reconstruction of signals on graph, IEEE Signal Process. Lett., 27, 76, 10.1109/LSP.2019.2956670

Segarra, 2016, Reconstruction of graph signals through percolation from seeding nodes, IEEE Trans. Signal Process., 64, 4363, 10.1109/TSP.2016.2552510

Kai, 2017, Time-varying graph signal reconstruction, IEEE J. Sel. Top. Signal Process., 11, 870, 10.1109/JSTSP.2017.2726969

Grassi, 2018, A time-vertex signal processing framework: scalable processing and meaningful representations for time-series on graphs, IEEE Trans. Signal Process., 66, 817, 10.1109/TSP.2017.2775589

Spelta, 2020, Normalized LMS algorithm and data-selective strategies for adaptive graph signal estimation, Signal Process., 167, 107326, 10.1016/j.sigpro.2019.107326

Lorenzo, 2017, Distributed adaptive learning of graph signals, IEEE Trans. Signal Process., 65, 4193, 10.1109/TSP.2017.2708035

Yan, 2022, Adaptive sign algorithm for graph signal processing, Signal Process., 108662, 10.1016/j.sigpro.2022.108662

Loukas, 2015, Distributed autoregressive moving average graph filters, IEEE Signal Process. Lett., 22, 1931, 10.1109/LSP.2015.2448655

Isufi, 2017, Autoregressive moving average graph filtering, IEEE Trans. Signal Process., 65, 274, 10.1109/TSP.2016.2614793

Chi, 2022, A distributed algorithm for reconstructing time-varying graph signals, Circuits, Syst., Signal Process., 41, 3624, 10.1007/s00034-021-01930-3

Liang, 2022, Multi-fidelity and learning-regularization for single image super resolution, J. Frankl. Inst., 359, 4489, 10.1016/j.jfranklin.2022.03.037

Jiang, 2019, Decentralised signal processing on graphs via matrix inverse approximation, Signal Process., 165, 292, 10.1016/j.sigpro.2019.07.010

Sun, 2006

(Dec. 2015). Sea Surface Temperature (SST) V2. [Online]. Available:http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html.

Zeng, 2017, Bipartite approximation for graphwavelet signal decomposition, IEEE Trans. Signal Process., 65, 5466, 10.1109/TSP.2017.2733489

2016, Sea-level pressure, 1948–2010.http://research.jisao.washington.edu/data_sets/reanalysis,

(May 2016). Air quality data. [Online] Available: Available: https://www.epa.gov/outdoor-airquality-data

(Mar. 2020). Germany Covid2019 data. [Online] Available: https://data.world/liz-friedman/covid-19-in-germany.