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Autoregressive Moving Average Graph Filters a Stable Distributed Implementation. / Isufi, Elvin; Loukas, Andreas; Leus, Geert.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. Piscataway, NJ : IEEE, 2017. p. 4119-4123 7952931.

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Harvard

Isufi, E, Loukas, A & Leus, G 2017, Autoregressive Moving Average Graph Filters a Stable Distributed Implementation. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings., 7952931, IEEE, Piscataway, NJ, pp. 4119-4123, ICASSP 2017, New Orleans, LA, United States, 5/03/17. https://doi.org/10.1109/ICASSP.2017.7952931

APA

Isufi, E., Loukas, A., & Leus, G. (2017). Autoregressive Moving Average Graph Filters a Stable Distributed Implementation. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (pp. 4119-4123). [7952931] Piscataway, NJ: IEEE. https://doi.org/10.1109/ICASSP.2017.7952931

Vancouver

Isufi E, Loukas A, Leus G. Autoregressive Moving Average Graph Filters a Stable Distributed Implementation. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. Piscataway, NJ: IEEE. 2017. p. 4119-4123. 7952931 https://doi.org/10.1109/ICASSP.2017.7952931

Author

Isufi, Elvin ; Loukas, Andreas ; Leus, Geert. / Autoregressive Moving Average Graph Filters a Stable Distributed Implementation. 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. Piscataway, NJ : IEEE, 2017. pp. 4119-4123

BibTeX

@inproceedings{6f09432f105c457f94a81ea8da5ef699,
title = "Autoregressive Moving Average Graph Filters a Stable Distributed Implementation",
abstract = "We present a novel implementation strategy for distributed autoregressive moving average (ARMA) graph filters. Differently from the state of the art implementation, the proposed approach has the following benefits: (i) the designed filter coefficients come with stability guarantees, (ii) the linear convergence time can now be controlled by the filter coefficients, and (iii) the stable filter coefficients that approximate a desired frequency response are optimal in a least squares sense. Numerical results show that the proposed implementation outperforms the state of the art distributed infinite impulse response (IIR) graph filters. Further, even at fixed distributed costs, compared with the popular finite impulse response (FIR) filters, at high orders our method achieves tighter low-pass responses, suggesting that it should be preferable in accuracy-demanding applications.",
keywords = "autoregressive moving average graph filters, graph filters, graph signal processing",
author = "Elvin Isufi and Andreas Loukas and Geert Leus",
year = "2017",
doi = "10.1109/ICASSP.2017.7952931",
language = "English",
pages = "4119--4123",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Autoregressive Moving Average Graph Filters a Stable Distributed Implementation

AU - Isufi, Elvin

AU - Loukas, Andreas

AU - Leus, Geert

PY - 2017

Y1 - 2017

N2 - We present a novel implementation strategy for distributed autoregressive moving average (ARMA) graph filters. Differently from the state of the art implementation, the proposed approach has the following benefits: (i) the designed filter coefficients come with stability guarantees, (ii) the linear convergence time can now be controlled by the filter coefficients, and (iii) the stable filter coefficients that approximate a desired frequency response are optimal in a least squares sense. Numerical results show that the proposed implementation outperforms the state of the art distributed infinite impulse response (IIR) graph filters. Further, even at fixed distributed costs, compared with the popular finite impulse response (FIR) filters, at high orders our method achieves tighter low-pass responses, suggesting that it should be preferable in accuracy-demanding applications.

AB - We present a novel implementation strategy for distributed autoregressive moving average (ARMA) graph filters. Differently from the state of the art implementation, the proposed approach has the following benefits: (i) the designed filter coefficients come with stability guarantees, (ii) the linear convergence time can now be controlled by the filter coefficients, and (iii) the stable filter coefficients that approximate a desired frequency response are optimal in a least squares sense. Numerical results show that the proposed implementation outperforms the state of the art distributed infinite impulse response (IIR) graph filters. Further, even at fixed distributed costs, compared with the popular finite impulse response (FIR) filters, at high orders our method achieves tighter low-pass responses, suggesting that it should be preferable in accuracy-demanding applications.

KW - autoregressive moving average graph filters

KW - graph filters

KW - graph signal processing

UR - http://www.scopus.com/inward/record.url?scp=85023780803&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2017.7952931

DO - 10.1109/ICASSP.2017.7952931

M3 - Conference contribution

SP - 4119

EP - 4123

BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings

PB - IEEE

CY - Piscataway, NJ

ER -

ID: 28022095