Distributed Analytical Graph Identification

Sundeep Prabhakar Chepuri; Mario Coutino; Antonio G. Marques; Geert Leus

doi:10.1109/ICASSP.2018.8461484

Distributed Analytical Graph Identification

Sundeep Prabhakar Chepuri, Mario Coutino, Antonio G. Marques, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

Original language	English (US)
Title of host publication	2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	4064-4068
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8461484
State	Published - Sep 13 2018
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700, the ASPIRE project (project 14926 within the STW OTP program), and MINECO (grants TEC2013-41604-R and TEC2016-75361-R). Mario Coutino is partially supported by CONA-CYT.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1109/ICASSP.2018.8461484

Cite this

@inproceedings{04b0cf84609f41c28b23d2cceed44f0f,

title = "Distributed Analytical Graph Identification",

abstract = "An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.",

author = "Chepuri, {Sundeep Prabhakar} and Mario Coutino and Marques, {Antonio G.} and Geert Leus",

note = "KAUST Repository Item: Exported on 2022-06-30 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700, the ASPIRE project (project 14926 within the STW OTP program), and MINECO (grants TEC2013-41604-R and TEC2016-75361-R). Mario Coutino is partially supported by CONA-CYT. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2018",

month = sep,

day = "13",

doi = "10.1109/ICASSP.2018.8461484",

language = "English (US)",

isbn = "9781538646588",

pages = "4064--4068",

booktitle = "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

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TY - GEN

T1 - Distributed Analytical Graph Identification

AU - Chepuri, Sundeep Prabhakar

AU - Coutino, Mario

AU - Marques, Antonio G.

AU - Leus, Geert

N1 - KAUST Repository Item: Exported on 2022-06-30 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700, the ASPIRE project (project 14926 within the STW OTP program), and MINECO (grants TEC2013-41604-R and TEC2016-75361-R). Mario Coutino is partially supported by CONA-CYT. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2018/9/13

Y1 - 2018/9/13

N2 - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

AB - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

UR - http://hdl.handle.net/10754/679445

UR - https://ieeexplore.ieee.org/document/8461484/

UR - http://www.scopus.com/inward/record.url?scp=85054214677&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2018.8461484

DO - 10.1109/ICASSP.2018.8461484

M3 - Conference contribution

SN - 9781538646588

SP - 4064

EP - 4068

BT - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

ER -

Distributed Analytical Graph Identification

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