Sparse electromagnetic tomography based on matching pursuit algorithms

Yang Tao; Wenbo Zhang; Yifei Zhao; Christos Ktistis; Wuliang Yin; Anthony Peyton

doi:10.1109/I2MTC.2016.7520511

Sparse electromagnetic tomography based on matching pursuit algorithms

Yang Tao, Wenbo Zhang, Yifei Zhao, Christos Ktistis, Wuliang Yin, Anthony Peyton

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

A sparse Electromagnetic Tomography (EMT) system based on Matching Pursuit (MP) algorithms is introduced in this paper. The sparse EMT problem with a linear model is essentially a pursuit for the sparse representation of the spatial conductivity. The framework of sparse representation theory is adopted for the EMT problem. Such notations as spark and mutual-coherence are introduced as tools in the analysis of the recovery results. MP algorithms are considered as effective and efficient solvers towards sparse representation. Some variations of MP are compared in this paper for a random dictionary and the EMT dictionary. The result of conductivity reconstruction proves the effectiveness of MP algorithms for the proposed EMT system.

Original language	English
Title of host publication	I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings
Publisher	IEEE
Volume	2016-July
ISBN (Electronic)	9781467392204
DOIs	https://doi.org/10.1109/I2MTC.2016.7520511
Publication status	Published - 25 Jul 2016
Event	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 - Taipei, Taiwan Duration: 23 May 2016 → 26 May 2016

Conference

Conference	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016
Country/Territory	Taiwan
City	Taipei
Period	23/05/16 → 26/05/16

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10.1109/I2MTC.2016.7520511

Cite this

Tao, Y., Zhang, W., Zhao, Y., Ktistis, C., Yin, W., & Peyton, A. (2016). Sparse electromagnetic tomography based on matching pursuit algorithms. In I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings (Vol. 2016-July). Article 7520511 IEEE. https://doi.org/10.1109/I2MTC.2016.7520511

@inproceedings{24d8725aee9d431f957aa74bc0dab29c,

title = "Sparse electromagnetic tomography based on matching pursuit algorithms",

abstract = "A sparse Electromagnetic Tomography (EMT) system based on Matching Pursuit (MP) algorithms is introduced in this paper. The sparse EMT problem with a linear model is essentially a pursuit for the sparse representation of the spatial conductivity. The framework of sparse representation theory is adopted for the EMT problem. Such notations as spark and mutual-coherence are introduced as tools in the analysis of the recovery results. MP algorithms are considered as effective and efficient solvers towards sparse representation. Some variations of MP are compared in this paper for a random dictionary and the EMT dictionary. The result of conductivity reconstruction proves the effectiveness of MP algorithms for the proposed EMT system.",

author = "Yang Tao and Wenbo Zhang and Yifei Zhao and Christos Ktistis and Wuliang Yin and Anthony Peyton",

year = "2016",

month = jul,

day = "25",

doi = "10.1109/I2MTC.2016.7520511",

language = "English",

volume = "2016-July",

booktitle = "I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 ; Conference date: 23-05-2016 Through 26-05-2016",

}

Tao, Y, Zhang, W, Zhao, Y, Ktistis, C, Yin, W & Peyton, A 2016, Sparse electromagnetic tomography based on matching pursuit algorithms. in I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. vol. 2016-July, 7520511, IEEE, 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016, Taipei, Taiwan, 23/05/16. https://doi.org/10.1109/I2MTC.2016.7520511

Sparse electromagnetic tomography based on matching pursuit algorithms. / Tao, Yang; Zhang, Wenbo; Zhao, Yifei et al.
I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. Vol. 2016-July IEEE, 2016. 7520511.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sparse electromagnetic tomography based on matching pursuit algorithms

AU - Tao, Yang

AU - Zhang, Wenbo

AU - Zhao, Yifei

AU - Ktistis, Christos

AU - Yin, Wuliang

AU - Peyton, Anthony

PY - 2016/7/25

Y1 - 2016/7/25

N2 - A sparse Electromagnetic Tomography (EMT) system based on Matching Pursuit (MP) algorithms is introduced in this paper. The sparse EMT problem with a linear model is essentially a pursuit for the sparse representation of the spatial conductivity. The framework of sparse representation theory is adopted for the EMT problem. Such notations as spark and mutual-coherence are introduced as tools in the analysis of the recovery results. MP algorithms are considered as effective and efficient solvers towards sparse representation. Some variations of MP are compared in this paper for a random dictionary and the EMT dictionary. The result of conductivity reconstruction proves the effectiveness of MP algorithms for the proposed EMT system.

AB - A sparse Electromagnetic Tomography (EMT) system based on Matching Pursuit (MP) algorithms is introduced in this paper. The sparse EMT problem with a linear model is essentially a pursuit for the sparse representation of the spatial conductivity. The framework of sparse representation theory is adopted for the EMT problem. Such notations as spark and mutual-coherence are introduced as tools in the analysis of the recovery results. MP algorithms are considered as effective and efficient solvers towards sparse representation. Some variations of MP are compared in this paper for a random dictionary and the EMT dictionary. The result of conductivity reconstruction proves the effectiveness of MP algorithms for the proposed EMT system.

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U2 - 10.1109/I2MTC.2016.7520511

DO - 10.1109/I2MTC.2016.7520511

M3 - Conference contribution

AN - SCOPUS:84980347851

VL - 2016-July

BT - I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings

PB - IEEE

T2 - 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016

Y2 - 23 May 2016 through 26 May 2016

ER -

Sparse electromagnetic tomography based on matching pursuit algorithms

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Sparse electromagnetic tomography based on matching pursuit algorithms

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Electromagnetic Sensing Group

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