Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device

Taesik Nam; Dong Hoon Choi; Eui Bum Lee; Jong Gwan Yook

doi:10.1109/EMCSI39492.2022.9889414

Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device

Taesik Nam, Dong Hoon Choi, Eui Bum Lee, Jong Gwan Yook

Department of Electrical and Electronic Engineering

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

2 Citations (Scopus)

Abstract

This paper proposes a learning-based denoising algorithm that improves the signal-to-noise ratio (SNR) of the information signal emitted from the display device. The information signal is easily degraded by noise and interference on the channel and has various SNR. In this situation, an algorithm that enhances the model's robustness is required to improve the degraded information signal into a learning-based denoising model. Therefore, this paper proposes a normalization method to enhance the robustness of the model.

Original language	English
Title of host publication	2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	268-271
Number of pages	4
ISBN (Electronic)	9781665409292
DOIs	https://doi.org/10.1109/EMCSI39492.2022.9889414
Publication status	Published - 2022
Event	2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022 - Spokane, United States Duration: 2022 Aug 1 → 2022 Aug 5

Publication series

Name	2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022

Conference

Conference	2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022
Country/Territory	United States
City	Spokane
Period	22/8/1 → 22/8/5

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Energy Engineering and Power Technology
Safety, Risk, Reliability and Quality

Access to Document

10.1109/EMCSI39492.2022.9889414

Cite this

Nam, T., Choi, D. H., Lee, E. B., & Yook, J. G. (2022). Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device. In 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022 (pp. 268-271). (2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMCSI39492.2022.9889414

Nam, Taesik ; Choi, Dong Hoon ; Lee, Eui Bum et al. / Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device. 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 268-271 (2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022).

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title = "Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device",

abstract = "This paper proposes a learning-based denoising algorithm that improves the signal-to-noise ratio (SNR) of the information signal emitted from the display device. The information signal is easily degraded by noise and interference on the channel and has various SNR. In this situation, an algorithm that enhances the model's robustness is required to improve the degraded information signal into a learning-based denoising model. Therefore, this paper proposes a normalization method to enhance the robustness of the model.",

keywords = "Convolutional neural network (CNN), Deep-learning, EMI, Wavelet transform",

author = "Taesik Nam and Choi, {Dong Hoon} and Lee, {Eui Bum} and Yook, {Jong Gwan}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022 ; Conference date: 01-08-2022 Through 05-08-2022",

year = "2022",

doi = "10.1109/EMCSI39492.2022.9889414",

language = "English",

series = "2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022",

address = "United States",

}

Nam, T, Choi, DH, Lee, EB & Yook, JG 2022, Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device. in 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022. 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 268-271, 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022, Spokane, United States, 22/8/1. https://doi.org/10.1109/EMCSI39492.2022.9889414

Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device. / Nam, Taesik; Choi, Dong Hoon; Lee, Eui Bum et al.
2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 268-271 (2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022).

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

TY - GEN

T1 - Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device

AU - Nam, Taesik

AU - Choi, Dong Hoon

AU - Lee, Eui Bum

AU - Yook, Jong Gwan

PY - 2022

Y1 - 2022

N2 - This paper proposes a learning-based denoising algorithm that improves the signal-to-noise ratio (SNR) of the information signal emitted from the display device. The information signal is easily degraded by noise and interference on the channel and has various SNR. In this situation, an algorithm that enhances the model's robustness is required to improve the degraded information signal into a learning-based denoising model. Therefore, this paper proposes a normalization method to enhance the robustness of the model.

AB - This paper proposes a learning-based denoising algorithm that improves the signal-to-noise ratio (SNR) of the information signal emitted from the display device. The information signal is easily degraded by noise and interference on the channel and has various SNR. In this situation, an algorithm that enhances the model's robustness is required to improve the degraded information signal into a learning-based denoising model. Therefore, this paper proposes a normalization method to enhance the robustness of the model.

KW - Convolutional neural network (CNN)

KW - Deep-learning

KW - EMI

KW - Wavelet transform

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DO - 10.1109/EMCSI39492.2022.9889414

M3 - Conference contribution

AN - SCOPUS:85140824994

T3 - 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022

SP - 268

EP - 271

BT - 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022

Y2 - 1 August 2022 through 5 August 2022

ER -

Nam T, Choi DH, Lee EB, Yook JG. Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device. In 2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 268-271. (2022 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2022). doi: 10.1109/EMCSI39492.2022.9889414

Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device

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