EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Li, Hongyi; Chen, Shengyu; Xu, Shaofeng; Song, Ziming; Chen, Jiaxin; Zhao, Debin

doi:10.1049/iet-spr.2017.0354

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Three typical enhancement systems based on deep learning are constructed as the baseline method, which further demonstrates the superiority of the proposed model. The first one uses deep auto-encoder (DAE) [22], which is a network structure commonly used for denoising. The second one uses convolutional neural network (CNN) for enhancement [23].…”

Section: Resultsmentioning

confidence: 99%

A Novel Approach Based on Generative Adversarial Network for Signal Enhancement in Wireless Communications

Zhu

Yao

et al. 2022

Wireless Communications and Mobile Computing

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Wireless communication signals are often affected by noise and interference in the channel during transmission, which makes it difficult for the receiver to analyze. The signal enhancement technology can suppress the noise and interference in the signal, so as to improve the communication quality. It is one of the main research directions of signal processing. Classical enhancement methods separate the signals through separable transform domain. Artificial construction of the corresponding separable transform domain requires prior information of noise and interference, but they have the characteristics of randomness. Further, these methods usually use high-level features and rely on statistics, so they can only deal with specific noise conditions. At present, deep learning is increasingly applied in the field of wireless communications due to its powerful feature extraction ability for large sample sets. In this paper, a communication signal enhancement model based on generative adversarial network (GAN) is proposed. Compared with classical methods, the signal is operated directly and the model is trained end-to-end. It can adapt to different noise conditions and avoid the above problems. An independent and invisible test set is used to evaluate several comparative methods. The experimental results confirm the effectiveness of the proposed model.

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Section: Resultsmentioning

confidence: 99%

A Novel Approach Based on Generative Adversarial Network for Signal Enhancement in Wireless Communications

Zhu

Yao

et al. 2022

Wireless Communications and Mobile Computing

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Compression and Enhancement of Multiscale Vibration Signals Based on Φ-OTDR

Zhang

Wang

2020

IOP Conf. Ser.: Earth Environ. Sci.

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Phase optical time domain reflection (Φ-OTDR) is a distributed optical fiber sensing technology. Its output data contains amplitude, time and other information, so it can accurately reflect various abnormal events. However, due to the high sampling frequency and the large number of sampling points, the amount of data of the vibration signal outputted by it is huge. In order to facilitate the analysis of signal features and optimize the storage space of data, this paper combines Fast Fourier Transform and multi-scale detail enhancement to enhance the vibration signal features while realizing the vibration signal compression.

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Stacked Autoencoder for Wavelet-Based the EMI Signal analysis

Yang

Zhen

et al. 2021

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)

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EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Cited by 3 publications

References 32 publications

A Novel Approach Based on Generative Adversarial Network for Signal Enhancement in Wireless Communications

A Novel Approach Based on Generative Adversarial Network for Signal Enhancement in Wireless Communications

Compression and Enhancement of Multiscale Vibration Signals Based on Φ-OTDR

Stacked Autoencoder for Wavelet-Based the EMI Signal analysis

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