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Compressed chaotic signal reconstruction based on deep learning

Author

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  • Deng, Jiarui
  • Lao, Huimin
  • Lyu, Shanxiang

Abstract

Chaotic signals are often compressed on account of limited hardware resources in the Internet of Things (IoT). In this paper, we show that using 1 or 2 bits/symbol in the front-end quantizer suffices for subsequent high-quality recovery at the back-end. Specifically, we introduce a general chaotic signal recovery scheme, which is conceived to be deployed at the level of IoT edge to reconstruct received compressed signals. The scheme relies on the simplified convolutional denoising auto-encoder (SCDAE), which has advantages in terms of network architecture and parameter amount. Compared with U-Net, SCDAE has 50 times smaller parameters of storage, and 26 times smaller computational complexity.

Suggested Citation

  • Deng, Jiarui & Lao, Huimin & Lyu, Shanxiang, 2023. "Compressed chaotic signal reconstruction based on deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000693
    DOI: 10.1016/j.chaos.2023.113168
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    References listed on IDEAS

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    1. Li, Yin & Li, Fagang & Lyu, Shanxiang & Xu, Meng & Wang, Shiyuan, 2021. "Blind extraction of ECG signals based on similarity in the phase space," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Lou, Shuting & Deng, Jiarui & Lyu, Shanxiang, 2022. "Chaotic signal denoising based on simplified convolutional denoising auto-encoder," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. Huang, Pengfei & Chai, Yi & Chen, Xiaolong, 2022. "Multiple dynamics analysis of Lorenz-family systems and the application in signal detection," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
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    Cited by:

    1. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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