Laser & Optoelectronics Progress, Volume. 62, Issue 5, 0530001(2025)

Deep Learning-Based Disordered-Dispersion Miniature Spectrometer

Jiajia Wang1、*, Qianqian Mo1, and Tao Yang1,2
Author Affiliations
  • 1Institute of Advanced Materials, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu , China
  • 2Henan Institute of Flexible Electronics, Zhengzhou 450046, Henan , China
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    References(25)

    [10] Han J Z, Zhao S J, Feng A W et al. Design of compact and broadband imaging spectrometer based on freeform surface[J]. Acta Optica Sinica, 43, 1422002(2023).

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    [20] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C], 807-814(2010).

    [21] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014).

    [24] Tihonov A N. On the solution of ill-posed problems and the method of regularization[J]. Doklady Akademii Nauk SSSR, 151, 501-504(1963).

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    Jiajia Wang, Qianqian Mo, Tao Yang. Deep Learning-Based Disordered-Dispersion Miniature Spectrometer[J]. Laser & Optoelectronics Progress, 2025, 62(5): 0530001

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    Paper Information

    Category: Spectroscopy

    Received: May. 20, 2024

    Accepted: Jun. 27, 2024

    Published Online: Feb. 21, 2025

    The Author Email:

    DOI:10.3788/LOP241318

    CSTR:32186.14.LOP241318

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