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Yiguang Yang

As seen in: MDPI, Optica
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Articles by Yiguang Yang

Study on the Influence of Water Color on Signal Directionality in Laser-Induced Acoustic Processes

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By Tongchang Zhang, Xiaolong Cao, Qi Tan, Yiguang Yang
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
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Acoustic Signal Reconstruction Across Water-Air Interface Through Millimeter-Wave Radar Micro-Vibration Detection

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By Yuchen Du, Xiaolong Cao, Yiguang Yang, Tongchang Zhang
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Extraction of Underwater Acoustic Signals Across Sea-Air Media Using Butterworth Filtering

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By Xiaolong Cao, Yiguang Yang, Qi Tan, Tengyuan Cui
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared View Duplicates (186) Issue with article?

Low SNR multimirror Fabry-Perot pressure sensor optic spectrum signal analysis and demodulation via SVM-KNN regressors

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By Li Wenhong, Hao Chen, Xujin Li, Yiguang Yang
| Optica Verified
We demonstrate an ensemble learning based method to solve the problem of low SNR Fabry–Perot sensor spectrum signal demodulation. Taking the eight-layer approximate coefficients of a multilevel discrete wavelet transform as input features, an ensemble model that combines multiple SVM and KNN learners is trained. Bootstrap and booting techniques are introduced for better modeling performance and stability.
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