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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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