Drift calibration method of Fabry-Perot filters using two-stage decomposition and hybrid modeling.

Although tunable Fabry-Perot (F-P) filters are widely acknowledged as fiber Bragg grating (FBG) demodulators, F-P filters exhibit drift error when subjected to ambient temperature and piezo-electrical transducer (PZT) hysteresis. To address the drift issue, the majority of the existing literature makes use of additional devices like the F-P etalon and gas chamber. In this study, a novel drift calibration method based on two-stage decomposition and hybrid modeling is proposed. The initial drift error sequences are broken down into three frequency components using the variational mode decomposition (VMD), and the medium-frequency components are further broken down using the secondary VMD. The initial drift error sequences are significantly simplified by the two-stage VMD. On this foundation, the long short-term memory (LSTM) network and polynomial fitting (PF) are used to forecast the low-frequency and high-frequency drift errors, respectively. The LSTM enables the prediction of intricate nonlinear local behaviors, while the PF method predicts the overall trend. The benefits of LSTM and PF can be effectively utilized in this manner. Compared to the single-stage decomposition, two-stage decomposition achieves superior results. The suggested method is an affordable and effective alternative to the current drift calibration techniques.

Hysteresis and temperature drift compensation for FBG demodulation by utilizing adaptive weight least square support vector regression.

Hysteresis and temperature drift deteriorate the demodulation performance of tunable Fabry-Perot (F-P) filters. This study addresses a novel adaptive weight least square support vector regression (AWLSSVR) to compensate for the hysteresis and temperature drift of F-P filters. The temperature drift of a referent fiber Bragg grating(FBG) is used to estimate the temperature drifts of other three sensing FBGs, and a novel adaptive weighting strategy with an asymmetric noise interval is proposed, to eliminate the effects of noise in the training dataset. The experimental results show that when the temperature-changing modes of the training and testing datasets were close to each other, the error of the proposed method is reduced to 8.7 pm, while the errors of the other three conventional methods based on LSSVR are more than 10.8 pm. Further, when the temperature-changing modes of the training and testing datasets were partly different, the error of the proposed method was reduced to 5.4 pm, while the errors of other methods were more than 11.9 pm. It was verified experimentally that the proposed AWLSSVR method is more accurate and robust than other versions of WLSSVR for training samples with noise, requires no additional hardware, and covers the entire C band.

A sequential regularization based image reconstruction method for limited-angle spectral CT.

In spectral computed tomography (CT), the object is respectively scanned under different x-ray spectra. Multiple projection data can be collectively used for reconstructing basis images and virtual monochromatic images, which have been used in material decomposition, beam-hardening correction, bone removal, and so on. In practice, projection data may be obtained in a limited scanning angular range. Images reconstructed from limited-angle data by conventional spectral CT reconstruction methods will be deteriorated by limited-angle related artifacts and basis image decomposition errors. Motivated by observations of limited-angle spectral CT, we propose a sequential regularization-based limited-angle spectral CT reconstruction model and its numerical solver. Both simulated and real data experiments validate that our method is capable of suppressing artifacts, preserving edges and reducing decomposition errors.