This paper proposes a snapshot Compressed Light Field Imaging Spectrometer based on compressed sensing and light field concept, which can acquire the two-dimensional spatial distribution, depth estimation and spectral intensity of input scenes simultaneously. The primary structure of the system contains fore optics, coded aperture, dispersion element and light field sensor. The detected data can record the coded mixture spatial-spectral information of the input scene with direction information of light rays. The datacube containing depth estimation can be recovered with the compressed sensing and digital refocus framework. We establish the mathematical model of the system and conduct simulations for verification. The reconstruction strategy is demonstrated for the simulation data.

A coherent-dispersion spectrometer (CODES) system using the radial velocity (RV) method for exoplanet searches is established in this paper. This spectrometer utilizes a new Sagnac interferometer with common-path and asymmetric designs. Compared with the traditional Michelson interferometer-based spectrometer for exoplanet detection, these designs markedly improve the stability of the optical path difference (OPD) to positional changes in optical elements or air density changes, reducing the need for active cavity stabilization. Furthermore, the asymmetric Sagnac design allows convenient separation of the two complementary outputs. In order to verify the feasibility of the CODES, an optical Doppler shift experiment is constructed in the laboratory. The RV signal was extracted by the phase shifts of the interference fringes produced by the spectrometer. The experimental results show that the obtained retrieved RV is 76.7 m s −1 , with an absolute error of 0.3 m s −1 compared to simulated values. And the root mean square error (RMSE) and the standard deviation (STD) are 21.3 m s −1 and 21.4 m s −1 , respectively. Error analyses show that the OPD change caused by the temperature variation is the main factor for the RMSE and STD.

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Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate selection, unstable solutions, weak generalization ability, and an ability to easily fall into local minimum points, the extreme learning machine (ELM) was applied to sulfur hexafluoride ( S F 6 ) concentration inversion research. To solve the problems of high dimensionality, collinearity, and noise of the spectral data input to the ELM network, a genetic algorithm was used to obtain fewer but critical spectral data. This was used as an input variable to achieve a genetic algorithm joint extreme learning machine (GA-ELM) whose performance was compared with the genetic algorithm joint backpropagation (GA-BP) neural network algorithm to verify its effectiveness. The experiment used 60 groups of S F 6 gas samples with different concentrations, made via a self-developed Fourier transform infrared spectroscopy instrument. The S F 6 gas samples were placed in an open optical path to obtain infrared interference signals, and then spectral restoration was performed. Fifty groups were randomly selected as training samples, and 10 groups were used as test samples. The BP neural network and ELM algorithms were used to invert the S F 6 gas concentration of the mixed absorbance spectrum, and the results of the two algorithms were compared. The sample mean square error decreased from 248.6917 to 63.0359; the coefficient of determination increased from 0.9941 to 0.9984; and the single running time decreased from 0.0773 to 0.0042 s. Comparing the optimized GA-ELM algorithm with traditional algorithms such as ELM and partial least squares, the GA-ELM algorithm had higher prediction accuracy and operating efficiency and better stability and generalization performance in the quantitative analysis of small samples of gas under complex noise backgrounds.