Abstract:To solve the problem that the traditional optical absorption cell can not meet the requirement of low cost while meets the requirement of high optical spot density, a new optical absorption cell combining spherical mirrors with astigmatic mirrors is designed according to the transfer matrix formalism. The relationship of the beam transmission number in the optical absorption cell with structure parameters, mirror rotation and emergent light slope is analyzed by numerical simulation. The results show that the combination of spherical mirrors with astigmatic mirrors has a good anti-interference capability. A design example for an optical absorption cell with the length of 65 mm and an optical path of 10 m is given. This optical absorption cell has a small interference effect. Its volume is merely 0.29 L. It is suitable for absorption spectroscopy.

Abstract:To avoid the loss of details of infrared video data during compression, a method for lossless compression of infrared video data is proposed. The method uses a FPGA as its core controller and uses an ADV212 chip as a compression chip. It can choose the appropriate operation mode for lossless compression. By switching its configuration on line, the saved infrared video data can be decompressed and played back. This method has been used in the portable infrared cameras we developed and good results are obtained.

Abstract:A visible and infrared image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Network (PCNN) is proposed. Firstly, the source images are decomposed by using NSCT and the low frequency subband coefficients and various bandpass directional subband coefficients are obtained. Secondly, a weighted averaging scheme based on the features of visible and infrared images is proposed for the low frequency subband coefficients. Then, the fusion method based on PCNN is proposed for each bandpass subband coefficient. Finally, the fused image is obtained after the inverse NSCT. The experimental result shows that the method is better than the wavelet-based fusion algorithms and the traditional NSCT-based fusion algorithms.

Abstract:A method for rapid non-destructive identification of Celastrus varieties by near-infrared spectroscopy is proposed. Six kinds of Celastrus are collected. Their spectra in the region from 12493 cm^-1 to 4000 cm^-1 are obtained with a spectrometer. Through the clustering analysis of pre-processed spectral data by principal component analysis (PCA), ten principal components are obtained. Then, several variety identification models are established by combining the PCA with different stoichiometries. Since the score disribution in PC1 and PC2 has a remarkable clustering effect for different samples, it can be used to discriminate Celastrus varieties qualitatively. 165 samples randomly selected from 220 samples are used as modeling sets so as to establish linear discrimination analysis (LDA), back-propagation artificial neural network (BPANN) and support vector machine (SVM) models. The remaining 55 samples are used to validate the prediction. After optimization of principal components, all models have an identification precision up to 100%. The result shows that the proposed method is effective in the classification and identification of Celastrus varieties.