This PDF file contains the front matter associated with SPIE Proceedings Volume 12558, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

Laser-induced breakdown spectroscopy (LIBS) is a promising technology for nuclear safeguard because of the advantages of rapid analysis, in situ and real-time detection. The potential application of LIBS is simulatively investigated for continuous uranium emission monitoring during the nuclear accident. The aerosol containing UOx is generated with laser ablation to simulate the uranium emission in laboratory. The laser induced plasma emission in the aerosol has been continuously analyzed with a spectroscopy. The characteristic spectral lines of uranium have been clearly identified. The intensity variation of uranium spectral lines agrees well with UOx particles emission and sedimentation process in aerosol. The potential of LIBS is demonstrated for emergency and continuous emission monitor in nuclear accidents.

In view of the current problems in online monitoring of non-methane total hydrocarbons (NMHC) in the exhaust gas from stationary sources, the study is based on the combination of tunable diode laser absorption spectroscopy (TDLAS) and hydrogen flame ionization detection (FID) technology in online monitoring of NMHC. The online measurement of trace methane content is realized by using a laser with a center wavelength near 1653.7 nm and a design based on the White-type multiple reflection absorption cell. At the same time, a heated FID detector is used to measure the total hydrocarbon (THC) content, and the NMHC concentration can be calculated through the detection of the total hydrocarbon and methane content. Measured (0-207) mg/mg³ NMHC system in laboratory and obtained NMHC linearity error of -0.59%F.S., with a repeatability and detection limit of 0.45% and 0.05 mg/m³ respectively, with advantages of low detection limit and good repeatability, high linearity, and strong anti-interference ability, etc., it meets the requirements of NMHC online monitoring applied to exhaust gas of stationary pollution sources.

Under the condition of normal gravity, it is difficult to perceive weak changes of microscopic matter caused by material combustion. To meet the requirement of long-term microgravity environment, it is necessary to establish a combustion science experimental system in space station. For combustion experiment on orbit, a compact optical observation facility is designed in this paper. The facility bases on schlieren imaging, which is able to observe density distribution and flow-field change in combustion experiment. According to the characteristics of space condition, a highly reliable optical lens and mechanical structure are designed. The simulation experimental results show that our design is of high reliability, which is able to be used in complex condition of space combustion experiment.

A spectrometer is a type of apparatus that measures the intensity of spectra at different wavelengths utilizing dispersive components and detectors. In recent years, with the increase of application scenarios and demands in scientific research and industrial fields, researchers have make efforts in the miniaturization of spectrometers and there have been many new developments. Among them, the application of metalenses in spectrometers has made great progress. As a kind of two-dimensional subwavelength microstructure, metalens can flexibly control the phase, amplitude and polarization of electromagnetic wave. On account of its small size, light weight and planar characteristics, researches have been conducted to replace conventional optical components with metalens. In this paper, we introduce the design principle of metalenses, and summerize different kinds of miniaturized spectrometers using metalenses, focusing on the dispersion mechanism, optical characteristics and performance of efficiency of these spectrometer system. These applications of metalenses to spectrometers take advantage of their refraction, differaction and polarization characteristics and can be beneficial to exploit other complex functions. We discuss current challenges, potential application fields and promising future developments of metalens design for spectral analysis.

Hadamard transform spectral imaging technology has high signal-to-noise ratio and the advantage in energy distribution. In recent decades, related works focus on dynamic coding Hadamard spectral imager instead of static coding ones. The latter has great potential value in spaceborne and airborne applications, but the accuracy of its restored spectral image data is poor at present. Therefore, it is necessary to further study the formation mechanism and correction method of errors in static coding Hadamard spectral imager. In this paper, the influencing mechanism and correction method of spectral overflow are studied. Firstly, the imaging and restoration process of the Hadamard coding spectral imager with spectral overflow is mathematically deduced, and the conclusion is compared with the simulated restoration results. Secondly, the simulated results are compared with the experimental data, verifying that spectral overflow is an important source of the error in experimental results. Finally, a correction method for eliminating spectral overflow errors under certain conditions is proposed, and the effectiveness of the method is verified by simulation. This paper can provide reference for the design and data restoration of static coding Hadamard spectral imager.

Chinese government advocates a low-carbon development and strives to achieve carbon peak and carbon neutralization. It is an important issue to safely allocate and use of natural gas, which is a relatively clean basic energy and owns a huge consumption demand. In this paper, a sensitive experimental device based on CRDS has been developed to measure natural gas leakage in the ambient atmosphere, while a wavelength scanning baseline correction method is proposed for measurement in multicomponent ambient atmosphere. The minimum Allan deviation of the CRDS device with the laser for CH4 detection reaches 9 × 10-11 cm-1 , corresponding to the NEA of 6.4 × 10-10 cm-1 Hz-1/2. The minimum value of Allan deviation becomes 1.1 × 10-10 cm-1 after replacing the laser for C2H6 detection. The measurement sensitivity of the device for methane and ethane is 0.24 ppbv and 2.44 ppbv respectively. The device is calibrated with 1 ppmv CH4/C2H6 standard gases, and the measurement precision is 4.1/18.6 ppbv respectively. In addition, the concentrations of CH4, H2O and CO2 in the ambient air are measured by the wavelength scanning method, and this method and results can be used to determine the baseline of field measurement in the future.

The development of heavy metal mining area will pollute the surrounding soil and do great harm to the ecological environment and human health. Soil classification in different mining areas is of great significance to soil management and environmental pollution control. Soil is easily affected by matrix effect due to its complex physical properties and composition. Thus, accurately classifying soils is a challenge. Laser-induced breakdown spectroscopy has developed rapidly in the past two decades. It has been widely used in the detection of various physical samples due to its characteristics of fast analysis speed and no need for sample pretreatment. However, traditional LIBS technology has disadvantages such as low sensitivity, obvious noise and poor repeatability, which affect the accuracy of quantitative analysis. In this paper, a soil classification method based on principal component analysis (PCA) based laser-induced breakdown spectroscopy (LIBS) and random forest (RF) algorithm was proposed, and the standard soil samples from six different mining areas were accurately identified and classified. The final prediction results based on this combination show that the accuracy of soil classification by PCA-RF machine learning model can reach 97.86%. From the aspect of classification accuracy, it can be found that laser-induced breakdown spectroscopy combined with PCA-RF can achieve rapid and accurate classification of soil in different mining areas, which also provides a new method for soil classification in heavy metal mining areas.

Spectral confocal technology uses chromatic aberration which is generated by a dispersion lens to detect surface shape. The axial dispersion generated by the dispersion lens will affect the measurement range of the whole spectral confocal displacement sensor. The refractive index of an axial GRIN (gradient index, GRIN) lens varies non-homogeneous along the axial direction and is constantly perpendicular to an optical axis of the plane. The paper explored the design of a dispersive objective lens for a spectral confocal displacement sensor based on the GRIN lens. Firstly, the optical power and axial dispersion models of the GRIN lens are established. The axial dispersion can be realized by focusing the light of different wavelengths at different positions of the optical axis. Secondly, based on the optic power and dispersion function of the GRIN lens, the refractive index distribution of the GRIN lens and the simulation design of the dispersive objective lens is obtained by using MATLAB and ZEMAX software respectively. Finally, the GRIN dispersive objective lens is optimized by setting different merit function operands. The experimental results show that the axial GRIN lens can achieve a focal shift of 1130μm within the wavelength range from 486nm to 656nm. Moreover, the linearity of the lens behaves well. All the blur spot is much smaller than the airy spot. The lens has well-focused as well as high-precision. The research results provide a reference and theoretical basis for the application of the GRIN lens in spectral confocal technology.

The Mars Surface Composition Detector (MarSCoDe) is impacted by the lack of energy supply, the ambient temperature outside, and the spectral line drift issue, which makes it very difficult to analyze flight and ground data together. The BEADS algorithm is used to eliminate the spectral baseline generated by ambient light, noise, dark background, and continuous radiation signals in the original spectrum. The influence of parameters like detecting distance, focusing level, and laser energy jitter can be reduced to the fullest extent through normalization processing. To determine the mineral types and quantitative evaluation of elements on the surface of Mars as much as possible, the team tested 35 national standard minerals and obtained1750 spectra in an effort to ascertain the types of minerals and quantitative evaluation of elements as possible on the surface of Mars.. A fuzzy decision of flight data based on assa-grnn is proposed and supported by the national standard mineral database on the ground. When combined with the decision results, the wavelength of the measured Mars data is modified twice, which supports the interpretation of flight data by laboratory data. Results demonstrate that assa-grnn can flawlessly improve the wavelength transfer between the ground database and flight data of MarSCoDe.

Mars exploration is the biggest hot spot of deep space exploration after lunar exploration, and it is also an important target of manned planetary exploration in the future. For Mars exploration, the detection of its surface material composition is one of the important scientific tasks. This paper mainly introduces a new method for the detection of material components on the surface of Mars - Raman spectroscopy technology, briefly explains the principle of Raman scattering, summarizes the research progress of Raman spectroscopy technology for the detection of material components on the surface of Mars at home and abroad, and analyzes the development prospect of Raman spectroscopy technology in the field of Mars exploration.

In order to realize space-based remote sensing for river, lake and ocean environment monitoring and break through the key technology of hyperspectral imaging system, based on the Ritchey-Chrétien (R-C) Cassegrain folding reflector telescope, we designed a front telescope system with a working altitude of 600 km, a working spectral band of 450-900 nm, a full field of view of 2.9°, a system focal length of 400 mm , the optical speed of F/4 and effective control of aberration and chromatic aberration of adding three-piece correction mirror to control the field of view is designed. The initial structural parameters of the front telescope system are solved according to the primary aberration theory, and the secondary mirror blocking ratio of the front telescope system is designed to be 0.35, and the simulation and optimization design are carried out in ZEMAX software. The system performance analysis shows that more than 80% of the energy of the imaging spot is concentrated within 12 μm.The maximum aberration is 0.78% and the maximum magnification chromatic aberration is 1.26 μm.The RMS (Rate-Monotonic Scheduling) radius of spot at all fields of view and wavelengths in the point column diagram is less than 2.6 μm (Airy radius is 3.3 μm). At the Nyquist frequency of 25 lp / mm, the MTF (Modulation Transfer Function) values of each spectral section in all fields of view are greater than 0.8.All the above evaluation indexes meet the performance requirements of hyperspectral imaging system

With unique properties, multispectral cameras have become a hot research direction in recent years. In our country's major space mission "Mars Exploration Project", the multispectral camera was mounted on the " Zhu Rong" rover as an important payload to complete a number of exploration missions. Due to the optical structure of the multispectral camera and other reasons, there are often deviations such as rotation, scale change, and displacement between the images of each channel. For multispectral images, there may be huge differences between the image grayscales of different channels. For the same target subject, the local grayscale contrast may even be opposite. Therefore, the conventional image registration method is difficult to solve the alignment issue between the subjects in each channel. In this paper, a calibration method based on a pyramid mixture model of the circular templet is proposed, and a set of accurate calibration parameters is obtained by using the templet images, so as to complete the channel registration of the Mars multispectral image. At the same time, based on the particularity of the template images, an objective evaluation criterion of geometric calibration is proposed.

With the improvement of spatial resolution, the focal length of space cameras and spectral imagers become longer. The thermal stability of image stability is more sensitive, with the temperature, especially in VNIR (visible and near-infrared). To solve the thermal stability of R-C(Ritchey-Chrétien) long focal length fore-telescope system, the relevant factors are discussed, on the basis of the LASIS(Large Aperture Static Imaging Spectrometer), and the change in the spacing between primary mirror and secondary mirror with temperature is proposed. Base on the calculation in theory, the method of mechanical passive athermalization design is developed. Mechanical test results indicate that the first natural frequency is 195Hz, above the 100 Hz. The thermal experiments show that the stability of primary mirror and secondary mirror spacing is 0.5μm.℃^-1 , consisting with the FEA(Finite Element Analysis) value

Raman spectroscopy technology is a spectral analysis technology based on Raman effect. With this technology, the molecular structure information can be identified and analyzed quickly, nondestructively and effectively, and it has high spectral specificity. Raman spectroscopy technology can provide the mineral components information of lunar soil samples, which is of great significance for lunar surface exploration and future resource utilization. In this paper, a 785nm Raman spectroscopy detection system was set up, and used to detect and identify phosphate components with different doping concentrations in various types of earth soils, which provided data support for further analysis of lunar samples.

In this paper, the near-infrared spectral data of five different types of starch were collected, and the starch species identification model was constructed by using a quaternion convolutional neural network (QCNN), we proved that the qualitative model based on QCNN has obtained higher prediction accuracy than traditional qualitative models. In the experimental results, the classification accuracy of QCNN for five different starches reached 0.996. The results show that the combination of the quaternion spectral fusion method and deep learning is more conducive to extracting and mining the deep information of NIR spectra and has important research significance and application value in the field of near-infrared spectroscopy technology

Space object images obtained through ground-based telescopes tend to be heavily blurred and degraded by the atmospheric turbulence as well as detection noise and aberrations of optical systems. Multi-Frame Blind Deconvolution (MFBD) is currently the mainstream image restoration algorithm for images degraded by the atmospheric turbulence. MFBD jointly estimates the original image of object and the corresponding point spread functions (PSFs) from a sequence of short-exposure images. From our experience, there are still a lot of space for the improvement of the traditional MFBD algorithm. The mixed-Gaussian noise model that accounts for both the photonic and detector noise is used to replace the stationary Gaussian noise model. The L₂-L₁ (quadratic-linear) regularization method is used to replace originally used TV regularization method or Tikhonov regularization method. The phase annealing method is used to improve the quality of initial phase estimation and the multi-round iterative MFBD algorithm is preliminarily implemented. The simulation results demonstrate that the restored images obtained by the multi-round iterative MFBD algorithm often have better quality than that restored by traditional MFBD.

At present, there are few studies on nondestructive testing of aircraft surface based on hyperspectral imaging at home and abroad. Therefore, an indoor near infrared (NIR) hyperspectral damage detection system with a spectral resolution of 5nm was established, and the paint damage on the sample surface was identified. The reflectance calibration, average reflectance calculation and principal component analysis (PCA) dimensionality reduction were performed on the collected hyperspectral data. On this basis, the unsupervised classification iterative self-organizing Data analysis algorithm (ISODATA) is used to identify the damaged samples. The results show that the spectral curves of the damaged and undamaged pixels of the sample are significantly different at about 910nm. The first 10 principal components selected can contain 97% of the sample data information, which can realize the effective identification of damage samples based on ISODATA. In this study, paint damage was taken as an experimental sample to verify the feasibility of using near-infrared hyperspectral imaging technology for damage identification. In addition, preliminary outfield experiment results also show that it is feasible to apply this technology to aircraft surface damage detection.

The detection of biological spore activity is the basis for effective prevention and control of plant and animal diseases. However, the reduction of its activity level during storage is one of the major problems affecting the application. A rapid and accurate method to detect the activity of biological spores is of great value for exploration and research. In this paper, UV-Vis spectroscopy combined with a one-dimensional convolutional neural network (1D-CNN) is used for the discrimination of dead and viable biological spore. The spectrum of three biological spores were collected and preprocessed by the standard normal variate transformation (SNV). Unsupervised clustering of the sample set was performed using principal component analysis (PCA). The activity discrimination model of biological spores is constructed based on 1D-CNN. The experimental results show that the model has a discriminative accuracy of 100%, which has the potential to replace the traditional methods of determining the dead and viable biological spore.

In recent years, pupil detection in human eye images or videos has played a key role in many fields. In the field of eye tracking, the position of the center of the pupil is a basic problem, and the error of pupil detection will be magnified in subsequent calculations, which will seriously affect the performance of eye tracking. In this paper, we propose to use the currently popular semantic segmentation network for pupil detection task. We first train the Unet architecture as a benchmark, then introduce two different attention modules into Unet, and compare with the benchmark network. The results show that our method has a higher detection rate within 1-15 pixel errors. We also added an ellipse fitting error term to the loss function of the network to further improve the network performance. The training of the model is done on the LPW dataset. Finally, we also investigate the effect of data augmentation on generalization performance, with the model trained on the LPW dataset and tested on the I-SOCIAL_DB dataset. Although data enhancement slightly reduces the detection rate of the model in the original data set, it can improve the generalization performance of the model.

Aiming at the current status that the reconstruction quality of spectral image is yet to be enhanced, an improved two-step iterative shrinkage/threshold (TwIST) algorithm is proposed in this paper. Firstly, according to the coded aperture spectral imaging principle, a mathematical model for spectral image reconstruction of coded aperture spectral imaging system based on compressed sensing is established. Then, taking the spatial smooth transition characteristics of spectral image as a priori knowledge, two improvements are proposed based on the traditional TwIST algorithm, selecting the total variation regular constraint terms and denoising the updated terms in each iteration. Finally, in order to verify the improved algorithm, the reconstructed spectral images are simulated. It shows the reconstructed spectral images retain the spatial details well, and the reconstructed spectral curve is in good agreement with the original spectral curve, indicating that the improved algorithm is effective in the high-precision reconstruction of spatial information and spectral information of the target.

A composite phosphorescent temperature measurement film based on rare earth phosphorescent powder and polymer derived ceramics (PDC) was proposed in this letter. The film was made on nickel-based alloy substrate with yttrium oxide doped europium (Y2O3:Eu3+) and polysilazane (PSN2) precursor ceramic as temperature sensing substances and high-temperature bonding layer respectively. We tested the thermal properties of the film, and built a high-temperature test system based on a calibration furnace. The phosphorescence emission spectrum and 611nm/587nm phosphor intensity ratio were measured with the 407nm excitation laser. The results showed that the film can survive for more than 3 hours at 1000℃, and the adhesion strength could reach up to 30 MPa. In the temperature range of 300~947°C, there is a linear relationship between temperature and phosphorescence intensity ratio. The temperature coefficient of phosphorescence intensity ratio is 0.0149/°C, and the temperature measurement error is less than 2.3%. The influence of high temperature thermal radiation on phosphorescence temperature measurement was studied, and the calibration curve of the sensor was corrected using the thermal radiation correction method based on the theory of incoherent light. The results show that the phosphorescence intensity changes from exponential function to linear relationship with temperature, and the upper limit of temperature measurement of the sensor increases from 947℃ to 1000℃.

A method of target material recognition based on spectral emissivity curve matching is proposed to solve the problem of target material recognition in complex field environment. In this paper, the target material recognition model of spectral emissivity curve is established, and the effectiveness of the model is verified by simulation experiments. The model input data is the spectral emissivity measurement data of the target material to be measured. The model construction mainly includes weighted deviation maximization model construction, Lagrange function extremum method solution and similarity function construction. Through the simulation experiment model, the similarity between different curves is calculated and the target is effectively identified. The influence of spectral emissivity noise on similarity is analyzed. The target spectral emissivity superposition noise amplitude is 0.01 ~ 0.05 times, and the similarity is greater than 0.6. This method can accurately identify the target material. The spectral emissivity identification method proposed in this paper can provide technical support for target identification in complex