The traditional optical compensated continuous zoom medium wave infrared optical system is improved by means of the combination of the zoom group, compensation group, and post-fixed group. The optical system with super-long focal length, super-large field of view, large zoom ratio, and small envelope is obtained. The mathematical model is deduced. Based on this model, a continuous zoom optical system with focal length of 11.56-982.6 mm and zoom ratio of 85× is designed by using 640×512 refrigerated medium wave detector. The application band of the optical system is 3.6-4.9 μm, the F number is 5.5,and the cold screen efficiency is 100%. The design results show that the envelope of the optical system is 333.5 mm×125 mm (local diameter of 177.5 mm)×80 mm (local diameter of 180 mm). The structure is compact, the zoom compensation curve is smooth, and the image plane is stable in zoom process. The image quality of the optical system is evaluated from the aspects of point diagram, optical transfer function, and distortion. The results show that the optical system has excellent image quality and high resolution, and meets the requirements of the thermal imager.

This paper proposes a de-occlusion stereo matching algorithm using regional prior information. According to this algorithm, the reference image is segmented using edge detection and region growing. The homogeneous regions after segmentation are regarded as the regional prior information and introduced into the cost calculation for weakening the sensitivity of the cost function to occlusion regions. Area consistency detection is used for correcting occlusions and mismatched pixels to obtain an accurate disparity map. The proposed algorithm is an additive algorithm that can effectively improve the matching effect of the original algorithm when applied to occlusion areas without significantly increasing the amount of calculation. The result of testing the proposed algorithm on the Middlebury dataset proves that the algorithm can effectively reduce the mismatching rate when analyzing occlusion regions.

Traffic sign recognition plays an important role in driver assistance systems for traffic safety. Convolutional neural networks (CNNs) have made a significant breakthrough in computer vision tasks and achieved considerable success in traffic sign detection and recognition. However, existing methods typically fail at achieving real-time recognition. Therefore, this study proposes a modified traffic sign recognition method based on a CNN, wherein inception modules are added, the network structure is extended, and a new loss function is used to overcome the original model's difficulty in detecting small targets. German traffic sign datasets are used to simulate the effectiveness of the proposed method. Simulation results show that the proposed method can obtain higher detection rates than those of existing methods at the processing time of only 0.015 s for each image.

The difficulty to detect small targets or occlusion aircrafts poses a great challenge to the accuracy and real-time of aircraft detection. In this paper, YOLO v3 algorithm with high real-time performance is applied to the field of aircraft detection in airport scene, and two improvements are proposed: replacing the convolution layer in backbone network with void convolution, maintaining high resolution and large field of receptivity and improving the accuracy of small target detection; optimizing the NMS algorithm by linear attenuation confidence score to improve the detection accuracy of occlusion aircrafts. The results show that the improved YOLO v3 can well detect small targets and occlusion aircraft, and the detection accuracy is improved from 72.3% to 83.7% as the real-time performance is ensured.

This study proposes a Harris-SIFT algorithm that combines the color information representation and illumination compensation to resolve the contradiction between matching real time and matching accuracy of the traditional SIFT algorithm. The algorithm calculates the color information compensation before the image is grayed. By using the Gaussian weight function, the illumination compensation amount is obtained, the image contrast is enhanced, and the matching accuracy is improved. The Harris corner detection and SIFT algorithms are combined to improve the system rapidity and eliminate badly characterized feature points, improving the real-time system performance. Further, the experimental results denote that the proposed matching algorithm improves the real-time performance of the algorithm while improving the matching accuracy.

An image compression model based on the FA-LMBP hybrid neural network algorithm is proposed by the combination of the firefly algorithm and LM (Levenberg-Marquardt) algorithm. First, a set of optimal approximate solutions of the backpropagation(BP)network weight threshold are obtained by the global search of the target function based on the firefly algorithm, then the approximate solution is used as the initial value of the BP model, and the LM algorithm is used to carry out the secondary optimization training for these weight thresholds, thereby obtaining the final image compression model. The experimental results show that the reconstruction quality of the compression image model based on the FA-LMBP hybrid neural network algorithm is obviously higher than that of the basic BP model and the LMBP model under the same training times and error accuracy.

Based on polarization imaging characteristics and deep feature classification requirements, an object detection method based on deep sparse feature learning of salient polarization parameters is proposed. First, the salient polarization parameter image is constructed as the source image based on polarization analysis. Then the sparse feature of the image to be detected is learned by discriminant dictionary pair, and the object is classified and located by the dictionary pair which is used as the classifier in CNN framework. Finally, the typical object and scene are selected for data acquisition and model training according to the practical application requirements of polarization imaging detection, and some simulation experiments are conducted. The results show that the detecting score and average detection precision of the proposed method are improved at different degrees by comparing to the polarization direction detection methods and the effectiveness of this method is verified. The proposed method has application value for improving the detection ability of polarization imaging effectively.

This study focuses on the output characteristics of an 808-nm laser diode (LD) end-pumped Nd∶YVO4 mode-locked laser with a semiconductor saturable absorber mirror (SESAM). Further, a mode-locked laser with variable output power is designed by the selection of the output mirror transmittance. First, the influence of the output mirror transmittance on the laser mode-locked power and threshold is systematically studied. An output mirror transmittance of 10% and a pumping power of 8 W result in the highest output power of 2.58 W that corresponds to a conversion efficiency of 32.3%, whereas an output mirror transmittance of 0.1% results in a low continuous-wave mode-locked threshold of 1 W and an output power of 0.58 mW. Second, the mode-locked pulse width is measured by using a home-made autocorrelator; the parasitic oscillations of the laser are analyzed based on the autocorrelation traces. Thus, we obtain mode-locked pulses with a pulse width of 13 ps at a repetition rate of 150 MHz by optimizing the output mirror to suppress the parasitic oscillations.

Herein, a versatile parabolic band integrating mirror for a rectangular spot is proposed. A segmented parabolic equation satisfying the ultraprecision turning process is derived using an established segmented parabola homogenization model. Using the integrating mirror, homogenization width in the integrating direction larger than that of the traditional rotating segmental linear integrating mirror can be obtained, which is considerably larger than the input spot diameter. The influence of the number of integrating mirror segments on the uniformity of the output spot is analyzed. To avoid large deviation errors caused by a geometric method, the transmission equation for traveling Gaussian waves is used to calculate the defocused spot size in the rotation direction. Two integrating mirrors with output spots of 100 mm×3.2 mm and 14 mm×3.3 mm are designed and simulated by SolidWorks and Zemax, respectively, and the simulated spot sizes are 100.2 mm×3.2 mm and 14.2 mm×3.3 mm. The 14 mm×3.3 mm integrating mirror is processed and tested with a 3-kW fiber laser. The spot size is measured to be 14.3 mm×3.3 mm, and the uniformity of the homogenization direction reaches up to 90%, which satisfies the application requirements for laser processing.

Laser transformation hardening of the vermicular graphite cast iron material is numerically simulated using a two-dimensional discrete 5×5 lattice spot exhibiting a uniform distribution of the laser heat flux density to improve the resistance of the material surface to wear and combine strength and toughness. Further, the changes of temperature field and hardened layer during the hardening process are analyzed by changing the laser power and laser loading time. The results denote that the distribution pattern of temperature field during the laser transformation hardening based on beam discretization corresponds to the spatial distribution of the lattice spot. At the end of laser loading, the center point temperature of each sub-spot reaches the peak value. The center point temperature of the whole area of the lattice spot is the largest because of the superposition of the temperature field of each sub-spot, and the temperature distribution along the center point of each sub-spot is wavy. As the depth increases along the cross section, the temperature gradually decreases, and the overall temperature of the material increases with the increasing laser power and laser loading time. The hardened layer of each laser spot is crescent-shaped. With an increase in the laser power, the distribution of the section of the hardened layer remains basically unchanged and exhibits a discrete distribution. With an increase in the laser loading time, the hardened layer changes from discrete to integral crescent shape. Additionally, the maximum depth of the hardened layer obtained using a numerical simulation increases with the increasing laser power and laser loading time. During the process of laser transformation hardening, increasing laser power combines strength and toughness between the high hardness of the laser irradiated reinforced area and the low hardness of the laser non-irradiated non-reinforced area on the surface of the material while increasing the depth of the hardened layer. Although increasing laser loading time can increase the depth of the hardened layer, the combination of strength and toughness of the material surface cannot be achieved because of the cumulative effects of heat conduction on the laser irradiated and non-irradiated areas on the material surface.

The tracking algorithm always receives an inaccurate object position because of the poor robustness of the features in the multi-domain network tracking (MDNet) algorithm network model and the loss of the target background information. In this study, we propose a multi-domain convolutional neural network visual tracking algorithm based on the combined reconstructed features. This algorithm performs the deconvolution upsampling operation on an advanced object feature to obtain reconstructed features containing the background information. This advanced object feature is extracted using the end convolutional layer and is combined with the reconstructed feature, which can enhance the robustness of the feature and effectively distinguish an object from the background,thereby improving the object tracking accuracy in situations such as object occlusion, illumination change, and object deformation. The proposed algorithm is tested using the OTB50 and VOT2015 tracking test sets. When compared with the MDNet algorithm, the tracking accuracy and tracking success rate of the proposed algorithm are improved by 1.53% and 2.03%, respectively.

A multi-filter collaborative tracking algorithm based on high-confidence updating strategy is proposed. First, the multi-layer convolutional features of the region around the target are extracted using VGG-Net-19, which is a convolutional network architecture, followed by an adaptive feature fusion strategy with the designed deep filter to get the initial position of the target. Meanwhile, a scale filter is constructed to detect the size change of the target. Then, a tracking confidence indicator named primary and secondary peak slope ratio is utilized, which helps to build a high-confidence model updating strategy. Finally, when the confidence is insufficient, the object region proposals are extracted by EdgeBox method, and the final position of the target is determined by the designed re-detection filter. The experimental results on OTB-100 and TC-128 datasets show that the proposed algorithm achieves high tracking precision and also tracks steadily under some complex circumstances, such as occlusion, illumination variation, and out-of-view.

To deal with disordered data involving white noise and random missing points, a three-dimensional point cloud registration method based on factor analysis was proposed. First, the mathematical model of a point cloud was extended to an orthogonal factor model, transforming the point cloud registration problem into the model parameter solution problem. Then, a Gaussian mixture model was used to fit the point clouds, and the factor load matrix of an orthogonal factor model was obtained via the exponential moving average (EMA) method. Finally, the factor load matrix was used to perform point cloud registration. In a simulation experiment, the registration accuracy of the factor analysis algorithm for noisy point cloud data with missing points was found to be equal to that of the classical iterative closest point (ICP) algorithm, and 70% higher than that of the classical ICP algorithm. The factor analysis algorithm did not fall into local minima and could yield clear improvements in efficiency, registration accuracy, and stability.

Herein, a new method for noncontact measurement is presented to quickly digitize vases and other objects with rotary properties. This study develops a cultural-relic digital three-dimensional (3D) reconstruction system, which overcomes the problems of missing inner cavity information and texture distortion in cultural-relic 3D reconstruction. For the inner cavity, the beam direction of the laser range finder is changed by an optical mirror. The point cloud data of the cultural-relic inner cavity is acquired by combining the movement of the turntable and the guide rail. The point cloud data of appearance is obtained by a line laser scanner combined with the turntable motion. High-resolution orthoscopic texture images of the cultural relic at different rotation angle positions are obtained using telecentric lens and a camera to reduce the distortion of the cultural-relic texture. All the extraction pixels are then stitched together to reconstruct a complete appearance texture unfolding. Finally, the method of calibration and compensation for the system measurement error is introduced. Experimental results show that the proposed method can realize the 3D digitization of vases. The overall reconstruction error of the cultural relic is less than 0.5 mm; hence, it can meet the demands of a rapid and safe 3D digitization of cultural relics in the museum site.

Criminal investigation is often conducted based on the surveillance video footage and crime-scene shoeprint identification. The basic principle of this method is to infer the type of shoe worn by the suspect based on the shoeprints identified at the crime scene and to subsequently search for the shoe in the surveillance video footage. To solve the problem of low automation associated with this criminal investigation method, a new shoe type recognition method using a convolutional neural network has been proposed in this study. According to the unique characteristics of shoe type recognition, a framework of convolutional neural network is designed on the basis of DeepID, and a shoe database containing 50 pairs of shoes and 160231 images is constructed. The experiments are conducted based on the Caffe framework using different network models. Initially, the network structure comprises two convolution layers, two pooling layers, and two full connection layers. Further, experiments are conducted to compare the effects of the number of output elements in the first layer of two full connection layers on the network performance and training efficiency, and the experimental results of different network depths are compared without changing the size of the output feature graph as well. Based on the optimization model, the optimal network model is obtained by using overlapping pooling. The experimental results denote that the proposed method achieves an excellent performance, with an accuracy of 96.06%. Therefore, the proposed method can be considered to be a promising new method for shoe type recognition.

To address the problems of large noise, multi-view, and difficult to locate and identify digital characters in road scenes, a robust method for recognizing digital characters in road scenes is proposed. According to this method, the maximally stable extremum region algorithm based on the color space and enhanced edge is used first to obtain candidate regions. Then, a geometrically constrained filter is designed and combined with the stroke width transform to filter non-character regions. The convergence function and pooling window of Lenet-5 are improved, and the localized character regions are normalized and input into the network to obtain the final recognition results. According to the experimental results, the recall rate of the proposed method is 90.0%, the comprehensive performance value is 0.89, and the character recognition rate is 88.6%. These results are higher than those of the existing algorithms.

Herein, based on the TE1 mode theory of the parallel-plate waveguide and assumption of approximately linearly polarized electric fields, we derive the expression of electric-field propagation of a cos-Gaussian beam in the parallel-plate waveguide. Under the paraxial approximation, the cos-Gaussian beam can be used as a fundamental solution for parallel-plate waveguides. The derived beam is tested by numerical simulation in copper parallel-plate waveguides at a frequency of 0.35 THz. Results show that the simulated amplitude and phase are in good agreement with those from the analytical expression of electric field. The deviation between the simulated phase and theoretical results at the z-axis and completeness of the electric-field divergence equation are also discussed.

We propose an optimized uniform illumination design method for the ultraviolet (UV) light-emitting diode (LED) array module considering its application in UV light purification systems. First, the geometrical optics theory is used to evaluate the illumination of the purification layer receiving surface based on the luminescence intensity distribution in case of a single LED. Then, by combining the requirements of the spacing between the array module and purification layer, the ratio of the minimum and maximum illuminances of the purification layer, and illumination angle of a single LED, the optimal array spacing and simulation results of illuminance distributions are obtained by the dichotomy method along with the TracePro simulation. Finally, an uniform illumination measurement test has been designed for the array module to verify the accuracy of the simulation results. The research results demonstrate that the proposed method can realize an optimal design of the LED array module under different requirements with respect to the illumination uniformity, structure, and light source; furthermore, the proposed method exhibits important guiding significance for the design and development of UV light purification systems.

The problem of leakage current limits the application of traditional semi-insulated gallium nitride (GaN) photoconductive semiconductor switches (PCSSs) in high-voltage applications. Therefore, a n-type epitaxial layer is grown on the semi-insulated GaN∶Fe substrate (as a laser-triggered region), in which a cell array of vertical double-diffusion-field-effect transistors (as a voltage-triggered region) is constructed. In other words, a reverse pn junction controlled by the gate voltage is introduced into the traditional vertical PCSS structure for restraining the leakage current of the semi-insulated GaN due to the carrier depletion effect of the pn-junction space-charge region. The device simulation results show that the 10-kV bias voltage is reasonably shared by the laser- and voltage-triggered regions and the leakage current of the device is two orders of magnitude less than that of the traditional PCSS with the same bias electric field. Moreover, the results show that the bias voltage of the voltage-triggered region can be transferred to the laser-triggered region quickly with the gate opening; thus, the laser energy efficiency is heightened because the dynamic bias electric field increment across the laser-triggered region brings a high photocurrent peak. Moreover, the relationships between the laser parameters and the device output characteristics are calculated and analyzed to ensure high laser energy efficiency.

Hyperspectral remote sensing image data have characteristics of high dimension, spatial correlation, and feature nonlinearity, based on which a spatial-spectral feature extraction classification method based on deep learning is proposed herein. First, the weight decay is added to a stacked sparse auto-encoder. Next, the principal component analysis method is used to reduce the dimensionality of the image data. Then, neighborhood information is sorted, deleted, reorganized, and stacked according to the difference between the first principal component of all pixels in the principal component image block and the central pixel. Finally, the obtained spatial-spectral information is input into a stacked sparse auto-encoder combined with the SoftMax classifier for classification. The comparison of two sets of experimental data reveals that the proposed classification algorithm improves the classification accuracy of hyperspectral images.

A method for generating tree skeletons by thinning the voxels of point cloud data has been proposed based on point cloud data acquired by a terrestrial three-dimensional laser scanner. First, the voxel space is constructed based on the point cloud data of a tree, and the voxel coordinates of point clouds are calculated simultaneously. Second, the noise points in the voxels are filtered according to the statistical information of point cloud data in each voxel. Third, the voxels without noise are thinned using thinning templates. The skeleton nodes are then fitted considering the thinned voxels. Finally, based on the connectivity of natural trees, a depth-first search algorithm is employed to connect nodes and generate tree skeletons. The proposed method is tested with a ginkgo tree and an Amygdalus triloba f. multiplex tree. The two trees are scanned by a terrestrial three-dimensional laser scanner. The effects of different parameters on the tree skeleton are analyzed by using the tree point clouds with different scanning accuracy. In comparison with the GSA method, the proposed method can reduce time consumption for the tree skeleton generation of the ginkgo tree and the Amygdalus triloba f. multiplex tree to 1/30 and 1/67, respectively. Experimental results show that the skeletons of the two trees generated by the proposed method are consistent with the original point clouds,and the proposed method is feasible and efficient.

In order to overcome the problems of tracking and registration based on a target point cloud in augmented reality, a robust Z-score hybrid tree registration algorithm is proposed. The noise is identified by the vertical distance from the point in the local neighborhood to the fitting plane and the distribution at normal point of the plane. The robustness of the Z-score is enhanced by utilizing the median absolute deviation; the hybrid tree algorithm is used to improve the efficiency of the nearest-point search. We demonstrate formulation by applying the proposed method to the imaging principle of augmented reality. The proposed algorithm is verified by using the point cloud dataset from a research group in Stanford University and real data. Experimental results show that, for the point cloud dataset with noise, the algorithm can maintain a certain accuracy while effectively improving the registration efficiency, which takes time about 5%-10% of that of the comparison algorithm.

Synthetic aperture radar images are characterized by salt & pepper noise that affects change detection accuracy. To solve this problem, an improved method based on intuitionistic fuzzy C-core mean clustering is applied to synthetic aperture radar image change detection. First, we use algebraic operation methods, such as the difference, ratio, and image regression methods, to construct three types of spectral variation difference images, and three different images are grouped to column vectors. Second, the principal component analysis algorithm is used to extract features from column vectors of the difference images. Finally, the change map is obtained using intuitionistic fuzzy C-core mean clustering. Experimental results show that the proposed method can reduce the influence of salt & pepper noise in synthetic aperture radar images, retain feature information, and improve the accuracy of change detection in the image.

Obtaining depth estimation of a scene from a two-dimensional image is a classic computer vision problem that plays an important role in three-dimensional reconstruction and scene perception. Monocular image depth estimation based on deep learning has been developing rapidly in recent years with new methods being proposed rapidly. This study discusses the application history and research progress in deep learning-based monocular depth estimation and analyzes several representative deep learning algorithms and network architectures in detail for both supervised and unsupervised learning. Finally, the research progress and trend of the deep learning in the monocular depth estimation field are summarized. Existing problems and future research priorities are discussed as well.

The development of semiconductor lithography technology requires light sources with short wavelengths. Excimer laser-based lithography light sources (i.e., KrF-248 nm and ArF-193 nm) are gradually replacing the previously used light sources based on a Hg lamp, which are the commonly used light sources in current semiconductor lithography technology. The optical components that are currently employed in lithographic light sources primarily use calcium fluoride (CaF2) materials, which have excellent transmission characteristics in the deep ultraviolet region. In this study, the damage characteristics of the laser-material interaction in the development of the light source are analyzed. The development of the research on ultraviolet resistance of CaF2 materials is comprehensively analyzed by investigating the physicochemical properties of CaF2 materials, characteristics of laser radiation, and damage mechanism of the laser-material interaction. The laser damage characteristics of CaF2 materials for different applications are compared. The approaches and methods to improve the damage thresholds of optical components are summarized.

Compared with the conventional laser processing, femtosecond laser processing exhibits a smaller melting zone and a negligible heat-affected zone. In addition, the shock waves and cracks caused by femtosecond laser processing are almost invisible under normal conditions. Therefore, the usage of femtosecond laser has already been extensively adopted in precision manufacturing, organism processing, special material processing, and other fields and exhibits promising prospects for future development. Herein, the processing of ultrahard material using the femtosecond laser technology is reviewed with respect to its mechanisms, methods, and applications at home and aboard in recent years. Machining and processing methods for ultrahard material tools are also introduced and evaluated using the recently developed femtosecond laser techniques. Finally, the potential problems and future prospects associated with the femtosecond laser technology applications in ultrahard material machining are discussed.

Green lasers having a compact structure, high efficiency, and stable performance are widely used in optical storage, laser printing, stage performance, medical treatment, and underwater communication, particularly in laser displays. All-solid-state lasers based on frequency doubling technology are currently the most effective means of generating green lasers because of the lack of efficient semiconductor lasers having the corresponding output spectrum. With the developed technology of a periodically poled crystal, green lasers based on quasi-phase-matched intracavity frequency doubling in PPMgLN have been rapidly developed in recent years. Hence, this study reviews the multiple structures, performance advantages, and development status of green lasers based on PPMgLN.

Space division multiplexing (SDM),high order modulation, digital coherent detection,and digital signal process have become the necessary technologies for realizing a optical fiber transmission system with ultra-large capacity, ultra-high rate, and ultra-long distance. SDM technology has become the key part to realize Pbit/s transmission, and has become a research hotspot in resent years. A multicore fiber amplifier with SDM is analyzed systematically. In this paper, the research progress of multicore amplifiers, including multicore erbium-doped fiber amplifiers, multicore few-mode erbium-doped fiber amplifiers, multicore erbium-ytterbium co-doped fiber amplifiers, multicore Raman amplifiers, multicore remote pump amplifier,and hybrid multicore amplifiers, are reviewed. Finally, the future of multicore amplifier technologies is prospected.

Magnesium alloys are the lightest structural material; thus, their demand in the automotive, aerospace, electronics, and medical fields has increased remarkably. The development of laser additive manufacturing technique allows for the production of high-performance magnesium alloy parts with complex structures. In this paper, we review the local and international manufacturing of magnesium alloys via laser additive manufacturing. The effects of laser process parameters and powder material on surface morphology, spheroidization, defect, porosity, loss of alloying elements, microstructure characteristics, mechanical properties, and numerical modeling are introduced. The limitations and gaps in the researches on magnesium alloys are summarized herein. In addition, an overview of future research prospects and the applications of magnesium alloys are discussed.

Tunable diode laser absorption spectroscopy (TDLAS) technique is a non-intrusive measurement method for measuring gas concentration and temperature,and it requires scanning over the entire spectrum. To relieve the overlapping spectral interference and increase the measurement rate, a fixed-wavelength absorption spectroscopy technique is used. To study the effect of fixed-wavelength absorption spectroscopy for temperature measurement, a distributed feedback laser with a center wavelength around 2.0 μm is used to measure the temperature of CO2. Since the absorption spectrum peak is affected by the lineshape function, the peak value of the lineshape function is simulated by querying the database, and then the relationship between the absorption peak ratio and temperature under the test condition is simulated. Finally, a tube furnace is used in the validation experiment. The experimental results show that the measured temperature is highly consistent with the set temperature. In the range of 673-1273 K, the average temperature measurement error is 1.22%, which proves the feasibility and accuracy of the fixed-wavelength absorption spectroscopy technique.

Calorific value is a major indicator of the quality of coal. In this study, we use nanosecond, femtosecond, and dual-pulse laser-induced breakdown spectroscopy (LIBS) to quantitatively analyze the calorific values of 18 standard coal samples exhibiting different calorific values. The comparison of nanosecond and femtosecond plasma spectra denotes that the femtosecond plasma spectra exhibit less continuous noise and better signal-to-noise ratio when compared with those exhibited by the nanosecond plasma spectra; further, the relative standard deviation of the spectral line intensity is smaller, whereas the stability is higher. Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) can obtain high linear fitness of the element content without any baseline correction but it has the disadvantage of weak spectral intensity. We construct a dual-pulse LIBS system using a femtosecond laser as the spectral excitation source and a nanosecond laser for plasma heating. Experiments denote that the dual-pulse LIBS can considerably enhance the emission lines. Finally, we combine nanosecond, femtosecond, and dual-pulse LIBS with the partial least squares (PLS) method to quantitatively determine the calorific values of coal samples. The fitting degrees of the calibration curves (R2) are 0.9553, 0.9897, and 0.9964 for the three aforementioned methods, respectively. The use of dual-pulse LIBS can effectively improve the accuracy of the quantitative analysis of the calorific value of coal.

Laser absorption spectroscopy is widely used for monitoring trace gases in the atmosphere. However, some gases have several overlapping absorption lines. Therefore, the peak signals overlap; this affects the measured results. For example, the NH3 peak near 6529 cm -1 includes four overlapping absorption lines. Absorption spectra at different pressures are simulated to obtain four absorption lines separated by Voigt-line profiles. First, the experimental platform is established by the direct absorption method. The experiment demonstrates that the peak value of the NH3 spectra at low gas concentration does not decrease. However, the peak value increases at a lower gas pressure. The phenomenon may be attributed to the absorbability of NH3. Therefore, the CH4 gas, which does not exhibit absorbability, is used to verify this assumption. Finally, by analyzing the NH3 absorption lines at different pressures, it is determined that the difference between the experimental and simulated spectra is the smallest when the gas pressure is 0.18 atm(≈18 kPa)and the maximum absorption peak of NH3 is proportional to the gas concentration. This result solves the interference problem, which will improve future gas-detection approaches.

For the spectral absorption curve of cavity ring-down spectroscopy (CRDS), this study uses the MATLAB software and the least-squares algorithm to program six different line-shape fitting algorithms (VP, GP, AVP, AGP, SDVP, and SDAVP algorithms) commonly used for gas absorption spectroscopy and designs interface windows for the parameter setting and fitting result display. The absorption spectrum of the carbon dioxide gas with known concentration is obtained using CRDS. The six aforementioned algorithms are used to fit the absorption spectrum, and the fitting residuals of six different line-shape fitting algorithms are compared. The fitting results denote that the fitting curve is almost coincident with the measuring points. The average AVP residual is the largest, which is 3.1748×10 -3, whereas GP and AGP denote the minimum average residual of 1.3212×10 -3. This study verifies the feasibility of the six aforementioned line-shape fitting algorithms with respect to the fitting of gas absorption spectrum. The AVP's operation time is the shortest(i.e., 1.7516 s), whereas the SDAVP's operation time is the longest (i.e., 389.682 s). The GP's operation time is less than that of AGP. Therefore, the GP is most suitable for performing the CRDS spectral line-shape fitting with respect to the residuals and operation time.