Inertial confinement fusion ignition achieved at the National Ignition Facility – an editorial
C. N. Danson, and L. A. Gizzi
On behalf of all at High Power Laser Science and Engineering we would like to congratulate the team at Lawrence Livermore National Laboratory (LLNL) on demonstrating fusion ignition at the National Ignition Facility. This major scientific achievement was realized on the 5 December 2022 at the LLNL and announced at a press briefing on the 13 December 2022 by the United States Department of Energy’s National Nuclear Security Administration. This was a historic milestone and the culmination of decades of effort.On behalf of all at High Power Laser Science and Engineering we would like to congratulate the team at Lawrence Livermore National Laboratory (LLNL) on demonstrating fusion ignition at the National Ignition Facility. This major scientific achievement was realized on the 5 December 2022 at the LLNL and announced at a press briefing on the 13 December 2022 by the United States Department of Energy’s National Nuclear Security Administration. This was a historic milestone and the culmination of decades of effort.
- Apr. 27, 2023
- High Power Laser Science and Engineering
- Vol. 11, Issue 3, 03000e40 (2023)
- DOI：10.1017/hpl.2023.38
Monitoring and characterization of particle contamination in the pulse compression chamber of the OMEGA EP laser system
B. N. Hoffman, N. Savidis, and S. G. Demos
The laser-damage performance of optics is known to be negatively affected by microscale particle contamination induced by the operational environment. This work investigates the properties of particles accumulating in various locations near critical optics inside the OMEGA EP grating compressor chamber during quarterly operational periods over a 2-year duration. The particles found were characterized using optical microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy. The analysis indicates significant concentrations of micrometer- to nanometer-scale particles inside the vacuum chamber, with higher values observed near the port leading to the OMEGA EP target chamber. The distribution of the chemical composition of these particles varies between collection periods. Although understanding of the mechanisms of particle generation and transport remains uncertain, the hypothesis is that this particle load represents a risk for contaminating the surfaces of high-value optics located inside the chamber, including the compression gratings and deformable mirrors, and therefore affecting their laser-damage resistance and overall operational lifetime.The laser-damage performance of optics is known to be negatively affected by microscale particle contamination induced by the operational environment. This work investigates the properties of particles accumulating in various locations near critical optics inside the OMEGA EP grating compressor chamber during quarterly operational periods over a 2-year duration. The particles found were characterized using optical microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy. The analysis indicates significant concentrations of micrometer- to nanometer-scale particles inside the vacuum chamber, with higher values observed near the port leading to the OMEGA EP target chamber. The distribution of the chemical composition of these particles varies between collection periods. Although understanding of the mechanisms of particle generation and transport remains uncertain, the hypothesis is that this particle load represents a risk for contaminating the surfaces of high-value optics located inside the chamber, including the compression gratings and deformable mirrors, and therefore affecting their laser-damage resistance and overall operational lifetime.
- Apr. 20, 2023
- High Power Laser Science and Engineering
- Vol. 11, Issue 3, 03000e39 (2023)
- DOI：10.1017/hpl.2023.34
Improved Angle Constraint Lidar Obstacle Detection Method
Chang Liu, Ming Ling, Xing Wang, Shulong Zhai, and Qipeng Rao
The traditional angle constraint algorithm to detect lidar disorders can cause excessive cutting when facing the point cloud with a special angle or lack of point clouds. Therefore, an improved angle constraint three-dimensional lidar obstacle detection method is proposed. In this study, the point cloud is converted to a deep map, a new breakpoint detector is used to complete the initial segmentation and construct the chart structure, a point cloud collection is described, and the point cloud set that meets the cluster distance is combined by searching the graph node. Compared with traditional methods, the breakpoint detector enhances segmentation robustness. Also, the graph structure search solves overcutting caused by the lack of point clouds and accelerates clustering speed. Moreover, compared with traditional methods, the average time consumption of the proposed method is reduced by 51.4% while the average positive detection rate is increased by 11.5 percentage points.The traditional angle constraint algorithm to detect lidar disorders can cause excessive cutting when facing the point cloud with a special angle or lack of point clouds. Therefore, an improved angle constraint three-dimensional lidar obstacle detection method is proposed. In this study, the point cloud is converted to a deep map, a new breakpoint detector is used to complete the initial segmentation and construct the chart structure, a point cloud collection is described, and the point cloud set that meets the cluster distance is combined by searching the graph node. Compared with traditional methods, the breakpoint detector enhances segmentation robustness. Also, the graph structure search solves overcutting caused by the lack of point clouds and accelerates clustering speed. Moreover, compared with traditional methods, the average time consumption of the proposed method is reduced by 51.4% while the average positive detection rate is increased by 11.5 percentage points.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1228008 (2023)
- DOI：10.3788/LOP221510
Hyperspectral Remote-Sensing Classification Combining Transformer and Multiscale Residual Mechanisms
Yuhan Chen, Bo Wang, Qingyun Yan, Bingjie Huang, Tong Jia, and Bin Xue
Convolutional neural networks (CNNs) have achieved impressive results in hyperspectral image classification. However, because of the limitations of convolution operations, CNNs cannot satisfactorily perform contextual information interaction. In this study, we use the Transformer for hyperspectral classification to address the problem of capturing hyperspectral sequence relationships at extended distances. We propose a multiscale mixed spectral attention model based on Swin Transformer (SMSaNet). The spectral features are modeled using the multiscale spectral enhancement residual fusion module and the spectral attention module in SMSaNet. The spatial features are then extracted using the improved Swin Transformer module, and hyperspectral image classification is realized using a fully connected layer. SMSaNet is compared with five other classification models on two public datasets, that is, the Indian Pines and University of Pavia. The results show that SMSaNet achieves the best classification effect compared to the other models. The overall classification accuracies reach 99.51% and 99.56%, respectively.Convolutional neural networks (CNNs) have achieved impressive results in hyperspectral image classification. However, because of the limitations of convolution operations, CNNs cannot satisfactorily perform contextual information interaction. In this study, we use the Transformer for hyperspectral classification to address the problem of capturing hyperspectral sequence relationships at extended distances. We propose a multiscale mixed spectral attention model based on Swin Transformer (SMSaNet). The spectral features are modeled using the multiscale spectral enhancement residual fusion module and the spectral attention module in SMSaNet. The spatial features are then extracted using the improved Swin Transformer module, and hyperspectral image classification is realized using a fully connected layer. SMSaNet is compared with five other classification models on two public datasets, that is, the Indian Pines and University of Pavia. The results show that SMSaNet achieves the best classification effect compared to the other models. The overall classification accuracies reach 99.51% and 99.56%, respectively.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1228002 (2023)
- DOI：10.3788/LOP220921
Modified Imaging Algorithm for Inverse Synthetic Aperture LiDAR Based on Optical Imaging Model
Chen Xu, Anpeng Song, Kai Jin, and Kai Wei
Inverse synthetic aperture LiDAR (ISAL) is a kind of coherent imaging system. It acquires images with speckles that affect target recognition and judgment. In recent years, some scholars proposes a model-based iterative reconstruction (MBIR) algorithm to solve the problem. The algorithm directly estimates the real valued reflectance instead of the complex valued one commonly used by traditional reconstruction methods, making the reconstructed image closer to the optical image. However, the MBIR algorithm faces the problems of complex optimization model, low efficiency, and difficult convergence when the gradient-free line search version is used. To address these problems, this study presents two proposals. First, the Markov relation between the distributions of the complex reflectance and reflectivity, and the measurement signal is obtained from the viewpoint of information transfer. The complex reflectance is assumed as a complete dataset of the reflectivity estimation that simplifies the optimization. Second, the surrogate function of a prior model, whose gradient is easier to obtain, and the logarithm transformation are used to improve the algorithm efficiency in which the original problem is transformed into an unconstrained problem with gradient. The effectiveness and efficiency of the proposed method are verified by simulation and outdoor experimental data from a target 7 km away. The results show that the proposed method can obtain better images within five iterations for echo data with carrier-to-noise ratio of 5 dB, 0 dB, and -5 dB.Inverse synthetic aperture LiDAR (ISAL) is a kind of coherent imaging system. It acquires images with speckles that affect target recognition and judgment. In recent years, some scholars proposes a model-based iterative reconstruction (MBIR) algorithm to solve the problem. The algorithm directly estimates the real valued reflectance instead of the complex valued one commonly used by traditional reconstruction methods, making the reconstructed image closer to the optical image. However, the MBIR algorithm faces the problems of complex optimization model, low efficiency, and difficult convergence when the gradient-free line search version is used. To address these problems, this study presents two proposals. First, the Markov relation between the distributions of the complex reflectance and reflectivity, and the measurement signal is obtained from the viewpoint of information transfer. The complex reflectance is assumed as a complete dataset of the reflectivity estimation that simplifies the optimization. Second, the surrogate function of a prior model, whose gradient is easier to obtain, and the logarithm transformation are used to improve the algorithm efficiency in which the original problem is transformed into an unconstrained problem with gradient. The effectiveness and efficiency of the proposed method are verified by simulation and outdoor experimental data from a target 7 km away. The results show that the proposed method can obtain better images within five iterations for echo data with carrier-to-noise ratio of 5 dB, 0 dB, and -5 dB.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1228001 (2023)
- DOI：10.3788/LOP221548
Main Factor Analysis for Imaging Performance of Hand-Held Full-Field Optical Coherence Tomography System with Dual Interferometers
Ran Li, Wanrong Gao, and Yifeng Tang
In this study, we report an improved hand-held full-field optical coherence tomography (FFOCT) system based on a combination of double interferometers. The system uses a Fizeau interferometer for detection and a Michelson interferometer for compensation. The initial system was modified by the addition of a plane mirror to alter its optical path and to make it easier to arrange samples horizontally. The analysis was conducted on how the system's imaging quality and compensation distance were affected by the angle deviation between the mirror and other key components of the system. The obtained results are useful for the design and optimization of the hand-held FFOCT system.In this study, we report an improved hand-held full-field optical coherence tomography (FFOCT) system based on a combination of double interferometers. The system uses a Fizeau interferometer for detection and a Michelson interferometer for compensation. The initial system was modified by the addition of a plane mirror to alter its optical path and to make it easier to arrange samples horizontally. The analysis was conducted on how the system's imaging quality and compensation distance were affected by the angle deviation between the mirror and other key components of the system. The obtained results are useful for the design and optimization of the hand-held FFOCT system.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1217002 (2023)
- DOI：10.3788/LOP221765
Evaluation of Line-Scan Imaging System's Ability to Detect Internal Defects in Tissue Using Improved Monte Carlo Simulation and Optical Density Algorithm
Danni Sun, Qibing Zhu, and Min Huang
We use Monte Carlo simulation and optical density algorithm to evaluate the detection performance of line-scan imaging system for internal defects of tested samples in this paper. First, a fine division for the irregular tissue boundary of the internal defects is achieved using a three-dimensional voxel segmentation method, as it is difficult to accurately simulate the optical transmission of complex tissues by the traditional Monte Carlo method. Then, the effects of the instrument parameters on the penetration depth of photons in the tissue, the detection depth of the detector, and the diffuse reflectance of surfaces are analyzed, and the optimal parameter configuration is determined. Finally, the optical density algorithm is used to evaluate the detection performance of the system for defects with different sizes and depths. The simulation results show that the line-scan imaging detection system can achieve a good balance between the photon detection depth and the surface reflectivity, under a light source with an incident angle of 15° and a distance of 1 mm between the light source and detector. For large (a=2 mm, b=3 mm, c=1 mm), medium (a=2 mm, b=2 mm, c=1 mm), and small (a=2 mm, b=1.5 mm, c=1 mm) ellipsoid defects, the defect depth detection limits of the system are 3.5 mm, 3 mm, and 2.7 mm, respectively. Hence, the study provides a theoretical basis for parameter optimization and performance evaluation of the line-scan imaging system for detecting the internal defects in agricultural products such as fruits.We use Monte Carlo simulation and optical density algorithm to evaluate the detection performance of line-scan imaging system for internal defects of tested samples in this paper. First, a fine division for the irregular tissue boundary of the internal defects is achieved using a three-dimensional voxel segmentation method, as it is difficult to accurately simulate the optical transmission of complex tissues by the traditional Monte Carlo method. Then, the effects of the instrument parameters on the penetration depth of photons in the tissue, the detection depth of the detector, and the diffuse reflectance of surfaces are analyzed, and the optimal parameter configuration is determined. Finally, the optical density algorithm is used to evaluate the detection performance of the system for defects with different sizes and depths. The simulation results show that the line-scan imaging detection system can achieve a good balance between the photon detection depth and the surface reflectivity, under a light source with an incident angle of 15° and a distance of 1 mm between the light source and detector. For large (a=2 mm, b=3 mm, c=1 mm), medium (a=2 mm, b=2 mm, c=1 mm), and small (a=2 mm, b=1.5 mm, c=1 mm) ellipsoid defects, the defect depth detection limits of the system are 3.5 mm, 3 mm, and 2.7 mm, respectively. Hence, the study provides a theoretical basis for parameter optimization and performance evaluation of the line-scan imaging system for detecting the internal defects in agricultural products such as fruits.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1215005 (2023)
- DOI：10.3788/LOP221515
Spherical Center Imaging Location Solution Method for Monocular Spherical Target
Yuanjiong Liu, Guan Cheng, Bo Tang, Maozheng He, and Guozhong Jiang
A solving spherical target imaging coordinate method based on monocular vision for resolving the deviation between the two-dimensional elliptical image center and the actual spherical center imaging coordinate in the monocular vision measurement system of the spherical auxiliary target is proposed as a result of this non-collinearity of the optical axis and the spherical center. First, the monocular imaging system is calibrated, the factors influencing the deviation between the spherical center imaging coordinate and the corresponding two-dimensional elliptical image center are examined, and the monocular spherical center imaging coordinate solution model is developed. Second, the improved Zernike moment method is used to realize the edge sub-pixel extraction of ellipse images. The simulation results demonstrate that the ellipse center positioning accuracy of the improved Zernike moment method is enhanced by more than 25.00%. Finally, the experimental plan for the monocular measurement of the spherical target distance is created, and the experimental validation is done on spherical targets with various intervals. The findings indicate that the maximum absolute deviation between the spherical target moving distance calculated by the proposed method and the actual value is less than 0.039 mm, which meets the requirements of target positioning accuracy and stability of monocular vision robot system.A solving spherical target imaging coordinate method based on monocular vision for resolving the deviation between the two-dimensional elliptical image center and the actual spherical center imaging coordinate in the monocular vision measurement system of the spherical auxiliary target is proposed as a result of this non-collinearity of the optical axis and the spherical center. First, the monocular imaging system is calibrated, the factors influencing the deviation between the spherical center imaging coordinate and the corresponding two-dimensional elliptical image center are examined, and the monocular spherical center imaging coordinate solution model is developed. Second, the improved Zernike moment method is used to realize the edge sub-pixel extraction of ellipse images. The simulation results demonstrate that the ellipse center positioning accuracy of the improved Zernike moment method is enhanced by more than 25.00%. Finally, the experimental plan for the monocular measurement of the spherical target distance is created, and the experimental validation is done on spherical targets with various intervals. The findings indicate that the maximum absolute deviation between the spherical target moving distance calculated by the proposed method and the actual value is less than 0.039 mm, which meets the requirements of target positioning accuracy and stability of monocular vision robot system.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1211005 (2023)
- DOI：10.3788/LOP221932
Registration Algorithm for Differently Scaled Point Clouds Based on Artificial Bee Colony Optimization
Yiping Fan, Baozhen Ge, and Lei Chen
This study proposed a differently scaled point cloud registration algorithm based on artificial bee colony optimization that can improve the accuracy and efficiency of differently scaled point cloud registration. The scale scaling factor, together with the three-dimensional rotation and translation parameters, was introduced as the variables to be solved in the registration process, and the artificial bee colony optimization method was used to optimize the solution. Furthermore, the proposed algorithm improved the Euclidean distance objective function based on the normalized scale factor, which eliminated the errors caused by optimizing the scale scaling factor to effectively improve the stability of the registration algorithm. Compared to currently employed methods, the proposed algorithm improves the accuracy and efficiency in different model registrations. The experimental results demonstrate that the proposed algorithm utilizes the excellent global optimization ability of the artificial bee colony optimization method and can therefore effectively realize the high-precision and fast registration for differently scaled point clouds.This study proposed a differently scaled point cloud registration algorithm based on artificial bee colony optimization that can improve the accuracy and efficiency of differently scaled point cloud registration. The scale scaling factor, together with the three-dimensional rotation and translation parameters, was introduced as the variables to be solved in the registration process, and the artificial bee colony optimization method was used to optimize the solution. Furthermore, the proposed algorithm improved the Euclidean distance objective function based on the normalized scale factor, which eliminated the errors caused by optimizing the scale scaling factor to effectively improve the stability of the registration algorithm. Compared to currently employed methods, the proposed algorithm improves the accuracy and efficiency in different model registrations. The experimental results demonstrate that the proposed algorithm utilizes the excellent global optimization ability of the artificial bee colony optimization method and can therefore effectively realize the high-precision and fast registration for differently scaled point clouds.
- Jun. 25, 2023
- Laser & Optoelectronics Progress
- Vol. 60, Issue 12, 1210023 (2023)
- DOI：10.3788/LOP221735
Optical beat notes assisted attosecond soft X-ray pulse generation in high-gain free electron lasers
Zhen Wang, and Chao Feng
Attosecond soft X-ray pulses are of great importance for the study of ultrafast electronic phenomena. In this paper, a feasible method is proposed to generate isolated fully coherent attosecond soft X-ray free electron laser via optical frequency beating. Two optical lasers with the opposite frequency chirps are used to induce a gradient frequency energy modulation, which helps to generate a gradually varied spacing electron pulse train. Subsequently, the undulator sections with electron beam delay lines are used to amplify the target ultra-short radiation. Numerical start-to-end simulations have been performed and the results demonstrate that an isolated soft X-ray pulse with the peak power of 330 GW and pulse duration of 620 as can be achieved by the proposed technique.Attosecond soft X-ray pulses are of great importance for the study of ultrafast electronic phenomena. In this paper, a feasible method is proposed to generate isolated fully coherent attosecond soft X-ray free electron laser via optical frequency beating. Two optical lasers with the opposite frequency chirps are used to induce a gradient frequency energy modulation, which helps to generate a gradually varied spacing electron pulse train. Subsequently, the undulator sections with electron beam delay lines are used to amplify the target ultra-short radiation. Numerical start-to-end simulations have been performed and the results demonstrate that an isolated soft X-ray pulse with the peak power of 330 GW and pulse duration of 620 as can be achieved by the proposed technique.
- Feb. 17, 2023
- High Power Laser Science and Engineering
- Vol. 11, Issue 3, 03000e33 (2023)
- DOI：10.1017/hpl.2023.15
Optics
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Theme Issue on Orbital Angular Momentum (2023)
In Progress
Editor (s): Xiao-Cong Yuan, Guixin Li, Junsuk Rho
Theme Issue on Orbital Angular Momentum (2023)
In Progress
Editor (s): Xiao-Cong Yuan, Guixin Li, Junsuk Rho
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