2022

• Record 445 of
  
  Title:Research Status and Prospect of Laser Scribing Process and Equipment for Chemical Milling Parts in Aviation and Aerospace
  
  Author(s):Wang, Jian(1,2); Liu, Qiang(1,3); Sun, Pengpeng(1,2); Zang, Chenxin(1,2); Wang, Liuquan(1,2); Ning, Zhiwei(1,2); Li, Ming(4); Wang, Hui(5)
  Source: Micromachines  Volume: 13  Issue: 2  DOI: 10.3390/mi13020323  Published: February 2022  
  Abstract:Laser scribing in chemical milling is an important process which can effectively improve the precision and efficiency of chemical milling, and is of great significance to improve the thrust– weight ratio and manufacturing efficiency of aviation and aerospace parts. According to the scribing requirements in chemical milling for aviation and aerospace parts, the process and mechanism of laser scribing were studied and the influence of different process parameters for the quality of laser scribing was analyzed. Based on the review of related research literature, the laser scribing process, the ablation mechanism and technology of different materials and the selective laser removal process for "laser–coating–substrate" are summarized and discussed. Based on the requirements of high-precision laser scribing on complex surfaces, the current situation of laser scribing equipment is summarized. Finally, the practical challenges and key technical problems for the laser scribing process are summarized, and the application and development of laser scribing in aerospace manufacturing are prospected. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
  Accession Number: 20220911728059
• Record 446 of
  
  Title:A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data
  
  Author(s):Li, Jiaojiao(1); Ma, Yinle(1,2); Song, Rui(1,2); Xi, Bobo(1,2); Hong, Danfeng(3); Du, Qian(4)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 60  Issue:   DOI: 10.1109/TGRS.2022.3213513  Published: 2022  
  Abstract:Data fusion of hyperspectral and light detection and ranging (LiDAR) is conducive to obtain more comprehensive surface information and thereby achieve better classification result in Earth monitoring systems. However, lack of labeled samples usually limits the performance of supervised classifiers, and the heterogeneity of multisource data also brings great challenges to data fusion. Aiming to address these issues, we propose a triplet semisupervised deep network (TSDN) for fusion classification of hyperspectral and LiDAR. Specifically, we utilize three basic pathways to extract deep learning features: 1-D convolutional neural network (CNN) for spectral features in hyperspectral, 2-D CNN for spatial features in hyperspectral, and Cascade Net for elevation features in LiDAR data. Furthermore, a novel label calibration module (LCM) is proposed to generate effective pseudo labels with high confidence based on the superpixel segmentation by comparing the multiview classification results for assisting semisupervised model training. In addition, we design a novel 3D-Cross Attention Block to enhance the complementary spatial features of multisource data. Experiments on three public HSI-LiDAR benchmarks, Houston, Trento, and MUUFL Gulfport, have demonstrated the effectiveness and superiority of our proposed method. © 1980-2012 IEEE.
  Accession Number: 20224212976083
• Record 447 of
  
  Title:Characterization of the laser-induced breakdown spectroscopy near the gas–liquid two-phase interface
  
  Author(s):Liu, Simeng(1,2); Liu, Yinghua(1,2); Xu, Boping(1,2); Lei, Bingying(1,2); Ran, Shuang(1,2); Wang, Yishan(1,2); Duan, Yixiang(1,3); Zhao, Wei(1,2); Tang, Jie(1)
  Source: Applied Optics  Volume: 61  Issue: 11  DOI: 10.1364/AO.451217  Published: April 10, 2022  
  Abstract:The characterization of laser-induced breakdown spectroscopy (LIBS) near the gas–liquid two-phase interface was investigated with the laser acting on the sample along the horizontal direction. Simulation of the laser beam focusing process and observation of laser beam spot images show that difference in focusing positions in the air and the solution results from refraction of the laser beam entering the solution from the air and the change of propagation direction on the container lateral. The peak power and mean irradiance of the focused laser beam spot increase with the distance away from the interface, which is attributed to the fact that the loss of laser energy due to the refraction and reflection of light at the interface decreases with the focusing position moving away from the interface. This variation trend of laser irradiance allows for the growth of the spectral line intensity and lifetime with increasing the distance from the interface. The plasma electron density and temperature decrease with the delay time but increase with the distance away from the interface at the same delay time. Our findings help us to gain more insight into the characteristics and evolution mechanisms of LIBS produced near the gas–liquid two-phase interface, which provides theoretical guidance for the correction of LIBS spectra especially in water pollution monitoring. © 2022 Optica Publishing Group
  Accession Number: 20221611966207
• Record 448 of
  
  Title:Investigation of the Spectral Characteristics of Laser-Induced Plasma for Non-Flat Samples
  
  Author(s):Lei, Bing-Ying(1,2); Xu, Bo-Ping(1,2); Wang, Yi-Shan(1,2); Zhu, Xiang-Ping(1,2); Duan, Yi-Xiang(3); Zhao, Wei(1,2); Tang, Jie(1)
  Source: Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis  Volume: 42  Issue: 10  DOI: 10.3964/j.issn.1000-0593(2022)10-3024-07  Published: October 2022  
  Abstract:Laser-induced breakdown spectroscopy (LIBS), a fast and real-time tool for elemental analysis, has attracted great attention due to its broad applications in trace detection, geological environment monitoring, and other fields. The sample surface is one of the key environmental factors that affect the generation and characteristics of plasma. In this work, a 1 064 nm-laser beam with a pulse width of 8 ns is used to produce plasma in ambient air and comparatively investigate the emission spectra of a series of natural rock samples under non-flat and flat samples surfaces. Based on the laser-supported detonation wave model, the influence of non-flat sample surface on spectral characteristics of laser-induced plasmais discussed. For time-integrated spectra, the results show that the spectral intensities of the atomic lines of the non-flat sample are reduced by nearly 70% compared to those of the flat sample. This indicates that the negative effect of the non-flat sample surface on the LIBS cannot be ignored. According to the signal intensity of the spectral lines, Fe Ⅰ 404.58 nm and Fe Ⅰ 438.35 nm from limonite sample under different laser energies, the variation of their peak intensities and reduction factor with the change of laser energy were studied under the conditions of flat and non-flat sample surfaces. It is found that the spectral intensity under the condition of the non-flat sample surface is lower than that under the condition of the flat sample surface. It is worth noting that the reduction factor of spectral intensity first decreases gradually with laser energy, reaches the minimum value at 33 mJ, and then increases with the further increase of laser energy. Further observations show that laser-plasma with lower electron density is generated on the non-flat sample surface, and the ratio of the electron density of the non-flat sample to that of the flat sample reaches its minimum at the laser energy of 33 mJ, which is consistent with the changing trend of reduction factor with laser energy. This mainly arises because a thinner energy absorption region in laser-plasma is formed due to the large laser incident angle on the non-flat sample surface, thereby increasing the laser energy threshold corresponding to the plasma shielding. Moreover, it is found that the sample surface and the laser energy have little effect on the plasma temperature. © 2022 Science Press. All rights reserved.
  Accession Number: 20224312991881
• Record 449 of
  
  Title:Ensemble of half-space trees for hyperspectral anomaly detection
  
  Author(s):Huang, Ju(1,2); Li, Xuelong(1,3,4)
  Source: Science China Information Sciences  Volume: 65  Issue: 9  DOI: 10.1007/s11432-021-3310-x  Published: September 2022  
  Abstract:Most methods for hyperspectral anomaly detection (HAD) construct profiles of background pixels and identify pixels unconformable to the profiles as anomalies. Recently, isolation forest-based algorithms were introduced into HAD, which identifies anomalies from the background without background modeling. The path length is used as a metric to estimate the anomaly degree of a pixel, but it is not flexible and straightforward. This paper introduces the half-space tree (HS-tree) method from the theory of mass estimation into HAD and proposes a metric involving mass information and tree depth to measure the anomaly degree for each pixel. More specifically, the proposed HS-tree-based detection method consists of three main steps. First, the key spectral-spatial features are extracted using the principal component analysis and the extended morphological attribute profile methods. Then, the ensemble of HS-trees are trained using different randomly selected subsamples from the feature map. Finally, each instance in the feature map traverses through each HS-tree and the anomaly scores are computed as the final detection map. Compared with conventional methods, the experimental results on four real hyperspectral datasets demonstrate the competitiveness of our method in terms of accuracy and efficiency. © 2022, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.
  Accession Number: 20223612696713
• Record 450 of
  
  Title:Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm
  
  Author(s):Zhao, Yubo(1,2,3); Yu, Tao(1,2); Hu, Bingliang(1,2); Zhang, Zhoufeng(1,2); Liu, Yuyang(1,2,4); Liu, Xiao(1,2); Liu, Hong(1,2,5); Liu, Jiacheng(1,2,4); Wang, Xueji(1,2); Song, Shuyao(1,2,4)
  Source: Remote Sensing  Volume: 14  Issue: 21  DOI: 10.3390/rs14215305  Published: November 2022  
  Abstract:With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients ((Formula presented.)) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future. © 2022 by the authors.
  Accession Number: 20224613127104
• Record 451 of
  
  Title:Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet
  
  Author(s):Li, Xuemei(1); Ye, Huping(2); Qiu, Shi(3)
  Source: Remote Sensing  Volume: 14  Issue: 19  DOI: 10.3390/rs14194815  Published: October 2022  
  Abstract:Multispectral remote sensing images have shown unique advantages in many fields, including military and civilian use. Facing the difficulty in processing cloud contaminated remote sensing images, this paper proposes a multispectral remote sensing image enhancement algorithm. A model is constructed from the aspects of cloud detection and image enhancement. In the cloud detection stage, clouds are divided into thick clouds and thin clouds according to the cloud transmitability in multi-spectral images, and a multi-layer cloud detection model is established. From the perspective of traditional image processing, a bimodal pre-detection algorithm is constructed to achieve thick cloud extraction. From the perspective of deep learning, the MobileNet algorithm structure is improved to achieve thin cloud extraction. Faced with the problem of insufficient training samples, a self-supervised network is constructed to achieve training, so as to meet the requirements of high precision and high efficiency cloud detection under the condition of small samples. In the image enhancement stage, the area where the ground objects are located is determined first. Then, from the perspective of compressed sensing, the signal is analyzed from the perspective of time and frequency domains. Specifically, the inter-frame information of hyperspectral images is analyzed to construct a sparse representation model based on the principle of compressed sensing. Finally, image enhancement is achieved. The experimental comparison between our algorithm and other algorithms shows that the average Area Overlap Measure (AOM) of the proposed algorithm reaches 0.83 and the Average Gradient (AG) of the proposed algorithm reaches 12.7, which is better than the other seven algorithms by average AG 2. © 2022 by the authors.
  Accession Number: 20224212981511
• Record 452 of
  
  Title:High precision reconstruction for compressed femtosecond dynamics images based on the TVAL3 algorithm
  
  Author(s):Yin, Fei(1,2); Meng, Yizhao(3); Yang, Qing(1); Kai, Lin(3); Liu, Yi(3); Hou, Xun(3); Lu, Yu(3); Chen, Feng(3)
  Source: Optical Materials Express  Volume: 12  Issue: 11  DOI: 10.1364/OME.468475  Published: November 1, 2022  
  Abstract:Compressed sensing (CS) has been successfully demonstrated to reconstruct ultrafast dynamic scenes in ultrafast imaging techniques with large sequence depth. Since compressed ultrafast imaging used a two-step iterative shrinkage/thresholding (TwIST) algorithm in previous image reconstruction, some details of the object will not be recovered when the amount of data compression is large. Here we applied a more efficient Total Variation (TV) minimization scheme based on augmented Lagrangian and alternating direction algorithms (TVAL3) to reconstruct the ultrafast process. In order to verify the effectiveness of the TVAL3 algorithm, we experimentally compare the reconstruction quality of TVAL3 algorithm and TwIST algorithm in an ultrafast imaging system based on compressed-sensing and spectral-temporal coupling active detection with highest frame rate of 4.37 trillion Hz. Both dynamic and static experimental results show that, TVAL3 algorithm can not only reconstruct a rapidly moving light pulse with a more precise profile and more fitted trajectory, but also improve the quality of static objects and the speed of reconstruction. This work will advance the ultrafast imaging techniques based on compressed sensing in terms of image reconstruction quality and reconstruction speed, which finally helps promoting the application of these techniques in areas where high spatial precision is required, such as phase transitions and laser filamentation in nonlinear solids, etc. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
  Accession Number: 20224713151320
• Record 453 of
  
  Title:STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples
  
  Author(s):Yu, Zhe(1); Sun, Bangyong(1,3); Liu, Di(2); de Dravo, Vincent Whannou(1); Khokhlova, Margarita(4); Wu, Siyuan(3)
  Source: Journal of Renewable Materials  Volume: 10  Issue: 5  DOI: 10.32604/jrm.2022.018262  Published: 2022  
  Abstract:In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is yielded after iterations of the high-pass filter. STRASS can be run directly without any machine learning. Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts. Image dehazing can be applied in the field of printing and packaging, our method is of great significance for image pre-processing before printing. © 2022, Tech Science Press. All rights reserved.
  Accession Number: 20220211440017
• Record 454 of
  
  Title:Generation of scalar/vectorial vortex beams by using the plasmonic metasurfaces
  
  Author(s):Zhang, Xiaodong(1,2,3); Kong, Depeng(4); Zhao, Yu(1); Ma, Ningtao(1)
  Source: Applied Optics  Volume: 61  Issue: 25  DOI: 10.1364/AO.463459  Published: September 1, 2022  
  Abstract:Scalar and vector vortex beams are characterized of a helical wavefront but different polarized states, which result in different applications. In this paper, we design and fabricate a plasmonic metasurface based on the geometric phase principle. The designed metasurfaces are capable of generating a scalar vortex beam with a topological charge of ±2 and a vectorial vortex beam with a topological charge of ±1 in the near-infrared band. The experimental results are in good agreement with the simulation results, and our work provides a new idea for the development of a multivortex beam converter. © 2022 Optica Publishing Group.
  Accession Number: 20223712720653
• Record 455 of
  
  Title:A High-Sensitivity Vacuum Diode Temperature Sensor Based on Barrier-Lowering Effect
  
  Author(s):Shen, Zhihua(1); Wang, Xiao(2); Li, Qiaoning(1); Ge, Bin(1); Jiang, Linlin(1); Tian, Jinshou(3); Wu, Shengli(4)
  Source: Micromachines  Volume: 13  Issue: 2  DOI: 10.3390/mi13020286  Published: February 2022  
  Abstract:A new kind of temperature sensor based on a vacuum diode was proposed and numerically studied in this paper. This device operated under different electron emission mechanisms according to the electron density in the vacuum channel. The temperature determination ability of this device was only empowered when working in the electric-field-assisted thermionic emission regime (barrier-lowering effect). The simulated results indicated that the temperature-sensing range of this device was around 273 K–325 K with a supply current of 1 μA. To obtain a linear dependency of voltage on temperature, we designed a proportional-to-absolute-temperature (PTAT) circuit. The mathematic derivation of the PTAT voltage is presented in this study. The temperature-sensing sensitivity was calculated as 7.6 mV/K according to the measured I-U (current versus voltage) characteristic. The structure and principle of the device presented in this paper might provide an alternative method for the study of temperature sensors. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
  Accession Number: 20220811671317
• Record 456 of
  
  Title:Experimental Study on Bottom-Up Detection of Underwater Targets Based on Polarization Imaging
  
  Author(s):Pan, Tianfeng(1,2); He, Xianqiang(1,2,3); Zhang, Xuan(2); Liu, Jia(2,4); Bai, Yan(2,3); Gong, Fang(2); Li, Teng(2,3)
  Source: Sensors  Volume: 22  Issue: 8  DOI: 10.3390/s22082827  Published: April-2 2022  
  Abstract:Previous studies on the polarization imaging of underwater targets mainly focused on top-down detection; however, the capacities of bottom-up detection were poorly known. Based on in situ experiments, the capability of bottom-up detection of underwater targets using polarization imaging was investigated. First, to realize the objective of bottom-up polarization imaging, a SALSA polarization camera was integrated into our Underwater Polarization Imaging System (UPIS), which was integrated with an attitude sensor. At Qiandao Lake, where the water is relatively clear, experiments were conducted to examine the capacity of the UPIS to detect objects from the bottom up. Simultaneously, entropy, clarity, and contrast were adopted to compare the imaging performance with different radiation parameters. The results show that among all the used imaging parameters, the angle of polarization is the optimal parameter for bottom-up detection of underwater targets based on polarization imaging, which may result from the different diffused reflectance of the target surface to the linear polarization components of the Stokes vector. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
  Accession Number: 20221511943473