2022
2022
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Record 61 of
Title:Contrasting time-resolved characteristics of laser-induced plasma spatially confined by conical cavities with different bottom diameters
Author(s):Liu, Yinghua(1,2); Xu, Boping(1,2); Lei, Bingying(1,2); Liu, Simeng(1,2); Wang, Jing(1,2); Zeng, Jianhua(1,2); Wang, Yishan(1,2); Duan, Yixiang(3); Zhao, Wei(1,2); Tang, Jie(1,2)Source: Applied Physics B: Lasers and Optics Volume: 128 Issue: 6 DOI: 10.1007/s00340-022-07823-w Published: June 2022Abstract:In this work, conical cavities with a fixed top diameter and varied bottom diameter have been utilized to improve the signal intensity, signal-to-noise ratio (SNR), and signal stability of laser-induced breakdown spectroscopy (LIBS). It is observed that the postponement of the maximum enhancement of spectral intensity and SNR occurs abnormally with decreasing the bottom diameter due to the energy dissipation from the plasma to the cavity walls. Optimization of the cavity size indicates that the conical cavity is superior to the widely-used cylindrical cavity in improving the performance of LIBS. It is also found that the emission enhancement in the conical cavity with larger bottom diameters is attributed to the increase in plasma temperature and electron number density, but the enhancement in the conical cavity with smaller bottom diameters is ascribed to the growth of electron number density. For the first time, the exact efficiencies of conical cavities suppressing the total number density fluctuation are acquired to evaluate the performance of improving signal stability through analysis of the signal uncertainty composition. ? 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Accession Number: 20222112145643 -
Record 62 of
Title:Mid-wave infrared planar optical device via femtosecond laser ablation on a sulfur-based polymeric glass surface
Author(s):Liu, Feng(1); Zhou, Liang(1); Cheng, Huachao(1); Li, Peng(1); Liu, Sheng(1); Mao, Shan(1); Jin, Chuan(2); Zhu, Xiangping(2); Zhao, Jianlin(1)Source: Optical Materials Express Volume: 12 Issue: 7 DOI: 10.1364/OME.459018 Published: July 1, 2022Abstract:Sulfur-based polymer materials are attractive for infrared (IR) applications, as they exhibit profoundly high IR transparency, low temperature processability, and higher refractive index relative to conventional organic polymers. In this paper, the laser induced surface damage threshold of such sulfur-based polymeric glass is experimentally studied with femtosecond laser pulse exposure. The single- and multi-shot laser damage thresholds are determined as 41.1 mJ/cm2 and 32.4 mJ/cm2, respectively, and line width of laser scanning is proved to be controllable by laser energy implantation dose. The results enrich the technical knowledge of such novel optical material, and predict its processability by laser surface inscription. While, the amplitude-type binary planar devices based on femtosecond laser ablation are fabricated, and their imaging abilities are performed both in visible light and mid-wave IR regions. ? 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing AgreementAccession Number: 20222512242723 -
Record 63 of
Title:Deep Pansharpening via 3D Spectral Super-Resolution Network and Discrepancy-Based Gradient Transfer
Author(s):Su, Haonan(1); Jin, Haiyan(1); Sun, Ce(2,3)Source: Remote Sensing Volume: 14 Issue: 17 DOI: 10.3390/rs14174250 Published: September 2022Abstract:High-resolution (HR) multispectral (MS) images contain sharper detail and structure compared to the ground truth high-resolution hyperspectral (HS) images. In this paper, we propose a novel supervised learning method, which considers pansharpening as the spectral super-resolution of high-resolution multispectral images and generates high-resolution hyperspectral images. The proposed method learns the spectral mapping between high-resolution multispectral images and the ground truth high-resolution hyperspectral images. To consider the spectral correlation between bands, we build a three-dimensional (3D) convolution neural network (CNN). The network consists of three parts using an encoder–decoder framework: spatial/spectral feature extraction from high-resolution multispectral images/low-resolution (LR) hyperspectral images, feature transform, and image reconstruction to generate the results. In the image reconstruction network, we design the spatial–spectral fusion (SSF) blocks to reuse the extracted spatial and spectral features in the reconstructed feature layer. Then, we develop the discrepancy-based deep hybrid gradient (DDHG) losses with the spatial–spectral gradient (SSG) loss and deep gradient transfer (DGT) loss. The spatial–spectral gradient loss and deep gradient transfer loss are developed to preserve the spatial and spectral gradients from the ground truth high-resolution hyperspectral images and high-resolution multispectral images. To overcome the spectral and spatial discrepancy between two images, we design a spectral downsampling (SD) network and a gradient consistency estimation (GCE) network for hybrid gradient losses. In the experiments, it is seen that the proposed method outperforms the state-of-the-art methods in the subjective and objective experiments in terms of the structure and spectral preservation of high-resolution hyperspectral images. ? 2022 by the authors.Accession Number: 20223812750843 -
Record 64 of
Title:Cross-domain heterogeneous residual network for single image super-resolution
Author(s):Ji, Li(1); Zhu, Qinghui(1); Zhang, Yongqin(1,2); Yin, Juanjuan(1); Wei, Ruyi(3); Xiao, Jinsheng(3); Xiao, Deqiang(4); Zhao, Guoying(5)Source: Neural Networks Volume: 149 Issue: DOI: 10.1016/j.neunet.2022.02.008 Published: May 2022Abstract:Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction- and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance. Code and pre-trained models are available at https://github.com/zhangyongqin/HRN. ? 2022 Elsevier LtdAccession Number: 20221111786493 -
Record 65 of
Title:Loss modulation assisted solitonic pulse excitation in Kerr resonators with normal group velocity dispersion
Author(s):Liu, Mulong(1); Dang, Yaai(1); Huang, Huimin(2); Lu, Zhizhou(3); Wang, Yuanyuan(1); Cai, Yanan(1); Zhao, Wei(4)Source: Optics Express Volume: 30 Issue: 17 DOI: 10.1364/OE.464145 Published: August 15, 2022Abstract:We demonstrate an emergent solitonic pulse generation approach exploiting the externally introduced or intrinsic loss fluctuation effects. Single or multiple pulses are accessible via self-evolution of the system in the red, blue detuning regime or even on resonance with loss perturbation. The potential well caused by the loss profile not only traps the generated pulses, but also helps to suppress the drift regarding high-order dispersion. Breathing dynamics is also observed with high driving force, which can be transferred to stable state by backward tuning the pump detuning. We further investigate the intrinsic free carrier absorption, recognized as unfavored effect traditionally, could be an effective factor for pulse excitation through the time-variant loss fluctuation in normal dispersion microresonators. Pulse excitation dynamics associated with physical parameters are also discussed. These findings could establish a feasible path for stable localized structures and Kerr microcombs generation in potential platforms. ? 2022 Optica Publishing Group.Accession Number: 20223312570687 -
Record 66 of
Title:Graphene-empowered dynamic metasurfaces and metadevices
Author(s):Zeng, Chao(1); Lu, Hua(1); Mao, Dong(1); Du, Yueqing(1); Hua, He(1); Zhao, Wei(2); Zhao, Jianlin(1)Source: Opto-Electronic Advances Volume: 5 Issue: 4 DOI: 10.29026/oea.2022.200098 Published: 2022Abstract:Metasurfaces, with extremely exotic capabilities to manipulate electromagnetic (EM) waves, have derived a plethora of advanced metadevices with intriguing functionalities. Tremendous endeavors have been mainly devoted to the static metasurfaces and metadevices, where the functionalities cannot be actively tuned in situ post-fabrication. Due to the intrinsic advantage of active tunability by external stimulus, graphene has been successively demonstrated as a favorable candidate to empower metasurfaces with remarkably dynamic tunability, and their recent advances are propelling the EM wave manipulations to a new height: from static to dynamic. Here, we review the recent progress on dynamic metasurfaces and metadevices enabled by graphene with the focus on electrically-controlled dynamic manipulation of the EM waves covering the mid-infrared, terahertz, and microwave regimes. The fundamentals of graphene, including basic material properties and plasmons, are first discussed. Then, graphene-empowered dynamic metasurfaces and metadevices are divided into two categories, i.e., metasurfaces with building blocks of structured graphene and hybrid metasurfaces integrated with graphene, and their recent advances in dynamic spectrum manipulation, wavefront shaping, polarization control, and frequency conversion in near/far fields and global/local ways are elaborated. In the end, we summarize the progress, outline the remaining challenges, and prospect the potential future developments. ? The Author(s) 2022.Accession Number: 20222512243130 -
Record 67 of
Title:Multi-modulation compatible miniaturization system for FSO communication assisted by chirp-managed laser
Author(s):Gao, Duorui(1,2); Li, Tianlun(3); Bai, Zhaofeng(1); Ma, Rong(1,2); Xie, Zhuang(1,2); Jia, Shuaiwei(1,2); Wang, Wei(1,2); Xie, Xiaoping(1,2)Source: Optics Express Volume: 30 Issue: 18 DOI: 10.1364/OE.465160 Published: August 29, 2022Abstract:In recent years, the thriving satellite laser communication industry has been severely hindered by the limitations of incompatible modulation formats and restricted Size Weight and Power (SWaP). A multi-modulation compatible method serving for free-space optical (FSO) communication has been proposed assisted by chirp-managed laser (CML). The corresponding demonstration system has been established for realizing free-switching between intensity (OOK) and phase modulation (RZ-DPSK). The feasibility and performance of system have been evaluated sufficiently when loading with 2.5 and 5 Gbps data streams, respectively. Additionally, a control-group system has been operated utilizing Mach-Zehnder modulator (MZM) for comparison between CML-based and MZM-based compatibility solutions. The OOK receiving sensitivities of CML-based system are ?47.02 dBm@2.5 Gbps and ?46.12 dBm@5 Gbps at BER of 1×10?3 which are 0.62 dB and 1.11 dB higher than that of MZM; the receiving sensitivities of RZ-DPSK are ?50.12 dBm@2.5 Gbps and ?47.03 dBm@5 Gbps which are 0.79 dB and 0.47 dB higher than that of MZM respectively. Meanwhile, CML-based transmitter abandoned the traditional modulator and its complicated supporting devices which can effectively contribute to the reduction of SWaP. The CML-based system has been proven to have the compatibility between intensity and phase modulation while also possesses a miniaturized design. It may provide fresh thinking to achieve a practical miniaturization system for satisfying the requirements of space optical network in future. ? 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.Accession Number: 20224012816583 -
Record 68 of
Title:Scientific objectives and payloads of the lunar sample return mission—Chang'E-5
Author(s):Zhou, Changyi(1); Jia, Yingzhuo(1); Liu, Jianzhong(2); Li, Huijun(1); Fan, Yu(1); Zhang, Zhanlan(1); Liu, Yang(1,3); Jiang, Yuanyuan(1); Zhou, Bin(4); He, Zhiping(5); Yang, Jianfeng(6); Hu, Yongfu(7); Liu, Zhenghao(1,8); Qin, Lang(1,8); Lv, Bohan(1); Fu, Zhongliang(9); Yan, Jun(10); Wang, Chi(1,3); Zou, Yongliao(1,3)Source: Advances in Space Research Volume: 69 Issue: 1 DOI: 10.1016/j.asr.2021.09.001 Published: January 1, 2022Abstract:In the early morning on December 17, 2020 Beijing time, China's chang'E-5 probe successfully returned to the Earth with 1731 g of lunar samples after completing drilling, shoveling, packaging of lunar soil and scientific exploration on lunar surface. It is the successful completion of the third phase of China's lunar exploration project, namely "circling, landing and returning to the moon". The scientific objectives of CE-5 mission are to carry out in situ investigation and analysis of the lunar landing region, laboratory research and analysis of lunar return samples. This paper analyzes scientific exploration tasks of CE-5 mission conducted on the lunar surface, and carries out the scientific payload system architecture design and individual scientific payload design with the scientific exploration task requirements as the target, and proposes the working mode and main technical index requirements of the scientific payloads. Based on the preliminary geological background study of the Mons Ruemker region which is the landing region of CE-5, the lunar scientific exploration and the laboratory physicochemical characterization of the return samples are of great scientific significance for our in-depth understanding of the formation and evolution of the Earth-Moon system and the chemical evolution history of the lunar surface. ? 2021 COSPARAccession Number: 20214010972542 -
Record 69 of
Title:Seeing Clear before Visual Tracking
Author(s):Zhang, Ximing(1); Wang, Yuanbo(1); Zhao, Hui(1); Fan, Xuewu(1)Source: 2022 IEEE 8th International Conference on Computer and Communications, ICCC 2022 Volume: Issue: DOI: 10.1109/ICCC56324.2022.10066016 Published: 2022Abstract:In this paper, we propose a two-stages visual tracking method mainly based on two branches including image deblurring and visual tracking. Our main motivation is to achieve the robust visual tracking when the tracker is suffering fast motion blur. Firstly, we present the hierarchical model based on Spatial Pyramid Matching that performs the fine-to-coarse deblurring and exploits localized-to-coarse operations. After achieving the deblurred images, the proposed method use transformer framework with spatial and channel attention for extracting features in order to obtain the spatial and channel features simultaneously to obtain the fast visual tracking with the balance of accuracy and robustness. We first train the one-stage deblurring network in the dataset of Gopro. Then, we train the second stage visusal tracking branch. Lastly, we conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the outperforming results on large tracking benchmarks, we also validate the effectiveness of our method against the fast motion blurring. ? 2022 IEEE.Accession Number: 20231513864397 -
Record 70 of
Title:Pairwise Comparison Network for Remote Sensing Scene Classification
Author(s):Zhang, Yue(1,2); Zheng, Xiangtao(1); Lu, Xiaoqiang(1)Source: arXiv Volume: Issue: DOI: 10.48550/arXiv.2205.08147 Published: May 17, 2022Abstract:Remote sensing scene classification aims to assign a specific semantic label to a remote sensing image. Recently, convolutional neural networks have greatly improved the performance of remote sensing scene classification. However, some confused images may be easily recognized as the incorrect category, which generally degrade the performance. The differences between image pairs can be used to distinguish image categories. This paper proposed a pairwise comparison network, which contains two main steps: pairwise selection and pairwise representation. The proposed network first selects similar image pairs, and then represents the image pairs with pairwise representations. The self-representation is introduced to highlight the informative parts of each image itself, while the mutual-representation is proposed to capture the subtle differences between image pairs. Comprehensive experimental results on two challenging datasets (AID, NWPU-RESISC45) demonstrate the effectiveness of the proposed network. The code are provided in https://github.com/spectralpublic/PCNet.git. Copyright ? 2022, The Authors. All rights reserved.Accession Number: 20220114265 -
Record 71 of
Title:A Laboratory Open-Set Martian Rock Classification Method Based on Spectral Signatures
Author(s):Yang, Juntao(1,2); Kang, Zhizhong(3,4); Yang, Ze(3,4); Xie, Juan(3,4); Xue, Bin(5); Yang, Jianfeng(5); Tao, Jinyou(5)Source: IEEE Transactions on Geoscience and Remote Sensing Volume: 60 Issue: DOI: 10.1109/TGRS.2022.3175996 Published: 2022Abstract:Rocks are one of the major surface features of Mars. The accurate characterization of the chemical and mineralogical composition of Martian rocks would yield significant evolutionary information about relevant geological processes and exobiological exploration. Many existing rock recognition systems generally assume that all testing classes are known during training. Over real planetary surfaces, the autonomous recognition system is likely to encounter an unknown category of rock that is crucial to the performance of the rock classification task. Therefore, we develop an open-set Martian rock-type classification framework based on their spectral signatures, with the subgoal of new/unknown rock-type recognition and category-incremental learning for expanding the recognition model. First, the spectral signatures of rock samples are captured to characterize their mineralogical compositions and physical properties, which serves as the input of the developed framework. To further produce the highly discriminative feature representation from the original spectral signatures, a transformer architecture integrated with contrastive learning is constructed and trained in an end-to-end manner to force instances of the same class to remain close-by while pushing those of a dissimilar class farther apart. Following this, according to the extreme value theorem (EVT), category-specific distance distribution analysis is conducted to detect and identify new/unknown types of rock samples due to the isolated characteristics of new/unknown rock samples in the latent feature space. Finally, the recognition model is incrementally updated to learn these identified 'unknown' samples without forgetting previously known categories when the associated labels are progressively obtained. The multispectral camera, a duplicated payload of the counterpart onboard the Zhurong rover, is used as the multispectral sensor for capturing the spectral information of the laboratory rock dataset shared by the National Mineral Rock and Fossil Specimens Resource Center for both qualitative and quantitative evaluations. Experimental results indicate the effectiveness and robustness of the developed in situ analysis framework. ? 1980-2012 IEEE.Accession Number: 20222112149619 -
Record 72 of
Title:Intelligent Classification for Emotional Issues by Deep Learning Network on EEG Signal Processing
Author(s):Yin, Shaokang(1,2,3); Zhu, Feiyu(1,2,3); Wei, Xiaojie(1,2,3); Han, Gongen(4); Zhang, Runqi(4); Liu, Xi(1,2,3); Hu, Bingliang(1,3); Wang, Quan(1,3)Source: 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2022 Volume: Issue: DOI: 10.1109/EEBDA53927.2022.9744774 Published: 2022Abstract:Identifying the risk of emotional issues is of great significance to the development of adolescents. Electroencephalography (EEG) signals can reflect brain activities, and imply the emotional status, therefore can be used to identify emotional issues. In this study, we designed an experimental paradigm for high school adolescents by displaying emotion-inducing pictures as stimuli and recorded their EEG signals simultaneously. The EEG signals was preprocessed and analyzed. In this paper we applied a convolutional network EEGNet to classify four emotional issues in adolescents: depression symptom, manic symptom, anxiety symptom and the control group. The results showed that the classification accuracy of the four groups can reach 94.24%. In addition, this paper explored using different types of picture stimuli on the classification and reached a result above chance level. This work extended the previous work on the classification of emotional issues to four categories, and achieved a good classification accuracy. ? 2022 IEEE.Accession Number: 20221712027366