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Intelligent Moiré deflection tomography using physically processed deep learning

Optics Express 2025年第11期33卷 23367-23375页

作者： Weihao Cheng Yunyun Chen Zhiqin Huang Zhaolou Cao Bing Tu State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Nanjing University of Information Science & Technology Nanjing 210044 China Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing University of Information Science & Technology Nanjing 210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology Nanjing 210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science & Technology Nanjing 210044 China

Moiré tomography is a vital technique for diagnosing high-temperature and complex flow fields. However, traditional information extraction and reconstruction methods lead to significant time and resource expenditures. In this paper, we propose a Deep Learning-based Moiré Deflection Tomography (DLMDT) method that achieves high-precision predictions of the refractive index distribution in measured flow fields while ensuring the results align closely with physical principles. A carefully designed two-step deep learning model serves as the foundation of this approach. The model's accuracy and computational efficiency are first validated through numerical simulations. Subsequently, the trained model is applied to the diagnos of real flow fields. The findings reveal that the proposed model accurately predicts the refractive index distribution of measured flow fields, exhibiting high precision and structural similarity. Compared to traditional moiré tomography, our proposed method significantly enhances the speed of high-precision diagnostics for high-temperature and complex flow fields, which provides a robust foundation for advancing the intelligent capabilities of moiré tomography.

关键词： Deep learning Digital holography Ghost imaging Numerical simulation Phase unwrapping Refractive index

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Noise Mitigation Based on Geometric Shaping of 32QAM for Discrete Spectrum Nonlinear Frequency Division Multiplexing Systems

Noise Mitigation Based on Geometric Shaping of 32QAM for Dis...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xue Tang Bo Liu Jianxin Ren Yaya Mao Xiangyu Wu Xiumin Song Shuaidong Chen Rahat Ullah Institute of Optics and Electronics Nanjing University of Information Science & Technology Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing China Beijing University of Posts and Telecommunications Beijing China School of communication and information engineering Nanjing University of Posts and Telecommunications Nanjing China

A noise mitigation scheme is proposed for nonlinear frequency division multiplexing systems. This scheme utilizes channel characteristics to design an olive shape geometry with greater ASE noise and phase noise tolera... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Phase noise Geometry Prevention and mitigation System performance Noise Frequency division multiplexing Optical noise Signal to noise ratio Photonics Testing

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Enhanced figure of merit for famatinite Cu_(3)SbSe_(4)via band structure tuning and hierarchical architecture

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Journal of Materiomics 2023年第6期9卷 1263-1272页

作者： Lijun Zhao Haolin Ye Xinmeng Wu Jian Yang Lihua Yu Zhongqi Shi Shahid Hussain Guanjun Qiao Junhua Xu Bangzhi Ge Li Wang Chongjian Zhou School of Materials Science and Engineering Jiangsu University of Science and TechnologyZhenjiang212100JiangsuChina State Key Laboratory of Solidification Processing Key Laboratory of Radiation Detection Materials and DevicesMinistry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi'an710072ShaanxiChina School of Materials Science and Engineering Jiangsu UniversityZhenjiang212013JiangsuChina State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong UniversityXi'an710049ShaanxiChina

The p-type Te-free Cu_(3)SbSe_(4)with famatinite structure is a potential candidate for thermoelectric materials due to the low cost and eco-friendly constituent ***,its strong bipolar effect and high lattice thermal conductivity(klat)are the main challenges for its performance ***,we report a new strategy to enhance its figure of merit zT~0.86 at 673 K for Cu_(3)Sb_(0.95)Fe_(0.05)Se_(2.8)S_(1.2)via band structure tuning and hierarchical ***,S substituted Se atoms in lattice can widen the band gap to alleviate the bipolar ***,Fe doping in Sb site significantly increases the density of states,thus increasing the carrier effective mass,and obtaining a remarkably high Seebeck coefficient of~560 mV/K at 300 ***,the induced hierarchical architecture defects resulting in a minimum klat of~0.48 W·m^(-1)·K^(-1)at 673 ***,the improved Seebeck coefficient combined with low thermal conductivity leads to an enhanced zT.

关键词： Cu3SbSe4-Based materials Defects Band gap Thermoelectric properties Solid solution

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HSI-MFormer: Integrating Mamba and Transformer Experts for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： He, Yan Tu, Bing Liu, Bo Li, Jun Plaza, Antonio Institute of Optics and Electronics State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing University of Information Science and Technology Nanjing210044 China China University of Geosciences Faculty of Computer Science Wuhan430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica 10003 Spain

Hyperspectral image (HSI) classification is fundamental to numerous remote sensing applications, enabling detailed analysis of material properties and environmental conditions. Recent Mamba built upon selective state space models (SSMs) (S6) have demonstrated exceptional advantages in long-range sequence modeling with linear computational efficiency, while Transformer based on self-attention mechanisms is particularly adept at capturing short-range dependencies. To leverage the complementary strengths of these models, this article introduces a novel hybrid Mamba-Transformer framework (HSI-MFormer), effectively exploring the multiscale properties of hyperspectral data for HSI classification. Initially, a multiscale token generation (MTG) module is developed, which converts the HSI cube into multiple spatial-spectral token groups across different scales. To adequately capture fine-grained multiscale spatial-spectral patterns, an inner-scale transformer expert (ITE) is designed, which incorporates grouped self-attention operations to perform short-range sequence modeling within token groups at each scale. Meanwhile, a cross-scale Mamba expert (CME) is introduced, which integrates a cross-scale serialization mechanism and bidirectional Mamba block for long-range sequence modeling, further exploring the interactions and complementarity between token groups across different scales. Several hybrid strategies for integrating the ITE and CME are investigated to maximize their complementarity, including parallel, interval, and serial structures. Extensive experiments demonstrate that the proposed HSI-MFormer significantly outperforms the state-of-the-art Transformer-based and Mamba-based HSI classification methods. © 1980-2012 IEEE.

关键词： Image classification

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Secondary electron emission and photoelectric emission with account of excited electrons’ moving backward emission surface

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Results in Physics 2024年 60卷

作者： Li, Xiang Xie, Ai-Gen Tu, Long School of Physics and Optoelectronic Engineering Nanjing University of Information Science & Technology Nanjing 210044 China Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science & Technology Nanjing 210044 China Jiangsu international Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology Nanjing 210044 China

From existing old photoelectric emission PEE and secondary electron emission SEE models and the process that 50 % excited electrons are treated as as those moving backward emission surface, this study develops new SEE model for negative electron affinity semiconductor NEAS with large l_SEE and new PEE model, respectively;l_SEE denotes mean escape depth of secondary electrons. From our investigations of PEE from metals and NEAS with large l_PEE and SEE from NEAS with large l_SEE, it can be concluded that the new SEE model for NEAS with large l_SEE and new PEE model developed here are correct;l_PEE denotes mean escape depth of photo-emitted electrons. The theories of SEE from insulators with 120 Å≤ l_SEE ≤ 1.0 × 10⁴ Å are also developed and discussed. © 2024 The Author(s)

关键词： Absolute quantum efficiency Excited electrons’ moving backward Mean surface escape probability Photoelectric emission Secondary electron emission

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Three-dimensional dynamic probabilistic shaping high-security transmission scheme based on dual physical layer encryption for seven-core fibers

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Chinese Optics Letters 2025年第3期 19-25页

作者：毛雅亚刘博黄景瑞任建新陈帅东吴翔宇吴泳锋宋秀敏戚志鹏崔杰 Institute of Optics and Electronics Nanjing University of Information Science & Technology Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science & Technology Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology School of Computer Science and Technology Tongji University

In this paper,a carrier-less amplitude and phase modulation passive optical network （CAP-PON） scheme is proposed based on dynamic probabilistic shaping （DPS） and Rubik’s cube encryption in optical access *** key is generated from a novel five-dimensional entangled chaos model for dynamic probabilistic shaping and Rubik’s cube *** verify the performance of the encryption scheme,an experimental demonstration of 70 Gb/s （7×10 Gb/s） encrypted DPS-3D-CAP signal transmission over 2 km weakly coupled 7-core fiber is *** key space of the new five-dimensional entangled chaos model reaches 10173,and the interference level reaches 100%.Experimental results show that the receiver sensitivity increases by 1.47 d B compared to the conventional uniform 3D-CAP due to the introduction of dynamic probabilistic shaping.

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3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification

arXiv

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arXiv 2024年

作者： He, Yan Tu, Bing Liu, Bo Li, Jun Plaza, Antonio The Institute of Optics and Electronics The Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean The Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China The Faculty of Computer Science China University of Geosciences Wuhan430074 China The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura C¨ceres 10003 Spain

Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the State Space Model integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-Spectral-Spatial Mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Hyperspectral imaging

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IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification

arXiv

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arXiv 2024年

作者： He, Yan Tu, Bing Jiang, Puzhao Liu, Bo Li, Jun Plaza, Antonio The Institute of Optics and Electronics The Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean The Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China The Faculty of Computer Science China University of Geosciences Wuhan430074 China The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura Cáceres‎10003 Spain

Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent Mamba architectures built upon the Selective State Space Models (S6) have demonstrated enormous potential in long-range sequence modeling. However, the high dimensionality of hyperspectral data and information redundancy pose challenges to the application of Mamba in HSI classification, suffering from suboptimal performance and computational efficiency. In light of this, this paper investigates a lightweight Interval Group Spatial-Spectral Mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multi-directional and multi-scale global spatial-spectral information extraction in a grouping and hierarchical manner. Technically, an Interval Group S6 Mechanism (IGSM) is developed as the core component, which partitions high-dimensional features into multiple non-overlapping groups at intervals, and then integrates a unidirectional S6 for each group with a specific scanning direction to achieve non-redundant sequence modeling. Compared to conventional applying multi-directional scanning to all bands, this grouping strategy leverages the complementary strengths of different scanning directions while decreasing computational costs. To adequately capture the spatial-spectral contextual information, an Interval Group Spatial-Spectral Block (IGSSB) is introduced, in which two IGSM-based spatial and spectral operators are cascaded to characterize the global spatial-spectral relationship along the spatial and spectral dimensions, respectively. IGroupSS-Mamba is constructed as a hierarchical structure stacked by multiple IGSSB blocks, integrating a pixel aggregation-based downsampling strategy for multiscale spatial-spectral semantic learning from shallow to deep stages. Extensive experiments demonstrate that IGroupSS-Mamba significantly outperforms the state-of-the-art methods in classification accuracy and achieves lower model parameters and floating po

关键词： Hyperspectral imaging

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A CVAE-GAN-based Approach to Process Imbalanced Datasets for Intrusion detection in Marine meteorological Sensor Networks

A CVAE-GAN-based Approach to Process Imbalanced Datasets for...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xin Su Tian Tian Lei Cai Baoliu Ye Hongyan Xing College of IOT Engineering Hohai University Changzhou Jiangsu China Information Department Hohai University Nanjing Jiangsu China Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Nanjing Jiangsu China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and Technology Nanjing Jiangsu China

In marine meteorological sensor networks (MMSN), there are massive data flows transmitted within numerous nodes, resulting in serious potential consequences once any anomalous traffic implied launches an attack. Therefore, accurate identification and fast response to abnormal traffic is vital for intrusion detection system (IDS) constructions. Dataset imbalances cause classification models to erroneously bias to normal traffic, significantly restricting IDS developments and applications. This paper proposes an approach to deal with dataset imbalances in intrusion detections. This approach mitigates dataset imbalance impacts on IDSs from the data perspective, which is liable to process the input data in classification models. In this approach, CVAE-GAN is adopted as the data generation module to synthesize specified minority class samples, thus reducing dataset imbalance rate. ordering points to identify the clustering structure (OPTICS) is taken as the denoising algorithm to remove outliers and decrease the overlap extent between majority classes. An experiment on NSL-KDD dataset demonstrates that the proposed method obtains a high-quality dataset with reasonable distribution. This approach improves the classifier's identification ability for potential anomalous traffic.

关键词： Training Measurement Noise reduction Intrusion detection Clustering algorithms Optics Data models

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Multi-target localization using distributed MIMO radar based on spatial sparsity

Multi-target localization using distributed MIMO radar based...

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2021 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2021

作者： Zhao, Chenyang Ke, Wei Wang, Tingting Nanjing Normal University Jiangsu Key Laboratory on Optoelectronic Technology in School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Nanjing University of Information Science Technology Jiangsu Key Lab of Meteorological Observation and Information Processing Nanjing China

ISBN: (纸本)9781665418676

This paper presents a sparsity-based multi-target localization method for multiple-input multiple-output (MIMO) radar systems using distributed antennas. Since targets usually lie at some points within the localization domain, we are able to exploit this sparsity to convert the radar localization problem into a distributed recovery solution. Based on this natural sparsity, in this paper we introduce a block-sparse illustration model for distributed MIMO radar and propose a completely unique block-sparse recovery algorithmic rule supported approximate l0 norm diminution. The novelty of this technique is using l0 norm to push inter-block sparsity within the signals and also the optimisation problem is resolved by an ordered procedure in conjunction with a conjugate-gradient technique for quick reconstruction. Moreover, the amount of targets doesn't be well-known in advance. The effectiveness of this technique is incontestable by simulation results that obtain better localization performance and reduce computation complexness for giant sized data. © 2021 IEEE.

关键词： MIMO radar

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