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Component recognition of ISAR targets via multimodal feature fusion

Chinese Journal of Aeronautics 2025年第2期38卷 256-273页

作者： Chenxuan LI Weigang ZHU Wei QU Fanyin MA Rundong WANG Graduate School Space Engineering UniversityBeijing 101400China Department of Electronic and Optical Engineering Space Engineering UniversityBeijing 101400China

Inverse Synthetic Aperture Radar(ISAR)images of complex targets have a low Signal-to-Noise Ratio(SNR)and contain fuzzy edges and large differences in scattering intensity,which limits the recognition performance of ISAR ***,data scarcity poses a greater challenge to the accurate recognition of *** address the issues of component recognition in complex ISAR targets,this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal *** scarcity of available data is mitigated by using a two-branch scattering feature encoding ***,the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes,thereby achieving significantly higher structural ***,the scattering trait enhancement module and the statistical quantification module are *** edge texture is enhanced based on the scatter quantization property,which alleviates the segmentation challenge of edge blurring under low SNR *** coupling of query/support samples is enhanced through four-dimensional ***,to overcome fusion challenges caused by information differences,multimodal feature fusion is guided by equilibrium comprehension *** this way,the performance potential of the fusion framework is fully unleashed,and the decision risk is effectively *** demonstrate the great advantages of the proposed framework in multimodal feature fusion,and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.

关键词： Few-shot Semantic segmentation Inverse Synthetic Aperture Radar(ISAR) Scattering Multimodal fusion

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PDINN sandwiched silver nanowires for solution-processed semitransparent organic photovoltaics with over 4% light utilization efficiency and nearly 100% bifaciality factor

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Journal of Energy Chemistry 2025年第5期104卷 1-9页

作者： Junjie Wu Menghua Cao Xin Liu Rihong Zhu Jie Zhou Jiangsheng Yu Gang Li School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Electrical and Electronic Engineering Research Institute for Smart Energy (RISE) Photonic Research Institute (PRI) The Hong Kong Polytechnic University School of Chemistry and Chemical Engineering Nanjing University of Science and Technology

Semitransparent organic photovoltaics(ST-OPVs) for building integration represent a pivotal direction in the development of photovoltaic industry. Solution-processed silver nanowires(AgNWs) are considered promising candidates for transparent electrodes in semitransparent devices due to their high transparency-conductivity-efficiency merit, large-scale processability, and low cost. In this work, we develop two solution-processed organic–inorganic hybrid electrodes, named AgNWs-PD and AgNWsPC, utilizing AgNWs as the conductive framework and aliphatic amine-functionalized perylene-diimide(PDINN) as the sandwiched material, while AgNWs-PC exhibits significantly improved electrical conductivity and enhanced contact area with the underlying electron transport layer. The optimized device achieves a power conversion efficiency of 9.45% with an open circuit voltage of 0.846 V, a high filling factor of 75.4%, and an average visible transmittance(AVT) of 44.0%, delivering an outstanding light utilization efficiency(LUE) of 4.16%, which is the highest reported value for all solution-processed ST-OPVs. In addition, by coupling a 30-nm tellurium dioxide atop AgNWs-PC, the bifaciality factor of derivative devices improves from 73.7% to 99.4%, while maintaining a high bifacial LUE over 3.7%. Our results emphasize the superiority and effectiveness of PDINN-sandwiched AgNWs electrodes for highperformance and all solution-processed ST-OPVs.

关键词： Bifaciality factor Conventional structure Light utilization efficiency Semitransparent organic photovoltaics Silver nanowire electrode Solution-processed

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Waveform Design of Carrier-Free UWB Sensors Based on Deep Learning Network

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IEEE Sensors Journal 2025年第6期25卷 10380-10389页

作者： Hou, Linsheng Chen, Si Tian, Wuqi Li, Jianchao Zhang, Shuning Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China

The development of radar sensors toward intelligent and adaptive is a major trend in the future. Adaptive waveform design is an important part to improve the performance of the sensor. This article investigates the signal waveform design problem of carrier-free ultra-wideband (UWB) radar sensors. The problem requires designing waveforms to detect and estimate extended targets in the presence of interference and clutter while imposing practical constraints on the designed signals, such as constant modulus or low peak-to-average power ratio (PAR). Since the resulting problem is high-dimensional and nonconvex (also known as NP-hard), finding the globally optimal solution through polynomial time algorithms is extremely challenging. We notice that deep convolutional neural networks (CNNs) are fundamentally nonlinear systems, making them well-suited for addressing the aforementioned problem. To this end, we propose a deep learning-based waveform design approach. This method is based on improved deep residual networks, utilizing the optimization problem construction in waveform optimization to form the loss function, and combining the Adam algorithm and the loss function to drive the optimization of deep residual networks. By leveraging the capability of deep convolutional networks to solve nonconvex problems, we achieve fast adaptive design of UWB sensor waveforms. Numerical examples are provided to validate the effectiveness of this approach. © 2001-2012 IEEE.

关键词： Polynomial approximation

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SAF-SFT-SRAF-Based Signal Coherent Integration Method for High-Speed Target Detecting in Airborne Radar

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Progress in Electromagnetics Research C 2025年 154卷 183-190页

作者： Xu, Wenwen Wang, Yuhang Huang, Jidan Wang, Hao Cao, Jianyin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

In radar target detection, long-term coherent integration (LTCI) is widely employed to improve the signal-to-noise ratio (SNR) and enhance the detection capability for weak and small targets. Meanwhile, the airborne radar, with advantages of wide-area surveillance, high sensitivity, and strong maneuver ability, demonstrates significant superiority in detecting high-speed targets. However, during the flight of the airborne radar platform, motion errors and the relative motion of high-speed targets can cause significant range migration (RM) and Doppler frequency migration (DFM), degrading coherent integration performance. To this end, this paper proposes a coherent integration method for high-speed target in airborne radar based on the symmetric autocorrelation function, scaled Fourier transform, and sequence reversing autocorrelation function (SAF-SFT-SRAF). Detailed comparisons between SAF-SFT-SRAF and several typical methods demonstrate that the proposed method effectively balances computational complexity and detection performance. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Aircraft detection

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Enhancing plasmonic structured illumination microscopy with surface plasmon coupled emission

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Optics Letters 2025年第2期50卷 483-486页

作者： Huang, Ming Jiang, Kaiwen Shen, Zhe School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

Plasmonic structured illumination microscopy (PSIM) is a super-resolution technique that utilizes surface plasmon polaritons (SPPs) with higher frequency as the structured light;thus, it is able to break the diffraction limit with a 3–4 times resolution enhancement. However, the low efficiency of near-field fluorescence collection results in a low imaging signal-to-noise ratio (SNR) of PSIM. In this paper, we propose a method to enhance the performance of PSIM with surface plasmon coupled emission (SPCE). Compared to conventional fluorescence detection, it achieves a 5-fold enhancement in fluorescence collection efficiency by collecting SPCE signals, which facilitates better collection of near-field information of targets. In our PSIM, samples with different fluorescence excitation wavelengths are distinguished by adjusting the collection angle, since SPCE has high wavelength sensitivity. Especially, the technique of fluorescence emission difference (FED) for SPCE was used to obtain a 5.7-fold resolution enhancement, which further enhanced the super-resolution capability of PSIM. This method improves the SNR and resolution of PSIM and facilitates more detailed observation of biological cells. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Fluorescence imaging

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Snapshot multispectral polarization imager using a narrowband reflection filter array

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Applied Optics 2025年第12期64卷 3109-3121页

作者： Wu, Wenxuan Yang, Yu Li, Liansheng Zhu, Haibo Huang, Huitong Liu, Yixuan Zong, Yi Li, Jianxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Beijing Institute of Control Engineering Beijing100190 China

Multispectral polarization imaging technology holds significance in various fields. This study proposes a multispectral polarization imaging scheme based on a narrowband reflection filter array (RFA), incorporating spatial dispersion that leverages the RFA to segment optical paths for multispectral image acquisition. The RFA was analyzed and designed, and a snapshot multispectral polarization imager was constructed. The feasibility of the spatial dispersion structure was experimentally validated. The proposed method can capture spectral polarization images at video frame rates with relatively consistent imaging quality across different bands and high spatial resolution, providing, to our knowledge, a new technical approach for realizing real-time multispectral polarization imagers. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Image acquisition

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Rapid Convergence and Low Data Dependency: Physics-Informed Tri-Module Multiplicative Network for DML-DD Link in Analog RoF Fronthaul

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Journal of Lightwave Technology 2025年第10期43卷 4616-4623页

作者： Zhang, Yikun Zhu, Yixiao Man, Lina Zhuge, Qunbi Hu, Weisheng Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication Systems and Networks School of Electronic Information and Electrical Engineering Department of Electronic Engineering Shanghai200240 China

The conventional analog radio-over-fiber (A-RoF) scheme offers high spectral efficiency, which can meet the growing capacity demand in 6G cloud radio access networks (C-RAN). With the trend towards dense small cells, the directly modulated laser with direct detection (DML-DD) link stands out as a cost-effective option. The main challenge in the DML-DD link-based A-RoF fronthaul is the nonlinear chirp-dispersion interaction including composite second-order (CSO) and composite triple beat (CTB) distortion. In this work, we propose and experimentally demonstrate a fast-convergence and low data dependency physics-informed tri-module multiplicative network (TriMNet) for C-band DML-DD link. Neural networks (NN) usually require substantial training set to learn and achieve high performance in a data-driven mode. By introducing the analytical form of the CSO and CTB models, TriMNet can extract nonlinear distortion features more quickly and accurately, offering significantly reduced training data requirements. Compared to the fully connected neural network (FNN), TriMNet reduces training symbols by 50% and training epochs by 62.5%, without extra computational complexity. Furthermore, TriMNet enhances the system capacity, achieving a common public radio interface (CPRI)-equivalent rate of 1.02 Tb/s from 0.9 Tb/s in 10-km transmission and 0.84 Tb/s from 0.72 Tb/s in 15-km transmission at 8% error vector magnitude (EVM) threshold compared to full-tap 3rd order Volterra-based feed-forward equalizer (VFE). © 1983-2012 IEEE.

关键词： Radio-over-fiber

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Topological transmission and topological corner states combiner in all-dielectric honeycomb valley photonic crystals

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Chinese Physics B 2025年第3期34卷 424-431页

作者： Ming Sun Xiao-Fang Xu Yun-Feng Shen Ya-Qing Chang Wen-Ji Zhou School of Mechanical Engineering Jiangsu UniversityZhenjiang 212003China School of Optical and Electronic Information Suzhou City University&Suzhou Key Laboratory of BiophotonicsSuzhou 215104China

We study the topological states(TSs)of all-dielectric honeycomb valley photonic crystals(VPCs).Breaking the space inversion symmetry of the honeycomb lattice by varying the filling ratio of materials for circular ring dielectric columns in the unit cell,which triggers topological phase transitions and thus achieves topological edge states(TESs)and topological corner states(TCSs).The results demonstrate that this structure has efficient photon transmission characteristics and anti-scattering *** particular,we have found that changing the type of edge splicing between VPCs with different topological properties produces a change in the frequency of TCSs,and then based on this phenomenon,we have used a new method of adjusting only the type of edge splicing of the structure to design a novel TCSs combiner that can integrate four TCSs with different *** work not only expands the variety and number of unexplored TCSs that may exist in a fixed photonic band gap and can be rationalized to be selectively excited in the fixed *** study provides a feasible pathway for the design of integrated optical devices in which multiple TSs coexist in a single photonic system.

关键词： valley photonic crystals topological phase transitions topological edge states topological corner states combiner

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Iterative Detection for Orthogonal Time Frequency Space Modulation with ADMM

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IEEE Communications Letters 2025年第5期29卷 993-997页

作者： Li, Xuefeng Shan, Chengzhao Zhao, Honglin Yuan, Weijie Zhang, Ruoyu Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Ministry of Education School of Electronic and Optical Engineering Nanjing210094 China

Orthogonal time frequency space (OTFS) modulation, because of its excellent performance in fast time-varying channels, has been proposed as an alternative to orthogonal frequency-division multiplexing modulation in high-mobility scenarios. Time and frequency diversity can be fully exploited in OTFS systems with proper signal detection. However, most existing signal detection algorithms are based on message passing (MP) algorithms, and their signal detection performance rapidly deteriorates when the channel has low sparsity. In this letter, we first model the OTFS signal detection problem as an optimization problem and propose an alternating direction method of multipliers (ADMM)-based algorithm to solve the problem. On this basis, we further propose a modified ADMM-based algorithm by replacing the penalty term to address the performance loss caused by constraint relaxation. The numerical results show that the proposed algorithms are effective in terms of convergence and detection performance. © 1997-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Fading suppression method based on redundant data within the spatial resolution and deep learning for a Φ-OTDR system

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Optics Express 2025年第9期33卷 18642-18658页

作者： Pan, Xianglei Cui, Ke Zheng, Aoran Ren, Zhongjie Ma, Jun Zhu, Rihong School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Science Nantong University Nantong226019 China

Weak light intensity positions induced by interference fading adversely affect the sensing performance of phase-sensitive optical time-domain reflectometry (Φ-OTDR). Most effective fading suppression methods rely on frequency or phase modulation of the light source, which requires complex hardware modifications. To solve the above issue, this paper proposes a novel multi-channel data synthesizing method based on deep neural network (MDS-DNN) to reduce the impact of interference fading on the signal-to-noise ratio (SNR) of Φ-OTDR. The proposed algorithm can work efficiently without any modification of the conventional Φ-OTDR setup. The spatial sampling rate of the Φ-OTDR systems is typically much higher than the spatial resolution. This means that neighboring sampling points carry the same external vibration signal, providing redundant information. Therefore, it is possible to perform comprehensive analysis on these multi-channel data to improve the suppression capability of interference fading noise. This work designs a long short-term memory (LSTM) network-based framework and an end-to-end training strategy to automatically learn the correlation between these multi-channel data and the ideal sensing signal. Simulation and experimental results show that the MDS-DNN algorithm can effectively suppress phase noise and improve the SNR at fading positions. Experiments using the data collected from the actual Φ-OTDR system demonstrate that the output SNR can reach 49.88 dB, which is 19.65 dB higher than the average level of the input channels. Moreover, the MDS-DNN method reduces the false alarm rate caused by interference fading by one order. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Deep neural networks

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