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6th Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems

作者： Zhu, Haixia Ni, Bo Feng, Zhenjie Qian, Yitong Zhou, Xiaoyan Ni, Haibin Yang, Lingsheng Chang, Jianhua Jiangsu Key Laboratory of Meteorological Observation and Information Processing School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9781510679672

ISBN: (纸本)9781510679665

In this paper, an absorber based on split square graphene is designed, which produces double absorption peaks at different polarization incidence. The absorption rates of x-polarized waves at 7.86 THz and 12.63THz are 97.9% and 91.2% respectively. The absorption rates of y-polarized waves at 6.30 THz and 10.52 THz are 94.1% and 93.2%. In this paper, an absorber based on split square graphene is designed, which produces double absorption peaks at different polarization incidence. The absorption rates of x-polarized waves at 7.86 THz and 12.63THz are 97.9% and 91.2% respectively. The absorption rates of y-polarized waves at 6.30 THz and 10.52 THz are 94.1% and 93.2%. The effect of Fermi level on the dual polarization absorptivity of graphene is discussed. Therefore, this work may have great reference value for terahertz sensing, selective spectral detection and other fields. The effect of Fermi level on the dual polarization absorptivity of graphene is discussed. Therefore, this work may have great reference value for terahertz sensing, selective spectral detection and other fields. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Polarization

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MSF-Net: A Multilevel Spatiotemporal Feature Fusion Network Combines Attention for Action Recognition

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Computer Systems Science & Engineering 2023年第11期47卷 1433-1449页

作者： Mengmeng Yan Chuang Zhang Jinqi Chu Haichao Zhang Tao Ge Suting Chen School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing210044China

An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction,information redundancy,and insufficient extraction of frequency domain information in channels in 3D convolutional neural ***,based on 3D CNN,this paper designs a new multilevel spatiotemporal feature fusion(MSF)structure,which is embedded in the network model,mainly through multilevel spatiotemporal feature separation,splicing and fusion,to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network parameters;In the second step,a multi-frequency channel and spatiotemporal attention module(FSAM)is introduced to assign different frequency features and spatiotemporal features in the channels are assigned corresponding weights to reduce the information redundancy of the feature ***,we embed the proposed method into the R3D model,which replaced the 2D convolutional filters in the 2D Resnet with 3D convolutional filters and conduct extensive experimental validation on the small and medium-sized dataset UCF101 and the largesized dataset *** findings revealed that our model increased the recognition accuracy on both *** on the UCF101 dataset,in particular,demonstrate that our model outperforms R3D in terms of a maximum recognition accuracy improvement of 7.2%while using 34.2%fewer *** MSF and FSAM are migrated to another traditional 3D action recognition model named C3D for application *** test results based on UCF101 show that the recognition accuracy is improved by 8.9%,proving the strong generalization ability and universality of the method in this paper.

关键词： 3D convolutional neural network action recognition MSF FSAM

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Design of Flexible Wearable Antenna Based on Artificial Magnetic Conductor Structure

Design of Flexible Wearable Antenna Based on Artificial Magn...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Zhou, Yong Zhuang, Jiajie Zhou, Jiahao Nuist Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing210044 China Nuist Collaborative Innovation Center for Atmospheric Environment and Equipment Technology Nanjing210044 China

ISBN: (纸本)9798350375909

This paper designs a dual-band wearable antenna based on a 2×2 artificial magnetic conductor (AMC) array, which is suitable for wireless body area network (WBAN) applications. It works in the ISM bands of 2.45 GHz and 5.8 GHz. The antenna has the characteristics of high gain, low profile, good flexibility and body adaptability. The antenna consists of three layers. The top layer is a T-shaped metal strip-loaded tuning branch antenna fed by coplanar waveguide printed on a PI material substrate, which has dual-band resonance frequency. The middle layer is the dielectric material, which is air in the study. The bottom layer is an AMC reflector composed of a 2 × 2 double circular strip array structure printed on a PDMS material substrate. The simulation results show that S11 = -18.2 dB and -25.5 dB in 2.45 GHz and 5.8 GHz, with the frequency range of 2.3 GHz-2.5 GHz and 5.6 GHz-5.9 GHz. The bandwidth is 200 MHz and 300 MHz for the two resonance frequencies. Also, the gain of the antenna reaches 1.1 dBi and 5.3 dBi in 2.45 GHz and 5.8 GHz respectively. As a comparison, the gain of the single antenna without AMC structure is -1.3 dBi at 2.45 GHz and 3.14 dBi at 5.8 GHz. Thus, the gain of the antenna increases 2.4 dB and 2.16 dB respectively after applied with AMC structure. The antenna with AMC structure is manufactured and measured in microwave anechoic chamber. The test results show that S11 = -22.8 dB and -19.7 dB at frequency 2.55 GHz and 5.65 GHz, with frequency band of 2.2 GHz to 2.7 GHz and 5.6 GHz to 6.0 GHz. The gain is 0.92 dBi and 4.59 dBi at 2.45 GHz and 5.8 GHz respectively. Three bending radii were chosen for bending experiments to investigate the effect of bending on the antenna radiation performance. Experimental results demonstrate that S11 and radiation pattern remain stable, with minimal impact. The test results show good agreement with simulation, and the gain is a little lower than simulation because of the manufacture error. The research demonstrates

关键词： Coplanar waveguides

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Lightweight image super-resolution via an adaptive information fusion attention network

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Soft Computing 2025年第8期29卷 4075-4089页

作者： He, Yi Huan, Hai Zou, Nan Zhang, Yi Xie, Yaqin Wang, Chao School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China School of Electronic & Information Engineering Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology Nanjing210044 China Nanjing Nanji Agricultural Machinery Science and Technology Research Institute Nanjing210019 China

The Convolutional Neural Networks (CNNs) have achieved outstanding performance in the field of Image Super-Resolution (SR). However, many existing methods focus excessively on increasing network depth, which leads to an excessive number of parameters and computational complexity. Some methods, aimed at reducing model capacity, significantly reduce the receptive field of the network, and employ a single upsampling method, resulting in suboptimal model performance. To better balance model size and performance, this paper proposes a lightweight Adaptive information Fusion Attention Network (AIFAN). Specifically, a Dual Path Block (DPB) is designed to adaptively aggregate multi-scale features from different paths through the Adaptive Path Fusion Block (APFB). To take full advantage of the hierarchical features, all DPBs are connected using a dense connection mode. Meanwhile, a receptive field attention (RFA) module is proposed to increase the receptive field of the network and force the network to pay attention to the region with more information in the feature. In addition, a hybrid up-sampling module is designed. The Adaptive Multi-scale Up-sampling (AMSU) module and Feature Update Up-sampling Block (FUUB) are combined in the module to obtain better reconstruction results. The experimental results show that the proposed AIFAN achieves an effective balance between performance and model complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： information fusion

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Hardware acceleration implementation of a ground-based cloud image classification algorithm

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Journal of Electronic Measurement and Instrumentation 2025年第2期39卷 21-31页

作者： Lin, Feng Wenqiang, Song Wei, Xu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology Nanjing210044 China

The automatic observation and recognition of ground-based clouds have guiding significance for analyzing atmospheric motion trends and weather forecasting. To solve the problem of low accuracy of ground-based cloud image classification algorithms and difficulties in deploying them on embedded terminals, a ground-based cloud image classification network model GBcNet based on residual network structure and a hardware implementation architecture based on ZYNQ are proposed. The PS end is used to load the weight parameters and cloud data of the model, and the PL end implements DDR3 read-write control and GBcNet hardware acceleration. For the GBcNet network, accelerated IP cores corresponding to each module were designed, including sliding window, convolutional layer, pooling layer, batch normalization layer, and fully connected layer. Experiments were conducted on the CCSN dataset, and the results showed that the GBcNet model achieved an accuracy of 96. 02% on a PC platform. After hardware acceleration, the accuracy remained at 94. 5%. Compared with the recognition rate of the PC model, the accuracy loss for each cloud class did not exceed 3%, and the overall accuracy loss was less than 1. 5%. The maximum resource usage on the FPGA did not exceed 48%, and the inference time for a single ground-based cloud image was 0. 13 seconds. Compared to existing ground-based cloud recognition methods, this approach demonstrates higher accuracy and shorter inference time. The proposed recognition model and acceleration method provide a reference solution for the development of multi-node, portable ground-based cloud observation equipment. © 2025 Editorial Office of EMI Journal. All rights reserved.

关键词： Motion analysis

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Deep learning-enabled inverse design of polarization-selective structural color based on coding metasurface

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Chinese Physics B 2025年第5期34卷 311-318页

作者： Haolin Yang Bo Ni Junhong Guo Hua Zhou Jianhua Chang Jiangsu Key Laboratory of Meteorological Observation and Information Processing School of Electronic and Information EngineeringNanjing University of Information Science and TechnologyNanjing 210044China College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China

Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color ***,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding *** this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding *** results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the ***,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse *** study provides a new path for the application and development of structural colors.

关键词： deep learning inverse design coding metasurface structural color polarization-selective

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Lightning current waveform measurement based on Rogowski coil and CEEMDAN

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Journal of Electronic Measurement and Instrumentation 2025年第2期39卷 102-112页

作者： Xu, Wei Li, Xiang Wang, Xuemeng Liu, Yan Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science & Technology Nanjing210044 China Guangzhou Meteorological Public Service Center Guangzhou511400 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Nanjing210041 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China

The lightning current waveform measurement module embedded in the surge protector provides data support for its aging analysis. To address the current issues in lightning current measurement within surge protectors, such as the inability to record complete waveforms and high noise levels, this study analyzes the characteristics of lightning current waveforms and designs a measurement circuit comprising a Rogowski coil, low-noise wideband amplification, single-ended to differential conversion, high-speed analog-to-digital converter, and FPGA. The processing of collected signals, caching and sending of data, and counting of lightning strikes are completed by FPGA. This design aims to reduce measurement errors in lightning current waveforms. A threshold and slope combining triggering method is employed to enhance the accuracy of counting lightning current impulses. Additionally, a method combining differential circuit and complete ensemble empirical mode decomposition with adaptive noise is proposed to reduce the influence of noise on lightning current waveform. Experimental tests were conducted using a lightning current composite wave generator from a lightning protection laboratory to evaluate the system and denoising methods. The results demonstrate that the system can accurately and comprehensively record lightning current waveforms. The lightning current measurement with the peak range of 1 ~ 10 kA has no leakage trigger phenomenon, a measurement error of ≤0. 2 μs for half-peak arrival time, and a measurement error of ≤2. 04% for the average slope of the front peak. The system will promote the intelligent development of surge protectors and provide data support for lightning research. © 2025 Editorial Office of EMI Journal. All rights reserved.

关键词： Electric current measurement

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Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

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Chinese Journal of Aeronautics 2022年第5期35卷 40-52页

作者： Quanbo GE Zhongcheng MA Jinglan LI Qinmin YANG Zhenyu LU Hong LI School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi UniversityHangzhou 310018China State Key Laboratory of Industrial Control Technology and the College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and TechnologyNanjing 210044China Chinese Flight Test Establishment Xi’an 710089China

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement *** this paper,first,a correlation multiplicative measurement noise model is *** is able to more accurately represent the measurement error caused by the distance sensor dependence ***,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is *** improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative *** practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even *** address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is ***,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

关键词： Adaptive CKF Correlated noise Estimation accuracy Multiplicative noise Performance analysis Target tracking

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Design of Flexible Wearable Antenna Based on Artificial Magnetic Conductor Structure

Design of Flexible Wearable Antenna Based on Artificial Magn...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yong Zhou Jiajie Zhuang Jiahao Zhou Jiangsu Key Laboratory of Meteorological Observation and Information Processing NUIST Nanjing China Collaborative Innovation Center for Atmospheric Environment and Equipment Technology NUIST Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This paper designs a dual-band wearable antenna based on a 2×2 artificial magnetic conductor (AMC) array, which is suitable for wireless body area network (WBAN) applications. It works in the ISM bands of 2.45 GHz and 5.8 GHz. The antenna has the characteristics of high gain, low profile, good flexibility and body adaptability. The antenna consists of three layers. The top layer is a T-shaped metal strip-loaded tuning branch antenna fed by coplanar waveguide printed on a PI material substrate, which has dual-band resonance frequency. The middle layer is the dielectric material, which is air in the study. The bottom layer is an AMC reflector composed of a 2 × 2 double circular strip array structure printed on a PDMS material substrate. The simulation results show that S 11 = −18.2 dB and −25.5 dB in 2.45 GHz and 5.8 GHz, with the frequency range of 2.3 GHz–2.5 GHz and 5.6 GHz–5.9 GHz. The bandwidth is 200 MHz and 300 MHz for the two resonance frequencies. Also, the gain of the antenna reaches 1.1 dBi and 5.3 dBi in 2.45 GHz and 5.8 GHz respectively. As a comparison, the gain of the single antenna without AMC structure is −1.3 dBi at 2.45 GHz and 3.14 dBi at 5.8 GHz. Thus, the gain of the antenna increases 2.4 dB and 2.16 dB respectively after applied with AMC structure. The antenna with AMC structure is manufactured and measured in microwave anechoic chamber. The test results show that S 11 = −22.8 dB and −19.7 dB at frequency 2.55 GHz and 5.65 GHz, with frequency band of 2.2 GHz to 2.7 GHz and 5.6 GHz to 6.0 GHz. The gain is 0.92 dBi and 4.59 dBi at 2.45 GHz and 5.8 GHz respectively. Three bending radii were chosen for bending experiments to investigate the effect of bending on the antenna radiation performance. Experimental results demonstrate that S 11 and radiation pattern remain stable, with minimal impact. The test results show good agreement with simulation, and the gain is a little lower than simulation because of the manufacture error. The research

关键词： Wireless communication Microwave measurement Microwave antennas Magnetic resonance Dual band Bending Wearable antennas

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A multimodal progressive fusion bearing fault diagnosis algorithm based on residual network

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IEEE Sensors Journal 2025年

作者： Zhu, Wenbing Ni, Haibin Li, Zhuo Cao, Ji Ni, Bo Chang, Jianhua Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Jiangsu JITRI Integrated Circuit Application Technology Innovation Center Wuxi214101 China Nanjing University of Information Science and Technology Jiangsu Provincial Key Laboratory of Meteorological Observation and Information Processing Nanjing210044 China

Bearings are crucial components of rotating machinery in critical industrial equipment such as wind turbines, high-speed trains, and aerospace engines. Existing methods for bearing fault diagnosis are generally confined to superficial integration of multi-sensor or multi-domain data, constrained by either poor heterogeneous information integration in early fusion approaches or information loss and imbalanced modality representations caused by late fusion strategies, resulting in limited diagnostic effectiveness under complex and dynamic industrial operating conditions. To solve this issue, we propose a multimodal progressive fusion bearing fault diagnosis algorithm based on residual networks. The algorithm integrates multi-sensor and multi-domain data and automatically extracts fault features using residual networks. Then, an improved progressive feature fusion technique is applied to optimise the utilisation of the multimodal features, which aims to allow earlier layers to access later fused features, avoiding the loss of important information and improving the fusion representation over multiple iterations. The diagnostic efficacy of the proposed method is validated using two different bearing datasets, achieving a diagnostic accuracy of 99.97% for composite faults. This advancement shows the great potential for implementation on industrial IoT platforms, especially in scenarios such as power generation and transport where predictive maintenance is required, reducing unplanned downtime and maintenance costs. © 2001-2012 IEEE.

关键词： Bearings (machine parts)

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