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A Novel Method for Rapidly Solving Wideband RCS by Combining UCBFM and Compressive Sensing

Progress In Electromagnetics Research C 2022年 124卷 33-42页

作者： Wang, Zhonggen Li, Chenwei Sun, Yufa Nie, Wenyan Wang, Pan Lin, Han School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Key Lab of Intelligent Computing & Signal Processing Anhui University Hefei230039 China School of Mechanical and Electrical Engineering Huainan Normal University Huainan232001 China

While analyzing wideband electromagnetic scattering problems using ultra-wideband characteristic basis function method (UCBFM), the reconstruction of a reduced matrix and the recalculation of an impedance matrix at each frequency point cost a large amount of time. To overcome this issue, a novel method that combines UCBFM with compressive sensing (CS) is proposed in this paper to rapidly analyse the wideband RCS. The proposed method makes the ultra-wideband characteristic basis functions (UCBFs) generated at the highest frequency as the sparse basis, introduces the CS theory, randomly extracts several rows from the original matrix as the measurement matrix, utilizes the corresponding excitation vector as the measurement value, and then employs the recovery algorithm, through which the solution of target induced current can be obtained. Due to partial filling of impedance matrix and efficient recovery algorithm, the wideband RCS computation time of the object is significantly reduced using the proposed method. Furthermore, the numerical simulation results show that the computation efficiency for the target wideband RCS can be further enhanced compared with that of the stand-alone UCBFM. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Computational efficiency

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Linear Frequency Modulated signal Denoising Based on Symplectic Geometric Mode Decomposition and Block Thresholding

Linear Frequency Modulated Signal Denoising Based on Symplec...

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International Conference on Communication Technology (ICCT)

作者： Jincheng Ni Jin Zhao Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China

In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step process: first, the signal is decomposed into a set of Symplectic Geometric Components (SGCs) using SGMD. Subsequently, each SGC is subjected to Block-Thresholding denoising. Finally, the denoised SGCs are recombined to obtain the denoised linear frequency modulation (LFM) signal. The experimental verification demonstrates the effectiveness of the SGMD-BT method in denoising LFM signals. This novel approach offers a fresh solution for the processing and analysis of LFM signals, holding significant application potential and research importance.

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Adversarial Example Generation Method Based on Probability Histogram Equalization

Adversarial Example Generation Method Based on Probability H...

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第42届中国控制会议

作者： Youqing Fang Jingwen Jia Yuhai Yang Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University

CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the security and robustness of *** transferability of adversarial examples is still low in black-box ***,an adversarial example method based on probability histogram equalization,namely HE-MI-FGSM(Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is *** each iteration of the adversarial example generation process,the original input image is randomly histogram equalized,and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial *** effectiveness of the method is verified on the ImageNet *** with the advanced method I-FGSM(Iterative Fast Gradient Sign Method) and MI-FGSM(Momentum I-FGSM),the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average,respectively.

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation

Improving Transferability Reversible Adversarial Examples Ba...

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作者： Youqing Fang Jingwen Jia Yuhai Yang Wanli Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding(RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example(RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper,we optimize the data hiding structure and combine data augmentation technology,which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image's visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios.

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A Wideband Six-Port 5G MIMO Mobile Phone Antenna 13

A Wideband Six-Port 5G MIMO Mobile Phone Antenna

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Shi, Ce Sun, Yufa Anhui University Key Lab Of Intelligent Computing Signal Processing Ministry Of Education Hefei230601 China

ISBN: (纸本)9781665434379

This paper introduces a compact wideband 6 port multiple-input multiple-output (MIMO) antenna for the 5G handset. Each antenna elements uses coupling feed, and I-shaped grounding structure is added to the unit to obtain better impedance matching and enhance the isolation from nearby antenna units. The 5G MIMO antenna is designed with a F-type antenna to cover 3.685.27 GHz. The simulation results show that it has good impedance matching, bandwidth and isolation with extremely small ground clearance. © 2021 IEEE.

关键词： 5G mobile communication systems

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A Dual-Band Compact Four-Element MIMO Antenna for Sub-6G Applications 13

A Dual-Band Compact Four-Element MIMO Antenna for Sub-6G App...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Pan, Tao Sun, Yufa Anhui University Key Lab Of Intelligent Computing Signal Processing Ministry Of Education Hefei230601 China

ISBN: (纸本)9781665434379

A compact Sub-6GHz multiple-input-multiple-output (MIMO) antenna is presented in this paper. The recommended MIMO antenna is electrically small (38mm × 38 mm× 1.6 mm). For good isolation and miniaturized size, four antenna elements are deposed orthogonally to each other. Four isolated grounds are devised to diminish the surface wave coupling as well as each ground is etched with orthogonal symmetric stub to decrease the near field coupling. Simulation outcomes conform to the return loss requirement of superior to 10 dB and insertion loss superior to 16 dB over the entire 3.24-3.68 and 4.77-5.04 GHz bandwidths (covering Sub-6GHz band in China). © 2021 IEEE.

关键词： Surface waves

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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FastICA based single-channel radar signal separation algorithm

FastICA based single-channel radar signal separation algorit...

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IET International Radar Conference (IRC 2023)

作者： Xinyu Wang Jin Zhao Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei People's Republic of China

ISBN: (数字)9781837240982

Aiming at the traditional blind source separation for the received single-channel mixed radar signal makes it difficult to accurately recover the source signal, a single-channel radar signal comprehensive separation method based on the combination of the FastICA algorithm and the variational modal decomposition (VMD) algorithm is proposed. The method utilizes VMD to first perform modal separation of single-channel mixed Linear Frequency Modulation (LFM) signal, then selects the modal component signal with the largest correlation coefficient with the mixed signal and expands it with single-channel mixed LFM signal into a virtual multi-channel mixed signal, and finally inputs it into FastICA algorithm to obtain the source signal. The simulation experiment results show that the comprehensive separation algorithm can accurately separate the LFM signal, overcoming the limitation that the FastICA algorithm cannot directly separate the single-channel mixed radar signal.

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Adversarial Example Generation Method Based on Probability Histogram Equalization

Adversarial Example Generation Method Based on Probability H...

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Chinese Control Conference (CCC)

作者： Youqing Fang Jingwen Jia Yuhai Yang Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively.

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A Dual-band Monopole Antenna with EBG for Wearable Wireless Body Area Networks

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Applied Computational Electromagnetics Society Journal 2021年第1期36卷 48-54页

作者： Wang, Chao Zhang, Liang Wu, Shenbing Huang, Shijie Liu, Changqing Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei City China AnHui Province Key Laboratory of Simulation and Design Electronic Information System Hefei Normal University Hefei City China

This paper proposes a dual-band wearable monopole antenna adopting an electromagnetic band-gap (EBG) structure, which operates at 2.45 and 5.8 GHz ISM bands and is suitable for wearable applications. Both the monopole antenna and the EBG structure are fabricated on an F4B semi-flexible substrate having a dielectric constant of 2.2. The EBG structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 97.5%. This improves the antenna gain and the peak gain reaches 9.1 dBi at 5.8 GHz. The wearable performance of the antenna showed that it can sustain good performance even under realistic human body loading. Besides, the antenna has a small size, which makes it ideal for wearable applications. © ACES

关键词： Monopole antennas

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