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 h...
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This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This m...
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ISBN:
(数字)9798350384437
ISBN:
(纸本)9798350384444
This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This method substitutes the original component recombination approach in SGMD with the K-means clustering algorithm, thus rendering the signal grouping more logically coherent. In the experiment, we compare the method proposed in this article with other denoising methods. The experimental findings demonstrate substantial advantages of the proposed method in mitigating noise in LFM signals, thereby enhancing both signal-to-Noise Ratio (SNR) and signal quality. The research outcomes underscore the feasibility and substantial potential of this method in mitigating noise in signals. In subsequent studies, additional investigation into its applicability across diverse domains can be pursued alongside the refinement of clustering and denoising algorithms.
The paper proposes a noise reduction algorithm based on symplectic Geometric Mode Decomposition (SGMD) and Savitzky-Golay (SG)filtering to address the issue of noise interference during signal transmission. Firstly, t...
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ISBN:
(数字)9798350384437
ISBN:
(纸本)9798350384444
The paper proposes a noise reduction algorithm based on symplectic Geometric Mode Decomposition (SGMD) and Savitzky-Golay (SG)filtering to address the issue of noise interference during signal transmission. Firstly, the noisy signal is decomposed using symplectic geometric mode decomposition to obtain several symplectic geometric components. The component with less noise is then selected and superimposed based on its energy entropy. Finally, the reconstructed signal undergoes denoising through SG filtering. The proposed method is applied to conduct comparison experiments on noise reduction for linear frequency modulation signals and multicomponent periodic signals. The results demonstrate a significant noise reduction effect of the proposed method, which holds great significance for signal analysis and research on noise reduction.
A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip pa...
A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications.
A wideband GCPW-SIW fed slots antenna in millimeter wave band is designed in this paper. The structure is composed of four inclined slots which are placed on the one side of the SIW with three shorting vias and fed by...
A wideband GCPW-SIW fed slots antenna in millimeter wave band is designed in this paper. The structure is composed of four inclined slots which are placed on the one side of the SIW with three shorting vias and fed by GCPW transmission line. The simulated results show that the operating frequency band of the proposed antenna is 24.15-27.59GHz (13.23%), the antenna has a stable gain with a maximum value of 9.67dBi and high radiation efficiency.
作者:
He, ZhikangZhu, HaoranAnhui Province
Anhui University Information Materials and Intelligent Sensing Laboratory Hefei230039 China Anhui University
Ministry of Education Key Lab of Intelligent Computing and Signal Processing Hefei230039 China
a U-shaped structure, based on the equivalent lumped circuit of differential transmission line, is proposed to suppress the noise of differential-common-mode conversion. With the equivalent lumped circuit, the cause o...
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作者:
Wang, JunZhu, HaoranAnhui University
Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University
Ministry of Education Key Lab of Intelligent Computing & Signal Processing Hefei230039 China
A miniaturized ultra-wideband microwave limiter with low insertion loss is presented in this paper. This limiter adopts a three-stage antiparallel diode structure. A T-type LC network topology consisting of two spiral...
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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 p...
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.
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 s...
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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