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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Excellent Student Training in Shielding Effectiveness Analysis with International Cooperation 10

Excellent Student Training in Shielding Effectiveness Analys...

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10th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2022

作者： Li, Yingsong Huang, Zhixiang Wang, Meiyu Jiang, Tao Sinkevich, Eugene Mordachev, Vladimir College of Information and Communication Engineering Harbin Engineering University Harbin15001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Belarusian State University of Informatics and Radioelectronics Emc RandD Laboratory Minsk Belarus

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

We present the excellent student training scheme in shielding effectiveness analysis with international cooperation of Belarus. As we know, the shielding is vital for modern information devices due to the radiating around us. Herein, the shielding effectiveness is important for training excellent students to master the design of the frequency selective components. In this method, we teach the students to use the EM simulation tools to construct different EM structures for obtaining the shielding, and the performance of these structures are verified from the simulations and measurements. Using this shielding effectiveness training scheme, a frequency selective surface is presented, simulated and discussed to analyze its performance. In future, the excellent student training methods given herein for shielding effectiveness can be expanded to develop other EM structures. © 2022 IEEE.

关键词： International cooperation

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Pctlfusion: A Progressive Fusion Network Via Contextual Texture Learning for Infrared and Visible Image

SSRN

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SSRN 2024年

作者： Lu, Yixiang Gong, Daiwei Zhao, Dawei Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Belarusian State University of Informatics and Radioelectronics Minsk Belarus

The objective of infrared and visible image fusion is to generate a fused image that contains rich texture details and salient targets. However, most of the existing fusion methods tend to focus on preserving texture details in visible image and salient targets in infrared image, ignoring the fact that infrared image have richer texture details and better visual effects than visible image under low-light conditions. In other words, differences in information sources under the condition of illumination imbalance is ignored in setting the task goal. In addition, existing network designs usually ignore multimodal information interactions during the coding phase, which may result in loss of information. In this work, we propose a progressive fusion network via contextual texture learning for infrared and visible image, termed as PCTLFusion. To enhance the limited texture information, we design a content and contextual texture learning module (CCTLM) to model the texture and content of both the source images, respectively. Then, an affine transform-based multimodal pre-fusion module (ATMPM) is designed to learn and pre-fuse the complementary and similar enhanced information, which achieves information interaction during encoding. To learn fully feature information using multilayers, we iterate CCTLM and ATMPM three times in the process of model forming. Finally, the fused image is reconstructed by decoding the output of the last ATMPM. Numerous experiments show that our proposed fusion algorithm is superior to the state-of-the-art existing methods in both subjective and objective evaluations. © 2024, The Authors. All rights reserved.

关键词： Textures

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A Circularly Polarized Antenna with a Loading Metal Strip for GPS L1 band

A Circularly Polarized Antenna with a Loading Metal Strip fo...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Lulu Meng Sixian Qian Zhixiang Huang Yingsong Li Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Electromagnetic & Eletrochemical Technologies Department National Physical Laboratory Middlesex United Kingdom

The paper presents a novel circular polarization(CP) antenna loading with a parasitic ring metal strip, which is designed for global positioning system (GPS) L1 band applications. The antenna consists of a defected ground plane with four arc-slots and a circular slot, a dielectric substrate, a parasitic ring metal strip, and a radiating patch (RP) with a arc-slot. By rotating the parasitic ring metal strip to opening slot on the RP, the antenna can be transformed from left-handed circular polarization (LHCP) to right-handed circular polarization (RHCP). The size of the antenna can be reduced by using slots on the ground. The simulation and optimization results show that impedance bandwidth of the L/RHCP antenna is over 100 MHz, and the axial ratio bandwidth is over 20 MHz.

关键词： Polarization Slot antennas Loading Metals Bandwidth Loaded antennas Optimization

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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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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Efficient polar coding scheme and implementation with shared information bits

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Science China(Information Sciences) 2022年第10期65卷 309-310页

作者： Wenyue ZHOU Xiaofeng ZHOU Yifei SHEN Qiang LIU Liping LI Chuan ZHANG Laboratory of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China Anhui University

Dear editor,Polar codes invented by Ar?kan [1] can achieve the capacity of symmetric binary-input discrete memoryless channels(B-DMCs) with an infinite code length. However, it suffers from unsatisfactory performance with a finite code length [2]. The performance degradation of polar codes with a finite code length is caused by the inadequate polarization phenomenon;

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Constrained Squared Sine Derived Adaptive Algorithm: Performance and Analysis

SSRN

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SSRN 2022年

作者： Li, Yingsong Chen, Yong Li, Liping Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

A constrained squared sine derived adaptive (CSSDA) algorithm is proposed in this paper, which provides better steady-state behavior than existing algorithms in impulsive-noise environments. The devised CSSDA is constructed via constructing a constrained cost function on the basis of squared sine functioned system estimate error. Theoretical analysis of the CSSDA is presented and compared with the simulations. The simulation results show that the theoretical analysis agrees well with simulations, helping to verify the effectiveness and correctness of the analysises. Also, the performance of the CSSDA is superior to the recent popular constraint adaptive algorithms for system identifications in a variety of environments with non-Gaussian impulsive-noises. © 2022, The Authors. All rights reserved.

关键词： Adaptive filtering

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Two-dimensional Eletrostatically Actuated MEMS Micromirror 5

Two-dimensional Eletrostatically Actuated MEMS Micromirror

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Tao, Wei Ren, Xingang He, Shuping Wu, Xianliang Cai, Xueyuan Huang, Zhixiang Key Laboratory of Target Recognition and Feature Extraction of Anhui Province Lu'an China Ministry of Education Anhui University Key Laboratory of Intelligent Computing & Signal Processing Hefei China Anhui University Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Hefei China School of Electronic Engineering and Intelligent Manufacturing Anqing Normal University Anqing China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Recently, the development of LiDAR in the field of vehicle has made the core micromirror with essential characteristics of large tilted angle and small size. We proposed a two-dimensional electrostatically actuated MEMS micromirror with a slender torsion bar. With the modified theoretical formula, two different torsional modes and corresponding torsional frequencies of the micromirror are obtained, which are then compared to the results by finite element simulation, and revealed a good agreement. The rotation angle of the micromirror is improved through optimizing the micromirror structure. Meanwhile, it is found that the driving voltage and the moment of inertia introduce the softening behavior of the spring, which will then affect the torsion frequency. To prevent the torsion bar stress is too large, with the sinusoidal driving voltage of 75V and 100V, the corresponding mechanical rotation angles are 14.8° and 15.3° at a driving frequency of 340.5Hz and 1500Hz, respectively. © 2022 IEEE.

关键词： Torsional stress

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SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

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arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

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