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Design of low-profile wide-band high-gain circularly polarized antenna based on metasurface

Design of low-profile wide-band high-gain circularly polariz...

International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Wei Zhang Liang Zhang Xianliang Wu key lab of intelligent computing & signal processing ministry of education anhui university Anhui University Hefei China key lab of intelligent computing & signal processing ministry of education anhui university AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei Normal University Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper proposes a wide-band low-profile high-gain microstrip slot antenna loaded with a mushroom-type metasurface structure. By directly loading the metamaterial structure on the upper surface of the slot antenna, low profile loading characteristics are realized and the antenna bandwidth is expanded. And by inserting the metal line array in the metamaterial dielectric board to improve the antenna gain. Change the gap width through the metasurface element with corner cut to produce a phase difference and realize circularly polarized. Simulation test and analysis of antenna through HFSS simulation software. The results show that the antenna with this metasurface has a profile height of only 0.09×λ(λ =27 mm), a relative impedance band-width of 20.4% (S 11

关键词： Slot antennas Microstrip antennas Metasurfaces Metamaterials Microstrip Loaded antennas Gain

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Characterization and construction of optimal binary linear codes with one-dimensional hull

arXiv

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arXiv 2022年

作者： Li, Shitao Shi, Minjia Kim, Jon-Lark The School of Mathematical Sciences Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The Department of Mathematics Sogang University Seoul Korea Republic of

The hull of a linear code over finite fields is the intersection of the code and its dual, and linear codes with small hulls have applications in computational complexity and information protection. Linear codes with the smallest hull are LCD codes, which have been widely studied. Recently, several papers were devoted to related LCD codes over finite fields with size greater than 3 to linear codes with one-dimensional or higher dimensional hull. Therefore, an interesting and non-trivial problem is to study binary linear codes with one-dimensional hull with connection to binary LCD codes. The objective of this paper is to study some properties of binary linear codes with one-dimensional hull, and establish their relation with binary LCD codes. Some interesting inequalities are thus obtained. Using such a characterization, we study the largest minimum distance done(n, k) among all binary linear [n, k] codes with one-dimensional hull. We determine the largest minimum distances done(n, n − k) for k ≤ 5 and done(n, k) for k ≤ 4 or 14 ≤ n ≤ 24. We partially determine the exact value of done(n, k) for k = 5 or 25 ≤ n ≤ *** Codes 94B05, 15B05, 12E10 Copyright © 2022, The Authors. All rights reserved.

关键词： Optimal systems

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High order SF-SFDTD method for solving the time-dependent Schrödinger equation

High order SF-SFDTD method for solving the time-dependent Sc...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Pan Pan Guoda Xie Zhixiang Huang Guilin Hou Ziheng Song School of Electronic and Information Engineering Anhui university Hefei China Education of Anhui Province. Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan Anhui China Ministry of Education. the Key Laboratory of Intelligent Computing and Signal Processing Hefei China Key Laboratory of Electromagnetic Environmental Sensing Anhui Province Anhui University Hefei China Anhui university Hefei China

A high-order spatial filtering-symplectic finite difference time domain (SF-SFDTD) scheme with controllable stability condition is proposed to solve the time-dependent Schrödinger equation (TDSE). Firstly, the higher-order symplectic framework for the discretization of Schrödinger equation is described, and then the spatial filtering method is carried out to extend the stability of the standard SFDTD method. Finally, a numerical example is given to evaluate the correctness of the SF-SFDTD method in numerical solving Schrödinger equation.

关键词： Filtering Filtering algorithms Computational electromagnetics Mathematical models Time-domain analysis Numerical stability Standards

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Chosen Plaintext Attack on Jpeg Image Encryption with Adaptive key and Run Consistency

SSRN

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SSRN 2022年

作者： He, Hongjie Yuan, Yuan Chen, Fan Ye, Yuyun Tai, Heng-Ming Key Lab of Signal & Information Processing Southwest Jiaotong University Sichuan Province Chengdu611756 China Department of Electrical and Computer Engineering University of Tulsa TulsaOK74104 United States School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

A JPEG image encryption with an adaptive key generated by a preserved characteristic not only lowers the cost of key management but also enhances the ability to resist various attacks. However, there are still security risks under the chosen-plaintext attack (CPA) conditions. This paper designs a novel CPA scheme to obtain details of the original image. First, the adaptive key can be reproduced from the encrypted image, so that the plaintext images with the same adaptive key can be constructed. Second, the existence of run consistency of MCUs (RCM), which facilitates rapid estimation. In addition, the single permutation for the runs of MCUs with RCM is designed for more accurate estimation. Experimental results show that two plaintext images are sufficient to get the image details. For an image of high quality factors (QFs≥90) or with rich complex texture, the accuracy ratio of estimated permutation sequence is close to 100%. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement

arXiv

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arXiv 2022年

作者： Yang, Yuting Huang, Pei Cao, Juan Li, Jintao Lin, Yun Dong, Jin Song Ma, Feifei Zhang, Jian Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing China National University of Singapore Singapore Laboratory of Parallel Software and Computational Science ISCAS Beijing China

Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerabilities. In this paper, we propose a novel prompt-based adversarial attack to compromise NLP models and robustness enhancement technique. We first construct malicious prompts for each instance and generate adversarial examples via mask-and-filling under the effect of a malicious purpose. Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs). Furthermore, we design a prompt-based adversarial training method to improve the robustness of PLMs. As our training method does not actually generate adversarial samples, it can be applied to large-scale training sets efficiently. The experimental results show that our attack method can achieve a high attack success rate with more diverse, fluent and natural adversarial examples. In addition, our robustness enhancement method can significantly improve the robustness of models to resist adversarial attacks. Our work indicates that prompting paradigm has great potential in probing some fundamental flaws of PLMs and fine-tuning them for downstream tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Self-Mutual Distillation Learning for Continuous Sign Language Recognition

Self-Mutual Distillation Learning for Continuous Sign Langua...

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International Conference on Computer Vision (ICCV)

作者： Aiming Hao Yuecong Min Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665428132

In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network. However, due to the limitation of chain rules in back-propagation, the visual module is hard to adjust for seeking optimized visual features. As a result, it enforces that the contextual module focuses on contextual information optimization only rather than balancing efficient visual and contextual information. In this paper, we propose a Self-Mutual Knowledge Distillation (SMKD) method, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously. Specifically, the visual and contextual modules share the weights of their corresponding classifiers, and train with CTC loss simultaneously. Moreover, the spike phenomenon widely exists with CTC loss. Although it can help us choose a few of the key frames of a gloss, it does drop other frames in a gloss and makes the visual feature saturation in the early stage. A gloss segmentation is developed to relieve the spike phenomenon and decrease saturation in the visual module. We conduct experiments on two CSLR bench-marks: PHOENIX14 and PHOENIX14-T. Experimental results demonstrate the effectiveness of the SMKD.

关键词： Training Deep learning Visualization Computer vision Gesture recognition Assistive technologies Benchmark testing

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Cross-domain Contrastive Learning for Unsupervised Domain Adaptation

arXiv

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arXiv 2021年

作者： Wang, Rui Wu, Zuxuan Weng, Zejia Chen, Jingjing Qi, Guo-Jun Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Seattle Cloud Lab Futurewei Technologies BellevueWA98004 United States

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing feature distances across domains. In this work, we build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets. Exploring the same set of categories shared by both domains, we introduce a simple yet effective framework CDCL, for domain alignment. In particular, given an anchor image from one domain, we minimize its distances to cross-domain samples from the same class relative to those from different categories. Since target labels are unavailable, we use a clustering-based approach with carefully initialized centers to produce pseudo labels. In addition, we demonstrate that CDCL is a general framework and can be adapted to the data-free setting, where the source data are unavailable during training, with minimal modification. We conduct experiments on two widely used domain adaptation benchmarks, i.e., Office-31 and VisDA-2017, for image classification tasks, and demonstrate that CDCL achieves state-of-the-art performance on both datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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Application of Maehly Approximation to em scattering from a Three-dimensional Rough Surface over a Frequency Band 5

Application of Maehly Approximation to em scattering from a ...

引用

5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Wang, An Qi Zhang, Xue Huang, Zhi Xiang Anhui University School of Electronics and Information Engineering Key Laboratory of Intelligent Computing Signal Processing Hefei China

ISBN: (纸本)9781538671115

In this paper, the Maehly approximation, which is based on Chebyshev series, is proposed to fast simulate the EM scattering from a three-dimensional (3D) rough surface over a frequency band. Comparing with the exact results obtained by the method of moments (MoM), results of the Maehly approximation have achieved an excellent agreement both in co-polarization and cross-polarization. Moreover, the Maehly approximation is superior to the MoM in terms of the CPU time required. © 2019 IEEE.

关键词： Method of moments

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Data gravitation-based three-way sampling method for imbalanced data classification

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Information Sciences 2025年 718卷

作者： Yuanting Yan Weiqing Wang Yingao Ma Yang Tian Peng Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui Province 230601 PR China School of Computer Science and Technology Anhui University Hefei Anhui Province 230601 PR China

Data resampling is widely used for addressing the class imbalance problem, with numerous methods developed over recent decades. Among these, the K-nearest neighbor (KNN) technique is widely adopted for leveraging local neighborhood information in rebalancing imbalanced data. However, KNN struggles to accurately characterize local data distribution, as samples with identical KNNs may also exhibit divergent local structures, which may further hamper imbalanced learning performance. We propose a hybrid sampling method called data gravitation-based three-way sampling (DGS) to improve classification performance on imbalanced data. DGS introduces the concept of data gravitation into KNN to evaluate the local importance of samples, and combines it with three-way decision theory to selectively oversample minority-class samples and remove risky majority-class samples. DGS introduces a cost-sensitive sampling mechanism and adaptive threshold estimation using a Gaussian-based model, reducing reliance on manually tuned parameters. Extensive experiments on 38 datasets and seven classifiers demonstrate the superiority of DGS over 18 resampling methods across three evaluation metrics. Our code is available at https://***/ytyancp/DGS .

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Long-Tailed Multi-label Classification with Noisy label of Thoracic Diseases from Chest X-Ray

Long-Tailed Multi-Label Classification with Noisy Label of T...

引用

IEEE International Symposium on Biomedical Imaging

作者： Haoran Lai Qingsong Yao Zhiyang He Xiaodong Tao S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei P.R. China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Medical Business Department iFlytek Co. Ltd Hefei China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML- MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at: https://***/laihaoran/LTML-MIMIC-CXR.

关键词： Design automation Annotations MIMICs Pipelines Tail Computer aided diagnosis Noise measurement

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