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Dual-layer Broadband High-gain Circularly Polarized Patch Antenna with L-shaped Stub

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Dual-layer Broadband High-gain Circularly Polarized Patch An...

IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Wen Li Wei Xue Yingsong Li Tian Hong Loh Kwok L. Chung College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Middlesex UK School of Computing Science and Engineering Huizhou University Huizhou China

This paper introduced a dual-layer broadband high-gain circularly polarized (CP) antenna. The proposed broadband CP antenna consists of the radiating patch and couping structure, which is made up of opening slot patch and L-shaped stub. An axial-ratio bandwidth of 26 % (4.36-5.66 GHz) is shown by this proposed dual-layer CP patch antenna with a simulated -10-dB reflection coefficient bandwidth of 37 % (4.23-6.15 GHz). The broadband characteristics offered by this CP antenna are promising for a wide range of future applications.

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Transient Electro-thermal Analysis of the Bipolar Transistor

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Transient Electro-thermal Analysis of the Bipolar Transistor

International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Hui Xiao Xingang Ren Yeqiang Yan Junjie Si Xueyuan Cai Bo Wu Zhixiang Huang Xianliang Wu Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Chizhou Anhui Province China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

This paper describes the transient electro-thermal properties of the bipolar transistor by a two-dimensional transient electro-thermal simulation, in which model the coupled heat conduction equation and drift-diffusion equations are solved. The backward difference scheme with the second-order accuracy was used to discretize the time derivative. The increment of bipolar transistor temperature will affect the drift and diffusion of carriers and thus its electrical characteristics. Through the transient study of electro-thermal characteristics of the bipolar transistor, the influence of the self-heating effect on bipolar transistor characteristic has considered in amplification mode. The developed model and results obtained in this work can be used to optimize the thermal design of bipolar transistor.

关键词： Performance evaluation Heating systems Bipolar transistors Temperature Electric variables Computational electromagnetics Mathematical models

An open problem and a conjecture on binary linear complementary pairs of codes

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arXiv 2023年

作者： Li, Shitao Shi, Minjia Ling, San The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China The State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China The School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore

The existence of q-ary linear complementary pairs (LCPs) of codes with q > 2 has been completely characterized so far. This paper gives a characterization for the existence of binary LCPs of codes. As a result, we solve an open problem proposed by Carlet et al. (IEEE Trans. Inf. Theory 65(3): 1694-1704, 2019) and a conjecture proposed by Choi et al. (Cryptogr. Commun. 15(2): 469-486, 2023). Copyright © 2023, The Authors. All rights reserved.

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KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation

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KERM: Knowledge Enhanced Reasoning for Vision-and-Language N...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiangyang Li Zihan Wang Jiahao Yang Yaowei Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The re-trieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://***/XiangyangLi20/KERM.

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A Novel Broadband EBG Structure For Simultaneous Switching Noise Suppression

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A Novel Broadband EBG Structure For Simultaneous Switching N...

International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Guoxing Sun Yeqiang Yan Xingang Ren Xueyuan Cai Bo Wu Huping Ju Wei Tao Gang Wang Bao Zou Zhixiang Huang Xianliang Wu Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Chizhou Anhui Province China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

In this paper, a compact electromagnetic bandgap (EBG) structure for suppressing simultaneous switching noise (SSN) in high-speed circuits is proposed. At the bandwidth level of 0.35GHz to 20GHz, an ultra-wideband SSN suppression feature with a suppression depth of -40dB is achieved. The basic unit structure of the presented EBG is formed by etching an annular groove in the square patch and connecting the separated patches inside and outside the groove with a bridge adjacent to the end. An improved L-type bridge is used between the EBG units to further improve the EBG performance. Compared with the traditional L-type bridge, the suppression depth of the proposed electromagnetic bandgap structure is improved from -30dB to - 40dB. In addition, the time domain eye simulation results show that the structure has good signal integrity.

关键词： Bridge circuits Bandwidth Switches Tail Metamaterials Etching Time-domain analysis

Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection

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arXiv 2023年

作者： Hu, Beizhe Sheng, Qiang Cao, Juan Zhu, Yongchun Wang, Danding Wang, Zhengjia Jin, Zhiwei Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China ZhongKeRuijian Technology Co. Ltd China

Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://***/ICTMCG/FTTACL23. © 2023, CC BY-NC-SA.

关键词： Forecasting

A spurious-free characteristic mode formulation based on surface integral equation for patch antenna structures

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arXiv 2021年

作者： Fan, Kun Zhao, Ran Cheng, Guang Shang Huang, Zhi Xiang Hu, Jun Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Chengdu611731 China

Conventional surface integral equation (SIE)-based characteristic mode formulation for the patch antenna structure with a finite substrate is susceptible to the spurious (nonphysical) modes due to the dielectric part. To avoid the contamination of spurious modes, we propose a novel generalized eigenvalue formulation based on the electric field integral equation coupled Poggio-Miller-Chang-Harrington-Wu-Tsai (EFIEPMCHWT) equation. In this formulation, the real and imaginary parts of the exterior integral operators are chosen to construct the finalized weighting matrices, to connect radiated power of the characteristic current. Compared with other SIE-based methods, this equation doesn't need additional post-processing since it can effectively avoid spurious modes. Numerical results compared with the volume-surface integral equation (VSIE) are investigated to validate the accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Microstrip antennas

Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint

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Data-Free Knowledge Distillation via Feature Exchange and Ac...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shikang Yu Jiachen Chen Hu Han Shuqiang Jiang 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 Peng Cheng Laboratory Shenzhen China

Despite the tremendous progress on data-free knowledge distillation (DFKD) based on synthetic data generation, there are still limitations in diverse and efficient data synthesis. It is naive to expect that a simple combination of generative network-based data synthesis and data augmentation will solve these issues. Therefore, this paper proposes a novel data-free knowledge distillation method (Spaceship-Net) based on channel-wise feature exchange (CFE) and multi-scale spatial activation region consistency (mSARC) constraint. Specifically, CFE allows our generative network to better sample from the feature space and efficiently synthesize diverse images for learning the student network. However, using CFE alone can severely amplify the unwanted noises in the synthesized images, which may result in failure to improve distillation learning and even have negative effects. Therefore, we propose mSARC to assure the student network can imitate not only the logit output but also the spatial activation region of the teacher network in order to alleviate the influence of unwanted noises in diverse synthetic images on distillation learning. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, Imagenette, and ImageNet100 show that our method can work well with different backbone networks, and outperform the state-of-the-art DFKD methods. Code will be available at: https://***/skgyu/Spaceship-Net.

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis