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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Penghao Run Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

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.

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Parallel Coupled Microstrip Wideband Bandpass Filter Loaded with CSRR and SRR Structures

Parallel Coupled Microstrip Wideband Bandpass Filter Loaded ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zijian Yu Xiaodi Song Chen Li Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

This paper presents a parallel coupled line bandpass filter for X-band applications, which has low insertion loss, broadband and easy processing. By loading the CSRR (Complementary Split Ring Resonator) and SRR (Split Ring Resonator) structures, three additional transmission zeros are added to improve the out-of-band rejection performance of the filter. Moreover, the rectangular DGS structure below the triple-parallel coupling line reduces the insertion loss of the filter. The simulation results show that the filter center frequency is 9.5GHz, the insertion loss is 0.8dB, S11

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Design of a Miniaturised Wide-Stopband SIW Filter

Design of a Miniaturised Wide-Stopband SIW Filter

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Chen Li Minquan Li Xin Qu Shuang Xiao Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we propose a miniaturized wide-stopband SIW dual-band filter. SIW limits the propagation of electromagnetic waves and thus enables the miniaturization of the filter. A passband below the waveguide cutoff frequency is generated by etching the CSRR structure on the surface of the SIW, while a modified CSRR-DGS structure is etched on the backside of the SIW structure, and the other passband is generated by coupling the CSRR etched on the surface of the SIW to the ground. In addition, the CSRR-DGS structure broadens the stopband without increasing the filter size. Through the circuit equivalent analysis and parameter optimization of the filter structure, the final simulation results are as follows: the filter has a passband of 3.5 GHz (WIMAX) and 5.2 GHz (WLAN), the 3dB bandwidths of the two bands are 130MHz and 180MHz respectively, the insertion loss is 0.5 dB and 0.65 dB, and the size is $0.18\lambda_{\mathrm{g}}\times 0.27\lambda_{\mathrm{g}}$ . Overall, it achieves high performance and also has a wide range of applications.

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A Bandstop Filter Based on Modified Defected Ground Structure with Compact Size

A Bandstop Filter Based on Modified Defected Ground Structur...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Boyan Zhang Minquan Li Shuang Xiao Chen Li Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

This paper reports a compact size bandstop filter using Defected Ground Structure (DGS). The proposed filter consists of squared shaped defect and U-shaped defect sticked on the ground plane. This design is based on a parallel LC circuit model and designed to work at 1 GHz. The filter is implemented on a FR-4 dielectric plate and its size is $0.049\lambda_{g}\times 0.049\lambda_{g}$ (where $\lambda_{g}$ is the wavelength in waveguide).

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Evolutionary neural architecture search for transformer in knowledge tracing 23

Evolutionary neural architecture search for transformer in k...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Shangshang Yang Xiaoshan Yu Ye Tian Xueming Yan Haiping Ma Xingyi Zhang Anhui University and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Guangdong University of Foreign Studies Anhui University and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province

Transformer has achieved excellent performance in the knowledge tracing (KT) task, but they are criticized for the manually selected input features for fusion and the defect of single global context modelling to directly capture students' forgetting behavior in KT, when the related records are distant from the current record in terms of time. To address the issues, this paper first considers adding convolution operations to the Transformer to enhance its local context modelling ability used for students' forgetting behavior, then proposes an evolutionary neural architecture search approach to automate the input feature selection and automatically determine where to apply which operation for achieving the balancing of the local/global context modelling. In the search space design, the original global path containing the attention module in Transformer is replaced with the sum of a global path and a local path that could contain different convolutions, and the selection of input features is also considered. To search the best architecture, we employ an effective evolutionary algorithm to explore the search space and also suggest a search space reduction strategy to accelerate the convergence of the algorithm. Experimental results on the two largest and most challenging education datasets demonstrate the effectiveness of the architecture found by the proposed approach.

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Research on patent classification based on hierarchical label semantics 3

Research on patent classification based on hierarchical labe...

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3rd International Conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Chen, Jie Zhao, Shu Lu, Fan Liu, Feng Zhang, Yangping Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665416665

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model. © 2022 IEEE.

关键词： Semantics

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under α-Stable Noise 6

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Sun, Dazhi Yu, Canping Li, Li Li, Yingsong Xiao, Wudang Chen, Yong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology Center of Aviation Safety Technology Cast Beijing101399 China

ISBN: (纸本)9798350399059

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance. © 2023 IEEE.

关键词： Adaptive filtering

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2023 7th International Conference on Communication and Information Systems An Improved IOTA Protocol Based on Optimal Midway Walk 7

2023 7th International Conference on Communication and Infor...

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7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Wu, Hongcheng Zhi, Hui Liu, Yangyang Anhui University School of Electronic and Information Engineering Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei230601 China Hefei Zhilian Technology Industry Investment Co. Ltd Hefei230601 China

ISBN: (纸本)9798350330540

IOTA (Internet of Things Applications) protocol aims to provide a secure and trustworthy protocol for realizing data-value interactions between IoT devices, and it has become the next possible direction for the development of Blockchain applications. IOTA protocol has the problem that the time-consuming cumulative weight update grows too fast with the increase of the number of transactions, this paper proposed an improved IOTA protocol based on optimal midway walk, the improved protocol uses cutting-edge transaction selection algorithm of weighted Monte Carlo Markov random walk to reduce cumulative weight update delay. Firstly, this improved IOTA protocol mainly ensures the efficiency of transaction confirmation without losing the security of consensus by introducing the optimal midway walk method, i.e., the starting point of each walk is chosen to depart at an optimal location. Secondly, this paper designs the indirect verification identity record algorithm and the layer degree of confirmation principle are designed to realize the improved IOTA protocol's resilience and resistance to Parasite chain attack. The experimental results show that the improved IOTA using random function to select the midway walk starting point from i-50λ, to i-75λsubstantially solves the system computational burden problem and greatly reduces the cumulative weight update delay. © 2023 IEEE.

关键词： cumulative weight IOTA midway walk Parasite chain attack

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A novel miniaturized l-band filter with great stopband characteristics using interdigitated coupled lines CRLH-TL structure

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Progress In Electromagnetics Research C 2021年 114卷 57-67页

作者： Wang, Peng Duan, Kaiyue Li, Minquan Zhang, Man Jin, Baokun Anqing Normal University Anqing246011 China Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei230601 China

This paper proposes a novel bandpass filter for L-band based on CRLH TL, which is mainly formed by coupling a high-pass characteristic module with a low-pass characteristic module in a cascade. The high-pass module consists of an interdigitated coupled line and a grounding via, owning to its singular characteristics, which the miniaturization is realized. The low-pass module is composed of a C-type resonator with high-low impedance lines, which can realize great sideband attenuation characteristics. To further improve its out-of-band rejection characteristics, a complementary split-ring resonator (CSRR) defective ground structure with single-pole attenuation characteristics is loaded, and a transmission zero is introduced at 2.5f0 out-of-band. The test results are in great agreement with the simulation ones, and the dimensions are only 0.20λg * 0.22λg. Compared with other similar types, the filter proposed in this paper has miniaturization, great passband selection characteristics, stopband characteristics, and the advantage of low insertion loss. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Po Zhao Jie Chen Chaochen Zhang Ke Ma Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University Hefei China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models.

关键词： Representation learning Sensitivity Prototypes Estimation Object detection Interference Feature extraction Radar polarimetry Data models Synthetic aperture radar

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