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A Broadband Circular Polarization Antenna Applied For Base-Station

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A Broadband Circular Polarization Antenna Applied For Base-S...

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

作者： Xiangkui Hu Haoran Zhu Xianliang Wu Zhixiang Huang Bo Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper studies the broadband circularly polarized antenna used in micro base stations. The antenna consists of a pair of rectangular loops. The ring patch is used to form circular polarization, and a pair of open resonant ring parasitic elements are loaded between the two rectangular rings to increase the bandwidth. By introducing balun feed to increase bandwidth, good impedance matching can be obtained. Add a metal reflector at the bottom of the antenna to form directional radiation. The antenna was printed on the Rogers RO4003 substrate, and the results showed that the -10dB return loss bandwidth was 43.4% (1.86-2.89GHz), and the -3dB axial ratio bandwidth was 41% (1.9-3GHz). The gain of the antenna in the frequency band is greater than 9dBi.

关键词： Polarization Bandwidth Broadband communication Broadband antennas Loaded antennas Substrates Antenna radiation patterns

Author classification using transfer learning and predicting stars in co-author networks

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Author classification using transfer learning and predicting...

作者： Abbasi, Rashid Kashif Bashir, Ali Chen, Jianwen Mateen, Abdul Piran, Jalil Amin, Farhan Luo, Bin School of Information and Communication Engineering University of Electronics Science and Technology of China Chengdu China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Computer Science Federal Urdu University of Arts Science and Technology Islamabad Pakistan Computer Engineering Department Sejong University Yeungnam Korea Republic of Department of Information and Communication engineering Yeungnam University Yeungnam Korea Republic of Key Lab of Intelligent Computing and Signal Processing of MOE & School of Computer and Technology Anhui University China

The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning;consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods. © 2020 John Wiley & Sons Ltd

关键词： Stars

Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Sun, Haifeng Wu, Hang Liang, Huadong Ding, Zhuanlian Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China iFlytek Co. Ltd. Hefei230088 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

Determining the functional roles of proteins is a vital task to understand life at molecular level and has great biomedical and pharmaceutical implications. With the development of novel high-throughput techniques, enormous amounts of protein-protein interaction (PPI) data are collected and provide an important and feasible way for studying protein function predictions. According to this, many approaches assign biological functions to all proteins using PPI networks directly. However, due to the extreme complexity of the topology structure of real PPI networks, it is very difficult and time consuming to seek the global optimization or clustering on the networks. In addition, biological functions are often highly correlated, which makes functions assigned to proteins are not independent. To address these challenges, in this paper we propose a two-stage function annotation method with robust feature selection. First, we transform the network into the low-dimensional representations of nodes via manifold learning. Then, we integrate the functional correlation into the framework of multi-lab.l linear regression, and introduce robust sparse penalty to achieve the function assignment and representative feature selection simultaneously. For the optimization, we design an efficient algorithm to iteratively solve several subproblems with closed-form solutions. Extensive experiments against other baseline methods on Saccharomyces cerevisiae data demonstrate the effectiveness of the proposed approach. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

On the structure of 1-generator quasi-polycyclic codes over finite chain rings

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arXiv 2021年

作者： Wu, Rongsheng Shi, Minjia Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China Lab I2M CNRS Aix-Marseille University Centrale Marseille Marseille France

Quasi-polycyclic (QP for short) codes over a finite chain ring R are a generalization of quasi-cyclic codes, and these codes can be viewed as an R[x]-submodule of Rm, where Rm := R[x]/hfi, and f is a monic polynomial of degree m over R. If f factors uniquely into monic and coprime basic irreducibles, then their algebraic structure allow us to characterize the generator polynomials and the minimal generating sets of 1-generator QP codes as R-modules. In addition, we also determine the parity check polynomials for these codes by using the strong Gröbner bases. In particular, via Magma system, some quaternary codes with new parameters are derived from these 1-generator QP codes. © 2021, CC0.

关键词： Polynomials

DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework

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arXiv 2023年

作者： Dai, Siran Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China BDKM University of Chinese Academy of Sciences China

The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testing examples are drawn i.i.d. from the same distribution, which is often unachievable in practice. Distributionally Robust Optimization (DRO) enhances model performance by optimizing it for the local worst-case scenario, but directly integrating AUC optimization with DRO results in an intractable optimization problem. To tackle this challenge, methodically we propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC) and build our optimization framework on top of it. Moreover, we highlight that conventional DRAUC may induce lab.l bias, hence introducing distribution-aware DRAUC as a more suitable metric for robust AUC learning. Theoretically, we affirm that the generalization gap between the training loss and testing error diminishes if the training set is sufficiently large. Empirically, experiments on corrupted benchmark datasets demonstrate the effectiveness of our proposed method. Code is availab.e at: https://***/EldercatSAM/DRAUC. © 2023, CC BY.

关键词： Optimization

Global-Local Attention Network for Semantic Segmentation in Aerial Images

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Global-Local Attention Network for Semantic Segmentation in ...

International Conference on Pattern Recognition

作者： Minglong Li Lianlei Shan Xiaobin Li Yang Bai Dengji Zhou Weiqiang Wang Ke Lv Bin Luo Si-Bao Chen University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Science Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Errors in semantic segmentation could be classified into two types: the large area misclassification and inaccurate local boundaries. Previously attention-based methods typically capture rich global contextual information, which benefits the large area classification but cannot address the local errors of boundaries. In this paper, we propose a Global-Local Attention Network (GLANet) which can simultaneously consider the global context and local details. Specifically, our GLANet consists of two branches: (1) the global attention branch and (2) local attention branch. Furthermore, three different modules are embedded in GLANet for respectively modelling the semantic interdependencies in spatial, channel and boundary dimension. Lastly, we merge the outputs of different branches to enhance the feature representation further, resulting in more precise segmentation. Overall, the proposed method achieves the competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over the existing baselines.

关键词： Image segmentation Image color analysis Image edge detection Semantics Vegetation mapping Vegetation Pattern recognition

A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning

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arXiv 2023年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University China BDKM University of Chinese Academy of Sciences China

Real-world datasets are typically imbalanced in the sense that only a few classes have numerous samples, while many classes are associated with only a few samples. As a result, a naïve ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes. To address this issue, one simple but effective approach is to modify the loss function to emphasize the learning on minority classes, such as re-weighting the losses or adjusting the logits via class-dependent terms. However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results. To bridge this gap, we propose a novel technique named data-dependent contraction to capture how these modified losses handle different classes. On top of this technique, a fine-grained generalization bound is established for imbalanced learning, which helps reveal the mystery of re-weighting and logit-adjustment in a unified manner. Furthermore, a principled learning algorithm is developed based on the theoretical insights. Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method. © 2023, CC BY.

关键词： Learning algorithms

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

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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