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Progress in Electromagnetic Research Symposium (PIERS)

作者： Qing Guo Yuting Chen Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

An X-band CMOS low noise amplifier (LNA) with flat gain and noise Figure (NF) is proposed in this paper. To achieve sufficient gain, the LNA adopts two-stage topology. A feed-back transformer is used at the input terminal to implement noise and wideband input matching. Transformer-based inter-stage and output matching networks are adopted to achieve well isolation and compact size. By introducing inductive peaking technique, the proposed LNA obtain lower NF and extend the bandwidth. Post simulation results show that the LNA achieves a gain of 14.6± 2dB and NF of 2. 5-3.3dB across a bandwidth from 8.5 GHz to 12.5 GHz. With the supply voltage of 1 V at the first stage and 1.8 V at the second stage, the dc power dissipation is 20.8 mW. The simulated input 1dB compression point ($\mathrm{P}_{\mathrm{l}\mathrm{d}\mathrm{B}}$) is -27.3dBm. The core chip area is only 0.41× 0.87mm 2 .

关键词： Low-noise amplifiers Impedance matching Simulation Voltage Transformer cores Transformers CMOS technology

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The interior penalty virtual element method for fourth-order singular perturbation problems

arXiv

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arXiv 2023年

作者： Feng, Fang Yu, Yue School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China

This paper is dedicated to the numerical solution of a fourth-order singular perturbation problem using the interior penalty virtual element method (IPVEM) proposed in [42]. The study introduces modifications to the jumps and averages in the penalty term, as well as presents an automated mesh-dependent selection of the penalty parameter. Drawing inspiration from the modified Morley finite element methods, we leverage the conforming interpolation technique to handle the lower part of the bilinear form. Through our analysis, we establish optimal convergence in the energy norm and provide a rigorous proof of uniform convergence concerning the perturbation parameter in the lowest-order case. © 2023, CC BY-NC-ND.

关键词： Numerical methods

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Tiny Object Tracking: A Large-scale Dataset and A Baseline

arXiv

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arXiv 2022年

作者： Zhu, Yabin Li, Chenglong Liu, Yao Wang, Xiao Tang, Jin Luo, Bin Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Anhui Higher Education Institutes Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China Peng Cheng Laboratory Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Anhui Higher Education Institutes Anhui University Hefei230601 China

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel Multilevel Knowledge Distillation Network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://***/mmic-lcl/Datasets-andbenchmark-code. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Compact Broadband Absorptive Bandstop Filter Based on Microstrip 2

Compact Broadband Absorptive Bandstop Filter Based on Micros...

引用

2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Lu, Qi Wu, Xianliang Wang, Chao Key Lab of Intelligent Computing and Signal Processing Ministry of Education AnHui University AnHui University Heifei AnHui230000 China

A compact broadband absorptive band rejection filter (ABSF) based on microstrip line is proposed. The filter consists of a pair of coupled wires and is loaded with a resistor. The coupling line of the filter has an open stub. The transmission zero point can be adjusted by adjusting the width of the coupling line. The center frequency can be adjusted by changing the length of the intermediate coupling line. Absorption performance can be achieved through loaded resistors. In this way, the signal not input in the filter will be dissipated in the filter and will not be reflected. When F0=3.8 GHz, 26% of the 20 dB bandwidth covers 3.15 GHz-4.2 GHz, the rejection depth of the stopband is less than-40 dB, and the in-band absorption is less than-10 dB. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Notch filters

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Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data

arXiv

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arXiv 2024年

作者： Wang, Zhenzhong Zeng, Qingyuan Lin, Wanyu Jiang, Min Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian Xiamen361005 China

While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the classification performance of prevailing GNNs on many real-world applications suffering from low-data regimes. Specifically, features extracted from scarce labeled nodes could not provide sufficient supervision for the unlabeled samples, leading to severe over-fitting. In this work, we point out that leveraging subgraphs to capture long-range dependencies can augment the representation of a node with homophily properties, thus alleviating the low-data regime. However, prior works leveraging subgraphs fail to capture the long-range dependencies among nodes. To this end, we present a novel self-supervised learning framework, called Multi-view subgraph neural networks (Muse), for handling the long-range dependencies. In particular, we propose an information theory-based identification mechanism to identify two types of subgraphs from the views of input space and latent space, respectively. The former is to capture the local structure of the graph, while the latter captures the long-range dependencies among nodes. By fusing these two views of subgraphs, the learned representations can preserve the topological properties of the graph at large, including the local structure and long-range dependencies, thus maximizing their expressiveness for downstream node classification tasks. Theoretically, we provide the generalization error bound based on Rademacher complexity to show the effectiveness of capturing complementary information from subgraphs of multiple views. Empirically, we show a proof-of-concept of Muse on canonical node classification problems on graph data. Experimental results show that Muse outperforms the alternative methods on node classification tasks with limited labeled data. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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An Overlapping Local Community Detection Algorithm Based on Node Transitivity and Modularity Density 15th

An Overlapping Local Community Detection Algorithm Based on ...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Huang, Xintong Wu, Ling Guo, Kun College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

ISBN: (纸本)9789811625398

The local community detection (LCD) method can discover the local community structure in which the seed node is located. Compared with global community detection, local community detection is characterized by its low cost and high efficiency. However, most existing LCD methods only return a non-overlapping community. Individuals in the real world may participate in multiple communities, which can only be discovered by using overlapping local community detection methods. In this study, an overlapping local community detection algorithm based on modularity and node transitivity. First, the scope and structure information of the overlapping communities are obtained according to the node transitivity. Second, NMF is used to obtain the number of overlapping communities. Finally, the local modularity density based on edge weights is used to refine the detected local communities. The experimental results validate the high performance of our method to the other method in comparison. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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The Design and Implementation of KMeans Based on Unified Batch and Streaming processing 15th

The Design and Implementation of KMeans Based on Unified Bat...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Chen, Hao Guo, Kun Chen, Yuzhong Guo, Hong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

引用

Journal of Cyber Security 2022年第2期4卷 65-77页

作者： Jianquan Ouyang Jinling Wang Yaowu Wang Tianming Liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan Province411100China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and *** must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM ***,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D *** propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy *** method uses a fully convolutional neural network to generate a keypoint *** heatmap value represents the probability that a micrograph pixel belongs to a particle center *** can process images of any size and can directly predict the center point and size of the *** network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection *** have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

关键词： Cryo-electron microscope deep learning particle picking object detection

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A Dual-Polarized Antenna For 5G N78 Application

A Dual-Polarized Antenna For 5G N78 Application

引用

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiakang Pan Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University China College of Computer Science and Technology Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A compact dual polarization dipole antenna for the application to 5G base station is investigated, which is composed of radiator, baluns, and metal reflector, having an impendence bandwidth of 23.6% with VSWR<1.5. The dual-polarized antenna has a high ports isolation, high cross-polarization and high gain in operating bandwidth. The results indicate that this work can operate in entire N78 band and provide potential application for 5G base station.

关键词： Base stations 5G mobile communication Dipole antennas Baluns Metals Bandwidth Reflector antennas

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