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A dual-band antenna designed for 5G communication based on a self-decoupled antenna

A dual-band antenna designed for 5G communication based on a...

引用

2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Fang, Rongzhen Wu, Xianliang Chen, Zhipeng Dong, Mengying Hu, Yunfeng Li, Yuanyuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University Department of Education of Anhui Province Hefei China

ISBN: (纸本)9781665452366

In this paper, a dual-band antenna for 5G communication based on an antenna design with self-decoupling properties is *** antenna is composed of a self-decoupled antenna unit vertically placed on an 30×80 mm2 ground plane and printed on 0.8 mm thick FR-4(ϵr = 4.4, tanδ = 0.02) substrate,and then added a pair of L-shaped coupling feeder structures on this *** shows that the antenna has two operating frequency bands of 3.3-4.2GHz and 4.8-5GHz, and has good transmission and isolation in the above two operating frequency ***,it also has the advantages of small size, self-decoupling,high isolation,simple structure and easy *** antenna can be used as 5G mobile phone communication antenna unit. © 2022 IEEE.

关键词： 5G mobile communication systems

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A Compact Double-Layer Wide Beam Circularly Polarized Beidou Antenna

A Compact Double-Layer Wide Beam Circularly Polarized Beidou...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lulu Meng Sipei Min Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

This paper proposes and designs a microstrip antenna that can work in the B1 band for Beidou with a double-layer substrate, and realizes the circularly polarized radiation characteristics. The antenna consists of a ground plane, two substrates, and two metal patches. The first layer of the patch is a rectangular structure as well as the second layer of the patch is a T-shaped structure with two vertical branches which are to achieve circular polarization. This structure adopts a simple coaxial feeding method, with the probe passing through the second layer of the substrate to connect the ground plane and the T-shaped strip. The simulation optimization results using the HFSS 2021 show that the axial ratio (AR) beam width in both planes exceeds 160°, and the AR at the design frequency is 0.18 dB. The impedance bandwidth is 70 MHz, which can meet the requirements of Beidou's operation.

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A Band-pass FSS with Wide Angular Stability and High Polarization Stability

A Band-pass FSS with Wide Angular Stability and High Polariz...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yunshuai Gao Shuying Ding Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A mimic multi-layer polarization-independent FSS with accessible angular-stability is reported using a three-layer structure, where the top layer and the bottom layer are realized based the metal rings that are same, and the middle is constructed by a slotted metal structure. The mentioned three layers are aimed to separate the used two dielectric substrates. For a well illustration, the constructed dimensions of the installed FSS are $\boldsymbol{5\times 5}\mathbf{mm}^{2}$ . As a case of the FSS cell design, here the dimension is limited as $\boldsymbol{0.15}\lambda\times \boldsymbol{0.15}\lambda$ . Since two different structure is used, the FSS has double resonace, making a wide operation. From the numerical evaluations, the FSS can reject the signal from C-and X-band.

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Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism

Classification of Cervical Images Based On Deep Neural Netwo...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yun Feng Lan Chen Xia Zhang Yingsong Li Liping Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL).

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Generative Negative Sample Enhancement Based Few-Shot Named Entity Recognition 11th

Generative Negative Sample Enhancement Based Few-Shot Named ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Duan, Zhen Xiao, Shenghua Chen, Jie Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei 230601 China

ISBN: (纸本)9783031927409

Few-shot Named Entity Recognition (Few-shot NER) aims to identify and classify unseen named entity types with a limited labeled samples. In recent years, large language models have achieved remarkable results in various NLP tasks due to their excellent zero/few-shot generalization capabilities. Recent research has extensively focused on using large language models to study the named entity recognition task. Negative sample entities have been demonstrated to be effective when performing this task using in-context learning paradigm. However, the problem of low efficiency and poor quality in manually constructing negative sample entities has been exposed. To address this issue, in this paper, we propose a Few-shot NER method based on generative negative sample enhancement (GNSE). Specifically, GNSE first utilizes a large language model to pre-identify potential entity spans in the support set text paragraphs in a zero-shot manner and generate the semantics of the spans based on the paragraph context. Then, the pre-identified candidate spans are compared with the ground-truth labels to determine positive or negative sample entity spans. Finally, based on Chain-of-Thought, the span semantics, positive and negative sample entities, and ground-truth labels are inserted into the example template for the in-context learning paradigm, and the test text from the query set is concatenated to construct a complete prompt. GNSE was evaluated on three widely used datasets: CoNLL’03, WNUT’17 and GUM. Experiments show that GNSE has achieved better performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Few-shot Named Entity Recognition In-context Learning Large Language Model Prompt Engineering

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A RIS-Supported DOA Estimation Using Combined Norm Constraint in Non-Gaussian Noise Environments

A RIS-Supported DOA Estimation Using Combined Norm Constrain...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lei He Liping Li Run Ying Canping Yu Qiang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Reconfigurable intelligent Surface (RIS) is a revolutionary technology in modern wireless communication systems, since it can adjust the wireless communication environment with passive components. In this paper, we consider the system environment in the presence of Base Station (BS) interference and implement the estimation of the target Direction-of-Arrival (DOA). To improve the estimation performance, we propose a DOA estimation algorithm based on RIS. The proposed algorithm consists of an error term for the lorentzian bound function and a regular term for the atomic norm (termed as LBF-AN), which are implemented to eliminate system noise and signal interference, respectively. The simulation results show that the DOA estimation performance of the proposed algorithm is better than existing algorithms.

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Dynamic Graph Network Augmented by Contrastive Learning for Radar Target Classification

Dynamic Graph Network Augmented by Contrastive Learning for ...

引用

IEEE National Conference on Radar

作者： Han Meng Yuexing Peng Wenbo Wang Intelligent Computing and Communication Group Wireless Signal Processing and Network Lab Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

In recent years, radar echo signals based multi-aircraft recognition has gained significant attention in the research community. Despite its potential, the extraction of valuable information from weak echo signals in complex environments remains a challenging task. Based on heterogeneous radar network, graph convolution network (GCN) is a powerful method to extract semantic features and then fuse them for reliable detection. However, owing to the limited knowledge, the design of a suitable adjacent matrix is vital for GCN. In this paper, an intelligent and partly interpretable method termed Dynamic Graph Network augmented by Contrastive Learning (CL-DGN) is proposed for classifying the radar targets in a heterogeneous radar network under low signal-to-noise ratio. The Zhao-Atlas-Marks (ZAM) transform and Gated Recurrent Unit with attention mechanism method are used for semantic feature enhancement, then the contrastive learning-based semantic similarity metric learning (SSML) is elaborated to construct an adjacency matrix to flexibly support dynamic networking for each target in dynamic heterogeneous radar network. Finally, graph convolution network method utilizes the adjacency matrix generated by SSML to extract and fuse the target spatial-temporal-frequency (STF) features, enabling the recognition and classification of radar targets. The simulation results verify the effectiveness of the proposed method in the low signal noise ratio scenarios. Specifically, it achieved 5% accuracy improvement at $\mathbfintelligent\ dB$ , compared to suboptimal performance method. Moreover, the adjacency matrix heatmap is introduced to highlight the similarity between radars.

关键词： Target recognition Fuses Convolution Atmospheric modeling Semantics Radar Transforms

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zhiyi Liu Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University HeFei China

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications.

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Design of Microstrip Type Broadband Directional Coupler

Design of Microstrip Type Broadband Directional Coupler

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Hang Yan Xianliang Wu Ruikang Wang Gang Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A microstrip line broadband directional coupler is designed based on microstrip line theory. The bandwidth of the coupler is extended by using a cascade of multiple coupled lines. The isolation of the directional coupler is greatly improved by covering the microstrip line with a dielectric layer [1] and by loading the branching compensation capacitor to compensate for the odd-even mode phase velocity difference. Simulation results of ANSYS HFSS 2019 show that the return loss ( $S_{11}$ ) is greater than 20 dB, the insertion loss ( $S_{21}$ ) is less than 1.7 dB, the coupling degree (S31) is about −6dB(±0.8dB), the isolation degree (S41) is greater than 23.4dB, and the minimum directionality is greater than 17dB during 700 MHz-3700 MHz. The physical size of coupler is $18\text{mm}\times 60\text{mm}$ , which meets the design requirements of 6dB coupler.

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Diao, Liangjin Shen, Longfeng Chen, Jiacong Ge, Fangzhen Li, Mengwen College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui Hefei China

ISBN: (纸本)9798331517021

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority. © 2024 IEEE.

关键词： Image segmentation

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