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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Tang, Huanrong Jiang, Aoyun Ouyang, Jianquan Key Laboratory of Intelligent Computing Information Processing Ministry of Education School of Computer Science and School of Cyberspace Science Xiangtan University Xiangtan Hunan411100 China

ISBN: (纸本)9783030786144

At present, with the development of science and medical level, more and more diseases can be cured. However, the cost of treating some diseases is high, and for many families with limited financial resources, it is difficult to spend so much money on treatment in a short period of time. As a result, more and more people are launching fundraising projects through third-party platforms such as Water Drops and Easy Fundraising, using the money raised to treat diseases. However, these third-party platforms have many problems, such as easy to disclose the private information of people who seeking help, the fundraising process is complex and slow, the use of project funds is difficult to timely monitor, the release of false fundraising projects for fundraising fraud, and so on. For these reasons, fewer members of the public will believe the authenticity of fundraising projects, so that it is not easy to donate to fundraising projects, which also makes it difficult for people who really need to raise funds to treat diseases. In view of this situation, this paper puts forward a blockchain and smart contract-based fundraising system for critical diseases, which can solve the problems existing in third-party platforms, and can help helpers quickly raise funds to cure diseases, it also can avoid the abuse of fundraising funds, false fundraising and other acts. Not only improve the transparency of fundraising behavior, but also enhance the enthusiasm of public participation in donations. © 2021, Springer Nature Switzerland AG.

关键词： Smart contract

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Fast Near-field Computational Imaging with Deep Neural Networks

Fast Near-field Computational Imaging with Deep Neural Netwo...

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IEEE International Conference on Radar

作者： Fafa Zhao Zhenhua Wu Xueli Pan Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education East China Research Institute of Electronic Engineering Hefei China

ISBN: (纸本)9781665468893

With the capability to sculpt complex radiative wavefronts and couple energy from the reference wave to the desired radiation pattern, metasurface antennas have gained tremendous progress in recent years. The flexibility in antenna design gives rise to a bunch of metasurface antennas, while there is still a comprise between the needed operation band and radiation field pattern coherence. Furthermore, the relatively low sampling ratio and overlap between frequency measurement modes would inevitably pose a great challenge for matched filter and other advanced sparsity-driven post-processing imaging algorithms. In this paper, we design a deep neural network to achieve fast scene image reconstruction. The trained network could fastly reconstruct the imaged object in both low scene compressed ratio and high coherence of the measurement modes. The imaging experiments with measured radiation field data are conducted to verify the effectiveness of our method.

关键词： Antenna measurements Deep learning Neural networks Imaging Coherence Radar imaging Radar antennas

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A Passive Mixed Loaded Over-Hundred-Octave Miniaturized Super-Wideband Antenna

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Xia, Yinfeng Li, Yingsong Xue, Wei Elsherbeni, Atef Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China Colorado School of Mines GoldenCO United States

An over-hundred-octave miniaturized super- wideband antenna based on passive elements mixed loading is proposed and analyzed in this paper. The proposed antenna is composed of inversed E-radiation patch, feeding line, several passive mixed loaded elements. The entire size of the proposed antenna is 0.012λ×0.01λ×0.00005λ (where λ is the lowest frequency of realized bandwidth). Derived from the analysis of impedance and equivalent circuit, by properly adjusting the inversed E-radiation patch and loading 5 passive elements into the construction of the antenna in position, the bandwidth is greatly improved. The simulated and measured results show that a -10dB fractional bandwidth of 196.2% is realized. Moreover, good omni-directional radiation patterns within the operating frequency band have been achieved from 10MHz to 1051MHz for HF, VHF and UHF applications. © 2002-2011 IEEE.

关键词： Antenna feeders

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TransMixer: A Hybrid Transformer and CNN Architecture for Polyp Segmentation

TransMixer: A Hybrid Transformer and CNN Architecture for Po...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Huang, Yanglin Tan, Donghui Zhang, Yuan Li, Xuanya Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Affiliated Hospital of Xiangnan University Chengzhou423000 China Baidu Inc Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou423000 China

ISBN: (纸本)9781665468190

Learning how to fully extract global representations and local features is a key factor in improving the performance of polyp segmentation. In this paper, we explore the potential of combined techniques of Transformers and convolutional neural networks (CNNs) to address the challenges of polyp segmentation. Specifically, we present TransMixer, a hybrid interaction fusion architecture of the Transformer branch and the CNN branch, which is able to enhance the local details of global representations and the global context awareness of local features. To achieve this, we first bridge the semantic gap between the Transformer branch and the CNN branch through the Interaction Fusion Module (IFM), and then make full use of both respective properties to enhance polyp feature representations. After that, we further propose the Hierarchical Attention Module (HAM) to collect polyp semantic information from high-level features to gradually guide the recovery of polyp spatial information in low-level features. Quantitative and qualitative results show that the proposed model is more robust to various complex situations compared to existing methods, and achieves state-of-the-art performance in polyp segmentation. © 2022 IEEE.

关键词： Parallel architectures

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Recursive Constrained Sine Second-Order Error Promoting Adaptive Algorithm

TechRxiv

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TechRxiv 2023年

作者： Li, Yingsong Chen, Yong Miao, Yongchun Zakharov, Yuriy Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China The School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom

This brief proposes a recursive constrained sine second-order error promoting adaptive (RCSSOEPA) algorithm. Compared with classical recursive method, the RCSSOEPA algorithm can achieve better steady state performance in impulsive noise. In general, the sine second-order error (SSOE) is constructed to devise a new recursive constrained adaptive-filtering within the least-squares framework for solving linear constrained optimization problems. The mean-square (MS) stability of the RCSSOEPA and its theoretical instantaneous MS deviation under Gaussian and non Gaussian noise are analyzed, numerically investigated and discussed in detail. Simulated results are reported to give a comfirmation of the theoretical analysis, and show that the RCSSOEPA outperforms recent developed constrained adaptive filtering algorithms in the estimation misalignment and when used for system identification under impulsive-noise. © 2023, CC BY.

关键词： Errors

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Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection 48

Automatic Segmentation of Nasopharyngeal Carcinoma in CT Ima...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wang, Qizhi Huang, Wei Zhang, Yuan Li, Xuanya Ye, Xiongjun Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China The First Hospital of Changsha Department of Radiology Changsha410005 China Baidu Inc. Beijing100085 China Chinese Academy of Medical Sciences Peking Union Medical College National Cancer Center Cancer Hospital Beijing100021 China

ISBN: (纸本)9781728163277

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation particularly challenging. To address these problems, we propose an accurate and effective NPC segmentation method using Dual Attention and Edge Detection Convolutional Neural Network (DAED-Net). Firstly, we combine a 2.5D convolutional neural network with UNet++ and propose a new backbone called Dual-dimension Dense UNet (DD-UNet), which can extract more beneficial features from 3D images. Secondly, a Dual Attention Module (DAM) is proposed to help the model better segment the target region of NPC by efficiently collecting spatial and channel attention information from feature maps. Moreover, an Edge Detection Module (EDM) is introduced in the network to enhance the segmentation of the target contours. Finally, we evaluate the proposed DAED-Net on the public MICCAI 2019 StructSeg NPC dataset from different perspectives. Numerical and visual results show that the proposed method outperforms nine state-of-the-art segmentation methods and yields more accurate NPC segmentation results. © 2023 IEEE.

关键词： Convolutional neural network Dual attention Edge detection Nasopharyngeal carcinoma segmentation

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OpenViewer: Openness-Aware Multi-View Learning 39

OpenViewer: Openness-Aware Multi-View Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)157735897X

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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Design of Low-profile Broadband Circularly Polarized Metasurface Antenna Based on CPW Feed

Design of Low-profile Broadband Circularly Polarized Metasur...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yue Ma Bo Wu Changqing Liu Xianliang Wu Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665409889

This paper presents a low-profile broadband circularly polarized metasurface antenna based on CPW feed. The antenna works in the 3.9-8.5 GHz frequency band, the metasurface consists of $5\times 5$ square cut corner metal patches printed on the upper surface of the dielectric substrate. In order to make the metasurface have the same characteristics for X polarization and Y polarization, the structure is designed to be symmetric in the slant -45° direction, and the size of the cut angle is changed to generate a circularly polarized wave. According to the phase of the incident wave at different positions on the metasurface, the distance between the 9 radiation elements at the center of the metasurface is 0.3 mm, and the distance between the 16 radiation elements in the outermost layer is 0.4 mm, so as to obtain a more suitable phase compensation. The lower surface of the dielectric substrate adopts a CPW coupling aperture structure as a feed source, and the coupling aperture adopts a stair-shape structure to achieve broadband impedance matching, and the stair-shape aperture is attached to the end of the CPW. The proposed antenna has a simple structure and a low profile, which can be realized by a single-layer printed circuit board (PCB) substrate. To verify the simulation results, a model antenna with an overall size of 0.9$\lambda_{0}$× 0.9$\lambda_{0}$× 0.06$\lambda_{0}$, where $\lambda_{0}$ is the free-space wavelength at 5 GHz, is fabricated. The measurement results show that the -10dB return loss bandwidth reaches 72.8%, the circular polarization axis ratio (AR) is less than 3 dB with a bandwidth of 21.6%, and the highest gain reaches 8.34 dBi, which is almost consistent with the simulation results.

关键词： Antenna measurements Polarization Bandwidth Apertures Metasurfaces Antenna feeds Loss measurement

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Design of Miniaturized UWB Antenna with Rectangular Slot

Design of Miniaturized UWB Antenna with Rectangular Slot

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Minquan Li Ziyun Tu Ran Zhao Guangxiu Zhao Yajing Yan Xiaming Mo Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665409889

A miniaturized ultra-wideband (UWB) antenna with excellent performance is designed in this paper. The antenna is based on hexagonal metal patch, and a circular arc structure is added on the top of the antenna, which widens the effective frequency band of the antenna. The current path is changed by opening rectangular slots on the surface and bottom of the antenna, which greatly improves the in-band characteristics of the antenna. The size of the antenna is 29mm × 31 mm × 1.5 mm after continuous optimization with HFSS. The final test results show that the impedance bandwidth of the antenna less than -10 dB is 3.5 GHz ∼ 19.5 GHz, and it has good radiation characteristics in this frequency band range. The antenna achieves the design goal of miniaturization and UWB, and has a good application prospect in communication system.

关键词： Surface impedance Slot antennas Simulation Ultra wideband communication Metals Bandwidth Ultra wideband antennas

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Entity Alignment Through Joint Utilization of Multiple Pretrained Models for Attribution Relationship

Entity Alignment Through Joint Utilization of Multiple Pretr...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Liu Yujia Xu Tao Su Zhehan Li Xinsong Xue Kaipeng Liu Yuxin Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Lanzhou Gansu Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Lanzhou Gansu College of Information Engineering Shanghai Maritime University Shanghai

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

Most existing models for entity alignment tasks are based on a single pre-training mode. Through multiple experiments, it has been verified that a single pre-training model is no longer sufficient to meet the needs of current entity alignment tasks. Based on considerations of multiple pre-training models, this paper proposes an entity alignment method that utilizes multiple pre-training models. The purpose of this method is to address the limited accuracy in entity alignment tasks, with a particular focus on separately pre-training the embedding of entity attribute information and link relationship information for matching. To achieve the goal of improving entity alignment accuracy, this paper adopts joint alignment using pre-training with BERT and GloVe word embeddings. Experimental evaluations are conducted on the DBP15k dataset, and the results demonstrate that leveraging multiple pre-training models for attribute and relationship joint alignment can enhance model performance. The evaluation metrics of the model on three different language datasets, Hits@1 and MRR, are as follows: 0.899, 0.930, 0.964, and 0.932, 0.951, 0.975, respectively. These metrics indicate that the model performs well in aligning entities and outperforms current mainstream entity alignment methods.

关键词： Training Measurement Deep learning Cloud computing Accuracy Computational modeling Semantics

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