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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Xinju Wang Sentao Wang Zhuo Chen Zongsheng Gan Haowei Zhang Yutong Yin Lu Liu Peng Chen School of Ocean Information Engineering Jimei University Xiamen China School of Transportation and Civil Engineering Nantong University Nantong China Fujian Provincial Key Laboratory of Maritime Communication and Intelligent Electronic Systems Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing School of Ocean Information Engineering Jimei University Xiamen China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

A new broadband high-gain microstrip quasi-yagi antenna is proposed. With the aim of designing an end-firing antenna for X-band by utilizing the good directivity of yagi antennas. The main radiating dipole patches of this antenna adopts an additional tapered trapezoidal-like structure to optimize its radiation characteristics. In addition, the operating bandwidth and main beam gain of the antenna are effectively adjusted by placing four identical microstrip metal strip patches on the bottom surface of the dielectric substrate to act as the reflector and the guide, respectively. Simulation results show that the designed antenna operates in the frequency bands of 8.33-13.07 GHz and 13.57-13.95 GHz with relative bandwidths of 44.3% and 2.76%, respectively, and achieves a peak gain of 8.9 dBi. The antenna has a simple structural design and is suitable for X-band wireless communication systems with a wide range of applications.

关键词： Satellite antennas Dipole antennas Simulation Metals Microstrip antennas Bandwidth Radar antennas Reflector antennas Broadband antennas Microstrip

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LSU-NET: LIGHTWEIGHT AUTOMATIC ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

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arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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Molecular dynamics simulation of heat transfer during liquid hydrogen boiling on surfaces at the nanoscale

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Nuclear Science and Techniques 2025年第7期36卷 1-12页

作者： Li, Xiao-Jia Chen, He-Ying Sun, Yi-Zheng Fan, Zhi-Chun School of Ocean Information Engineering Jimei University Xiamen361021 China Department of Mechanical Engineering City University of Hong Kong Hong Kong Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen361021 China

Liquid hydrogen, known for its high energy density and eco-friendly properties, has garnered significant attention in the context of sustainable development and clean energy. A comprehensive understanding of its nucleation mechanisms and boiling heat transfer characteristics is crucial. However, current experimental and macroscopic simulation methods offer limited insights. This study employs molecular dynamics simulations to investigate the vaporization nucleation and boiling heat transfer properties of liquid hydrogen at the microscopic scale, with a focus on the effects of hydrogen film thickness, surface temperature, and wettability. The results indicate that hydrogen film thickness plays a critical role in nucleation. Thinner layers disrupt the shape of liquid films, leading to increased errors, whereas a thickness of 7 nm ensures film stability. Different heating methods and temperatures influence nucleation in various ways. Rapid heating results in a higher heat flux, while an increase in temperature under the same heating method accelerates nucleation, resulting in earlier nucleation and enhanced surface heat flow. Surfaces with varying wettability levels exhibit distinct nucleation behaviors. Specifically, an increase in α delays nucleation, causing a shift from the surface to within the liquid film due to stronger solid–liquid interaction forces. This study offers a microscale perspective on the nucleation and boiling processes of liquid hydrogen and provides valuable insights for phase transition studies. © The Author(s), under exclusive licence to China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society 2025.

关键词： Electric heating

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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SNR improvement for Φ-OTDR with improved adaptive non-local means denoising method

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Optics Communications 2025年 587卷

作者： Zeng, Weiming Sun, Yizheng Shen, Hengwei Chen, Wen Liu, Jing Chen, Yushan Fan, Zhichun Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing School of Ocean Information Engineering Jimei University Xiamen361021 China

An improved adaptive non-local means (NLM) denoising method, called side window NLM (SW-NLM), was proposed in this study. Unlike the centrally symmetric square windows used in traditional NLM filtering, the SW-NLM method utilized rectangular side windows to further improve the signal-to-noise ratio (SNR) of phase-sensitive optical time domain reflectometer (Φ-OTDR). Additionally, SW-NLM method incorporated adaptive parameter estimation by calculating the SNR of the filtered traces and optimizing smoothing coefficients and window sizes accordingly. SW-NLM algorithm increased the SNR by up to 45.85 dB, achieving a 25.5% improvement over the NLM filtering while better preserving effective vibration information through the side window technique. Moreover, SW-NLM was applied to dual vibration condition to validate its capability of multipoint detection. This work contributes to enhancing the performance of Φ-OTDR and improving its signal recognition capability. © 2025 Elsevier B.V.

关键词： Image denoising

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Optical Music Recognition Based On Localityconstrained Linear Coding and Double Distribution Support Vector Machine 5

Optical Music Recognition Based On Localityconstrained Linea...

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The 5th International Symposium on Electromagnetic Compatibility

作者： XiaXia Zeng Fanyong Cheng Wenzhong Lin Haibo Luo Zhiqiang Ruan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control（Minjiang University）

ISBN: (纸本)9781509051861;9781509051854

Optical Music Recognition（OMR） has received increasing attention in recent *** this paper,we adopt the descriptor of Locality-constrained Linear Coding（LLC） and Double Distribution Support Vector Machine（DDSVM） to address music symbol classification in *** can obtain stronger feature representation ability compared with original music ***,DDSVM is presented to obtain stronger robustness and generalization ***,DDSVM is adopted to improve the classification performance in ***,One Versus Rest Double Distribution Support Vector Machine（OVR-DDSVM） is proposed for multi-class music symbol ***-DDSVM can obtain high accuracy only using linear kernel due to LLC descriptor,and this speeds the classification ***-DDSVM is tested on more than10000 music symbol images of 20 classes,and experimental results verify the superiority of LLC+DDSVM to other algorithms.

关键词： OMR SVM classification

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Multi-bit nonvolatile memory device based on the reverse polarization under the channel of organic ferroelectric thin-film transistor

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Surface and Coatings Technology 2025年 509卷

作者： Chen, Qiusong Zhou, Yulan Sanchez, Juan E. Lin, Dong Zhu, Guodong Xiong, Zuhong School of Physics and Electronics Science & Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing Guizhou Education University Guiyang550018 China DEVSIM LLC PO Box 50096 AustinTX78763 United States Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen361021 China Department of Materials Science Fudan University No. 220 Handan Road Shanghai200433 China Chongqing Key Laboratory of Micro & Nano Structure Optoelectronics School of Physical Science and Technology Southwest University Chongqing400715 China

Ferroelectric field thin-film transistors (Fe-TFTs) are a promising candidate for multi-bit nonvolatile memory devices. Different theories and methods have been proposed to realize this kind of device. This paper introduces a novel operation procedure that utilizes reverse polarization under the organic ferroelectric thin-film transistors (OFe-TFTs) channel to achieve the multi-bit nonvolatile memory device. The potential difference between the source and the drain contact of Fe-TFT was utilized to implement a reverse distribution of the polarization under the channel of Fe-TFT. A self-designed technology computer-aided design platform of DEVSIM was used to exemplify our method of reverse polarization distribution in the bottom-gate top-contact Fe-TFTs. Then, an organic Fe-TFT using poly (vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) as the dielectric and Poly [2,5-(2-octyldodecyl) -3, 6- diketopyrrolopyrrole -alt-5,5- (2,5-di (thien-2-yl) thieno [3, 2-b] thiophene)] (DPP-DTT) as the semiconductor layers was fabricated to realize a 2-bit memory device. The measured transfer characteristics and retention properties perform a good verification of our originality. © 2025 Elsevier B.V.

关键词： Ferroelectric thin films

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Mid-infrared band multi-Fano resonance-based sensing of high-Q concave-convex arrays of metasurface with full control of Fano spectral profile

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Photonics and Nanostructures - Fundamentals and Applications 2025年 65卷

作者： Mengyang Xu Dong Lin Haoyuan Cai Haoran Wang Zhichun Fan Jing Liu Yushan Chen School of Ocean Information Engineering Jimei University Xiamen 361021 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen 361021 China

In this study, a concave-convex array metasurface with a height difference characterized structure is proposed, combining in-plane and out-of-plane asymmetry to achieve a high Q-factor, refractive index sensing, and full control of the Fano spectral profile. Notably, both the concave model and the convex model, can only produce one Fano peak. But it can yield multiple Fano resonance peaks in the mid-infrared band following a simple combination. Herein, we have performed both near-field and far-field analysis for each Fano resonance generation. The designed metasurface achieves a remarkably high Q-factor of 9.63637 × 10 5 . Moreover, it exhibits excellent reflection resonances under different polarization directions. In terms of refractive index sensing, the designed metasurface attains a sensitivity of 600 nm/RIU under TE polarization, while under TM polarization, it achieves full control of the Fano spectral profile by changing the incidence angle. The integration of periodic arrays with height differences in metasurfaces shows tremendous potential in controlling the Fano spectral profile and enhancing sensing capabilities. Additionally, this study will provide new inspirations for the sensing metasurface design with high optical performance.

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