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A 3–8 decoder of terahertz metamaterials and its sensing application

Diamond and Related Materials 2025年 156卷

作者： Zhu, Aijun Wei, Wenrui Hou, Weigang Cheng, Lei Hu, Cong Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400000 China Jincheng Research Institute of Opto-mechatronics Industry Shanxi Jincheng048000 China Shanxi Key laboratory of Advanced semiconductor Optoelectronic Devices and Integrated Systems Shanxi Jincheng048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin541004 China

A 3–8 decoder based on terahertz metamaterial was proposed for the first time, which can achieve both 3–8 decoding function and dual band selectable 2–4 decoding function. The function of this device mainly relies on VO2 acting as a conductor or insulator. The finite integration in time domain (FITD) method was used to simulate and optimize the performance of the metamaterial absorber (MMA), and its working mechanism was analyzed through electric field distribution diagram and equivalent circuit model (ECM). When the device is used as a 3–8 decoder, the maximum modulation depth (MD) is 99.60 %, the minimum extinction ratio (ER) is 17.7 dB, the maximum insertion loss (IL) is 0.76 dB, and it has the property of being insensitive to the incident angle, with stable decoding ability. In addition, the device can also be used as a sensor with a refractive index sensitivity (S) of 0.55 THz/RIU, a quality factor (Q) of 24.714, and a FOM of 3.93RIU−1, demonstrating excellent sensing capabilities. This work provides new ideas for improving the decoding capability of terahertz devices and designing dynamic devices. © 2025

关键词： Refractive index

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Dynamic Simulation Analysis of the Flexible Swap Device

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Journal of Donghua University(English Edition) 2015年第2期32卷 300-304页

作者：崔艳波赵腊生张强张建新 Key Laboratory of Advanced Design and Intelligent Computing Dalian University

With the development of space science and technology,the on-orbit servicing technologies of spacecraft get more and more *** to the design criterion of existing spacecraft in orbit module replacement technology,the flexible swap device is designed and the dynamics simulation of institutions by the automatic dynamic analysis of mechanical systems(ADAMS)simulation software is *** the analysis process,this paper studies the effect of collision force of flexible mechanism and provides a basis for the optimization of flexible plug agencies.

关键词： collision swap spacecraft institutions trajectory freedom kinematic criterion constraints replacement

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Exploiting a novel perturbation structure to overcome manufacturing defect and modal overlap in mode localization MEMS gyroscope

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Nonlinear Dynamics 2025年 1-30页

作者： Zhang, Kunpeng Li, Wei Hao, Shuying Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin People’s Republic of China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin People’s Republic of China

This study presents a micro-electro-mechanical system (MEMS) gyroscope featuring an innovative perturbation structure in the perturbation stiffness induced by angular velocity is applied to a serpentine coupling beam. Considering the symmetry of the initial structure of resonators and the asymmetry in stiffness resulting from initial manufacturing defects, the dynamic characteristics in the linear regime are analyzed using the complex exponential method. By integrating the backbone curve, energy balance method, amplitude-frequency response (AFR), and bifurcation analysis, we reveal the gyroscope's dynamics, mode localization phenomenon, and sensitivity performance in the geometrically nonlinear case. Subsequently, the excitation threshold at which bifurcation occurs in the out-of-phase nonlinear normal mode (NNM) is determined. Taking the excitation voltage exceeds the bifurcation threshold, the results indicate that the mode localization due to nonlinear characteristics increases the sensitivity from 0.22 ppm/°/s to 789.4 ppm/°/s when initial manufacturing defects are not considered. Furthermore, owing to the implementation of out-of-phase dual-resonator-driven, only the out-of-phase NNM is excited and forms a mode localization phenomenon, so avoiding the phenomenon of modal overlap. Accounting for the stiffness error (± 3%) caused by initial manufacturing defects, the amplitude ratio difference sensitivity improves by 314% in the nonlinear case compared to the linear case and by 103.3% relative to the nonlinear symmetric regime. The gyroscope sensitivity escalates with the rising absolute value of the stiffness error. The novel perturbation structure design for mode localization MEMS gyroscope exploits initial stiffness errors and nonlinearity to achieve a higher sensitivity output than the initial symmetric structure.

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Multi-process digital twin closed-loop machining through shape-feature state update and error propagation knowledge graph

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advanced Engineering Informatics 2025年 65卷

作者： Lin, Zhiwen Zhang, Yueze Chen, Chuanhai Guo, Jinyan Qi, Baobao Yan, Jun Liu, Zhifeng Key Laboratory of CNC Equipment Reliability Ministry of Education Jilin University Jilin Province Changchun130025 China Jilin Provincial Key Laboratory of Advanced Manufacturing and Intelligent Technology for High-end CNC Equipment Jilin University Jilin Province Changchun130025 China School of Mechanical and Aerospace Engineering Jilin University Jilin Province Changchun130025 China Beijing Key Laboratory of Advanced Manufacturing Technology Beijing University of Technology Beijing100124 China Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology Beijing University of Technology Beijing100124 China

Machining digital twin systems represent a novel platform for achieving full autonomy in machine tools. However, in multi-process machining, error propagation between different processes presents significant challenges to current digital twin systems: real-time model updates, error evolution analysis and optimization. To address these issues, this study proposes a method for rapid state updates of shape features and the construction of an error propagation knowledge graph, enabling dynamic control in a multi-process machining digital twin system. First, a state update method based on multi-level voxel computation is introduced, utilizing the transformation relationship between cutter-workpiece engagement (CWE) and voxels to ensure rapid updates of multi-process digital twin models. Second, digital threads for multi-process machining are developed to identify machining states and track errors. To analyze error evolution and implement effective control measures, an error propagation knowledge graph based on Taylor expansion model is constructed, enabling closed-loop control. Finally, the proposed system was validated for its advancement in error control and closed-loop optimization through the machining of typical multi-process components in the transportation field. The results of comparative experiments and ablation experiments indicate that the error updating efficiency based on multi-level voxel computation threads improved by an average of 59.26% compared to other benchmarks. The error propagation knowledge graph based on the Taylor model achieved a 42.75% improvement in reasoning accuracy compared to the ablated Taylor model. The developed digital twin closed-loop optimization system successfully ensured that the errors in 1280 processes of the case study remained within the tolerance range. © 2025 Elsevier Ltd

关键词： Machining centers

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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