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2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Tinghao Li, Yachao Song, Xuan Zhang, Tao Wu, Chunfeng Sun, Ziyue Xidian University National Laboratory of Radar Signal Processing Xi'an China Chengdu Aerospace Science and Technology Microelectronics System Research Institute Chengdu China University of Exeter BEng Electronic Enginnering Cornwall United Kingdom

ISBN: (纸本)9781665498142

Accurate two dimension frequency spectrum (2D FS) with acceleration, two-way range coupling terms, and spatial variant Doppler parameters are main problems to be solved in translational variant bistatic forward-looking SAR (TV BFSAR) with curved trajectory. For these issues, a modified Omega-K imaging algorithm is derived in this paper. The maximum usage of 2D FS based on method of series reversion (MSR) is achieved by linear range cell migration correction and 2D FS is linearized in bistatic range by using high-order polynomial fitting. Then, a method of azimuth resampling is introduced to implement compensation of spatial variant Doppler parameters. And the small-aperture data is focused in the azimuth frequency domain to avoid azimuth aliasing without padding zeros. Finally, the raw bistatic SAR data verifies the effectiveness of the proposed method © 2021 IEEE.

关键词： bistatic Omega-K algorithm space variance Translational variant bistatic forward-looking synthetic aperture radar (TV BFSAR)

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Optically transparent transmissive orbital angular momentum (OAM) metasurface with high transmittance and broadband characteristics

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Optics Express 2025年第12期33卷 24385-24395页

作者： Yong Cai Yuying Dai Yue Ma Gang Wang Guanghui Xu Xingang Ren Hui Wang Xiaohu Zhao Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei 230039 China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University Hefei 230039 China wang_toward@*** ghxu86@*** Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei 230039 China xgren@*** Hefei Normal University Hefei 230009 China

This work proposes a high-performance broadband orbital angular momentum (OAM) beam-generating metasurface based on optically transparent media. A broadband polarization-converting transmissive metasurface, which operates in the 18 - 24 (28.57% fractional bandwidth) GHz range, has been realized by collaboratively designing dual C-split ring resonator structures and bidirectional lattice-patterned surface networks. The array structure demonstrates typical helical phase distribution features in the K-band, and experimental results verify simulation design. Innovatively employing a composite structure of quartz glass substrate and patterned conductive thin films, the design achieves compatible optimization of 77% visible-light transmittance and microwave broadband performance. This approach breaks through the mutually exclusive limitations of traditional metasurfaces’ optical/electromagnetic properties, providing a highly integrated solution for transparent antenna radomes in satellite communication systems and multi-mode beam multiplexing applications.

关键词： Orbital angular momentum multiplexing Phase plates Ring resonators Spatial light modulators Spiral phase Thin films

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Modulating Excited State Properties of Thermally Activated Delayed Fluorescence Molecules by Hybrid Long-Range and Short-Range Charge Transfer Strategy

SSRN

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SSRN 2023年

作者： Zhao, Xin Liu, Huanling Mu, Qingfang Fan, Jianzhong Xu, Yuanyuan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China School of Science Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

Balancing the rapid radiative decay process and the fast reverse intersystem crossing (RISC) process of thermally activated delayed fluorescence (TADF) molecule remains a great challenge and efficient molecular design strategies are highly desired. Herein, from a theoretical perspective, excited state properties of three reported TADF molecules (1TICz, 1BOICz and 2BOICz) are investigated based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations coupled with the thermal vibration correlation function (TVCF) method. Results indicate that, by introducing the multi-resonance (MR) acceptor, 1BOICz possesses hybrid long-range and short-range charge transfer features, balanced small energy gap (ΔEST) and large oscillator strength (f) is obtained. Furthermore, by incorporating double equivalent MR acceptors in 2BOICz, largely enhanced f with slightly changed ΔEST is achieved, inner mechanism for remarkable photophysical property is illustrated. Keep this strategy, seven new TADF molecules (2pDBA-bICz-1, 2pDBA-bICz-2, 2OSBA-bICz, 2DQAO-bICz, 2QAO-bICz, 2SQAO-bICz and 2OQAO-bICz) are theoretically designed, detailed physical parameters are analyzed and excited state energy consumption process is studied. Strong electrophilicity on acceptor is determined and the strength of nucleophilic sites on the bridge-phenyl of 2DQAO-bICz, 2QAO-bICz, 2SQAO-bICz and 2OQAO-bICz is increased, this promotes the short-range charge transfer property. In addition, the excitation processes for all studied molecules are dominated by long-range charge transfer from donor to acceptors, and supplemented by the short-range charge transfer on the bridge-phenyl with MR effect. Compromise energy gap and oscillator strength as well as large spin orbit coupling (SOC) constant are obtained for designed molecules. Thus, by regulating the long-range and short-range charge transfer ratios, excited state properties are successfully modulated and new efficient TA

关键词： Energy gap

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HU-Net: Hessian-incorporated U-Net for CBCT projection-domain metal segmentation with guidance from SAM

HU-Net: Hessian-incorporated U-Net for CBCT projection-domai...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Chen Jiang Tianling Lyu Gege Ma Yan Xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China Hangzhou Institute for Advanced Study of UCAS Hangzhou China Shanghai First-Imaging Tech. Shanghai China Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Metal artifact is a prevailing factor reducing the image quality of CBCT in minimally invasive spine surgery. In CBCT, conventional metal artifact reduction algorithms pay more attention to the inpainting of the metal traces, but metal segmentation is also challenging. Despite the current success in image segmentation with deep learning, the substantial expense associated with annotating metal traces in the projection domain makes most of these approaches impractical for this task. To address this, we propose a Hessian-incorporated U-Net (HU-Net) for CBCT projection-domain metal segmentation with guidance from SAM. The proposed method has been tested on both digital phantom data and clinical CBCT data. According to the experimental results, our method has demonstrated notable improvement in the segmentation of metal traces across both datasets. This paper presents a feasible approach for metal trace segmentation in the projection domain with deep learning and has the potential to be applied in clinical applications.

关键词： Deep learning image quality image segmentation Minimally invasive surgery Mechatronics Metals Phantoms

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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiasheng Xu Tianyi Zhang Yangqian Wu Jie Yang Guang–Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai CHINA Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CHINA MOE Key Laboratory of System Control and Information Processing Shanghai P.R. China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

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Robust multiple model-based control proposed for a SISO CSTR Benchmark

Robust multiple model-based control proposed for a SISO CSTR...

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signal, Control and Communication (SCC), International Conference on

作者： Marwa Yousfi Tarek Garna Hassani Messaoud Research Laboratory of Automatic Signal and Image Processing University of Monastir Monastir Tunisia Higher Institute of Applied Science and Technology of Sousse University of Sousse Sousse Tunisia

ISBN: (纸本)9781665402712

In this paper, robust multiple model-based LSDP (Loop-Shaping Design Procedure) controller is proposed for a SISO system where each linear submodel is represented in the form of a SISO ARX (Auto Regressive with exogenous input) Laguerre model. The proposed controller guarantees robustness in term of stability and reference tracking of the SISO system in presence of parametric uncertainties. Simulation example on the SISO CSTR (Continuous Stirred Tank Reactor) Benchmark is given to show the efficiency and the robustness of the proposed controller in handling the parametric uncertainties of the process.

关键词： Uncertainty Perturbation methods Process control Benchmark testing Numerical simulation Robustness Stability analysis

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

DOA Estimation for RIS Assisted Multi-User Wireless Communic...

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

作者： Canping Yu Li Li Yingsong Li Dazhi Sun Liping Li Zhixiang Huang Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Teddington Middlesex UK

Reconfigurable intelligent surface (RIS) can control the phase of reflected signals, and has become a hotspot in the field of wireless communication. In this paper, we propose a direction-of-arrival (DOA) estimation algorithm for RIS assisted multi-user wireless communication systems with squared-sine error criterion and Lawson norm constraint. This algorithm utilizes the sparsity of the signal and can achieve fast and accurate DOA estimation under the interference of impulsive noises. To suppress the line-of-sight (LOS) interference and thus to maximize the received signal-to-interference-plus-noise ratio (SINR), we also propose a random optimal phase selection (ROPS) technique to design the control matrix of RIS elements. The simulation results show that the proposed algorithm performs better than the existing methods.

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CHALLENGES IN IMPLEMENTING A RECOMMENDER SYSTEM FOR HISTORICAL RESEARCH IN THE HUMANITIES

arXiv

引用

arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Trattner, Christoph Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria University of Bergen & Media Futures Bergen Norway

This extended abstract describes the challenges in implementing recommender systems for digital archives in the humanities, focusing on ***, a platform for historical legal documents. We discuss three key aspects: (i) the unique characteristics of so-called charters as items for recommendation, (ii) the complex multi-stakeholder environment, and (iii) the distinct information-seeking behavior of scholars in the humanities. By examining these factors, we aim to contribute to the development of more effective and tailored recommender systems for (digital) humanities research. © 2024, CC BY-SA.

关键词： Recommender systems

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication

Leveraging Semisupervised Hierarchical Stacking Temporal Con...

引用

作者： Cheng, Yongliang Xu, Yan Zhong, Hong Liu, Yi Anhui Engineering Laboratory of IoT Security Technologies Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Shenzhen Key Laboratory of Intelligent Media and Speech Peking University Shenzhen Institute Shenzhen China

The rapid development of the Internet of Things (IoT) accumulates a large number of communication records, which are utilized for anomaly detection in IoT communication. However, only a small part of these records can be labeled, which increases the difficulty in anomaly detection. This article proposes a semisupervised hierarchical stacking temporal convolutional network (HS-TCN), which is the first semisupervised model for anomaly detection in IoT communication, and it can train unlabeled data based on a small number of labeled data. Furthermore, HS-TCN fully considers the features of streaming data in IoT communication and can weed out uncertain records. Finally, the experimental results demonstrate that HS-TCN promotes the performance of anomaly detection and attains better efficiency. © 2014 IEEE.

关键词： Internet of things

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