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32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Huiming Gao, Wei Yu, Zhuozhen Zhao, Tiesong Li, Ge School of Electronic and Computer Engineering Shenzhen Graduate School Peng Cheng Laboratory Peking University Shenzhen China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

ISBN: (纸本)9798400706868

With the rise of immersive media applications such as digital museums, virtual reality, and interactive exhibitions, point clouds, as a three-dimensional data storage format, have gained increasingly widespread attention. The massive data volume of point clouds imposes extremely high requirements on transmission bandwidth in the above applications, gradually becoming a bottleneck for immersive media applications. Although existing learning-based point cloud compression methods have achieved specific successes in compression efficiency by mining the spatial redundancy of their local structural features, these methods often overlook the intrinsic connections between point cloud data and other modality data (such as image modality), thereby limiting further improvements in compression efficiency. To address the limitation, we innovatively propose a view-guided learned point cloud geometry compression scheme, namely ViewPCGC. We adopt a novel self-attention mechanism and cross-modality attention mechanism based on sparse convolution to align the modality features of the point cloud and the view image, removing view redundancy through Modality Redundancy Removal Module (MRRM). Simultaneously, side information of the view image is introduced into the Conditional Checkboard Entropy Model (CCEM), significantly enhancing the accuracy of the probability density function estimation for point cloud geometry. In addition, we design a View-Guided Quality Enhancement Module (VG-QEM) in the decoder, utilizing the contour information of the point cloud in the view image to supplement reconstruction details. The superior experimental performance demonstrates the effectiveness of our method. Compared to the state-of-the-art point cloud geometry compression methods, ViewPCGC exhibits an average performance gain exceeding 10% on D1-PSNR metric. © 2024 ACM.

关键词： Redundancy

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CHRONIC STROKE LESIONS SEGMENTATION BASED ON COMPOUND LOSS FUNCTION AND TRANSFORMER

CHRONIC STROKE LESIONS SEGMENTATION BASED ON COMPOUND LOSS F...

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第十六届国际体视学与图像分析学术会议

作者： Zhiyong GAO Li KANG Jianjun HUANG College of Electronics and Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing

Ischemic stroke is a cerebrovascular disease with high disability and mortality rates *** and accurate segmentation of stroke lesions from medical images is crucial for early diagnosis and ***,stroke lesion segmentation faces many challenges,such as different locations of stroke occurrences,large shape variations,unclear lesion boundaries,and strong data *** accurately segment stroke lesions,our paper proposes an end-to-end deep learning method based on a compound loss function and Transformer to automatically segment lesion regions from stroke MRI *** our proposed method,we use the unified segmentation framework MMSegmentation,with a UNet network as the main architecture,and introduce a Transformer-based attention mechanism to explore more detailed local *** addition,to alleviate the problem of data heterogeneity,we propose a weighted compound loss function and hard example mining strategy,which enables our network parameters to be updated in a faster and smoother *** evaluate the proposed method on the ATLAS dataset and compare it with several state-of-the-art *** the experimental results,the proposed method achieves better stroke segmentation performance.

关键词： chronic stroke medical image segmentation transformer

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INFODEMIC MECHANISM BASED ON THE SIR MODEL: A POPULATION MOBILITY PERSPECTIVE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第2期85卷 89-102页

作者： Li, Cun-Lin Li-Dan Mai, Xiao-Jun Thoo, Ai-Chin The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China Azman Hashim International Business School Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Faculty of Management Universiti Teknologi Malaysia Johor Bahru81300 Malaysia

Ease of access and transmission of information through social media has led to ambiguity, the scope of misinformation, and uncertainty, especially during the time of public health emergencies when there is a huge demand for health information due to the lack of scientific knowledge of health knowledge among online users. Inaccurate health information will induce social disorders such as panic during the epidemic Covid-19 outbreak, which has drawn increasing attention from scholars and the government. The purpose of the study is to construct a dynamic misinformation fading model by initially considering the population mobility possibility based on the classic SIR model, thus making the infordemic spreading process more realistic and apparent. The proposed model helps to understand the impact of misinformation online by deriving and simu-lating the important parameters: misinformation-free equilibrium, spreading duration, and the social stability index to revealing the misinformation development trend. Fi-nally, the research identifies more efficient measures that could be employed in times of government officials or companies during public health emergencies. © 2023, Politechnica university of Bucharest. All rights reserved.

关键词： Public health

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 11

Anomaly Handwritten Text Detection for Automatic Descriptive...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Chatterjee, Nilanjana Shivakumara, Palaiahnaakote Pal, Umapada Lu, Tong Lu, Yue Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods. © 2022 ACM.

关键词： Students

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Research on Thermal Stability of SOI Ring Resonator

Research on Thermal Stability of SOI Ring Resonator

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intelligent Design (ICID), International Conference on

作者： Wei Zhang Yang Liu Yue He Ruolan Wang Shanxi Provincial Key Laboratory of Intelligent Sensing and Multi-dimensional Information Processing Taiyuan University Taiyuan China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

As the core sensitive unit of optical waveguide gyro, SOI optical waveguide resonator has attracted much attention due to its micrometer size and high quality factor. In order to reduce the influence of temperature change on the performance of SOI micro-ring resonator, the thermal stability of the microring resonator is studied, and the single ring, cascaded double ring and three-ring microcavity structures based on silicon on insulator are designed and fabricated. The prepared micro-ring resonator was heated by the digital temperature control heating system, and the resonator resonance curve was obtained by using the laser wavelength scanning method. Through linear fitting analysis, the relationship between the resonant wavelength drift and temperature of the single ring, cascade double ring and three-ring resonators was calculated as $111 \text{pm} /{ }^{\circ} \mathrm{C}, 90 \text{pm} /{ }^{\circ} \mathrm{C}$ and $77 \text{pm} /{ }^{\circ} \mathrm{C}$ respectively. The experimental results show that with the increase of the number of rings, the temperature coefficient of the resonator decreases and the temperature stability increases. The experimental results provide a certain basis for the subsequent research on the thermal stability of the optical waveguide gyro.

关键词： Integrated optics Optical device fabrication Silicon-on-insulator Laser stability Stability analysis Gyroscopes Optical sensors Optical ring resonators Thermal stability Optical waveguides

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POWERFUL LOSSY COMPRESSION FOR NOISY IMAGES

arXiv

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arXiv 2024年

作者： Cai, Shilv Liang, Xiaoguo Cao, Shuning Yan, Luxin Zhong, Sheng Chen, Liqun Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China

Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method;and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio (SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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A Tetrahedral Spectral Element Method for Thermo-Mechanical Analysis of Electronic Devices

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Zhang, Shuai Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

For the coupled thermo-mechanical simulation of complex electronic devices, we propose a Tetrahedral Spectral Element Method (TSEM), which truly combines the high accuracy of spectral methods with the strong geometric adaptability of Finite Element Methods (FEMs). The TSEM employs a collapse transformation method and successfully converts the orthogonal Gauss-Lobatto-Legendre (GLL) basis functions from a hexahedron to a tetrahedral reference element. This ensures numerical accuracy and stability for arbitrary high-order interpolation. We also generate non-equidistant GLL spectral interpolation nodes for tetrahedral elements through Warp&Blend transformation, enabling direct nodal numerical integration calculation through Vandermonde matrices. This approach replaces the classical Gaussian quadrature in the FEM, not only significantly enhancing the computational efficiency of element matrix but also avoiding potential numerical dissipation and dispersion associated with Gaussian integration, thus achieving rapid convergence. In the TSEM thermo-mechanical coupling, we further construct a separate solver based on the implicit Newmark-β method, which realizes unconditional stability for temporal integration for arbitrary time step increment. Moreover, it supports independent inversion of system matrices for temperature and stress fields, without increasing computational resource consumption due to the coupling of multi-physics equations. By quantitatively comparing with the COMSOL Multiphysics software, several typical case studies are used to demonstrate the significant advantages of the TSEM in numerical accuracy, efficiency, and stability. © 2025 IEEE. All rights reserved.

关键词： Orthogonal functions

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A Nonconformal SETI-DP Method for Transient Thermal Simulation

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Li, Hongyang Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

This paper proposes a novel nonconformal meshbased dual-primal spectral element tearing and interconnecting (SETI-DP) method designed for the efficient transient simulation of complex large-scale heat conduction problems utilizing a domain decomposition method (DDM). This method allows for the division of complex, large-scale problems into smaller subdomain problems. By applying Robin-type transmission conditions at the subdomain interfaces, rapid convergence of the global interface iterative solution is achieved for arbitrary non-conformal subdomain problems. This non-conformal DDM approach supports independent meshing of subdomains, eliminating the need to generate a globally continuous mesh for large-scale problems, thereby significantly enhancing meshing efficiency. In addition, this non-conformal meshing strategy enables high-contrast spatial discretization and mixed-order spectral interpolation between different subdomains. This significantly increases meshing flexibility, minimizes the number of degrees of freedom (DoF) in multi-scale problems, and reduces computational costs. In addition, by developing a subdomain-coupled time iteration scheme, the global corner problem of residual transfer is explicitly given, which greatly improves the versatility and flexibility of the algorithm. Finally, to demonstrate the accuracy, efficiency, and robustness of our algorithm, we conduct benchmark tests on several representative examples. © 2011-2012 IEEE.

关键词： Domain decomposition methods

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

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Frontiers of information Technology & Electronic Engineering 2023年第11期24卷 1541-1556页

作者： Shihmin WANG Binqi ZHAO Zhengfeng ZHANG Junping ZHANG Jian PU Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China

As one of the most fundamental topics in reinforcement learning(RL),sample efficiency is essential to the deployment of deep RL *** most existing exploration methods that sample an action from different types of posterior distributions,we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the ***,we first employ clustering methods in the policy sampling process to generate an action candidate *** we introduce a clustering buffer for modeling the internal hierarchy,which consists of on-policy data,off-policy data,and expert data to evaluate actions from the clusters in the action candidate set in the exploration *** this way,our approach is able to take advantage of the supervision information in the expert demonstration *** on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective *** particular,on the LGSVL task,our method can reduce the number of convergence steps by 46.7%and the convergence time by 28.5%.Furthermore,our code is open-source for *** code is available at https://***/Shihwin/SelectiveSampling.

关键词： Reinforcement learning Sample eficiency Sampling process Clustering methods Autonomous driving

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Performance Analysis of Aerial Base Station Cellular Network 12

Performance Analysis of Aerial Base Station Cellular Network

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12th International Conference on Electronics, Communications and Networks, CECNet 2022

作者： Ding, Xue Qian, Gongbin He, Chunlong Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China National Mobile Communications Research Laboratory Southeast University Nanjing211189 China

ISBN: (纸本)9781643683683

In this paper, we model the cellular network consisting of aerial base stations (ABSs) which follow homogeneous Poisson Point Process (PPP) and an independent collection of random users is considered. We assume that each user communicates with the closest ABS. In particular, we deal the interference signals with Laplace transform. As a result, we can obtain a tractable expression of the coverage probability. We have found that there is a maximum coverage probability which can be reached by an optimal ABS density and with low signal-to-noise ratio (SNR), the optimal ABS density gets larger when path loss exponent (PLE) increases. Simulations under high or low SNR condition have shown that the influence of PLE on coverage probability is quite different in spite of changing signal-to-interference-plus-noise ratio (SINR) threshold or ABS density. © 2022 The authors and IOS Press.

关键词： Base stations

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