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Hybrid Lifting Wavelet-Like Transform for Solution of Electromagnetic Integral Equation

Chinese Physics Letters 2008年第3期25卷 1000-1003页

作者：陈明生吴先良沙威黄志祥 Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039

A hybrid lifting wavelet-like transform scheme is successfully applied to the solution of electric field integral equation using Rao-Wilton-Glisson basis functions. To speed up the matrix transform process, the lifting scheme is adopted. Numerical examples of different three-dimensional perfectly electric conducting objects are considered. Compared with the method of moments, the proposed matrix transform scheme can save considerable CPU time and memory.

关键词： supernova explosion proto-neutron star shock wave

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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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Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges

arXiv

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arXiv 2025年

作者： Zhou, Xiaotian Zehmakan, Ahad N. Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China School of Computing Australian National University Canberra Australia

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation

arXiv

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arXiv 2025年

作者： Xiang, Wenzhao Liu, Chang Yu, Hongyang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Peng Cheng Laboratory China University of Chinese Academy of Sciences China Department of Electronic Engineering Shanghai Jiao Tong University China

Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for downstream tasks. However, images inherently contain abundant redundant information, leading the pixel-based MIM reconstruction process to focus excessively on finer details such as textures, thus prolonging training times unnecessarily. Addressing this challenge requires a shift towards a compact representation of features during MIM reconstruction. Frequency domain analysis provides a promising avenue for achieving compact image feature representation. In contrast to the commonly used Fourier transform, wavelet transform not only offers frequency information but also preserves spatial characteristics and multi-level features of the image. Additionally, the multi-level decomposition process of wavelet transformation aligns well with the hierarchical architecture of modern neural networks. In this study, we leverage wavelet transform as a tool for efficient representation learning to expedite the training process of MIM. Specifically, we conduct multi-level decomposition of images using wavelet transform, utilizing wavelet coefficients from different levels to construct distinct reconstruction targets representing various frequencies and scales. These reconstruction targets are then integrated into the MIM process, with adjustable weights assigned to prioritize the most crucial information. Extensive experiments demonstrate that our method achieves comparable or superior performance across various downstream tasks while exhibiting higher training efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Frequency domain analysis

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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Subband Adaptive Filtering Based on the Squared Sine Error Criterion: Analysis and Applications

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1949-1959页

作者： Zhikai Gong Yingsong Li Xinqi Huang Liping Li Zhixiang Huang Rodrigo. C. de Lamare Yingying Zhu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil School of Physics Engineering and Technology University of York York U.K.

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

关键词： Noise Prediction algorithms signal processing algorithms Filtering algorithms Nonlinear filters Maximum likelihood detection Convergence Vectors Mathematical models Information filters

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Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images

arXiv

引用

arXiv 2025年

作者： Yan, Jiebin Rao, Jiale Chen, Junjie Tan, Ziwen Liu, Weide Fang, Yuming School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330032 China Harvard Medical School Harvard University United States

Omnidirectional image quality assessment (OIQA) has been widely investigated in the past few years and achieved much success. However, most of existing studies are dedicated to solve the uniform distortion problem in OIQA, which has a natural gap with the non-uniform distortion problem, and their ability in capturing non-uniform distortion is far from satisfactory. To narrow this gap, in this paper, we propose a multitask auxiliary network for non-uniformly distorted omnidirectional images, where the parameters are optimized by jointly training the main task and other auxiliary tasks. The proposed network mainly consists of three parts: a backbone for extracting multiscale features from the viewport sequence, a multitask feature selection module for dynamically allocating specific features to different tasks, and auxiliary sub-networks for guiding the proposed model to capture local distortion and global quality change. Extensive experiments conducted on two large-scale OIQA databases demonstrate that the proposed model outperforms other state-of-the-art OIQA metrics, and these auxiliary sub-networks contribute to improve the performance of the proposed model. The source code is available at https://***/RJL2000/MTAOIQA. Copyright © 2025, The Authors. All rights reserved.

关键词： Image quality

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HTL-WASI preconditioner for finite element discretization of complex ADR equation and its application

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Journal of Computational and Applied Mathematics 2025年 470卷

作者： Yang, Ronghua Wang, Junxian Shu, Shi School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan Hunan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China

The convection diffusion reaction (ADR) equation is widely used in engineering applications. In this paper, we focus on a complex ADR equation arising from Helmholtz equation with impedance boundary conditions, prove the coerceiveness and boundedness of the sesquilinear functional and discuss the fast solver of the discretization. Firstly, we design a Weighted Additive Schwarz with Impedance (WASI) preconditioner, in order to overcome the dependence of the WASI preconditioner on the number of subdomains and the overlapping width, an ideal coarse space is designed. Although it can be proved that the corresponding hybrid two-level preconditioner is the exact inverse of the ADR coefficient matrix, the dimension of this coarse space is too big. In order to overcome this deficiency, a smaller dimensional coarse space was constructed by introducing local generalized eigenvalue problems (GEP) on each subdomain, and the descent rate of the corresponding two-level preconditioned GMRES method was rigorously analyzed which does not depend on mesh size, overlapping width, wave number k, and the absorption parameter. Since the size of the coarse space is sensitive to k and the GEP on each subdomain needs to be solved, the computational cost is too high. Therefore, we design an iterative method based on an economical two-level preconditioner, and establish a heuristic convergence theory that is supported by numerical results. Numerical experiments show the robustness of GMRES with the corresponding economical two-level preconditioner(HTLE-WASI-GMRES). Finally, for the Helmholtz finite element discretization, by using the Shifted Laplace technique and ADR equation, we design a novel fast solver combining with HTLE-WASI-GMRES for ADR equation. Numerical experiments have demonstrated the effectiveness of the algorithm. © 2025

关键词： Convergence of numerical methods

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High-order diversity feature learning for pedestrian attribute recognition

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Neural Networks 2025年 188卷 107463页

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou China Australian Artificial Intelligence Institute University of Technology Sydney NSW Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou China School of Electrical and Data Engineering University of Technology Sydney NSW Australia

Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation or region detection methods to localize body parts and learn corresponding attribute-specific feature from the corresponding regions, where the performance heavily depends on the accuracy of body region localization. The latter adopts the embedded attention modules or transformer attention to exploit detailed feature. However, it can focus on certain body regions but often provide coarse attention, failing to capture fine-grained details, the learned feature may also be interfered with by irrelevant information. Meanwhile, these methods overlook the global contextual information. This work argues for replacing coarse attention with detailed attention and integrating it with global contextual feature from ViT to jointly represent attribute-specific regions. To tackle this issue, we propose a High-order Diversity Feature Learning (HDFL) method for PAR based on ViT. We utilize a polynomial predictor to design an Attribute-specific Detailed Feature Exploration (ADFE) module, which can construct the high-order statistics and gain more fine-grained feature. Our ADFE module is a parameter-friendly method that provides flexibility in deciding its utilization during the inference phase. A Soft-redundancy Perception Loss (SPLoss) is proposed to adaptively measure the redundancy between feature of different orders, which can promote diverse characterization of features. Experiments on several PAR datasets show that our method achieves a new state-of-the-art (SOTA) performance. On the most challenging PA100K dataset, our method outperforms previous SOTA by 1.69% and achieves the highest mA of 84.92%. © 2025 The Authors

关键词： Contrastive Learning

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

引用

7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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