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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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A fusion algorithm which can reduce PAPR and redundancy 4

A fusion algorithm which can reduce PAPR and redundancy

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4th World Conference on Computing and Communication Technologies, WCCCT 2021

作者： Dong, Jian'E Xie, Yu'Rui Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Southwest Forestry University Yunnan province Kunming City650224 China College of Big Data and Intelligence Engineering Southwest Forestry University Yunnan province Kunming City650224 China

ISBN: (纸本)9780738144986

The precoding method based on pulse forming reduces the effect of the OFDM system on PAPR, but also introduces a large amount of redundant information, which reduces the frequency band utilization of the system. In order to further improve the PAPR of OFDM system value, and reduce the redundancy of the system, this paper proposes a grouping optimization selection of SLM algorithm, Combining it with the precoding method can reduce the redundant information of the system while reducing PAPR greatly. Theoretical analysis and experimental results show that the new SLM and precoding fusion algorithm for redundant information is far lower than the traditional fusion algorithm, under the condition of the same PAPR values. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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Nailfold Capillary Segmentation Based on U-Net and Attention Mechanism 23

Nailfold Capillary Segmentation Based on U-Net and Attention...

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3rd International Conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Chen, Shupin Wei, Dan Gu, Shenming Yang, Zhangru School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Department of Radiation Oncology Shanghai Chest Hospital Shanghai Jiao Tong University No. 241 West Huaihai Road Shanghai200030 China

ISBN: (纸本)9798400700200

Microcirculation refers to the blood circulation composed of arterioles and venules, which can be used as a reference indicator for doctors to diagnose diseases. The traditional manual reading method is relatively time-consuming and labor-consuming and is not conducive to the standardization and digitization of diagnosis. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. In this study, we proposed a method to segment the nailfold vascular images based on the U-Net backbone network combined with the residual mechanism and the attention mechanism. The results show that: 1. Adding the CBAM attention mechanism can improve the anti-noise ability of the model. 2. Using residual blocks to replace the convolution blocks of the backbone network can improve the accuracy of models. 3. Contrast enhancement can improve the accuracy of blood vessel segmentation. The optimal method in this study is Res_CBAM_Unet and the contrast enhancement uses CLAHE (clipLimit = 2). The overall accuracy is 94.31%, the Dice score is 0.8297, the IoU score is 0.7090, the VOE score is 0.2910. The generalization ability of the proposed method is verified on the public dataset DRIVE. The results show that the accuracy of the proposed method is equivalent to the state-of-the-art methods, and it has an advantage in the sensitivity. This paper provides an effective method for nailfold capillary segmentation, which lays a foundation for the automation of diagnosis based on microcirculation images. © 2023 ACM.

关键词： Microcirculation

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FuzzyTrack: User Adaptive Cervical Spine Motion Prediction with Earable Inertial Sensing

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IEEE Transactions on Fuzzy Systems 2024年

作者： Luo, Chengwen Li, Yaxue Chen, Gecheng Li, Xing Zhang, Jin Wei, Bo Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Dongguan University of Technology School of Electrical Engineering & Intelligentization Guangdong Dongguan523808 China Northeastern University China State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China Newcastle University United Kingdom

The widespread use of electronic devices has contributed to an increase in poor posture, particularly when it comes to the cervical spine, leading to various cervical vertebral pain disorders. In this paper, we focus on accurately monitoring the motion status of the cervical spine using the accelerometers and gyroscope sensors embedded in earphones. Our aim is to gain a better understanding of cervical spine health. To address the individual differences among subjects, we introduce fuzzy rules to the Re-ISDA method, proposing a novel approach known as FuzRe-ISDA. Unlike traditional domain adaptation methods, the FuzRe-ISDA method offers flexibility in adjusting the contribution from different source domains. It takes into account the collective impact of multiple models on predicting new user behavior. Moreover, this method can quickly adapt to new users without requiring extensive datasets. Experimental results demonstrate that our FuzRe-ISDA approach outperforms popular domain adaptation methods in terms of accuracy when predicting cervical motion. This highlights the effectiveness of our approach in addressing individual differences and improving the reliability of cervical spine motion prediction. © 1993-2012 IEEE.

关键词： Fuzzy rules

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Delving Globally into Texture and Structure for Image Inpainting

arXiv

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arXiv 2022年

作者： Liu, Haipeng Wang, Yang Wang, Meng Rui, Yong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Lenovo Research Beijing China

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch is reconstructed from all other patches across the whole image, to match the coarsely filled information, especially the structure information over the masked regions. Unlike the current decoder-only transformer within the pixel level for image inpainting, our model adopts the transformer pipeline paired with both encoder and decoder. On one hand, the encoder captures the texture semantic correlations of all patches across image via self-attention module. On the other hand, an adaptive patch vocabulary is dynamically established in the decoder for the filled patches over the masked regions. Building on this, a structure-texture matching attention module anchored on the known regions comes up to marry the best of these two worlds for progressive inpainting via a probabilistic diffusion process. Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting. The extensive experiments over the benchmarks validate its superiority. Our code is available here. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

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Obstacle-Avoidance Path Planning of Robot Arm Based on Improved RRT Algorithm

Obstacle-Avoidance Path Planning of Robot Arm Based on Impro...

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Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)

作者： Chong Hu Chunyang Mu Ma Xing Chuntao Zhang Wenya Zhou Ke Yang School of Electrical and Information Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province College of Mechanical and Electrical Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province School of Electrical and Information Engineering North Minzu University Yinchuan China College of Mechanical and Electrical Engineering North Minzu University Yinchuan China

In the application of RRT (Rapidly-exploring Random Trees) algorithm in obstacle avoidance path planning of redundant robot arms in high-dimensional space, the sampling area of random sampling points is large, the search time is long, the path twists and turns, and the redundant points are too many. In this paper, an efficient sampling RRT algorithm with adaptive step size and fast automatic convergence is proposed. Firstly, by adding the optimized gravity function, the step size of the extended tree is changed adaptively and the convergence is stronger. Secondly, the limited sampling of parent node area expansion is proposed to avoid useless point sampling and repeated point sampling, so that the utilization of sampling points is greatly improved. Finally, by changing the way of parent node reconnection and re-selection, the path tortuous degree is lower, the path planning cost and redundancy points are reduced, and MATLAB software is used to carry out the path planning simulation experiment. By comparing RRT, gravitational field RTT and RRT* algorithm, the improved RRT algorithm has significantly improved in distance, time and number of nodes. It has important engineering application value.

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HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints

arXiv

引用

arXiv 2024年

作者： Li, Yang Liu, Ya-Feng The Shenzhen Research Institute of Big Data Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper studies the quality-of-service (QoS) constrained multi-group multicast beamforming design problem, where each multicast group is composed of a number of users requiring the same content. Due to the nonconvex QoS constraints, this problem is nonconvex and NP-hard. While existing optimization-based iterative algorithms can obtain a suboptimal solution, their iterative nature results in large computational complexity and delay. To facilitate real-time implementations, this paper proposes a deep learning-based approach, which consists of a beamforming structure assisted problem transformation and a customized neural network architecture named hierarchical permutation equivariance (HPE) transformer. The proposed HPE transformer is proved to be permutation equivariant with respect to the users within each multicast group, and also permutation equivariant with respect to different multicast groups. Simulation results demonstrate that the proposed HPE transformer outperforms state-of-the-art optimization-based and deep learning-based approaches for multi-group multicast beamforming design in terms of the total transmit power, the constraint violation, and the computational time. In addition, the proposed HPE transformer achieves pretty good generalization performance on different numbers of users, different numbers of multicast groups, and different signal-to-interference-plus-noise ratio targets. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Optimizing Age of Information in Random Access Networks With Correlated Sources

Optimizing Age of Information in Random Access Networks With...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Long Liang Siyuan Zhou Bin Tang Guoping Tan College of Information Science and Engineering Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Random access is a wireless network access technique that can meet the transmission requirements of a massive number of Internet of Things terminals under limited resource constraints. To address the needs of delay-sensitive applications, it is essential to study random access strategies from the perspective of information timeliness. Existing research often assumes that the information transmitted by terminals is independent of each other, neglecting the correlation between information in the design process of random access strategies. However, in scenarios such as video surveillance and sensor monitoring, adjacent terminal transmissions exhibit spatial correlation. To overcome this limitation, this paper proposes a random access strategy based on information timeliness and information correlation. Specifically, we establish information correlation between information sources and analyze the coupling variation of Age of Information (AoI) in multipoint transmission scenarios, deriving an expression for the average network AoI. Next, by jointly optimizing the random access probabilities of various terminals, we minimize the average network AoI. To address the non-convex and nonlinear optimization problem, we design an optimization algorithm based on Sequential Quadratic Programming and propose an approach to obtain initial values for improved algorithm stability. Simulation results demonstrate that the proposed method significantly reduces the average network AoI compared to random strategies that do not consider correlation.

关键词： Correlation Wireless networks Simulation Signal processing algorithms Signal processing Reliability theory Information age Video surveillance Stability analysis Quadratic programming

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Confidence-aware Contrastive Learning for Selective Classification

arXiv

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arXiv 2024年

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model’s confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. © 2024, CC BY.

关键词： Classification (of information)

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A Study on The Multi-Attribute Decision Theory and Methods 9th

A Study on The Multi-Attribute Decision Theory and Methods

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Feng, Jinyuan Yan, Yunxi Huang, Manling Du, Yang Lu, Zhichen Li, Biao School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Dance Minzu University of China Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision theory

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