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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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Pan-media liquid metal 3D printing

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The Innovation 2025年第4期6卷 7-8页

作者： Xiaohui Shan Weichen Feng Minghui Guo Xuelin Wang Jing Liu School of Biomedical Engineering Tsinghua UniversityBeijing 100084China Key Laboratory of Cryogenic Science and Technology Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China Beijing Advanced Innovation Center for Biomedical Engineering Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Engineering MedicineBeihang UniversityBeijing 100191China

INTRODUCTION The manufacturing industry has evolved from traditional forging and cutting to three-dimensional(3D)printing,a revolutionary technology that expands human imagination by creating everything of complex shapes and intricate structures,making it a cornerstone of smart manufacturing.1 However,conventional fabrications may encounter bottlenecks that seriously restrict their printing across different inks,ambient,and *** existing 3D printing methods to produce target functional components on diverse working media,therefore,requires significant *** highly adaptable 3D printing inks,liquid metals(LMs)open large spaces to address such challenges due to their versatile capabilities,such as fluidities,conductivities,easy solid-liquid transitions,and *** continuous efforts are being increasingly made in LM printing in different media,2 there is still no generalized methodology or concept proposed to unite all LM 3D printing techniques,inks,and media *** achieve this intriguing target,here we present a unified 3D printing concept,termed pan-media liquid metal 3D printing,to manufacture customized end-user devices as ***-media 3D printing means that it is able to administrate printing with any ink and any working ambient,from gases and liquids to soft matters,bio-tissues,and rigid media,transcending the boundaries of traditional printing ***,with intentionally introduced physical or chemical processing between LM inks and ambient,pan-media LM 3D printing could achieve a much wider variety of 3D object and targeted functions over existing *** synthesizing the pan-media theory of LM 3D printing,we prospect a pan-media manufacturing center equipped with functional ink storage,media library,printing head system,and control units,all integrated to fully address the desired printing tasks.

关键词： expands human imagination Liquid metals creating everything complex shapes intricate forging cutting smart manufacturing d printing produce target functional com D printing

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MADNet: Based on Multiple Attention and Dilated Convolution for CSI Feedback

MADNet: Based on Multiple Attention and Dilated Convolution ...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Zhao, Xiang Yang, Chuansheng Wang, Chao Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510667662

The massive multiple-input and multiple-output (MIMO) system based on channel state information (CSI) is the core technology of next-generation communication. As the complexity of the CSI matrix gradually increases, CSI feedback becomes more challenging. CSI feedback technology based on deep learning (DL) has been successful in frequency-division duplex (FDD) MIMO systems. In this paper, we propose a complex-valued lightweight neural network MADNet for CSI feedback. The network is based on an encoder-decoder structure, which enriches the information extraction of the CSI matrix by adopting channel information extraction modules and coordinates information extraction modules, which reduces the computational complexity of the network by using lightweight convolution. Experimental results show that our network outperforms most CNN-based network architectures at multiple compression rates and performs significantly better in both indoor and outdoor scenarios. © 2023 SPIE.

关键词： Channel state information

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Electrochemical response of MoS_(2) nanosheets/SiO_(2)-NH_(2) nanoparticles modified glassy carbon electrode to phenylacetic acid

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Rare Metals 2023年第12期42卷 4142-4152页

作者： Tian-Yao Qi Zhen-Yu Yuan Fan-Li Meng College of Information Science and Engineering Northeastern UniversityShenyang 110819China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China

Phenylacetic acid(PAA)is a primary raw material for illegal Methamphetamine(MATM)synthesis under the strong precursor chemicals supervisions of safrole and ***,trace detection of PAA at ultra-low concentration is a strategic technique and an urgent issue in the field of drug *** this paper,trace determination of PAA at sub-nmol-L-1 concentration level is achieved by hydrogen bond adsorption and electrochemical catalysis through the prepared aminated SiO_(2)nanoparticles(SiO_(2)-NH_(2) NPs)and MoS_(2) nanosheets(NSs)modified glassy carbon electrode(GCE).The prepared MoS_(2) NS s/SiO_(2)-NH_(2) NPs modified electrode represents a detecting limit of 0.0989 nmol·L^(-1)and an obvious increasing linear range before the concentration increasement up to 60 nmol·L^(-1)in square wave voltammetry(SWV)responses of *** SWV response of the modified electrode to PAA in the concentration range within 100 nmol·L^(-1)is higher than phenol,acetic acid(HOAc)and benzoic Acid(BEN).This electrochemical method for trace detection of PAA in aqueous solution with desired performance provides a feasible scheme for the detection of other drugs and aromatic precursor chemicals.

关键词： Phenylacetic acid detection Modified electrode MoS_(2)nanosheet Square wave voltammetry

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Research on a quantification model of online learning cognitive load based on eye-tracking technology

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Multimedia Tools and Applications 2024年第18期84卷 1-15页

作者： Xue, Yaofeng Zhu, Fangqing Li, Jiaxuan Department of Educational Information Technology and Shanghai Engineering Research Center of Digital Educational Equipment East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China

Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learners. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Eye movements

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Dynamic community detection algorithm based on hyperbolic graph convolution

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International Journal of Intelligent Computing and Cybernetics 2024年第3期17卷 632-653页

作者： Weijiang Wu Heping Tan Yifeng Zheng College of Information Science and Engineering/College of Artificial Intelligence China University of Petroleum BeijingBeijingChina Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum BeijingBeijingChina School of Computer Science Minnan Normal UniversityZhangzhouChina

Purpose:Community detection is a key factor in analyzing the structural features of complex ***,traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic *** address this challenge,a hyperbolic space-based dynamic graph neural network community detection model(HSDCDM)is ***/methodology/approach:HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincare and Lorentz models to realize feature fusion and information *** addition,the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended ***,the community clustering module divides the community structure by combining the node characteristics of the space domain and the time *** evaluate the performance of HSDCDM,experiments are conducted on both artificial and real ***:Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical *** shows an average improvement of 7.29%in NMI and a 9.07%increase in ARI across datasets compared to traditional *** complex networks with nonEuclidean geometric structures,the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space,provides a more compact embedding that preserves the data structure,and offers advantages over methods based on Euclidean geometry ***/value:This model aggregates the potential information of nodes in space through manifoldpreserving distribution mapping and hyperbolic graph topology ***,it optimizes the Simple Recurrent Unit(SRU)on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space,thereby enhancing computing efficiency by eliminating t

关键词： Complex networks Dynamic community detection Hyperbolic graph neural network

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LIGI: low-light image enhancement via gray image

LIGI: low-light image enhancement via gray image

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Sun, Yue Ye, Qihong Chen, Hongming Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510671881

Low-light images are usually affected by problems such as low illumination, noise, and color distortion, resulting in unsatisfactory image enhancement. Low-light image enhancement aims to improve the quality and visibility of low-light images. In this paper, a gray image based low-light image enhancement deep learning method is proposed, which converts the low-light image to gray image and then extracts the important information in the image as branches to assist the main network. To achieve better enhancement performance, we proposed denoising center block (DC block), which focusing on color restoration and information preservation. The experimental results show that the method proposed in this paper outperforms the existing low-light image enhancement methods in both objective and subjective evaluation metrics, and can effectively improve the clarity, contrast, and color reproduction of low-light images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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Mal-Net: Multi-scale feature extraction and attention mechanism lightweight network for CSI feedback 3

Mal-Net: Multi-scale feature extraction and attention mechan...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Song, Jinjie Chen, Hongming Zhao, Xiang Zhang, Chenyu Wu, Yiting Ye, Qihong School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province China

ISBN: (纸本)9781510668522

Massive multiple-input multiple-output (MIMO) system is one of the wireless technologies with great research significance based on channel state information (CSI). As the complexity of the CSI matrix increases, CSI feedback faces many challenges and limitations. This paper proposes a neural network composed of the encoder-decoder framework, which can effectively compress and recover CSI. The network utilizes a channel information generation module and multi-scale feature extraction module to enhance the information acquisition ability of the CSI matrix. At the same time, it reduces the computational complexity of the network by using lightweight architecture. Experimental results show that the network outperforms other advanced deep-learning methods at multiple compression rates. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Channel state information

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Solving Multiobjective Combinatorial Optimization via Learning to Improve Method

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第3期9卷 2122-2136页

作者： Ye, Te Zhang, Zizhen Zhang, Qingfu Chen, Jinbiao Wang, Jiahai Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510275 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Recently, neural combinatorial optimization (NCO) methods have been prevailing for solving multiobjective combinatorial optimization problems (MOCOPs). Most NCO methods are based on the "Learning to Construct"(L2C) paradigm, where the trained model(s) can directly generate a set of approximate Pareto optimal solutions. However, these methods still suffer from insufficient proximity and poor diversity towards the true Pareto front. In this paper, following the "Learning to Improve"(L2I) paradigm, we propose weight-related policy network (WRPN), a learning-based improvement method for solving MOCOPs. WRPN is incorporated into multiobjective evolutionary algorithm (MOEA) frameworks to effectively guide the search direction. A shared baseline for proximal policy optimization is presented to reduce variance in model training. A quality enhancement mechanism is designed to further refine the Pareto set during model inference. Computational experiments conducted on two classic MOCOPs, i.e., multiobjective traveling salesman problem and multiobjective vehicle routing problem, indicate that our method achieves remarkable results. Notably, our WRPN module can be easily integrated into various MOEA frameworks such as NSGA-II, MOEA/D and MOGLS, providing versatility and applicability across different problem domains. © 2025 IEEE.

关键词： Deep reinforcement learning

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Contactless Vital Signs Monitoring for Animals

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IEEE Internet of Things Journal 2025年第11期12卷 16789-16796页

作者： Pitafi, Zaid Farooq Song, Yingjian Xie, Zaipeng Brainard, Benjamin Song, Wenzhan University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China University of Georgia College of Veterinary Medicine AthensGA30602 United States

Monitoring vital signs such as Heart Rate (HR) and Respiratory Rate (RR) is critical for veterinary medicine. The existing contact based systems are difficult to use on animals for longer periods as they may cause movement restriction. Contactless solutions have recently gained more popularity due to their ease-of-use. However, there are very few validated systems for animals. In this study, we propose a contactless vital signs monitoring system, CageDot for animals. Our system provides continuous real-time monitoring of HR and RR during hospitalization. The CageDot is placed under the animal cage and detects the heart vibrations using a geophone sensor. The CageDot also has a signal quality control algorithm to address the problem of obtaining high-quality cardiac data in real-life noisy environments such as hospitals. Compared to the existing works that use controlled environments, this is especially significant. The algorithm includes several steps that include background noise/movement removal, subject movement detection, heartbeat extraction and vital signs estimation. The experimental results on 16 hospitalized dogs and cats show that this system can achieve high accuracy for vital signs monitoring with a Mean Absolute Error (MAE) of 4.71 for Heart Rate (3.8 percent error rate) and 1.28 (8 percent error rate) for Respiratory Rate. © 2014 IEEE.

关键词： Heart

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