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Enabling intelligent Connectivity: A Survey of Secure ISAC in 6G Networks

IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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CESFusion: Cross-Frequency Enhanced Spatial—Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong Tianjin University of Technology School of Computer Science and Engineering Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China School of Computer Science and Engineering Tianjin300384 China Hangzhou Dianzi University Institute of Intelligent Media Computing Hangzhou310018 China

The fusion of hyperspectral and multispectral images (MSIs) involves integrating a high spectral resolution hyperspectral image (HSI) and a high spatial resolution MSI to generate an HSI with high-resolution HSI (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this article, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency fusion module (CFFM) to perform global analysis through the Fourier transform (FT) and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on a state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 1980-2012 IEEE.

关键词： Image fusion

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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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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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy Systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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newBalance: Efficient Data Space Management and Algorithmic Optimization for Large-Scale Storage Systems

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Chinese Journal of Electronics 2017年第3期26卷 493-501页

作者： XU Guangping LIN Sheng SHI Kai ZHANG Hua Tianjin Key Laboratory of Intelligence Computing and New Software Technology Computer and Communication Engineering School Tianjin University of Technology

Fragmentation usually occurs when data space of original storage nodes has to be reallocated to new added storage nodes during the scale-out evolution of the large-scale storage *** greatly influences its performance and becomes a challenge to manage the whole *** present an efficient space management framework,called new Balance,to reduce fragmentation with the minimum data movement while keeping the storage system load *** space management framework has two phases including the collection phase and the allocation *** the collection phase,we propose a novel algorithm,called the greedy bi-direction collector,which collects enough space for the new storage *** the allocation phase,we formally represent it as a variant of the bin packing problem and then utilize some bin packing heuristics including the first fitting and the best fitting to allocate collected intervals to new added storage *** experimental results show that the amount of intervals can be reduced by 20%;5%and our algorithmic optimization improves the data lookup performance by at least 10%and the scale-out performance by 2X;X.

关键词： Large-scale storage systems Space management Algorithmic optimization Performance evaluation

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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MR image denoising method for brain surface 3D modeling

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Optoelectronics Letters 2014年第6期10卷 477-480页

作者：赵德新刘朋杰张德干 Tianjin Key Laboratory of Intelligent Computing & Novel Software Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology

Three-dimensional(3D) modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance(MR) images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.

关键词： Magnetic resonance Magnetic resonance imaging Medical imaging Three dimensional computer graphics Wavelet analysis

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A method for brain 3D surface reconstruction from MR images

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Optoelectronics Letters 2014年第5期10卷 383-386页

作者：赵德新 Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Key Laboratory of Computer Vision and SystemMinistry of EducationTianjin University of Technology

Due to the encephalic tissues are highly irregular, three-dimensional （3D） modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction from magnetic reso- nance （MR） images of head, which is helpful to surgery planning and tumor localization. A heuristic algorithm is pro- posed foi＂ surface triangle mesh generation with preserved features, and the diagonal length is regarded as the heuristic information to optimize the shape of triangle. The experimental results show that our approach not only reduces the computational complexity, but also completes 3D visualization with good quality.

关键词： Heuristic algorithms Magnetic resonance imaging Mesh generation Surface reconstruction Three dimensional computer graphics

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