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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Fuzzy Rule-based Differentiable Representation Learning

arXiv

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arXiv 2025年

作者： Zhang, Wei Deng, Zhaohong Wang, Guanjin Choi, Kup-Sze School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China School of Information Technology Murdoch University WA Australia The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong

Representation learning has emerged as a crucial focus in machine and deep learning, involving the extraction of meaningful and useful features and patterns from the input data, thereby enhancing the performance of various downstream tasks such as classification, clustering, and prediction. Current mainstream representation learning methods primarily rely on non-linear data mining techniques such as kernel methods and deep neural networks (DNNs) to extract abstract knowledge from complex datasets. However, most of these methods are black-box, lacking transparency and interpretability in the learning process, which constrains their practical utility. To this end, this paper introduces a novel representation learning method grounded in an interpretable fuzzy rule-based model. Specifically, it is built upon the Takagi-Sugeno-Kang fuzzy system (TSK-FS) to initially map input data to a high-dimensional fuzzy feature space through the antecedent part of the TSK-FS. Subsequently, a novel differentiable optimization method is proposed for the consequence part learning which can preserve the model’s interpretability and transparency while further exploring the nonlinear relationships within the data. This optimization method retains the essence of traditional optimization, with certain parts of the process parameterized corresponding differentiable modules constructed and a deep optimization process implemented. Consequently, this method not only enhances the model’s performance but also ensures its interpretability. Moreover, a second-order geometry preservation method is introduced to further improve the robustness of the proposed method. Extensive experiments conducted on various benchmark datasets validate the superiority of the proposed method, highlighting its potential for advancing representation learning methodologies. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Research on Lightweight Sensing technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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A Novel Two-Electrode Microchip Based Electrochemiluminescence Point-Of-Care Testing System For Rapid And Highly Sensitive Multiple Biomarkers Analysis

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yuan, Haojun Gao, Wanlei Yang, Ping Cao, Xinyi Wu, Yangbo Guo, Zhiyong Cong, Hui Zhang, Xiaowei Jin, Qinghui Zhejiang Fashion Institute of Technology Mechanical and Electrical Engineering School Zhejiang Ningbo315211 China Ningbo University College of Information Science and Engineering Ningbo315211 China Ningbo University School of Materials Science and Chemical Engineering Zhejiang Ningbo315211 China Affiliated Hospital of Nantong University Center of Laboratory Medicine Jiangsu Nantong226000 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Chinese Academy of Sciences State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Shanghai200050 China

Point-of-care testing (PoCT) method is suitable for rapid detection and daily monitoring of various acute diseases, which can effectively improve patients’ survival rates. Current lateral flow assay-based PoCT systems, with insufficient sensitivity, cannot meet the simultaneous detection requirements of rare multiple biomarkers. Therefore, we developed an electrochemiluminescence (ECL) microchip based PoCT system, realizing rapid and highly sensitive detection of multiple biomarkers. This microchip incorporated specific aptamer probes assembled working electrode array and a shared counting electrode, thus forming a two -electrode based detection units for initiating Ru(bpy)32+/TPrA based ECL reaction. Integrated with photomultiplier tube (PMT) and a constant transient potential model, the PoCT system was capable of sensitively detecting three Acute Myocardial Infarction (AMI) biomarkers-cardiac troponin I (cTnI), myoglobin (Myo), and creatine kinase MB (CK - MB)-on a single microchip within just 20 min. The system exhibited an excellent detection limit of 0.25 pg/mL, 0.05 pg/mL, and 5 pg/mL for cTnI, Myo, and CK-MB, in a wide linear range of 1 pg/mL-1 μ g/mL, 0.1 pg/mL-10 ng/mL, and 10 pg/mL-1 μ g/mL, respectively. Furthermore, the analysis of AMI biomarkers in mixture and serum samples was conducted with high sensitivity and specificity, as well as low *** platform synergized high sensitivity of ECL with the multiplexing of micro-chip, thereby holding tremendous potential for application in PoCT. © 1963-2012 IEEE.

关键词： Integrated circuits Biomarkers Electrodes Microfluidics Sensors Testing Sensitivity Myocardium Liquid crystal displays Biosensors

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing 210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing 210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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Three-dimensional dynamic probabilistic shaping high-security transmission scheme based on dual physical layer encryption for seven-core fibers

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Chinese Optics Letters 2025年第3期23卷 030602-030602页

作者： Mao, Yaya Liu, Bo Huang, Jingrui Ren, Jianxin Chen, Shuaidong Wu, Xiangyu Wu, Yongfeng Song, Xiumin Qi, Zhipeng Cui, Jie Institute of Optics and Electronics Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology Nanjing210044 China School of Computer Science and Technology Tongji University Shanghai201804 China

In this paper, a carrier-less amplitude and phase modulation passive optical network (CAP-PON) scheme is proposed based on dynamic probabilistic shaping (DPS) and Rubik’s cube encryption in optical access networks. The key is generated from a novel five-dimensional entangled chaos model for dynamic probabilistic shaping and Rubik’s cube encryption. To verify the performance of the encryption scheme, an experimental demonstration of 70 Gb/s (7 × 10 Gb/s) encrypted DPS-3D-CAP signal transmission over 2 km weakly coupled 7-core fiber is performed. The key space of the new five-dimensional entangled chaos model reaches 10173, and the interference level reaches 100%. Experimental results show that the receiver sensitivity increases by 1.47 dB compared to the conventional uniform 3D-CAP due to the introduction of dynamic probabilistic shaping. © 2025 Chinese Optics Letters.

关键词： Passive optical networks

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup 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 School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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A diffusion model based on multi-scale spatial Mamba for medical image segmentation

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Li, Chun Sun, Qiule Zhang, Muqing Zhang, Jianxin School of Medical Information Wannan Medical College Wuhu Anhui 241002 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning 110819 China School of Information and Communication Engineering Dalian University of Technology Dalian Liaoning 116024 China School of Computer Science and Engineering Dalian Minzu University Dalian Liaoning 116600 China

Medical image segmentation plays a key role in disease diagnosis, treatment planning, and monitoring disease progression. Recently, denoising diffusion models have shown significant promise in generating accurate pixel-level semantic representations. In this study, we introduce a diffusion model based on multi-scale spatial mamba (MSM-Diff) designed for precise medical image segmentation. MSM-Diff integrates the strengths of diffusion models, Mamba architecture, and convolutional neural networks to efficiently capture both global and local contextual information from complex volumetric data. The core of MSM-Diff is the Mamba-based U-shaped feature encoder (MUFE), which combines the three-dimensional multi-scale spatial Mamba model (MS-Mamba) with extracted features for enhanced multi-scale and global feature extraction. By using the mamba architecture, the model maintains linear computational complexity. Additionally, MSM-Diff incorporates a multi-scale gated spatial convolution (MS-GSC) module within MUFE to further refine spatial feature representations. Extensive evaluations of three public datasets demonstrate that MSM-Diff consistently outperforms current methods, achieving state-of-the-art performance in DSC and HD95. This model provides a robust solution for medical image segmentation by effectively capturing global context and accurately delineating boundaries, thereby improving diagnostic and treatment planning outcomes for radiologists and clinicians. © 2025 Elsevier Ltd

关键词： Denoising diffusion model Global features Mamba Medical image segmentation State space model

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