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IEEE Transactions on Green Communications and Networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

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Hydrogen and biomethane pathways to achieve sustainable transportation in circular economic concept: A review

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International Journal of Hydrogen Energy 2025年

作者： Saravanakumar, Ayyadurai Sudha, M.R. Chen, Wei-Hsin Pradeshwaran, Vijayakumar Centre for Research in Environment Sustainability Advocacy and Climate Change Directorate of Research and Virtual Education SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Chemistry SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India

Amidst the imperative need to reduce emissions and the rising demand for transportation, the logistics sector is at a crossroads. On a global scale, where road freight plays a significant role in the economy, the search for environmentally friendly fuel alternatives has led to the emergence of biomethane as a potential solution to assist fleets in decarbonizing their operations in the present day. The reliance of the transportation industry on fossil fuels is a cause for concern due to its environmental consequences and the depletion of resources. This study investigates the feasibility of hydrogen and biomethane as an eco-friendly transportation fuel in a circular economy. The analysis focuses on the production method, environmental advantages, and the role of biomethane in closing resource loops. The study examines constraints such as infrastructural development, feedstock availability, and economic viability. The significant findings on hydrogen suggest its strong potential as a sustainable fuel for long-haul heavy vehicles, showing better climate performance even when fossil electricity is used in production. Furthermore, in a circular economy, hydrogen-powered heavy-duty vehicles could become more advantageous than diesel, particularly as diesel faces reduced acceptance due to environmental issues. © 2025 Hydrogen Energy Publications LLC

关键词： Circular economy

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Astrophysical expedition: Transit search heuristics for fractional Hammerstein control autoregressive models

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Modern Physics Letters B 2025年第7期39卷

作者： Altaf, Faisal Chang, Ching-Lung Chaudhary, Naveed Ishtiaq Ali Khan, Taimoor Khan, Zeshan Aslam Shu, Chi-Min Raja, Muhammad Asif Zahoor Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Electrical and Computer Engineering International Islamic University Islamabad Capital Territory Islamabad 44000 Pakistan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

This study presents an astrophysics-inspired transit search optimization (TSO) algorithm based on exoplanet search divided into five phases: galaxy phase, star phase, transit phase, neighbor phase and exploitation phase for effective parameter estimation of fractional Hammerstein control autoregressive (Fr-HCAR) systems. Various physical phenomena and real processes can be modeled with Fr-HCAR systems and estimating the Fr-HCAR parameters becomes a vital task. The mean-square error (MSE)-based criterion function is developed, and efficacy of the TSO for Fr-HCAR identification is deeply analyzed for different fractional orders, disturbance levels and degrees of freedom. The TSO remained accurate, convergent, robust and stable for all variations in Fr-HCAR but the accuracy level degrades a little bit for high disturbance and increased degrees of freedom. The reliability and trustworthiness of the TSO for Fr-HCAR identification are endorsed through statistical analyses conducted on sufficient autonomous executions of the scheme. © 2025 World Scientific Publishing Company.

关键词： Astrophysics fractional calculus Hammerstein model nonlinear systems transit search optimization

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Can 2D materials enable passively Q-switched lasers in the nanoscale?

arXiv

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arXiv 2025年

作者： Christopoulos, Thomas Hizanidis, Johanne Nousios, Georgios Kriezis, Emmanouil E. Tsilipakos, Odysseas Theoretical and Physical Chemistry Institute National Hellenic Research Foundation Athens11635 Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Institute of Electronic Structure and Laser Foundation for Research and Technology-Hellas Crete Herakleio70013 Greece Institute of Nanoscience and Nanotechnology National Center for Scientific Research "Demokritos" Athens15341 Greece Istituto per la Microelettronica e Microsistemi Consiglio Nazionale delle Ricerche Roma00133 Italy

Achieving compact on-chip pulsed lasers with attractive performance metrics and compatibility with the silicon photonics platform is an important, yet elusive, goal in contemporary nanophotonics. Here, the fundamental question of whether 2D materials can be utilized as both gain and saturable absorption media to enable compact integrated passively Q-switched nanophotonic lasers is posed and addressed by examining a broad range of 2D material families. The study is conducted by developing a temporal coupled-mode theory framework involving semi-classical rate equations that is capable of rigorously handling gain and saturable absorption by 2D materials, allowing to perform stability and bifurcation analysis covering broad parameter spaces. The range of pulse-train metrics (repetition rate, pulse width, peak power) that can be obtained via different 2D materials is thoroughly assessed. Our work illustrates that nanophotonic cavities enhanced with 2D materials can enable passive Q-switching with repetition rates ranging up to 50 GHz, short pulse duration down to few picoseconds, and peak power exceeding several milliwatts. Such attractive metrics, along with the ultrathin nature of 2D materials and the ability to electrically tune their properties, demonstrate the potential of the proposed platform for compact and flexible integrated laser sources. Copyright © 2025, The Authors. All rights reserved.

关键词： Silicon photonics

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Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

arXiv

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arXiv 2025年

作者： Yoo, Seong Jong Liu, Sisung Arshad, Muhammad Zeeshan Kim, Jinhyeok Kim, Young Min Aloimonos, Yiannis Fermüller, Cornelia Joo, Kyungdon Kim, Jinwook Hong, Je Hyeong Department of Computer Science University of Maryland College ParkMD United States Department of Artificial Intelligence Hanyang University Korea Republic of Faculty of Health Sciences University of Ottawa ON Canada Artificial Intelligence Graduate School UNIST Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Intelligence and Interaction Research Center Korea Institute of Science and Technology Korea Republic of Artificial Intelligence Graduate School The Department of Computer Science and Engineering UNIST Korea Republic of Department of Electronic Engineering Hanyang University Korea Republic of

Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous false positive matches and hinder large-scale puzzle solving. Existing global approaches, which optimize all potential fragment pairs simultaneously or data-driven models, are prone to local minima and face scalability issues when multiple pots are intermixed. Motivated by Structure-from-Motion (SfM) for 3D reconstruction from multiple images, we propose an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time, called Structure-from-Sherds++ (SfS++). Our method extends beyond simple replication of incremental SfM and leverages multi-graph beam search to explore multiple registration paths. This allows us to effectively filter out indistinguishable false matches and simultaneously reconstruct multiple pots without requiring prior information such as base or the number of mixed objects. Our approach achieves 87% reassembly accuracy on a dataset of 142 real fragments from 10 different pots, outperforming other methods in handling complex fracture patterns with mixed datasets and achieving state-of-the-art performance. Code and results can be found in our project page https://***/sfs/. Copyright © 2025, The Authors. All rights reserved.

关键词： Historic preservation

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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SSRN

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SSRN 2025年

作者： He, Xiao Liu, Kai Zhang, Yifan He, Huihong School of Computer and Communication Engineering University of Science and Technology Beijing No. 30 Xueyuan Road Haidian District Beijing100083 China Engineering Research Center of Intelligent Supercomputing Ministry of Education No. 30 Xueyuan Road Haidian District Beijing100083 China China National Tendering Center of MACH. & ELEC. Equipment Government Procurement Center of MIIT No. 46 Enjizhuang Haidian District F Area Beijing100142 China

Context:Efficient version management of models in model-driven software engineering is vital for modeling tools, necessitating model matching, differencing, and merging to incorporate various model versions. Although similarity-based matching is the most general method, its computational complexity escalates at a cubic rate with the number of elements. Objective:This paper introduces a subtree-equivalence-based approach to accelerate similarity model matching, inspired by the observation that consecutive version changes typically impact only a small portion of a model. Methods:Our approach initially decomposes a model into a series of subtrees. Rather than performing element-wise matching directly, our approach tries to finds equivalent (i.e., either identical or closely similar) subtrees, representing the unchanged portion of a model, thus enabling quick pairing of elements within these *** effectively identify equivalent subtrees, this paper develops two hash functions for equality and similarity comparison of model trees. Results:Experiments using open-source Ecore and UML models indicate that our approach is 1.27 to 22.5 times faster on average compared to the state-of-the-art model matching tool while reducing the error rates in most ***:Our approach combines subtree matching and element-wise matching, and can improve the efficiency and the quality of similarity-based model matching. © 2025, The Authors. All rights reserved.

关键词： Open source software

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Few-Shot Photovoltaic Film Defect Detection with Contextual Ensemble Language-Image Multimodal Network

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IEEE Transactions on Industrial Informatics 2025年

作者： Wang, Huiyan Peng, Ruihao Zhu, Yiheng Li, Jiachen Zhou, Mengchu Ying, Ming Wang, Shouguang Zhejiang Gongshang University School of Computer Science and Technology Hangzhou 310018 China Qilu University of Technology (Shandong Academy of Sciences) Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan 250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan 250353 China New Jersey Institute of Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering Newark 07102 NJ United States Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou 310018 China

The automatic detection of defects in industrial photovoltaic film is crucial for ensuring the reliability of photovoltaic modules. Key challenges include limited defect samples, interclass feature similarities, and interference from complex backgrounds. Existing deep learning-based methods require large-scale datasets and focus solely on visual data, which limits their effectiveness in few-shot defect detection (FSDD). To address these challenges, we propose Contextual Ensemble Language-Image multimodal Network (CELIN), which enhances FSDD in photovoltaic films by incorporating textual information through prompt tuning. Unlike traditional language-image models that rely on single fixed text prompts, CELIN employs a position-aware context ensemble strategy to integrate position-specific prompt vectors, enabling the model to capture global contextual information and reduce background interference. In addition, a cross class mask method is introduced to differentiate between similar defect categories by blocking interclass interactions during attention computation, thereby minimizing misclassification. Extensive experiments on our own few-shot photovoltaic film defect dataset and various public benchmarks demonstrates that CELIN significantly outperforms existing methods. © 2005-2012 IEEE.

关键词： Contextual ensemble prompt tuning few-shot surface defect detection photovoltaic film defect detection vision-language multimodal model

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TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining

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computer Methods and Programs in Biomedicine 2025年 267卷 108809-108809页

作者： Zhang, Qing Wu, Xiaoxiao Li, Xiang Ma, Wei Wu, Tongquan Li, Liuyue Hu, Fan Xie, Yicheng Wu, Xinglong School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Hubei Wuhan430205 China School of Mechanical Engineering Hefei University of Technology Hefei China Faculty of Applied Science and Engineering University of Toronto Canada UNISKIN Research Institute on Skin Aging Inertia Shanghai Biotechnology Co. Ltd Shanghai China DermaHealth Shanghai Biotechnology Co. Ltd. Shanghai China Department of Dermatology Children's Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Zhejiang Province Hangzhou310052 China

Background: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, conventional supervised or semi-supervised methodologies are contingent on expert labels and incur substantial labeling costs, In contrast self-supervised pre-training strategies leverage unlabeled data during the pre-training phase and utilise a limited amount of labeled data in the fine-tuning phase, thereby greatly reducing labor costs. Furthermore, the fine-tuning does not need to learn the feature representations from scratch, enhancing the efficiency and transferability of the model. Methods: The proposed methodology is outlined below. The deep learning framework, TransAnno-Net, is based on transfer learning and a Transformer architecture. It has been designed for efficient and accurate cell type annotations in large-scale scRNA-seq datasets of mouse lung organs. Specifically, TransAnno-Net is pre-trained on the scRNA-seq lung data of approximately 100,000 cells to acquire gene-gene similarities via self-supervised learning. It is then migrated to a relatively small number of datasets to fine-tune specific cell type annotation tasks. To address the issue of imbalance in cell types commonly observed in scRNA-seq data, we applied a random oversampling technique is applied to the fine-tuned dataset. This is done to mitigate the impact of distributional imbalance on the annotation outcomes. Results: The experimental findings demonstrate that TransAnno-Net exhibits superior performance with an AUC of 0.979, 0.901, and 0.982, respectively, on three mouse lung datasets, outperforming eight state-of-the-art (SOTA) methods. In addition, TransAnno-Net demonstrates robust performance on cross-organ, cross-platform datasets, and is competitive with the fully supervised learning-based method. Conclusion: The TransAnno-Net method

关键词： Self-supervised learning

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