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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

IEEE Internet of Things Journal 2025年第12期12卷 20757-20772页

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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Exploring Y1 communications and Services in O-RAN: Background, Privacy, and Security

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the communications Society 2025年 6卷 4638-4666页

作者： Sachinkumar B. Mallikarjun Mohammad Asif Habibi Mihirraj Dixit Xavier Costa-Pérez Mérouane Debbah Hans D. Schotten Department of Electrical and Computer Engineering Division of Wireless Communications and Radio Navigation University of Kaiserslautern (RPTU) Kaiserslautern Germany 6G Networks Research and Development Department NEC Laboratories Europe Heidelberg Germany Network Engineering Department Universitat Politècnica de Catalunya Barcelona Spain Department of AI Driven Systems i2CAT Research Center Barcelona Spain Department of AI Driven Systems Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain Department of Computer and Information Engineering KU 6G Research Center Khalifa University of Science and Technology Abu Dhabi UAE CentraleSupelec University Paris-Saclay Gif-sur-Yvette France Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

In the latest open radio access network (O-RAN) architecture, the Y1 interface has been introduced to grant authorized parties, known as Y1 consumers, access to and utilization of Y1 data within the near-real-time RAN intelligent controller (Near-RT RIC). The primary objective of enabling communication between Y1 consumers and the Near-RT RIC is to facilitate the introduction of value-added services to O-RAN. This paper comprehensively explores Y1 communications, services, interface, data, and other essential concepts required to understand the subject matter thoroughly. We examine the security and privacy aspects of Y1 communications to ensure that all authorized parties involved in these interactions are equipped to withstand potential threats and vulnerabilities. More importantly, our research proposes a novel architectural framework for Y1 communications that integrates advanced functionalities and specifies secure communication protocols. This framework addresses the limitations of the state-of-the-art Y1 architecture by enhancing its robustness, adaptability, and efficiency. In addition, we outline the procedural flow for Y1 communications within the proposed framework, detailing the functionalities and processes it introduces. Beyond security and privacy procedures, this paper elaborates on the various phases of Y1 communications. Furthermore, a series of experiments are conducted to evaluate the effectiveness of the proposed Y1 framework. Our assessment focuses on the authentication and authorization schemes, revealing that the mutual transport layer security (mTLS) 1.3-based certificate-bound access token (CBAT) scheme demonstrated superior performance in ensuring secure and efficient communication. Additionally, the robustness of the proposed architecture is validated by evaluating its functionality in both subscription and query scenarios. Moreover, the paper discusses the proposed Y1 framework, primarily focusing on its role and functionality within the

关键词： Open RAN Security Privacy Protocols computer architecture Authentication Standards Robustness Guidelines Authorization

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Low-modal-crosstalk doped-fiber amplifiers in few-mode-fiber-based systems

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Photonics research 2024年第8期12卷 1768-1775页

作者： SHUAILUO HUANG LEI SHEN GANG QIAO YUANPENG DING YUYANG GAO JIAN CUI BAOLONG ZHU SIYUAN LIU MINGQING ZUO JINGLONG ZHU LEI ZHANG JIE LUO YONGQI HE ZHANGYUAN CHEN JUHAO LI State Key Laboratory of Advanced Optical Communication Systems and Networks Peking UniversityBeijing 100871China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Yangtze Optical Fibre and Cable Joint Stock Limited CompanyWuhan 430073China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Networks China Mobile Communications Group Co.Ltd.Beijing 100033China Department of Fundamental Network Technology China Mobile Research InstituteBeijing 100053China Pengcheng Laboratory Shenzhen 518055China

Independent light propagation through one or multiple modes is commonly considered as a basic demand for mode manipulation in few-mode fiber(FMF)-or multimode fiber(MMF)-based optical systems such as transmission links,optical fiber lasers,or distributed optical fiber ***,the insertion of doped-fiber amplifiers always kills the entire effort by inducing significant modal *** this paper,we propose the design of doped-fiber amplifiers in FMF-based systems adopting identical multiple-ring-core(MRC)index profiles for both passive and doped fibers to achieve low modal *** develop the direct-glass-transition(DGT)modified chemical vapor deposition(MCVD)processing for precise fabrication of few-mode erbium-doped fibers(FM-EDFs)with MRC profiles of both refractive index and erbium-ion doping ***,a few-mode erbium-doped-fiber amplifier(FM-EDFA)with a maximum gain of 26.08 dB and differential modal gain(DMG)of 2.3 dB is realized based on fabricated FM-EDF matched with a transmission FMF supporting four linearly polarized(LP)*** the insertion of the FM-EDFA,60+60 km simultaneous LP_(01)∕LP_(11)∕LP_(21)∕LP_(02)transmission without inter-modal multiple-input multiple-output digital signal processing(MIMO-DSP)is successfully *** proposed design of low-modal-crosstalk doped-fiber amplifiers provides,to our knowledge,new insights into mode manipulation methods in various applications.

关键词： fiber crosstalk amplifiers

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Accounting for the Special Role of Infrastructure-assisted Collective Perception

Accounting for the Special Role of Infrastructure-assisted C...

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International Conference on Intelligent Transportation

作者： Florian Alexander Schiegg Anna–Lisa Rueeck Jörg Gamerdinger Hugues Tchouankem Edmir Xhoxhi Georg Volk Distributed Intelligence and Systems Corporate Research Robert Bosch GmbH Hildesheim Germany Function Development 3D Surround View Engineering Parking Functions Robert Bosch GmbH Feuerbach Germany Department of Computer Science Embedded Systems Group University of Tübingen Tübingen Germany Institute of Communication Technology Leibniz University Hanover Hanover Germany

The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communication is regarded as one of the most promising emerging technologies to enhance road safety. It allows traffic participants to exchange relevant information to increase their environmental perception. Cooperative Awareness, a V2X service that is already being deployed in Europe, enables road users to share their dynamic states. Collective Perception (CP), currently in the final stage of standardization in the different regions of the world, enables the exchange of sensor-detected objects. The latter is thus not only relevant for connected vehicles but can further be used by sensor-equipped infrastructure to support vehicular perception, opening a whole new range of possibilities. The objective of this work is to investigate mechanisms to account for the special role of Infrastructure-assisted Collective Perception (ICP). Packet duplication is introduced for ICP to enhance the communication reliability. Additionally, different message generation rules prioritizing ICP are proposed and compared based on the enabled environmental perception. Simulations show significant improvements of the average precision, reaching an average increase of about 60% for ICP in comparison to V2V -based CP in scenarios with low connected vehicle densities.

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Implicit Multi-Scale Swin Transformer Network for Image Denoising

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

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Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

arXiv

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arXiv 2022年

作者： Kokkonen, Henna Lovén, Lauri Motlagh, Naser Hossein Kumar, Abhishek Partala, Juha Nguyen, Tri Pujol, Víctor Casamayor Kostakos, Panos Leppänen, Teemu González-Gil, Alfonso Sola, Ester Angulo, Iñigo Liyanage, Madhusanka Bennis, Mehdi Tarkoma, Sasu Dustdar, Schahram Pirttikangas, Susanna Riekki, Jukka Center for Ubiquitous Computing University of Oulu Finland Center for Machine Vision and Signal Analysis University of Oulu Finland Center for Wireless Communications University of Oulu Finland School of Computer Science University College Dublin Ireland Department of Computer Science University of Helsinki Finland Distributed Systems Group TU Wien Austria R&D Department *** S.L. Spain

Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration. © 2022, CC BY-NC-ND.

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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