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arXiv 2023年

作者： Rodgers, Lila V.H. Nguyen, Suong T. Cox, James H. Zervas, Kalliope Yuan, Zhiyang Sangtawesin, Sorawis Stacey, Alastair Jaye, Cherno Weiland, Conan Pershin, Anton Gali, Adam Thomsen, Lars Meynell, Simon A. Hughes, Lillian B. Bleszynski Jayich, Ania C. Gui, Xin Cava, Robert J. Knowles, Robert R. de Leon, Nathalie P. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States Princeton University Department of Chemistry PrincetonNJ08540 United States School of Physics Center of Excellence in Advanced Functional Materials Suranaree University of Technology Nakhon Ratchasima30000 Thailand School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Science RMIT University MelbourneVIC3000 Australia Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Wigner Research Centre for Physics POB 49 BudapestH-1525 Hungary Department of Atomic Physics Budapest University of Technology and Economics Műegyetem rakpart 3. BudapestH-1111 Hungary Australian Synchrotron Australian Nuclear Science and Technology Organisation 800 Blackburn Road ClaytonVIC3168 Australia Physics Department University of California Santa Barbara Santa BarbaraCA93106 United States Materials Department University of California Santa Barbara Santa BarbaraCA93106 United States

Nitrogen-vacancy (NV) centers in diamond are a promising platform for nanoscale nuclear magnetic resonance (NMR) sensing1. Despite significant progress towards using NV centers to detect and localize nuclear spins down to the single spin level2,3, NV-based spectroscopy of individual, intact, arbitrary target molecules remains elusive. NV molecular sensing requires that target molecules are immobilized within a few nanometers of NV centers with long spin coherence time. The inert nature of diamond4 typically requires harsh functionalization techniques such as thermal annealing2,5 or plasma processing6–8, limiting the scope of functional groups that can be attached to the surface. Solution-phase chemical methods can be more readily generalized to install diverse functional groups, but they have not been widely explored for single-crystal diamond surfaces. Moreover, realizing shallow NV centers with long spin coherence times requires highly ordered single-crystal surfaces, and solution-phase functionalization has not yet been shown to be compatible with such demanding conditions. In this work, we report a versatile strategy to directly functionalize C–H bonds on single-crystal diamond surfaces under ambient conditions using visible light. Hydrogen atom abstraction from surface C–H bonds generates carbon-centered radicals that can either intercept various radical acceptors or be oxidized to carbocations to form C–F, C–Cl, C–S, and C–N bonds at the surface. We verify covalent bond formation using lab and synchrotron-based surface analysis. This functionalization method is compatible with charge stable NV centers within 10 nm of the surface with spin coherence times comparable to the state of the art5. As a proof of principle, we use shallow ensembles of NV centers to detect nuclear spins from functional groups attached to the surface. Our approach to surface functionalization based on visible light-driven C–H bond activation opens the door to deploying NV centers as a br

关键词： Chemical activation

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Combination of curcumin or chitosan with photodynamic therapy as an effective alternative therapy for overcoming wound infection associated with multidrug-resistant Acinetobacter baumannii

Microbe (Netherlands)

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Microbe (Netherlands) 2025年 7卷

作者： Yonpiam, Manta Wannigama, Dhammika Leshan Shein, Aye Mya Sithu Liao, Tingting Hurst, Cameron Monk, Peter N. Amarasiri, Mohan Phattharapornjaroen, Phatthranit Ounjai, Puey Saethang, Thammakorn Badavath, Vishnu Nayak Luk-in, Sirirat Nilgate, Sumanee Rirerm, Ubolrat Srisakul, Sukrit Ngamwongsatit, Natharin Leelahavanichkul, Asada Ragupathi, Naveen Kumar Devanga Kanjanabuch, Talerngsak Narkban, Phurin Miyanaga, Kazuhiko Cui, Longzhu Boontaveeyuwat, Einapak Higgins, Paul G. Kicic, Anthony Hamamoto, Hiroshi Abe, Shuichi Hongsing, Parichart Chatsuwan, Tanittha Department of Microbiology Faculty of Medicine Chulalongkorn University King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Center of Excellence in Antimicrobial Resistance and Stewardship Research Faculty of Medicine Chulalongkorn University Bangkok Thailand Dual degree Medical Science program Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan School of Medicine Faculty of Health and Medical Sciences The University of Western Australia Nedlands WA Australia Biofilms and Antimicrobial Resistance Consortium of ODA receiving countries The University of Sheffield Sheffield United Kingdom Pathogen Hunter's Research Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan Department of Infectious Diseases Faculty of Medicine Yamagata University Yamagata Japan Department of Clinical Epidemiology Faculty of Medicine Thammasat University Rangsit 10120 Thailand Department of Infection Immunity & Cardiovascular Disease University of Sheffield Medical School United Kingdom Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Miyagi Japan Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok 10210 Thailand HRH Princess Chulabhorn Disaster and Emergency Medicine Center Chulabhorn Royal Academy Bangkok 10210 Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand School of Pharmacy & Technology Management SVKM's Narsee Monjee Institute of Management Studies (NMIMS) Hyderabad 509301 India Department of Clinical Microbiology and Applied Technology Faculty of Medical Technology Mahidol University Bangkok Thailand Department of Clinical Sciences and Public Health Faculty of Veteri

The increasing prevalence of multidrug-resistant Acinetobacter baumannii as an opportunistic pathogen in wound infections raises significant concerns due to its antibiotic resistance and biofilm-mediated antibiotic tolerance. This underscores the urgent need to explore an alternative approach to effectively managing wound infections caused by MDR A. baumannii. In this study, combining subinhibitory concentrations of curcumin (25 µg/ml) or chitosan (156 µg/ml) with photodynamic therapy-PDT (10 J/cm²) resulted in a significant reduction of planktonic viabilities and biofilm biovolume of MDR A. baumannii clinical isolates in vitro. In murine A. baumannii-associated wound infection, a combination of curcumin (25 µg/ml) or chitosan (156 µg/ml) with PDT (10 J/cm²) resulted in complete bacterial eradication with wound healing at Day 15 of post-treatments with these combination therapies. Our study demonstrated that combining curcumin or chitosan with PDT at their subinhibitory concentrations represents a promising alternative strategy as localized therapy for effectively managing A. baumannii-associated wound infection. © 2025 The Authors

关键词： Acinetobacter baumannii Alternative therapy Biofilm eradication Chitosan Curcumin Multidrug-resistant (MDR) Photodynamic therapy (PDT) Wound infection

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On-the-fly Autonomous control of Neutron Diffraction via Physics-Informed Bayesian Active Learning

arXiv

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arXiv 2021年

作者： McDannald, Austin Frontzek, Matthias Savici, Andrei T. Doucet, Mathieu Rodriguez, Efrain E. Meuse, Kate Opsahl-Ong, Jessica Samarov, Daniel Takeuchi, Ichiro Ratcliff, William Kusne, A. Gilad Materials Measurement Laboratory Nist GaithersburgMD20899 United States Neutron Sciences Ornl Oak RidgeTN37831 United States Department of Chemistry and Biochemistry University of Maryland College ParkMD20742 United States Maryland Quantum Materials Center College ParkMD20742 United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Rice University HoustonTN United States Information Technology Laboratory Nist GaithersburgMD20899 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Nist Center for Neutron Research Nist GaithersburgMD20899 United States

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the Autonomous Neutron Diffraction Explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe1.09Te all while providing a 5-fold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery. Copyright © 2021, The Authors. All rights reserved.

关键词： Neutron diffraction

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Industry 4.0 in Germany, Austria and Hungary: Interpretation, strategies and readiness models 16

Industry 4.0 in Germany, Austria and Hungary: Interpretation...

引用

16th IMEKO TC10 Conference 2019 on Testing, Diagnostics and Inspection as a Comprehensive Value Chain for Quality and Safety

作者： Nick, Gábor Gallina, Viola Szaller, Ádám Várgedő, Tamás Schumacher, Andreas Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Fraunhofer Austria Research GmbH Wien Austria Széchenyi István University Research Center of Vehicle Industry Győr Hungary University of Technology and Economics Department of Manufacturing Science and Engineering Budapest Hungary

ISBN: (纸本)9789299008416

Global economy increasingly responds to trends of industrial digitization and start to develop their own digitalization strategies e.i. through application of Industry 4.0 concepts. Thereby, initiatives and developments are often carried out on a company-, industry- or country-level, which can result in a lack of international co-operation and utilization of synergy-effects. To analyse the Industry 4.0 development status on a macro level and to enhance transnational developments, in the first part of this study we introduce the national Industry 4.0 platforms of three EU member states – the frontrunner Germany, the solidly rising Austria and Hungary that is in the process of catching up. Our analysis are carried out in four dimensions: economic background, country-specific Industry 4.0 interpretation, and local platforms as well as initiatives. To analyse the actual Industry 4.0 development status of industrial enterprises and of development platforms, existing Industry 4.0 maturity models form all three countries are presented and compared. Our analysis helped to capture the different phases of digitization and Industry 4.0 in Germany, Austria and Hungary in regard to defined goals and strategies and further of resulting problems with respect to Industry 4.0. Building on our comparison-studies and captured problems, decision makers should be enabled to carry out transnational Industry 4.0 projects and initiatives more effectively. © 2019 International measurement Confederation (IMEKO). All rights reserved.

关键词： Industry 4.0

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Constructing Epidermal Rete Ridges Using Composite Hydrogel to Enhance Multiple Signaling Pathways for the Maintenance of Epidermal Stem Cell Niche

SSRN

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SSRN 2023年

作者： Shen, Zhizhong Liu, Zixian Sun, Lei Li, Meng Han, Lu Wang, Jianming Wu, Xunwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China General Hospital of TISCO North Street Xinghualing District Taiyuan030809 China Engineering Laboratory for Biomaterials and Tissue Regeneration Ningbo Stomatology Hospital Savaid Stomatology School Hangzhou Medical College Ningbo China Department of Tissue Engineering and Regeneration School and Hospital of Stomatology Cheeloo College of Medicine Shandong University Shandong Key Laboratory of Oral Tissue Regeneration Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration Shandong Jinan China Cutaneous Biology Research Center Massachusetts General Hospital Harvard Medical School BostonMA United States

The undulating microstructure rete ridge (RR) located at the junction between the dermis and epidermis plays a crucial role in improving skin mechanical properties and maintaining skin homeostasis. However, the investigation of RR microstructures are usually neglected in current tissue engineering for skin regeneration. Here, to create an epidermal model with RR microstructures, keratinocytes were inoculated onto a patterned GelMA-PEGDA hydrogel constructed using molding technology. Furthermore, grafting acryloylated Arg-Gly-Asp (RGD) peptides on the hydrogel surface significantly improved cell adhesion, fusion, and development. RT-PCR, Western blot, and immunofluorescence staining confirmed that cells on RR microstructures exhibited higher gene and protein expression associated with epidermal stem cells. RNA sequencing analysis of cells on RR microstructure showed higher gene expression profiles related to stem cell maintenance, basement membrane formation, and epidermal development. Furthermore, RT-PCR analysis of epidermal models of various dimensions demonstrated that smaller microstructures were more conducive to epidermal stem cell marker gene expression, which is analogous to human skin. Overall, we have successfully developed a method for integrating RR microstructures into an epidermal model that mimics natural skin to maintain epidermal stem cell niche, providing a valuable reference for researching skin regeneration within the fields of tissue engineering and regenerative medicine. © 2023, The Authors. All rights reserved.

关键词： Microstructure

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Coalition game based user association for mmWave mobile relay systems in rail traffic scenarios

arXiv

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arXiv 2021年

作者： Chen, Chen Niu, Yong Mao, Shiwen Zhang, Xiaodan Han, Zhu Ai, Bo Gao, Meilin Xiao, Huahua Wang, Ning The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Management Shenzhen Institute of Information Technology Shenzhen518172 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The Tsinghua Space Center Tsinghua University Beijing100091 China The ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. Copyright © 2021, The Authors. All rights reserved.

关键词： Game theory

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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