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Showing 441 - 460 results of 1,810 for search '((\ sources detection functions\ ) OR (( (resources OR sources) OR resource) detection function\ ))', query time: 0.34s Refine Results
  1. 441

    XFP-recognizer: detecting cross-file browser fingerprinting by Xiaoxi Wang, Zhenxu Liu, Chunyang Zheng, Xinyu Liu, Wei Liu, Yuling Liu, Qixu Liu

    Published 2025-07-01
    “…XFP-Recognizer complements existing detection methods, and the constructed split dataset also serves as a foundational resource for future research.…”
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    Article
  2. 442

    Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

    Published 2025-04-01
    “…Abstract The timely and accurate detection of unidentified drones is crucial for public safety. …”
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    Article
  3. 443

    Electrochemical microfluidic biosensor for the detection of CD4+ T cells by Katarzyna Białas, Hui Min Tay, Chayakorn Petchakup, Razieh Salimian, Stephen G. Ward, Mark A. Lindsay, Han Wei Hou, Pedro Estrela

    Published 2025-04-01
    “…This work presents an innovative electrochemical microfluidic device that, with further development, could be applied for HIV management in low resource settings. The setup integrates an electrochemical sensor within a PDMS microfluidic structure, allowing for on-chip electrode functionalization and cell detection. …”
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    Article
  4. 444
  5. 445

    A deep learning-based reconstruction model for 3D sound speed field combining underwater vertical information by Hongchen Li, Yuhang Liu, Ming Li, Penghao Wang, Yuhang Zhu, Kefeng Mao, Xi Chen

    Published 2025-06-01
    “…Rapid acquisition of underwater three-dimensional (3D) sound speed fields is essential for target detection, acoustic communication, and underwater navigation. …”
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    Article
  6. 446

    YOLO-PEL: The Efficient and Lightweight Vehicle Detection Method Based on YOLO Algorithm by Zhi Wang, Kaiyu Zhang, Fei Wu, Hongxiang Lv

    Published 2025-03-01
    “…YOLOv8-PEL shows outstanding performance in detection accuracy, computational efficiency, and generalization capability, making it suitable for real-time and resource-constrained applications. …”
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    Article
  7. 447

    Revealing connectivity patterns of deep brain stimulation efficacy in Parkinson’s disease by Eva Výtvarová, Martin Lamoš, Jaroslav Hlinka, Sabina Goldemundová, Ivan Rektor, Martina Bočková

    Published 2024-12-01
    “…Abstract The aim of this work was to study the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on the subnetwork of subcortical and cortical motor regions and on the whole brain connectivity using the functional connectivity analysis in Parkinson’s disease (PD). …”
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  8. 448
  9. 449

    DWS-YOLO: A Lightweight Detector for Blood Cell Detection by Yihai Mao, Hongyi Zhang, Wanqing Wu, Xingen Gao, Zhibin Lin, Juqiang Lin

    Published 2024-12-01
    “…Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. …”
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    Article
  10. 450
  11. 451
  12. 452

    Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

    Published 2025-01-01
    “…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. …”
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    Article
  13. 453

    Toward global rooftop PV detection with Deep Active Learning by Matthias Zech, Hendrik-Pieter Tetens, Joseph Ranalli

    Published 2024-12-01
    “…It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. …”
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    Article
  14. 454

    Detection of water surface targets based on improved Deformable DETR by Pengjiu WANG, Junbin Gong, Wei LUO, Xiao HUANG, Junjie GUO

    Published 2025-06-01
    “…Objective With technological advancements and the increasing demand for water resource exploration, water surface target detection plays a crucial role in various applications, such as ship navigation and maritime safety. …”
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  15. 455
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  17. 457

    A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

    Published 2025-05-01
    “…Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based on You Only Look Once v7 (YOLOv7). …”
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  18. 458

    Radar Detection Simulation by Digital Twins of Target and Antenna System by A. S. Grigoriev, A. A. Kazantsev, A. M. Terentyev, B. S. Stavtsev

    Published 2025-03-01
    “…The signal-to-noise dynamic dependence of the given radar system, space object, and observation scenario, presented by their digital models, was calculated. The function of detection probability density was calculated, which demonstrated an insufficient detection capacity of a radar system in the case of observation of such type of objects.Conclusion. …”
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  19. 459

    Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images by Taojuan Li, Wen Liu, Mingxian Song, Zheng Gu, Ling Hai

    Published 2025-01-01
    “…Thus, the proposed model effectively balances detection performance and lightweight design, making it well-suited for real-time applications on resource-constrained computing devices.…”
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  20. 460

    A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

    Published 2025-04-01
    “…Introduce SIoU instead of the original loss function to accelerate model convergence. Finally, the introduction of the Ghost module to optimize the backbone network can reduce the computational and parametric quantities of the model without losing the model performance, improve the detection speed, and make the model easier to be deployed on resource-constrained devices. …”
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