Showing 441 - 460 results of 1,810 for search '((( resources OR resourcess) detection function ) OR ( source detection functions ))', query time: 0.33s Refine Results
  1. 441
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    Design of a Non-Destructive Seed Counting Instrument for Rapeseed Pods Based on Transmission Imaging by Shengyong Xu, Rongsheng Xu, Pan Ma, Zhenhao Huang, Shaodong Wang, Zhe Yang, Qingxi Liao

    Published 2024-12-01
    “…Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. …”
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  3. 443
  4. 444

    Performance optimization of active RIS-assisted near-shore dual-function radar communicaiton system by XU Miao, YUE Dianwu, SUN Yu, WU Xiangkai, YUAN Meng

    Published 2025-01-01
    “…To address the issues of poor communication quality, high demand for radar detection, and scarce scarcity of spectrum resources for maritime users in coastal waters, an offshore dual-function radar communication (DFRC) system assisted by a shipborne active reconfigurable intelligent surface (ARIS) was studied. …”
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  5. 445

    Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strateg... by Bingxin Yu, Shengze Yu, Yuandi Zhao, Jin Wang, Ran Lai, Jisong Lv, Botao Zhou

    Published 2025-05-01
    “…This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. …”
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  6. 446

    FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

    Published 2025-01-01
    “…The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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  7. 447

    Hybrid Deep Neural Network Optimization with Particle Swarm and Grey Wolf Algorithms for Sunburst Attack Detection by Mohammad Almseidin, Amjad Gawanmeh, Maen Alzubi, Jamil Al-Sawwa, Ashraf S. Mashaleh, Mouhammd Alkasassbeh

    Published 2025-03-01
    “…The strength of DNNs is derived from their ability to model complex functions and to improve detection engines through deeper architecture. …”
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  8. 448
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    Computational Temporal Ghost Imaging Using Intensity-Only Detection Over a Single Optical Fiber by Jiang Tang, Yongwen Tang, Kun He, Luluzi Lu, Di Zhang, Mengfan Cheng, Lei Deng, Deming Liu, Minming Zhang

    Published 2018-01-01
    “…We propose a method of computational temporal ghost imaging over a single optical fiber using simple optical intensity detection instead of coherent detection. The transfer function of a temporal ghost imaging system over a single optical fiber is derived, which is a function of the total fiber dispersion and the power density spectrum of the light source. …”
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  10. 450
  11. 451

    A fuzzy track-to-track association algorithm with dynamic time warping for trajectory-level vehicle detection by Siqi Wan, Huaqiao Mu, Ke Han, Taesu Cheong, Chi Xie

    Published 2025-03-01
    “…Multi-source track-to-track association (TTTA), which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle, is an important data fusion technique widely applied to vehicle detection in the fields of road, marine, and aviation transportation. …”
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  12. 452

    Vulnerability detection using BERT based LLM model with transparency obligation practice towards trustworthy AI by Jean Haurogné, Nihala Basheer, Shareeful Islam

    Published 2024-12-01
    “…The result shows higher accuracy of 91.8 % for the vulnerability detection and model explainability outcome is highly influenced by “vulnerable”, “function”, ''mysql_tmpdir_list'', “strmov” tokens using both SHAP and LIME framework. …”
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  13. 453

    GloFinder: AI-empowered QuPath plugin for WSI-level glomerular detection, visualization, and curation by Jialin Yue, Tianyuan Yao, Ruining Deng, Siqi Lu, Junlin Guo, Quan Liu, Juming Xiong, Mengmeng Yin, Haichun Yang, Yuankai Huo

    Published 2025-04-01
    “…Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli—key functional units of the kidney—from whole slide images (WSIs) in kidney pathology. …”
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    Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model by He Gong, Xiaodan Ma, Ying Guo

    Published 2024-12-01
    “…In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. …”
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  17. 457
  18. 458

    YOLOv8-CBSE: An Enhanced Computer Vision Model for Detecting the Maturity of Chili Pepper in the Natural Environment by Yane Ma, Shujuan Zhang

    Published 2025-02-01
    “…Additionally, SRFD and DRFD modules are introduced to replace the original convolutional layers, effectively capturing features at different scales and enhancing the diversity and adaptability of the model through the feature fusion mechanism. To further improve detection accuracy, the EIoU loss function is used instead of the CIoU loss function to provide more comprehensive loss information. …”
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  19. 459

    Let ants find them: Using ants as eDNA samplers for detecting the invasive spotted lanternfly by Wei-Jiun Lin, Fang-Ling Chloe Liu, Lucie Cho, Chin-Cheng Scotty Yang

    Published 2025-04-01
    “…Compared to non-ant insects, ants exhibited higher SLF DNA concentrations, suggesting that honeydew ingestion serves as the primary eDNA source. These findings demonstrate that ants can function as efficient SLF eDNA samplers, providing a scalable and cost-effective alternative to existing SLF detection methods.…”
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  20. 460

    PdYOLO: A Lightweight Algorithm for Detecting Peach Fruits Against a Peach Tree Background by Jiajun Zhang, Jialin Zhang, Kazuki Kobayashi

    Published 2024-01-01
    “…First, the CIOU regression loss function in YOLOv8s is replaced with the WIoUv2 regression loss function, effectively alleviating the negative impact of uneven distribution of positive and negative samples during model training through a more balanced gradient gain distribution strategy, which significantly improves detection accuracy. …”
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