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  1. 421

    Detection of VOCs and Biogenic Amines Through Luminescent Zn–Salen Complex-Tethered Pyrenyl Arms by Roberta Puglisi, Caterina Testa, Sara Scuderi, Valentina Greco, Giuseppe Trusso Sfrazzetto, Manuel Petroselli, Andrea Pappalardo

    Published 2024-12-01
    “…Biogenic amines, resulting from the natural decarboxylation of amino acids, are released into the environment from both natural and industrial sources. Several methods have been developed so far to detect amines in the environment. …”
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  2. 422

    VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

    Published 2025-01-01
    “…Accurate detection of vulnerable road users (VRUs) is critical for enhancing traffic safety and advancing autonomous driving systems. …”
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  3. 423
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    DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

    Published 2025-06-01
    “…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. Entropy and throughput metrics are employed as decision conditions at each sensing channel, enhancing uncertainty measurement and maintaining detection robustness under low Signal-to-Noise Ratio (SNR) conditions. …”
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  5. 425
  6. 426

    TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

    Published 2025-05-01
    “…Finally, the EIOU loss function is introduced to measure the overlap between the predicted box and the real box more accurately and improve the detection accuracy. …”
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  7. 427
  8. 428

    YOLOv8n-DDSW: an efficient fish target detection network for dense underwater scenes by Jinwang Yi, Wei Han, Fangfei Lai

    Published 2025-04-01
    “…Therefore, the YOLOv8n-DDSW fish target detection algorithm was proposed in this article to resolve the detection difficulties resulting from fish occlusion, deformation and detail loss in complex intensive aquaculture scenarios. (1) The C2f-deformable convolutional network (DCN) module is proposed to take the place of the C2f module in the YOLOv8n backbone to raise the detection accuracy of irregular fish targets. (2) The dual-pooling squeeze-and-excitation (DPSE) attention mechanism is put forward and integrated into the YOLOv8n neck network to reinforce the features of the visible parts of the occluded fish target. (3) Small detection is introduced to make the network more capable of sensing small targets and improving recall. (4) Wise intersection over union (IOU) rather than the original loss function is used for improving the bounding box regression performance of the network. …”
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  9. 429

    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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  10. 430

    Smart Fault Detection, Classification, and Localization in Distribution Networks: AI-Driven Approaches and Emerging Technologies by Jianxian Wang, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hazlee Azil Illias, Agileswari K. Ramasamy, Xingyu Wu, Siqi Wang

    Published 2025-01-01
    “…However, with nations worldwide actively pursuing carbon neutrality and emission peak goals, sustainable energy sources such as solar and wind are increasingly penetrating distribution networks, posing significant challenges to conventional fault detection, classification, and localization techniques due to bidirectional power flows, dynamic fault currents, and rising network complexity. …”
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  14. 434

    Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data. by Qihai Liu, Kevin H Lee, Hyun Bin Kang

    Published 2025-01-01
    “…In this work, we propose finite mixtures of functional graphical models (MFGM), which detect the heterogeneous subgroups of the population and estimate single graph for each subgroup by considering the correlation structures. …”
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  15. 435

    Asynchronous bearing only tracking management approach in distributed multi-function integrated sensors by ZHANG Wei, YANG Qiu, LI Hao

    Published 2024-12-01
    “…The distributed multi-function system requires only one integrated sensor to switch to electronic support measure (ESM) mode within each tracking cycle to update the angle measurement information of target radiation source, while the other integrated sensors still work in the original planned mode and task. …”
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  16. 436

    STar-DETR: A Lightweight Real-Time Detection Transformer for Space Targets in Optical Sensor Systems by Yao Xiao, Yang Guo, Qinghao Pang, Xu Yang, Zhengxu Zhao, Xianlong Yin

    Published 2025-02-01
    “…Optical sensor systems are essential for space target detection. However, previous studies have prioritized detection accuracy over model efficiency, limiting their deployment on resource-constrained sensors. …”
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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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    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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