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Showing 241 - 260 results of 2,295 for search '((( resourcessssss OR resources) detection functions ) OR ( source selection functions ))', query time: 0.33s Refine Results
  1. 241

    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
  2. 242

    Statistically Optimized Near-Field Acoustic Holography Using Prolate Spheroidal Wave Functions by Xuxin Zhang, Jingjun Lou, Jinfang Lu, Ronghua Li, Shijian Zhu

    Published 2023-01-01
    “…Near-field acoustic holography (NAH) is an effective tool for realizing accurate sound field reconstruction in three-dimensional space on the prerequisite that appropriate elementary wave functions are selected or constructed to match the characteristics of the sound sources. …”
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    Article
  3. 243

    A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

    Published 2025-02-01
    “…This approach allows for a preliminary evaluation of the effectiveness of different algorithms for attacking APUFs without access to challenge-response datasets from real instances of physically unclonable functions. Attacks were conducted on models of basic and modified variants of APUFs from the open-source library "pypuf", using classical logistic regression and artificial neural networks (ANNs). …”
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    Article
  4. 244

    Forecasting springtime rainfall in southeastern Australia using empirical orthogonal functions and neural networks by S. Marčelja

    Published 2025-08-01
    “…In addition to standard ocean climate indicators such as El Niño or the Indian Ocean Dipole, other typical patterns of variation are captured in terms of the temperatures of selected ocean areas. When characteristic patterns of correlation are discovered, they are included in the predictor selection in the form of expansion in terms of the empirical orthogonal functions (EOFs). …”
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  5. 245

    Development of curd semi-finished product with functional properties by L. Yu. Lavrova, E. L. Bortsova, S. A. Ermakov

    Published 2023-09-01
    “…The article is devoted to the development of a curd semifinished product with a high content of dietary fiber. The food fibre source is represented by the use of natural apple pectin introduced in various dosages into curd cheesecakes instead of prime grade wheat flour. …”
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    Article
  6. 246

    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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    Article
  7. 247

    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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    Article
  8. 248

    Improved CSW-YOLO Model for Bitter Melon Phenotype Detection by Haobin Xu, Xianhua Zhang, Weilin Shen, Zhiqiang Lin, Shuang Liu, Qi Jia, Honglong Li, Jingyuan Zheng, Fenglin Zhong

    Published 2024-11-01
    “…The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. …”
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    Article
  9. 249

    Dynamic Risk Assessment System for Power System Based on Multi-source Information of the Ubiquitous Power Internet of Things by Shenhua WANG, Xiangwei HE, Xiaofang FANG, Bingsong CHEN, Chuangxin GUO

    Published 2019-12-01
    “…Finally, based on the architecture, the corresponding software system is designed. The data source, data platform and main function modules of the system are also described. …”
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    Article
  10. 250

    The functional mechanisms of phosphite and its applications in crop plants by Zhenyi Li, Xiangjiu Kong, Zhiqiang Zhang, Fang Tang, Mingjiu Wang, Mingjiu Wang, Yan Zhao, Fengling Shi

    Published 2025-04-01
    “…Notably, ptxD also acts as an ideal selectable marker because its resistant is specific to Phi, thereby eliminating the risk of false positive clones. …”
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    Article
  11. 251

    A Two-Step Method for Impact Source Localization in Operational Water Pipelines Using Distributed Acoustic Sensing by Haonan Wei, Yi Liu, Zejia Hao

    Published 2025-08-01
    “…STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. …”
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  12. 252
  13. 253

    Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

    Published 2025-04-01
    “…Finally, to further enhance the classification performance of defect prediction, a heterogeneous data stacking ensemble learning algorithm (HEDSE) based on feature selection is proposed. Experimental evaluations on 16 open-source software defect datasets indicate that the proposed HEDSE outperforms existing methods, providing a novel and effective solution for software defect prediction. …”
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    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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