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Showing 201 - 220 results of 1,810 for search '(( source detection functions ) OR (( resources OR resources) detection function ))', query time: 0.34s Refine Results
  1. 201

    Bias‐Independent True Random Number Generator Circuit using Memristor Noise Signals as Entropy Source by Jinwoo Park, Hyunjoong Kim, Hyungjin Kim

    Published 2025-06-01
    “…Finally, the performance of the designed TRNG circuit is evaluated using autocorrelation functions and National Institute of Standards and Technology tests, confirming its capability to produce random number bitstreams.…”
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    Article
  2. 202

    Infrared Satellite Detection Limits for Monitoring Atmospheric Ammonia by Mark W. Shephard, Shailesh K. Kharol, Enrico Dammers, Christopher E. Sioris, Andrew Bell, Rik Jansen, Jerome Caron, Ralph Snel, Emanuela Palombo, Karen E. Cady-Pereira, Chris A. McLinden, Erik Lutsch, Robert O. Knuteson

    Published 2025-01-01
    “…Information on the frequency of a given detection limit, and the cumulative probability of detection, are provided as a function of instrument spectral resolution and noise. …”
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  3. 203

    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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  4. 204
  5. 205

    High-Quality and Enhanced-Resolution Single-Pixel Imaging Based on Spiral Line Array Laser Source by Guozhong Lei, Haolong Jia, Wenchang Lai, Wenhui Wang, Wenda Cui, Yan Wang, Hao Liu, Kai Han

    Published 2024-01-01
    “…We propose a spiral line array laser source which can generate random illumination light fields without periodicity in the normalized second-order correlation function <italic>g</italic><sup>(2)</sup>. …”
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  6. 206

    Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou, Jun Zhao

    Published 2025-03-01
    “…The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. …”
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  7. 207
  8. 208

    Varying Activity and the Burst Properties of FRB 20240114A Probed with GMRT Down to 300 MHz by Ajay Kumar, Yogesh Maan, Yash Bhusare

    Published 2024-01-01
    “…All of the bursts we detect are faint (<10 Jy ms) and thus probe the lower end of the energy distribution. …”
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    Article
  9. 209

    Potential Source Regions and Transportation Pathways of Reactive Gases at a Regional Background Site in Northwestern China by Quanwei Zhao, Qing He, Lili Jin, Jianlin Wang

    Published 2021-01-01
    “…Wind rose, cluster analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) methods were adopted for identifying the transport pathways and potential source regions of these atmosphere components at Akedala. …”
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  10. 210
  11. 211

    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
  12. 212

    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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  13. 213

    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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  14. 214

    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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  15. 215

    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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  16. 216
  17. 217

    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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  18. 218

    Algorithms for Automatic Detection and Location of Infrasound Events in the PSDL System by Asming Vladimir Ernestovich, Fedorov Andrey Viktorovich

    Published 2024-12-01
    “…An algorithm for detecting infrasound signals by calculating the cross-correlation function between records of individual sensors in a array is described. …”
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  19. 219

    Detection, Parameter Estimation and Direction Finding of Periodic Pulse Signals by V. B. Manelis, I. S. Faustov, V. A. Kozmin

    Published 2025-07-01
    “…Simple-to-implement algorithms for detecting periodic pulse signals, evaluating their parameters, and direction finding of the source have been developed. …”
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  20. 220

    Code vulnerability detection method based on graph neural network by Hao CHEN, Ping YI

    Published 2021-06-01
    “…The schemes of using neural networks for vulnerability detection are mostly based on traditional natural language processing ideas, processing the code as array samples and ignoring the structural features in the code, which may omit possible vulnerabilities.A code vulnerability detection method based on graph neural network was proposed, which realized function-level code vulnerability detection through the control flow graph feature of the intermediate language.Firstly, the source code was compiled into an intermediate representation, and then the control flow graph containing structural information was extracted.At the same time, the word vector embedding algorithm was used to initialize the vector of basic block to extract the code semantic information.Then both of above were spliced to generate the graph structure sample data.The multilayer graph neural network model was trained and tested on graph structure data features.The open source vulnerability sample data set was used to generate test data to evaluate the method proposed.The results show that the method effectively improves the vulnerability detection ability.…”
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