Showing 1,981 - 2,000 results of 2,900 for search '(feature OR features) parameters (computation OR computational)', query time: 0.17s Refine Results
  1. 1981

    Investigation of the Pulmonary Artery Hypertension Using an Ad Hoc OpenFOAM CFD Solver by Francesco Duronio, Paola Marchetti

    Published 2024-12-01
    “…We performed CFD simulations with the OpenFOAM C++ library using a purposely developed solver that features the Windkessel model as a pressure boundary condition. …”
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    Article
  2. 1982

    Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment by Zhihang Qu, Xiao Liang, Sicheng Liang, Xiumei Guo

    Published 2025-01-01
    “…The model employs StarNet to refine the C3k2 structure, reducing computational complexity without compromising detection accuracy, and integrates the MBConv module into the detection head to boost feature extraction while further minimizing computational load. …”
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    Article
  3. 1983

    Fingerprint-based Wi-Fi indoor localization using map and inertial sensors by Xingwang Wang, Xiaohui Wei, Yuanyuan Liu, Kun Yang, Xuan Du

    Published 2017-12-01
    “…Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. …”
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    Article
  4. 1984

    Frequency-Domain Collaborative Lightweight Super-Resolution for Fine Texture Enhancement in Rice Imagery by Zexiao Zhang, Jie Zhang, Jinyang Du, Xiangdong Chen, Wenjing Zhang, Changmeng Peng

    Published 2025-07-01
    “…Experiments on the 4× downsampled rice test set demonstrate that the proposed method achieves a 62% reduction in parameters compared to EDSR, 41% lower computational cost (30 G) than MambaIR-light, and an average PSNR improvement of 0.68% over other methods in the study while balancing memory usage (227 M) and inference speed. …”
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    Article
  5. 1985
  6. 1986

    Analytical Approach to Designing a Combined-Mode Resonator Filter on Surface Acoustic Waves Using the Model of Coupling of Modes by A. S. Koigerov

    Published 2022-04-01
    “…The relevant research task of reducing the design time and optimizing the filter’s cost can be solved by either using modern computational software or improving existing modeling tools.Aim. …”
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    Article
  7. 1987

    Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

    Published 2025-03-01
    “…Moreover, the proposed ML models provide user-friendly empirical equations to calculate the mean limiting pressure factor, requiring minimal computational expertise, thus bridging the gap between theoretical stochastic studies and practical field applications. …”
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    Article
  8. 1988

    DDoS Attack Detection in SDN-Assisted Federated Learning Environment Based on Contrastive Learning by Minghong Fan, Jinghua Lan, Yiyi Zhou, Mengshuang Pan, Junrong Li, Daqiang Zhang

    Published 2025-01-01
    “…Software-defined networking (SDN)-assisted federated learning (FL) is an emerging network computing environment. It can not only shorten the training time of federated learning while maintaining high learning performance, but also enhance the security of the FL network. …”
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    Article
  9. 1989
  10. 1990

    An Evolutionary Toolchain for Morphological Filter Mapping on Many-Core Architectures by Emerson C. Pedrino, Denis P. Lima, Igor F. Gallon, Valentin O. Roda, Naijia Liu, Gianluca Tempesti

    Published 2025-01-01
    “…A set of standard benchmark applications was used to determine the optimal parameters for the algorithms, which were then validated on two real-world application examples involving the detection of features in high-resolution PCB images. …”
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    Article
  11. 1991

    Lightweight underwater object detection method based on multi-scale edge information selection by Shaobin Cai, Xin Zhou, Wanchen Cai, Liansuo Wei, Yuchang Mo

    Published 2025-07-01
    “…These modules are individually incorporated into the C3K2 module of the backbone network, with the aim of extracting features at multiple scales, emphasizing edge information, and efficiently selecting key features that are highly relevant to the target task, thereby improving the model’s accuracy in recognizing critical targets. …”
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    Article
  12. 1992

    Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video by Yu Liu, Xiaoyan Wang

    Published 2021-01-01
    “…First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. …”
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    Article
  13. 1993
  14. 1994
  15. 1995
  16. 1996
  17. 1997

    Identification of leaves of wild Ussurian Pear (Pyrus ussuriensis) based on YOLOv10n-MCS by Niman Li, Niman Li, Xingguang Dong, Yongqing Wu, Luming Tian, Ying Zhang, Hongliang Huo, Dan Qi, Jiayu Xu, Chao Liu, Zhiyan Chen, Yulu Mou

    Published 2025-07-01
    “…Accurately and efficiently identifying wild Ussurian Pear accession is a prerequisite for germplasm conservation and utilization.MethodsWe proposed YOLOv10n-MCS, an improved model featuring: (1) Mixed Local Channel Attention (MLCA) module for enhanced feature extraction, (2) Simplified Spatial Pyramid Pooling-Fast (SimSPPF) for multi-scale feature capture, and (3) C2f_SCConv backbone to reduce computational redundancy. …”
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    Article
  18. 1998

    Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

    Published 2025-05-01
    “…The model’s parameter count and computational complexity require reduction. …”
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    Article
  19. 1999

    A Novel Attention-Guided Enhanced U-Net With Hybrid Edge-Preserving Structural Loss for Low-Dose CT Image Denoising by Muhammad Zubair, Helmi Md Rais, Talal Alazemi

    Published 2025-01-01
    “…However, it significantly reduces the quality of LDCT images and introduces noise and artifacts, degrading the diagnostic accuracy of the Computer Aided Diagnostic (CAD) system. This study presents a novel U-Net architecture, featuring several key enhancements. …”
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    Article
  20. 2000