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Showing 101 - 120 results of 1,810 for search '((( resource OR sourcessss) detection function ) OR ( sources detection function ))', query time: 0.29s Refine Results
  1. 101

    Functional and Genomic Evidence of L-Arginine-Dependent Bacterial Nitric Oxide Synthase Activity in <i>Paenibacillus nitricinens</i> sp. nov. by Diego Saavedra-Tralma, Alexis Gaete, Carolina Merino-Guzmán, Maribel Parada-Ibáñez, Francisco Nájera-de Ferrari, Ignacio Jofré-Fernández

    Published 2025-06-01
    “…Although nitric oxide (NO) production in bacteria has traditionally been associated with denitrification or stress responses in model or symbiotic organisms, functionally validated L-arginine-dependent nitric oxide synthase (bNOS) activity has not been documented in free-living, non-denitrifying soil bacteria. …”
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  2. 102

    Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight by Yu Ren, Zongliang Xie, Yihan Luo, Shaoxiong Xu, Haotong Ma, Yi Tan

    Published 2020-01-01
    “…Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. …”
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  3. 103

    Using an ensemble approach to predict habitat of Dusky Grouse ( Dendragapus obscurus ) in Montana, USA by Elizabeth A Leipold, Claire N Gower, Lance McNew

    Published 2024-12-01
    “…Consensus between the resource selection function and random forest models was high (93%) and the ensemble map had higher predictive accuracy when classifying the independent dataset than the other two models. …”
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  4. 104
  5. 105

    Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data by Fatimah Alzahrani, Steve Maddock, Heidi Christensen

    Published 2025-06-01
    “…Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). …”
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  6. 106
  7. 107

    SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

    Published 2025-01-01
    “…These results indicate that SDES-YOLO successfully combines efficiency and precision in fall detection. Through these innovations, SDES-YOLO not only improves detection accuracy but also optimizes computational efficiency, making it effective even in resource-constrained environments.…”
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  8. 108
  9. 109

    Spin-dependent edge detection and imaging enabled by optical circularly polarised states by Jiale Chen, Zhao-xian Chen, Zi-xin Zhou, Yan-qing Lu, Jun-long Kou

    Published 2025-04-01
    “…Furthermore, these two independent functions can be easily switched by altering the circular polarisation state of the light source.…”
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  10. 110

    Image Deconvolution to Resolve Astronomical X-Ray Sources in Close Proximity: The <i>NuSTAR</i> Images of SXP 15.3 and SXP 305 by Sayantan Bhattacharya, Dimitris M. Christodoulou, Silas G. T. Laycock

    Published 2025-03-01
    “…The broad point spread function of the <i>NuSTAR</i> telescope makes resolving astronomical X-ray sources a challenging task, especially for off-axis observations. …”
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  11. 111
  12. 112

    Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar by Jingfeng Yu, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, Qianchuan Zhao

    Published 2025-03-01
    “…Compared to 2D sonar images, 3D sonar images offer superior spatial positioning capabilities, although the data acquisition cost is higher and lacks open source references for data annotation, target detection, and semantic segmentation. …”
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  13. 113

    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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  14. 114
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  16. 116

    QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity by Nan Xu, Behnaz Yousefi, Nmachi Anumba, Theodore J. LaGrow, Xiaodi Zhang, Shella Keilholz

    Published 2025-02-01
    “…To address these challenges, we present QPPLab, an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. …”
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  17. 117

    Anisotropic Diffusion of e± in Pulsar Halos over Multiple Coherence of Magnetic Fields by Kai Yan, Sha Wu, Ruo-Yu Liu

    Published 2025-01-01
    “…Also, the halo’s morphology may appear less asymmetric, especially after being smoothed by the point-spread function of instruments. This largely relaxes the tension between the asymmetric morphology of halos predicted by the model and the lack of apparent asymmetric halos detected so far. …”
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  18. 118

    Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation by N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov

    Published 2023-12-01
    “…Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. …”
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  19. 119

    Reusable and robust fuzzy extractor for CRS-dependent sources by Yucheng Ma, Peisong Shen, Xue Tian, Kewei Lv, Chi Chen

    Published 2025-01-01
    “…Abstract Fuzzy extractors allow for the extraction and reproduction of a nearly uniform string from a noisy and non-uniform source. Reusable and robust fuzzy extractors further require that the output string should remain pseudorandom under multiple extractions and any modification of public value should be detectable. …”
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  20. 120

    Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks by Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler, Ricardo E. Pérez Guzmán

    Published 2025-03-01
    “…The proposed system is formulated as a Markov Decision Process (MDP), where encryption level selection is driven by a reward function that optimizes the trade-off between energy efficiency and security robustness. …”
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