Showing 81 - 100 results of 133 for search '"NYU"', query time: 0.05s Refine Results
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    Comprehensive FR1(C) and FR3 Lower and Upper Mid-Band Propagation and Material Penetration Loss Measurements and Channel Models in Indoor Environment for 5G and 6G by Dipankar Shakya, Mingjun Ying, Theodore S. Rappaport, Hitesh Poddar, Peijie Ma, Yanbo Wang, Idris Al-Wazani

    Published 2024-01-01
    “…Here we present the world’s first comprehensive indoor propagation measurement and channel modeling study at 6.75 GHz and 16.95 GHz in mid-band spectrum conducted at the NYU WIRELESS Research Center spanning distances from 11–97 m using 31 dBm EIRP transmit power with 15 and 20 dBi gain rotatable horn antennas at 6.75 GHz and 16.95 GHz, respectively. …”
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    Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation by Wei-Jong Yang, Chih-Chen Wu, Jar-Ferr Yang

    Published 2024-12-01
    “…The experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed autoencoder, which increases the first accuracy rate about 28% and reduces the root mean square error about 27% compared to an existing method in the NYU dataset.…”
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    RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning by Yang Jing, Li Weiya

    Published 2025-01-01
    “…The QPSO module is responsible for global path optimization, using quantum mechanics to avoid local optima, while the DRL module adjusts strategies in real-time based on environmental feedback, thus enhancing decision-making capabilities in complex high-dimensional scenarios.Results and discussionExperiments were conducted on multiple datasets, including Cityscapes, NYU Depth V2, Mapillary Vistas, and ApolloScape, and the results showed that RL-QPSO Net outperforms traditional methods in terms of accuracy, computational efficiency, and model complexity. …”
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    Improving Monocular Depth Estimation Through Knowledge Distillation: Better Visual Quality and Efficiency by Chang Yeop Lee, Dong Ju Kim, Young Joo Suh, Do Kyung Hwang

    Published 2025-01-01
    “…These evaluations were conducted on benchmark datasets, including NYU Depth V2 and SUN RGB-D for indoor environments and KITTI for outdoor scenarios, to ensure a rigorous and comprehensive assessment of robustness and generalization capabilities. …”
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    Understanding North Korea: Rimjin-gang Citizen Journalists Out to Cure the “Sick Man of Asia”? by Suzy Kim

    Published 2010-12-01
    “…Carter Journalism Institute at NYU in "a discussion about journalism in and about North Korea" (according to the event flyer). …”
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