Showing 41 - 53 results of 53 for search '"Nvidia"', query time: 0.04s Refine Results
  1. 41

    The Nexus Between Talent Management Attention and Artificial Intelligence: Evidence from Companies Operating Within the AI Domain

    Published 2025-01-01
    “…AI company performance is measured via the stock returns of Microsoft, Google, Amazon, and NVIDIA, which represent key players in the AI sector. …”
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  2. 42

    OW-YOLO: An Improved YOLOv8s Lightweight Detection Method for Obstructed Walnuts by Haoyu Wang, Lijun Yun, Chenggui Yang, Mingjie Wu, Yansong Wang, Zaiqing Chen

    Published 2025-01-01
    “…Experiments were conducted using the PyTorch framework on an NVIDIA GeForce RTX 4060 Ti GPU. The results demonstrate that OW-YOLO outperforms other models, achieving an mAP@0.5 (mean average precision) of 83.6%, mAP@[0.5:0.95] of 53.7%, and an F1 score of 77.9%. …”
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  3. 43

    EventSegNet: Direct Sparse Semantic Segmentation from Event Data by Pengju Li, Yuqiang Fang, Jiayu Qiu, Jun He, Jishun Li, Qinyu Zhu, Xia Wang, Yasheng Zhang

    Published 2024-12-01
    “…Compared to the baseline model, the optimized network model reduces the F1 score by 1.1% but is more than twice as fast computationally, enabling real-time inference on the NVIDIA AGX Xavier.…”
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  4. 44

    A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations by Odysseas Ntousis, Evangelos Makris, Panayiotis Tsanakas, Christos Pavlatos

    Published 2025-01-01
    “…The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. …”
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  5. 45

    EgoSep: Egocentric On-Screen Sound Source Separation for Real-Time Edge Computing by Donghyeok Jo, Jun-Hwa Kim, Jihoon Jeon, Chee Sun Won

    Published 2025-01-01
    “…Additionally, real-time feasibility is validated on the NVIDIA Jetson Nano Developer Kit, achieving a real-time factor (RTF) of 0.17, demonstrating its practicality for wearable applications. …”
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  6. 46

    DSC-SeNet: Unilateral Network with Feature Enhancement and Aggregation for Real-Time Segmentation of Carbon Trace in the Oil-Immersed Transformer by Liqing Liu, Hongxin Ji, Junji Feng, Xinghua Liu, Chi Zhang, Chun He

    Published 2024-12-01
    “…For a 512 × 512 input, it achieved 84.7% mIoU, which is 6.4 percentage points higher than that of the baseline short-term dense convolution network (STDC), with a speed of 94.3 FPS on an NVIDIA GTX 2050Ti. This study provides technical support for real-time segmentation of carbon traces and transformer insulation assessment.…”
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  7. 47

    LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

    Published 2025-01-01
    “…Additionally, deployment on the NVIDIA Jetson AGX Orin edge computing device confirms its high real-time performance and suitability for underwater applications, further showcasing the exceptional capabilities of LFN-YOLO.…”
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  8. 48

    A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

    Published 2024-06-01
    “…Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. …”
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  9. 49

    GAZE DIRECTION MONITORING MODEL IN COMPUTER SYSTEM FOR ACADEMIC PERFORMANCE ASSESSMENT by Olesia Barkovska, Yaroslav Liapin, Tetiana Muzyka, Ihor Ryndyk, Pavlo Botnar

    Published 2024-02-01
    “…Real-time gaze control capability is implemented by using massive parallel processing system (NVIDIA GeForce GTX 1650 graphics card) for calculations. …”
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  10. 50

    AIPerf: Automated Machine Learning as an AI-HPC Benchmark by Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

    Published 2021-09-01
    “…We perform evaluations on various systems to ensure the benchmark’s stability and scalability, from 4 nodes with 32 NVIDIA Tesla T4 (56.1 Tera-OPS measured) up to 512 nodes with 4096 Huawei Ascend 910 (194.53 Peta-OPS measured), and the results show near-linear weak scalability. …”
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  11. 51

    A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching by Jose Moises Araya-Martinez, Jose Moises Araya-Martinez, Vinicius Soares Matthiesen, Vinicius Soares Matthiesen, Simon Bøgh, Jens Lambrecht, Rui Pimentel de Figueiredo

    Published 2025-01-01
    “…For instance, with a rotation step of 10° in the template database, we achieve an average rotation error of 10°, matching the template quantization level, and an average translation error of 14% of the object’s size, with an average processing time of 0.3s per image on a small form-factor NVIDIA AGX Orin device. We also evaluate robustness under partial occlusions (up to 10% occlusion) and noisy inputs (signal-to-noise ratios [SNRs] up to 10 dB), with only minor losses in accuracy. …”
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  12. 52

    6G-oriented cross-modal signal reconstruction technology by Ang LI, Jianxin CHEN, Xin WEI, Liang ZHOU

    Published 2022-06-01
    “…In order to implement the proposed cross-modal signal reconstruction framework into practical application scenarios, a teleoperation platform was further built using the robot and Nvidia development board for the industrial scenario of The results of running on this platform show that the actual mean absolute error is 0.0126,the total end-to-end delay is 127ms and the reconstruction model delay is 98ms.A questionnaire was also used to assess user satisfaction,where the mean value of haptic realism satisfaction is 4.43 with a variance of 0.72 and the mean value of time delay satisfaction is 3.87 with a variance of 1.07. …”
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  13. 53

    Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics by Stefan Schoder

    Published 2025-01-01
    “…It outperformed the finite element analysis and the standard PINN in terms time needed to obtain the solution, needing 15 min and 30 s on an AMD Ryzen 7 Pro 8840HS CPU (AMD, Santa Clara, CA, USA) for the FEM, compared to about 20 min (standard PINN) and just under a minute of the feature-engineered PINN, both trained on a Tesla T4 GPU (NVIDIA, Santa Clara, CA, USA).…”
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