Showing 581 - 600 results of 1,806 for search '"Convolutional neural network', query time: 0.09s Refine Results
  1. 581

    Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review by Nafizul Alam, Sk Hasan, Gazi Abdullah Mashud, Subodh Bhujel

    Published 2025-01-01
    “…By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. …”
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  2. 582

    Siamese Neural Networks in Unmanned Aerial Vehicle Target Tracking Process by Athraa Sabeeh Hasan Allak, Jianjun Yi, Haider M. Al-Sabbagh, Liwei Chen

    Published 2025-01-01
    “…Aiming at the problem of low recognition accuracy of small target, a target tracking model of UAV based on siamese neural network (SNN) is studied. Firstly, based on the YOLOv5 recognition model, convolutional attention module and multi-scale feature fusion network are introduced. …”
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  3. 583

    Spiking Neural Networks for Energy-Efficient Acoustic Emission-Based Monitoring by Federica Zonzini, Wenliang Xiang, Luca de Marchi

    Published 2024-01-01
    “…Furthermore, the event-driven capabilities of neuromorphic architectures (and spiking neural networks (SNNs) in particular) in processing spiky and sparse temporal information are exploited to retrieve ToA in a beyond state-of-the-art power-efficient manner and negligible loss of performance with respect to standard models. …”
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    Inception neural network for human activity recognition using wearable sensor by Duo CHAI, Cheng XU, Jie HE, Shao-yang ZHANG, Shi-hong DUAN, Yue QI

    Published 2017-11-01
    “…The experience from computer vision was learned,an innovative neural network model called InnoHAR (inception neural network for human activity recognition) based on the inception neural network and recurrent neural network was put forward,which started from an end-to-end multi-channel sensor waveform data,followed by the 1×1 convolution for better combination of the multi-channel data,and the various scales of convolution to extract the waveform characteristics of different scales,the max-pooling layer to prevent the disturbance of tiny noise causing false positives,combined with the feature of GRU helped to time-sequential modeling,made full use of the characteristics of data classification task.Compared with the state-of-the-art neural network model,the InnoHAR model has a promotion of 3% in the recognition accuracy,which has reached the state-of-the-art on the dataset we used,at the same time it still can guarantee the real-time prediction of low-power embedded platform,also with more space for future exploration.…”
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