deep neural networks
Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Generally, pedestrian interviews and surveys used to evaluate walkability, but this is expensive and lack professional insight. To address limitations in current methods for evaluating pedestrian pathways, we propose a novel approach utilizing wearable sensors and deep learning.
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An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.
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281 Views
Tri-gate ferroelectric FETs with HZO gate insulator for memory and neuromorphic applications are fabricated and characterized for multi-level operation. The conductance and threshold voltage exhibit highly linear and symmetric characteristics. A compact analytical model is developed to accurately capture FET transfer characteristics, including series resistance, coulombic scattering, and vertical field dependent mobility degradation effects, as well as the evolvement of threshold voltage and mobility with ferroelectric polarization switching.
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751 Views
Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.
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2216 Views
This is the images and the image masks used in the paper "Z. Petrou and Y. Tian, Prediction of Sea Ice Motion with Convolutional Long Short-Term Memory Networks,IEEE Transactions on Geoscience and Remote Sensing."
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318 Views