Showing 1,201 - 1,220 results of 1,248 for search '(((pinnau OR pinn) OR hinges) OR (((ping OR espina) OR (pengna OR pengguna)) OR ling))', query time: 0.17s Refine Results
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    P4-network : a networking infrastructure for intra-aircraft cabin wireless communication by Pham, Chau Khoa.

    Published 2009
    “…In particular, a pinging test over a path of four hops takes less than a hundred millise-conds. …”
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    Final Year Project (FYP)
  13. 1213

    Software defined network : user centric network by Lim, Derrick Teck Leong.

    Published 2013
    “…A test of the application was done by running two continuous pings, one from each host to the other. Then, the path in use was disconnected. …”
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    Final Year Project (FYP)
  14. 1214
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  16. 1216

    Preliminary Investigation of 4D Printing Technology for Deployable UAV Development by Chua, Chee Kai, Teoh, J. E. M., Liu, Yong, An, J., Li, Y.

    Published 2016
    “…The investigation has also shown that the folding angles (0°, 15°, 30° and 60°) of the hinge for the back wing of UAV model is programmable and controllable. …”
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    Conference Paper
  17. 1217

    Dragonfly (Sympetrum flaveolum) flight : kinematic measurement and modelling by Chen, Y. H., Martin, Skote., Zhao, Y., Huang, W. M.

    Published 2013
    “…By applying more tracking points along the leading edge around the nodus, it is shown that the leading edge is not one rigid piece, but two pieces hinged at the nodus with physical constraint of forty degrees. …”
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    Journal Article
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    Cross-Modal Deep Variational Hashing by Liong, Venice Erin, Lu, Jiwen, Zhou, Jie, Tan, Yap Peng

    Published 2017
    “…Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep fusion neural network to learn non-linear transformations from image-text input pairs, such that a unified binary code is achieved in a discrete and discriminative manner using a classification-based hinge-loss criterion. We then design modality-specific neural networks in a probabilistic manner such that we model a latent variable to be close as possible from the inferred binary codes, at the same time approximated by a posterior distribution regularized by a known prior, which is suitable for out-of-sample extension. …”
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    Conference Paper