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    A cellular neural network methodology for deformable object simulation

    Access Status
    Fulltext not available
    Authors
    Zhong, Yongmin
    Shirinzadeh, B.
    Alici, G.
    Smith, J.
    Date
    2006
    Type
    Journal Article
    
    Metadata
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    Citation
    Zhong, Yongmin and Shirinzadeh, Bijan and Alici, Gursel and Smith, Julian. 2006. A cellular neural network methodology for deformable object simulation. IEEE Transactions on Information Technology in Biomedicine 10 (4): pp. 749-762.
    Source Title
    IEEE Transactions on Information Technology in Biomedicine
    DOI
    10.1109/TITB.2006.875679
    ISSN
    1089-7771
    URI
    http://hdl.handle.net/20.500.11937/39838
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extra polating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation.

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