A Cellular Neural Network Methodology for Deformable Object Simulation Export

@article{citeulike:953232, 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 extrapolating 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.}, author = {Zhong, Y. and Shirinzadeh, B. and Alici, G. and Smith, J.}, citeulike-article-id = {953232}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1707688}, journal = {Information Technology in Biomedicine, IEEE Transactions on}, keywords = {deformation}, number = {4}, pages = {749--762}, posted-at = {2006-11-20 12:59:00}, priority = {2}, title = {A Cellular Neural Network Methodology for Deformable Object Simulation}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1707688}, volume = {10}, year = {2006} }

TY - JOUR ID - citeulike:953232 L3 - citeulike-article-id:953232 N2 - 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 extrapolating 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. IS - 4 JF - Information Technology in Biomedicine, IEEE Transactions on EP - 762 TI - A Cellular Neural Network Methodology for Deformable Object Simulation VL - 10 SP - 749 KW - deformation AU - Zhong, Y AU - Shirinzadeh, B AU - Alici, G AU - Smith, J PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1707688 ER -