Dynamic images such as computer graphics animation, moving things captured by video camera are always composed of several images as frames. This paper proposes one of the mathematical formulations for such animations. The key idea of our formulation is that each of the pixels representing an image is regarded as a kind of potentials in vector fields. Based on the vector calculus in classical physics, any static and dynamic images can be represented by the Poisson-and Helmholtz-type partial differential equations, respectively. This makes it possible to handle any images as analytical and continuous quantity even though these are given in terms of the discrete ones.Further, it is clarified that the animation technology is closely related with the simulation one. In the present paper, we carry out one of the simulations for magnetodynamic fields in ferro-magnetic material to show the relationship between the animation and simulation methodologies. As a result, it is demonstrated that our animation methodology reproduces the simulated magnetodynamic fields. Thus, it is found that our approach has versatile capability for analyzing the electric and electronic devices.

No abstract

A new reconstruction method called generalized vector sampled pattern matching (GVSPM) has been applied to an ill-posed inverse problem involving electrical capacitance computed tomography for solid-air two-phase flow. This new method is able to achieve stable convergence without the use of an empirical value. This accurate reconstruction is accomplished using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance. The GVSPM method is compared with the conventional Landweber (LW) and iterative Tikhonov regularization (ITR) methods in terms of capacitance residual, image error and image correlation. Overall, the accuracy is strongly dependent upon the image type and the iteration number, however the GVSPM method proved superior to the LW and the ITR methods in the case of annular pseudo particle images.

No abstract

This paper presents a new application of a generalized vector sample pattern matching (GVSPM) method for image reconstruction of conductivity changes in electrical impedance tomography. GVSPM is an iterative method for linear inverse problems. The key concept of the GVSPM is that the objective function is defined in terms of an angular component between the inner product of the known vector and solution of a system of equations. Comparisons are presented between images of simulated and experimental data, reconstructed using truncated singular value decomposition and GVSPM. In both cases, a normalized sensitivity matrix is constructed using the finite volume method to solve the forward problem.