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This paper presents a method for effectively modeling inlay/onlay prostheses restoring a tooth that are partially destroyed. An inlay/onlay is composed of internal surface adhering to an abutment, and external surface revealed to the outside sight. Internal surfaces are modeled using Minkowski sum expanding the grinded surface parts of abutments so that the internal surfaces can adhere to the abutments with closer contact. In modeling external surfaces, we exploit 3D mesh deformation techniques: DMFFD(direct manipulation free-form deformation)[19] and MWD(multiple wires deformation)[17] with three kinds of informations: standard teeth models, mesh data obtained by scanning a plaster cast of a patient`s tooth, FGP(functionally guided plane) measuring the occlusion of the patients`s teeth. The standard teeth models are used for building up the basic shapes of external surfaces, while the plaster fast and FGP data are used for reflecting the unique properties of adjacent md occlusal surfaces of the patients`s teeth, which are slightly different to each other but very important for correct functioning. With these informations as input data, the adjacent and occlusal surfaces are automatically generated as mesh data using the techniques of DMFFD and m, respectively. Our method was implemented so that inlay/onlay prostheses fan be designed more accurately by visualizing the generated mesh models with requirements by dentists.

In array signal processing applications involving eigenstructure methods, direction finding algorithms are often derived by using the second order information of source signals. But in some cases the noise is not spatially white or the number of source signals is larger than that of the sensors, and the sources cannot be correctly resolved by using conventional algorithms exploiting second order statistics. A wideband source localization method utilizing higher order statistics (HOS) is presented. The information of signals of higher order greater than two is immune from additive Gaussian noise whether they are spatially, temporally white or colored, so that a priori knowledge on the noise statistics is unnecessary. In the algorithm presented the fourth order cumulant matrix is calculated in the transformed domain and a MUSIC-like estimator is used. However, this technique can be generalized for other eigenstructure methods such as ESPRIT and WSF.

This paper presents an optimum space-time moving target indication (MTI) processor for the airborne radar. The optimization is based on a stochastic target model, rather than deterministic target models adopted in most space-time MTI processor designs. The optimum solution that maximizes the improvement factor yielded by the processor is shown to be the generalized eigenvector corresponding to the smallest generalized eigenvalue of the signal and clutter covariance matrices. A suboptimal, but computationally simpler solution to this problem is also derived. This approach requires the solution of a linearly constrained minimum variance (LCMV) problem. Unlike typical LCMV problems, our solution also calculates the response vector specifying the frequency response along the look direction. Experimental results demonstrating the usefulness of our methods are included in the paper. The results indicate that the suboptimal solution does not suffer from significant performance loss.