Mitsubishi

About: Mitsubishi is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 53115 authors who have published 54821 publications receiving 870150 citations. The organization is also known as: Mitsubishi Group of Companies & Mitsubishi Companies.

Machining apparatus with automatic restart function

Abstract: A laser cutting apparatus which allows cutting to be automatically restarted or redone without intervention from an operator once a cutting error or fault occurs. In a first embodiment, when the fault occurs the apparatus discontinues processing on the product corresponding to the fault. Thereafter, it moves to, and restarts cutting at, a starting position of the next product to be cut. In a second embodiment, the apparatus corrects a fault and continues cutting on the product corresponding to the fault by restarting cutting at the fault point. It determines whether a restart is possible at the point of the fault based on the type or definition of the fault. If a predetermined number of restarts are attempted at the point of fault without a successful restart, then the apparatus moves to a new start position. In another embodiment, the apparatus uses a profile sensor to prevent machine damage by sensing the position of the cutting head relative to the workpiece.

Model-Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Nonnegative Eigenvalues

Abstract: Model-based decomposition of polarimetric radar covariance matrices holds the promise that specific scattering mechanisms can be isolated for further quantitative analysis. In this paper, we show that current algorithms suffer from a fatal flaw in that some of the scattering components result in negative powers. We propose a simple modification that ensures that all covariance matrices in the decomposition will have nonnegative eigenvalues. We further combine our nonnegative eigenvalue decomposition with eigenvector decomposition to remove additional assumptions that have to be made before the current algorithms can be used to estimate all the scattering components. Our results are illustrated using Airborne Synthetic Aperture Radar data and show that current algorithms typically overestimate the canopy scattering contribution by 10%-20%.

Beyond eigenfaces: probabilistic matching for face recognition

Abstract: We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image differences. Specifically we model lure mutually exclusive classes of variation between facial images: intra-personal (variations in appearance of the same individual, due to different expressions or lighting) and extra-personal (variations in appearance due to a difference in identity). The likelihoods for each respective class are learned from training data using eigenspace density estimation and used to compute similarity based on the a posteriori probability of membership in the intra-personal class, and ultimately used to rank matches in the database. The performance advantage of this probabilistic technique over nearest-neighbor eigenface matching is demonstrated using results front ARPA's 1996 "FERET" face recognition competition, in which this algorithm was found to be the top performer.

A superplastic covalent crystal composite

Abstract: SUPERPLASTICITY is defined phenomenologically as the ability of a material to exhibit exceptionally large elongations during tensile deformation1. It is a property of some poly crystalline solids, and is well established for metals and alloys2. Superplasticity has also been observed in some ionic crystals, such as Y2O3-stabilized tetragonal ZrO2 polycrystals3,4, but has not been found previously for covalent crystals. Here we report superplastic elongation (by more than 150%) of a covalent crystal composite, Si3N4/SiC. The superplasticity is probably related to the presence of an inter-granular liquid phase. Combined with its hardness, this property suggests several useful applications for the novel material: for example, to form engine components—superplasticity will make it readily mouldable at high temperatures.