A signature verification system using a neuro-computer is presented. The neuro-computer executes the backpropagation learning algorithm for multilayer neural networks faster than a super computer. The system uses signals obtained from pen pressure and writing speed. A method for generating deformed signature patterns is also presented. The learning of about 500 patterns is converged within several seconds using four-layer networks (768-119-61-1). The evaluation result based on 70 persons' signatures is also presented. The reject ratio (false rejection) is 7.97% and the error ratio (false acceptance) is 0.61%.>

An algorithm to normalize the skew of document images is proposed. The skew angle is detected in two stages. In the first stage, connected regions in an image are extracted and some feature parameters are extracted for each region. In the second stage, the Hough transform is calculated for the parameters, and the angle which gives the minimum of the transform is estimated as the skew angle. In experiments using CCITT standard documents, a detection accuracy of less than 0.1 degrees is obtained for printed documents. When graphical elements are included in the documents in addition to printed characters, the accuracy deteriorates to 0.2 degrees .>

PACS-32 is a prototype supercomputer used for a broad range of scientific calculations.It has a synchronizable MIMD processor array, and on the basis of this facility, it provides efficient nearest neighbor communication.Processors are combined into a rectangular array with endaround connections, and can synchronize at user-specified points with little overhead.The hardware has been constructed with 32 processors, and the operating software includes a highlevel language.The PACS-32 system has performed in a number of applications, such as Poisson equation solving, nuclear reactor calculation, aerodynamics, Monte Carlo simulation of plasma particles, and molecular dynamics modeling in solid state physics.The processor efficiency measure ranges from 78 percent through 99 percent in these applications.