• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2052-2058
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• 2009

As the operating frequency of digital system increases and voltage swing decreases, an accurate and high speed analysis of PCB board becomes very important. Transmission matrix method, which use the multiple products of unit column matrix, is the highest speedy method in PCB board analysis. In this paper a new method to reduce the calculation time of PCB board impedances is proposed. First, in this method the eigenvalue and eigenvectors of the transmission matrix for unit column of PCB are calculated and the transmission matrix for the unit column is transformed using similarity transform to reduce the number of multiplication on the matrix elements. This method using the similarity transform can reduce the calculation time greatly comparing the previous method. The proposed method is applied to the 1.3 inch by 1.9 inch board and shows about 10 times reduction of calculation time. This method can be applied to the PCB design which needs a lots of repetitive calculation of board impedances.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.2
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• pp.31-43
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• 2000

This paper presents a novel algorithm lot extraction of the eye and mouth fields (facial features) from 2D gray level face images. First of all, it has been found that Eigenfeatures, derived from the eigenvalues and the eigenvectors of the binary edge data set constructed from the eye and mouth fields are very good features to locate these fields. The Eigenfeatures, extracted from the positive and negative training samples for the facial features, ate used to train a MultiLayer Perceptron(MLP) whose output indicates the degree to which a particular image window contains the eye or the mouth within itself. Second, to ensure robustness, the ensemble network consisting of multiple MLPs is used instead of a single MLP. The output of the ensemble network becomes the average of the multiple locations of the field each found by the constituent MLPs. Finally, in order to reduce the computation time, we extracted the coarse search region lot eyes and mouth by using prior information on face images. The advantages of the proposed approach includes that only a small number of frontal faces are sufficient to train the nets and furthermore, lends themselves to good generalization to non-frontal poses and even to other people's faces. It was also experimentally verified that the proposed algorithm is robust against slight variations of facial size and pose due to the generalization characteristics of neural networks.