• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.162-170
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• 2010

Adaptive arrays reject interferences while preserving the desired signal, exploiting a priori information on its arrival angle. Subspace-based adaptive arrays, which adjust their weight vectors in the signal subspace, have the advantages of fast convergence and robustness to steering vector errors, as compared with the ones in the full dimensional space. However, the complexity of theses subspace-based methods is high because the eigendecomposition of the covariance matrix is required. In this paper, we present a simple subspace-based method based on the PASTd (projection approximation subspace tracking with deflation). The orignal PASTd algorithm is modified such that eigenvectora are orthogonal to each other. The proposed method allows us to significantly reduce the computational complexity, substantially having the same performance as the beamformer with the direct eigendecomposition. In addition to the simple beamforming method, we present theoretical analyses on the SINR (signal-to-interference plus noise ratio) of subspace beamformers to see their behaviors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7A
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• pp.557-562
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• 2003

An output multiplexer of manifold type is widely used in a recent satellite transponder for its mass and volume reduction. For correct operation, the filters of such a multiplexer must be singly terminated. In this paper, a simple synthesis method of a 6-pole dual-mode singly-terminated filter is described. From the transfer function of the filter, network parameters such as in/output terminations and coupling coefficients are obtained easily without complicated matrix algebra such as orthogonal projection and similarity transformation. Two different-structure filters are taken into consideration and the network parameters of each filter have been extracted from the same transfer function. It is shown that the responses of two filters are same to each other since their network parameters are obtained from the same transfer function. The method described in this paper can be applied to the other degree singly terminated filter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.5
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• pp.694-704
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• 2001

In this work, We evaluate the performance of eigencanceller which can suppress directional interferences and noise effectively while maintaining specified beam pattern constraints. The constraints and optimal weight vector of eigencanceller vary by using interference and noise or desired signal, interference and noise as array input signal. From the analysis results in the steady state, We show that weight vectors in each case are simplified the form of projection equation that belongs to desired subspace orthogonal to interference subspace and eigencanceller has the better performance than DMI method through mathematical analysis and simulation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.50-55
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• 2002

The standard approach of image resampling is to fit the original image with continuous model and resample the function at a desired rate. We used the B-spline function as the continuous model because it oscillates less than the others. The main purpose of this paper is the derivation of a nonuniform optimal resampling algorithm. To derive it, needing approximation can be computed in three steps: 1) determining the I-spline coefficients by matrix inverse process, 2) obtaining the transformed-spline coefficients by the optimal resampling algorithm derived from the orthogonal projection theorem, 3) converting of the result back into the signal domain by indirect B-spline transformation. With these methods, we can use B-spline in the non-uniform resampling, which is proved to be a good kernel in uniform resampling, and can also verify the applicability from our experiments.