• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.1
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• pp.29-34
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• 2010

The QR method is one of the most common methods for calculating the eigenvalues of a square matrix, however its understanding would require complicated and sophisticated mathematical logics. In this article, we present a simple way to understand QR method only with a minimal mathematical knowledge. A deflation technique is introduced, and its combination with the power iteration leads to extracting all the eigenvectors. The orthogonal iteration is then shown to be compatible with the combination of deflation and power iteration. The connection of QR method to orthogonal iteration is then briefly reviewed. Our presentation is unique and easy to understand among many accounts for the QR method by introducing the orthogonal iteration in terms of deflation and power iteration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.868-876
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• 2016

MUSIC (multiple signal classification) algorithm is a representative algorithm estimating the angle of arrival using the eigenvalues and eigenvectors. Generally, the eigenvalues and eigenvectors are obtained through the eigen-analysis, but this analysis requires high computational complexity and late convergence time. For this reason, it is almost impossible to construct the real-time system with low-cost using this approach. Even though QR iteration is considered as the eigen-analysis approach to improve these problems, this is inappropriate to apply to the MUSIC algorithm. In this paper, we analyze the problems of conventional method based on the eigenvalues for convergence decision and propose the improved decision algorithm using the eigenvectors.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.1
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• pp.6-14
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• 2011

This paper presents a high-speed QR decomposition architecture for the multi-input-multi-output (MIMO) receiver based on Givens rotation. Under fast-varying channel, since the inverse matrix calculation has to be performed frequently in MIMO receiver, a high performance and low latency QR decomposition module is highly required. The proposed QR decomposition architecture is composed of Sign-Select Lookahead (SSL) coordinate rotation digital computer (CORDIC). In the SSL-CORDIC, the sign bits, which are computed ahead to select which direction to rotate, are used to select one of the last iteration results, therefore, the data dependencies on the previous iterations are efficiently removed. Our proposed QR decomposition module is implemented using TSMC 0.25 ${\mu}M$ CMOS process. Experimental results show that the proposed QR architecture achieves 34.83% speed-up over the Compact CORDIC based architecture for the 4 ${\times}$ 4 matrix decomposition.

• The Pure and Applied Mathematics
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• v.2 no.2
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• pp.133-140
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• 1995

The D-optimal design criterion for precise parameter estimation in nonlinear regression analysis is called the determinant criterion because the determinant of a matrix is to be maximized. In this thesis, we derive the gradient and the Hessian of the determinant criterion, and apply a QR decomposition for their efficient computations. We also propose an approximate form of the Hessian matrix which can be calculated from the first derivative of a model function with respect to the design variables. These equations can be used in a Gauss-Newton type iteration procedure.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.28-35
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• 2013

The Krylov-Schur algorithm has been applied to reveal the eigen-properties of the wave guide having the square cross section. The eigen-matrix equation has been constructed from FEM with the basis function of the tangential edge-vectors of the triangular element. This equation has been treated firstly with Arnoldi decomposition to obtain a upper Hessenberg matrix. The QR algorithm has been carried out to transform it into Schur form. The several eigen values satisfying the convergent condition have appeared in the diagonal components. The eigen-modes for them have been calculated from the inverse iteration method. The wanted eigen-pairs have been reordered in the leading principle sub-matrix of the Schur matrix. This sub-matrix has been deflated from the eigen-matrix equation for the subsequent search of other eigen-pairs. These processes have been conducted several times repeatedly. As a result, a few primary eigen-pairs of TE and TM modes have been obtained with sufficient reliability.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.428-433
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• 2007

This paper describes the new eigenvalue algorithm exploiting the Implicit Restarted Arnoldi Method (IRAM) and its application to power systems. IRAM is a technique for combining the implicitly shifted mechanism with a k-step Arnoldi factorization to obtain a truncated form of the implicitly shifted QR iteration. The numerical difficulties and storage problems normally associated with the Arnoldi process are avoided. Two power systems, one of which has 36 state variables and the other 150 state variables, have been tested using the ARPACK program, which uses IRAM, and the eigenvalue results are compared with the results obtained from the conventional QR method.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.393-395
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• 2005

This paper describes implicitly restarted Arnoldi method (IRAM), which is a technique for combining the implicitly shifted QR mechanism with a k-step Arnoldi factorization to obtain a truncated form of the implicitly shifted QR-iteration. IRAM avoids numerical difficulties and storage problems normally associated with Arnoldi. This paper deals with the basic algorithms of IRAM as an intial research phase for developing the full featured eigenvalue analysis program for large power system up to 30,000 states.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.10-14
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• 2014

Krylov-Schur iteration method has been applied to the 2-Dim. waveguides of the varied geometrical structure. The eigen-equations for them have been constructed from FEM based on the tangential edge vectors of triangular elements. The eigen-values and their modes have been determined from the diagonal components of the Schur matrices and its transforming matrices. The eigen-pairs as the results have been revealed visually in the schematic representations.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.142-148
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• 2014

Krylov-Schur iteration method has been applied to the 3-Dim. resonant cavity of a cylindrical form. The vector Helmholtz equation has been analysed for the resonant field strength in homogeneous media by FEM. An eigen-equation has been constructed from element equations basing on tangential edges of the tetrahedra element. This equation made up of two square matrices associated with the curl-curl form of the Helmholtz operator. By performing Krylov-Schur iteration loops on them, Eigen-values and their modes have been determined from the diagonal components of the Schur matrices and its transforming matrices. Eigen-pairs as a result have been revealed visually in the schematic representations. The spectra have been compared with each other to identify the effect of boundary conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.103-108
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• 2023

We study the problem of selecting a subset of sensor nodes in sensor networks in order to maximize the performance of parameter estimation. To achieve a low-complexity sensor selection algorithm, we propose a greedy iterative algorithm that allows us to select one sensor node at a time so as to maximize the log-determinant of the inverse of the estimation error covariance matrix without resort to direct minimization of the estimation error. We apply QR factorization to the observation matrix in the log-determinant to derive an analytic selection rule which enables a fast selection of the next node at each iteration. We conduct the extensive experiments to show that the proposed algorithm offers a competitive performance in terms of estimation performance and complexity as compared with previous sensor selection techniques and provides a practical solution to the selection problem for various network applications.