• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.657-665
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• 1991

Although orthogonal function is introduced in control theory in early 1970's, it is not perfect. Since the concept of integral operator by Chen and Hsiao in mid 1970's, orthogonal function (for example Walsh, Block-pulse, Haar, Laguerre, Legendre, Chebychev etc) has been widely applied In system's analysis and identification, model reduction, state estimation, optimal control, signal processing, image processing, EEG, and ECG etc. The reason why Walsh Functions introduces in control theory is that as integral of Walsh function is also developed in Walsh orthogonal function, if we transfer give system into integral equation and introduce Walsh function. We can know that system's characteristic by algebraical expression. This approach is based on least square error and that result is expressed as computer calculation and partly continuous constant value which is easy to apply. Such a Walsh function has been actively studied in USA, TAIWAN, INDO, CHINA, EUROPE etc and in domestic, author has studied it for 10 years since it was is introduced in 1982. This paper is consider the that author has studied for 10 years and Walsh function's efficiency.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.1
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• pp.164-169
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• 1986

Walsh analysis is applied to the numerical specification of the volume distribution which is the key parameter in the formulation of the constitutive equations of heterogeneous media, indicating the geometrical state of the mixture. An example of two-dimensional volume distribution, its approximation, and the Walsh correlation coefficients are presented and the change of the information distribution in the operations is investigated. The phenomena of information concentration upon the large-scale Walsh coefficients are applied to the volumetric response of porous slids, clarifying the validity of the spherical-model calculation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.331-337
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• 2007

In this paper, we propose a novel Walsh coded training signal design and Walsh decoding method to estimate the channel response in MIMO-OFDM systems. The Walsh coded training signals are designed to have orthogonal property in time domain. Using the orthogonal property, the Walsh decoding process makes it possible to separate the desired training signal from the received signal and to estimate the channel response. The computer simulation results show that the proposed method exhibits almost the same performance as Li's original method using the optimal training sequence, even though the proposed method has much lower complexity.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.304-312
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• 2008

Orthogonal codes like Walsh and Golay codes may have large correlation value when they are not synchronized, hence they are seldom used in asynchronous CDMA systems. Wysocki[1] showed that by multiplying the original Walsh-Hadamard matrix with an orthogonal transformation matrix the resultant matrix sustains orthogonality between row vectors and their cross-correlation can be reduced. Soberly and Wysocki[2] proposed similar scheme on Golay codes. This implies that using the proper orthogonal transformation cross-correlation of Walsh and Golay codes can be reduced, and the transformed codes can be used for user separation in the CDAM reverse link. In this paper we discuss cross-correlation related parameters which affect the performance of an asynchronous CDMA link, and we investigate the correlation properties of the transformed codes. When we designed orthogonal transformation matrices for Walsh and Golay codes, we minimized the maximum value of aperiodic cross-correlation of the codes ($ACC_{max}$) or the mean square value of the aperiodic cross-correlation($R_{cc}$) with preserving the orthogonality of the modified codes. We also evaluate the asynchronous CDMA system that uses the transformed Walsh and Golay codes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.3
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• pp.95-101
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• 1986

This paper describes two methods for analyzing distributed parameter systems (DPS) via Walsh series expansions. Firstly, a Walsh-Galerkin expansion approach technique (WGA) introduced by S.G. Tzafestas. is considered. The method which is based on Galerkin scheme, is well established by using Walsh series. But then, there are some difficulty in finding the proper basic functions at each systems. Secondly, a double Walsh series approach technique (DWA) is developed. The essential feature of DWA propoesed here is that it reduces the analysis problem of DPS to that of solving a set of linear algebraic equation which is extended in double Walsh series.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.610-616
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• 2000

This paper presents an active noise control algorithm via Walsh transform domain controller learned by genetic algorithm. Typical active noise control algorithms such as the filtered-x lms algorithm are based on the gradient algorithm. Gradient algorithm have two major problems; local minima and eigenvalue ratio. To solve these problems, we propose a combined algorithm which consist of genetic learning algorithm and discrete Walsh transform called Walsh Transform Domain Genetic Algorithm(WTDGA). Analyses and computer simulations on the effect of Walsh transform to the genetic algorithm are performed. The results show that WTDGA increase convergence speed and reduce steady state errors.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.758-760
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• 1995

The purpose of noise cancellation is to estimating signals corrupted by additive noise or interference. In this paper, an adaptive noise canceller is built from a Walsh filter with a new adaptive algorithm. The Walsh filter consists of a Walsh function. Since the Walsh functions are either even or odd functions, the covariance matrix in the tap gain adjustment algorithm can be reduced to a simple form. In this paper, minimization of the mean squre error is accomplished by a proposed adaptive algorithm. The conventional adaptation techniques use a fixed time constant convergence factor by trial and error methods. In this paper, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.601-606
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• 1999

A Walsh function method is proposed in this report for the analysis and optimal control of linear time-delay systems, which is based on the Picard's iterative approximation and fast Walsh transformation. In this research, the following results are obtained: 1) The differential and integral equation can be solved by transforming into a simple algebraic equation as it was possible with the usual orthogonal function method: 2) General orthogonal function methods require usage of Walsh operational matrices for delay or advance and many calculations of inverse matrices, which are not necessary in this method. Thus, the control problems of linear time-delay systems can be solved much faster and readily.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.914-921
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• 1992

In this paper, we introduce a Walsh network and an LMS algorithm, and show how these can be realized as an adaptive equalizer. The Walsh network is built from a set of Walsh and Block pulse functions. In the LMS algorithm, the convergence factor is an important design parameter because it governs stability and convergence speed, which depend on the proper choice of the convergence facotr. The conventional adaptation techniques use a fixed time constant convergence factor by the method of trial and error. In this paper, we propose an optimal method in the choice of the convergence factor. The proposed algorithm depends on the received signal and the output of the Walsh network in real time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.640-643
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• 2003

A Walsh function based associative memory is capable of storing m patterns in a single pattern storage space with Walsh encoding of each pattern. Furthermore, each stored pattern can be matched against the stored patterns extremely fast using algorithmic parallel processing. As such, this special type of memory is ideal for real-time processing of large scale information. However this incredible efficiency generates large amount of crosstalk between stored patterns that incurs mis-recognition. This crosstalk is a function of the set of different sequencies [number of zero crossings] of the Walsh function associated with each pattern to be stored. This sequency set is thus optimized in this paper to minimize mis-recognition, as well as to maximize memory saying. In this paper, this Walsh memory has been applied to the problem of face recognition, where PCA is applied to dimensionality reduction. The maximum Walsh spectral component and genetic algorithm (GA) are applied to determine the optimal Walsh function set to be associated with the data to be stored. The experimental results indicate that the proposed methods provide a novel and robust technology to achieve an error-free, real-time, and memory-saving recognition of large scale patterns.