• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.577-582
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• 2013

In this paper Legendre wavelets are used to approximate the solutions of linear time-invariant system. The Legendre wavelet and its integral operational matrix are presented and an efficient algorithm to solve the Sylvester matrix equation is proposed. The algorithm is based on the decomposition of the Sylvester matrix equation and the preorder traversal algorithm. Using the special structure of the Legendre wavelet's integral operational matrix, the full order Sylvester matrix equation can be solved in terms of the solutions of pure algebraic matrix equations, which reduce the computation time remarkably. Finally a numerical example is illustrated to demonstrate the validity of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.281-282
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• 2007

We address a new representation of DCT/DFT/Wavelet matrices via one hybrid architecture. Based on an element inverse matrix factorization algorithm, we show that the OCT, OFT and Wavelet which based on Haar matrix have the similarrecursive computational pattern, all of them can be decomposed to one orthogonal character matrix and a special sparse matrix. The special sparse matrix belongs to Jacket matrix, whose inverse can be from element-wise inverse or block-wise inverse. Based on this trait, we can develop a hybrid architecture.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.347-354
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• 1996

This paper presents digital hearing aids signal processing system in WT(wavelet transform) domain. For implementation of hearing aids in WT domain, the gain in frequency domain is approximated in WT domain. We also present the gain selection algorithm to deal with the change of input signal power. Most transform methods produce blocking effect, and this effect degrades the convergence rate of feedback canceller. As a solution, we proposed wavelet transform bascd feedback canceller. To evaluate the performance, we compared it with LOT (lapped orthogonal transform) method in the frequency domain. This system has not shown the blocking effect, and improves convergence rate as compared with the LOT based feedback canceller.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.305-316
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• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2706-2713
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• 1997

The fast implementation algorithm of M-band discrete wavelet transform is propsed using the factorization of lossless matrix when the length of discrete orthogonal wavelet bases equals to 2M. In computational complexity when direct filtering method is employed, the number of multiplicationand addition is (2M$^{2}$) and (2M$^{2}$ -M), respectively. But by proposed algorithm, it can be reduced to (M$^{2}$+M) and (M$^{2}$+2M-1), respectively. and it is possible to reduce the compuatational complexity further when unitary matrix employed to design the discrete or thogonal wavelet basis has the fast algorithm.

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Proceedings of the KSRS Conference
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• v.1
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• pp.243-246
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• 2006

Wavelet approach is regarded as a useful methodology for geo-environment analysis with respect to spatial objects with periodicity and spatial pattern, compared to autocorrelation analysis, Fourier analysis, variogram analysis and so on. However, there are a few case studies for geo-lineament characterization with the actual geo-based information such as remotely sensed imagery and DEM. In this study, wavelet approach in the Uiseung caldera region are carried out to delineate characterization for geolineament spatial pattern. There are high possibilities of the development of radial lineaments from the centre of round crater due to the eruption of a volcano and the subsidence of a crater. We have grasped the directionality of the whole linear structures of the caldera via rose diagram, and then performed wavelet analysis on the profiles of orthogonal directions for main directions of the lineaments. The result of this study is likely to be used as a fundamental data in order to grasp the outline of caldera structure prior to the close estimation

• Journal of Broadcast Engineering
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• v.4 no.1
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• pp.1-11
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• 1999

This paper introduces orthogonally multiplexed modulation and demodulation methods based on Wavelet Packet Bases and particularly describes Wavelet Packet Modulation (WPM) techniques that provide the designer of transmission signal set in time-frequency domain with tree structural information which can be adapted to given channel characterristics. Multi-dimensional signaling methods are also contrasted to common and different characteristics of conventional QAM. multi-tone modulation methods. The paper addresses the mothod how to find a best tree structure that has more adaptivity to impulse and narrowband tone pulse noises using a tunning algorithm which arbitrarily partitions the time-frequency space and makes a suitable orthogonal signaling waveforms. Simulation results exhibits a favorable performance over existing mod/demod methods specially for narrowband tone pulse and impulse interferences.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.511-516
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• 2002

The neural networks may have problem such that the amount of calculation for the network learning goes too big according to the dimension of the dimension. To overcome this problem, the wavelet neural networks(WNN) which use the orthogonal basis function in the hidden node are proposed. One can compose wavelet functions as activation functions in the WNN by determining the scale and center of wavelet function. In this paper, when we compose the WNN using wavelet functions, we set a single scale function as a node function together. We intend that one scale function approximates the target function roughly, the other wavelet functions approximate it finely During the determination of the parameters, the wavelet functions can be determined by the global search for solutions suitable for the suggested problem using the genetic algorithm and finally, we use the back-propagation algorithm in the learning of the weights.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1015-1018
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• 2002

We show how any discrete wavelet transform or two band subband filtering with finite filters can be decomposed onto a finite sequence of simple filtering steps, which we call lifting steps but that are also known as ladder structures, We present a self-contained derivations, building the decomposition from the basic principles such as the Euclidean algorithm, with a focus on a applying it to wavelet filtering. This factorization provides an alternative for the lattice factorization, with the advantage that it can also be used in the bi-orthogonal, i.e, non-unitary case. Lifting leads to a speed-up when compared to the standard implementation. We show that this lifting scheme can be applied in image compression efficiently