• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.59-67
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• 1998

This paper propose a new vector-quantizer design method for coefficients of wavelet transformed images. In conventional wavelet transform, it is quite often to employ wavelet transformed coefficients, not containing images to be encoded, as training sequences for designing a vector-quantizer. This method has a serious drawback ; it is not known how to find a proper set of training images. This paper investigates characteristics of images that should be considered in the design of vector-quantizers for wavelet transformed images. Besides the statistical parameters such as correlation and standard deviation, edge components are shown to characterise wavelet transform images. Training sequences established in accordance with the above knowledge are used in the design of quantizers having guaranteed range of applicable images. Results of computer simulations are shown to demonstrate the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.645-650
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• 2000

The discrete wavelet transform is utilized as processing of neural network(NN) to identifying aging state of internal partial discharge in transformer. The discrete wavelet transform is used to produce wavelet coefficients which are used for classification. The mean values of the wavelet coefficients are input into an back-propagation neural network. The networks, after training, can decide if the test signals is aging early state or aging last state, or normal state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1613-1623
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• 1996

In this paper, an image coding scheme using removal of isolated coefficients and quadtree structure in wavelet transformed domain, is suggested. The number of significant coefficients can be reduced by quantizing in different stages according to the weights of the each band. The quadtree structure preseves the location information while significantly reducing the number of bits required to represent the locality of an image. To increase the efficienty of the wavelet transformed images using quadtree, the energy of the isolated coefficients is analyzed and those with low energy are eliminated. Furthermore, to control the bit rate the entropy of each block is sorted according to its energy concentration and the number of bits required for encoding, and the blocks with low energy are removed to achieve the desired compression rate.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.313-316
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• 2000

Wavelet transform has specific properties for image coding. The property used at this Paper is clustering of significant coefficients across subband. These coefficients are classified in significant coefficient and insignificant coefficient on a threshold value, and symbolized EZW decreases symbol-position information using zero-trees, but threshold value fall for raising resolution, then coding cost of significant coefficients is expensive. To avoid this fact, this paper uses quad-tree representing coefficient-position information. a magnitude of significant coefficient is represented on matrix used at EZW. the proposed algorithm is hoped for raising a coding cost.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.5-28
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• 2001

This paper investigates a classification method of the electrocardiogram (ECG) into different disease categories. The features for the classification of the ECG are the coefficients of the discrete wavelet transform (DWT) of ECG signals. The coefficients are calculated with Haar wavelet, and after DWT we can get 64 coefficients. Each coefficient has morphological information and they may be good features when conventional time-domain features are not available. Since all of them are not meaningful, it is needed to reduce the size of meaningful coefficients set. The distributions of each coefficient can be the rules to classify ECG signal. The optimally reduced feature set is obtained by fuzzy c-means algorithm and rough set theory. First, the each coefficient is clustered by fuzzy c-means algorithm and the clustered ...

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.30-35
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• 1998

Given a noisy sampled at equispaced points with white noise, we consider problems where the signal to be recovered is known to be positive; for example, images, chemical spectra or other measurements of intensities. Shrinking noisy wavelet coefficients via thresholding offers very attractive alternatives to existing methods of recovering signals from noisy data. In this paper, we propose a method of recovering the original signal from a corrupted noisy signal, guaranteeing the recovered signal positive. We first obtain wavelet coefficients by thresholding, and use a nonlinear optimization to find the denoised signal which must be positive. Numerical examples are used to illustrate the performance of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.112-119
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• 2002

We propose an enhanced wavelet packet image coder algorithm which is based on the coefficients partition. The proposed wavelet packet image coder uses the first-order entropy to reduce the total compression time, and achieves low bit rates and rate-distortion performance by the zero-tree based coding using correlations between coefficients partition. This new algorithm represents new parent-children relationships for reducing image reconstruction error using the correlations between each frequency subbands and then the wavelet packet coefficients are Partitioned by a new order. The computer simulations demonstrate higher PSNR under the same bit rate and improved image compression time and enhanced rate control compare with conventional algorithms. From the simulation results, it is shown that the encoding and decoding process of proposed coder are much simple and accurate than present method against texture images , which include many mid-frequency elements.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.189-194
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• 1998

We introduce a new zerotree scheme that effectively exploits the inter-scale self-similarities found in the octave decomposition by a wavelet transform. A zerotree is useful to efficiently code wavelet coefficients and its efficiency was proved by Shapiro's EZW. In the coding scheme, wavelet coefficients are symbolized and entropy-coded for more compression. The entropy per symbol is determined from the produced symbols and the final coded size is calculated by multiplying the entropy and the total number of symbols. In this paper, were analyze produced symbols from the EZW and discuss the entropy per symbol. Since the entropy depends on the produced symbols, we modify the procedure of symbolic streaming out for the purpose. First, we extend the relation between a parent and children used in the EZW to raise a probability that a significant parent has significant children. The proposed relation is flexibly extended according to the fact that a significant coefficient is highly addressed to have significant coefficients in its neighborhood. The extension way is reasonable because an image is decomposed by convolutions with a wavelet filter and thus neighboring coefficients are not independent with each other.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.143-150
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• 2002

This paper presents the application of continuous wavelet transform for determination of dynamic parameters of continuous beam subjected to moving load. Morlet wavelet is used as mother wavelet in wavelet transform. Dynamic parameters are estimated from the magnitudes and arguments of the wavelet coefficients obtained by wavelet transforming the response time histories of joints on the beam. This study shows that the estimated parameters such as natural frequencies, dmping ratios and mode shapes are to be well-compared with those of the finite element analysis.

• Korean Journal of Mathematics
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• v.22 no.2
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• pp.289-305
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• 2014

In this paper we propose a new approach to the variable selection problem for a primary resolution and wavelet basis functions in wavelet regression. Most wavelet shrinkage methods focus on thresholding the wavelet coefficients, given a primary resolution which is usually determined by the sample size. However, both a primary resolution and the basis functions are affected by the shape of an unknown function rather than the sample size. Unlike existing methods, our method does not depend on the sample size and also takes into account the shape of the unknown function.