• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2956-2958
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• 1999

The classical solution to the noise removal problem is the Wiener filter, which utilizes the second-order statistics of the Fourier decomposition. We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most application. For the prior specified, the posterior median yields a thresholding procedure

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.759-769
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• 2005

We first discuss jump discontinuity in higher dimension, and then prove a local convergence theorem for wavelet approximations in higher dimension. We also redefine the concept of Gibbs phenomenon in higher dimension and show that wavelet expansion exhibits Gibbs phenomenon.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.5 no.1
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• pp.37-42
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• 2006

A reversible audio watermarking algorithm is presented in this paper. This algorithm transforms the audio signal with the integer wavelet transform first in order to enhance the correlation between neighbor audio samples. Audio signal has low correlation between neighbor samples, which makes it difficult to apply difference expansion scheme. Second, a novel difference expansion scheme is used to embed more data by reducing the size of location map. Therefore, the difference expansion scheme used in this paper theoretically secures high embedding capacity under low perceptual distortion. Experiments show that this scheme can hide large number of information bits and keeps high perceptual quality.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.50-57
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• 1996

In this paper, we proposed the method of optimal wavelet selection and wavelet expansion of AR(autoregressive) parameters by selected wavelet using F-test. A cost function is introduced as a wavelet selection method. Using this cost function, wavelets (D4 to D20) are tested to the synthesized signal. With this selected wavelet, we get the wavelet coefficients of AR parameters to both synthesized signal and real speech signal. To evaluate the proposed method, this wavelet based algorithm is compared with the Kalman filering algorithm. As a results, the proposed method shows a better performance by about 5-10dB than the Kalman filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1828-1834
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• 2007

This study proposes a new digital watermarking technique based on wavelet transformation on color image. First the $YC_bC_r$ coordinates obtain from RGB color space. then, the correlation of watermark is decreased by Arnold transformation. Next, watermark which has been enlarged by Linear Bit-expansion is inserted at a given intensity in Color images' low frequency sub-bands. When detecting the presence of watermark, F-norm function is applied. As a result of the various experiments on color images, the proposed watermarking technique has outstanding quality in regards to fidelity and robustness.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.657-666
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• 2002

ThE Gibbs' phenomenon in the classical Fourier series is well-known. It is closely related with the kernel of the partial sum of the series. In fact, the Dirichlet kernel of the courier series is not positive. The poisson kernel of Cesaro summability is positive. As the consequence of the positiveness, the partial sum of Cesaro summability does not exhibit the Gibbs' phenomenon. Most kernels associated with wavelet expansions are not positive. So wavelet series is not free from the Gibbs' phenomenon. Because of the excessive oscillation of wavelets, we can not follow the techniques of the courier series to get rid of the unwanted quirk. Here we make a positive kernel For Meyer wavelets and as the result the associated summability method does not exhibit Gibbs' phenomenon for the corresponding series .

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.55-59
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• 2007

Wavelet Transform is amenable to efficient algorithms. So wavelet transform was adopted many signal processing and communication applications. For example, the wavelet transform was adopted as the transform for JPEG2000. However, Wavelet has weakness about smoothness along the contours and limited directional information. Hence, recently, some new transforms have been introduced to take advantage of this property. So we use to other transform, called contourlet transform in compression. In this paper, we propose a new method for image compression based on the contourlet transform, which has been recently introduced. Contourlet transform has a good result about images with smooth contours. Moreover, Contourlet is feasible multiresolution and multidirection expansion using non-separable filter bank. This treatise shows a good image representation after compressing using contourlet transform.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.613-620
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• 2004

The concept of Gibbs’ phenomenon has not been made for higher dimension in wavelets. In this paper we extend the concept in two dimensional wavelets. We give the fundamental concept of jump discontinuity in two dimensions. We provide the criteria for the existence of Gibbs phenomenon for both separable and tensor product wavelets.

• Journal of the Korean Society of Radiology
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• v.2 no.1
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• pp.23-29
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• 2008

The present study proposed a method that dissolves ultrasonographic images into multiple resolutions using wavelet conversion and a boundary detection filter and improves the quality of ultrasonographic images through boundary detection filtering. In order to reduce noises and strengthen edges, the proposed method adjusted selectivity coefficient by area step by step from a low resolution image obtained from wavelet converted images to a high resolution image and performed edge filtering in consideration of direction. Through this method, we generated a selective low pass filtering effect in areas except edges by decreasing the wavelet coefficient for pixels in spot areas, improved continuity by smoothing edges in the tangential direction, and enhanced contrast by thinning in the normal direction. Through an experiment, we compared the filtering method using a non linear anisotropic expansion model and the filtering method using wavelet contraction structure in single resolution.