• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.344-353
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• 2012

In this paper, we propose a super-resolution algorithm using discrete wavelet transform. In general super-resolution algorithms for single-image, probability based operations have been used for searching high-frequency components. Consequently, the complexity of the algorithm causes the increase of processing time. In the proposed algorithm, we use discrete wavelet transform to find high-frequency sub-bands. We perform inverse discrete wavelet transform using input image and high-frequency sub-bands of the same resolution as the input image which are obtained by performing discrete wavelet transform without down-sampling and then we obtain image with high-resolution. In the proposed algorithm, we use the down-sampled version of the original image ($512{\times}512$) as a test image ($256{\times}256$) to compare the performance of algorithms. Through experimental results, we confirm the improved efficiency of the proposed algorithm comparing with conventional interpolation algorithms and also decreased processing time comparing the probability based operations.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.777-783
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• 2008

Nowadays wavelet transforms are widely used for the analyses of PIV velocity vector fields. This is bemuse the wavelet provides not only spatial information of the velocity vectors but also of time and frequency domains. In this study, a discrete wavelet trC1f1$form has been applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform The performances of the discrete wavelet transform is investigated by changing the level of power of discretization. The decomposed images by the wavelet multiresolution showed conspicuous characteristics of the bubbly flows according to the level changes. The high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, at which high leveled wavelets could play a dominant roles to reveal the flow characteristics.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.237-247
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• 2014

This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.473-478
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• 2001

Image processings using the two-dimensional wavelet transform (2DWT) have been a very active research area in recent years because the 2DWT possess many good properties. However, the discrete 2DWT can not be used for pattern recognition directly because it does not have the translation property. In this paper, we show why conventional discrete two-dimensional wavelet transforms cannot be used for pattern recognitions directly. Then, we propose a new method that makes it possible to use discrete 2DWT to pattern recognition without modification of standard pyramidal algorithms. The main idea of our method is to postprocess the wavelet transformed images using the second-order neural network. To justify the validity of the method, evaluations with test images were performed. The effectiveness of the method can be shown by the evaluation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1139-1145
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• 2002

Wavelet transformed data can filter signal with each frequency band, because it includes detail information about original signal. Therefore, in this paper, important two noises were removed by wavelet. About AWGN environment UDWT(undecimated discrete wavelet transform), applying hard-threshold, was used and about impulse noise environment, it can be possible to recognize edge of original signal as well as superior denoising effect by using two methods, denoising by threshold and slope of signal by wavelet. SNR was used as a judgemental criterion of a denoising effect and Blocks and DTMF(dual tone multi frequency) were used as a test signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.853-860
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• 2001

This paper presents the new two denoising methods using wavelet. One is new spatially selective noise filtration(NSSNF) using spatial correlation and the other is undecimated discrete wavelet transform (UDWT) threshold-based. NSSNF got the flexible gain special property of SNR adding new parameter at the existing SSNF and UDWT had superior denosing effect than orthogonal wavelet transform(OWT) applied soft-threshold by applied hard-threshold. We selected additive white gaussian noise(AWGN) in this test environment. Also we analyzed and compared ousting denoising method using SNR as standard of judgement of improvemental effect.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.461-469
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• 2000

A wavelet analysis in a field of analytics is to create a reconstruction theorem of Plancherel form. And a series of wavelet is to create a discrete is to create a discrete reconstruction theorem for a frame theory and a multiresolution analysis theory. As a generation of reconstruction theorem, a wavelet correspond to it is generated. That is to be like a basic wavelet which is satisfied an admissibility condition in CWT and a Daubechies wavelet using MRA in wavelet series and a Meyer wavelet using a frame theory. In this paper, we discover a discrete reconstruction theorem which is superior to a conventional discrete reconstruction theorem by extending admissibility condition used in CWT and develop a New $L^1$-wavelet group. A new $L^1$-wavelet is applied to a signal reconstruction and a signal analysis in time-frequency region.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.727-732
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• 2012

This paper presents an approach to classify normal and arrhythmia from the MIT-BIH Arrhythmia Database using Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) and neural network. In the first step, Discrete Cosine Transform is used to obtain the representative 15 coefficients for input features of neural network. In the second step, Discrete Wavelet Transform are used to extract maximum value, minimum value, mean value, variance, and standard deviation of detail coefficients. Neural network classifies normal and arrhythmia beats using 55 numbers of input features, and then the accuracy rate is 98.8%.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.309-314
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• 2003

Impact Echo method has been successful in detecting a variety of defects in concrete structure. This study has the objectives to show important aspects of applying the Discrete Wavelet Transform(DWT) to signal processing of Modified Impact Echo(ModIE) Measurement systems and to the understanding of the seismic wave propagation. The data of ModIE were processed by DWT and compared with the results of conventional ModIE Analysis. Although it is inconsistent in the evaluated thickness of concrete lining, the DWT provides the features of separation, synthesis and de-noising in the original signal. The application of technique by wavelet was explained numerically with ABAQUS and performed experimentally with a real scale model in this work. Further works on the possible ways for creating new mother wavelet are specially needed for the enhancement of seismic signal analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.2
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• pp.126-135
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• 2011

In this study, numerical analysis was carried out by using the finite element method to construct the first mode shape of damaged stiffened plates, and the damage locations were detected with two-dimensional discrete wavelet analysis. In the experimental analysis, four different damaged stiffened structures were observed. Firstly, each damaged structure was hit with a shaker, and then accelerometers were used to measure the vibration responses. Secondly, the first mode shape of each structure was obtained by using the wavelet packet, and the location of cracks were also determined by two-dimensional discrete wavelet analysis. The results of the numerical analysis and experimental investigation reveal that the proposed method is applicable to detect single crack or multi-cracks of a stiffened structure. The experimental results also show that fewer measurement points are required with the proposed technique in comparison to those presented in previous studies.