• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.583-586
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• 1997

In this study, we proposed an application of wavelet transform or analysis of ultrasound echo signals to improve troubles of convenianced methods such as SDM, SSM. We examined method using wavelet transform to prove again our proposal which we have proposed prior time. At first, we made phantoms by adding 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05($g/cm^3$) on constant quantity of distilled water and agar, and collected echo signals. We used SDM(spectral difference method) and WTM(wavelet transform method) as signal processing method. To compare with WTM, SDM was used. In WTM, we selected detail signals of level 3 of Daubechies 16, and got derivative, calculated area of it. Next, we calculated slopes. In SDM, it was 0.0308 and in WTM, it was 0.5248. As a result, we knew that we could know that the values using WTM showed more detailed than those using SDM. So we could concluded wavelet transform is very useful and powerful in ultrasound tissue characterization.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.172-177
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• 2007

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of the reduction method for expedient fault analysis and protection assessment.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.93-100
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• 2007

In this paper, a novel approach based on wavelet transform is proposed to process the scraped character which is represented on digital image. The basis idea is that the scraped character is described by its textured neighborhood, and it is decomposed into multiresolution features at different levels with its background region. The image is first decomposed into sub bands by applying Daubechies wavelets. Character features are extracted from the low frequency sub-bands by partition, FCM clustering and area-based region process. High frequency ones are activated by applying local energy density over a moving mask. Features are synthesized in order to reconstruct the original image state through inverse wavelet transform Background region is eliminated and character is extracted. The experimental results demonstrate the effectiveness of the proposed method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.165-176
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• 2013

This paper introduces the high density discrete wavelet transform using quincunx sampling, which is a discrete wavelet transformation that combines the high density discrete transformation and non-separable processing method, each of which has its own characteristics and advantages. The high density discrete wavelet transformation is one that expands an N point signal to M transform coefficients with M > N. The high density discrete wavelet transformation is a new set of dyadic wavelet transformation with two generators. The construction provides a higher sampling in both time and frequency. This new transform is approximately shift-invariant and has intermediate scales. In two dimensions, this transform outperforms the standard discrete wavelet transformation in terms of shift-invariant. Although the transformation utilizes more wavelets, sampling rates are high costs and some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. A solution to this problem is a non separable method. The quincunx lattice is a non-separable sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Proposed wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, This method services good performance in image processing fields.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.133-145
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• 2012

This paper describes the high density discrete wavelet transformation which is one that expands an N point signal to M transform coefficients with M > N. The double-density discrete wavelet transform is one of the high density discrete wavelet transformation. This transformation employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. And it is nearly shift-invariant. Similarly, triple-density discrete wavelet transformation is a new set of dyadic wavelet transformation with two generators. The construction provides a higher sampling in both time and frequency. Specifically, the spectrum of the first wavelet is concentrated halfway between the spectrum of the second wavelet and the spectrum of its dilated version. In addition, the second wavelet is translated by half-integers rather than whole-integers in the frame construction. This arrangement leads to high density wavelet transformation. But this new transform is approximately shift-invariant and has intermediate scales. In two dimensions, this transform outperforms the standard and double-density discrete wavelet transformation in terms of multiple directions. Resultingly, the proposed wavelet transformation services good performance in image and video processing fields.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.9
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• pp.1302-1310
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• 2002

A direct numerical simulation in turbulent curved channel flow is performed. The drifting Taylor-Gortler vortices are identified by applying a conditional averaging. A new algorithm is proposed based on the wavelet transform of the wall information. A continuous wavelet transform with Marr wavelets is employed to decompose the flow signals at a chosen length scale. An active cancellation is applied to attenuate the Taylor-Gortler vortices and to reduce the wall skin friction.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.370-384
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• 2016

Computer-aided materials design requires new modeling approaches to characterize and represent fine-grained geometric structures and material compositions at multiple scales. Recently, a dual-Rep approach was developed to model materials microstructures based on a new basis function, called surfacelet. As a combination of implicit surface and wavelets, surfacelets can efficiently identify and represent planar, cylindrical, and ellipsoidal geometries in material microstructures and describe the distribution of compositions and properties. In this paper, these primitive surfacelets are extended and composite surfacelets are proposed to model more complex geometries. Composite surfacelets are constructed by Boolean operations on the primitives. The surfacelet transform is applied to match geometric features in three-dimensional images. The composition of the material near the identified features can then be modeled. A cubic surfacelet and a v-joint surfacelet are developed to demonstrate the reverse engineering process of retrieving material compositions from material images.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.168-174
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• 2009

An application of wavelet analysis to power system transient generated signals is presented in this paper. With the time-frequency localisation characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualised scheme. This feature is very important for non-stationary signals analysis such as the ones generated from power system disturbances. Unlike the Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. For time intervals where the function changes rapidly, this method can zoom in on the area of interest for better visualisation of signal characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1580-1586
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• 2009

Wavelet transform is utilized to the field of the signal processing and the digital communication. In this paper, the performance for wavelets is analyzed for Haar and Daubechies series in the wavelet shift keying. It is mainly utilized to Haar, Daubechies 4tap, 8tap and 12tap in this paper. The analysis scheme is utilized by the eye pattern and the error probability. As a results of simulation, we confirmed that the proposed scheme was superior to performance when the number of the filler coefficient is small.