• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.505-508
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• 2005

Wavelet scheme can be applied to the various remote sensing problems: conventional multi-resolution image analysis, compression of large image sets, fusion of heterogeneous sensor image and segmentation of features. In this study, we attempted wavelet-based filtering and its analysis. Traditionally, statistical methods and adaptive filter are used to manipulate noises in the image processing procedure. While we tried to filter random noise from optical image and radar image using Discrete Wavelet Transform (DW1) and Stationary Wavelet Transform (SW1) and compared with existing methods such as median filter and adaptive filter. In result, SWT preserved boundaries and reduced noises most effectively. If appropriate thresholds are used, wavelet filtering will be applied to detect road boundaries, buildings, cars and other complex features from high-resolution imagery in an urban environment as well as noise filtering

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.45-50
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• 2010

In this study, Discrete Wavelet Transform(DWT), which can detect more correct QRS complex, approximated through impulse response for reducing complexity to suit real-time system during exercise. Also, rhythm information, which is Arrythmia, Bradycardia and Tachycardia, is provided through continuously monitoring QRS complex. Proposed algorithm is evaluated by computer simulation of ECG signal that is measured during exercise.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.298-300
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• 2004

In the decoding process of interframe wavelet coding, the inverse wavelet transform requires huge computational complexity. However, the decoder may need to be used in various devices such as PDAs, notebooks, PCs or set-top Boxes. Therefore, the decoder's complexity should be adapted to the processor's computational power. A decoder designed in accordance with the processor's computational power would provide optimal services for such devices. So, it is natural that the complexity scalability and the low complexity codec are also listed in the requirements for scalable video coding. In this contribution, we develop a method of controlling and lowering the complexity of the spatial wavelet transform while sustaining almost the same coding efficiency as the conventional spatial wavelet transform. In addition, the proposed method may alleviate the ringing effect for certain video data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.149-152
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• 2000

This paper addresses the wavelet fuzzy modeling using Virus-Evolutionary Genetic Algorithm (VEGA). We build a fuzzy system model which is equivalent to the wavelet transform after identifying the coefficients of wavelet transform. We can obtain an accurate system model with a small number of coefficients due to the energy compaction property of the wavelet transform. It thus means that we can construct a fuzzy system model with a small number of rules. In order to identify the wide-ranged coefficients of the wavelet transform, VEGA is adopted, which has prominent ability to avoid premature local convergence that is suitable to complex optimization problems. We demonstrate the superiority of our proposed fuzzy system modeling method over the previous results by modeling nonlinear function.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.243-252
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• 2005

Wavelet transform and its inverse processing provide the effective framework for data fusion. The purpose of this study is to investigate applicability of wavelet transform using texture images for the urban remote sensing application. We tried several experiments regarding image fusion by wavelet transform and texture imaging using high resolution images such as IKONOS and KOMPSAT EOC. As for texture images, we used homogeneity and ASM (Angular Second Moment) images according that these two types of texture images reveal detailed information of complex features of urban environment well. To find out the useful combination scheme for further applications, we performed DWT(Discrete Wavelet Transform) and IDWT(Inverse Discrete Wavelet Transform) using texture images and original images, with adding edge information on the fused images to display texture-wavelet information within edge boundaries. The edge images were obtained by the LoG (Laplacian of Gaussian) processing of original image. As the qualitative result by the visual interpretation of these experiments, the resultant image by each fusion scheme will be utilized to extract unique details of surface characterization on urban features around edge boundaries.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.369-378
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• 2021

This paper reviewed a few output-only system identification algorithms and identified the shortcomings of those popular blind source separation methods. To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain. The wavelet coefficients with different dominant frequencies can be used to address the under-determinate problem, while complex mode shapes are addressed by introducing the complex wavelet transformation. Numerical simulations with both high and low signal-to-noise ratios validate that CWMSOBI can overcome the above-mentioned issues while obtaining more accurate identified results than other blind identification methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.13-19
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• 2012

There are many dynamic properties in measured end-milling force. So, it is difficult to predict the real static property of end-milling force. Also the behavior of end-milling force is very complex to predict with the measured one. To extract the static property from measured force, it must be filtered and its problem is closely related to a de-noising one. Also this paper presents alternative de-noising method of end-milling force using wavelet filter bank, based on the wavelet transform and its inverse one. In this paper, by comparing the measured force and its wavelet filtered one, the fundamental end-milling force property after wavelet transform is well reviewed and analyzed. This result of wavelet filtering with filter bank shows the static force of end-milling which has severe dynamic properties occurring in entry and exit state of edge emersion into the workpiece.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.10-16
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• 2004

The detection and analysis of chatter behaviour in endmilling is very complex and difficult so it is necessary to detect and diagnose this chatter phenomenon clearly. This paper presents a new method for detecting the abnormal chatter in endmilling operation, based on the wavelet transform. Using AR spectrum the data that has chatter phenomenon was verified and the fundamental property of chatter and its characteristics in endmilling by using the wavelet transform is reviewed. This result obtained by wavelet transform proves the possibility and reliability of detecting the chatter in endmilling operation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.