• Journal of KSNVE
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• v.8 no.5
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• pp.957-963
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• 1998

A new signal processing technique, the directional harmonic wavelet map(dHWM), is presented to characterize the instantaneous planar motion of a measurement point in a structure from its transient complex-valued vibration signal. It is proven that the directional auto-HWM essentially tracks the shape and directively of the instantaneous planar motion, whereas the phase of the directional cross-HWM indicates its inclination angle. Finally, the technique is suessfully applied to an automobile engine for characterization of its transient motion during crank-on/idling/engine-off.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.143-152
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• 2000

As muscular contraction is sustained, the Fourier spectrum of the myoelectric signal is shifted toward the lower frequency. This spectral density is associated with muscle fatigue. This paper describes a quantitative measurement method that performs the measurement of localized muscle fatigue by tracking changes of median frequency based on stationary wavelet transform. Applying to the human masseter muscle, the proposed method offers the much information for muscle fatigue, comparing with the conventional FFT-based method for muscle fatigue measurement.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.397-400
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• 2002

This paper has been studied speech enhancement method in noisy environment. By mean of that we prefer human auditory mechanism which is perfect system and applied wavelet transform. Multi-resolution of wavelet transform make possible multiband spectrum analysis like human ears. This method was verified very effective way in noisy speech enhancement.

• Nuclear Engineering and Technology
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• v.56 no.11
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• pp.4688-4697
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• 2024

Accurate nuclide identification in γ-spectrum analysis of low- and intermediate-level radioactive waste with high-purity germanium detectors necessitates initial forced fitting with a nuclide library, yet inaccuracies in library data may lead to misidentification and missing nuclides. To this end, background clipping strategies were hereby analyzed, and a novel deconvolution spectrum analysis method was proposed, which utilized continuous wavelet transform for peak searching and Gaussian first-order derivative quadratic convolution for calculating peak width. Furthermore, to effectively realize the nuclide identification and peak area calculation, a response filter function model was established through the peak shape calibration. By eliminating the need for nuclide library parameter settings prior to overlapping peak separation, the issue of inaccurate matching arising from reliance on the precision of the nuclide library was addressed. Moreover, spectrum analysis experiments were carried out on standard point sources and 200 L drums, and the results were compared and analyzed using GammaVision. Compared to the GammaVision results set by the accurate nuclide library, the area error of strong peaks decreased from 27.5 % to 4.82 %, while that of weak peaks witnessed a decline from 49.98 % to 27.5 %. Finally, the accuracy of the proposed method was verified using the Pakistan Nuclear Library.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.389-395
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• 2000

Digital Watermarking is a research area which aims at hiding secret information in digital multimedia content such as images, audio, and video. In this paper, we propose a new watermarking method with visually recognizable symbols into the digital images using wavelet transform, spread spectrum method and multilevel threshold value in considering the wavelet coefficients. The information of watermark can be extracted by subtracting wavelet coefficients with the original image and the watermarked image. The results of this experiment show that the proposed algorithm was superior to other similar watermarking algorithms. We showed Watermarking algorithm in JPEG lossy compression, resizing, LSB(Least Significant Bit) masking, and filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2434-2453
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• 2019

To improve the separation performance of time-frequency overlapped radar and communication signals from a single channel, this paper proposes an effective separation method based on an improved empirical wavelet transform (EWT) that introduces a fast boundary detection mechanism. The fast boundary detection mechanism can be regarded as a process of searching, difference optimization, and continuity detection of the important local minima in the Fourier spectrum that enables determination of the sub-band boundary and thus allows multiple signal components to be distinguished. An orthogonal empirical wavelet filter bank that was designed for signal adaptive reconstruction is then used to separate the input time-frequency overlapped signals. The experimental results show that if two source components are completely overlapped within the time domain and the spectrum overlap ratio is less than 60%, the average separation performance is improved by approximately 32.3% when compared with the classic EWT; the proposed method also improves the suitability for multiple frequency shift keying (MFSK) and reduces the algorithm complexity.

• Journal of the Korean Magnetic Resonance Society
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• v.5 no.2
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• pp.66-72
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• 2001

The noise suppression of time domain NMR data by discrete wavelet transform with high order Daubechies wavelet coefficients exhibits severe peak distortion and incomplete noise suppression near real signal. However, the fact that even a shift averaged Harr wavelet transform with a set of Daubechies wavelet coefficients (1/2, -l/2) can be used as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signal is introduced. New algorithms of shift averaged Harr wavelet were developed and quantitatively evaluated in terms of threshold and signal to noise ratio (SNR).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.155-162
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• 2008

In this paper we proposed a video watermarking algorithm based on a three dimension discrete wavelet transform (3D DWT) and direct spread spectrum (DSS). In the proposed method, the information watermark is embedded into followed frames, after sync watermark is embedded into the first frame. Input frames are divided into sub frames which are located odd row and even row. The sub frames are arranged as 3D frames, and transformed into 3D wavelet domain. In this domain the watermark is embedded using DSS. Existing video watermarking using 3D DWT is non-blind method but, proposed algorithm uses blind method. The experimental results show that the proposed algorithm is robust against frame cropping, noise addition, compression, etc. acquiring BER of 10% or below and sustains level of 40dB or above on the average.

• Journal of Korean Ophthalmic Optics Society
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• v.17 no.2
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• pp.203-207
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• 2012

Purpose: Electroretinogram(ERG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of retinal-related diagnosis with removing signal noise. Methods: Sampling signal was made with generating 60 Hz noise and white noise. The noise were removed using wavelet transforms and bandpass filter. De-noising frequency was compared with Fourier transform spectrum. Removed noises were compared numerically using SNR(signal to noise ratio). Results: The result compared Fourier transform spectrum was showed that 60 Hz noise removed completely and most of white noise was removed by wavelet transforms. 60 Hz and the white noise remained using bandpass filters. The result compared SNR showed that wavelet transforms was 22.8638 and bandpass filter was 4.0961. Conclusions: Wavelet transform showed less signal distortion in removing noise. ERG signal is expected to improve the accuracy of retinal-related diagnosis.

• Progress in Medical Physics
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• v.10 no.3
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• pp.141-149
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• 1999

This research analyzed the HRV signals by using wavelet transform to observe the activities of autonomous nervous system in a sleep state. This research also restructured the HRV signals from electrocardiogram and by using coefficient which was obtained through wavelet transform, analyzed the signals by frequency bandwidth. Then compared the analyzed results with existing frequency analyzing method using AR model techniques. The suggested wavelet coefficient from power spectrum component in the study shows a similar tendency with the results from FFT or AR model technique. Therefore, it can be found that power spectrum analyzing method by wavelet coefficient is a useful as a tool for analyzing autonomous nervous system activities using HRV signals. Since the suggested method able to clearly depict the progression of change in time zone, which was once impossible with the existing methods, it is presumed that it will be useful in other physiological signals.