• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.64-67
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• 2004

Points of sharp variations in images are the most important components when we analyze singularities of images. Therefore a lot of researches for detecting those edges have been continuing even now. However, existing methods do not have excellent performance in the image which exists noise and can not detect edge selectively. In the meantime, the wavelet transform which is presented as a new technique of signal processing field is able to detect multiscale edge and is being applied widely in many fields that analyze singularities such as edge. for this reason, this paper detected image's line-edge elements with 2-D wavelet function, which is independent of line's width, in noise environment.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.189-194
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• 1998

We introduce a new zerotree scheme that effectively exploits the inter-scale self-similarities found in the octave decomposition by a wavelet transform. A zerotree is useful to efficiently code wavelet coefficients and its efficiency was proved by Shapiro's EZW. In the coding scheme, wavelet coefficients are symbolized and entropy-coded for more compression. The entropy per symbol is determined from the produced symbols and the final coded size is calculated by multiplying the entropy and the total number of symbols. In this paper, were analyze produced symbols from the EZW and discuss the entropy per symbol. Since the entropy depends on the produced symbols, we modify the procedure of symbolic streaming out for the purpose. First, we extend the relation between a parent and children used in the EZW to raise a probability that a significant parent has significant children. The proposed relation is flexibly extended according to the fact that a significant coefficient is highly addressed to have significant coefficients in its neighborhood. The extension way is reasonable because an image is decomposed by convolutions with a wavelet filter and thus neighboring coefficients are not independent with each other.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.535-539
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• 2008

With the advancement which development and information communication of the computer is quick about lower with multimedia technical diffusion of network base the necessity the use of the digital image technique which is various regarding the protection of sharp increasing multimedia contents raised its head in the social whole. The copyright protection against a multimedia data hazard the authentication techniques it will be able to confirm the content authentication, ownership authentication, illegal copy of the image which stands is demanded. confronts to the water marking technique of existing and the researches the fact that against a picture is most. In this paper, It does not damage the video data. As inserts copyright information and it protects the copyright. It is with a copyright protection which leads the wavelet transform which is a watermark of frequency base from visual system viewpoint in the human being center with it proposes the watermarking technique against a forgery prevention.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.11-18
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• 2005

In this paper, we proposed a video watermarking algerian based on wavelet transform using statistical characteristic of video according to the energy distribution and the spread spectrum technique. In the proposed method, the original video is splitted by spatial difference metric and classified into the motion region and the motionless region according to the motion degree. The motion region is decomposed into 3-levels using 3D DWT and the motionless region is decomposed into 2-levels using 2D DWT The baseband of the wavelet-decomposed image is not utilized because of the image quality. So that the standard deviation of the highest subband coefficients except for the baseband is used to determine the threshold. Binary video watermarks preprocessed by the random permutation and the spread spectrum technique are embedded into selected coefficients. In computer experiments, the proposed algorithm was found to be more invisible and robust than the conventional algorithms.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.95-98
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• 2003

DCT or Wavelet based on Image compression technique occurred to compression artifact. In order to remove this problem, we can apply many improvement algorithms. But in these cases, these algolithms resulted in bad results against the good quality image. Therefore this paper proposes an automatic image interpretation technique not to apply to the good quality images. In this paper, we exploit the basic nature of DCT.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.7
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• pp.328-336
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• 2005

Power quality has become concern both utilities and their customers with wide spread use of electronic and power electronic equipment. The poor quality of electric power causes malfunctions, instabilities and shorter lifetime of the load. In power system operation, power system disturbances such as faults, overvoltage, capacitor switching transients, harmonic distortion and impulses affects power quality. For diagnosing power quality problem, the causes of the disturbances should be understood before appropriate actions can be taken. In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. This paper deals with the use of a multi-resolution analysis by a discrete wavelet transform to detect power system disturbances such as interruption, sag, swell, transients, etc. We also proposed do-noising and threshold technique to detect power system disturbances in a noisy environment. To find the better mother wavelet for detecting disturbances, we compared the performance of the disturbance detection with the several mother wavelets such as Daubechies, Symlets, Coiflets and Biorthogonals wavelets. In our analysis, we adopt db4 wavelet as mother wavelet because it shows better results for detecting several disturbances than other mother wavelets. To show the effectiveness of the proposed method, a various case studies are simulated for the example system which is constructed by using PSCAD/EMTDC. From the simulation results. proposed method detects time Points of the start and end time of the disturbances.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.1-9
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• 2011

This paper presents a novel scratch detection and removal approach for old film images in wavelet-domain. Various scratch detection and removal algorithms have been proposed for past decades. However, accurate scratch detection and removal with a moderate amount of computing effort is still a problem because of trade off between the quality of the film and computational load. For overcoming this problem, we first decompose an input image using a 3-level wavelet transform, and then remove the scratch by shrinking wavelet coefficients using linear interpolation. Experimental results show that the proposed algorithm can efficiently detect and remove the scratch in damaged films, and also be incorporated into old film restoration systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.160-170
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• 2010

This paper proposes a Haar Wavelet Feature Detector (HWFD) based on the Haar wavelet transform and average box filter. By decomposing the original image using the Haar wavelet transform, the proposed detector obtains the variance information of the image, making it possible to extract more distinctive features from the original image. For detection of interest points that represent the regions whose variance is the highest among their neighbor regions, we apply the average box filter to evaluate the local variance information and use the integral image technique for fast computation. Due to utilization of the Haar wavelet transform and the average box filter, the proposed detector is robust to illumination change, scale change, and rotation of the image. Experimental results show that even though the proposed method detects fewer interest points, it achieves higher repeatability, higher efficiency and higher matching accuracy compared with the DoG detector and Harris corner detector.