• 디지털산업정보학회논문지
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• 제8권2호
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• pp.131-143
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• 2012

This paper introduces the double-density discrete wavelet transform using 3 direction separable processing method, which is a discrete wavelet transform that combines the double-density discrete wavelet transform and quincunx sampling method, each of which has its own characteristics and advantages. The double-density discrete wavelet transform is nearly shift-invariant. But there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. The dual-tree discrete wavelet transform has a more computationally efficient approach to shift invariance. Also, the dual-tree discrete wavelet transform gives much better directional selectivity when filtering multidimensional signals. But this transformation has more cost complexity Because it needs eight digital filters. Therefor, we need to hybrid transform which has the more directional selection and the lower cost complexity. A solution to this problem is a the double-density discrete wavelet transform using 3 direction separable processing method. The proposed wavelet transformation services good performance in image and video processing fields.

• 한국인터넷방송통신학회논문지
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• 제19권1호
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• pp.69-75
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• 2019

과표본화 이산 웨이브렛 변환의 가장 대표적으로 응용되는 분야는 디지털 영상에 존재하는 잡음을 제거하는 기술이다. 이중 밀도 이산 웨이브렛 변환을 이중 트리 이산 웨이브렛 변환과 비교하면, 거의 유사한 특징을 가진다. 본 논문에서는 잡음이 포함된 디지털 영상에 여러 이산 웨이브렛 변환들을 수행하고 생성된 부대역에 임계값 처리 기법을 적용하여 잡음을 제거한 다음 복원한 영상의 성능을 평가하는 실험을 수행하였다. 적당한 임계값을 설정하여 효과적인 잡음제거가 가능하다. 본 논문에서는 여러 방법의 실험 결과에서 제안하는 3방향 분리처리 2차원 이중 밀도 이산 웨이브렛 변환 방법이 우수하다는 것을 확인할 수 있었다.

• 산업기술연구
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• 제19권
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 추계학술대회 논문집
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• pp.169-173
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• 2001

This study presents the results of decoupling of free decay roll test data by discrete wavelet transform. Free roll decay test was performed to decide the coefficients of damping terms in equation of motion. During the experiment, a slight yaw motion was found while the model was in the free roll decay motion. Discrete wavelet transform was applied to the signal to extract the pure roll motion. The results were compared to those of the Fourier transform. DWT was able to decouple the two signals efficiently while the Fourier transform was not.

• 디지털산업정보학회논문지
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• 제8권3호
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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.

• 디지털산업정보학회논문지
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• 제10권3호
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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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.585-591
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• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• 디지털산업정보학회논문지
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• 제9권1호
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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.

• 한국전자통신학회논문지
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• 제7권4호
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• pp.727-732
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• 2012

본 논문은 DCT, DWT와 역전파 신경망을 이용하여 MIT-BIH 부정맥 데이터베이스의 심전도 신호로부터 정상파와 부정맥 분류를 제안하였다. 역전파 신경망에 사용할 특징입력을 추출하기 위해 첫 번째 단계에서는 DCT 변환을 이용하여 15개의 계수를 선택하였다. 두 번째 단계에서는 DWT 변환 후 각 detail 계수들의 최대값, 최소값, 평균, 분산, 표준편차를 추출하였다. 역전파 신경망은 55개의 특징입력을 이용하여 정상파와 부정맥 파형을 분류하였고, 98.8%의 분류 성능을 나타냈다.

• 방송공학회논문지
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• 제17권2호
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• pp.344-353
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• 2012

본 논문에서는 이산 웨이블릿 변환(Discrete Wavelet Transform: DWT)을 이용한 새로운 초고해상도 기법을 제안한다. 기존의 단일 영상에 적용되는 초고해상도 기법들의 경우 영상에서의 고주파 대역을 찾기 위하여 확률 기반의 방법들을 사용하였다. 그로 인한 연산의 복잡도 증가는 처리시간 증가라는 문제점을 발생시켰다. 제안된 기법에서는 고주파 대역을 찾기 위한 방법으로 DWT를 이용한다. DWT 수행 시 수반되는 다운 샘플링 과정을 수행하지 않음으로써 입력 받은 영상과 동일한 크기의 고주파 부대역(sub-band)들을 생성하고, 이 부대역들과 입력 받은 영상을 조합하여 이산 웨이블릿 역변환(Inverse Discrete Wavelet Transform: Inverse DWT)을 수행함으로써 고해상도의 영상을 획득한다. 제안하는 기법에서 사용한 실험영상은 원본영상($512{\times}512$)을 다운 샘플링하여 획득한 실험영상($256{\times}256$)을 사용한다. 실험을 통하여 제안된 기법이 기존의 보간법에 비해 향상된 효율을 보이며, 확률 기반의 기법들에 비해 처리시간이 줄어드는 것을 확인하였다.