• 전자공학회논문지CI
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• 제37권3호
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• pp.45-55
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• 2000

본 논문에서는 영상처리를 위한 웨이브렛 변환 디지틀 필터 설계의 하드웨어 구조를 제안한다. 웨이브렛 변환을 위하여 필터 뱅크 피라미드 알고리즘을 이용하고 각각의 필터는 FIR 필터로 구현하였다. 그리고 메모리 제어기를 하드웨어로 구현하여 DWT 계산이 수행되므로 단순한 파라미터 입력만으로 영상 데이터의 다중해상도 분해를 효율적으로 처리할 수 있었다. 본 논문에서의 영상처리 결과는 FPGA의 하드웨어적 제한으로 인한 11bit의 가수처리 때문에, 512×512 흑 백영상에 대하여 33㏈의 PSNR이 나타났다. 그리고 QMF(Quadrature Mirror Filter)의 특성을 이용하여 DWT(Discrete Wavelet Transform) 계산에 필요한 승산기의 수를 절반으로 줄임으로써 하드웨어의 크기도 감소하였다. 그러므로 제안된 방법은 하드웨어 크기의 감소에 따른 영상처리의 효율성을 증대할 수 있다. DWT 필터 뱅크의 제안된 하드웨어 설계는 VHDL 코딩으로 설계합성을 하여 테스트 보드가 제작되었으며, 실행프로그램은 MFC++로, 영상복원 디코드 응용프로그램은 C++언어를 이용하여 구현하였다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국인터넷방송통신학회논문지
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• 제9권6호
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• pp.155-161
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• 2009

SAR영상의 가장 큰 문제점은 경계선 부근에서 스패클(Speckle)잡음을 어떻게 줄이느냐 하는 것이다. 본 논문에서는 제안한 방법을 이용하여 경계선을 보존할 수 있는 효과적인 필터를 개발하고자 한다. 스패클 잡음을 줄이면서 에지 영역에 대한 블러링 없는 영상을 추출하기 위하여 웨이브렛 기반의 sigma 필터를 적용하였다. 실험 결과 에지정보에 대한 블러링을 줄인 출력 영상을 구성하였다. 제안한 방법을 미디언 필터와 비교한 결과, 스패클 잡음을 효과적으로 제거한 우수한 영상을 얻을 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.376-379
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• 1999

The wavelet transform chip is implemented with Daubechies' 4 tap filter. It works at 20MHz in Field Programmable Gate array (FPGA) implementation of Quadrature Mirror Filter(QMF) Lattice Structure. In this paper, the structure contains taro-channel quadrature mirror filter, data format converter(DFC), delay control unit(DCU), and three 20$\times$8 bits real multiplier. The structures for the DFC and DCU need to he regular and scalable, require minimum number of regular, and thereby lead to an efficient and scalable architecture for the Discrete Wavelet Transform(DWT). These results present the possibility that it can be used in Digital Signal Processing(DSP) application faster than Fourier transform at small area with lour cost.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.1077-1080
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• 2002

In building acoustics, reverberation time is an important acoustic parameter. However, it is often difficult to measure short reverberation times at low frequencies using the traditional band pass filter bank if the product of bandwidth (B) and reverberation time (T) is small. It is well known that the minimum permissible product of bandwidth and reverberation time of the traditional band pass filter is at least 16 ［F. Jacobsen, J. Sound Vib. 115, 163-170 (1987)]. This strict requirement makes it difficult to measure short reverberation times of an acoustic room at low frequencies exactly. In order to reduce this strict requirement, recently, the wavelet filter bank is developed and the minimum permissible product of bandwidth and reverberation time is replaced with 4 [S. K. Lee, J, Sound Vib. 252, 141-153 (2002)]. In the present paper, it is demonstrated how the short reverberation times at low frequencies are successfully measured by using the wavelet filter bank. In order to present this job, two synthetic signals and one measured signal are used for impulse responses of an acoustic room.

• 전자공학회논문지B
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• 제31B권12호
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• pp.20-26
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• 1994

Lossless system has been employed to design the perfect reconstruction filter banks and has particularly a close relation with the desing of orthogonal wavelet filter (OWF). With such a relation, we generalize 2-band OWF to the M-band OWF which has an improved performances. The improvement is achieved using the two techniques. One is that the wavelet low pass filter has (N-1)th order regularity with extra zeros while the existing filter has N-th order regularity. The other is that unitary matrix is designed by adding the zeros to the proper positions. As a result, M-band OWF designed by propose method has better performance than M-band OWF designed by exsiting method.

• 대한수학회지
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• 제46권2호
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• pp.281-294
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• 2009

Accuracy of the scaling function is very crucial in wavelet theory, or correspondingly, in the study of wavelet filter banks. We are mainly interested in vector-valued filter banks having matrix factorization and indicate how to choose block central symmetric matrices to construct multi-wavelets with suitable accuracy.

• 한국통신학회논문지
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• 제35권11C호
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• pp.871-876
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• 2010

현재 전력선 통신은 기술의 발전으로 인하여 고속통신이 가능하게 되었다. 하지만 전력선 통신에 이용되는 전력선은 통신용 배선이 아닌 전력을 실어 나르는 배선이기 때문에 고주파를 전송하다 보면 의도치 않게 무선통신시스템에 영향을 주게 된다. 이러한 단점을 보완하기 위하여 notch 필터를 이용하여 간섭을 줄이는 연구가 진행되고 있다. 이와는 다르게 Wavelet기반 OFDM 방법을 이용하여 간섭을 줄이는 방법이 사용되고 있다. Wavelet기반 OFDM은 기존의 전력선에서 사용되던 FFT를 이용한 일반적인 OFDM구조를 대신하여 CMFB 필터구조를 이용하여 신호를 생성한다. 이렇게 함으로써, 주파수대 마다 세세하게 신호를 컷 해, 고효율 고속도를 실현하는 방법이다. 이는 깊은 필터 특성을 가져, 유연한 노치필터를 외부 회로 없이 실현할 수 있다는 장점이 있다. 본 논문에서는 Wavelet OFDM 기법을 이용한 전력선 통신 시스템을 설계하고 시뮬레이션 하여 그 결과를 제시하였다.

• 대한의용생체공학회:의공학회지
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• 제19권3호
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• pp.269-278
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• 1998

본 논문에서는 스트레스 심전도를 분석함에 있어서 가장 중요한 파라미터인 ST 세그먼트를 측정하기 위해서 웨이브렛 변환을 이용하여 Wavelet Adaptive Filter(WAF)와 QRS콤플렉스 검출 알고리즘을 설계하였다. WAF는 웨이브렛 변환부와 적응필터부로 구성되어 있으며, 웨이브렛 변환부에서는 웨이브렛 함수를 이용하여 입력되는 심전도 신호를 저주파 대역과 고주파 대역으로 각각 j=-7레벨까지 분할하고, 적응필터부에서는 웨이브렛 변환에 의해 분할된 신호중 j=-7레벨의 저주파 대역 신호를 주입력으로 사용하여 필터링 한다. QRS 콤플레스는 합산신호를 구성한 후 문턱치를 RR간격에 변화에 따라 변화시키면서 검출하였으며, 합산신호는 웨이브렛 변환에 의해 QRS 콤플렉스의 주파수 성분이 포함되어 있는 고주파 대역의 신호를 더하여 구성하였다. WAF는 표준 필터와 일반적인 적응필터와 성능을 비교하였으며, 잡음제거 특성과 신호왜곡도 측면에서 비교필터에 비해 우수한 성능을 보였다. QRS 콤플렉스 검출성능을 평가하기 위해서 MIT/BIH데이터베이스를 이용하여 기존의 QRS 검출 알고리즘들의 검출 방법과 비교하였으며, 웨이브렛에 의한 합산신호를 이용할 경우에 99..67%로써 더 좋은 검출성능을 보였다. 또한 측정된 ST세그먼트의 정확도를 비교.평가를 위하여 European ST-T 데이터베이스와 실제 임상데이터를 이용하였으며 심박수의 변화에 따라 적응적으로 ST세그먼트를 측정할 수 있었다.