• Kyungpook Mathematical Journal
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• v.14 no.2
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• pp.267-272
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• 1974

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.809-812
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• 2001

본 논문에서는 AMR(Adaptive Multi-Rate)과 MLT (Modulated Lapped Transform) 벡터 양자화 방법을 이용하여 광대역 음성부호화기를 설계하였다. 제안한 음성부호화 알고리즘은 split-band 구조를 가지고 있으며 16kHz로 샘플링 된 신호를 입력받아 QMF 필터에 의해 두 개의 대역으로 나누어, 각각 8kHz 샘플링 신호로 변환시킨 후 저대역(0Hz-3400Hz)의 신호와 고대역(3400Hz -7000Hz)의 신호로 나누어 각각 부호화한다. 나누어진 두 개의 협대역 음성신호는 AMR(Adaptive Multi-Rate)부호화기와 MLT (Modulated Lapped Transform)벡터 양자화 방법을 사용하여 각각 부호화되어 전송된다. 수신단에서는 각 대역을 AMR과 IMLT(Inverse MLT) 벡터 양자화 방법으로 역부호화하여 음성신호를 합성한다. 제안한 음성부호화기는 20.2kbps에서 12.15kbps까지의 다전송률로 동작된다. 설계된 광대역 음성부호화기는 MOS시험 결과로부터 G.722의 56 kbps 음성이 설계된 코더의 20.2 kbps와 비슷한 음질을 갖음을 확인할 수 있었다.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.783-786
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• 2000

본 논문에서는 웨이브렛 변환을 적용한 광대역 음성부호화 알고리즘을 제안하였다. 제안한 음성부호화 알고리즘은 split-band 구조를 가지며, 16 kHz로 sampling된 입력신호를 QMF를 이용해서 동일한 대역폭을 갖는 두 개의 subband 신호로 나누고 이를 8kHz의 sampling율을 갖도록 downsampling 한다. 그리고 저대역 신호는 GSM-EFR 음성부호화 알고리즘을 이용하여 부호화하고, 고대역 신호는 DWT(Discrete Wavelet Transform)을 적용하여 subband로 나누어 부호화하였다. 각 subband에서 양자화 된 파라미터는 IDWT(Inverse DWT)과정을 거쳐서 upsampling되고 합성 QMF를 통과시켜 최종 합성음을 구하였다. 제안한 음성부호화기는 저대역 신호의 GSM-EFR 부호화에 12.2 kbps, 웨이브렛 변환을 이용한 고대역 신호의 부호화에 7.8 kbps로 전체 20 kbps의 전송율을 가지면서 G.722 표준안의 56 kbps에서의 합성음과 비슷한 음질을 나타내었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.112-115
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• 2010

본 논문은 그림 1과 같은 네트워크 환경에서 두 송신단이 LT code를 오류정정 부호로 사용할 때, 두 송신단이 생성하는 인코딩 심볼과 수신단이 수신하는 인코딩 심볼들의 degree distribution의 관계에 대해 다룬다. LT code를 복호하기 위해 belief propagation 방법을 사용했을 때, 수신단이 받은 인코딩 심볼들의 degree distribution은 robust soliton distribution(RSD)을 따를 때, overhead 대비 가장 높은 확률로 복호에 성공한다. 하지만 그림 1과 같은 네트워크 환경에서, 두 송신단 모두 RSD에 따라 인코딩 심볼을 생성하여 송신하면, 수신단에서 수신한 심볼은 RSD를 따르지 않는다. 본 논문은 한 송신단($S_1$) 이 생성하는 인코딩 심볼의 degree distribution을 알 때, 수신단에서의 인코딩 심볼의 분포가 근사적으로 RSD를 따르도록 하는 또 다른 송신단($S_2$)에서의 degree distribution을 구하는 방법을 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.864-867
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• 2005

In this paper we introduce a design of parallel architecture for wavelet transformation on FPGA. We implement wavelet transforms though lifting scheme and apply Daubechies4 transform equations. This technique has an advantage that we can obtain perfect reconstruction of the data. We divide our process to high pass filter and low pass filter. With this division, we can find coefficients from low and high pass filters simultaneously using parallel processing properties of FPGA to reduce processing time. From the equations, we have to design real number computation module, referred to IEEE754 standard. We choose 32 bit computation that is fine enough to reconstruct data. After that we arrange the real number module according to Daubechies4 transform though lifting scheme.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2611-2613
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• 2000

This paper deals with a novel approach to unknown inputs observer(UIO) design for linear time-invariant dynamical systems using a fast Walsh transform and Walsh function's differential operation. Generally, UIO has a derivation of system outputs which is not available from the measurement directly. And it is an obstacle to estimate the unknown inputs properly when unexpected measurement noises are presented. Therefore, this paper propose an algebraic approach to eliminate such problems by using a Walsh function's differential operation.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.192-199
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• 1999

We propose a rotation invariant fingerprint identification system based on the circular harmonic filter(CHF) and binary phase extraction joint transform correlator(BPEJTC) for validation and security verification. It is shown that this system has the shift and rotation robust properties and can recognize the fingerprint in real-time. The complex circular harmonic filter, which is used to obtain the rotation invariance, is converted into the real-valued filter for real-time implementation. Experimental results show that this system has a good performance in the rotated fingerprints.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1385-1392
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• 2013

There is a variety of researches about recognition system using biometric data these days. In this study, we propose a new algorithm, uses simultaneous equation that made of the edge of objects, to segment an iris region without threshold values from an anterior eye image. The algorithm attempts to find a center area through calculated outskirts information of an iris, and decides the area where the most points are accumulated. To verify the proposed algorithm, we conducted comparative experiments to Hough transform and Daugman's method, based on 50 images anterior eye images. It was found that proposed algorithm is 5 and 75 times faster than on each algorithm, and showed high accuracy of detecting a center point (95.36%) more than Hough transform (92.43%). In foreseeable future, this study is expected to useful application in diverse department of human's life, such as, identification system using an iris, diagnosis a disease using an anterior image.