• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.40 no.4
• /
• pp.34-41
• /
• 2003

A new technique for detecting parametric faults in mixed signal circuits is proposed Pseudo-random sequence from linear feedback shift register(LFSR) is fed to circuit-under-test (CUT) as stimulus and wavelets are used to compact the transient response under this stimulus into a small number of signature. Wavelet based scheme decomposes the transient response into a number of signal in different frequency bands. Each decomposed signal is compacted into a signature using digital integrator. The digital pulses from LFSR, owing to its pseudo-randomness property, are almost uniform in frequency domain, which generates multi-frequency response when passed through CUT. The effectiveness of this technique is demonstrated in our experimental results.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.263-270
• /
• 2004

Recently, wavelet transform have been applied to various kinds of problems in many fields. In this paper, we propose method of Daubechies wavelet to detect several kinds of important characteristic waves in tasks EEG that are needed to diagnose EEG. We show that our system could be attained higher performance in detecting characteristic waves than the other methods. In this system, the architecture of the neural network is a three layered feed-forward networks with one hidden layer which implements the error back propagation teaming algorithm. Applying the algorithms to 4 subjects show 92% classification rates. The proposed system shows a little more accurate diagnosis for task EEG by Wavelet and neural network. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and quantitative diagnosis of task EEG.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.724-726
• /
• 2004

본 논문에서는 말스버그가 주장한 가보 웨이블릿을 기반으로 하는 얼굴 인식과 표정에 강인한 얼굴 인식에 대한 내용을 소개하였다. 표정을 분류하는 방법론에 대한 연구는 활발한 편이지만, 유사한 표정을 지니는 타인에 대한 구분이라던가, 동일인의 다양한 표정을 한 사람으로 정확히 인식하는 연구는 전무한 실정이다. 본 논문에서는 얼굴을 구성하는 특징 중에서 표정에 가장 많이 영향을 받는 특징을 분석하기 위한 실험과정과 결과, 그리고 근거를 제시하였고, 그에 따르는 방법론에 대한 연구를 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.419-422
• /
• 2002

본 논문에서는 신경망에 비해 보다 단순화되고 빠르게 수렴하는 특성을 보이는 방사 기준함수 구조를 초기에 설계하기 위한 초기화 방법을 제안한다. 이를 위해 웨이블릿 변환을 이용한 분석 기법을 사용하였고, 주어진 문제에 적합한 방사 기준 함수 구조를 초기에 최적 상태로 결정하였다. 시간-주파수 평면에서 지역화 특성이 대상 함수를 근사할 수 있는 특성을 지닌 방사 기준 함수를 선택, 결정하여 은닉층을 구성할 경우, 근사 능력을 지닌 초기 구조를 결정함에 있어서 장점을 지닌다. 제안된 구조는 다층 전방향 신경망 또는 정규 배열된 방사 기준함수 구조에 비해 주어진 문제에 대해 좋은 성능을 보인다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10a
• /
• pp.820-822
• /
• 2003

웨이블릿 변환 영역에 제안한 알고리즘으로 여러 공격에 강인한 워터마크를 삽입하고 성능을 분석하였다. 영상의 저작권 보호를 위해 웨이블릿 변환영역의 LH, HL, HH의 중대역에 128비트열의 워터마크를 삽입하였다. 제안한 방법의 강인성을 실험하기 위해 명도 변화, 콘트라스트 변화, 노이즈, 가우시안 등 과 같은 영상처리를 하였다. 실험결과 화질의 열화를 최소로 하여 워터마크를 삽입하여 높은 효율을 얻었다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2002.04a
• /
• pp.157-166
• /
• 2002

원격탐측의 다양한 응용분야 중 저해상도의 다중분광(multispectral) 영상으로부터 고해상도의 영상을 생성하기 위한 영상융합의 연구가 진행되어 오고 있다. 지금까지 융합 결과에 있어서 공간해상력은 향상되었지만 영상의 질에 있어서는 그다지 만족스럽지 못한 결과를 보여주고 있다. 본 연구에서는 최근 여러 분야에서 응용되고 있는 웨이블릿 변환을 이용하여 영상융합을 시도 하고자 한다. 실험영상으로 2001년 11월에 촬영된 대전지역의 IKONOS 공간 해상력 1m 전정색(panchromatic)영상과 4m의 다중분광영상(Blue, Green, Red, NIR)을 이용하여 Daubechies 웨이블릿기반 영상 융합방법을 통해 1m의 칼라영상을 생성하였으며 기존에 일반적으로 사용되고 있는 방법과 그 결과를 비교 분석하였다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.4
• /
• pp.521-526
• /
• 2015

Research on how to use the biological characteristics of human to confirm the identity of the individual is being actively conducted. Electrocardiogram(: ECG) based biometric system is difficult to counterfeit and does not cause skin irritation on the subject. It can be easily combined with conventional biometrics such as fingerprint and face recognition to give multimodal biometric systems. In this thesis, biometric identification method analysing ECG waveform characteristics from Discrete Wavelet Transform(DWT) coefficients is suggested. Feature selection is performed on the 9 coefficients of DWT using the correlation analysis. The verification is achieved by using the error back propagation neural networks. Using the proposed approach on 24 subjects of MIT-BIH QT Database, 98.88% verification rate has been obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.422-423
• /
• 2018

In this paper, we implement Frenelet transform for decomposition of the fringe pattern and analyze its characteristics. The implemented wavelet-like basis functions are well suited for reconstruction and processing of optically generated Fresnel holograms. After analyzing the characteristics of the B-spline function, we will discuss the wavelet-like multi-resolution analysis method. Through this process, we implemented a transform tool that can decompose fringe patterns effectively. We have implemented a B-spline function with various decomposition properties and showed the results of decomposing the fringe pattern.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.489-492
• /
• 2005

In this paper, we present a system for robust face detection based on wavelet transform using the standard models of image distortion. In the previous works, it was known to be difficult to treat a distorting of image information such as noises and light of the images obtained by a still camera and a movie camera. we analyze the high frequence information by using wavelet transform. This information is used for testing the image distortion and constructing the standard models of image distortion. The experimental results show that our extracting method based on standard models of image distortion is very effective.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.821-827
• /
• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.