• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.435-438
• /
• 1997

One of the main techniques or diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source such 60Hz powerline interference, motion artifact and baseline drift. in this paper, we performed the extracting parameters from and recovering the ECG signal using wavelet transform that has recently been applying to various fields.

• Smart Structures and Systems
• /
• v.8 no.3
• /
• pp.303-320
• /
• 2011

Comparing to active damage monitoring, impact localization on composite by using time reversal focusing method has several difficulties. First, the transfer function of the actuator-sensor path is difficult to be obtained because of the limitation that no impact experiment is permitted to perform on the real structure and the difficulty to model it because the performance of real aircraft composite is much more complicated comparing to metal structure. Second, the position of impact is unknown and can not be controlled as the excitation signal used in the active monitoring. This makes it not applicable to compare the difference between the excitation and the focused signal. Another difficulty is that impact signal is frequency broadband, giving rise to the difficulty to process virtual synthesis because of the highly dispersion nature of frequency broadband Lamb wave in plate-like structure. Aiming at developing a practical method for on-line localization of impact on aircraft composite structure which can take advantage of time reversal focusing and does not rely on the transfer function, a PZT sensor array based phase synthesis time reversal impact imaging method is proposed. The complex Shannon wavelet transform is presented to extract the frequency narrow-band signals from the impact responded signals of PZT sensors. A phase synthesis process of the frequency narrow-band signals is implemented to search the time reversal focusing position on the structure which represents the impact position. Evaluation experiments on a carbon fiber composite structure show that the proposed method realizes the impact imaging and localization with an error less than 1.5 cm. Discussion of the influence of velocity errors and measurement noise is also given in detail.

• Smart Structures and Systems
• /
• v.12 no.3_4
• /
• pp.345-361
• /
• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.8
• /
• pp.691-697
• /
• 2002

Myocardial ischemia is a disorder of cardiac function caused by insuficient blood flow to the muscle tissue of the heart. We can diagnose myocardial ischemia by observing the change of ST-segment, but this change is temporary. Our primary purpose is to detect the temporary change of the 57-segment automatically In the signal processing, the wavelet transform decomposes the ECG(electrocardiogram) signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily. Amplitude comparison method is adopted to detect QRS complex. Reducing the effect of noise to the minimum, we grouped ECG by 5 data and compared the amplitude of maximum value. To recognize the ECG .signal pattern, we adopted the polynomial approximation partially and statistical method. The polynomial approximation makes possible to compare some ECG signal with different frequency and sampling period. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. After removing the distorted ECG by calculating the difference between the orignal ECG and the approximated ECG for polynomial, we compared the approximated ECG pattern with the database, and we detected and classified abnormality of ECG.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.10
• /
• pp.49-57
• /
• 2010

In this thesis, We studied on hand-written character recognition, that characters entered into a digital input device and remove noise and separating character elements using preprocessing. And processed character images has done thinning and 3-level wavelet transform for making normalized image and reducing image data. The structural method among the numerical Hangul recognition methods are suitable for recognition of printed or hand-written characters because it is usefull method deal with distortion. so that method are applied to separating elements and analysing texture. The results show that recognition by analysing texture is easily distinguished with respect to consonants. But hand-written characters are tend to decreasing successful recognition rate for the difficulty of extraction process of the starting point, of interconnection of each elements, of mis-recognition from vanishing at the thinning process, and complexity of character combinations. Some characters associated with the separation process is more complicated and sometime impossible to separating elements. However, analysis texture of the proposed character recognition with the exception of the complex handwritten is aware of the character.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.3
• /
• pp.34-41
• /
• 2001

In an IMBE (Improved Multi-band Excitation) speech coder, initial pitch estimation occupies most of the total computing time for the coder due to complex cost function and exhaustive search over candidate pitches. Future frames in initial pitch estimation cause inevitable time delay. Therefore, it is difficult to implement a real-time coder. Furthermore, unvoiced frames use the unnecessary pitch estimation as in the voiced frames. In this paper, each frame is determined voiced or unvoiced by Dyadic Wavelet Transform (DyWT) and, then, initial pitch estimation is performed only for voiced frame. Therefore different pitch estimation algorithms are employed between voiced and unvoiced frames incurring reduced time delay at transmitter and receiver. Simulation result show that the relative complexity of initial pitch estimation is reduced by 23％, and the processing time decreases down to 1/10 ∼ 1/1l of the IMBE coder while speech quality is almost maintained.

• Journal of Broadcast Engineering
• /
• v.26 no.2
• /
• pp.143-154
• /
• 2021

In this paper, we propose a method of compressing digital hologram standardized data provided by JPEG Pleno. In numerical reconstruction of digital holograms, the addition of random phases for visualization reduces speckle noise due to interference and doubles the compression efficiency of holograms. Holograms are composed of completely complex floating point data, and due to ultra-high resolution and speckle noise, it is essential to develop a compression technology tailored to the characteristics of the hologram. First, frequency characteristics of hologram data are analyzed using various wavelet filters to analyze energy concentration according to filter types. Second, we introduce the subband selection algorithm using energy concentration. Finally, the JPEG2000, SPIHT, H.264 results using the Daubechies 9/7 wavelet filter of JPEG2000 and the proposed method are used to compress and restore, and the efficiency is analyzed through quantitative quality evaluation compared to the compression rate.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.1140-1146
• /
• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• Smart Structures and Systems
• /
• v.19 no.2
• /
• pp.195-202
• /
• 2017

The aim of this paper is to develop a novel method to determine the severity of a damage in a thin plate. This paper presents a novel fault detection and diagnosis approach employing a new electromagnetic acoustic transducer, called EMAT, together with a complex signal processing method. The method consists in the recognition of a fault that exists within the structure, the fault location, i.e. the identification of the geometric position of damage, and the determining the significance of the damage, which indicates the importance or severity of the defect. The main scientific novelties presented in this paper is: to develop of a new type of electromagnetic acoustic transducer; to incorporate wavelet transforms for signal representation enhancements; to investigate multi-parametric analysis for noise identification and defect classification; to study attenuation curves properties for defect localization improvement; flaw sizing and location algorithm development.

• Korean Journal of Remote Sensing
• /
• v.26 no.3
• /
• pp.305-316
• /
• 2010

The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.