• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.6
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• pp.76-86
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• 1999

In this paper, it is shown that the car number plate are segmented and extracted more efficiently by using wavelet transform. A car image is decomposed by wavelet transform, and the high frequency image of the decomposed image are selected as feature images. Three selected feature images are synthesized of a single feature image, and a region including the plate is segmented by the correlation coefficient between the feature image and the synthesized image. For segmented plate region, the car plate region is extracted by deciding the Y-axis region composed by vertical region, the car plate region is extracted by deciding the Y-axis region composed by vertical histogram and the X-axis region composed by the variance histogram. Some experiment results of the various image and shown. It has been shown from the results with the high rate of 96% that the car number plates can be segmented and extracted more extractly and efficiently than converntional method.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.13-22
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• 2017

Emotion has a direct influence such as decision-making, perception, etc. and plays an important role in human life. For the convenient and accurate recognition of high-arousal negative emotion, the purpose of this paper is to design an algorithm for analysis using the bio-signal. In this study, after two emotional induction using the 'normal' / 'fear' emotion types of videos, we measured the Galvanic Skin Response (GSR) signal which is the simple of bio-signals. Then, by decomposing Tonic component and Phasic component in the measured GSR and decomposing Skin Conductance Very Slow Response (SCVSR) and Skin Conductance Slow Response (SCSR) in the Phasic component associated with emotional stimulation, extracting the major features of the components for an accurate analysis, we used a discrete wavelet transform with excellent time-frequency localization characteristics, not the method used previously. The extracted features are maximum value of Phasic component, amplitude of Phasic component, zero crossing rate of SCVSR and zero crossing rate of SCSR for distinguishing high-arousal negative emotion. As results, the case of high-arousal negative emotion exhibited higher value than the case of low-arousal normal emotion in all 4 of the features, and the more significant difference between the two emotion was found statistically than the previous analysis method. Accordingly, the results of this study indicate that the GSR may be a useful indicator for a high-arousal negative emotion measurement and contribute to the development of the emotional real-time rating system using the GSR.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.56-62
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• 1993

OWT is a tool for block transform coding with wavelet basis functions that overlap adjacent blocks. The OWT can reduce the block effect. In this paper performances of OWT are evaluated for AR model. Some simulation results show that performances are nearly same to DCT, but block effect is reduced to very low level.

• Journal of Industrial Technology
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• v.17
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• pp.331-338
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• 1997

In this paper, we propose a new neural pattern recognition from wavelet transform. We first analysis in BFT(Binary Field Transform) in character image. The proposed neural network and wavelet transform is able to improve learning time and scaling. The ability and effectiveness of identifying image using the proposed wavelet transform will be demonstrated by computer simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.83-93
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• 1996

This paper describes a new method for reducing blocking effet in block coded images using wavelet transform. In this method, all processings are one-dimensionally executed based on the fact that blocking effect occures both horizontal and vertical directions in image. Experimental results show that the proposed method yields PSNR improvement and better subjective quality.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2142_2143
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• 2009

Many waveform characteristics of arc signal support the detection of hazardous arc faults. The shoulder is one of main characteristics of arc signal. This paper provide a method for the detection of shoulder from series arc fault based on discrete wavelet transform. The simulation results show that the proposed method is reliable and simple.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.305-307
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• 2006

The choice of threshold is an important in the compression algorithms. It plays a major role in the removal of coefficients which are not preserved the important features. In this paper two threshold method, hard threshold(HTH) and soft threshold(STH) are applied to the suggested wavelet based compression algorithm of power quality signal and compared the compression ratio and NMSE. The simulation results show that STH is bottom than HTH.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.589-592
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). The earlier noise reduction techniques can not effectively cancellation complex noise from the noisy ECG such powrline interference, baseline drift, muscle artifact. In this paper, we performed the extrude noise from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2244-2247
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• 2003

In this paper, we present a method which can minimize distortion from desired signal in thoracic sound signal processing. We firstly chose the proper wavelet mother function to reduce noise components. Secondly, we chose a clean thoracic sound, then added Gaussian noise and 3 step(10, 15, 20db) uniform noise to it. Finally, the various wavelet functions are applied for noise cancellation. To evaluate the efficiency of this study, we computed SNR and RSE value. Then we found the optimal mother wavelet function for thoracic sound.