• Biomedical Engineering Letters
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• v.8 no.4
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• pp.373-382
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• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.486-493
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• 2002

In this paper, we proposed the effective multispectral image compression method using CIBP(classified interband bidrectional prediction) and extended SPIHT(set partition in hierarchical trees) in wavelet domain. We determine separately feature bands that have the highest correlation with other bands in the visible range and in the infrared range of wavelengths. Feature bands are coded to remove the spatial redundancy with SPIHT in the wavelet domain. Prediction bands that have high correlation with feature bands are wavelet transformed and they are classified into one of three classes considering reflection characteristics of the baseband. For Prediction bands, CIBP is performed to reduce the spectral redundancy. for the difference bands between prediction bands and the predicted bands, They are ordered to upgrade the compression efficiency of extended SPIHT with the largest error magnitude. The arranged bands are coded to compensate the prediction error with extended SPIHT. Experiments are carried out on the multispectral images. The results show that the proposed method reconstructs higher quality images than images reconstructed by the conventional methods at the same bit rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2460-2470
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• 2009

In this study, the parameter extraction for evaluation of the anesthesia depth in each anesthesia stages was conducted. An object of the this experiment study has studied 5 adult patients (mean $\pm$ SD age:$42{\pm}9.13$), ASA classification I and II, undergoing surgery of obstetrics and gynecology. Anaesthesia was maintained with Enflurane. HRV signal was created by R-peak detection algorithm form ECG signal. The HRV data were preprocessing algorithm. It has tried find out the anesthesia parameter which responds the anesthesia events and shows objective anesthesia depth according to anesthesia stage including pre-anesthesia, induction, maintenance, awake and post-anesthesia. In this study, proposed algorithm to analysis the HRV(heart rate variability) signal using wavelet transform in anesthesia stage. Three sorts of wavelet functions applied to PSD. In the result, all of the results were showed similarly. But experiment results of Daubeches 10 is better. Therefore, this parameter is the best parameter in the evaluation of anesthesia stage.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.981-989
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• 2011

In this paper, we performed radar target classification for the five scale models using ultra-wideband measured signal. In order to compare the performance, the 2 GHz(2~4 GHz), 4 GHz(2~6 GHz), and 6 GHz(2~8 GHz) bandwidth were used. Short time Fourier transform(STFT) and continuous wavelet transform(CWT) are used for target feature extraction. Extracted feature vectors are used as input for the multi-layerd perceptron(MLP) neural network classifier. The results show that as the bandwidth is wider, the performance is better.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.163-166
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• 2000

In this paper, we will present a lossy data compression method for coding multispectral images. The proposed method uses both spatial and spectra] correlation inherent in multispectral images. First, band 2 and band 6 are vector quantized. Secondly, band 4 is estimated with the quantized band 2 using the predictive coding. Errors of band 4 are encoded at a second stage based on the magnitude of the errors. Thirdly, remaining bands are calculated with the quantized band 2 and band 4. Errors of residual bands are wavelet transformed and then we apply the SPIHT coding on the transformed coefficients. We classify classes without extra information transmitting and then use linear predictor. And errors can be encoded by SPIHT coding at any target rate we are want. It is shown that this method has better performance than FPVQ. Average PSNR rises 0.645 dB at the same bit rate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.110-113
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• 2005

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted fuzzy Membership Funcstions, NEWFM)을 이용하여 심전도 신호로부터 조기심실수축(Premature Ventricular Contraction, PVC)을 판별하는 퍼지규칙을 추출하고 있다. NEWFM은 자기적응적(self adaptive) 가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이를 기반으로 정상 파형과 PVC 파형을 구분한다. 분류 성능 평가를 위하여 MIT/BIH 부정맥 데이터 베이스를 사용하였으며, NEWFM의 입력은 심전도의 파형에 웨이블릿 변환을 적용하여 추출된 웨이블릿 계수를 사용하였다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 계수를 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 추출된 2개의 웨이블릿 계수를 사용한 퍼지규칙은 $96\%$의 PVC 분류성능을 보여준다.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.14-19
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• 2007

2차원 웨이블릿이나 3차원 웨이블릿 변환은 주파수 방향으로 나타나는 분광특성을 고려할 수 있는 장점이 있다. 그러나 다중분광 영상에서 3차원 웨이블릿 변환을 이용하여 분류한 연구사례는 발표되거나 보고된 사례가 거의 없다. 따라서 본 연구에서는 기존의 전통적인 분류기법에 의한 처리결과를 제시하고 3차원 웨이블릿 변환 계수와 에너지 변수량들을 이용한 분류 처리결과를 분류 정확도 측면에서 비교하여 분석하였다. 3D 웨이블릿의 경우 공간적인 변화양상과 주파수에 따른 분광정보의 변화 양상을 동시에 알려주는 계수로 표현되기 때문에 본 연구의 처리 기법은 다양한 분광특성을 지니는 객체들이 조밀하고 복합적으로 구성되어 있는 도시지역의 고 해상도 위성영상자료에 효과적으로 적용될 수 있다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.4
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• pp.45-54
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• 1999

본 논문에서는 웨이브렛 변환 대역에서 영역분할 기법을 적용하여 얻어진 각 영역을 중요 영역과 비 중요 영역으로 분류하고 각각의 영역을 그의 특성에 적합한 방식으로 부호화 하는 기법을 제안하였다. 중요 영역은 전체 영역가운데 매우 작은 부분을 차지하지만 영상 복원에 매우 큰 영향을 주기 때문에 이러한 영역 부호화를 위해 기존의 EZW 방식보다 성능이 우수하며 단일계수 전송에 성능이 뛰어난 SPIHT 알고리즘을 적용하였다. 그러나 비 중요영역은 영상복원에 미치는 영향이 적을 뿐만 아니라, 매우 큰 동질 영역을 형성하기 때문에 텍스춰 모델링을 이용할 경우 높은 압축률을 얻을 수 있다. 또한 이 방식을 이용할 경우 인위적인 에러가 거의 없기 때문에 이용할 경우 높은 압축률을 얻을 수 있다. 또한 이 방식을 이용할 경우 인위적인 에러가 거의 없기 때문에 시각적으로도 좋은 영상을 복원 할 수 있다. 실험결과 제안한 시스템은 다양한 영상에 대하여 적응성이 있음을 보였고 특히 0.2bpp 이하의 매우 낮은 비트 율에서도 EZW 와 같은 기존의 웨이브렛 기반 부호화기보다 좋은 성능을 나타내었다.