• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2217-2224
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• 2014

This paper deals with system development which measures 12 channel ECG using medical analog front end(AFE) and discriminates arrythmia through signal analysis. Recently, occurrences of cardiac arrest have been increased. So the need of system that diagnoses an arrythmia which results in cardiac arrest is increasing. There are some drawbacks of conventional 12 channel ECG system that it occupies bulk and consists of complicated circuit. To improve those, we made up the system composed of medical AFE, algorithm for discriminating arrythmia and DSP for signal processing. This system can be monitored 12 channel ECG waveforms and the discriminant analysis result of arrhythmia through 7" LCD and received the input through touch pannel. In this study, we conducted normal operation test about output signal of ECG simulator(normal/abnormal ECG signal) to verify the implemented system and performance evaluation of the optimization process for applying arrhythmia algorithm to an embedded environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.512-515
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• 2010

SIGINT(SIGnal INTelligence) includes several parameters intercepted by measurement and analysis of the RF(Radio frequency) signal from free space. One of the important parameters is frequency information. Expecially, in order to perform instantaneous frequency measurement of Radar and Missile seeker's RF signals, we use dedicated RF modules as a DFD(Digital Frequency Discriminator) to provide frequency information by measurement of the relative phase difference between signals via intended RF delay lines. It must measure and provide realtime based frequency information on short pulsed RF signal up to 100 nSec or less. This document proposes Ultra wideband DFD consisted of a RF input section of Wideband 4 channel RF delay line and correlator, a digital processing section to measure and provide frequency information from I/Q signal, and a frequency calibration section. Also, it will show design suitability based on test results measured under test condition of very short input pulse signals.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.70-73
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• 2010

최근 IT 기술이 융합된 지능형 자동차 기술에 대한 관심이 높아짐에 따라 이에 대한 연구가 활발히 진행되고 있다. 차선 검출은 지능형 자동차의 주요 과제인 첨단 안전자동차 기술의 핵심적인 부분으로 국내외에서 다양한 방법들에 대한 연구가 진행되었다. 차량의 안전을 향상시키기 위해서는 충분한 제동거리 확보가 가능한 거리까지 정확하고 빠른 차선 검출이 이루어져야 한다. 기존의 경계선 검출기반 차선 검출은 소실점 근처에서 경계선 검출이 이루어지지 않았다. 이는 차선과 도로의 색이 잘 구분되지 않는 채널을 사용하는 문제에서 기인한다. 따라서 본 논문에서는 선형 판별 분석 기법을 이용하여 차선과 도로 색을 가장 잘 구분할 수 있는 RGB 가중치를 계산하여 이로부터 projection 영상을 만들고, 변환한 영상에서 경계선 검출을 수행함으로써 보다 정확한 경계선 검출 결과를 얻는 방법을 제안한다. 제안한 방법으로 얻은 영상과 기존의 흑백 영상에 동일한 경계선 검출기를 적용하여 성능을 비교하고, 이를 적용한 차선검출 실험결과를 제시한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.70-75
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• 2014

Super-resolution near-field structure (super-RENS) read-out samples are affected by a nonlinear and noncausal channel, which results in inter-symbol interference (ISI). In this study, we investigate asymmetry or domain bloom in super-RENS in terms of equalization. Domain bloom is caused by writing process in optical recording. We assume in this work that the asymmetry symbol conversion scheme is to generate asymmetric symbols, and then a linear finite impulse response filter can model the read-out channel. For equalizing this overall nonlinear channel, the read-out signals are deconvolved with the finite impulse response filter and its output is decided based on the decision rule table that is developed from the asymmetry symbol conversion scheme. The proposed equalizer is investigated with the simulations and the real super-RENS samples in terms of raw bit error rate.

• Information Systems Review
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• v.20 no.4
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• pp.59-78
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• 2018

With the advent of the various device types and marketing communication, customer's search and purchase behavior have become more complex and segmented. However, extant research on multichannel marketing effects of the purchase funnel has not reflected the specific features of device User Interface (UI) and User Experience (UX). In this study, we analyzed the marketing channel effects of multi-device shoppers using a unique click stream dataset from global online retailers. We examined device types that activate online shopping and compared the differences between marketing channels that promote visits. In addition, we estimated the direct and indirect effects on visits and purchase revenue through customer's accumulated experience and channel conversions. The findings indicate that the same customer selects a different marketing channel according to the device selection. These results can help retailers gain a better understanding of customers' decision-making process in multi-marketing channel environment and devise the optimal strategy taking into account various device types. Our empirical analyses yield business implications based on the significant results from global big data analytics and contribute academically meaningful theoretical framework using an economic model. We also provide strategic insights attributed to the practical value of an online marketing manager.

• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.99-106
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• 2003

In this paper, a method of discriminating EEG for diagnoses of mental activity is proposed. The proposed method for classification of schizophrenia and normal EEG is based on the wavelet transform and the artificial neural network. The wavelet coefficients of $\alpha$ band, $\beta$ band, $\theta$ band, and $\delta$ band are obtained using the wavelet transform. The magnitude, mean, and variance of wavelet coefficients for each EEG band are applied to the input data of the system's ANN. The architecture of the ANN s a four layered feedforward network with two hidden layer which implements the error back propagation learning algorithm. Through the classification of schizophrenia composed of 19 ANNs corresponding to 19 channels, the classifying system show that it can classify the 100% of the normal EEG group and the 86.67% of the schizophrenia EEG group.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.81-88
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• 2018

Spectrum sensing, the key technology of the cognitive radio networks, is used by a secondary user to determine the frequency state of a primary user. The energy detection in the spectrum sensing determines the presence or absence of a primary user according to the intensity of the allocated channel signal. Since this technique simply uses the strength of the signal for spectrum sensing, it is difficult to detect the signal of a primary user in the low SNR band. In this paper, we propose a way to combine spectrum sensing and support vector machine using wavelet packet decomposition to overcome performance degradation in low SNR band. In our proposed scheme, the sensing signals were extracted by wavelet packet decomposition and then used as training data and test data for support vector machine. The simulation results of the proposed scheme are compared with the energy detection using the AUC of the ROC curve and the accuracy according to the SNR band. With simulation results, we demonstrate that the proposed scheme show better determining performance than one of energy detection in the low SNR band.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.182-187
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• 2003

In this thesis, we propose the algorithm to improve the performance of speaker verification that is mapping feature parameters by using RBF neural network. There is a big difference between wire vector region and wireless one which comes from the same speaker. For wire/wireless speakers model production, speaker verification system should distinguish the wire/wireless channel that based on speech recognition system. And the feature vector of untrained channel models is mapped to the feature vector(LPC Cepstrum) of trained channel model by using RBF neural network. As a simulation result, the proposed algorithm makes 0.6%∼10.5% performance improvement compared to conventional method such as cepstral mean subtraction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1B
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• pp.79-89
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• 2002

A synchronization method is presented for IEEE 802.11a wireless OFDM system. First the coarse symbol synchronization is achieved by measuring the moving power average of the received envelope signal. The detection probabilities and optimum thresholds for the symbol synchronization are derived. By measuring the correlation between the short training signal and received envelope signal, fine symbol synchronization can be acquired. And the frequency synchronization is achieved using long training signal. A symbol synchronization error causes a phase rotation of the constellation. After the compensation for fading channel, the rotation due to the symbol timing error can be corrected. With this method, synchronization can be well achieved over frequency selective channels.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.310-315
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• 2015

A single channel VAD (Voice Activity Detection) algorithm for nonstationary noise environment is proposed in this paper. Threshold values of the feature parameter for VAD decision are updated adaptively based on estimates of means and standard deviations of past non-speech frames. The feature parameter, SPD-TE (Spectral Power Difference-Teager Energy), is obtained by applying the Teager energy to the WPD (Wavelet Packet Decomposition) coefficients. It was reported previously that the SPD-TE is robust to noise as a feature for VAD. Experimental results by using TIMIT speech and NOISEX-92 noise databases show that decision accuracy of the proposed algorithm is comparable to several typical VAD algorithms including standards for SNR values ranging from 10 to -10 dB.