• Journal of Biomedical Engineering Research
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• v.6 no.1
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• pp.31-36
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• 1985

In this paper, we describe the digital signal-processing for ultrasonic echo signals for the improvement of range resolution. The problem is to find the magnitude of analytic signals that are consistent with the arrival-rate of energy. It is also baaed upon the fact that the shapes of echo signals do not change, although the ampli- tudes and widths of the echo signals become smaller and wider than those of the transmitted signals. We have made the improvement in range resolution by using the quadrature-low pass filter and the area filter which are made on the basis of the theory discussed above.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.957-966
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• 2018

Recently, the demand for remote patient monitoring based on IoT has been increased due to aging population and an increase in single-person household. A non-contact biological signal measurement system using multiple IR-UWB radars for remote patient monitoring is proposed in this paper. To reduce error signals, a multilayer Subtraction algorithm is applied because when the background subtraction algorithm was applied to the biological signal processing, errors occurred such as voltage noise and staircase phenomenon. Therefore, a multilayer background subtraction algorithm is applied to reduce error occurrence. The multilayer background subtraction algorithm extracts the signal by calculating the amount of change between the previous clutter and the current clutter. In this study, the SVD algorithm is used. We applied the improved multilayer background subtraction algorithm to biological signal measurement and computed the respiration rate through Fast Fourier Transform (FFT). To verify the proposed system using IR-UWB radars and multilayer background subtraction algorithm, the respiration rate was measured. The validity of this study was verified by obtaining a precision of 97.36% as a result of a control experiment with Neulog's attachment type breathing apparatus. The implemented algorithm improves the inconvenience of the existing contact wearable method.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.9-15
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• 2019

As the human population increases in the world, the ratio of health doctors is rapidly decreasing. Therefore, it is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies. Usually this happens due to stressful situations during everyday activities including work. This paper presents a machine learning approach to reliably estimating the level of human mental stress using wearable physiological sensors. And also, this paper presents an Android- and Arduino-based stress monitoring and relief system.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2804-2806
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• 2003

생체신호는 생리학이나 해부학에서 주로 다루어졌으나, 최근 컴퓨터 시스템의 발전으로 공학적인 접근이 활발히 진행되고 있다. 특히 뇌의 정보를 보여주는 EEG(Electroencephalogram) 신호의 각 주파수 대역 별 에너지 분석은 의학분야에서도 매우 큰 비중을 두고 있다. 특정 뇌신경 관련질환이 갖는 대역별 주파수 특징과 Spike등을 분석하는 것은 치료와 예방에 아주 좋은 방법의 하나가 될 수 있다. 본 논문에서는 신호처리에서 높은 효율을 보이는 Wavelet Transform을 이용하여 알츠하이머병의 EEG 신호를 분석하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.139-146
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• 1986

Pulsed ultrasonic Doppler system is a useful diagnostic instrument to measure blood-flow-velocity, velocity profile, and volume-blood-flow. This system is more powerful compare with 2-dimensional B-scan tissue image. A system has been deve- loped and ii being evaluated using TMS 32010 DSP. We use this DSP for the purpose of real-time spectrum analyzer to obtain spectrogram in singlegate pulsed Doppler system and for the serial comb filter to cancel clutter and zero crossing counter to estimate Doppler mean frequency in multigate pulsed Doppler system. The Doppler shift of the backscattered signals is sensed in a phase detector. This Doppler signal corresponds to the mean velocity over a some region in space defined by the ultrasonic beam dimensions, transmitted pulse duration, and transducer ban(iwidth. Multi- gate pulsed Doppler system enable the transcutaneous and simultaneous assessment of the velocities in a number of adjacent sample volumes as a continuous function of time. A multigate pulsed Doppler system processing the information originating from presented.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.29-32
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• 2006

본 논문에서는 기존의 생체인식에서 사용하지 않았던 방법으로 개인의 치열 영상을 이용하는 생체 인식 방법을 제안한다. 제안한 치열 인식 시스템은 데이터의 중복성 제거와 관측벡터의 차원 감소를 위하여 2D-DCT를 특징 파라미터로 사용하고, 음성인식 및 얼굴인식 분야에서 사용하는 EHMM 기술을 사용한다. EHMM은 3개의 super-state로 구성되며 각각의 super-state는 3개, 5개, 3개의 상태를 갖는 1D-HMM으로 구성된다. 치열인증 시스템의 성능 평가는 모델 훈련에 사용하지 않은 치열 영상으로 인식 실험하여 평가한다. 치열인식 실험에는 남자 10명과 여자 10명에 대하여 각각 10개의 이미지로 구성된 총 200개의 치열 영상을 사용한다. 치열인식 실험에서 제안한 치열인식 시스템의 인식률은 98.5%를 보였고, 참고문헌 [4]의 EHMM을 사용한 얼굴인식 시스템이 갖는 98%와 대등한 성능을 나타내는 것을 확인하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.217-225
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• 2004

In this paper, a new automatic classification method for the normal EEC and schizophrenia EEC using hidden Markov model(HMM) is proposed. We used the feature parameters which are the variance for statistical stationary interval of the EEC and power spectrum ratio of the alpha, beta, and theta wave. The results were shown that high classification accuracy of 90.9％ in the case of normal person, and 90.5％ in the case of schizophrenia patient. It seems that proposed classification system is more efficient than the system using complicate signal processing process. Hence, the proposed method can be used at analysis and classification for complicated biosignal such as EEC and is expected to give considerable assistance to clinical diagnosis.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.273-277
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• 1995

많은 생리적 지능 연구자들은 생체신호 또는 생리적 측정치들을 지능의 새로운 측정치로서 제안하고 있는데, 이들의 기본 가정은 현재의 지능검사는 교육, 가정의 양육태도, 부모의 사회경제적 지위와 같은 문화적 요인에 의해 크게 좌우되므로 지능의 편파적인 측정치가 될 수밖에 없고, 이러만 요인에 가장 적게 영향받는 생리적 측정치만이 순수한 지능 즉정치가 될 수 있다는 것이다. 따라서, 본 연구는 이러한 생리적 신호들 중 EOG(electro-oculograph)를 이용하여 안구의 움직임을 기록함으로서 피검자의 문제해결을 위한 집중력과 이해력 등을 알아보고자 하였다. 또한, 피검자의 정보처리과정을 알아보기 위해 반응시간(RT, reaction time)을 측정하였다. 17명의 건강한 남녀 학생들이 실험에 참여하였으며, 첫단계로 Raven's Advanced Matrices 검사와 IQ 검사를 실시하고, 검사 결과에 근거하여 각각을 세 그룹으로 나누었다. 두번째 단계로는 슬라이드로 제작된 도형유추과제를 푸는 동안 EOG와 RT를 측정하였다. IQ 검사 점수, RT, 그리고 EOG로부터 추출된 변수들 간의 상관관계를 구하기 위해 GLM(general linear model) 및 Duncan's multiple range test를 위한 통계분석을 수행하였다. 분석결과를 통하여 도형유추과제의 난이도가 높을수록 EOG변수들에 의한 지능점수의 분류능력이 높음을 알 수 있었으며, Raven그룹보다는 IQ그룹에 대한 분류가 더욱 잘 이루어졌음을 발견하였다. 그리고, RT는 Raven그룹과 IQ그룹 모두에서 높은 변별력을 나타내고 있음을 확인하였다.