• Journal of IKEEE
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• v.24 no.4
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• pp.975-981
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• 2020

A 10 GHz LC voltage-controlled oscillator (VCO), which controls an amplitude of output signal, is proposed to improve the phase noise. The proposed amplitude control circuit for the LC VCO consists of a peak detector, an amplifier, and a current source. The peak detector is performed detecting the lowest voltage of the output signal by using two diode-connected NMOSFET and a capacitor. The proposed 10 GHz LC VCO with an amplitude control circuit for output signal is designed using a 55 nm CMOS process with a supply voltage of 1.2 V. Its area is 0.0785 ㎟. The amplitude control circuit used in the proposed LC VCO reduces the amplitude variation 242 mV generated in the output signal of the conventional LC VCO to 47 mV. Furthermore, it improves the peak-to-peak time jitter from 8.71 ps to 931 fs.

• Journal of IKEEE
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• v.24 no.4
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• pp.1081-1085
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• 2020

Instantaneous frequencies (IF) of the AM-FM signal is estimated based on the inflection point detection (IPD) method. Local maxima/minima are detected using the IPD, and they are exploited to find the IF of AM and FM components, respectively. The envelope of the maxima/minima is obtained to estimate the IF of the AM part. And the distance between neighboring maxima (or minima) is used to estimate the IF of the FM component. Computer simulation shows that the proposed method properly estimates the IF of the AM and FM when the signal has fixed frequencies for both parts. In the case of the time-varying IF of the FM part, the estimated IF shows some deviation from the true IF due to the rough sampling effect of the maximum/minimum points. Thus, the post-processing such as the lowpass filtering of the estimated IF is required to refine the resulting IF estimation.

• Journal of IKEEE
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• v.26 no.4
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• pp.568-576
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• 2022

Automatic modulation classification(AMC) is a core technique in Software Defined Radio(SDR) platform that enables smart and flexible spectrum sensing and access in a wide frequency band. In this study, we propose a simple yet accurate deep learning-based method that allows AMC for variable-size radio signals. To this end, we design a classification architecture consisting of two Convolutional Neural Network(CNN)-based models, namely main and small models, which were trained on radio signal datasets with two different signal sizes, respectively. Then, for a received signal input with an arbitrary length, modulation classification is performed by augmenting the input samples using a self-replicating padding technique to fit the input layer size of our model. Experiments using the RadioML 2018.01A dataset demonstrated that the proposed method provides higher accuracy than the existing methods in all signal-to-noise ratio(SNR) domains with less computation overhead.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.194-199
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• 2007

최근 차량증가에 따라 교통사고가 날로 증가하고 있고, 안전운전을 위한 보조장치 개발의 필요성이 증대되고 있다. 특히 교통사고 원인중 운전자의 졸음으로 인한 사고가 30%에 달하고 있어 졸음운전 예방장치의 개발이 시급한 실정이다. 이에 본 연구에서는 졸음운전예방을 위한 효율적인 졸음경보 장치시스템을 제안한다. 제안하는 방법은 HRV와 안구활동 신호를 BP(역전파알고리즘)을 이용 보다 더 정확한 측정 및 판단을 행하여 운전자에게 음성경고를 보내주는 방식이다. 제안된 방식은 기존의 연구된 피부전기활동, 영상처리를 이용한 졸음운전 감지 시스템, 심박변동신호 분석 방법보다 훨씬 효율적인 졸음경보를 할 수 있을 것으로 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1334-1340
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• 2003

In this paper, We have proposed an Heterodyne technique to generate millimeter-wave signal. Microwave signals in cellular broadband mobile communication networks and distributed networks can favorably be generated and distributed by optical techniques. In principle, these techniques have already been investigated for optical control of phase- array antennas, characterization of photo-detector and phase locking of millimeter-wave oscillators and now being applied to wireless communications. The generation and transmission of millimeter-wave radio signals by optical means is of interest for future pico-cell broadband mobile communication system, especially for systems operating at frequencies of 300Hz.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.402-405
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• 2000

In this paper we propose a method ef pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through leaning and therefore it can classify input patterns. The raw EMC signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.31-36
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• 2001

In this paper we propose a method of pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through learning and therefore it can classify input patterns. The raw EMG signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.220-225
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• 1997

졸음에 의한 순간적 과오는 자동차운전을 비롯한 각종 산업안전에 인명피해를 포함하는 치명적 손실 을 수반한다. 따라서 이분야에 대한 연구가 국내를 포함한 전세계에서 활발히 진행되어 상업화가 추진 중이다. 그러나 이러한 연구는 실용적 차원에서 주로 피부전기활동(Electrodermal Activity: EDA)과 눈 깜박임 등의 측정방법에 의존하고 있으며 졸음의 첫 지시치로서 중요하고 객관적인 각성-졸음 과도기 뇌파를 포함하는 수면 다원생리신호 측정에 관한 연구는 이 방법이 피험자에게 구속성을 주고 측정 자체가 까다로워서 현실적으로어려운 실정이다. 본 연구에서는 그 동안 Medilog SAC847 Polysomnography를 이용한 수면에 관련된 종합적 생리신호를 측정.분석 연구해온 경험을 토대로 정상적인 성인의 각성-졸음 과도기 생리신호특징으로서 뇌전도(Electroencephalogram:EEG), 턱 및 다리근전도(Electromyogram:EMG), 심전도( Electrocardiogram:ECG), 안전도(Electrooculogram:EOG) 등을 종합적으로 분석한 결과 졸음상태가 각성상 태에 비하여 EEG의 주파수는 감소하고, EMG와 ECG의 진폭은 줄어들고, EOG에서는 느린 안구운동의 특징을 갖는 것을 알 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.82-87
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• 2004

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.5-7
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• 2012

선교에는 항해사에게 정보를 전달하기 위하여 다양한 신호가 존재한다. 항해 및 통신 장비로부터 나오는 음향 신호 등 다양한 청각 신호들이 존재하지만 이러한 청각 신호, 청각 경고음에 대한 인간의 인지능력에 관한 연구는 미흡한 실정이다. 청각 경고음은 크게 음성(speech), 함축적 소리(abstract sound), 청각 아이콘(auditory icon)으로 구분 할 수 있다. 본 연구에서는 청각 경고음 중 청각아이콘을 활용하여 5가지의 경보상황(엔진, 화재, 조타, 전기, 충돌)에서 청각아이콘에 대한 감성평가를 통해 각 상황에 적합한 청각아이콘을 선별하였다. 본 연구 결과는 선교 내 청각표시장치와 통합선교알람관리시스템을 위한 기초자료로 활용될 수 있을 것으로 기대된다.