• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.580-582
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• 2000

본 논문에서는 MAM을 이용한 특징 벡터의 보정을 기반으로 하는 헤드 제스처 인식에 관해 기술한다. 제안된 시스템은 얼굴 움직임 검출 모듈과 눈 영역 추적 모듈, 미 측정된 벡터 보정 모듈, 측정된 제스처에 대한 인식모듈로 구성된다. 신경망과 모자이크 이미지를 이용하여 얼굴 영역을 검출하고, 이 영역에서 눈 영역을 검출한다. 만약 눈의 쌍이 검출되지 않는다면 시스템은 특징 벡터 보정(MAM)을 수행하여 손실된 정보를 예측한다. 검출된 눈 영역은 정규화된 벡터로 변경된다. 이 벡터의 분산을 이용하여 긍정, 부정, 중립의 제스처를 판단한다. 제스처의 인식은 직접 관측, 이중 HMM, 삼중 HMM을 사용한 다중 인식기를 이용한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.13-16
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• 2019

영상이 포함하고 있는 풍부한 정보를 검출하고 이해하기 위해서는, 영상속의 일관된 상호관계를 갖는 영상 객체 그룹을 이해하고 표현하는 영상 구문 검출 기술(Visual Phrase Detecting Technology)이 필수적이다. 영상 구문 검출 기술은, 영상이 포함하고 있는 다양하고 풍부한 정보를 추출하고 활용하기 위한 핵심 기술로 이를 이용한 자동차 주행중 전방 감시, 영상의 자동 주석 달기, 동영상의 검색, 쇼핑 공간에서의 부정행위 검출(fraud detection) 등 다양한 분야에 적용할 수 있다.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.835-838
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• 2012

최근 스마트폰의 폭팔적인 증가와 함께 사용 환경개선도 이루어 지고 있다. 또한 Wi-Fi 존의 증가와 LTE같은 빠른 네트워크 환경은 사용자 중심의 수 많은 앱을 탄생시키고 있다. 안드로이드는 애플의 iOS와는 다른 오픈소스 정책으로 플랫폼 소스가 공개되어 있어 많은 개발자가 쉽게 접근이 가능하다. 그러나 안드로이드는 앱(App) 검증 체계가 미흡하기 때문에 악성코드 등으로 인한 위협요소가 존재하고 있다. 또한 파일 시스템은 임의적 접근제어방식으로 공격자가 취약점을 통해 관리자 권한을 얻어 시스템 자원을 제어할 수 있기 때문에 위협요소가 다분하다. 본 논문에서는 스마트폰 앱이 호출하는 시스템 API 및 네트워크 자원사용 패턴을 분석하여 부정 앱을 차단하는 방법을 제안하였다. 제안 방법으로 실험한 결과 API호출 빈도 및 자원 사용률이 최소 기준치 이하로 검출된 경우를 제외한 평가대상은 모두 검출하여 보안성 강화에 효과적인 것으로 실험을 통하여 검증하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.731-739
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• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.945-952
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• 2013

The R wave of QRS complex is the most prominent feature in ECG because of its specific shape; therefore it is taken as a reference in ECG feature extraction. But R wave detection suffers from the fact that frequency bands of the noise/other components such as P/T waves overlap with that of QRS complex. ECG signal processing must consider efficiency for hardware and software resources available in processing for miniaturization and low power. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, efficient QRS detection based on SOM(Subtractive Operation Method) is presented in this paper. For this purpose, we detected R wave through the preprocessing method using morphological filter, empirical threshold, and subtractive signal. Also, we applied dynamic backward searching method for efficient detection. The performance of R wave detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41% in R wave detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.437-444
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• 2016

Most methods for detecting arrhythmia require pp interval, diversity of P wave morphology, but it is difficult to detect the p wave signal because of various noise types. Therefore it is necessary to use noise-free R wave. In this paper, we propose algorithm for premature contraction arrhythmia classification through ECG pattern analysis and template threshold. For this purpose, we detected R wave through the preprocessing method using morphological filter, subtractive operation method. Also, we developed algorithm to classify premature contraction wave pattern using weighted average, premature ventricular contraction(PVC) and atrial premature contraction(APC) through template threshold for R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 6 record of MIT-BIH arrhythmia database that included over 30 PVC and APC. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 94.91%, 95.76% in PVC and APC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.7
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• pp.661-668
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• 2004

A novel method to reject the spurious signals which are occurred at Weaver-type low-IF transmitter was proposed in this paper. The spurious signals are generated by the gain and phase imbalances of I/Q channel or imperfect characteristics of 90$^{\circ}$ phase shifter in local oscillator for I/Q channel source. By deriving the gain and phase-based functions from RF spurious signal with the channel imbalance information, the lie channel imbalances were deduced as functions with magnitude and sign dependent on I/Q channel imbalance degree. The proposed method compensates the estimated I/Q channel imbalances by correlation values between the down-converted signal obtained by squaring the output signal itself using a simple mixer and the modified baseband signal. By comparing two signals after A/D conversion, the magnitude and sign of each type of imbalances can be determined separately and simultaneously. Based on the I/Q channel imbalance compensation, the spurious signals can be reduced by adjusting the gain and phase values of I or Q channel signal. The way to estimate the channel imbalances of the up-conversion mixer was presented and verified by using theoretical derivations and computer simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.