• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.41-43
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• 2017

본 논문에서는 IoS (Internet of Signage) 환경에서 실감형 데이터 전송을 위한 상황 인지 기반 적응형 전송기법을 적용하고 이를 분석한다. 사이니지 네트워크 환경에서는 임의적인 다중 경로 페이딩, 간섭 등과 같은 왜곡 요소가 존재할 수 있기 때문에 이를 대처하기 위해 무선 송신기에서는 채널 환경에 대한 인지 이후, 그에 따른 MCS (Modulation and Coding Scheme)를 선정 및 안테나 기법을 선정한다. MCS 및 안테나 기법 선정을 위해 무선 송신기에서는 사전에 알고 있는 통계적인 SNR (Signal-to-Noise Ratio) 값을 통해 문턱값을 계산하고, 수신 사이니지로부터 얻은 CSI (Channel State Information)와의 크기 비교를 한다. 시뮬레이션 결과에서는 상황 인지를 기반으로 매 순간 최적의 전송률을 달성할 수 있음을 볼 수 있다.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.76-81
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• 2010

In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the second-order conditional maximum a posteriori (CMAP). In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the current observation and the speech activity decisions in the pervious two frames. Experimental results show that the proposed approach yields better results compared to the statistical model-based and the CMAP-based VAD using the LR test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.321-327
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• 2015

In this paper, we propose an algorithm for achieving robust Visual Voice Activity Detection (VVAD) for enhanced speech recognition. In conventional VVAD algorithms, the motion of lip region is found by applying an optical flow or Chaos inspired measures for detecting visual speech frames. The optical flow-based VVAD is difficult to be adopted to driving scenarios due to its computational complexity. While invariant to illumination changes, Chaos theory based VVAD method is sensitive to motion translations caused by driver's head movements. The proposed Local Variance Histogram (LVH) is robust to the pixel intensity changes from both illumination change and translation change. Hence, for improved performance in environmental changes, we adopt the novel threshold estimation using total variance change. In the experimental results, the proposed VVAD algorithm achieves robustness in various driving situations.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.3-7
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• 2001

In active sonar system using CW signal, when the noise included reverberation has not the white characteristics, the CFAR detector estimates high threshold. Because of this reason it cannot detect targets and not resolve the closely spaced multiple targets. In order to solve these problems, we propose an adaptive reverberation rejection filter The proposed filter is composed of an adaptive filter and a fixed filter with its coefficients. To study the performance of the proposed adaptive reverberation rejection filter, various experiments have been performed under In moving active sonar environments. As a results, the proposed method has the improved performance than the previous methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2453-2458
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• 2012

In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4
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• pp.14-22
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• 1998

본 연구에서는 Texas Instruments 사의 저가형 부동소수점 디지털 신호 처리기 (Digital Singnal Processor, DSP)인 TMS320C32를 이용하여 실시간 화자종속 음성인식 하 드웨어 모듈(VR32)을 개발하였다. 하드웨어 모듈의 구성은 40MHz의 TMS320C32 DSP, 14bit 코덱인 TLC32044(또는 8bit μ-law PCM 코덱), EPROM과 SRAM 등의 메모리와 호 스트 인터페이스를 위한 로직 회로로 이루어졌다. 뿐만 아니라 이 하드웨어 모듈을 PC사에 서 평가해보기 위한 PC 인터페이스용 보드 및 소프트웨어도 개발하였다. 음성인식 알고리 즘의 구성은 에너지와 ZCR을 기반으로 한 끝점검출(Endpoint Detection) 침 10차 가중 LPC 켑스터럼(Weighted LPC Cepstrum) 분석이 실시간으로 이루어지며 이후 Dynamic Time Warping(DTW)를 통하여 최고 유사 단어를 결정하고 다시 검증과정을 거쳐 최종 인식을 수행한다. 끝점검출의 경우 적응 문턱값(Adaptive threshold)을 이용하여 잡음에 강인한 끝 점검출이 가능하며 DTW 알고리즘의 경우 C 및 어셈블리를 이용한 최적화를 통하여 계산 속도를 대폭 개선하였다. 현재 인식률은 일반 사무실 환경에서 통상 단축다이얼 용도로 사 용할 수 있는 30 단어에 대하여 95% 이상으로 매우 높은 편이며, 특히 배경음악이나 자동 차 소음과 같은 잡음환경에서도 잘 동작한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.166-174
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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. The important problem in recording ECG signal is a baseline wandering, which is occurred by rhythm of respiration and muscle contraction attaching to an electrode. Particularly, 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 region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, baseline wander removing method based on morphological filter for efficient QRS detection method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. The signal distortion ratio of the proposed method is compared with other filtering method. Also, R wave detection is evaluated by using MIT-BIH arrhythmia database. Experiment result show that proposed method removes baseline wanders effectively without significant morphological distortion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2181-2190
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• 1997

The synchronous stream cipher has the problem of synchronization loss by cycle slip. Synchronization loss make the state which sender and receiver can't communicate and it may make the receiving system disordered. To lessen the risk, we usually use a continuous resynchronization which achieve resynchronization at fixed timesteps by inserting synchronization pattern and session key. While we can get effectively resynchronizationby continuous resynchronization, there are some problems. In this paper, we proposed an adaptive resynchronization algorithm for cipher system using LAPB protocol. It is able to solve the problem of the continunous resynchronization.The proposed adaptive algorithm make resynchronization only in the case that the resynchronization is occurred by analyzing the address field of LAPB. It measure the receiving rate of the address field in the decesion duration. If the receiving rate is smaller than threshold value, it make resynchronization or not. By using adaptively resynchronization, it solves the problems of continunous resynchronization. When the proposed adaptive algorithm is applied to the synchronous stream cipher system which is used in X.25 packet network, it reduced the time for resynchronization by ten times. It means that 11.3% of total data for transmit is compressed.