• Title/Summary/Keyword: Signal Detection

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The Design of Error Detection Auto Correction for Conversion of Graphics to DTV Signal

  • Ryoo-Dongwan;Lee, Jeonwoo
    • Proceedings of the IEEK Conference
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    • 2002.07a
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    • pp.106-109
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    • 2002
  • In the integrated systems, that is integrated digital TV(DTV) internet and home automation, like home server, is needed integration of digital TV video signal and computer graphic signal. The graphic signal is operating at the high speed and has time-divide-stream. So the re-request of data is not easy at the time of error detection. therefore EDAC algorithm is efficient. This paper presents the efficiency error detection auto correction(EDAC) for conversion of graphics signal to DTV video signal. A presented EDAC algorithms use the modified Hamming code for enhancing video quality and reliability. A EDAC algorithm of this paper can detect single error, double error, triple error and more error for preventing from incorrect correction. And it is not necessary an additional memory. In this paper The comparison between digital TV video signal and graphic signal, a EBAC algorithm and a design of conversion graphic signal to DTV signal with EDAC function is described.

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Signal Space Detection for High Data Rate Channels (고속 데이터 전송 채널을 위한 신호공간 검출)

  • Jeon , Taehyun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.42 no.10 s.340
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    • pp.25-30
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    • 2005
  • This paper generalizes the concept of the signal space detection to construct a fixed delay tree search (FDTS) detector which estimates a block of n channel symbols at a time. This technique is applicable to high speed implementation. Two approaches are discussed both of which are based on efficient signal space partitioning. In the first approach, symbol detection is performed based on a multi-class partitioning of the signal space. This approach is a generalization of binary symbol detection based on a two-class pattern classification. In the second approach, binary signal detection is combined with a look-ahead technique, resulting in a highly parallel detector architecture.

Extended Early-Late Phase Scheme using Combined Pseudo-Random Noise Signal to Detect GPS Repeat-Back Jamming Signals (GPS 재방송 재밍신호 검출을 위한 통합 의사잡음신호를 사용한 확장된 ELP 기법)

  • Yoo, Seungsoo;Yeom, Dong-Jin;Jee, Gyu-In;Kim, Sun Yong
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.6
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    • pp.483-489
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    • 2016
  • This paper proposes a repeat-back jamming signal detection scheme that utilizes a combined pseudo random noise signal that is effective for processing a global positioning system (GPS) repeat-back jamming signal with the early minus late phase scheme to alleviate any existing multipath signal detection. The proposed scheme uses the combined pseudo random noise signal to treat repeat-back jamming signals like similar multipath signals and can effectively detect a repeat-back jamming signal by applying the early minus late phase scheme to a combined pseudo random noise signal. Through a Monte-Carlo simulation, the detection probability of the proposed scheme is better than the one of the conventional scheme under low jamming to signal power ratio.

A study on EPD(End Point Detection) controller on plasma teaching process (플라즈마 식각공정에서의 EPD(End Point Detection) 제어기에 관한 연구)

  • 최순혁;차상엽;이종민;우광방
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.415-418
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    • 1996
  • Etching Process, one of the most important process in semiconductor fabrication, has input control part of which components are pressure, gas flow, RF power and etc., and plasma gas which is complex and not exactly understood is used to etch wafer in etching chamber. So this process has not real-time feedback controller based on input-output relation, then it uses EPD(End Point Detection) signal to determine when to start or when to stop etching. Various type EPD controller control etching process using EPD signal obtained from optical intensity of etching chamber. In development EPD controller we concentrate on compensation of this signal intensity and setting the relative signal magnitude at first of etching. We compensate signal intensity using neural network learning method and set the relative signal magnitude using fuzzy inference method. Potential of this method which improves EPD system capability is proved by experiences.

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Deep-learning Sliding Window Based Object Detection and Tracking for Generating Trigger Signal of the LPR System (LPR 시스템 트리거 신호 생성을 위한 딥러닝 슬라이딩 윈도우 방식의 객체 탐지 및 추적)

  • Kim, Jinho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.17 no.4
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    • pp.85-94
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    • 2021
  • The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

Switching Signal Patterns to Prevent Short Circuit of AC Choppers (교류초퍼에서 단락사고 방지를 위한 스위칭 신호 패턴)

  • Jang, Do-Hyeon;Yeon, Jae-Eul
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.50 no.9
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    • pp.445-452
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    • 2001
  • Two switching signal patterns are proposed to prevent short circuit of PWM ac choppers. The voltage detection method and the current detection method are proposed to execute two switching signal patterns. In the voltage detection method, the dead-time has to be inserted to the switching signals after polarity of input voltage is checked by voltage transducer at input side. In the current detection method, the direction of load current is checked by current transducer at output side, and the dead-time delay is not considered. Controlling circuit built by current detection method is simple because the dead-time delay is considered. The experimental results are presented to prevent short circuit of ac chopper safely.

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Development of Voice Signal Detection System using FPGA (FPGA를 이용한 음성 신호 감지 시스템 개발)

  • Kim, Jang-Won
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.15 no.6
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    • pp.141-146
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    • 2015
  • In order to classify and analyze variously compounded sound and voice signal from FPGA microphone, there are numerous systems to detect abnormality signal, however, they have a lot of problems to implement the abnormality signal detection efficiently and effectively. Therefore, we proposed a method that implements classifying the signal effectively and outputting the detection efficiently based on the algorithm applied FIFO structure (First-in First-out) by using microphone sensor which able to input the sound signal, and statistical variance and coefficient of variation (CV). The result showed 96.3% detection when the experiment was performed more than 100 times with the proposed algorithm applied system.

Real-time malfunction detection of plasma etching process using EPD signal traces (EPD 신호궤적을 이용한 플라즈마 식각공정의 실시간 이상검출)

  • Cha, Sang-Yeob;Yi, Seok-Ju;Koh, Taek-Beom;Woo, Kwang-Bang
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.2
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    • pp.246-255
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    • 1998
  • This paper presents a novel method for real-time malfunction detection of plasma etching process using EPD signal traces. First, many reference EPD signal traces are collected using monochromator and data acquisition system in normal etching processes. Critical points are defined by applying differentiation and zero-crossing method to the collected reference signal traces. Critical parameters such as intensity, slope, time, peak, overshoot, etc., determined by critical points, and frame attributes transformed signal-to symbol of reference signal traces are saved. Also, UCL(Upper Control Limit) and LCL(Lower Control Limit) are obtained by mean and standard deviation of critical parameters. Then, test EPD signal traces are collected in the actual processes, and frame attributes and critical parameters are obtained using the above mentioned method. Process malfunctions are detected in real-time by applying SPC(Statistical Process Control) method to critical parameters. the Real-time malfunction detection method presented in this paper was applied to actual processes and the results indicated that it was proved to be able to supplement disadvantages of existing quality control check inspecting or testing random-selected devices and detect process malfunctions correctly in real-time.

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A Case Study of the Characteristics of Fire-Detection Signals of IoT-based Fire-Detection System (사례 분석을 통한 IoT 기반 화재탐지시스템의 화재 감지신호 특성)

  • Park, Seung Hwan;Kim, Doo Hyun;Kim, Sung Chul
    • Journal of the Korean Society of Safety
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    • v.37 no.3
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    • pp.16-23
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    • 2022
  • This study aims to provide a fundamental material for identifying fire and no-fire signals using the detection signal characteristics of IoT-based fire-detection systems. Unlike analog automatic fire-detection equipment, IoT-based fire-detection systems employ wireless digital communication and are connected to a server. If a detection signal exceeds a threshold value, the measured values are saved to a server within seconds. This study was conducted with the detection data saved from seven fire accidents that took place in traditional markets from 2020 to 2021, in addition to 233 fire alarm data that have been saved in the K institute from 2016 to 2020. The saved values demonstrated variable and continuous VC-Signals. Additionally, we discovered that the detection signals of two fire accidents in the K institution had a VC-Signal. In the 233 fire alarms that took place over the span of 5 years, 31% of smoke alarms and 30% of temperature alarms demonstrated a VC-Signal. Therefore, if we selectively recognize VC-Signals as fire signals, we can reduce about 70% of false alarms.

R-Peak Detection Algorithm in ECG Signal Based on Multi-Scaled Primitive Signal (다중 원시신호 기반 심전도 신호의 R-Peak 검출 알고리즘)

  • Cha, Won-Jun;Ryu, Gang-Soo;Lee, Jong-Hak;Cho, Woong-Ho;Jung, YouSoo;Park, Kil-Houm
    • Journal of Korea Multimedia Society
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    • v.19 no.5
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    • pp.818-825
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    • 2016
  • The existing R-peak detection research suggests improving the distortion of the signal such as baseline variations in ECG signals by using preprocessing techniques such as a bandpass filtering. However, preprocessing can introduce another distortion, as it can generate a false detection in the R-wave detection. In this paper, we propose an R-peak detection algorithm in ECG signal, based on primitive signal in order to detect reliably an R-peak in baseline variation. First, the proposed algorithm decides the primitive signal to represent the QRS complex in ECG signal, and by scaling the time axis and voltage axis, extracts multiple primitive signals. Second, the algorithm detects the candidates of the R-peak using the value of the voltage. Third, the algorithm measures the similarity between multiple primitive signals and the R-peak candidates. Finally, the algorithm detects the R-peak using the mean and the standard deviation of similarity. Throughout the experiment, we confirmed that the algorithm detected reliably a QRS group similar to multiple primitive signals. Specifically, the algorithm can achieve an R-peak detection rate greater than an average rate of 99.9%, based on eight records of MIT-BIH ADB used in this experiment.