• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.294-301
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• 2013

OFDM technique uses multiple sub-carriers for the data transmission. Therefore, bit error rate increases because of inter-carrier interference caused by nonlinear high power amplifier and carrier frequency offset. Wireless OFDM transmission over multi path fading channels is characterized by small transmission gain in multiple sub-carrier frequency interval. Therefore bit error rate increases because of burst errors. Inter-leaver and convolution error control coding are effective for the reduction of this burst error. Pilot symbol is used for the channel estimation in OFDM systems. However, imperfect channel estimates in this systems degrade the performance. The performance of this convolution coding OFDM systems using inter-leaver, gauged by the bit error rate, is analyzed considering the nonlinear high power amplifier, carrier frequency offset and channel estimation error.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.5
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• pp.731-737
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• 2000

In this paper we propose a decoding algorithm of Ungerboeck TCM/PSK in the concept of Radix-trellis, which has been applied to the decoding of convolutional codes for the high speed decoding. As an example we choose 16-state trellis coded 8-ary PSK. For Radix-4 and Radix-16 trellis decoding, we explain the path metric(PM) and the branch metric(BM) calculation. By using the simulation, we evaluate the bit error rate(BER) performance according to the number of binary digits for I-Q value, PM and BM registers. The proper number of binary digits of each register has been derived.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.630-633
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• 2020

무의식적인 손-얼굴의 접촉으로 인한 감염의 문제점을 해결하기 위해, 얼굴 만지기 행동을 인식할 필요가 있다. 본 연구는 최근 각광을 받는 딥러닝 기술을 이용하여 비디오 영상에서 얼굴 만지기 행동 인식에 대한 연구이다. 우선, 비디오 영상에서 얼굴 만지기와 관련된 11 가지 행동에 대한 시, 공간적 특징을 컨볼루션 신경망을 통해 추출한다. 추출된 정보는 각 행동 레이블로 인코딩되어 비디오 영상에서 얼굴 만지기 행동을 분류한다. 또한, 3D, 2D 컨볼루션 신경망의 대표 네트워크인 I3D, MobileNet v3에 대해 비교 실험을 진행한다. 제안하는 시스템을 적용하여 인간의 행동을 분류하는 실험을 진행했을 때, 얼굴을 만지는 행동을 99%의 확률로 구분했다. 이 시스템을 이용하여 일반인이 무의식적인 얼굴 만지기 행동에 대해서 정량적으로 또는 적시적으로 인식을 하여, 안전한 위생 습관을 확립하여 감염의 확산방지에 도움을 줄수 있기를 바란다.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1068-1074
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• 2011

In this paper, the proposed scheme is STBC(space-time block codes) system combined with convolution code which is the most popular channel coding to ensure the reliability of data transmission for a high data rate wireless communication. The STBC is one of MIMO(multi-input multi-output) techniques. In addition, this scheme uses a modified viterbi algorithm in order to get a high system gain when data is transmitted. Because we combine STBC and convolution code, the proposed scheme has a little high quantity of computation but it can get a maximal diversity gain of STBC and a high coding gain of convolution code at the same time. Unlike existing viterbi docoding algorithm using Hamming distance in order to calculate branch matrix, the modified viterbi algorithm uses Euclidean distance value between received symbol and reference symbol. Simulation results show that the modified viterbi algorithm improved gain 7.5 dB on STBC 2Tx-2Rx at $BER=10^{-2}$. Therefore the proposed scheme using STBC combined with convolution code can improve the transmission reliability and transmission efficiency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.357-359
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• 2010

The Viterbi Decoding is one of the most researched areas of the convolutional decoding methods. In this paper, we use various parameters for the substantial Viterbi decoding and discuss some viterbi decoding methods. And, the viterbi algorithms of the methods, we discuss 'Hard Decision' and 'Soft Decision'. So, we compare differences of two methods about decoding methods, performance. Because of having various parameters and decision methods, we discuss the values of various parameter and decision methods in the Gaussian channel about the viterbi decoding methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.645-652
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• 2001

In this paper is adopted the One-Ray Rician channel model for AVHS's platoon to short-range IVC (Inter-Vehicle Communication System) and analyzed packet probability characteristics in 60㎓ millimeter wave with very powerful to MP(multipath-wave). Both Convolution coding and MRC diversity is adopted that Multimedia service is satisfied following user's desire increase in the next and analyzed packet probability characteristics of DS-CDMA/QPSK systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.73-75
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• 2019

인터레이스 영상은 지난 수 십 년간 방송 및 비디오 레코딩 등에 광범위하게 사용되고 있으며 디인터레이스의 성능을 향상 시키기 위한 많은 연구가 이루어졌다. 이를 위한 것으로써 본 논문에서는 컨볼루션 신경망을 이용한 비디오 디인터레이스 기법을 제안한다. 제안한 방법은 SKIP 연결을 사용하여 낮은 수준 특징 정보를 뒷 단의 레이어까지 전달함으로써 성능 향상을 달성하였다. 실험 결과는 FFMPEG 에서 제공하는 디인터레이스 기법에 비해 전 영상에 걸쳐 우수한 성능을 제공하며, 특히 복잡한 영상에서 기존 알고리즘 대비 큰 폭의 성능향상을 보인다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.27-32
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• 2014

A doppler shift is generated by moving a transmitter or receiver operated in communication systems. The doppler frequency shift between a transmitter and a receiver or the frequency offset present in transceivers must be removed to get the wanted system performance. FTS is used for preventing an accident from operating abnormally and for guaranteeing public protection. A launching vehicle's initial velocity is very fast in order to escape the earth and the amount of doppler shift is large. Recently many studies to adopt the next generation FTS are ongoing. To introduce new FTS, the effects of doppler shift on the performance of the new FTS must be studied. In this paper the doppler effect caused by launching vehicle's velocity affecting the performance of FTS receiver is investigated into two cases, one is for EFTS as a digital FTS and the other is for FTS using a tone signal. Noncoherent DPSK and noncoherent CPFSK are considered as the modulation methods of EFTS. In the cases of the doppler frequency shift of 200Hz present in EFTS using noncoherent DPSK and noncoherent CPFSK are simulated. Simulation results show that $E_b/N_o$ of 0.5dB deteriorates in the region of near BER of about $10^{-5}$ in RS coding. And there is no performance variation in $E_b/N_o$ or $E_b/N_o$ is worsened about 0.1dB in the same BER region for the case of using convolutional and BCH coding. Quadrature detector used in FTS using tone signals is not influenced by the doppler frequency shift.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.