• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.149-152
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• 2002

In this paper, an adaptive interference canceller (AIC) Is applied to the space-time coded DS-CDMA system in fading channels with arrival time difference from multiple transmit antennas In a CDMA system, arrival time difference causes not only inter-antenna and Inter-symbol interference but also multiple-access Interference even in a downlink. To mitigate the effect of the Interferences an AIC and ML decoding joint scheme Is proposed fur a space-time coded DS-CDMA system in which an adaptation process of tile AIC is merged in the Viterbi decoding algorithm. The performance of the proposed receiver is evaluated for the system with two transmit antennas. It is shown that the proposed receiver achieves significant performance improvement over the ML decoding receiver without the AIC

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.124-134
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• 1996

Korean has a large set of characters, and has a two-dimensional formation: each character is composed of graphemes in two dimensions. Whereas connections between characters are rare, connections inside a grapheme and between graphemes happen frequently and these connections generate many cursive strokes. To deal with the large character set and the cursive strokes, using the graphemes as a recognition unit is an efffective approach, because it naturally accommodates the structural characteristics of the characters. In this paper, we propose a grapheme-based on-line recognition method for cursive korean characters. Our method uses a TDNN recognition engine to segment cursive strokes into graphemes and a graph-algorithmic postprocessor based on korean grapheme composition rule and viterbi search algorithm to find the best recognition score path. We experimented the method on freely hand-written charactes and obtained a recognition rate of 94.5%.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.445-447
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• 2005

본 논문은 규칙 기반 방법과 통계 기반 방법을 동시에 사용함으로써 두 가지 방법의 장단점을 상호 보완한다. 한 문장에 대한 최적의 품사열은 HMM을 기반으로 Viterbi Algorithm을 사용하여 선택한다. 이때 파라미터 값은 규칙에 의한 가중치 값과 통계 정보를 사용한다. 최소한의 일반규칙을 사용하여 구축한 규칙의 적용에 따라 가중치 값을 구하며 규칙을 적용받지 못하는 경우는 비감독학습으로 추출한 통계정보에 기반을 둔 가중치 값을 이용하여 파라미터 값을 구한다. 이러한 기본 모델을 여러 회 반복하여 학습함으로써 최적의 통계기반 가중치를 구한다. 규칙과 비감독 학습으로 추출한 통계정보를 이용한 본 품사 태깅 시스템의 어절 기반 정확도는 $97.78\%$이다.

• Journal of the Korean Institute of Navigation
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• v.20 no.2
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• pp.77-84
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• 1996

This is the first report about the development of INMARSAT-C Satellite communication terminal. We analyze the existing Inmarsat-C terminal and examine each rules(IMO rule, domestic rules) about terminal. With that result, we design the basic hardware and software of terminal. This report consists of ; 1) the contents of the overall of operating situation and resources of INMARSAT-C system as like operation of communication system, communication channels and services 2) the contents of the specification of Inmarsat-C terminal hardware and software and the rules of IMO and Type approval 3) the design of basic hardware and reserch of signal modulation/demodulation using Viterbi algorithm 4) the design of software algorithms and functions focused in korean situations.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1627-1630
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• 2002

본 논문은 역추적 예견 알고리즘을 사용한 비터비 복호기에서의 TB단의 Polling 구조의 단순화 방법을 제시한다. 비터비 복호기의 3대 Unit중 하나인 Trace Back에서 역추적 예견 알고리즘을 사용할 경우 복호화 시점에서의 최소 State Metric 값을 찾아야 하는 번거로움을 줄일 수 있다. 하지만 복호 신호의 신뢰도 분산에 따라 Polling Unit 이 추가되어야 함에 따라 실제 하드웨어 복잡도에서의 이득은 미미한 것으로 알려져 있다. 제시된 구조에서는 Polling Unit을 단순화 할 수 있는 방법을 적용하였다. 기존 하드웨어와의 비교 평가를 위하여 IEEE802.11a의 표준에 따른 부호화율 1/2, 구속장 7을 갖는 비터비 디코더에 대하여 역추적 예견 알고리즘과 파이프라인 구조만을 갖는 경우와 제안된 단순화한 Polling Unit을 적용한 구조와의 비교에서 Trace Back Unit에서 약 45%의 감소 효과를 보였다.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.207-215
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• 2020

Part of speech (POS) tagging is an indispensable part of major NLP models. Its progress can be perceived on number of languages around the globe especially with respect to European languages. But considering Indian Languages, it has not got a major breakthrough due lack of supporting tools and resources. Particularly for Odia language it has not marked its dominancy yet. With a motive to make the language Odia fit into different NLP operations, this paper makes an attempt to develop a POS tagger for the said language on a HMM (Hidden Markov Model) platform. The tagger judiciously considers bigram HMM with dynamic Viterbi algorithm to give an output annotated text with maximum accuracy. The model is experimented on a corpus belonging to tourism domain accounting to a size of approximately 0.2 million tokens. With the proportion of training and testing as 3:1, the proposed model exhibits satisfactory result irrespective of limited training size.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.6
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• pp.768-775
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• 1998

The equalizer is the central part of the receiver and its performance significantly affects the overall performance of the system in the mobile communication. A proposed equalizer is composed of the channel estimator, MLSE based on the Viterbi algorithm and GMSK decoder. The approximation of GMSK with QPSK has great impact on the equalizer design, because it allows us to use the existing simple and efficient algorithms for designing optimal QPSK equalizer. In order to estimate efficiently channel, we use a sliding window algorithm based on energy calculation and cross-correlator. And also a tuning scheme is presented in order to improve the equalizer performance. Simulation results indicate that a proposed equalizer meets the GSM standards easily in terms of performance.

• ETRI Journal
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• v.33 no.2
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• pp.155-162
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• 2011

The scalable extension of the H.264/AVC video coding standard (SVC) demonstrates superb adaptability in video communications. Joint source and channel coding (JSCC) has been shown to be very effective for such scalable video consisting of parts of different significance. In this paper, a new JSCC scheme for SVC transmission over packet loss channels is proposed which performs two-dimensional optimization on the quality layers of each frame in a rate-distortion (R-D) sense as well as on the temporal hierarchical structure of frames under dependency constraints. To compute the end-to-end R-D points of a frame, a novel reduced trellis algorithm is developed with a significant reduction of complexity from the existing Viterbi-based algorithm. The R-D points of frames are sorted under the hierarchical dependency constraints and optimal JSCC solution is obtained in terms of the best R-D performance. Experimental results show that our scheme outperforms the existing scheme of [13] with average quality gains of 0.26 dB and 0.22 dB for progressive and non-progressive modes respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.2
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• pp.105-114
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• 2000

In this paper, we propose a bandwidth-efficient channel coding scheme using the turbo trellis-coded modulation with multiple symbol detection. The turbo code can achieve good bit error rates (BER) at low SNR. That comprises two binary component codes and an interleaver. TCM codes combine modulation and coding by optimizing the euclidean distance between codewords. This can be decoded with the Viterbi or the symbol-by- symbol MAP algorithm. But we present the MAP algorithm with branch metrics of the Euclidean distance of the first phase difference as well as the Lth phase difference. The study shows that the turbo trellis-coded modulation with multiple symbol detection can improve the BER performance at the same SNR.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.11-14
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• 2018

The aim of this study is to rectify the misclassified image features and enhance the performance of image classification tasks by incorporating a channel- coding technique, widely used in telecommunication. Specifically, the proposed algorithm employs the error - correcting mechanism of convolutional coding combined with the convolutional neural networks (CNNs) that are the state - of- the- arts image classifier s. We develop an encoder and a decoder to employ the error - correcting capability of the convolutional coding. In the encoder, the label values of the image data are converted to convolutional codes that are used as target outputs of the CNN, and the network is trained to minimize the Euclidean distance between the target output codes and the actual output codes. In order to correct misclassified features, the outputs of the network are decoded through the trellis structure with Viterbi algorithm before determining the final prediction. This paper demonstrates that the proposed architecture advances the performance of the neural networks compared to the traditional one- hot encoding method.