• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• 한국통신학회논문지
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• 제24권12B호
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• pp.2424-2429
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• 1999

본 논문은 길쌈 부호화 (convolutional encoding) 및 인터리빙(interleaving) 기법을 사용하는 채널 부호기에 있어서 메모리 크기를 최소화한 설계 방법에 관한 것이다. 기존의 구현방식에서는 프레임 데이터를 보관하는 입력버퍼 RAM과 인터리빙을 위한 인터리버 RAM을 별도로 사용해야 한다. 본 논문에서는 메모리 크기가 큰 인터리버 RAM을 사용하는 대신 입력버퍼 RAM 1개를 추가로 사용하여 길쌈 부호화 및 인터리빙을 동시에 처리할 수 있는 새로운 채널 부호기 설계 방법을 제안하였다. 이 설계 방법을 여러 디지털 이동통신 모뎀의 채널 부호기에 적용한 결과 기존 설계 방식에 비해 33% ∼60%의 메모리 크기 감소 효과가 있었으며, 프레임 데이터 수신 시 처리 절차가 간편해지고 타이밍 마진을 늘일 수 있는 장점이 있었다.

• 디지털콘텐츠학회 논문지
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• 제7권3호
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• pp.169-174
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• 2006

RFID는 각종 서비스 산업은 물론 물류, 산업 현장, 제조 공장과 물품의 흐름이 있는 곳이면 어디에서나 적용이 가능하여 사회 여러 분야로부터 큰 관심을 받고 있다. 하지만 현재 900Mhz 대역의 RFID에서 사용하는 ISO/IEC 18000-6의 프로토콜에서는 에러검출을 위한 CRC16만을 사용하여, 에러정정능력을 갖추지 못해 그 신뢰성이 떨어질 것으로 여겨진다. 본 논문에서는 이러한 RFID 시스템의 신뢰성 향상을 위해 Reader에서 Tag로의 명령어 및 데이터 전송 시에 콘벌루션 부호를 적용하여 시스템의 신뢰성 향상을 목적으로 하며, 이런한 방식을 적용했을 때와 적용하지 않았을 때의 에러율을 측정 비교하였다.

• 전기학회논문지
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• 제67권6호
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• 로봇학회논문지
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• 제14권1호
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• pp.8-13
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• 2019

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

• 대한전자공학회논문지TC
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• 제42권8호
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• pp.11-16
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• 2005

본 논문은 스크램블링(Scrambling), 길쌈부호화(Convolutional Encoding), 펑처링(Puncturing), 인터리빙(Interleaving) 등과 같은 연산에 공통적으로 필요한 비트 조작(Bit Manipulation)을 효율적으로 지원하기 위한 비트 조작 연산 가속기를 제안한다. 기존의 DSP는 곱셈 및 가산 연산을 기본으로 연산기가 구성되어 있으며 워드 단위로 동작을 함으로 비트 조작 연산의 경우 비효율적인 연산을 수행할 수밖에 없다. 그러나 제안한 가속기는 비트 조작 연산을 다수의 데이터에 대해 병렬 쉬프트와 XOR 연산, 비트 추출 및 삽입 연산을 효율적으로 수행할 수 있다. 제안한 가속기는 VHDL로 구현 하여 삼성 $0.18\mu m$ 표준 셀 라이브러리를 이용하여 합성하였으며 가속기의 게이트 수는 1,700개에 불과하다. 제안한 가속기를 통해 스크램블링, 길쌈부호화, 인터리빙을 수행시 기존의 DSP에 비해 $40\~80\%$의 연산 사이클의 절감이 가능하였다.

• 한국통신학회논문지
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• 제37권4A호
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• pp.260-268
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• 2012

통신, 컴퓨터를 포함한 많은 분야에서는 디지털 데이터를 신뢰성 있게 전달하기 위하여 채널 부호화를 사용하게 된다. 채널 부호화 된 신호가 전송 되었을 때 수신 측에서 채널 부호화 기법과 생성 파라미터를 알지 못한다면 복호는 어렵게 된다. 본 논문에서는 채널 부호화 기법 중 하나인 길쌈 부호(convolutional code)와 천공 패턴이 적용된 천공 길쌈 부호의 생성 파라미터 분석 방법을 제시한다. 또한 AWGN 채널 환경에서 시뮬레이션을 통하여 검증한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 추계학술대회
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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.

• ETRI Journal
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• 제25권2호
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• pp.144-147
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• 2003

We propose a new serial concatenation scheme for space-time and recursive convolutional codes, in which a space-time code is used as the outer code and a single recursive convolutional code as the inner code. We discuss previously proposed serial concatenation schemes employing multiple inner codes and compare them with the new one. The proposed method and the previous one with joint decoding, both performing a combined decoding of the simultaneous output signals from multiple antennas, give a large performance gain over the separate decoding method. In decoding complexity, the new concatenation scheme has a lower complexity compared with the multiple encoding/joint decoding scheme due to the use of the single inner code. Simulation results for a communication system with two transmit and one receive antennas in a quasi-static Rayleigh fading channel show that the proposed scheme outperforms the previous schemes.

• Journal of Communications and Networks
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• 제7권3호
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• pp.243-247
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• 2005

We propose an efficient block least-mean-square (BLMS) adaptive algorithm, in conjunction with error control coding, for direct-sequence code division multiple access (DS-CDMA) systems. The proposed adaptive receiver incorporates decision feedback detection and channel encoding in order to improve the performance of the standard LMS algorithm in convolutionally coded systems. The BLMS algorithm involves two modes of operation: (i) The training mode where an uncoded training sequence is used for initial filter tap-weights adaptation, and (ii) the decision-directed where the filter weights are adapted, using the BLMS algorithm, after decoding/encoding operation. It is shown that the proposed adaptive receiver structure is able to compensate for the signal-to­noise ratio (SNR) loss incurred due to the switching from uncoded training mode to coded decision-directed mode. Our results show that by using the proposed adaptive receiver (with decision feed­back block adaptation) one can achieve a much better performance than both the coded LMS with no decision feedback employed. The convergence behavior of the proposed BLMS receiver is simulated and compared to the standard LMS with and without channel coding. We also examine the steady-state bit-error rate (BER) performance of the proposed adaptive BLMS and standard LMS, both with convolutional coding, where we show that the former is more superior than the latter especially at large SNRs ($SNR\;\geq\;9\;dB$).