• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.193-200
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• 2000

본 논문에서는 소형으로 설계된 칼라 4화면 분할기를 위한 메모리 제어 알고리듬을 제안한다. 4화면 분할기는 크게 비디오 디코더부, 메모리부, 비디오 인코더부, OSD (On Screen Display)부, MICOM부, 제어부로 구성되어 있다. 본 논문의 칼라 4화면 분할기는 비디오 디코더부와 비디오 인코더부를 각각 원칩을 이용하여 설계하였으며, 제어부를 FPGA를 사용하여 원칩으로 제작하였다. 화면 4분할을 위해서 메모리 읽기 신호를 실 시간으로 제어하여 비디오 시스템을 제작하였다. 사용된 메모리 제어알고리듬은 비디오신호제어 및 디지털 메모리를 이용하는 다른 시스템에 적용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.676-678
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• 2003

최근에 2.5G나 3G 이동 단말 장치를 위한 프로세서로, 다양한 멀티미디어가 가미된 응용구현이 가능하도록 RISC 프로세서와 DSP를 포함하는 단일 칩 프로세서 기술이 등장하고 있다. 이에 따라 듀얼 프로세서 구조에서 비디오 인코딩/디코딩의 처리 속도를 향상시키기 위안 비디오의 인코더/디코더 구조를 제안한다. 기존의 연구에서는 비디오의 인코딩/디코딩의 전 과정을 DSP가 담당하도록 설계하였으나 많은 비트 연산이 필요한 부분에서는 RISC 칩보다 효율성이 낮게 된다. 이러한 문제점을 해결하기 위하여 본 논문에서는 비디오 신호 처리의 인코딩/디코딩을 구성하는 모듈들을 DSP와 RISC의 특성에 맞도록 분리해 수행시킴으로써 효율성을 높이고자 한다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.10a
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• pp.15-18
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• 1984

The majority - logic decoding algorithm for Geometry code is more simply imlemented than the known decoding algorithm for BCH codes. Thus, the moderate code word, Geometry codes provide rather effective error control. The purpose of this paper is to investigate the Reed - Muller code and to design the encoder and decoder circuit and to find the performance for (15, 11) Reed - muller code. Experimental results show that the system has not only single error - correcting ability but also good performance.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.325-343
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• 2012

This paper proposes the coding method to predict the split structure of LCU in the current frame on the basis of the reference frame or temporally-previous frame. HEVC encoder determines split structure according to image characteristics in LCU which is an basic element of CU. The split structure of the current LCU is very similar to the split structure of collocated LCU in the reference frame or temporally-previous frame. Thus, this paper proposes the method to reduce the encoder computational complexity by predicting split structure of the current LCU on the basis of that of collocated LCU in the reference frame or temporally-previous frame. And it also proposes the method to reduce the BD-Bitrate by coding after the prediction of the CU split information. The simulation results of changing only encoder showed that the mean of encoder computational complexity was lower by 21.3%, the decoder computational complexity was negligible change and the BD-Bitrate increase by the maximum of 0.6%. Also, the method changing encoder, bitstream, and decoder improves the mean of encoder computational complexity was lower by 22%, the decoder computational complexity was negligible change and the BD-Bitrate is improved to the maximum of 0.3%. When compared with the conventional method, indicating that the proposed method is superior.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.119-124
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• 2009

In this paper, AMR-WB+ audio coder is implemented, in real-time, using Intel 400MHz Xscale PXA250 with 32bit RISC processor ARM9E-J(R)core. The assembly code for ARM9E-J(R)core is developed through the serial process of C code optimization, cross compile, assembly code manual optimization and adjusting the optimized code to Embedded Visual C++ platform. C code is trimmed on Visual C++ platform. Cross compile and assembly code manual optimization are performed on CodeWarrior with ARM compiler. Through these stages the code for both ARM EVM board and PDA is implemented. The average complexities of the code are 160.75MHz on encoder and 33.05MHz on decoder. In case of static link library(SLL), the required memories are 65.21Kbyte, 32.01Kbyte and 279.81Kbyte on encoder, decoder and common sources, respectively. The implemented coder is evaluated using 16 test vectors given by 3GPP to verify the bit-exactness of the coder.

• Journal of The Korean Association of Information Education
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• v.20 no.4
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• pp.333-340
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• 2016

When elementary students write their own sentences, it is often educationally beneficial to compare them with other people's similar sentences. However, it is impractical for use in most classrooms, because it is burdensome for teachers to look up all of the sentences written by students. To cope with this problem, we propose a novel approach for automatic selection of similar sentences based on a three-step process: 1) extracting the subword units from the word-level sentences, 2) training the model with the encoder-decoder architecture, and 3) using the approximate k-nearest neighbor search algorithm to find the similar sentences. Experimental results show that the proposed approach achieves the accuracy of 75% for our test data.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.94-101
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• 2022

The evaluation of the drag and lift as the aerodynamic performance of airfoils is essential. In addition, the analysis of the velocity and pressure fields is needed to support the physical mechanism of the force coefficients of the airfoil. Thus, the present study aims at establishing two different deep learning models to predict force coefficients and flow fields of the airfoil. One is the convolutional neural network (CNN) model to predict drag and lift coefficients of airfoil. Another is the Encoder-Decoder (ED) model to predict pressure distribution and velocity vector field. The images of airfoil section are applied as the input data of both models. Thus, the computational fluid dynamics (CFD) is adopted to form the dataset to training and test of both CNN models. The models are established by the convergence performance for the various hyperparameters. The prediction capability of the established CNN model and ED model is evaluated for the various NACA sections by comparing the true results obtained by the CFD, resulting in the high accurate prediction. It is noted that the predicted results near the leading edge, where the velocity has sharp gradient, reveal relatively lower accuracies. Therefore, the more and high resolved dataset are required to improve the highly nonlinear flow fields.

• Korean Journal of Optics and Photonics
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• v.33 no.3
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• pp.106-112
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• 2022

A two-dimensional optical code division multiple access (OCDMA) encoder/decoder, which is composed of add/drop filters and all-pass filters for delay operation, is proposed. An example design is presented, and its feasibility is illustrated through numerical simulations. The chip area of the proposed OCDMA encoder/decoder could be about one-third that of a previous OCDMA device employing delay waveguides. Its performance is numerically investigated using the transfer-matrix method combined with the fast Fourier transform. The autocorrelation peak level over the maximum cross-correlation level for incorrect wavelength hopping and spectral phase code combinations is greater than 3 at the center of the correctly decoded pulse, which assures a bit error rate lower than 10^-3, corresponding to the forward error-correction limit.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.55-61
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• 2021

In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.