• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.7-13
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• 1996

MASF is a kind of temporal filter proposed for noise reduction and temporal band limitation. MASF uses motion vectors to extract temporal information in spatial domain. Therefore, inaccurate motion information causes some distortions in MASF operation. Currently, bilinear interpolation after BMA(Block Matching Algorithm) is used for the motion estimation sheme of MASF. But, this method results in unreliable estimation when the object in image sequence has larger movement than the maximum displacement assumed in BMA or the input images are severely corrupted with noise. In order to i:;olve this problem, we analyse the effect of inaccurate motion on MASF and propose a hierarchical motion estimation algorithm based on the analysis results. Experimental results show that the proposed method produces reliable output under large motion and noisy situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7A
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• pp.1028-1035
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• 1999

To increase the throughput of data transmission in burst-mode TDMA communication systems and also to get a good BER performance at the same time, it is essential to rapidly acquire the carrier while keeping the desirable tracking performance. To achieve this goal, in this paper, a new decision-directed carrier recovery algorithm is presented. The proposed scheme does not incorporate the PLL and suppress the Gaussian random process of input noise by the pre-stage low pass filter so as to get both the fast acquisition and a good performance. Through computer simulations, the performance of the scheme is analyzed with respect to the acquisition time and bit error rate. The cycle slip in the proposed scheme is seldom observed at very low SNR environment in contrast to the previous proposed one. Because of this merit, it is not required to do the differential encoding and decoding in the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.112-120
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• 2010

In this paper, we propose semi-ML adaptive parallel QRDM (APQRDM) and iterative QRDM (AIQRDM) algorithms based on set grouping. Using the set grouping, the tree-search stage of QRDM algorithm is divided into partial detection phases (PDP). Therefore, when the treesearch stage of QRDM is divided into 4 PDPs, the APQRDM latency is one fourth of that of the QRDM, and the hardware requirements of AIQRDM is approximately one fourth of that of QRDM. Moreover, simulation results show that in $4{\times}4$ system and at Eb/N0 of 12 dB, APQRDM decreases the average computational complexity to approximately 43% of that of the conventional QRDM. Also, at Eb/N0 of 0dB, AIQRDM reduces the computational complexity to about 54% and the average number of metric comparisons to approximately 10% of those required by the conventional QRDM and AQRDM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1219-1226
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm because discriminant and prediction system for Red Tide is insufficient development and the study of red tide are focused for the investigation of chemical and biological causing. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for Red Tide. We used K-Nearest Neighbor algorithm for recommend best similar case and input 375 EA by case for Red Tide case base. As a result, conducted 10-fold cross verification for minimal impact from learning data and acquired confidence, we obtained about 84.2% average accuracy for Red Tide case and the best performance results in case by number of similarity classification k is 5. And, we implemented Red Tide monitoring system using inference result.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.175-182
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• 2013

This paper proposes a readability enhancement filter for Patterned Retarder (PR) display. In general, when some texts in stereoscopic images are shown on PR display, their readability tends to be lowered. In order to overcome this problem, we present a readability enhancement algorithm which consists of readability filtering stage and post-processing stage for specific characters. First, each input stereo image is divided into an odd line image and an even line image. Then, they are independently up-scaled vertically by using Lanczos filter. Next, two up-scaled line images are averaged considering vertical phase difference. In post-processing stage, two specific characters which are normally difficult to read on PR display are detected, and they are filtered for additional readability enhancement. Here, this additional filtering is based on a specific brightness adjustment, and is applied only for two characters. The experiment results show that the proposed method achieves significant improvement in terms of readability in comparison with the previous scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.462-468
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• 2012

In this paper, an adaptive digital automatic gain control(AGC) algorithm with two stages is proposed. AGC technique is crucial for mobile communication equipment because path loss in wireless channel and gain fluctuation in receiver front-end continuously change the received signal strength. Furthermore, adaptive criteria should be applied to the design of AGC algorithm in order to support many waveforms with one SDR communication device. With these reasons, a two-stage structure is proposed to satisfy both flexibility and adaptiveness. Compared with conventional method, it also requires shorter convergence time. Numerical results show that the gain value of variable gain amplifier(VGA) is converged within proper time and proposed scheme controls the input level of analog to digital converter(ADC) to be stable during long range of time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.478-486
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• 2007

Double-talk detection errors can result in either large residual echo or distorting the near-end talker's input speech. Thus accurate double-talk detection is an important problem in the acoustic echo canceller to improve the speech quality. In the double-talk detection algorithm using a cross-correlation coefficient, double-talk detection errors can occur in the initial convergence period of an adaptive filter or in noisy environment since the cross-correlation coefficient becomes large in such situations. In this paper, we propose a new double-talk detection algorithm based on the cross-correlation method using a normalized error signal power to reduce the double-talk detection errors. The experimental results have shown the performance improvement of an acoustic echo canceller as well as the noise-robustness of the proposed double-talk detector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10A
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• pp.809-815
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• 2011

In this paper, we have proposed and implemented a novel architecture which can be used to effectively design the modified Euclidean (ME) algorithm for key equation solver (KES) block in high-speed Reed-Solomon (RS) decoder. With polynomial expressions of newly-defined state variables for controlling each processing element (PE), the proposed architecture has simple input/output signals and requires less hardware complexity because no degree computation circuits are needed. In addition, since each PE circuit is independent of the error correcting capability t of RS codes, it has the advantage of linearly increase of the hardware complexity of KES block as t increases. For comparisons, KES block for RS(255,239,8) decoder is implemented using Verilog HDL and synthesized with 0.13um CMOS cell library. From the results, we can see that the proposed architecture can be used for a high-speed RS decoder with less gate count.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1549-1557
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• 1999

In this paper, we propose a face recognition algorithm based on the differential image method-DCT This algorithm uses neural networks which is flexible for noise. Using the same condition (same luminous intensity and same distance from the fixed CCD camera to human face), we have captured two images. One doesn't contain human face. The other contains human face. Differential image method is used to separate the second image into face region and background region. After that, we have extracted square area from the face region, which is based on the edge distribution. This square region is used as the characteristics region of human face. It contains the eye bows, the eyes, the nose, and the mouth. After executing DCT for this square region, we have extracted the feature vectors. The feature vectors were normalized and used as the input vectors of the neural network. Simulation results show 100% recognition rate when face images were learned and 92.25% recognition rate when face images weren't learned for 30 persons.