• International Journal of Contents
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• v.6 no.3
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• pp.5-9
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• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.35-41
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• 2007

In this paper, a modified Fuzzy C-Means (MFCM) algorithm is presented for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed with the elements of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.7 s.337
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• pp.27-36
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• 2005

This paper presents an area-efficient architecture to implement the high-speed Reed-Solomon(RS) decoder, which is used in a variety of communication systems such as wireless and very high-speed optical communications. We present the new pipelined-recursive Modified Euclidean(PrME) architecture to achieve high-throughput rate and reducing hardware-complexity using folding technique. The proposed pipelined recursive architecture can reduce the hardware complexity about 80$\%$ compared to the conventional systolic-array and fully-parallel architecture. The proposed RS decoder has been designed and implemented with the 0.13um CMOS technology in a supply voltage of 1.2 V. The result show that total number of gate is 393 K and it has a data processing rate of S Gbits/s at clock frequency of 625 MHz. The proposed area-efficient architecture can be readily applied to the next generation FEC devices for high-speed optical communications as well as wireless communications.

• International Journal of Contents
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• v.12 no.1
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• pp.1-5
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• 2016

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.95-102
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• 2006

This paper proposes the KB-Fano algorithm which has lower decoding complexity by applying modified Fano-like metric bias to the conventional K-best algorithm. Additionally, an efficient K-best decoding algorithm, named the KR-Fano scheme, is proposed by jointly combining the K-reduction and the KB-Fano schemes. Simulations show that the proposed algerian provides the remarkable improvement from the viewpoints of the BER performance and the decoding complexity as compared to the conventional K-best scheme.

• ETRI Journal
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• v.26 no.2
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• pp.101-111
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• 2004

This paper presents a near-optimum blind decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television. By adopting a modified trellis decoder (MTD) with a trace- back depth of 1 for the decision device in the DFE, we obtain a hardware-efficient, blind DFE approaching the performance of an optimum DFE which has no error propagation. In the MTD, the absolute distance is used rather than the squared Euclidean distance for the computation of the branch metrics. This results in a reduction of the computational complexity over the original trellis decoding scheme. Compared to the conventional slicer, the MTD shows an outstanding performance improvement in decision error probability and is comparable to the original trellis decoder using the Euclidean distance. Reducing error propagation by use of the MTD in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer, and the difference is prominent at the trellis decoder following the blind DFE.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.407-412
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적함수(fitness function)와 가우시안 가중치가 적용된 멤버쉽 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 벡터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing (SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.