• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.709-712
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• 1987

In this paper, a mathematical model for the purpose of QRS detection is considered in the case of the occurrence of nonoverlapping pulse-shaped waveforms corrupted with white noise. The number of waveforms, the arrival times, amplitudes, and widths of QRS complexes are regarded as random variables. The joint MAP estimation of all the unknown quantities consists of linear filtering followed by an optimization procedure. Because of time-consuming, the optimization procedure is modified so that a threshold test is obtained. The model formulation with nonoverlapping waveforms leads to a standard procedure covering a segment before as well as after an accepted event. Adaptivity of the detector is gained by utilizing past signal properties in determining threshold for QRS detection.

• ETRI Journal
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• v.34 no.2
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• pp.184-189
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• 2012

In this paper, we propose a novel approach to statistical model-based voice activity detection (VAD) that incorporates a second-order conditional maximum a posteriori (CMAP) criterion. As a technical improvement for the first-order CMAP criterion in [1], we consider both the current observation and the voice activity decision in the previous two frames to take full consideration of the interframe correlation of voice activity. This is clearly different from the previous approach [1] in that we employ the voice activity decisions in the second-order (previous two frames) CMAP, which has quadruple thresholds with an additional degree of freedom, rather than the first-order (previous single frame). Also, a soft-decision scheme is incorporated, resulting in time-varying thresholds for further performance improvement. Experimental results show that the proposed algorithm outperforms the conventional CMAP-based VAD technique under various experimental conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.905-908
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• 2008

In this paper, an Adaptive Modulation (AM) system with an efficient turbo-coded Vertical-Bell-lab Layered Space-Time (V-BLAST) technique is proposed. The proposed decoding algorithm adopts iteratively the extrinsic information from a Maximum a Posteriori (MAP) decoder as a priori probability in the two decoding procedures of the V-BLAST scheme of ordering and slicing. In this analysis, each MIMO channel is assumed to be a part of the system of performance improvement.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.261-262
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• 2006

According to the previous work, various well known methods including maximum a posteriori probability classifier (MAP) and multi layer perceptron networks classifier (MLP) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can be examined by routine biopsy. We incorporated an ambiguous category in MAP and MLP. Classification results involving 216 spectra gave 100% sensitivity for the case of MLP.

• Information and Communications Magazine
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• v.24 no.12
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• pp.14-21
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• 2007

This paper aims to improve the design of iterative detection and decoding(IDD) based on the soft interference cancellation with minimum mean squared error(SC-MMSE) detector, which shows low performance compared to the maximum a posteriori(MAP) detector. By means of extrinsic information transfer(EXIT) chart analysis, such low performance may be attributed to that the "pure"(original) turbo principle is not always best for IDD. Thus, we propose a new IDD architecture based on the SC-MMSE detector which uses new a priori information. Simulation results show that the performance of the proposed IDD is very close to that of IDD based on the MAP detector.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.105-117
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• 1998

In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration.The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. And, we proposed new probabilistic models in order to simplify the joint probability distribution of disparities in the configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of swuared difference(SSD) based algorithm and Scharstein's method. We canconclude that the derived formular generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation, and the propsoed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% of the generalized formula.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.1-5
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• 2008

In this paper, we propose and analyze the AMC (Adaptive Modulation and Coding) system with efficient turbo coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique. The proposed algorithm adopts extrinsic information from a MAP (Maximum A Posteriori) decoder with iterative decoding as a priori probability in two decoding procedures of V-BLAST scheme; the ordering and the slicing. Also, we consider the AMC system using the conventional turbo coded V-BLAST technique that simply combines the V-BLAST scheme with the turbo coding scheme. And we compare the proposed decoding algorithm to a conventional V-BLAST decoding algorithm and a ML (Maximum Likelihood) decoding algorithm. In addition, we apply a STD (Selection Transmit Diversity) scheme to the systems for better performance improvement. Results indicate that the proposed systems achieve better throughput performance than the conventional systems over the entire SNR range. In terms of transmission rate performance, the suggested system is close in proximity to the conventional system using the ML decoding algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1045-1051
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• 2003

The SOVA, which produces the soft decision value, can be used as a sub-optimum solution for concatenated codes such as turbo codes, since it is computationally efficient compared with the optimum MAP algorithm. In this paper, we propose an efficient implementation of the SOVA used for decoding turbo codes, by reducing the number of calculations for soft decision values and trace-back operations. In order to utilize the memory efficiently, the whole block of turbo codes is divided into several sub-blocks in the proposed algorithm. It is demonstrated that the proposed algorithm requires less computation than the conventional algorithm, while providing the same overall performance.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.511-516
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• 2004

수치표고모델, 정사영상과 같은 공간영상정보를 구축하기 위해서는 입체영상을 이동한 영상정합(image matching)의 과정이 필수적이며, 단영상 또는 스테레오 영상을 이용하여 대상물의 3차원 정보를 재구성하고 복원하는 기술은 사진측량 및 컴퓨터 비전 분야의 주요 연구 중의 하나이다. 본 연구에서는 화소값의 유사성과 상호관계성을 고려하는 MRF 모델을 이용하여 영상정합을 수행하였다. MRF 모델은 공간분석이나 물리적 현상의 전후관계(contextural dependencies)의 분석을 위한 확률이론의 한 분야로 다양한 공간정보를 통합할 수 있는 방법을 제공한다. 본 연구에서는 기준영상의 화소에 시차를 할당하는 접근 방법으로 확률모델의 일종인 마르코프 랜덤필드(MRF)모델에 기반한 영상정합기법을 제안하였고, 공간내 화소의 상호관계를 고려해주므로 대상물의 경계부분에서의 매칭 정확도를 향상시켰다. 영상정합문제에서의 MRF 기본가정은 영상 내 특정화소의 시차는 그 주위화소의 시차에 의한 부분정보에 따라 결정이 가능하다는 것이다. 깁스분포(gibbs distribution)를 사용하여 사후(posteriori) 확률값을 유도해내고, 이를 최대사후확률(MAP: Maximum a Posteriori)추정법을 이용하여 에너지함수를 생성하였다. 생성된 에너지함수의 최적화(Optimization)를 위하여 본 연구에서는 전역최적화기법인 multiway cut 기법을 사용하여 영상정합에 있어 에너지함수를 최소로 하는 이미지화소에 대한 시차레이블을 구하여 영상정합을 수행하였다.