• Title/Summary/Keyword: Maximum a Posteriori Probability

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Improved Super-Resolution Algorithm using MAP based on Bayesian Approach

  • Jang, Jae-Lyong;Cho, Hyo-Moon;Cho, Sang-Bock
    • Proceedings of the KIEE Conference
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    • 2007.04a
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    • pp.35-37
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    • 2007
  • Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

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Automatic Basal Cell Carcinoma Detection using Confocal Raman Spectra (공초점 라만스펙트럼을 이용한 자동 기저세포암 검출)

  • Min, So-Hee;Park, Aaron;Baek, Seong-Joon;Kim, Jin-Young
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.255-256
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    • 2006
  • Raman spectroscopy has strong potential for providing noninvasive dermatological diagnosis of skin cancer. In this study, we investigated two classification methods with maximum a posteriori (MAP) probability and multi-layer perceptron (MLP) classification. The classification framework consists of preprocessing of Raman spectra, feature extraction, and classification. In the preprocessing step, a simple windowing method is proposed to obtain robust features. Classification results with MLP involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic Basal Cell Carcinoma (BCC) detection.

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Break Strength Prediction Using Maximum a Posterior Probability (MAP 확률을 이용한 끊어 읽기 강도 예측)

  • Kim Sanghun;Park Jun;Lee Youngjik
    • Proceedings of the Acoustical Society of Korea Conference
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    • spring
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    • pp.75-78
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    • 2000
  • 본 논문은 자연스러운 합성음 생성을 위한 끊어 읽기 강도 예측에 관한 것으로, 문장에 대한 품사열이 주어졌을 때 Posteriori 확률을 최대화하는 끊어 읽기 강도를 비터비 디코딩으로 예측한다. 훈련용 데이터는 여성화자 1인이 발성한 2,100 문장이며, 음성 데이터로부터 휴지길이(pause)에 따라 끊어 읽기 강도를 2단계로 할당하고, 텍스트에서는 30개의 품사 태그 심볼을 이용하여 형태소분석 및 태깅을 수행하였다. 관측확률은 3개 연속하는 품사열이 발생할 확률로 하고 끊어 읽기 강도 천이확률은 bigram으로 했을 때, cross validation 방법으로 성능 평가를 수행하였다 평가결과, 훈련데이타에 대해서는 $89.7\%$, 테스트 데이터에 대해서는 $84.9\%$의 예측정확률을 보였다.

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Speech Recognition Using the Energy and VQ (에너지와 VQ를 이용한 음성 인식)

  • Hwang, Young-Soo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.6 no.3
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    • pp.87-94
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    • 2007
  • In this paper, the performance of the speech recognition and speaker adaptation methods are studied. The speech recognition using energy state and VQ(Vector Quantization) is suggested and the speaker adaptation methods(Maximum a posteriori probability estimation, linear specrum estimation) are considered. The experimental results show that recognition ration using energy state is 2-3 % better than that of general VQ.

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Iterative Turbo Decoding Using Three Cascade MAP Decoder (3개의 직렬 MAP 복호기를 이용한 반복 터보 복호화기)

  • 김동원;이호웅;강철호
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.6B
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    • pp.709-716
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    • 2001
  • 반복 복호 알고리듬에 의해 복호화된 터보 코드는 가산성 백색 가우시안 잡음(AWGN) 채널 환경에서 이론적으로 Shannon의 한계에 근접한 뛰어난 코딩 이득을 나타내는 것으로 보여지고 있다. 그러나, 터보 코드의 성능은 터보 부호화기에서 프레임의 크기 즉, 인터리버의 크기에 의존한다. IMT-2000과 같은 이동 통신 채널 환경에서 음성을 전송하는 경우에는 터보 코드의 프레임 크기는 매우 작다. 그리고, 그것은 터보 코드의 성능을 떨어뜨리는 직접적인 원인이 된다. 본 논문에서는 차세대 이동 통신 시스템에서 프레임 크기가 작은 음성 프레임을 이용하여 터보 코드의 성능을 검증하며, 작은 프레임 크기에 알맞은 3개의 직렬 MAP(Maximum A Posteriori probability) 복호기를 이용한 반복 복호의 터보 코드를 제안하고 부호율 1/3, 구속장의 길이 3 또는 4, 프레임 크기 24, 192 비트에 대하여 컴퓨터 모의실험을 통해 터보 코드의 성능을 분석한다.

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Iterative V-BLAST Decoding Algorithm in the AMC System with a STD Scheme

  • Lee, Keun-Hong;Ryoo, Sang-Jin;Kim, Seo-Gyun;Hwang, In-Tae
    • 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.

Low Dimensional Multiuser Detection Exploiting Low User Activity

  • Lee, Junho;Lee, Seung-Hwan
    • Journal of Communications and Networks
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    • v.15 no.3
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    • pp.283-291
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    • 2013
  • In this paper, we propose new multiuser detectors (MUDs) based on compressed sensing approaches for the large-scale multiple antenna systems equipped with dozens of low-power antennas. We consider the scenarios where the number of receiver antennas is smaller than the total number of users, but the number of active users is relatively small. This prior information motivates sparsity-embracing MUDs such as sparsity-embracing linear/nonlinear MUDs where the detection of active users and their symbol detection are employed. In addition, sparsity-embracing MUDs with maximum a posteriori probability criterion (MAP-MUDs) are presented. They jointly detect active users and their symbols by exploiting the probability of user activity, and it can be solved efficiently by introducing convex relaxing senses. Furthermore, it is shown that sparsity-embracing MUDs exploiting common users' activity across multiple symbols, i.e., frame-by-frame, can be considered to improve performance. Also, in multiple multiple-input and multiple-output networks with aggressive frequency reuse, we propose the interference cancellation strategy for the proposed sparsity-embracing MUDs. That first cancels out the interference induced by adjacent networks and then recovers the desired users' information by exploiting the low user activity. In simulation studies for binary phase shift keying modulation, numerical evidences establish the effectiveness of our proposed MUDs exploiting low user activity, as compared with the conventional MUD.

Optimizations for Mobile MIMO Relay Molecular Communication via Diffusion with Network Coding

  • Cheng, Zhen;Sun, Jie;Yan, Jun;Tu, Yuchun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.4
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    • pp.1373-1391
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    • 2022
  • We investigate mobile multiple-input multiple-output (MIMO) molecular communication via diffusion (MCvD) system which is consisted of two source nodes, two destination nodes and one relay node in the mobile three-dimensional channel. First, the combinations of decode-and-forward (DF) relaying protocol and network coding (NC) scheme are implemented at relay node. The adaptive thresholds at relay node and destination nodes can be obtained by maximum a posteriori (MAP) probability detection method. Then the mathematical expressions of the average bit error probability (BEP) of this mobile MIMO MCvD system based on DF and NC scheme are derived. Furthermore, in order to minimize the average BEP, we establish the optimization problem with optimization variables which include the ratio of the number of emitted molecules at two source nodes and the initial position of relay node. We put forward an iterative scheme based on block coordinate descent algorithm which can be used to solve the optimization problem and get optimal values of the optimization variables simultaneously. Finally, the numerical results reveal that the proposed iterative method has good convergence behavior. The average BEP performance of this system can be improved by performing the joint optimizations.

A Baseline Correction for Effective Analysis of Alzheimer’s Disease based on Raman Spectra from Platelet (혈소판 라만 스펙트럼의 효율적인 분석을 위한 기준선 보정 방법)

  • Park, Aa-Ron;Baek, Sung-June
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.49 no.1
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    • pp.16-22
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    • 2012
  • In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

Reliability Improvement of Automatic Basal Cell Carcinoma Classifier with an Ambiguous Pattern Class (모호한 패턴 클래스 도입을 통한 기저 세포암 분류기의 신뢰도 향상)

  • Park, Aa-Ron;Baek, Seong-Joon;Jung, In-Wook;Song, Min-Gyu;Na, Seung-Yu
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.44 no.1
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    • pp.64-70
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    • 2007
  • Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability (MAP) and multilayer perceptron networks (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 pattern class in MAP, linear classifier using minimum squared error (MSE), MLP and reduced coulomb energy networks (RCE). The experiments involving 216 confocal Raman spectra showed that every methods could perfectly classify BCC by screening out some ambiguous patterns. The best results were obtained with MSE. According to the experimental results, MSE gives perfect classification by screening out 8.8% of test patterns.