• Journal of Communications and Networks
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• 제13권3호
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• pp.257-265
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• 2011

In this paper, a cooperative relaying protocol called soft-decision-and-forward (SDF) with multiple antennas in each node is introduced. SDF protocol exploits the soft decision source symbol values from the received signal at the relay node. For orthogonal transmission (OT), orthogonal codes including Alamouti code are used and for non-orthogonal transmission (NT), distributed space-time codes are designed by using a quasi-orthogonal space-time block code. The optimal maximum likelihood (ML) decoders for the proposed protocol with low decoding complexity are proposed. For OT, the ML decoders are derived as symbolwise decoders while for NT, the ML decoders are derived as pairwise decoders. It can be seen through simulations that SDF protocol outperforms AF protocol for both OT and NT.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.280-284
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• 2008

In this paper, a best channel matrix selection scheme(BCMS) is proposed to approximate maximum likelihood(ML) detection for a multiple-input multiple-output system. For a one stage BCMS scheme, one of the transmitted symbols is selected to perform ML detection and the other symbols are detected by zero forcing(ZF). To increase the diversity of the symbols that are detected by ZF, multi-stage BCMS detection scheme is used to further improve the system performance. Simulation results show that the performance of the proposed BCMS scheme can approach that of ML detection with a significant reduction in complexity.

• 품질경영학회지
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• 제25권2호
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• pp.1-14
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• 1997

This paper considers maximum likelihood (ML) estimation of lifetime distribution under stress bounded ramp tests in which the stress is increased linearly from used condition stress to the stress u, pp.r bound. The following assumptions are used: exponential lifetime distribution under a constant stress, an inverse power law relationship between stress and mean of exponential lifetime distribution, and a cumulative exposure model for the effect of changing stress. Likelihood equations for the parameters involved in the model and asymptotic distribution of the estimators are obtained, and a numerical example is given.

• Communications for Statistical Applications and Methods
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• 제19권5호
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• pp.697-704
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• 2012

The maximum likelihood(ML) estimation of the scale parameters of an exponential distribution based on progressive Type II censored samples is given. The sample is multiply censored (some middle observations being censored); however, the ML method does not admit explicit solutions. In this paper, we propose multiply progressive Type II censoring. This paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply progressive Type II censoring. The scale parameter is estimated by approximate ML methods that use two different Taylor series expansion types ($AMLE_I$, $AMLE_{II}$). We also obtain the maximum likelihood estimator(MLE) of the scale parameter under the proposed multiply progressive Type II censored samples. We compare the estimators in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20 and 40 and various censored schemes. The $AMLE_{II}$ is better than MLE and $AMLE_I$ in the sense of the MSE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4099-4113
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• 2022

In this paper, we propose improved maximum access delay time, noise variance, and power delay profile (PDP) estimation schemes for orthogonal frequency division multiplexing (OFDM) system in multipath fading channels. To this end, we adopt the approximate maximum likelihood (ML) estimation strategy. For the first step, the log-likelihood function (LLF) of the received OFDM symbols is derived by utilizing only the cyclic redundancy induced by cyclic prefix (CP) without additional information. Then, the set of the initial path powers is sub-optimally obtained to maximize the derived LLF. In the second step, we can select a subset of the initial path power set, i.e. the maximum access delay time, so as to maximize the modified LLF. Through numerical simulations, the benefit of the proposed method is verified by comparison with the existing methods in terms of normalized mean square error, erroneous detection, and good detection probabilities.

• 한국멀티미디어학회논문지
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• 제4권2호
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• pp.108-115
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• 2001

본 연구에서는 RGB칼라영상에서 세 칼라 객체의 색상에 따라 정량적으로 분류하기 위하여 ML(Maximum Likelihood) 분류법 을 개선 시도하여 보았다. RGB 칼라 영상이라 하면 빨강, 초록, 파랑의 세 밴드로 이루어진다. 스펙트룸과 공간상의 요소를 고려한다면 3차원적인 구조를 갖게 된다. 이러한 3차원 구조의 voxel를 RGB cube에 투사하여 이로부터 ML분류법의 개선 방법론을 적용하여 보았다. 전례적으로 쉽게 사용되어지는 Box 분류법과 비교 검토하여 보았으며 Bayesian decision 이론을 기반으로한 통계학적인 ML 분류법을 사용하였다. 유방암 항체조직영상에 이 방법론을 응용하며 양성 세포핵 음성 세포핵 그리고 배경을 분류하는데 좋은 결과를 얻어 임상에서 유방암 환자의 예후 및 진단에 사용할 수 있도록 연구하였다.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• 한국통신학회논문지
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• 제30권6C호
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• pp.523-531
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• 2005

덧셈꼴 일차 이동 평균 잡음이 충격성일 때, 가장 비슷함 검파기와 준최적 가장 비슷함 검파기의 결정영역을 얻는다. 가장 비슷함 검파기와 준최적 가장 비슷함 검파기를 쌍극 신호 시스템에 적용하여 비트 오류율 성능을 견준다. 준최적 가장 비슷함 검파기는 가장 비슷함 검파기보다 덜 복잡하고 얼개도 간단하지만 성능은 거의 같다는 것을 보인다. 한편, 잡음 충격성이 심해질수록 일차 이동 평균 잡음에 알맞게 설계한 검파기와 독립이고 분포가 같은 잡음에 알맞게 설계한 검파기의 성능 차이가 커진다는 것도 보인다.

• 한국전자파학회논문지
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• 제23권6호
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• pp.748-751
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• 2012

모노 펄스 estimator의 성능은 Monopulse Ratio(MR) 커브에 의해 결정된다. 모노 펄스 estimator의 성능을 향상시키기 위해서는 배열 구조 파라메타와 관련된 MR 커브의 수학적 표현이 필요하다. 본 논문에서는 Maximum Likelihood(ML) 기반 모노 펄스 estimator의 MR 커브의 역함수를 이용한 모노 펄스 estimator를 제안한다. 평면 배열 모노 펄스 레이더 안테나에서 제안된 estimator의 MR 커브의 선형 영역을 확장시키는 과정을 보이고, 시뮬레이션을 통해 기존의 ML 기반 estimator와 성능을 비교한다.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.145-154
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• 2020

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.