• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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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.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.25-31
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• 2011

In the underwater communications, transmitted acoustic signal is corrupted by interference from multipath. A parametric array transducer is capable of radiating a narrow beam with very low sidelobe levels. In certain cases, the parametric array transducer can help the multipath problem. To improve the performance of the underwater communications, the statistical signal processing methods will be required. In the paper, the communication system using a parametric array transducer was demonstrated. To detect the received signal of the communication system based on the on-off keying, the maximum likelihood method using averaged signal for a particular window size is used. The communication system has GUI using LebVIEW which allows the user to change the parameter. The GUI can also be easily modified based on the characteristics of a parametric array transducer. The implemented system can effectively evaluate the performance of the parametric array transducer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11A
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• pp.1167-1175
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• 2006

The ML-DFE(Maximum Likelihood-Decision Feedback Equalization) can be viewed as either a suboptimal signal detection method for reducing hardware complexity of ML or an enhanced detection method for reducing the effect of error propagation of SIC(Successive Interference Cancellation) in spatially multiplexed MIMO systems such as V-BLAST. The ML-DFE can achieve a higher diversity in rich scattering environments as well as reducing the error propagation effect by combing ML decoding with the DFE. In this paper, an MML-DFE(Modified Maximum Likelihood-Decision Feedback Equalization) is proposed to reduce the hardware complexity of the ML-DFE, without compromising performance. It is shown by FPGA implementation that the proposed MML-DFE can achieve the same performance as the ML-DFE with significantly reduced hardware complexity.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.529-536
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• 2016

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.102-107
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• 2021

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.295-302
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• 2000

We study the efficient method to estimate the parameters for the Weibull model with covariates which occupies an important position in survival analysis. A treatment period may be divided by the stages of treatments under the different treatment arams. The piecewise method is considered to obtain the estimators of the parameters by maximum likelihood method. We explore the real data to show that the piecewise is more efficient than the nonpiecewise to estimate the parameters.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.67-81
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• 2005

Most of the previously proposed methods for the frailty model do not work well when there are many tied observations. This is partly because the empirical likelihood used is not suitable for tied observations. In this paper, we propose a new method for the frailty model with many ties. The proposed method obtains the posterior distribution of the parameters using the binomial form empirical likelihood and Bayesian bootstrap. The proposed method yields stable results and is computationally fast. To compare the proposed method with the maximum marginal likelihood approach, we do simulations.