• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1467-1470
• /
• 2002

The minimum entropy method is one of blind equalization method. A conventional algorithm using the minimum entropy method has two problems : slower convergence and lower reliability of recovered signals. We propose a new algorithm using the minimum entropy method for solving the two problems. Pina31y, we confirm the validity of the proposed algorithm through computer simulation.

• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.657-667
• /
• 2006

The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.2
• /
• pp.206-209
• /
• 2003

An $H_2$/$H_{\infty}$ -controller realization is carried out by considering an entropy integral. Using J-spectral factorization, the parametrizations of all $H_{\infty}$ stabilizing controllers are derived. By the relation of a mixed $H_2$/$H_{\infty}$ control problem and a minimum entropy/$H_{\infty}$ control problem, the mixed $H_2$/$H_{\infty}$-controller state-space realization is presented.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.04a
• /
• pp.672-673
• /
• 2013

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1999.06a
• /
• pp.83-88
• /
• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.5
• /
• pp.366-374
• /
• 2018

In cases of airborne equipped with SAR, because the occurrence of motion is inevitable, it is necessary to apply autofocus techniques to SAR images to improve the image performance degradations caused by residual errors. Herein, a robust autofocus algorithm based on the minimum entropy criteria is proposed for the real SAR data in the spotlight mode. The convergence condition of the phase error estimation is checked at every iteration and if it is violated, the size of the phase error estimation is adjusted to the convergence condition. The real SAR raw data is used to demonstrate the excellent performance of the proposed algorithm.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.999-1001
• /
• 1996

In this paper, we represented the relation of minimum entropy/$H_{\infty}$-controller and mixed $H_2/H_{\infty}$-controller. An $H_2$ controller design problem involving a constraint on $H_{\infty}$ disturbance attenuation is considered. By the equivalence of the mixed $H_2/H_{\infty}$ control problem and the minimum entropy/$H_{\infty}$-control problem, we presented the controller state-space realization. Decentralized case was illustrated briefly.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.04a
• /
• pp.826-829
• /
• 2014

본 연구에서는 야외 지형 영상 및 항공 영상 등에 대하여 각각의 영역들의 속성을 분할 및 인식 하기 위해 minimum entropy clustering 기반의 군집화 기법과 over-segmentation을 반복 적용하여 군집화 하는 두 방법을 융합한 기법을 제안하였다. 이 기법들을 기반으로 각 군집의 대표 영역을 추출한 후에 학습 데이터를 기반으로 만들어진 텍스톤 사전과 학습 데이터 각각의 텍스톤 모델을 이용하여 텍스톤 히스토그램 매칭을 통해 매칭 포인트를 얻어내고 얻어낸 매칭 포인트를 기반으로 영역의 카테고리를 결정한다. 본 논문에서는 인터넷에서 얻은 일반 야외 영상들로부터 자체적으로 제작한 지형 데이터 셋을 통해 제안한 기법의 우수성을 검증하였으며, 본 실험에서는 영역을 토양, 수풀 그리고 물 지형으로 하여 영상내의 영역을 분류 및 인식하였다.

• Current Optics and Photonics
• /
• v.5 no.1
• /
• pp.16-22
• /
• 2021

We present the Shannon entropy as an indicator of the spatial resolutions of the morphologies of the resonance mode patterns in an optical resonator. We obtain each optimized number of mesh points, one of minimum size and the other of maximum one. The optimized mesh-point number of minimum size is determined by the identifiable quantum number through a chi-squared test, whereas the saturation of the difference between Shannon entropies corresponds to the other mesh-point number of maximum size. We also show that the optimized minimum mesh-point increases as the (real) wave number increases and approximates the proportionality constant between them.

• Journal of Communications and Networks
• /
• v.7 no.4
• /
• pp.438-449
• /
• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.