• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.958-964
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• 2007

In this paper, we derive the relationship between the bit error probability (BEP) of maximum a posteriori (MAP) bit detection and the bit minimum mean square error (MMSE), that is, the BEP is greater than a quarter of the bit USE and less than a half of the bit MMSE. By using this result, the lower and upper bounds of the derivative of the mutual information are derived from the BEP and the lower and upper bounds are easily obtained in the multiple-input multiple-output (MIMO) communication systems with the bit-linear linear-dispersion (BLLD) codes in the Gaussian channel.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Asia pacific journal of information systems
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• v.13 no.1
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• pp.197-211
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• 2003

Intensified competition, splintered mass market, shortened product life cycles, and advanced technology and automation let companies to crease the IT investment to meet the changes, Although IT investment increased, IS didn't show the visible outcome. One of the major interest of IS managers is how to demonstrate the business value of the firm's investment in information technology. This paper proposes the revised model of Nelson & Cooprider(1990) regarding shared knowledge between IS and line groups. Knowledge can be shared through mutual trust, mutual influence and communication between these two groups. The revised model including communication is tested empirically using LISREL. The results show that shared knowledge mediated the relationship between IS performance and mutual trust, mutual influence and communication. And shared knowledge between IS and line groups increase IS performance. IS managers should develop mutual trust, mutual influence and communication between these groups to achieve more shared knowledge and higher IS performance.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.1017-1025
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• 2015

In mathematical statistics education, we can use mutual information as a tool for evaluating the degree of dependency between two random variables. The ordinary correlation coefficient provides information only on linear dependency, not on nonlinear relationship between two random variables if any. In this paper as a measure of the degree of dependency between random variables, we suggest the use of symmetric uncertainty and ${\lambda}$ which are defined in terms of mutual information. They can be also considered as generalized correlation coefficients for both linear and non-linear dependence of random variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1840-1855
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• 2015

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.104-107
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• 2004

In this study, using brain phantom for multi-modality imaging, we acquired CT, MR and PET images and performed registration of these anatomical images and nuclear medicine functional images. The algorithms and program applied for registration were Chamfer Matching and Mutual Information Maximization algorithm which have been using frequently in clinic and verified accuracy respectively. In result, both algorithms were useful methods for CT-MR, CT-PET and MR-PET. But Mutual Information Maximization was more effective algorithm for low resolution image as nuclear medicine functional image.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.547-557
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• 2014

This paper presents a method for designing circular template used in similarity measurement for image registration. Circular template has translation and rotation invariant property, which results in correct matching of control points for image registration under the condition of translation and rotation between reference and sensed images. Circular template consisting of the pixels located on the multiple circumferences of the circles whose radii vary from zero to a certain distance, is converted to two-dimensional Discrete Polar Coordinate Matrix (DPCM), whose elements are the pixels of the circular template. For sensed image, the same type of circular template and DPCM are created by rotating the circular template repeatedly by a certain degree in the range between 0 and 360 degrees and then similarity is calculated using mutual information of the two DPCMs. The best match is determined when the mutual information for each rotation angle at each pixel in search area is maximum. The proposed algorithm was tested using KOMPSAT-2 images acquired at two different times and the results indicate high accurate matching performance under image rotation.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.53-56
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• 2002

This paper discusses the quorum based algorithm for group mutual exclusion defined by Yuh-Jzer Joung. Group mutual exclusion[4,5,6] is a generalization of mutual exclusion that allows a resource to be shared by processes of the same group, but requites processes of different groups to use the resource in a mutually exclusive style. Joung proposed a quorum system, which he referred to as the surficial quorum system for group mutual exclusion and two modifications of Maekawa's algorithm[6]. He mentioned that when a process may belong to more than one group, the process must identify one of the groups it belongs when it wishes to enter CS(Critical Section). However, his solution didn't provide mechanism of identifying a group which maximizes the possibility to enter CS. In this paper, we provide a mechanism for identifying that each process belongs to which group.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.258-261
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• 2002

This paper addresses the problem of merging two feedforward neural networks into one network. Merging is accomplished at the level of hidden layer. A new network selects its hidden layer's units from the two networks to be merged We uses information theoretic criterion (quadratic mutual information) in the selection process. The hidden unit's output and the target patterns are considers as random variables and the mutual information between them is calculated. The mutual information between hidden units are also considered to prevent the statistically dependent units from being selected. Because mutual information is invariant under linear transformation of the variables, it shows the property of the robust estimation.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.304-309
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• 2000

For understanding the information processing in human brain, we analyze the EEG, a spontaneous electric activity on the scalp of the human. In this paper, we used the mutual information to analyze EEG. The mutual information is used to show the stochastic correlation between signals which are generated in the communication and information theory. The used EEG is evoked by each auditory stimulus in positive and negative emotional states. As a result, we found thet there is some difference at the mutual information in each emotional state.