• Journal of Korea Multimedia Society
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• v.2 no.2
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• pp.137-144
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• 1999

Error diffusion is excellent for reproducing grey-scale images to binary images. The output of conventional error diffusion produces correlated pattern. In this paper, a new error diffusion coefficient set is proposed to reduce correlated pattern and to enhance edge through frequency analysis of the error diffusion coefficients. The error diffusion coefficients of the previous line are designed to enhance the edge. The error diffusion coefficient of the previous pixel of the current pixel is selected to symmeterize the coefficient set. Because the proposed coefficient-set consists of 1 and 2, a few computations are required. As results of experiments, it is shown that the binary image using the proposed coefficients have better quality than conventional ones.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.196-202
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• 2021

DNA sequencing provides fundamental data in genomics, bioinformatics, biology and many other research areas. With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Those large volumes of data also require a fast transmission, effective storage, superior functionality and provision of quick access to any record. Data storage costs have a considerable proportion of total cost in the formation and analysis of DNA sequences. In particular, there is a need of highly control of disk storage capacity of DNA sequences but the standard compression techniques unsuccessful to compress these sequences. Several specialized techniques were introduced for this purpose. Therefore, to overcome all these above challenges, lossless compression techniques have become necessary. In this paper, it is described a new DNA compression mechanism of pattern matching extended Compression algorithm that read the input sequence as segments and find the matching pattern and store it in a permanent or temporary table based on number of bases. The remaining unmatched sequence is been converted into the binary form and then it is been grouped into binary bits i.e. of seven bits and gain these bits are been converted into an ASCII form. Finally, the proposed algorithm dynamically calculates the compression ratio. Thus the results show that pattern matching extended Compression algorithm outperforms cutting-edge compressors and proves its efficiency in terms of compression ratio regardless of the file size of the data.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.483-488
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• 2015

This paper presents a local binary pattern(LBP) for effectively classifying textures, which is based on the multidimensional intensity difference between the adjacent pixels in the block image. The intensity difference by considering the a extent of 4 directional changes(verticality, horizontality, diagonality, inverse diagonality) in brightness between the adjacent pixels is applied to reduce the computation load as a results of decreasing the levels of histogram for classifying textures of image. And the binary patterns that is represented by the relevant intensities within a block image, is also used to effectively classify the textures by accurately reflecting the local attributes. The proposed method has been applied to classify 24 block images from USC Texture Mosaic #2 of 128*128 pixels gray image. The block images are different in size and texture. The experimental results show that the proposed method has a speedy classification and makes a free size block images classify possible. In particular, the proposed method gives better results than the conventional LBP by increasing the range of histogram level reduction as the block size becomes larger.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1365-1373
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• 2019

Meltdown and Spectre are vulnerabilities that exploit out-of-order execution and speculative execution techniques to read memory regions that are not accessible with user privileges. OS patches were released to prevent this attack, but older systems without appropriate patches are still vulnerable. Currently, there are some research to detect Meltdown and Spectre attacks, but most of them proposed dynamic analysis methods. Therefore, this paper proposes a binary signature that can be used to detect Meltdown and Spectre malware without executing them. For this, we collected 13 malicious codes from GitHub and performed binary pattern analysis. Based on this, we proposed a static detection method for Meltdown and Spectre malware. Our results showed that the method identified all the 19 attack files with 0.94% false positive rate when applied to 2,317 normal files.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.407-416
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• 2021

An active phased array antennas can not only electrically steer the beam by controlling the weighting of the excitation signal, but can also form a pattern null in the direction of the interference source. The weight of the excitation signal to steer the main beam can be easily calculated based on the position of the radiating element. In addition, the weight of the excited signal for pattern null formation can also be calculated by setting the required radiation pattern and using WLSM(Weighted Least Squares Method). However, in a general wireless communication network environment, the location of the interference source is unknown. Therefore, an adaptive pattern null synthesis is needed. In this paper, it was confirmed that pattern null synthesis according to the required radiation characteristic was possible. And based on this, adaptive pattern null synthesis into the direction of an interference source was studied using a binary search algorithm based on observation area. As a result of conducting a simulation based on the presented technique, it was confirmed that adaptive pattern null forming into the direction of an interference is possible in efficient way.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.539-547
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• 2016

We discussed how to handle missing binary data clinical trials. Patterns of occurring missing data are discussed and introduce missing binary data imputation methods that include the modified method. A simulation is performed by modifying actual data for each method. The condition of this simulation is controlled by a response rate and a missing value rate. We list the simulation results for each method and discussed them at the end of this paper.

• Journal of The Korean Astronomical Society
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• v.42 no.3
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• pp.33-37
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• 2009

We propose a diagnostic that can resolve the planet/binary degeneracy of central perturbations in caustic-crossing high-magnification microlensing events. The diagnostic is based on the difference in the morphology of perturbation inside the central caustics induced by a planet and a wide-separation binary companion. We find that the contours of excess exhibit a concentric circular pattern around the caustic center for the binary-lensing case, while the contours are elongated or off-centered for the planetary case. This difference results in the distinctive features of the individual lens populations in the residual of the trough region between the two peaks of the caustic crossings, where the shape of the residual is symmetric for binary lensing while it tends to be asymmetric for planetary lensing. We determine the ranges of the planetary parameters for which the proposed diagnostic can be used. The diagnostic is complementary to previously proposed diagnostics in the sense that it is applicable to caustic-crossing events with small finite-source effect.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.