• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.79-86
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• 2006

Recently the tagSNP selection problem has been researched for reducing the cost of association studies between human's diversities and SNPs. General approach for this problem is that all of SNPs are separated into appropriate blocks and then tagSNPs are chosen in each block. Marsel in this paper is the system that involved the concept of linkage disequilibrium for overcoming the problem that the existing block partitioning approaches have short of biological meanings. In most approaches, the contiguous regions, which recombinations have LD coefficient |D'| and then tagSNP selection step is performed. And MarSel guarantees the minimum tagSNP selection using entropy-based optimal selection algorithm when tagSNPs are chosen in each block, and enables chromosome-level association studies using efficient memory management technique when input is very large-scale dataset that is impossible to be processed in the existing systems.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.77-83
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• 2012

In this paper, a low-complexity embedded compression (EC) Codec algorithm for the wavelet video coder is applied to reduce excessive external memory requirements. The EC algorithm is used to achieve a fixed compression ratio of 50 % under the near-lossless-compression constraint. The EC technique can reduce the 50 % memory requirement for intermediate low-frequency coefficients during multiple discrete wavelet transform stages compared with direct implementation of the wavelet video encoder of this paper. Furthermore, the EC scheme based on a forward adaptive quantization and fixed length coding can save bandwidth and size of buffer between DWT and SPIHT to 50 %. Simulation results show that our EC algorithm present only PSNR degradation of 0.179 and 0.162 dB in average when the target bit-rate of the video coder are 1 and 0.5 bpp, respectively.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.388-398
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• 2004

This paper focuses on the reduction method of message traffic occurred when multiple participants collaborate in the distributed virtual environment. It is referred to as filtering method of update message to minimize data traffic exchanged in the virtual environment, and it is the essential process to improve the scalability of the virtual environment. Spatial partitioning method, a popular filtering technique, divides the whole environment into smaller sub-parts in order to make it small the regions to be maintained consistently. However, it is less flexible and adaptable because the information of area of interest must be configured before simulation. In this paper, the concept of dynamic area of interest is proposed, which is highly adaptable by applying dynamic environmental elements to AOI management. It uses multiple layering and multiple consistency algorithms for dynamic management, and makes it possible to consume system resource efficiently. For performance evaluation, experiments are performed with virtual avatars, measuring message traffic. Finally, the proposed model could be applied to the AOI management systems which accommodate massive users like MMORPG, or virtual communities.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.79-87
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• 2005

In e-commerce applications, a public key cryptosystem is an important and indispensible element for the basic security operations such as authentication, digital signaturing, and key distribution. In wired network environments, the public key infrastructure certificate, which is based on X.509 specification, has been widely used. On the other hand, it still remains difficult to use the certificate information in wireless network environments due to the inherent limitations of the hand-held devices such as low computational power and short battery life. In this paper, we facilitate a codesign approach by implementing a software public-key cryptosystem and classifying its internal computation overheads quantitatively using a software profiling technique. Moreover, we propose a method to analyze the profiled data and apply it to the problem of software/hardware partitioning in a codesign approach. As an illustrative example, we analyze the computational overheads of an EC-Elfagamal application and examine a critical computational path.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.135-144
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• 2003

A method for the automatic segmentation of speech signals is described. The method is dedicated to the construction of a large database for a Text-To-Speech (TTS) synthesis system. The main issue of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Market Model(HMM). Multi-layer perceptron (MLP) was used as a phone boundary detector. To increase the performance of segmentation, a technique which individually trains an MLP according to phonetic transition is proposed. The optimum partitioning of the entire phonetic transition space is constructed from the standpoint of minimizing the overall deviation from hand labelling positions. With single speaker stimuli, the experimental results showed that more than 95% of all phone boundaries have a boundary deviation from the reference position smaller than 20 ms, and the refinement of the boundaries reduces the root mean square error by about 25%.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.12-17
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• 2005

The segment matching method shows better performance than the global and points-based methods to compare reference signature with an input signature. However, the segment-to-segment matching method has the problem of decreasing recognition rate according to the variation of partitioning points. This paper proposes a fusion model based on the segment matching and HMM to construct a more reliable authentic system. First, a segment matching classifier is designed by conventional technique to calculate matching values lot dynamic information of signatures. And also, a novel HMM classifier is constructed by using the principal component analysis to calculate matching values for static information of signatures. Finally, SVM classifier is adopted to effectively combine two independent classifiers. From the various experiments, we find that the proposed method shows better performance than the conventional segment matching method.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.89-95
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• 2008

In this paper, we designed a low power face detection hardware and analysed its power consumption. The face detection hardware was fabricated using Samsung 0.18um CMOS technology and it can detect multiple face locations from a 2-D image. The hardware is composed of 6 functional modules and 11 internal memories. We introduced two operating modes(SLEEP and ACTIVE) to save power and a clock gating technique was used at two different levels: modules and registers. In additional, we divided an internal memory into several pieces to reduce the energy consumed when accessing memories, and fully utilized low power design option provided in Synopsis Design Compiler. As a result, we could obtain 68% power reduction in ACTIVE mode compared to the original design in which none of the above low power techniques were used.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.7
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• pp.677-684
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• 2003

This paper addresses the subblock phase weighting(SPW) method to reduce the PAPR in OFDM system. This method divides the input block of OFDM signal into many subblocks and lower the peak power by weighting the phase of each subblocks properly. SPW method can be realized by only one IFFT. PAPR reduction performance is novelly examined when the adjacent, interleaved and random subblock partitioning schemes are used in the SPW system. The random subblock partition scheme has the most effective. More subblocks can effectively reduce the PAPR, but there is a problem that the processing time of iteration is increased. We propose a new weighting factor combination of the complementary sequence characteristic with threshold technique. OFDM data can be recovered by the inserted side information of weighting factor in the feed forward type. Also, BER performance of this SPW system is analyzed when error happens in the side information.

• Journal of IKEEE
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• v.19 no.3
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• pp.357-365
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• 2015

Recently, embedded systems such as aircraft and automobilie, are developed as modular architecture instead of federated architecture because of SWaP(Size, Weight and Power) issues. In addition, partition operating system that support multiple logical node based on partition concept were recently appeared. Distributed recovery block is fault tolerance design scheme that applicable to mission critical real-time system to support real-time take over via real-time synchronization between participated nodes. Because of real-time synchronization, single-core based computer is not suitable for partition based distributed recovery block design scheme. Multi-core and AMP(Asymmetric Multi-Processing) based partition architecture is required to apply distributed recovery block design scheme. In this paper, we proposed design scheme of distributed recovery block on the multi-core based supervised-AMP architecture partition operating system. This paper implements flight control simulator for avionics to check feasibility of our design scheme.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.149-156
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• 2014

NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.