• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.1-16
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• 2007

A spatial data warehouse is a system to support decision making using a spatial data cube. A spatial data cube is composed of a dimension table and a fact table. For decision support using this spatial data cube, the concept hierarchy of spatial dimension and the summarized information of spatial fact should be provided. In the previous researches, however, spatial summarized information is deficient. In this paper, the spatial aggregation for spatial summarized information in a spatial data warehouse is proposed. The proposed spatial aggregation is separated of both the numerical aggregation and the object aggregation. The numerical aggregation is the operation to return a numerical data as a result of spatial analysis and the object aggregation returns the result represented to object. We provide the extended struct of spatial data for spatial aggregation and so our proposed method is efficient.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.141-149
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• 2006

In this paper, we introduce an improved algorithm for computing matrix triple product that commonly arises in primal-dual optimization method. In computing $P=AHA^{t}$, we devise a single pass algorithm that exploits the block diagonal structure of the matrix H. This one-phase scheme requires fewer floating point operations and roughly half the memory of the generic two-phase algorithm, where the product is computed in two steps, computing first $Q=HA^{t}$ and then P=AQ. The one-phase scheme achieved speed-up of 2.04 on Intel Itanium II platform over the two-phase scheme. Based on memory latency and modeled cache miss rates, the performance improvement was evaluated through performance modeling. Our research has impact on performance tuning study of complex sparse matrix operations, while most of the previous work focused on performance tuning of basic operations.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.5
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• pp.245-258
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• 2001

VLIW에서는 프로그램 코드를 병렬화 하는 작업이 모두 컴파일러에 의해서만 이루어진다. 따라서 병렬로 수행될 연산어들을 명시적으로 나타내 주어야 하며, 이를 위한 명령어 인코딩 방식으로 전개 인코딩 방식과 압축 인코딩 방식이 사용되어 왔다. 각 인코딩 방식들은 명령어의 적재 및 검색을 위해 서로 다른 캐쉬 구조를 필요로 하는데, 전개 인코딩 방식으로 비압축 캐쉬를 압축 인코딩 방식으로 압축 캐쉬를 사용하고 있다. 그러나 이들은 각각 무효 연산어로 인한 메모리 활용 효율 저하와 복원 과정으로 인한 명령어 인출 오버헤드의 증가라는 문제점을 안고 있다. 본 논문에서는 부분적으로 명령어 길이를 일정하게 유지하는 부분 압축 인코딩을 사용해 메모리 활용 효율을 높이는 동시에 명령어 인출 오버헤드를 줄일 수 있는 분할 캐쉬 구조를 제안한다. 각 캐쉬 구조를 구현하는데 필요한 칩 영역을 계산하여, 분할 캐쉬가 비교적 비용 효율적인 캐쉬 구조임을 확인하였다. 모의 실험을 통한 메모리 활용 효율 측정 결과 하드웨어 비용의 증가를 고려하더라도 분할 캐쉬는 비압축 캐쉬에 비해 최고 약 3배의 메모리 활용 효율을 얻을 수 있었다. 각 캐쉬 구조를 일차 캐쉬로 하는 VLIW 시스템들의 성능 측정 결과는 TCSC(블록 집중형 분할 캐쉬)를 사용한 시스템이 비용 대비 성능 면에서 가장 우수한 것으로 나타났다.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.313-320
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• 2005

The 2-D discrete cosine transform (DCT) is an integral part of video and image processing; it is used in both the PEG and MPEG enciding standards. As streaming video is brought to mobile devices, it becomes important that it is possible to calculate the DCT in an energy-efficient manner. In this paper, we present a new algorithm the DCT with a linear array PEs. This design is optimized for energy efficiency. We analyze the energy, area, and latency tradeoffs available with this design and then compare its energy dissipation, area, and latency to those of Xilinx's optimized IP core.

• Annual Conference of KIPS
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• 2013.11a
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• pp.835-838
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• 2013

클라우드 컴퓨팅을 사용하면 컴퓨팅 자원을 구축하는 비용을 절감할 수 있다는 장점이 있다. 문제는 클라이언트가 데이터 센터와 서비스제공자를 완전히 신뢰할 수 없다는 것이다. 예를 들어, 클라우드에 저장된 파일이 손실되었을 때 서비스 제공자는 서비스의 신뢰도가 떨어지는 것을 막기 위해 이를 숨길 수 있다. 이때, 데이터가 저장 후에 손실되었다는 것을 증명하지 못하면, 그 피해는 클라이언트에게 돌아오게 된다. 따라서, 클라이언트의 데이터를 보호하기 위하여 무결성을 검증할 수 있는 적절한 기법을 적용하여야 한다. 기존 연구로는 homomorphic tags 기반의 기법들이 많이 제안되었으나 이 기법은 많은 지수연산을 필요로 하므로 상용화할 수 있을 만큼의 효율성을 가지지 못한다. 특히, 클라이언트가 증거 생성을 위해 많은 연산을 부담해야 한다. 본 논문에서는 효율성에 중점을 둔, 특히 클라이언트의 효율성에 중점을 둔 무결성 검증 기법을 제안한다. 제안하는 기법은 Modular arithmetic을 기반으로 설계되었으며, 무결성 검증뿐만 아니라 데이터가 자주 업데이트 되는 환경을 지원한다. Simulation result는 제안하는 기법이 기존 기법에 매우 효율적임을 보여준다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1998.06a
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• pp.813-816
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• 1998

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.133-142
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• 2009

Recently, with the advance of wireless internet and the frequent use of mobile devices, demand for LBS(Location Based Service) is increasing, and research is required on spatial indexes for the storage and maintenance of spatial data to provide efficient LBS in mobile device environments. In addition, the use of flash memory as an auxiliary storage device is increasing in order to store large spatial data in a mobile terminal with small storage space. However, the application of existing spatial indexes to flash-memory lowers index performance due to the frequent updates of nodes. To solve this problem, research is being conducted on flash-memory based spatial indexes, but the efficiency of such spatial indexes is lowered by low utilization of buffer and flash-memory space. Accordingly, in order to solve problems in existing flash-memory based spatial indexes, this paper proposed FR-Tree (Flash-Memory based R-Tree) that uses the node compression technique and the delayed write operation technique. The node compression technique of FR-Tree increased the utilization of flash-memory space by compressing MBR(Minimum Bounding Rectangle) of spatial data using relative coordinates and MBR size. And, the delayed write operation technique reduced the number of write operations in flash memory by storing spatial data in the buffer temporarily and reflecting them in flash memory at once instead of reflecting the insert, update and delete of spatial data in flash-memory for each operation. Especially, the utilization of buffer space was enhanced by preventing the redundant storage of the same spatial data in the buffer. Finally, we perform ed various performance evaluations and proved the superiority of FR-Tree to the existing spatial indexes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.1
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• pp.64-70
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• 2008

The H.264 standard is widely used due to the high compression rate and quality. The deblocking filter of the H.264 standard improves the quality of images by eliminating blocking artifacts of pictures, and it requires a lot of computation. We propose a new hardware architecture for the deblocking filter with pipelined architecture, 1-D filters which support both horizontal and vertical filtering and efficient memory management. Four memory blocks are configured for the efficient storage and access of the current macroblock and adjacent referenced sub-macroblocks, and the pixel data from the motion compensation unit can be transferred without waiting during the computation cycles of the deblocking filter. The number of computation cycles and the hardware area are reduced using the proposed architecture, and the performance of the H.264 decoder is improved. We design the deblocking filter using Verilog-HDL and implement using an FPGA. The designed deblocking filter can be used for decoding HD quality images at 77 MHz.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.502-510
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• 2009

Multiprecision squaring is one of the most significant algorithms in the core public key cryptography operation. The aim of this work is to present a new improved squaring algorithm compared with the MIRACL's multi precision squaring algorithm in which the previous work [1] on multiprecision multiplication is implemented. First, previous works on multiprecision multiplication and standard squaring are analyzed. Then, our new Lazy Doubling squaring algorithm is introduced. In MIRACLE library [3], Scott's Carry-Catcher Hybrid multiplication technique [1] is applied to implementation of multiprecision multiplication and squaring. Experimental results of the Carry-Catcher hybrid squaring algorithm and the proposed Lazy Doubling squaring algorithm both of which are tested on Atmega128 CPU show that proposed idea has achieved significant performance improvements. The proposed Lazy Doubling Squaring algorithm reduces addition instructions by the fact $a_0\;{\ast}\;2\;+\;a_1\;{\ast}\;2\;+\;...\;+\;a_{n-1}\;{\ast}\;2\;+\;a_n\;{\ast}\;2\;=\;(a_0\;+\;a_1\;+\;...\;+\;a_{n-1}\;+\;a_n)\;{\ast}\;2$ while the standard squaring algorithm reduces multiplication instructions by the fact $S_{ij}\;=\;x_i\;{\ast}\;x_j\;=\;S_{ij}$. Experimental results show that the proposed squaring method is 25% faster than that in MIRACL.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.