• Title/Summary/Keyword: 유클리드 알고리즘

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A Study on Clutter Rejection using PCA and Stochastic features of Edge Image (주성분 분석법 및 외곽선 영상의 통계적 특성을 이용한 클러터 제거기법 연구)

  • Kang, Suk-Jong;Kim, Do-Jong;Bae, Hyeon-Deok
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.47 no.6
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    • pp.12-18
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    • 2010
  • Automatic Target Detection (ATD) systems that use forward-looking infrared (FLIR) consists of three stages. preprocessing, detection, and clutter rejection. All potential targets are extracted in preprocessing and detection stages. But, this results in a high false alarm rates. To reduce false alarm rates of ATD system, true targets are extracted in the clutter rejection stage. This paper focuses on clutter rejection stage. This paper presents a new clutter rejection technique using PCA features and stochastic features of clutters and targets. PCA features are obtained from Euclidian distances using which potential targets are projected to reduced eigenspace selected from target eigenvectors. CV is used for calculating stochastic features of edges in targets and clutters images. To distinguish between target and clutter, LDA (Linear Discriminant Analysis) is applied. The experimental results show that the proposed algorithm accurately classify clutters with a low false rate compared to PCA method or CV method

A Hierarchical Cluster Tree Based Fast Searching Algorithm for Raman Spectroscopic Identification (계층 클러스터 트리 기반 라만 스펙트럼 식별 고속 검색 알고리즘)

  • Kim, Sun-Keum;Ko, Dae-Young;Park, Jun-Kyu;Park, Aa-Ron;Baek, Sung-June
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.3
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    • pp.562-569
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    • 2019
  • Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

A Versatile Reed-Solomon Decoder for Continuous Decoding of Variable Block-Length Codewords (가변 블록 길이 부호어의 연속 복호를 위한 가변형 Reed-Solomon 복호기)

  • 송문규;공민한
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.41 no.3
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    • pp.187-187
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    • 2004
  • In this paper, we present an efficient architecture of a versatile Reed-Solomon (RS) decoder which can be programmed to decode RS codes continuously with my message length k as well as any block length n. This unique feature eliminates the need of inserting zeros for decoding shortened RS codes. Also, the values of the parameters n and k, hence the error-correcting capability t can be altered at every codeword block. The decoder permits 3-step pipelined processing based on the modified Euclid's algorithm (MEA). Since each step can be driven by a separate clock, the decoder can operate just as 2-step pipeline processing by employing the faster clock in step 2 and/or step 3. Also, the decoder can be used even in the case that the input clock is different from the output clock. Each step is designed to have a structure suitable for decoding RS codes with varying block length. A new architecture for the MEA is designed for variable values of the t. The operating length of the shift registers in the MEA block is shortened by one, and it can be varied according to the different values of the t. To maintain the throughput rate with less circuitry, the MEA block uses both the recursive technique and the over-clocking technique. The decoder can decodes codeword received not only in a burst mode, but also in a continuous mode. It can be used in a wide range of applications because of its versatility. The adaptive RS decoder over GF($2^8$) having the error-correcting capability of upto 10 has been designed in VHDL, and successfully synthesized in an FPGA chip.

A Versatile Reed-Solomon Decoder for Continuous Decoding of Variable Block-Length Codewords (가변 블록 길이 부호어의 연속 복호를 위한 가변형 Reed-Solomon 복호기)

  • 송문규;공민한
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.41 no.3
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    • pp.29-38
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    • 2004
  • In this paper, we present an efficient architecture of a versatile Reed-Solomon (RS) decoder which can be programmed to decode RS codes continuously with my message length k as well as any block length n. This unique feature eliminates the need of inserting zeros for decoding shortened RS codes. Also, the values of the parameters n and k, hence the error-correcting capability t can be altered at every codeword block. The decoder permits 3-step pipelined processing based on the modified Euclid's algorithm (MEA). Since each step can be driven by a separate clock, the decoder can operate just as 2-step pipeline processing by employing the faster clock in step 2 and/or step 3. Also, the decoder can be used even in the case that the input clock is different from the output clock. Each step is designed to have a structure suitable for decoding RS codes with varying block length. A new architecture for the MEA is designed for variable values of the t. The operating length of the shift registers in the MEA block is shortened by one, and it can be varied according to the different values of the t. To maintain the throughput rate with less circuitry, the MEA block uses both the recursive technique and the over-clocking technique. The decoder can decodes codeword received not only in a burst mode, but also in a continuous mode. It can be used in a wide range of applications because of its versatility. The adaptive RS decoder over GF(2$^{8}$ ) having the error-correcting capability of upto 10 has been designed in VHDL, and successfully synthesized in an FPGA chip.

Efficient Processing of k-Farthest Neighbor Queries for Road Networks

  • Kim, Taelee;Cho, Hyung-Ju;Hong, Hee Ju;Nam, Hyogeun;Cho, Hyejun;Do, Gyung Yoon;Jeon, Pilkyu
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.10
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    • pp.79-89
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    • 2019
  • While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.