• Title/Summary/Keyword: Gaussian kernel

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Communication Equalizer Algorithms with Decision Feedback based on Error Probability (오류 확률에 근거한 결정 궤환 방식의 통신 등화 알고리듬)

  • Kim, Nam-Yong;Hwang, Young-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.5
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    • pp.2390-2395
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    • 2011
  • For intersymbol interference (ISI) compensation from communication channels with multi-path fading and impulsive noise, a decision feedback equalizer algorithm that minimizes Euclidean distance of error probability is proposed. The Euclidean distance of error probability is defined as the quadratic distance between the probability error signal and Dirac-delta function. By minimizing the distance with respect to equalizer weight based on decision feedback structures, the proposed decision feedback algorithm has shown to have significant effect of residual ISI cancellation on severe multipath channels as well as robustness against impulsive noise.

Laser Process Proximity Correction for Improvement of Critical Dimension Linearity on a Photomask

  • Park, Jong-Rak;Kim, Hyun-Su;Kim, Jin-Tae;Sung, Moon-Gyu;Cho, Won-Il;Choi, Ji-Hyun;Choi, Sung-Woon
    • ETRI Journal
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    • v.27 no.2
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    • pp.188-194
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    • 2005
  • We report on the improvement of critical dimension (CD) linearity on a photomask by applying the concept of process proximity correction to a laser lithographic process used for the fabrication of photomasks. Rule-based laser process proximity correction (LPC) was performed using an automated optical proximity correction tool and we obtained dramatic improvement of CD linearity on a photomask. A study on model-based LPC was executed using a two-Gaussian kernel function and we extracted model parameters for the laser lithographic process by fitting the model-predicted CD linearity data with measured ones. Model-predicted bias values of isolated space (I/S), arrayed contact (A/C) and isolated contact (I/C) were in good agreement with those obtained by the nonlinear curve-fitting method used for the rule-based LPC.

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Decision Feedback Equalizer based on Maximization of Zero-Error Probability (영확률 최대화에 근거한 결정궤환 등화)

  • Kim, Nam-Yong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.8C
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    • pp.516-521
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    • 2011
  • In this paper, a nonlinear algorithm that maximizes zero-error probability (MZEP) with decision feedback (DF) is proposed to counteract both of severely distorted multi-path fading effect and impulsive noise. The proposed MZEP-DF algorithm has shown the immunity to impulsive noise and the ability of the feedback filter section to cancel the remaining intersymbol interference as well. Compared with the linear MZEP algorithm, it yields above 10 dB enhancement of steady state MSE performance in severely distorted multipath fading channels with impulse noise where the least mean square (LMS) algorithm does not converge below -3dB of MSE.

Investigations on the Optimal Support Vector Machine Classifiers for Predicting Design Feasibility in Analog Circuit Optimization

  • Lee, Jiho;Kim, Jaeha
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.15 no.5
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    • pp.437-444
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    • 2015
  • In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter ${\gamma}$ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher ${\gamma}$ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low ${\gamma}$ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by $10{\times}$.

Scene-based Nonuniformity Correction Algorithm Based on Temporal Median Filter

  • Geng, Lixiang;Chen, Qian;Qian, Weixian;Zhang, Yuzhen
    • Journal of the Optical Society of Korea
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    • v.17 no.3
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    • pp.255-261
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    • 2013
  • Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.

Photometric Properties and Spatial Distribution of RSGs of Nearby Galaxy System: Leo Triplet

  • Lee, Sowon;Chiang, Howoo;Sohn, Young-Jong
    • The Bulletin of The Korean Astronomical Society
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    • v.43 no.1
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    • pp.60.2-60.2
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    • 2018
  • We present the near infrared JHK photometric properties and the spatial distribution of red supergiants(RSGs) of NGC 3623, NGC 3627 and NGC 3628 in the Leo Triplet system using the data obtained with 3.8m UKIRT(United Kingdom Infra-Red Telescope) at Hawaii. We checked interaction between the three galaxies by making a spatial density map of RSGs. From (J-K,K)0 Color-Magnitude Diagram which include resolved stars in three galaxy and control field with PARSEC isochrone, we figured out the RSG candidates of the Leo triplet are at 0.9<(J-K)0<1.2, mK<17.5 and separated them from background and foreground sources. Using gaussian kernel density estimation, we drew spatial density map of RSGs in the Leo triplet with an assumption that all RSGs are an identical population. The density map shows extended features of NGC 3628 to NGC 3627 along the declination direction. The asymmetries between NGC 3627 and NGC 3628 might be evidence for that the distribution of actual star components(RSGs) follows the neutral hydrogen distribution and also for interaction between two galaxies. And the extended features along the right ascension direction might be a supporting evidence for the existence of a TDG(Tidal Dwarf Galaxy). In case of NGC 3623, we could not see any sign of interaction in density map.

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A Study on Power Variations of Magnitude Controlled Input of Algorithms based on Cross-Information Potential and Delta Functions (상호정보 에너지와 델타함수 기반의 알고리즘에서 크기 조절된 입력의 전력변화에 대한 연구)

  • Kim, Namyong
    • Journal of Internet Computing and Services
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    • v.18 no.6
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    • pp.1-6
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    • 2017
  • For the algorithm of cross-information potential with delta functions (CIPD) which has superior performance in impulsive noise environments, a new method of employing the information of power variations of magnitude controlled input (MCI) in the weight update equation of the CIPD is proposed in this paper where the input of CIPD is modified by the Gaussian kernel of error. To prove its effectiveness compared to the conventionalCIPD algorithm, the distance between the current weight vector and its previous one is analyzed and compared under impulsive noise. In the simulation results the proposed method shows a two-fold improvement in steady state stability, faster convergence speed by 1.8 times, and 2 dB - lower minimum MSE in the impulsive noise situation.

A Max-Flow-Based Similarity Measure for Spectral Clustering

  • Cao, Jiangzhong;Chen, Pei;Zheng, Yun;Dai, Qingyun
    • ETRI Journal
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    • v.35 no.2
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    • pp.311-320
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    • 2013
  • In most spectral clustering approaches, the Gaussian kernel-based similarity measure is used to construct the affinity matrix. However, such a similarity measure does not work well on a dataset with a nonlinear and elongated structure. In this paper, we present a new similarity measure to deal with the nonlinearity issue. The maximum flow between data points is computed as the new similarity, which can satisfy the requirement for similarity in the clustering method. Additionally, the new similarity carries the global and local relations between data. We apply it to spectral clustering and compare the proposed similarity measure with other state-of-the-art methods on both synthetic and real-world data. The experiment results show the superiority of the new similarity: 1) The max-flow-based similarity measure can significantly improve the performance of spectral clustering; 2) It is robust and not sensitive to the parameters.

Classification of universities in Daegu·Gyungpook by support vector cluster analysis (서포트벡터 군집분석을 이용한 대구·경북지역 대학의 분류)

  • Park, Hye Jung;Kim, Jong Tae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.4
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    • pp.783-791
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    • 2013
  • There are sixteen indicators of "College Information" found on the website of College Information Disclosure Center. Among these indicators, the current study examined an enrollment rate and an employment rate based on health insurance coverage, and focused on twenty-four universities in Daegu and Gyeongbuk area. The universities were classified into groups by the enrollment rate and employment rate. This study investigated the characteristics pertaining to those different groups. Hierarchical cluster analysis and support vector cluster analysis were conducted in order to analyze the characteristics of the groups statistically.

Depth-hybrid speeded-up robust features (DH-SURF) for real-time RGB-D SLAM

  • Lee, Donghwa;Kim, Hyungjin;Jung, Sungwook;Myung, Hyun
    • Advances in robotics research
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    • v.2 no.1
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    • pp.33-44
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    • 2018
  • This paper presents a novel feature detection algorithm called depth-hybrid speeded-up robust features (DH-SURF) augmented by depth information in the speeded-up robust features (SURF) algorithm. In the keypoint detection part of classical SURF, the standard deviation of the Gaussian kernel is varied for its scale-invariance property, resulting in increased computational complexity. We propose a keypoint detection method with less variation of the standard deviation by using depth data from a red-green-blue depth (RGB-D) sensor. Our approach maintains a scale-invariance property while reducing computation time. An RGB-D simultaneous localization and mapping (SLAM) system uses a feature extraction method and depth data concurrently; thus, the system is well-suited for showing the performance of the DH-SURF method. DH-SURF was implemented on a central processing unit (CPU) and a graphics processing unit (GPU), respectively, and was validated through the real-time RGB-D SLAM.