• 디지털산업정보학회논문지
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• 제13권3호
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• pp.75-81
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• 2017

This paper estimate a desired signal in a correlation wireless communication. The transmitted signal is mixed with the information signal, interference, and noise in wireless channel, and it is incident on the receiver. In this paper, we apply MUSIC algorithm and sub-array method to recover the total rank of the correlation matrix in order to estimation a desired signal among receiving signals. Through simulation, we analyze to compare the proposed method with the classical MUSIC algorithm. As a result of the simulation, the proposed method improved the resolution about 10degrees compared to the conventional MUSIC algorithm. We prove the superiority of the proposed method for the desired signal estimation in correlation channel.

• 한국시뮬레이션학회논문지
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• 제13권2호
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• pp.1-12
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• 2004

This paper estimated the arrival angle and electric wave delay time using the space method law and the directions of arrival (DOA) estimation algorithm in case of signal correlation. Space method law is the method used to repress cross correlation before applying the weight value to the receiving signal. The values of the diagonal elements in the correlation matrix were averaged to replace as the diagonal elements value. In the area of wireless communication or mobile communication, there are high correlations in case of low delay time difference in multiple waves. This causes the quality of the communication to drop due to interference with the desired signal elements. This paper estimated the arrival angle and electric wave delay time using the space method law and the MUSIC algorithm. With the arrival angle algorithm, the arrival angle cannot be estimated below 5 in case of signal correlations because the angle resolution capacity decreases accordingly. The super resolution capacity was estimated to determine the arrival angle below 5 in this paper. In addition, the proposed algorithm estimated the short delay time difference to be below 20ns.

• 한국전자파학회논문지
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• 제27권5호
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• pp.430-437
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• 2016

일반적으로 잡음 측정결과는 회로망분석기의 측정결과에 비해 상당히 큰 불규칙 리플을 보인다. 이에 대한 주 원인은 회로망분석기는 측정 시에 상대적인 전력의 비를 이용하고, 잡음 측정은 절대적인 잡음전력 측정에 의존하기 때문이다. 본 논문에서는 잡음측정의 불규칙 리플을 최소화하기 위해서 상대적인 잡음비를 이용한 새로운 잡음상관행렬 측정방법을 제안하였다. 제안된 방법은 5개의 입력에 대해 측정된 잡음전력들 중 하나를 기준으로 4개의 상대적인 잡음비를 통해 불규칙 리플을 최소화하고, 이를 이용하여 잡음상관행렬 및 잡음 파라미터를 측정하는 방법이다. 제안된 방법을 이용한 잡음파라미터 측정결과, 수동 DUT인 0.5 dB 감쇠기의 경우, S-파라미터를 이용하여 계산되어진 잡음 파라미터의 이론값과 거의 일치하는 결과를 보였다. 그리고 1 dB 이하의 잡음지수를 갖는 능동 DUT에 대해서도 신뢰할 수 있는 잡음파라미터 측정이 가능함을 보였다.

• 전자공학회논문지 IE
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• 제48권1호
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• pp.7-15
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• 2011

본 논문에서는 상관계수를 바탕으로 신호부공간을 추정하는 COPAST(correlation-based projection approximation subspace tracking)의 성능을 향상시키기 위하여 상관계수를 구하는 부분을 순방향 신호 벡터로부터 상관계수를 구하고 동시에 역방향 신호 벡터에서도 상관계수를 구하여 신호 부공간을 추정하는 방법을 제안한다. 또 매번 갱신되는 상관행렬의 안정성을 도모하고자 제곱근 행렬 갱신을 하도록 하였다. 컴퓨터 모의 실험을 통해서 제안된 방법이 기존의 COPAST에 비해서 약 5dB의 신호 부공간 추정 정확도에 향상이 있었음을 확인하였다.

• ETRI Journal
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• 제40권2호
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• pp.197-206
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• 2018

Multiple-input multiple-output (MIMO) systems offer significant enhancements in terms of their data rate and channel capacity compared to traditional systems. However, correlation degrades the system performance and imposes practical limits on the number of antennas that can be incorporated into portable wireless devices. The use of switched parasitic antennas (SPAs) is a possible solution, especially where it is difficult to obtain sufficient signal decorrelation by conventional means. The covariance matrix represents the correlation present in the propagation channel, and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the "water-pouring algorithm" to modify the covariance matrix, the channel capacity is significantly improved compared to traditional systems, which spread transmit power uniformly across all the antennas. A condition number is also proposed as a selection metric to select the optimal pattern configuration for MIMO-SPAs.

• 대한치과의사협회지
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• 제54권11호
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• pp.850-864
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• 2016

The Generalized Estimating Equations (GEE) approach is a widely used statistical method for analyzing longitudinal data and clustered data in clinical studies. In dentistry, due to multiple outcomes obtained from one patient, the outcomes produced from an individual patient are correlated with one another. This study focused on the basic ideas of GEE and introduced the types of covariance matrix and working correlation matrix. The quasi-likelihood information criterion (QIC) and quasi-likelihood information criterion approximation ($QIC_u$) were used to select the best working correlation matrix and the best fitting model for the correlated outcomes. The purpose of this study is to show a detailed process for the GEE analysis using SPSS software along with an orthodontic miniscrew example, and to help understand how to use GEE analysis in dental research.

• 대한안전경영과학회지
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• 제2권4호
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• pp.113-124
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• 2000

First, in order to improve selecting method of quality characteristic level desired by customers, S/N(Signal-to-Noise) ratio of Taguchi in larger-the-better characteristics was applied. Second, the Matrix classification standard of ACE(Attribute Categorization Evaluation) is presented using KANO model on difference analysis of importance and satisfaction through questionnaire from customers. This is for reflecting the diverse EC which customers want in EC quality sufficiently. Also, establishing sales point will be helpful in business strategy through presenting types that are able to decide planning quality. Third, the important measure of EC about correlation among quality characteristics and a new weight of EC are calculated depending on importance of EC and the weight of customer attribute and materials of relationship matrix through correlation matrix analysis.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 춘계학술대회
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• pp.481-490
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• 2000

First, in order to improve selecting method of quality characteristic level desired by customers, S/H(Signal-to-Noise) ratio of Taguchi in larger-the-better characteristics was applied. Second, the Matrix classification standard of ACE(Attribute Categorization Evaluation) is presented using KANO model on difference analysis of importance and satisfaction through questionnaire from customers. This is for reflecting the diverse EC which customers want in EC quality sufficiently. Also, establishing sales point will be helpful in business strategy through presenting types that are able to decide planning quality. Third, the important measure of EC about correlation among quality characteristics and a new weight o( EC are calculated depending on importance of EC and the weight of customer attribute and materials of relationship matrix through correlation matrix analysis.

• ETRI Journal
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• 제36권4호
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• pp.545-553
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• 2014

This paper proposes an algorithm to sense orthogonal frequency-division multiplexing (OFDM) signals in cognitive radio (CR) systems. The basic idea behind this study is when a primary user is occupying a wireless channel, the covariance matrix is non-diagonal because of the time domain cross-correlation of the cyclic prefix (CP). In light of this property, a new decision metric that measures the power of the data found on two minor diagonals in the covariance matrix related to the CP is introduced. The impact of synchronization errors on the signal detection is analyzed. Besides this, a likelihood-ratio test is proposed according to the Neyman-Pearson criterion after deriving probability distribution functions of the decision metric under hypotheses of signal presence and absence. A threshold, subject to the requirement of probability of false alarm, is derived; also the probabilities of detection and false alarm are computed accordingly. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.