• ETRI Journal
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• v.31 no.3
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• pp.292-297
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• 2009

The power spectral density of a signal can be estimated most accurately by using a window with a narrow bandwidth and large sidelobe attenuation. Conventional windows generally control these characteristics by only one parameter, so there is a trade-off problem: if the bandwidth is reduced, the sidelobe attenuation is also reduced. To overcome this problem, we propose using a Butterworth window with two control parameters for power spectral density estimation and analyze its characteristics. Simulation results demonstrate that the sidelobe attenuation and the 3 dB bandwidth can be controlled independently. Thus, the trade-off problem between resolution and spectral leakage in the estimated power spectral density can be overcome.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.78-79
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• 1998

This paper describes a real time algorithm for blood flow estimation of LDF(laser Doppler flowmeter). Many algorithms for blood flow estimation are using power spectral density of Doppler signal by blood flow. In these research, the fast Fourier transformation is used to estimate power spectral density. This is a block processing procedure rather than real time processing. The algorithm in this paper used parametric spectral estimation. This has real time capability by estimation of AR(autoregressive) parameters sample by sample, and has smoothing power spectrum. Also, the frequency resolution is not limited by number of samples used to estimate AR parameter. Another advantage of this algorithm is that AR model enhance SNR.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1439-1444
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• 2015

Measuring spectral reflectance can be regarded as obtaining inherent color parameters, and spectral reflectance has been used in image processing. Model-based spectrum recovering, one of the method for obtaining spectral reflectance, uses ordinary camera with multiple illuminations. Conventional model-based methods allow to recover spectral reflectance efficiently by using only a few parameters, however it requires some parameters such as power spectrum of illuminations and spectrum sensitivity of camera. In this paper, we propose an enhanced model-based spectrum recovering method without pre-measured parameters: power spectrum of illuminations and spectrum sensitivity of camera. Instead of measuring each parameters, spectral reflectance can be efficiently recovered by estimating and using the spectrum characteristic matrix which contains spectrum parameters: basis function, power spectrum of illumination, and spectrum sensitivity of camera. The spectrum characteristic matrix can be easily estimated using captured images from scenes with color checker under multiple illuminations. Additionally, we suggest fast recovering method preserving positive constraint of spectrum by nonnegative basis function of spectral reflectance. Results of our method showed accurately reconstructed spectral reflectance and fast constrained estimation with unmeasured camera and illumination. As our method could be conducted conveniently, measuring spectral reflectance is expected to be widely used.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.3
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• pp.107-114
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• 1985

This paper presents the iterative algorithm for obtaining the ME PSE(Maximum Entropy Power Spectral Estimation) of 2-dimensional signals. This problem involves a correction matching power spectral estimate that can be represented as the reciprocal of the spectral of 2-dimensional signals. This requires two matrix inversion every iterations. Thus, we compensate the matrix to be constantly positive definite with relaxational parameters. Using Row/Column decomposition Discrete Fourier Transform, we can decrease a calculation quantity. Using Lincoln data and white noise, this paper examines ME PSE algorithms. Finally, the results output at the graphic display device. The 2-dimensional data have the 3-dimensional axis components, and, this paper develops 3-dimensional graphic output algorithms using 2-dimensional DGL(Device Independent Graphic Library) which is prepared for HP-1000 F-series computer.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.39-46
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• 2001

In this paper we propose a new method that can be estimation the spectral power distribution of the light source from three-band images. the light source is estimated by dividing the reflected spectral power distribution of the maximum achromatic region(L(λ)) by the corresponding surface reflectance(Ο(λ)). In order to obtain reflected spectral power distribution of the maximum achromatic region from three-bend images, a modified gray world assumption algorithm is adapted. And the maximum surface reflectance is estimated using the principal component analysis method along with achromatic population. The achromatic population is created from a set of given Munsell color chips whose chroma vector is less than threshold. Cumulative contribution ratio of principal components from the first to the third for classified achromatic population was about 99.75%. The reconstruction of illumination spectral power distribution by using achromatic population and three-band digital images captured under various light source was examined, and evaluated by RMSE between the original and reconstructed illumination spectral power distribution. This work was supported by grant No (2000-1-30200-005-3) from the Basic Research Program of the Korea Science & Engineering Foundation.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• Journal of the Optical Society of Korea
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• v.15 no.1
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• pp.103-108
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• 2011

Recently reported asynchronous-optical-sampling terahertz (THz) time-domain spectroscopy enables high-resolution spectroscopy due to a long time-delay window. However, a long-lasting tail signal following the main pulse is often measured in a time-domain waveform, resulting in spectral fluctuation above a background noise level on a high-resolution THz amplitude spectrum. Here, we adopt the wavelet power spectrum estimation technique (WPSET) to effectively remove the spectral fluctuation without sacrificing spectral features. Effectiveness of the WPSET is verified by investigating a transmission spectrum of water vapor.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.69-74
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• 1987

The power spectrum of background EEG is estimated by the Plsarenko Harmonic Decomposition with the stochastic process whlch consists of the nonhamonic sinus Bid and the white nosie. The estimation results are examined and compared with the results from the maximum entropy spectral extimation, and the optimal order of this from the maximum entropy spectral extimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this method is possible to estimate the power spectrum of background EEG.

• Journal of Korean Society of Disaster and Security
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• v.10 no.2
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• pp.21-28
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• 2017

Wind turbulence data is required for engineering calculations of gust speeds, mean and fluctuating loading. Spectral densities are required as input data for methods used in assessing dynamic response. This study is concerned with the estimation of daily instantaneous maximum wind velocity in the meteorological major cities (selected each 6 points) during the yearly 1987-2016.12.1. The purpose of this paper is to present the power spectral densities of the daily instantaneous maximum wind velocity. In the processes of analysis, used observations data obtained at Korea Meteorological Adminstration(KMA), it is assumed as a random processes. From the analysis results, in the paper estimated power spectral densities function(Blunt model) shows a very closed with von Karman and Solari's spectrum models.