• ETRI Journal
• /
• 제31권3호
• /
• pp.292-297
• /
• 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.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1998년도 추계학술대회
• /
• pp.78-79
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제18권12호
• /
• pp.1439-1444
• /
• 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
• /
• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
• /
• pp.134-136
• /
• 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.

• 대한전기학회논문지
• /
• 제34권3호
• /
• pp.107-114
• /
• 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.

• 융합신호처리학회논문지
• /
• 제2권2호
• /
• pp.39-46
• /
• 2001

본 논문에서 우리는 3밴드 이미지로부터 광원의 분광 방사 에너지 분포를 추정 할 수 있는 새로운 방법을 제안한다. 광원은 표면 반사(Ο(λ))에 대응하는 최대 무채색 영역(L(λ))의 반사되는 분광 방사 에너지 분포에 의해 추정된다. 3밴드 이미지로부터 최대 무채색 영역의 분광 방사 에너지 분포를 획득하기 위하여 수정된 그레이월드가정 알고리즘을 채택했다. 그리고 최대 표면 반사는 무채색 모집단으로 주성분 분석 방법을 사용해서 추정을 하였다. 무채색 모집단은 먼셀 컬러 색표에서 문턱값 보다 낮은 크로마 벡터를 사용해서 만들었다. 분리된 무채색 모집단의 제1에서 제3차까지의 누적 기여율은 약 99.75%이다. 무채색 모집단에 의해 광원의 분광 방사 에너지 분포의 재구성 그리고 여러 가지 광원 하에서 획득된 3밴드 디지털 이미지는 원본과 재현된 광원의 분광 방사 에너지 분포를 RMSE에 의해 평가하고 실험하였다.

• 대한의용생체공학회:의공학회지
• /
• 제9권1호
• /
• pp.101-108
• /
• 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
• /
• 제15권1호
• /
• pp.103-108
• /
• 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.

• 대한의용생체공학회:의공학회지
• /
• 제8권1호
• /
• pp.69-74
• /
• 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.

• 한국방재안전학회논문집
• /
• 제10권2호
• /
• pp.21-28
• /
• 2017

시공간적으로 불규칙하게 작용하는 변동 풍속 난류의 자료는 풍공학적으로 돌풍계수 평균풍속 변동 풍하중등의 계산에서 요구되지만, 내풍 및 사용성에 따른 동적응답의 평가에서는 변동 풍속의 파워 스펙트럴 밀도함수가 요구된다. 본 논문에서는 1987-2016.12.1일까지의 일순간최대풍속 자료를 확률과정으로 가정했고, 이 실측된 자료와 확률이론을 근거로 평균류방향 파워 스펙트럴 밀도 함수에 대한 기초적 자료를 얻고자 대표지점(6개 지점)을 선정했다. 선정된 각 지점에 대한 일순간최대풍속자료는 기상청으로부터 획득했다. 해석결과 본 논문에서 평가된 스펙트럼 모델은 저진동수 영역에서는 Solari, 고진동수 영역에서는 von Karman의 모델과 근접한 현상을 나타냈다.