• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.963-980
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• 2009

In this paper, spectral scheme based on Hermite interpolation for solving partial differential equations is presented. The idea of this Hermite spectral method comes from the spectral method on arbitrary grids of Carpenter and Gottlieb [J. Comput. Phys. 129(1996) 74-86] using the Lagrange interpolation.

• 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.

• Current Optics and Photonics
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• v.6 no.3
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• pp.244-251
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• 2022

We introduce a spectral reconstruction method to enhance the spectral resolution in a static modulated Fourier-transform spectrometer. The optical-path difference and the interferogram in the focal plane, as well as the relationship of the interferogram and the spectrum, are discussed. Additionally, for better spectral reconstruction, applications of phase-error correction and apodization are considered. As a result, the transfer function of the spectrometer is calculated, and then the spectrum is reconstructed based on the relationship between the transfer function and the interferogram. The spectrometer comprises a modified Sagnac interferometer. The spectral reconstruction is conducted with a source with central wave number of 6,451 cm^-1 and spectral width of 337 cm^-1. In a conventional Fourier-transform method the best spectral resolution is 27 cm^-1, but by means of the spectral reconstruction method the spectral resolution improved to 8.7 cm^-1, without changing the interferometric structure. Compared to a conventional Fourier-transform method, the spectral width in the reconstructed spectrum is narrower by 20 cm^-1, and closer to the reference spectrum. The proposed method allows high performance for static modulated Fourier-transform spectrometers.

• Journal of KSNVE
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• v.6 no.1
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• pp.83-88
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• 1996

The significance of spectral element method is that it can treat the mass and stiffness distribution exactly in contrast to the conventional finite element method, and therefore the dynamic behaviors within each spectral element can be obtained exactly. The present study provides the derivation of the spectral element of a curved beam, while the previous ones presented that of a straight structure. Further, in order to verify the derived spectral element, the natural frequencies of a ring by the spectral element method are compared with those by the analytical method and those by the FEM. From the verification, derived spectral element is admissible. And the dynamic behaviors of curved beam are simulated by using the derived spectral element of a curved beam.

• Communications of the Korean Mathematical Society
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• v.34 no.3
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• pp.1005-1014
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• 2019

A new simple method is proposed for reconstructing phylogenetic trees, which we call the spectral method. The most common distance based method is the neighbor-joining method which is based on the minimum evolution principle. The spectral method shows similar performance to the neighbor-joining method for simulated data generated by seq-gen. For real data, the spectral method shows much better performance than the neighbor-joining method. Hence it can be a complementary method for reconstructing phylogenetic trees.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.280-287
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evacuate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth ColorChecker. As a result, the proposed method showed a lower mean square ems between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.121-128
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• 2008

Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

• East Asian mathematical journal
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• v.37 no.3
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• pp.295-305
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• 2021

In this paper, we analyze the efficiency of a domain decomposition method for the two-dimensional telegraph equations. We formulate the theoretical spectral radius of the iteration matrix generated by the domain decomposition method, because the rate of convergence of an iterative algorithm depends on the spectral radius of the iteration matrix. The theoretical spectral radius is confirmed by the experimental one using MATLAB. Speedup and operation ratio of the domain decomposition method are also compared as the two measurements of the efficiency of the method. Numerical results support the high efficiency of the domain decomposition method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• Speech Sciences
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• v.4 no.2
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• pp.5-15
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• 1998

This paper proposes a speech state-dependent spectral subtraction method to regulate the blind spectral subtraction for improved enhancement. In the proposed method, a modified subtraction rule is applied over the speech selectively contingent to the speech state being voiced or unvoiced, in an effort to incorporate the acoustic characteristics of phonemes. In particular, the objective of the proposed method is to remedy the subtraction induced signal distortion attained by two state-dependent procedures, spectrum sharpening and minimum spectral bound. In order to remove the residual noise, the proposed method employs a procedure utilizing the masking effect. Proposed spectral subtraction including state-dependent subtraction and residual noise reduction using the masking threshold shows effectiveness in compensation of spectral distortion in the unvoiced region and residual noise reduction.