• Biomedical Science Letters
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• v.11 no.2
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• pp.129-134
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• 2005

Three different deconvolution techniques for quantifying cerebral blood flow (CBF) from whole brain $T2^{\ast}-weighted$ bolus tracking images were implemented (parametric Fourier transform P-FT, parametric single value decomposition P-SVD and nonparametric single value decomposition NP-SVD). The techniques were tested on 206 regions from 38 hyperacute stroke patients. In the P-FT and P-SVD techniques, the tissue and arterial concentration time curves were fit to a gamma variate function and the resulting CBF values correlated very well $(CBF_{P-FT}\;=\;1.02{\cdot}CBF_{p-SVD},\;r^2\;=\;0.96)$. The NP-SVD CBF values correlated well with the P-FT CBF values only when a sufficient number of time series volumes were acquired to minimize tracer time curve truncation $(CBF_{P-FT}\;=\;0.92{\cdot}CBF_{NP-SVD},\;r^2\;=\;0.88)$. The correlation between the fitted CBV and the unfitted CBV values was also maximized in regions with minimal tracer time curve truncation $(CBV_{fit}\;=\;1.00{\cdot}CBV_{ Unfit},\;^r^2\;=\;0.89)$. When a sufficient number of time series volumes could not be acquired (due to scanner limitations) to avoid tracer time curve truncation, the P-FT and P-SVD techniques gave more reliable estimates of CBF than the NP-SVD technique.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.149-156
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• 2016

We proposed a blind watermarking scheme using singular vectors based on Discrete Wavelet Transform (DWT) and Redundant Discrete Wavelet Transform (RDWT) combined with Singular Value Decomposition (SVD) for copyright protection application. We replaced the 1st left and right singular vectors decomposed from cover image with the corresponding ones from watermark image to overcome the false-positive problem in current watermark systems using SVD. The proposed scheme realized the watermarking system without a false positive problem, and shows high fidelity and robustness.

• Korean Journal of Optics and Photonics
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• v.4 no.4
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• pp.363-372
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• 1993

An optimization technique with variable orthogonalization and SVD(singular value decomposition) is examined in a double-Gauss type photographic lens design and its convergence and stability are compared with ordinary least squares and DLS(damped least squares) method. It is known that there are close relationship between the stability of optimization and condition number of nomal equation, the ratio between maximum and minimum of eigenvalues. In this study, the stability is greatly improved by limiting the condition number, the SVD, as expeded. The case of DLS with small damping, orthogonalization and SVD shows the most rapid convergence and stability. It means that the unstability of DLS method with small damping is overcome by using the variable orthogonalization and SVD.

• Bulletin of the Korean Chemical Society
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• v.24 no.7
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• pp.971-974
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• 2003

By utilizing singular value decomposition (SVD) and shift averaged Harr wavelet transform (WT) with a set of Daubechies wavelet coefficients (1/2, -1/2), a method that can simultaneously eliminate an unwanted large solvent peak and noise peaks from NMR data has been developed. Noise elimination was accomplished by shift-averaging the time domain NMR data after a large solvent peak was suppressed by SVD. The algorithms took advantage of the WT, giving excellent results for the noise elimination in the Gaussian type NMR spectral lines of NMR data pretreated with SVD, providing superb results in the adjustment of phase and magnitude of the spectrum. SVD and shift averaged Haar wavelet methods were quantitatively evaluated in terms of threshold values and signal to noise (S/N) ratio values.

• Journal of the Korean Magnetic Resonance Society
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• v.4 no.2
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• pp.116-124
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• 2000

Singular value decomposition (SVD) has been used during past few decades in the advanced NMR data processing and in many applicable areas. A new modified SVD, piecewise polynomial truncated SVD (PPTSVD) was developed far the large solvent peak suppression and noise elimination in U signal processing. PPTSVD consists of two algorithms of truncated SVD (TSVD) and L$_1$ problems. In TSVD, some unwanted large solvent peaks and noises are suppressed with a certain son threshold value while signal and noise in raw data are resolved and eliminated out in L$_1$ problem routine. The advantage of the current PPTSVD method compared to many SVD methods is to give the better S/N ratio in spectrum, and less time consuming job that can be applicable to multidimensional NMR data processing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8455-8461
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• 2015

In this paper, we propose new search algorithms using SVD(Singular Value Decomposition) for fast search of Raman spectrum. In the proposed algorithms, small number of the eigen vectors obtained by SVD are chosen in accordance with their respective significance to achieve computation reduction. By introducing pilot test, we exclude large number of data from search and then, we apply partial distance search(PDS) for further computation reduction. We prepared 14,032 kinds of chemical Raman spectrum as the library for comparisons. Experiments were carried out with 7 methods, that is Full Search, PDS, 1DMPS modified MPS for applying to 1-dimensional space data with PDS(1DMPS+PDS), 1DMPS with PDS by using descending sorted variance of data(1DMPS Sort with Variance+PDS), 250-dimensional components of the SVD with PDS(250SVD+PDS) and proposed algorithms, PSP and PSSP. For exact comparison of computations, we compared the number of multiplications and additions required for each method. According to the experiments, PSSP algorithm shows 64.8% computation reduction when compared with 250SVD+PDS while PSP shows 157% computation reduction.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.63-70
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• 2008

Narrow-band interference (NBI) from the coexisting narrow-band services affects the performance of ultra wideband (UWB) systems considerably due to the high power of these narrow-band signals with respect to the UWB signals. Specifically, IEEE 802.11a systems which operate around 5 GHz and overlap the band of UWB signals may interfere with UWB systems significantly. In this paper, we suggest a novel NBI suppression technique based on singular value decomposition (SVD) algorithm in time hopping UWB (TH-UWB) systems. SVD is used to approximate the interference which then is subtracted from the received signals. The algorithm precision and closed-form bit error rate (BER) expression are derived in the wireless multipath channel. Comparing with the conventional suppression methods such as a notch filter and a RAKE receiver, the proposed method is simple and robust and especially suitable for UWB systems.

• ETRI Journal
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• v.41 no.1
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• pp.109-116
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• 2019

This paper explores new techniques that are based on a hidden-layer linear transformation for fast speaker adaptation used in deep neural networks (DNNs). Conventional methods using affine transformations are ineffective because they require a relatively large number of parameters to perform. Meanwhile, methods that employ singular-value decomposition (SVD) are utilized because they are effective at reducing adaptive parameters. However, a matrix decomposition is computationally expensive when using online services. We propose the use of an extended diagonal linear transformation method to minimize adaptation parameters without SVD to increase the performance level for tasks that require smaller degrees of adaptation. In Korean large vocabulary continuous speech recognition (LVCSR) tasks, the proposed method shows significant improvements with error-reduction rates of 8.4% and 17.1% in five and 50 conversational sentence adaptations, respectively. Compared with the adaptation methods using SVD, there is an increased recognition performance with fewer parameters.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.1
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• pp.87-94
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• 2001

In multibody dynamics, differential and algebraic equations which can satisfy both equation of motion and kinematic constraint equation should be solved. To solve this equation, coordinate partitioning method and constraint stabilization method are commonly used. The coordinate partitioning method divides the coordinate into independent and dependent coordinates. The most typical coordinate partitioning method arc LU decomposition, QR decomposition, projection method and SVD(sigular value decomposition).The objective of this research is to find a efficient coordinate partitioning method in flexible multibody systems and a hybrid decomposition algorithm which employs both LU and projection methods is proposed. The accuracy of the solution algorithm is checked with a slider-crank mechanism.