• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.302-308
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• 2001

SVD(Singular Value Decomposition) filter for the suppression of EMG in multi-lead stress ECG is studied. SVD filter consists of two parts. In the first part, the basis vectors were chosen from the averaged singular vectors obtained from the decomposed noise-free ECG. The singular vector is computed from the stress ECG and is compared itself with basis vectors to know whether the noise exist in stress ECG. In the second part, the existing elimination method is used, when one(or two) channels is(or are) contaminated by noise. But the proposed enhanced SVD filter is used in case of having the noise in the many channels. During signal decomposition and reconstruction, the noise-free channel or the least noisy channel have the weight of 1, the next less noisy channel has the weight of 0.8. In this way, every channel was weighted by decreased of 0.2 in proportion to the amount of the added noise. For the evaluation of the proposed enhanced SVD filter, we compared the SNR computed by the enhanced SVD filter with the standard average filter for the noise-free signal added with artificial noise and the patient data. The proposed SVD filter showed better in the SNR than the standard average filter. In conclusion, we could find that the enhanced SVD filter is more proper in processing multi-lead stress ECG.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.20-25
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• 1980

A singular value decomposition (SVD) pseudo-inversion method has been applied to the image reconstruction from projections. This approach is relatively unknown and differs from conventionally used reconstructioll methods such as the Foxier convolution and iterative techniques. In this paper, two SVD pseudo-inversion methods have been discussed for the search of optimum reconstruction and restoration, one using truncated inverse filtering, the other scalar Wiener filtering. These methods partly overcome the ill-conditioned nature of restoration problems by trading off between noise and signal quality. To test the SVD pseudo-inversion method, simulations were performed from projection data obtained from a phantom using truncated inversefiltering. The results are presented together with some limitations particular to the applications of the method to the general class of 3-D image reconstruction and restoration.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.3-11
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• 2001

This paper describes an implementation of inverse filter using SVD in order to recover the input in multi-channel system. The matrix formulation in SISO system is extended to MIMO system. In time and frequency domain we investigates the inversion of minimum phase system and non-minimum phase system. To execute an effective inversion of non-minimum phase system, SVD is introduced. First of all we computes singular values of system matrix and then investigates the phase property of system. In case of overall system is non-minimum phase, system matrix has one (or more) very small singular value (s). The very small singular value (s) carries information about phase properties of system. Using this property, approximate inverse filter of overall system is founded. The numerical simulation shows potentials in use of the inverse filter.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1784-1787
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• 2002

Two-dimensional (2-D) digital filters are widely useful inn image processing and other 2-D digital signal processing fields, but designing 2-D filters is much more difficult than designing one-dimensional (1-D) ones. This paper provides a new insight into the existing singular value decomposition (SVD)-based design approach in the sense that the SVD-based design can be performed more efficiently by exploiting the symmetries of the given 2-D magnitude specifications. By using the specification symmetries. only half of the 1-D filters (sub-filters) need to be designed. which significantly simplifies the design process and reduces the computer storage required for 1-D sub-filter coefficients. Another novel point of this paper si that an objective criterion is proposed for selecting appropriate sub-filter orders in order to reduce the hardware implementation cost. A design example is given to illustrate the effectiveness of the SVD-based design approach by exploiting specification symmetry and new order-selecting criterion.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.154-161
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• 2013

In this paper, Gabor cosine and sine transform considered as human visual filter is applied to watermarking methods for digital images. Four algorithms by using singular values or principal components of SVD in the frequency domain are proposed for watermark embedding and extraction. Two dimensional image is used as an embedded watermark. To measure the similarity between the embedded watermark image and the extracted one, a normalized correlation value is computed for the comparison of the four proposed methods with various attacks. Extracted watermark images are also provided for visual inspection. The proposed GCST-SVD method which embeds a watermark image into the lowest vertical or horizontal ac frequency band can provide useful watermarking algorithm with high correlation values and visual watermark features from experimental results for various attacks.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.543-546
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• 1996

We propose a new visual tracking system for grasping which can find grasping points of an unknown polygonal object. We construct the system with the image prediction technique and Extended Kalman Filter algorithm. The Extended Kalman Filter(EKF) based on the SVD can improve the accuracy and processing time for the estimation of the nonlinear state variables. By using it, we can solve the numerical unstability problem that can occur in the visual tracking system based on Kalman filter. The image prediction algorithm can reduce the effect of noise and the image processing time. In the processing of a visual tracking, we can construct the parameterized family and can found the grasping points of unknown object through the geometric properties of the parameterized family.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.997-1002
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• 2001

This paper describes an implementation of inverse filter using SVD in order to recover the input in multi-channel system. The matrix formulation in SISO system is extended to MIMO system. In time and frequency domain we investigates the inversion of minimum phase system and non-minimum phase system. To execute an effective inversion of non-minimum phase system, SVD is introduced. First of all we computes singular values of system matrix and then investigates the phase property of system. In case of overall system is non-minimum phase, system matrix has one (or more) very small singular value(s). The very small singular value(s) carries information about phase properties of system. Using this property, approximate inverse filter of overall system is founded. The numerical simulation shows potentials in use of the inverse filter.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.187-193
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• 1998

This paper proposes an advanced visual tracking algorithm for the stable grasping of a moving target(2D). This algorithm is programmed to find grasping points of an unknown polygonal object and execute visual tracking. The Kalman Filter(KF) algorithm based on the SVD(Singular Value Decomposition) is applied to the visual tracking system for the tracking of a moving object. The KF based on the SVD improves the accuracy of the tracking and the robustness in the estimation of state variables and noise statistics. In addition, it does not have the numerical unstability problem that can occur in the visual tracking system based on Kalman filter. In the grasping system, a parameterized family is constructcd, and through the family, the grasping system finds the stable grasping points of an unknown object through the geometric properties of the parameterized family. In the previous studies, many researchers have been studied on only 'How to track a moving target'. This paper concern not only on 'how to track' but also 'how to grasp' and apply the grasping theory to a visual tracking system.

• ETRI Journal
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• v.29 no.2
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• pp.237-239
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• 2007

Interference from IEEE 802.11a systems affects ultra-wideband (UWB) systems significantly. In this letter, we suggest a novel narrow-band interference (NBI) suppression technique based on the singular value decomposition (SVD) algorithm in time-hopping pulse amplitude modulation (TH-PAM) UWB systems. The SVD algorithm is used to approximate the interference which then is subtracted from the received signals. In contrast to the conventional notch filter and rake receiver, our method is more effective and the receiver complexity can be greatly reduced.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1887-1890
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• 2003

This paper presents a three-dimensional (3D) head pose estimation algorithm using the stereo image. Given a pair of stereo image, we automatically extract several important facial feature points using the disparity map, the gabor filter and the canny edge detector. To detect the facial feature region , we propose a region dividing method using the disparity map. On the indoor head & shoulder stereo image, a face region has a larger disparity than a background. So we separate a face region from a background by a divergence of disparity. To estimate 3D head pose, we propose a 2D-3D Error Compensated-SVD (EC-SVD) algorithm. We estimate the 3D coordinates of the facial features using the correspondence of a stereo image. We can estimate the head pose of an input image using Error Compensated-SVD (EC-SVD) method. Experimental results show that the proposed method is capable of estimating pose accurately.