• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.347-354
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• 1994

This paper describes a diagnostic protocol of nonlinear dynamic characteristics of biological system using chaos theory. An integrated chaos analysis system for the diagnosis of biological system was designed. We suggest a procedure of attractor reconstruction for reliable qualitative and quantitative analysis. The effect of autonomic nervous system activity on heart rate variability with power spectral analysis and its characteristics of chaotic attractors are investigated. The results show the applicability to evaluate the mental and physical conditions using nonlinear characteristics of biological signal.

• Proceedings of the KIEE Conference
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• 1979.08a
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• pp.151-152
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• 1979

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.105-114
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• 1997

The phase retrieval problem is concerned with the reconstruction of a signal or its fourier transform phase form the fourier transform magnitude of the signal. This problem does not have a unique solution, in general. If, however, the desired signal is minimum-phase, then it can be decided uniquely. This paper shows that we can make a minimum-phase signal by adding a delta function having a large value at the origin of an arbitrary one-dimensional signal, and a two-dimensional signal can be uniquely specified from its fourier transform magnitude if it is added by a delta function having a large value at the origin, and finally we can solve a two-dimensional phase retrieval problem by decomposing it into several ine-dimensional phase retrieval problems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.561-566
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• 2005

This paper introduces a compression technique for power qualify disturbance signal via discrete wavelet transform(DWT). The proposed approach is based on a previous estimation of the stationary component of power quality disturbance signal, so that it could be subtracted from the original signal in order to reduce a dynamic range of signal and generate transient events signal, which is subsequently applied to the compression technique. The compression techniques is performed through the difference signal decomposition, thresholding of wavelet coefficients, and signal reconstruction. It presents the relation between compression efficiency and threshold. It shouts that the wavelet transform leads to a power quality data compression approach with high compression efficiency, small compression error and good de-nosing effect.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.12-19
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• 2017

In this paper a super-resolution time delay estimation algorithm that estimates the time delays of spread spectrum signals using sparse signal reconstruction approach is introduced. So far, the correlation method has been mostly used to estimate the time delays of spread spectrum signals. However it fails to accurately estimate the time delays in the case where the signals are spaced within approximately 1 PN chip duration and a further processing should be applied to the correlation outputs in order to enhance the resolution capability. Recently sparse signal approaches attract much interest in the area of directions-of-arrival estimation, of which SPICE is the most representative. Thus we introduce a super-resolution time delay estimation algorithm based on the SPICE approach and compare its performance with that of MUSIC algorithm by applying them to the ISO/IEC 24730-2.1 RTLS system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2563-2568
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• 2010

SAII (synthetic aperture integral imaging) is a useful technique to record many multi view images of 3D objects by using a moving camera and to reconstruct 3D depth images from the recorded multiviews. This is largely composed of two processes. A pickup process provides elemental images of 3D objects and a reconstruction process generates 3D depth images computationally. In this paper, a signal model for SAII is presented. We defined the granular noise and analyzed its characteristics. Our signal model revealed that we could reduce the noise in the reconstructed images and increase the computational speed by reducing the shifting distance of a single camera.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.309-315
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• 2009

Image reconstruction of Inverse Fourier Transform after Frequency Domain Data is filtered applies to Image signal acquired from MR. There are various kinds of image processing techniques; image preprocessing, image reconstruction, image compression, image restoration image mixture, noise and artifact elimination, and image quality improvement. In this paper, optimum filter applicable to diagnosis in clinic by comparing and analyzing the characteristics of the filter will be explained. Fermi-Dirac filter will improve the image quality better than the previous MR image.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.240-249
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• 2014

According to the compressive sensing theory, it is possible to perfectly reconstruct a signal only with a fewer number of measurements than the Nyquist sampling rate if the signal is a sparse signal which satisfies a few related conditions. From practical viewpoint for image applications, it is important to reduce its computational complexity and memory burden required in reconstruction. In this regard, a Block-based Compressive Sensing (BCS) scheme with Smooth Projected Landweber (BCS-SPL) has been already introduced. However, it still has the computational complexity problem in reconstruction. In this paper, we propose a method which modifies its stopping criterion, tolerance, and convergence control to make it converge faster. Experimental results show that the proposed method requires less iterations but achieves better quality of reconstructed image than the conventional BCS-SPL.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.287-293
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• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.4
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• pp.34-41
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• 1999

Unlike the traditional radar system, Synthetic Aperture Radar(SAR) system is capable of imaging a target scene to ceertain degree of cross-range resolution. And this resolution is mainly depends on the size of aperture synthesized. Thus, a good system model and inversion scheme should be developed to actually give effect of synthesizing aperture size, which in turn gives better cross range resolution of reconstructed target scene. Among several inversion schemes for SAR imaging, we used an inversion scheme called wavefront reconstruction which has no approximation in wave propagation analysis, and tried to verify whether the collected data with synthesized aperture actually give the same support as that with physical aperture in the same size. To do this, we performed a signal subspace comparison of two imaging models with physical and synthesized arrays, respectively. Theoretical comparisons and numerical analysis using Gram-Schmidt procedures have been performed. The results showed that the synthesized array data fully span the physical array data with the same system geometry. This result strongly supports the previously proposed inversion scheme valuable in high resolution radar imaging.