• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.57-60
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• 2000

This paper presents a new adaptive fast motion estimation algorithm along with its architecture. The conventional algorithm such as full - search algorithm, three step algorithm have some disadvantages which are related to the amount of computation, the quality of image and the implementation of hardware, the proposed algorithm uses spatial correlation and a slope of motion vector in order to reduce the amount of computation and preserve good image quality, The proposed algorithm is better than the conventional Block Matching Algorithm(BMA) with regard to the amount of computation and image quality. Also, we propose an efficient at chitecture to implement the proposed algorithm. It is suitable for real time processing application.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.49-58
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• 2012

This paper is on an improved orthogonal projection method which can cancel the acoustic feedback signals in the digital hearing aids. Comparing with the NLMS algorithm which is widely used for simplicity and stability, it shows that this method has the improvement of the convergence performances, and has small computational quantities, for signals with the large auto-correlation as speech signals. This uses the improved orthogonal projection algorithm which reduces the correlation of signals. To verify the convergence characteristics of the proposed algorithm, we simulated about various input signals. The acoustic feedback canceller has a 12-bit resolution with 64-tap adaptive FIR filter. And we compared the results of simulation for this algorithm with the ones for the NLMS algorithm. By these works, it is proved that the feedback canceller adopting the proposed algorithm shows about 3.5dB more high SNR than the NLMS algorithm in the colored input signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.245-246
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• 2008

The Correlation FXLMS (Co-FXLMS) algorithm was developed to improve the control performance. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Modified Co-FXLMS is presented in comparison with that of the CO-FXLMS algorithm. Simulation results show that active noise control using Modified Co-FXLMS is effective to control performance of algorithm and prevent divergence.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.410-416
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• 2001

An algorithm of 3-D particle image velocimetry(3D-PIV) was developed for the measurement of 3-D velocity field of complex flows. The measurement system consists of two or three CCD camera and one RGB image grabber. In this study, stereo photogrammetty was applied for the 3-D matching of tracer particles. Epipolar line was used to decect the stereo pair. 3-D CFD data was used to estimate algorithm. 3-D position data of the first frame and the second frame was used to find velocity vector. Continuity equation was applied to extract error vector. The algorithm result involved error vecotor of about 0.13 %. In Pentium III 450MHz processor, the calculation time of cross-correlation for 1500 particles needed about 1 minute.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.199-202
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• 2009

This paper proposes a novel adaptive algorithm for deinterlacing. In the proposed algorithm, the previously developed Enhanced ELA [6], Chen [9] and Li [10] algorithms were used as a basis. The fundamental mechanism was the selection and application of the appropriate algorithm according to the correlation with the previous and next field using temporal information. Extensive simulations were conducted for video sequences and showed good performance in terms of peak signal-to-ratio (PSNR) and subjective quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• Journal of applied mathematics & informatics
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• v.30 no.1_2
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• pp.201-210
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• 2012

A modified multiplicative update algorithms is presented for computing the nearest correlation matrix. The convergence property is analyzed in details and a sufficient condition is given to guarantee that the proposed approach will not breakdown. A number of numerical experiments show that the modified multiplicative updating algorithm is efficient, and comparable with the existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.495-513
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• 2020

The correlation identification of the subsurface is a novel electrical prospecting method which could suppress stochastic noise. This method is increasingly being utilized by geophysicists. It achieves the frequency response of the underground media through division of the cross spectrum of the input & output signal and the auto spectrum of the input signal. This is subject to the spectral leakage when the cross spectrum and the auto spectrum are computed from cross correlation and autocorrelation function by Discrete Fourier Transformation (DFT, "To obtain an accurate frequency response of the earth system, we propose an improved correlation identification method which uses all phase Fast Fourier Transform (APFFT) to acquire the cross spectrum and the auto spectrum. Simulation and engineering application results show that compared to existing correlation identification algorithm the new approach demonstrates more precise frequency response, especially the phase response of the system under identification.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2324-2328
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• 2010

In this paper we proposed a new algorithm that can reduce the open-loop pitch estimation time of G.723.1. The time domain cross-correlation method is simple but has long processing time by recursive multiplication. For reduction of processing time, we use the method that compute the cross-correlation by multiplying the Fourier value of speech by it's complex conjugate. Also, we can reduce the processing time by omitting the bit-reversing of FFT and IFFT for time-frequency domain transform. As a result, the processing time of improved hybrid domain cross-correlation algorithm is reduced by 67.37% of conventional time domain cross-correlation.