• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.1
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• pp.31-40
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• 1992

This paper presents an automatic feature recognition system for recognizing and extracting feature information needed for the process planning input from a 3D CAD system. A given part is modeled by using the AutoCAD and feature information is automatically extracted from the AutoCAD database. The type of parts considered in this study is prismatic parts composed of faces perpendicular to the X, Y, Z axes and the types of features recognized by the proposed system are through steps, blind steps, through slots, blind slots, and pockets. Features are recognized by using the concept of convex points and concave points. Case studies are implemented to evaluate feasibilities of the function of the proposed system. The developed system is programmed by using Turbo Pascal on the IBM PC/AT on which the AutoCAD and the proposed system are implemented.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.43-46
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• 1999

Blind equalizers recover the transmitted data using signal's statistical characteristics only. Because of its computational simplicity and fast convergence rate, CMA is widely used in practice. Blind equalizers, however, converge much slowly than conventional equalizers which use the training signals. In order to improve the convergence rate, many modified blind equalization algorithms have been proposed. Among those, Normalized CMA (NCMA) was applied to increase the convergence rate by using the large step size. Unfortunately it can only be applied for the constant modulus signal constellation scheme. this paper, we propose the Selective NCMA (SNCMA) that improve the convergence rate of blind equalization algorithms by using NCMA for non-constant modulus signalling method such as QAM constellation. We achieved fast start-up convergence rate and reduced steady-state residual error.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.331-335
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• 2001

In this paper, we propose two novel blind LMS algorithms, called exponential step sire LMS algorithms (ES-LMS), for adaptive array antennas whose convergence speed is increased, hence they are much more capable of tracking the desired signal than the conventional LMS algorithms. Both of the algorithms require neither spatial knowledge nor reference signals since they use the finite symbol property of digital signal. Computer simulations were carried cot in CDMA environment affected by multi-path Rayleigh fading to verify the performance of the two proposed algorithms.

• The Journal of the Acoustical Society of Korea
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• v.34 no.5
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• pp.394-402
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• 2015

CMA (Constant Modulus Algorithm) is one of the well-known algorithms in blind acoustic channel equalization. Generally, CMA converges slowly and the speed of convergence is dependent on a step-size in the CMA procedure. Many researches have tried to speed up the convergence speed by applying a variable step-size to CMA, e.g. the orthogonal projection CMA and algebraic optimal step-size CMA. In this paper, we summarize these two algorithms, and we propose a new CMA with improved convergence performance. The improvement comes from the switching between the orthogonal projection CMA and algebraic optimal step-size CMA. In simulation results, we show the performance improvement in the time invariant channels as well as in time varying channel.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.99-105
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• 2001

In this paper, the selective normalized constant modulus algorithm(SNCMA) is applied to a fractionally spaced equalizer. The fractionally spaced equalizer is insensitive to the sampling timing because it processes received signals with the sampling rate larger than the symbol rate. The SNCMA improves the convergence rate by using the large step size for the most outer covering symbol belonging to the trust-level. This blind equalizer exhibits a fast start-up convergence rate as well as a reduced steady-state residual error compared to the fractionally spaced blind equalizer and the T-spaced blind equalizer using conventional blind algorithms.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1962-1965
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• 2004

In this paper, the new algorithm for blind adaptive decorrelating parallel interference canceller detector in direct-sequence code division multiple access (DS-CDMA) synchronous communication systems is proposed. The goal of this paper is to improve the performance of the blind adaptive decorrelating parallel interference cancellation detector (BAD/PIC). The proposed blind adaptive decorrelating detector is using optimum step-size technique bootstrap algorithm as an initial stage of PIC, which does not require a training sequence. Therefore, this algorithm has a superior view of utilizing bandwidth and reduces the complexity of computation of inversion cross-correlation matrix. The computer simulation results show that the bit error rate performance of the proposed algorithm for the new structure of detector is better than that of the other detectors such as matched filters, the conventional PIC, and the blind adaptive decorrelating PIC detector.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.129-135
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• 2011

The alternate adaptation algorithm (AAA) is proposed to improve the convergence characteristics and steady-state performance of the constant modulus algorithm (CMA). The alternate adaptation algorithm is a new equalization method which adapts an equalizer alternately by the algorithm with excellent blind convergence characteristics or the algorithm with better steady-state error performance. In this paper, it is introduced that the alternate adaptation equalization of the vsCMA (variable step-size CMA) and the decision-directed (DD) algorithm. We, first, designed the vsCMA with variable step-size to improve the steady-state error performance of the CMA, and combined it with the DD by alternate adaptation. As a result, it was mitigated that the sensitivity of performance fluctuation due to switching timing in CMA-DD switching method, and it was improved that the convergence speed and steady-state error performance of the CMA. Through computer simulations, under multipath channel condition, the usefulness of the proposed method was confirmed for 16-QAM.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.413-421
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• 2015

Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) / L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera / mobile cameras.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.640-645
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• 2009

CMA (Constant Modulus Algorithm) is one of the famous algorithms in blind channel equalization. Generally, CMA converges slowly and the speed of convergence is dependent on a step-size in the CMA procedure. Many researches have tried to speed up the convergence speed by applying a variable step-size to CMA. In this paper, we propose a new CMA algorithm with improved convergence performance. The improvement comes from an orthogonal projection of an average error gradient. We show the improvement in simulation results.