• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.64-67
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication. is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.169-177
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• 2012

A non-decimation second-order spatial moving average (SMA) discrete-time (DT) filter is proposed with reconfigurable null frequencies. The filter coefficients are changeable, and it can be controlled by switching sampling capacitors. So, interferers can be rejected effectively by flexible nulls. Since it operates without decimation, it does not change the sample rate and aliasing problem can be avoided. The filter is designed with variable weight of coefficients as $1:{\alpha}:1$ where ${\alpha}$ varies from 1 to 2. This corresponds to the change of null frequencies within the range of fs/3~fs/2 and fs/2~2fs/3. The proposed filter is implemented in the TSMC 0.18-${\mu}m$ CMOS process. Simulation shows that null frequencies are changeable in the range of 0.38~0.49fs and 0.51~0.62fs.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.852-855
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• 2002

This paper presents a design of FIR near-equiripple Nyquist filters having zero-intersymbol interference (ISI) and low sensitivity to timing jitter, with coefficients taking only discrete values. Using an affine scaling linear programming algorithm, an optimum discrete coefficient set can be obtained in a feasible computational time. Also presented is a pipelined multiplier-free FIR filter realization with periodically time-varying (PTV) coefficients based on a hybrid form suitable for Nyquist filter. The realization exploits the coefficient symmetry to reduce the hardware by about one half. High speed computation and low power consumption are achieved by its pipelined and low fan-out structure.

• Honam Mathematical Journal
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• v.32 no.1
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• pp.101-112
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• 2010

Using some interesting convolution, we find kernels recovering the given function f. By a slight change of this convolution, we obtain an identity filter related to the Fourier series in the discrete time domain. We also introduce some techniques to decompose an impulse into several dilated pieces in the discrete domain. The detail examples deal with specific constructions of those decompositions. Also we obtain localized moving averages from a decomposition of an impulse to make hybrid Bollinger bands, that might give various strategies for stock traders.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.61-65
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• 1999

This paper derives a sufficient condition on the stability of recursive third-order Volterra filters based on their filter coefficients. A Volterra filter is very effective in modeling nonlinear systems with memory. However, it is well-known that the nonrecursive Volterra filter requires a large number of filter coefficients to describe a nonlinear system. For this reason, recursive Volterra filters are usually considered because the recursive implementation requires a smaller number of coefficients compared to the nonrecursive one. Unfortunately, the main problem of the recursive Volterra filters is their inherent instablility. In this paper. we present a simple condition for the output of a recursive discrete-time third-order Volterra filter to be bounded whenever the input signal to the recursive Volterra filter is bounded by a finite constant.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.171-182
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• 2015

The general wavelet transform has profitable property in non-stationary signal analysis specially. The tunable Q-factor wavelet transform is a fully-discrete wavelet transform for which the Q-factor Q and the asymptotic redundancy r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The transform is based on a real valued scaling factor and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its over-sampling rate, with modest over-sampling rates being sufficient for the analysis/synthesis functions to be well localized. This paper describes filter design of 2D discrete-time wavelet transform for which the Q-factor is easily specified. With the advantage of this transform, perfect reconstruction filter design and implementation for performance improvement are focused in this paper. Hence, the 2D transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. Therefore, application for performance improvement in multimedia communication field was evaluated.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.52-58
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• 2007

We propose a new contact detection algorithm for fine pitch wire bonding. Fast and stable contact detection of the z-axis in wire bonding is extremely important to maintain the quality of fine pitch gold wire bonding processes, which use a small pad less than $70{\mu}m$ in diameter. A small perturbation in the contact detection time causes a large difference in the size of the formed squashed ball. The new detection method is based on a statistical approach and designed for a discrete Kalman filter. It is faster and has smaller detection time variations than conventional detection methods. Experimental results are presented to demonstrate the advantages of the proposed algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.502-508
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• 2014

In this paper, a delay-dependent $H_{\infty}$ filtering problem is investigated for discrete-time delayed nonlinear systems which include a more general sector nonlinear function instead of employing the commonly used Lipschitz-type function. By using the Lyapunov-Krasovskii functional approach, a less conservative sufficient condition is established for the existence of the desired filter, and then, the corresponding solvability condition guarantee the stability of the filter with a prescribed $H_{\infty}$ performance level. Finally, two simulation examples are given to show the effectiveness of the proposed filtering scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.