• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.663-664
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• 2006

This paper presents a reconfigurable digital signal processing(rDSP) architecture that is effective for implementing adaptive digital signal processing in the applications of smart health care system. This rDSP architecture employs an evolution capability of FIR filters using genetic algorithm. Parallel genetic algorithm based rDSP architecture evolves FIR filters to explore optimal configuration of filter combination, associated parameters, and structure of feature space adaptively to noisy environments for an adaptive signal processing. The proposed DSP architecture is implemented using Xilinx Virtex4 FPGA device and SMIC 0.18um CMOS Technology.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.143-148
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• 2006

The goal of this paper is to analyze, through simulations and experiments, GNSS interference mitigation performance under various types of antenna structures against wideband and narrowband interferences using spatial-temporal adaptive signal processing (STAP) techniques. The STAP approach, which combines spatial and temporal processing, is a viable means of GNSS array signal processing that enhancing the desired signal quality and providing protection against interference. In this paper, we consider four types of 3D antenna array structure - Uniform Linear Array (ULA), Uniform Rectangular Array (URA), Uniform Circular Array (UCA), and the Single-Ring Cylindrical Array (SRCA) under an interference environment. Analytical evaluation and simulations are performed to investigate the system performance. This is followed by simulation GPS orbits in interfered environment are used to evaluate the STAP performance. Furthermore, experiments using a 2x2 URA hardware simulator data show that with the removal of wideband and narrowband interference through the STAP techniques, the signal tracking performance can be enhanced.

• Journal of electromagnetic engineering and science
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• v.5 no.2
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• pp.49-60
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• 2005

In this paper, an efficient performance enhancement scheme for an adaptive antenna array under the flat and the frequency-selective Rayleigh fadings is proposed. The proposed signal enhancement scheme is the modified linear signal estimator which combines the rank N approximation by reducing noise eigenvalues(RANE) and Toeplitz matrix approximation(TMA) methods into the linear signal estimator. The proposed performance enhancement scheme is performed by not only reducing the noise component from the signal-plus-noise subspace using RANE but also having the theoretical property of noise-free signal using TMA. Consequently, the key idea of the proposed performance enhancement scheme is to greatly enhance the performance of an adaptive antenna array by removing all undesired noise effects from the post-correlation received signal. The proposed performance enhancement scheme applies at the Wiener maximal ratio combining(MRC) method which has been widely used as the conventional adaptive antenna array. It is shown through several simulation results that the performance of an adaptive antenna array using the proposed signal enhancement scheme is much superior to that of a system using the conventional method under several environments, i.e., a flat Rayleigh fading, a fast frequency-selective Rayleigh fading, a perfect/imperfect power control, a single cell, and a multicell.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.44-48
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• 2002

This paper describes an adaptive signal processing of digital receiver with DDC(Digital Down Convertor), DDC is implemented by using NCO(Numerically Controlled Oscillator), digital low pass filter. for the passband sampling, we present the results of digital receiver simulation with DDC. We confirm that the low IP signal is converted to zero IF by DDC. DOA(Direction Of Arrival) estimation technique using MUSIC(Multiple SIgnal Classification) algorithm with high resolution is presented. We Cow that an accurate resolution of DOA depends on the input sampling number.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.17-21
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• 2002

Least mean square(LMS) algorithm one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation. But the convergence speed of time domain adaptive algorithm is slow when the spread width of eigen values is wide. Moreover we have to choose the step size well for convergency. in this paper, ie use adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm of wavelet transform. And we propose a high speed wavelet based adaptive algorithm with variable step size, which is linear to absolute value of error signal. We applied this algorithm to adaptive noise canceler. Simulation results are presented to compare the performance of the proposed algorithm with the usual algorithms.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.145-154
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• 2020

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.2
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• pp.68-73
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• 2002

Many paper about the adaptive algorithm based to LS(Least Square) to improve convergence speed are already presented. In this paper, we propose a wavelet based adaptive algorithm which improves the convergence speed and reduces computational complexity, and adapt two kinds of adaptive noise cancelers using the characteristic of speech signal. We compared the performance of the nosed algorithm with time and frequency domain adaptive algorithm using computer simulation of adaptive noise canceler based on synthesis speech. As the result the proposed algorithm is suitable for adaptive signal processing area using speech or acoustic signal.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.53-59
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• 2006

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as system identification and signal prediction. This paper proposes the gamma neural network(GAM), which uses gamma memory kernel in the hidden layer of feedforward multilayered network, to improve dynamics of networks and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The proposed network is evaluated in nonlinear signal prediction and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of prediction performance. Simulation results show that the GAM network performs better with respect to the convergence speed and prediction accuracy, indicating that it can be a more effective prediction model than conventional multilayered networks in nonlinear prediction for nonstationary signals.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.6
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• pp.77-83
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• 2003

This paper deals with a position control problem of a hydraulic proportional position control system using a nonlinear friction compensation control. As nonlinear friction, stiction and coulomb friction forces are considered and modeled as deadzone and external disturbance respectively. In order to compensate this nonlinearities, we designed the controller which is the adaptive friction compensator using discrete time Model Reference Adaptive Control method in this paper. Digital Signal Processing board is employed for data acquisition and manipulation. The experimental results show that response is slow and steady-state error cannot be compensated properly without friction compensation but this compensator is effective to obtain fast response and good steady-state response.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.19-25
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• 2012

An adaptive system is a well-known method for removing noise from noise-corrupted speech. In this paper, we perform a least mean square (LMS) based on wavelet adaptive algorithm. It establishes the faster convergence rate of as compared to time domain because of eigenvalue distribution width. And this paper provides the basic tool required for the FIR algorithm whose algorithm reduces the arithmetic complexity. We consider a new fast short-length running FIR structure in discrete wavelet adaptive algorithm. We compare FIR algorithm and short-length fast running FIR algorithm (SFIR) to the proposed fast short-length running FIR algorithm(FSFIR) for arithmetic complexities.