• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.137-142
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• 2017

A general linearly constrained narrowband adaptive array is examined in the eigenvector space. The optimum weight vector in the eigenvector space is shown to have the same performance as in the standard coordinate system, except that the input signal correlation matrix and look direction steering vector are replaced with the eigenvalue matrix and transformed steering vector. It is observed that the variation in gain factor results in the variation in the distance between the constraint plane and the origin in the translated weight vector space such that the increase in gain factor decreased the distance from the constraint plane to the origin, thus affecting the nulling performance. Simulation results showed that the general linearly constrained adaptive array performed better at an optimal gain factor compared with the conventional linearly constrained adaptive array in a coherent signal environment and the former showed similar performance as the latter in a noncoherent signal environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.653-655
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• 2011

EPCglobal proposed a Q-algorithm, which is used for selecting a slot-count in the next query round. However, it is impossible to allocate an optimized slot-count because the original Q-algorithm did not define an optimized weight C value. In this paper, we propose an adaptive Q-algorithm, in which we differentiate the weight values with respect to collision and empty slots. The weight values are defined with the identification time as well as the collision probability.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.177-184
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• 2020

In this paper, we studied to direction of arrival (DoA) estimation to use DoA and optimum weight algorithms in coherent interference channels. The DoA algorithm have been considerable attention in signal processing with coherent signals and a limited number of snapshots in a noise and an interference environment. This paper is a proposed method for the desired signal estimation using MUSIC algorithm and adaptive beamforming to compare classical subspace techniques. Also, the proposed method is combined the updated weight value with LCMV beamforming algorithm in adaptive antenna array system for direction of arrival estimation of desired signal. The proposed algorithm can be used with combination to MUSIC algorithm, linearly constrained minimum variance beamforming (LCMV) and the weight value method to accurately desired signal estimation. Through simulation, we compare the proposed method with classical direction of in order to desired signals estimation. We show that the propose method has achieved good resolution performance better that classical direction arrival estimation algorithm. The simulation results show the effectiveness of the proposed method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.63-70
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• 2014

Method a target estimation in spatial are mobile wireless communication using network cell and GPS. It have much error that mobile wireless communication depend on cell size. GPS method can't find a target in shadow and inner area. In this paper, we estimate a target as direction of arrival method using adaptive array antenna system. Adaptive array antenna system can obtain desired signal to remove other signal This paper studied digital beamforming method in order to estimation a target. Proposed method is modified optimum weight and antenna error correction to estimation an optimal receive signal. Digital beamforming method decided a signal phase and amplitude from received signal on array antenna element. But if it is not to do error correction of received signal, system performance have decreased. Firstly, we proposed modified optimum weight in order to finding desired target. Secondly, we are error correction of antenna incident signals by optimal weight before digital beamforming method. Thirdly, throughly simulation, we showed that system performance of proposed method compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.305-309
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• 2011

A sparse reconfigurable adaptive filter (SRAF) based on a photonic switch determines the appropriate time delays and weight values for an optical switch implementation of tapped-delay-line (TDL) systems. It is well known that the choice of switch delays is significantly important for efficiently implementing the SRAF. If the same values exist as calculating the sum of weight magnitudes for implementing the connection constraint required by the SRAF, conventional connection algorithm based on sequentially selection the maximum elements might not work perfectly. In an effort to increase the effectiveness of system identification, an upgraded connection algorithm used progressive calculation to obtain the better solution is considered in this paper. The performance of the proposed connection constraint algorithm is illustrated by computer simulation for a system identification application.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2723-2740
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• 2017

Steganographic schemes which are based on minimizing an additive distortion function defined the overall impacts after embedding as the sum of embedding costs for individual image element. However, mutual impacts during embedding are often ignored. In this paper, an adaptive JPEG steganographic method based on weight distribution for embedding costs is proposed. The method takes mutual impacts during embedding in consideration. Firstly, an analysis is made about the factors that affect embedding fluctuations among JPEG coefficients. Then the Distortion Update Strategy (DUS) of updating the distortion costs is proposed, enabling to dynamically update the embedding costs group by group. At last, a kind of adaptive JPEG steganographic algorithm is designed combining with the update strategy and well-known additive distortion function. The experimental result illustrates that the proposed algorithm gains a superior performance in the fight against the current state-of-the-art steganalyzers with high-dimensional features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.509-513
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• 2002

This paper presents a semi-blind Minimum Mean Square Error (MMSE) beamforming adaptive algorithm used far OFDM/CDMA combined system. The proposed algorithm exploits the transmitting pilot signal in the initial period of the transmission to update the weight vector. Then it applies the blind adaptive period to update the weight vector, in which the pilot signal is no longer used. The derivation of the algorithm based on the Mean Square Error (MSE) criterion is also presented. Computer simulation is carried out to verify the performance of the proposed approach.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.1-8
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• 2015

This paper presents a Electrocardiogram(ECG) identification method using adaptive weight based on Least Mean Square Error(LMSE) optimization. With a preprocessing for noise suppression, we extracts the average ECG signal and its standard deviation at every time instant. Then the extracted information is stored in database. ECG identification is achieved by matching an input ECG signal with the information in database. In computing the matching scores, the standard deviation is used. The scores are computed by applying adaptive weights to the values of the input signal over all time instants. The adaptive weight consists of two terms. The first term is the inverse of the standard deviation of an input signal. The second term is the proportional one to the standard deviation between user SAECGs stored in the DB. Experimental results show up to 100% recognition rate for 32 registered people.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.81-90
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• 2018

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.