• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.136-139
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• 2010

We derived Stop-and-go normalized DD, dual-mode normalized Sato, dual-mode NCMA blind equalization algorithm for complex data in this research. And then, the convergence characteristics of the proposed SG-NDD, dual-mode NSato blind equalization algorithms are compared with those of SG-DD, dual-mode Sato algorithms. In general, the normalized blind equalization algorithms have better convergence characteristics than the conventional algorithms.

• ETRI Journal
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• v.33 no.3
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• pp.401-406
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• 2011

This paper presents a fast block-matching algorithm based on the normalized cross-correlation, where the elimination order is determined based on the gradient magnitudes of subblocks in the current macroblock. Multilevel Cauchy-Schwartz inequality is derived to skip unnecessary block-matching calculations in the proposed algorithm. Also, additional complexity reduction is achieved re-using the normalized cross correlation values for the spatially neighboring macroblock because the search areas of adjacent macroblocks are overlapped. Simulation results show that the proposed algorithm can improve the speed-up ratio up to about 3 times in comparison with the existing algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1227-1234
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• 2004

In this paper, we propose a modified Normalized-BP algorithm by changing the normalization factor according to the reliability of updated messages. Proposed algorithm has almost same decoding complexity as Normalized-BP algorithm and achieves a bit-error probability of $10^4$within 0.02dB away from compared with LLR-BP algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11C
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• pp.1127-1133
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• 2007

This paper proposes one of normalized QR-typed LMS (Least Mean Square) algorithms with computational complexity of O(N). This proposed algorithm shows the normalized property in terms of theoretical characteristics. This proposed algorithm is one of algorithms which normalize variance of input signal in terms of mean because QR-typed LMS is proportional to variance of input signal. In this paper, convergence characteristic analysis of normalized algorithm was made. Computer simulation was made by the algorithms used for echo canceller. Proposed algorithm has similar performance to theoretical value. And, we can see that proposed method shows similar one to performance of NLMS.by comparison among different algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1203-1210
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• 1999

Coefficients of the adaptive filter are misadjusted by primary signals which are uncorrelated with reference signals of the adaptive filter. In this paper, the normalized sign algorithm is analyzed and compared with the NLMS algorithm by the steady state performance and the transient characteristics when target signals are included in primary signals. The excess mean square error of the NLMS algorithm is proportional to the power of target signals. That of normalized sign algorithm is proportional to the square root of the target signal power. However, the convergence speed of the normalized sign algorithm is slower than that of NLMS algorithm. In this paper, it is shown that theoretical analysis of the steady state performance and the transient characteristics are well consisted with the results of computer simulation.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.280-287
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• 2016

In this paper, we propose a normalization algorithm that can be applied to adaptive filters for multi-channel active noise control. The FxLMS (Filtered-x Least Mean Square) algorithm for the single-channel active noise control can be normalized in the same way as the NLMS (Normalized Least Mean Square) algorithm, whereas in case of the multi-channel active noise control, the single-channel normalization for the FxLMS algorithm cannot be extended to the normalization for the multi-channel FxLMS algorithm straightforwardly. First, we adopt a generalized normalization algorithm for the multi-channel FxLMS algorithm based on the principle of minimal disturbance and then, proposed a normalized algorithm considering only diagonal elements to avoid computation for matrix inversion. We carried out performance comparisons of the proposed algorithm with other algorithms without normalization. It is shown that the proposed algorithm presents better convergence characteristics under non-stationary environments.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.3
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• pp.159-165
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

• ETRI Journal
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• v.26 no.5
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• pp.432-436
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• 2004

In this paper, we propose the modified uniformly most powerful (UMP) belief-propagation (BP)-based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP-based algorithm. This modified UMP BP-based algorithm shows better performance than that of the normalized UMP BP-based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP-based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP-based algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.782-787
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• 2007

This paper presents the D(Diagonal)-QR-RLS algorithm which normalizes the fast algorithm minimizes the MSE by using Givens rotated inputs and analyzes its characteristic. This proposed one has computational complexity of O(N) and the merit that it obtains the coefficients of TDL filter directly. Although this proposed normalized algorithm has the similar form to NLMS algorithm, we can see that D-QR-RLS has superior convergence characteristic to NLMS by computer simulation.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.518-524
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• 2013

In this paper, we propose a variable step size algorithm to enhance the normalized subband adaptive filter which has been proposed to improve the convergence characteristics of the conventional full band adaptive filter. The well-known Kwong's variable step size algorithm is simple, but shows better performance than that of the fixed step size algorithm. However, in case that large additive noise is present, the performance of Kwong's algorithm is getting deteriorated in proportion to the amount of the additive noise. We devised a variable step size algorithm which does not depend on the amount of additive noise by exploiting a normalized adaptation error which is the error subtracted and normalized by the estimated additive noise. We carried out a performance comparison of the proposed algorithm with other algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments.