• Korean Journal of Optics and Photonics
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• v.13 no.6
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• pp.486-492
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• 2002

In this paper. we presented 1 & 2 dimensional minimum mean-squared-error (MMSE) equalization scheme in a digital holographic data storage system to improve bit-error-rate (BER) and to mitigate inter-symbol interference (ISI) which were generated during the data storage and retrieval processes. We showed experimentally for ten data pages retrieved from the holographic storage system that BER and signal-to-noise ratio (SNR) were improved by adopting MMSE equalization.

• Wind and Structures
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• v.18 no.6
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.376-386
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• 1995

Designs for polynomial approximation to the unknown response function are considered. Optimality criteria are monotone functions of the mean squared error matrix of the least squares estimator. They correspond to the classical A-, D-, G- and Q-optimalities. Optimal first order designs are chosen from the invariant designs and then compared with optimal second order designs.

• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.13-19
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• 1995

This paper considers model robust accelerated life test plans for estimating the logmean or percentile of product lige which is exponentially distributed. A linear relationship between the log mean life and the stress is assumed as usual, while the true relationship is quadratic. Optimum plans are then obtained by minimizing asymptotic mean squared error of maximum likelihood estimator of the log mean life.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.22-28
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• 2015

In this paper, we propose a new computationally efficient joint iterative multi-input multi-output (MIMO) detection scheme using a soft interference cancellation and minimum mean squared-error (SIC-MMSE) method. The critical computational burden of the SIC-MMSE scheme lies in the multiple inverse operations of the complex matrices. We find a new way which requires only a single matrix inversion by utilizing the Taylor series expansion of the matrix, and thus the computational complexity can be reduced. The computational complexity reduction increases as the number of antennas is increased. The simulation results show that our method produces almost the same performances as the conventional SIC-MMSE with reduced computational complexity.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.384-390
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• 2011

The paper derives formulas for the mean-squared error distortion and resulting signal-to-noise (SNR) ratio of a fixed-rate scalar quantizer designed optimally in the minimum mean-squared error sense for a Gaussian density with the standard deviation ${\sigma}_q$ when it is mismatched to a Laplacian density with the standard deviation ${\sigma}_q$. The SNR formulas, based on the key parameter and Bennett's integral, are found accurate for a wide range of $p\({\equiv}\frac{\sigma_p}{\sigma_q}\){\geqq}0.25$. Also an upper bound to the SNR is derived, which becomes tighter with increasing rate R and indicates that the SNR behaves asymptotically as $\frac{20\sqrt{3{\ln}2}}{{\rho}{\ln}10}\;{\sqrt{R}}$ dB.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.109-110
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• 2008

본 논문은 MIMO(Multiple Input Multiple Output)- OFDM(Orthogonal Frequency Division Multiplexing) 시스템에서 V-BLAST (Vertical-Ball Laboratories Layered Space Time) 수신기에 대하여 성능을 비교하고 평가한다. 신호는 각각 송신 안테나에서 독립적으로 전송되며 QPSK(Quadrature Phase Shift Keying) 방식을 이용하여 변조 되고, 송 수신단에 각각 2개의 안테나와 각각 4개의 안테나를 사용한다. V-BLAST 수신기로 ZF(zero-Forcing), MMSE(Minimum Mean Squared Error), ZF-OSIC(Zero Forcing - Ordered Successive Interference Cancellation), MMSE-OSIC(Minimum Mean Squared Error - Ordered Successive Interference Cancellation)를 사용한다. 모의실험 결과에서 MMSE 방식이 ZF 방식 보다 좋은 BER(Bit Error Rate)을 보이고, ZF-OSIC 방식은 ZF 방식과 MMSE 방식 보다 더 좋은 BER을 가지는 것을 확인 할 수 있으며, MMSE-OSIC 방식은 사용된 방식 중 가장 좋은 성능을 보인다.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.1-8
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• 2017

In this paper, an improved ordered block minimum mean squared error (OB-MMSE) detector for generalized spatial modulation (GSM) systems is presented. It is based on an ordered successive interference cancellation (OSIC) technique. Its bit error rate (BER) performance and computational complexity are compared with those of the corresponding original OB-MMSE detector. It is shown that the proposed OSIC-based OB-MMSE detector outperforms the OB-MMSE detector in terms of BER without noticeable complexity increase.