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2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2017
In this work we investigate the hybrid analog and digital combining applicable to both multi-user single-input multiple-output (SIMO) and single user multiple-input multiple-output (MIMO) systems via modeling equivalence. We consider the mean square error (MSE) of the estimated signal after the combiner, and indicate that the optimal MSE can be ...
Ming-Chun Lee, Wei-Ho Chung
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In this work we investigate the hybrid analog and digital combining applicable to both multi-user single-input multiple-output (SIMO) and single user multiple-input multiple-output (MIMO) systems via modeling equivalence. We consider the mean square error (MSE) of the estimated signal after the combiner, and indicate that the optimal MSE can be ...
Ming-Chun Lee, Wei-Ho Chung
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Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, 2016
The image blurring is a common artifact effecting the image quality in terms of details. In medical imaging such image details play a very crucial role e.g. CT and MRI[7] scan images. There are many deconvolution techniques available like Wiener[1], RC Lucy[3] and Blind [4] etc. which help in restoration of blurred[12] images.
Poonam Sharma +2 more
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The image blurring is a common artifact effecting the image quality in terms of details. In medical imaging such image details play a very crucial role e.g. CT and MRI[7] scan images. There are many deconvolution techniques available like Wiener[1], RC Lucy[3] and Blind [4] etc. which help in restoration of blurred[12] images.
Poonam Sharma +2 more
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Multiresponse robust design: Mean square error (MSE) criterion
Applied Mathematics and Computation, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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MSE < Variance? A pitfall in calculating the mean square error
Model Assisted Statistics and Applications, 2011When calculating the mean square error (MSE), it is possible to encounter a situation where the variance of a parameter of interest is larger than its mean square error. In theory, this is impossible because MSE is the sum of variance and bias squared; even when bias is zero, the MSE should be equal to, and not less than, the variance.
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Plant Disease
Research synthesis methods such as meta-analysis rely primarily on appropriate summary statistics (i.e., means and variance) of a response of interest for implementation to draw general conclusions from a body of research. A commonly encountered problem arises when a measure of variability of a response across a study is not explicitly provided in the
Vinicius C. Garnica +3 more
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Research synthesis methods such as meta-analysis rely primarily on appropriate summary statistics (i.e., means and variance) of a response of interest for implementation to draw general conclusions from a body of research. A commonly encountered problem arises when a measure of variability of a response across a study is not explicitly provided in the
Vinicius C. Garnica +3 more
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Test, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A New Variable-Step LMS Algorithm Based on the Convergence Ratio of Mean-Square Error(MSE)
2008A new variable step-size(VSS) LMS adaptive algorithm based on the convergence ratio of MSE and the correlation between reference signal and output error is proposed in the paper. Theory analyzing and simulation results prove that the new algorithm improves the convergent speed of general LMS algorithm and optimizes the trace ability of time-varying ...
Hong Wan +3 more
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Communications in Statistics - Simulation and Computation, 2019
This paper is concerned with using the E-Bayesian method for computing estimates of Pareto index.
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This paper is concerned with using the E-Bayesian method for computing estimates of Pareto index.
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The Mean Square Error (MSE) Performance Criteria
1986Adaptive signal processing algorithms generally attempt to optimize a performance measure that is a function of the unknown parameters to be identified. The most pervasive of these performance measures are based upon squared prediction errors, although the specific prediction error used in adaptation often depends upon the particular algorithm.
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Journal of Geodesy, 2007
In a linear Gauss–Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed.
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In a linear Gauss–Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed.
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