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Minimum mean-squared error transform coding and subband coding
IEEE Transactions on Information Theory, 1996Summary: Knowledge of the power spectrum of a stationary random sequence can be used for quantizing the signal efficiently and with minimum mean-squared error. A multichannel filter is used to transform the random sequence into an intermediate set of variables that are quantized using independent scalar quantizers, and then inverse-filtered, producing ...
Knut C. Aas, Clifford T. Mullis
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A Minimum Mean-Square Error Equalizer for Nonlinear Satellite Channels
IEEE Transactions on Communications, 1987The problem of designing and evaluating the performance of a minimum mean-square error equalizer (MMSEE) for binary PSK transmission over band-limited nonlinear satellite channels is considered in this correspondence. The effect of intersymbol interference followed by AM/AM and AM/PM conversions are taken into account while optimizing the performance ...
Aly F. Elrefaie, Ludwick Kurz
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Hardware accelerator for minimum mean square error interference alignment
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015A dedicated hardware architecture for the digital baseband processing of minimum mean square error interference alignment is presented. The computationally intensive task of calculating the precoding and decoding matrices has been implemented and the underlying algorithm has been optimized for real-time capability, efficiency and flexibility.
Markus Kock, Steffen Busch, Holger Blume
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Strong Universal Consistent Estimate of the Minimum Mean Squared Error
2013Consider the regression problem with a response variable Y and a feature vector X. For the regression function m(x) = E{Y ∣X = x}, we introduce new and simple estimators of the minimum mean squared error Mean squared error—( Minimum mean squared error—( \({L}^{{\ast}} = \mathbf{E}\{{(Y - m(\mathbf{X}))}^{2}\}\), and prove their strong ...
Luc Devroye +3 more
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Robust estimate with minimum mean squared error
Australian Surveyor, 1991The result of any robust estimate depends on its weight function. But the weight function and its parameters are determined personally.
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Minimum mean square error estimation for a class of chaotic systems
1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 2002We develop the Bayesian maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators for sequences generated by a class of discrete-time chaotic systems. This class (of which the sawtooth map, an archetype of chaotic behavior, is a member) consists of maps of the unit interval whose dynamics are precisely equivalent to the output of a ...
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MINIMUM MEAN SQUARE ERROR UNBIASED ESTIMATION
Australian Journal of Statistics, 1974openaire +2 more sources
Minimum mean square error equalization on the 2-sphere
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014Parastoo Sadeghi +2 more
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Minimum mean-squared-error estimators for simulation experiments
Communications of the ACM, 1981James H. Donnelly, Robert E. Shannon
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Speech Enhancement Using a Minimum Mean-Square Error Log-Spectral Amplitude Estimator
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985Y Ephraim
exaly