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The analytical model we develop to estimate throughput is based on probability models and order statistics results assuming random storage.
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Firstly, a regularized least-square estimator is developed to estimate the states of the nonlinear system.
A local linear two stage least squared estimation is developed to estimate coefficient functions.
Many other estimators were also developed to estimate the angular parameters of multiple ID sources.
A decoupled parameter-estimation strategy is also developed to estimate the parameters of the equivalent model.
A segment-based noise estimation algorithm is also developed to estimate the noise statistics efficiently.
A maximum likelihood estimator combined with a bandwidth extrapolation technique is also developed to estimate the scale parameters.
Formulae have been developed to estimate the exposure time that different photographs can bear before detrimental change might occur.
The wavelet procedures are developed to estimate the regression function.
Subspace methods are also developed to estimate mode shapes.
The correlation was also developed to estimate cuttings bed height.
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