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We observe that the number of CDs is hardly affected, whereas the number of FA increases (decreases) for over (under -assumptions of K 0 dunder -assumptionsansitiof, but declines quicKly with increasing M. The signature length M should be chosen such that the algorithm operates in the successful regime where the support estimation features only CDs and no FAs.
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A further advantage is an automated a posteriori error estimation feature that enables a systematic increase in the orders of the SSM computation until the required accuracy is reached.
We included all participants despite small amounts of missing data by using the maximum likelihood estimation feature.
Further details on the formulation, estimation methods, features of the model, and properties of the estimators have been published elsewhere [ 18- 20].
Quantitative estimation of features from sectional images of microstructures is fundamental to determining the microstructural influences on material behavior.
In Section 2, we review the blind parameter estimation using features based on autocorrelation and cyclic autocorrelation for CP-OFDM signals.
An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method.
In the DT algorithm, the estimation of feature importance is calculated from the feature usage based on information gain.
The details of parameter estimation and feature extraction are summarized in the following subsections.
As shown in Fig. 2, this algorithm consists of two major steps: parameter estimation and feature extraction.
Then, filtering, parameter estimation, and/or feature extraction is performed using the standard means.
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