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The ROC curve is used to evaluate the discrimination power along a dimension of projection: if the dimension offers a linear discrimination between the good and bad signals, the respective AUC will be 1.
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For determining the dimensions of projections columns, we identified the numbers of row and arcs in TC slices by their relative proximity to hippocampal fornix and CA3 region (Figs 5 A and 6 A ).
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Track the signal subspace and determine the dimension of the projection subspace as listed in Table 2.
When the dimension of the projection subspace is equal to the dimension of the signal subspace, the output SNR can be maximized.
In this section, determining the dimension of the projection subspace is transformed to the maximization of the output SNR of the proposed beamformer by a power-associated method.
The estimated dimension of the projection subspace, i.e., r d (t), with the power-associated method is shown in Fig. 3 a, b, and c.
A crucial problem for our method is to estimate the dimension of the projection subspace especially when the number of signals is changed.
In this paper, we develop a power-associated method which maximizes the output signal to noise ratio (SNR) to determine the dimension of the projection subspace.
Then, we develop a power-associated method to determine the dimension of the projection subspace and combine it with Bi-LS subspace tracking to adapt to the dimensional variation of the signal subspace.
The bi-iterative least-square (Bi-LS) subspace tracking is used to update the signal subspace, and a power-associated method is developed to determine the dimension of the projection subspace automatically.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com