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We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records.
Theoretical justifications and extensive numerical assessments were conducted to calculate the design parameters and evaluate the performance of our methods.
We demonstrate the performance of our methods on several numerical examples, and we show that they have both the ability to handle highly complex geometry and the potential to solve large-scale problems.
In demonstrating this system, we run 2 pilot studies: one evaluates the performance of our methods with 14 subjects to analyze 6 common postures; the other is a series of overnight studies to verify continuous performance.
We prototype the system by using Impinj Speedway R420 reader and passive tags to evaluate the performance of our methods.
Table 5 Comparison of performance of our methods with some recent state-of-the-art methods in cub.
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Cross-validation was performed to evaluate the performance of our method and for parameter optimization.
We perform three sets of simulations to examine the performance of our method.
We illustrate the performance of our method with numerical experiments.
Experiments demonstrate the efficiency and excellent performance of our method.
We validated the performance of our method on two separate data sets from confocal and split detector AOSLO systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com