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This approach to screening is predicated on some "filtering" to determine the appropriate individuals or groups to be screened.
Our BCI system builds a computational model that employs filter banks to cover the full frequency range of EEG rhythms, together with common spatial pattern filtering to determine subject-specific spatial-spectral patterns in the EEG for discriminating attentive and inattentive states.
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Airborne magnetic data were filtered to determine magnetic and heat source depth information of the Soutpansberg Basin in South Africa.
After 72 h, the incubation was stopped and supernatant pH was determined, and then filtered to determine apparent degraded substrate (ADS).
Data were further filtered to determine whether their expression was specific to a tissue type.
These differentially expressed contigs were further filtered to determine whether they were overexpressed in a particular tissue.
When both plate counts were within the detection limit, concentrations were added and divided by the total volume filtered to determine the sample concentration.
This data was filtered to determine tags with either the same, or less, count in the relapse compared with the primary of R2.
The results obtained from the CompPASS analysis were filtered to determine proteins with a NWD score>1 or high confidence interactors (HCIPs).
In principle, any control problem can be solved in two steps: (1) building an optimal filter (a so-called Kalman filter) to determine the best estimate of the present state vector; (2) determining an optimal control law and mechanizing it by substituting into it the estimate of the state vector obtained in step 1.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com