The MMSE filter weights are optimal as they require knowledge of the desired signal, a common assumption used in the Wiener filter [3] derivation and previous MMSE FRESH filter weight derivations [2, 28, 29].
Many techniques have been proposed so far, which are typically reliable in terms of performances but often not suitable to real-time computing, for example, they require knowledge of the whole piece to perform optimally, or they are too computationally intensive for most embedded processors.
Phosphospecific antibodies (raised against specific tyrosine phosphorylated sites) can be sensitive and specific, but have the disadvantage that they require knowledge of the relevant phosphorylated sites, and they are available for only a small fraction of known sites.
Computational approaches are successful in identifying conserved miRNAs in many plants and animals, but they require knowledge of the complete genome sequence, which is unavailable for most plant species.
Double-ended methods such as NEB cannot be applied to find transition states, since they require knowledge of both the initial and final states, whereas the AKMC simulation has information only about the initial state.
Tofu, Tempeh, and Seitan are great meat alternatives, but they require knowledge of how to cook them (which isn't too difficult).
This first generation of tools was primarily used by experts, since they required knowledge of the UNIX environment and command lines to perform analyses.
For example, they usually require knowledge of the number of players and of the actions played by all the other players.
All these methods rely solely on the stoichiometric structure and thermodynamic (i.e. reversibility) constraints of reactions and they do not require knowledge of kinetic mechanisms and parameters.
Such efforts require knowledge of matrix surface chemistry and the cell responses they elicit.
That, investigators say, would require knowledge of his intent.
