The key generation class has 3 methods: generate : This method generates two 128-bit AES keys.
This method generates efficient motions exploiting the intrinsic characteristics of the mechanism and the environment.
We show that this method generates robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations.
This method generates a response whose deviation from the desired response is evenly distributed across the passband and stopband.
(If you're interested, in order to dampen the extreme differences that this method generates we then took the cube root of this score).
For measurement, we use a laser probe method that we developed in advance; this method generates statistical measurements method using a laser distance meter.
This method generates anisotropic triangular grids and the corresponding polynomial approximation degrees based on the minimization of the interpolation error in the Lq-norm (q∈[1,∞]).
This method generates a feature-based model from a B-rep model by sequentially and iteratively applying the four volume decomposition methods (fillet, round, and chamfer decomposition; wrap-around decomposition; volume split decomposition; and cell-based decomposition).
To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process.
This method generates a global VHDL file for the selected initial set of components based on inserting multiplexers at the gate inputs of the circuit which helps to perform the simulations in only one session.
Because x and y are coprime, so that x and N are coprime, it follows (from some theorem of the theory of cyclical groups that I forgot a long time ago) that this method generates the entire group, i.e., all integers 0, 1, …, N (and N is the last such generated, i.e., is adjacent to 0 on the clockwise side).
