For a given frequency band, spectrum sensing is generally formulated as a binary hypotheses as follows: H 0 : r i ( t ) = n i ( t ), i = 1, 2 …, N H 1 : r i ( t ) = h i ( t ) s i ( t ) + n i ( t ), i = 1, 2 …, N, (1).
The extract is generally formulated as tablets or capsules.
Our problem is generally formulated as J/STsd/Cmax.
Discrete network design problem (DNDP) is generally formulated as a bi-level programming.
The continuous network design problem (CNDP) is generally formulated as a mathematical program with equilibrium constraints (MPEC).
While things have been typically detected by sliding window or Hough transform based methods, detection of stuff is generally formulated as a pixel or segment-wise classification problem.
The simultaneous heat exchanger network (HEN) synthesis optimization problem is generally formulated as a mixed integer non-linear program (MINLP) by using the concept of HEN superstructures.
This method was generally formulated as a parabolic nonlinear equation.
This method generally formulated as a parabolic nonlinear equation, thus linearization process is used to derive analytical solution.
It should be mentioned that the above SSR methods in frequency domain are generally formulated as a multi-dictionary/multi-measurement (MD/MM) joint optimization problem.
Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM) problem which can be solved by using group sparsity techniques.
