assumption on noise and data, p(φ|h) can be approximated as a complex normal distribution with mean μ = [ μ 1 T, …, μ s ′ T ] T and covariance R φ = diag[R1,..., R s ' ].
Thus, we assume that the residual ISI after FDE, for SC-FDE, can be approximated as a complex-valued random Gaussian variable.
Consequently, since the self interference due to imperfect self-information removal can be approximated as a complex-valued random Gaussian variable (see Section 2.2), the sum of the residual ISI, the self interference, and AWGN can be treated as a new complex-valued random Gaussian noise with variance given by (12).
In this regime, the diffracted amplitude can be approximated as: 1where is the complex sample transmission function, with an amplitude due to absorption and a phase due to the shift of the wave propagating through the sample.
It is shown that large-scale CD processes can be approximated as RCMs and then diagonalized by complex Fourier matrices, allowing analysis in terms of a family of SISO transfer functions across the spatial frequencies.
In addition, the good linear MSD fits suggest that the motions of the unattached ParB/ parS complex can be approximated as a normal diffusion at a minute scale (i.e., on the time-scale of segregation).
For a back of the envelope calculation, the longitudinal and lateral complexes can be approximated as prolate ellipsoids with axial ratios of 2.1 and 1.43, respectively.
However, complexing of IKK with IκBα can also be approximated as a catalytic influence of IKK on IκBα, using the quasi-steady-state approximation [ 20] for all complexes involving IKK.
If the step-size μ of the complex gain algorithm is small, it can be approximated as (mu sigma _{u}^{2} /2).
