Therefore, in the implementation of the weighting method, we excluded the p-values which have been already statistically significant.
For an implementation of the spectral weighting, we have to consider short-time spectra of the microphone signals and estimate the short-time power spectral densities (PSD) of the speech signal and the noise components.
These equations represent useful design tools when dealing with super-directive arrays since the relative errors occurring in the implementation of the beamformer weights usually map onto amplified relative errors on the array gains.
As mentioned earlier, an array operating in super-directive mode has to face peculiar issues, such as a pronounced sensitivity to electro-mechanical imperfections and errors in the implementation of the beamforming weights, as well as increased vulnerability to internal noise, in comparison with a conventional uniformly weighted beamformer.
Furthermore, the implementation of the inverse weighting required for marginal structural models is not straightforward when dose densities are mixtures distributions with continuous components, as in the present study.
A mathematical implementation of the feature weighting account has been put forward by Maljkovic and Martini (2005), and updated in Martini (2010).
The algorithm was implemented in Python 2.6 using the NetworkX (Hagberg et al., 2008) package, as well as Joris van Rantwijk's implementation of the maximum-weight mapping algorithm based on the blossom method for finding augmenting paths and the primal dual method for finding a maximum-weight matching (Galil, 1986).
Implementation of the weight-restricted linear program requires specifying the 2M = 10 parameters γm, βm.
It is possible that in an occupational setting with a higher prevalence of overweight, the implementation of a weight loss intervention will be more successful.
As the implementation of proportional weights does not change the outcome tremendously, we decide to use the unweighted data and consider the results as exploratory rather than representative.
