Sentence examples for implementation of a mixture from inspiring English sources

Results obtained through the implementation of a mixture of these frameworks show that the Vibrochord facilitated an increased accuracy rate over the traditional piano keyboard, when inexperienced vibrotactile musicians tried to repeat vibrotactile patterns or "melodies".

These authors further demonstrated that the correct implementation of a mixture model dealing with heterotachy, first proposed by KT [ 33], renders ML largely superior to MP under conditions where standard ML was outperformed [ 35].

Implementation of a mixture model always decreases the objective function because effectively, the data are split into subsets and fit using different functions.

48 The key innovations of this approach are the simulation of observations for non-responders, and the explicit incorporation of differential associations for non-responders and responders for any given age/sex/deprivation/region combination by factoring in an alternative hospital admission/death rate for the non-responders by implementation of a pattern mixture-based approach.

The synchronous parallel implementation is a mixture of Jacobi and Gauss Seidel style iteration, where it becomes more Jacobi like as the number of processors increases.

Availability: A platform-independent implementation of the mixture modeling approach is available in terms of a MATLAB/Octave toolbox at http://gobics.de/peter/taxy.de/peter/taxy

The implementation of the mixture of Gaussian models using a lookup table makes the skin detection algorithm proper for real-time applications as well.

In India, based on the guidelines provided by Kandhal [16], Indian Roads Congress (IRC) has issued a special publication for SMA [17] in 2008, but the implementation of this mixture in field is very limited.

The implementation of a step-by-step concrete mixture design procedure to determine an optimized aggregate gradation is described.

The implementation of Gaussian finite mixture models used here estimates a range of possible models using a combination of class configurations (See Figure 2 and Table 3 of Scrucca et al. [29]) and numbers of components/classes and selecting the combination that gives the highest BIC goodness of fit.

Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem.