Sentence examples for approximation is detailed from inspiring English sources

In Subsection 2.3, the algorithm for segment detection and polygonal approximation is detailed.

The parameters identification methodology is presented and the fractional derivative approximations are detailed.

Syllable boundaries were manually marked according to the concept of target approximation to be detailed later in Section 2.4.

An image compression simulation and hardware implementation for the proposed and existing approximation DCT are detailed and analyzed in Section 3. Conclusion and final remarks are given in Section 4. Haweel[17] introduces the approximation DCT method by applying the signum function operator to the DCT element in Equation 1.

Methods related to construction of the stoichiometric matrix and the required steady-state approximation for intracellular metabolite concentrations have been detailed elsewhere [ 57].

Specific properties dealing with moment excitation and mid-frequency approximation are also detailed to provide complementary insights into their dynamic meaning and behavior.

This method of approximation is discussed in detail in the paper.

This approximation is described in detail in from the point of view of the distance functions in Sect.

In Zhou et al. [ 34], parametric approximations are only detailed for two self-contained gene set test statistics (sum of the score statistics and sum of the squares of the score statistics) and one non-directional competitive test statistic (a weighted sum of the squares of local score statistics).

We may thus approximate Q i (x) by single Gaussians whose standard deviations we will denote as δ i. (The validity of this approximation is taken up in further detail below).

Sequential Mann Kendall analysis on the different wavelet detail components (with their approximation component added) that result from the time series decomposition was also used and found to be helpful because it depicts how harmonious each of the detail components (plus approximation) is with respect to the original data.