Sentence examples for estimation in terms of from inspiring English sources

Figure 9 compares the MC code with the C/A code for range estimation in terms of their CRLB and their maximum likelihood estimators (MLE) by displaying the root mean square error (RMSE) in meter versus SNR in order to demonstrate the proposed theory in (7).

Although the forecast result using the 3 10-min data provides good estimation in terms of the maximum coastal tsunami height along the coast, the estimated source shows false subsidence region in the south of the Nankai Trough.

We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs.

However, simulations show that OLS is generally superior to IV estimation in terms of MSE, even in the presence of endogeneity.

This paper deals with cooperative position estimation in terms of pure relative localisation, which is based only on mutual observations among the robots.

The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV).

Results indicate that the proposed Depth-based approach leads to more homogeneous sub-regions in terms of H heterogeneity measure, and leading to more efficient quantile estimations in terms of relative bias and relative root mean square error, than those obtained by the traditional approach.

The representation of bio-liquid by a model compound (i.e. palmitic acid in case of algal oil) may result in good estimations in terms of mass (0.845 kgG-Diesel/kgalgal-oil product yield) and heat balance of the total process (94.9% thermal efficiency) but all the detected fatty acid is suggested be taken into account for a more detailed analysis.

In this paper, a detailed study about the performance of soft biometric estimations, in terms of accuracy as well as robustness under real-world conditions, has been presented.

The capacity estimations in terms of maximal number of voice and data users (MAL) which can independently be served by single BS sector at different Tx powers have been presented in Figure 4a,b, respectively.

In practice, these elements are estimations (in terms of a percentage of change between the baseline year and the target year) made by expert groups consisting of representatives of three stakeholder groups: professionals, health insurers and training institutions.