Sentence examples for mass estimation techniques from inspiring English sources

Using a three-dimensional computer model of an exceptionally complete Stegosaurus specimen, we compare mass estimation techniques based on volumetric and traditional bivariate regressions to test if estimates generated from limb bone dimensions alone are biologically plausible for taxa with morphologies lacking close modern analogues or for specimens that have not attained full adult size.

Many systems tools that have been attempted applied structural mass estimation techniques based on historical data and curve fitting techniques that are difficult and cumbersome to apply to new vehicle concepts and missions.

Recent years, however, have brought additional data and related analyses: results of three ground tests, better on-orbit size and mass estimation techniques, more regular orbital tracking and reporting, additional radar resources dedicated to the observation of small objects, and simply a longer time period with which to observe the debris and their decay.

> > This is the first study to apply both volumetric and linear bivariate mass estimation techniques to the same Stegosaurus individual.

Consequently, many mass estimation techniques for fossil taxa rely upon measurements taken from commonly preserved skeletal elements.

At least some of the inconsistency we find here between mass estimation techniques may therefore be due to the ontogenetic stage of the specimen.

Current mask estimation techniques rely on explicit estimation of the characteristics of the corrupting noise.

Overall, these results show that our SPECT/CT technique of lung parenchyma mass estimation was accurate but only moderately precise.

Although our SPECT/CT technique for patient-specific lung parenchyma mass estimation was shown to be accurate, it was only moderately precise.

The technique becomes less accurate in terms of both total mass estimation and the ability to resolve the vertical distribution of DNAPL, when a source zone contains more pools than residuals.

DCM for electrophysiological data combines a neural mass model with a forward model that translates the neural dynamics into predicted measurements; estimation techniques based on a variational Bayes allow one to infer the parameters of the neuronal system from the observed data.