Sentence examples for adaptive distance from inspiring English sources

adaptive distance vector.

The algorithm was originally proposed by Lavallée and Szeliski [33] for biplanar projection, and is based on 3D adaptive distance maps.

To evaluate the performance of TCP with fixed RTO, three routing protocols, two on-demand (Ad-Hoc on Demand Distance Vector (AODV) and Dynamic Source Routing (DSR)) and one adaptive proactive routing protocol (Adaptive Distance Vector (ADV)), have been considered.

For a faster quantification of the distance between the line and the model surface, and to define the sampling step of, adaptive distance maps (ADM) of the models surface were pre-computed and stored.

In the present study, we analysed, by means of computer simulation, the convergence property of a modified version of the pose estimation algorithm proposed by Lavalleé and Szelinsky [33] and based on adaptive distance maps (ADM), in order to better understand the influence of local minima and to optimize the pose estimation in terms of accuracy and precision.

Numerical tests with the optimised set of model parameters reveal that the IVM is very efficient, in terms of adaptive distance, in generating high-quality synthetic turbulent fluctuations over a moderate distance: 6h for channel flow and 21δ for flat-plate boundary layer, with h and δ being respectively the half channel height and the nominal boundary layer thickness.

To identify the nodules successfully, an adaptive distance-based threshold technique is applied to segment the contour of each candidate.

This paper aims at improving the vortex method (VM) of Sergent that demands long adaptive distances (12 times the half channel height, for a channel flow at Reτ= 395) to achieve high quality turbulence, and evaluating the equilibrium of the flow field obtained in terms of both the equilibrium of the mean flow and that of the turbulence (inter-scale turbulent energy transfer).

Hence, we introduce a latency-adaptive distance-based multi-candidate selection scheme for greedy forwarding to find routes with a small number of hops and acceptable delivery latency.

Furthermore, an adaptive spatial distance is integrated with ASGHSOM, in which local spatial information is considered in the clustering process to reduce the noise effect and the classification ambiguity.

These principles were implemented in the software by the use of three adaptive dimensions (distance, speed and conceptual complexity), which together form a multidimensional learning space.