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The algorithm enables rapid design and testing of future CFAs by automatically generating the image processing pipeline.
The algorithm enables a biped walking robot to effectively recover balance following external disturbance.
The algorithm enables to produce uniform mesh, as well as anisotropic adaptive mesh with refinement in the large gradient regions.
The algorithm enables us to calculate impurity and band edge states for systems of 100,000 atoms in about one day on 32 cores.
In addition to that, the algorithm enables identification of the bias term that appears due to currents (in yaw models) and difference between weigh and buoyancy (in heave models).
The algorithm enables to reproduce the subjective aspects of the example (extraordinary vertex layout) while minimizing the induced distortion; allowing users to leverage reference meshes considered of quality for fast prototyping.
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Combined, the algorithms enable simple and computationally efficient motion planning with support in both R2 and R3 exemplified in a real-time implementation.
The proposed algorithm enables the 3D garment fitting simulation result directly to react to the modification in the 2D patterns.
Therefore, the adaptive algorithm enables the solution of two-scale models with continuous inter-scale dependencies on large computational macroscopic and microscopic grids within an acceptable period of time for computation.
The GMDH algorithm enables the automatic selection of input variables during model construction, as well as a hierarchical polynomial regression of necessary complexity (Farlow 1984).
Moreover, the distributed nature of the proposed algorithm enables it to perform localization at the sensor level which eliminates the need for centralized processing.
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