The interest of this algorithm is the approximation of the solution and the leading singular coefficient which has a physical significance in the propagation of cracks.
Due to the uneven surface of the skull, there may be more than one local minimum inside the region of interest, so the algorithm uses the signed addition of derivatives within a specified neighborhood of the local minimal point.
This additional complexity therefore limits the interest of this algorithm in comparison to Algorithm 1, especially when one observes a large number n of pattern occurrences.
After the user manually draws a polygon surrounding the area of interest, the algorithm returns the minimal rectangle containing this image area (Figure 4). Figure 4 Example of the selection of the active area.
'environment search' In case only a single protein of interest exists, the algorithm can be used to observe the dynamics of expression in the environment of this protein using the 'environment search'.
Despite the growing interests, all of the algorithms are designed based on seemingly the same principle.
To use a computational analogy, we are really interested only in "computable" functions from context to content, functions that can be implemented somehow in a human mind, and this suggests that it is not the function itself that is of interest but rather the algorithm by means of which it is computed.
If there are a large number of SNPs in the region of interest, the convergence time of the algorithm will increase.
The intensity within the region of interest increases while the algorithm optimizes the spatial phase distribution of the incoming light.
It is of interest to extend the algorithm (2.25) to certain Banach spaces.
