A quantitative SA measurement tool named Computational Representation of Situation Awareness with Graphical Expressions (CoRSAGE) is introduced to resolve some of these concerns.
When we move from formal systems to computational systems, the situation is more complex.
Computational models of situation analysis processes are in many cases of a distributed nature, being composed of multiple autonomously operating agents that each react in an asynchronous manner to discrete events distributed in space and time.
Here we introduce two new improvements for the existing algorithm, adding significant computational speedup in situations with many moment constraints, where the original algorithm is known to converge slowly.
Indeed, most of analytical solutions contain infinite series and special functions, and thus are complicated and inconvenient for computational and numerical situations.
You have a similar situation with computational tools like audio or video codecs and with anything that relies on semiconductors integrated circuits, memory chips, even light emitting diodes.
In this situation, the computational capacity of the Condor pool will not be fully exploited, and the benefits of running in parallel will be negated.
Let us think about a new situation: both computational and life science sides vested in the science project: co-developing the problem, co-solving the problem, both having ownership of the outcome.
Examples of this situation, drawn from computational geometry, numerical parallel computation, VLSI theory, and computational biology, will be reviewed and examined in detail.
There are currently no specific and standardized measures for computing the similarity between the fixation density maps and saliency maps created using computational models in 3D situations.
