Sentence examples for algorithmic transformation from inspiring English sources
Typically, given a dataflow algorithm and a set of architectural constraints, the high-level synthesis performs algorithmic transformation and produces the optimal scheduling.
In this paper, an algorithmic transformation from the automaton-based Compositional Interchange Format (CIF) for hybrid systems to the equation-based Modelica language is defined.
A hypothesis based upon the concepts of "informational entropy" and "algorithmic transformation" is developed to explicate/rationalize these results.
Thus, in the case of the algorithmic transformation described above, a single regulatory event could precipitate comprehensive changes to the algorithmic information content of the CTD.
Thus, in the same sense that a Turing machine can be classified as "universal," algorithmic transformation of the CTD can also be thought of as a universal system.
- We define an algorithmic transformation to be a genetically controlled change in the symbolic structure of the CTD according to a simple rule (or "algorithm").
This incapacity, however, doesn't worry him, because the value of numbers for him is not that they produce or confirm readings, but that they provoke those who look at them to flights of interpretive imagination: "algorithmic transformations can provide the alternative visions that give rise to … readings" ("Reading Machines").
Our solution is based on the adaptation of previous positioning solutions to the application context, on algorithmic transformations to improve data reuse and reduce processing load, and on a new architectural solution for object drawing.
The methodology is based on the deductive transformation and algorithmic checking of hybrid diagrams.
To achieve low power implementation of DSP circuits, pipelining, parallel processing, algebraic transformations, and algorithmic modifications are generally employed [10].
However, due to the size of these data and algorithmic complexity of map projections, the transformation of big spatial data between alternative projections represents a pressing computational challenge.
