Along with information architecture and its implementation, the chosen prototype DFM components and architectural aspects requirements are outlined.
This is done by selecting a randomly chosen prototype C s, and replacing it by a randomly selected data vector x i : c_{s} leftarrow x_{i} quad left| {quad s = {text{rand}}left( {1,k} right),quad i = {text{rand}}left( {1,N} right)} right.
Using the meta-analysis scheme described above, we were able to identify genes whose expression was significantly associated with each chosen prototype (Additional data file 3).
Section 2 describes the chosen prototyping platform and the methodology that supports embedded application software coupled with custom FPGA logic and analog interfacing.
Stated differently, we choose the prototype η p to fit the average expression of all genes assigned to prototype p. This again is a simple optimization in our model.
If you're old enough, you should remember having to choose from prototype, jquery, moo and a bunch of other libraries in the early days of web 2.0.
Due to this re-weighting, all points in the vicinity of the chosen cluster prototype receive a larger γ-index.
For example, the material and manufacturing process chosen to prototype the gears resulted in gears with significant burrs which required removal before the gear train could be operated.
Implementable designs (use case, database, dataflow, work flow, system sequence, class and components) were then drawn based on the finalized functional requirements, the most suitable technologies (both software and hardware) were chosen, comprehensive prototype development was performed and the end product was successfully deployed and tested in-house.
Due to their increased γ-index, they are less likely chosen as prototypes in the next iteration.
