Sentence examples for models for signal from inspiring English sources

Techniques and models for signal, data and information processing, with emphasis on incomplete data, non-ordered index sets and robust low-complexity methods.

A complementary body of work on network MIMO focuses on the performance evaluation of specific system architectures and signal processing techniques, usually by numerical simulation, with more realistic models for signal propagation and cochannel interference (accounting, at least, for distance-based power loss and shadow fading effects).

Several large and detailed mathematical models for signal transduction pathways exist in the literature.

Frequency domain models for signals and frequency response for linear time-invariant systems are investigated.

In this chapter, we explore parametric models for signals and systems with a focus on the estimation of model parameters under a variety of scenarios.

At the same time, more potentially scalable models for signaling are emerging.

Although rules can be used to define large-scale biochemical reaction networks in a compact efficient manner, the shear size of such networks, has posed a formidable barrier to the development and analysis of models for signal-transduction systems that account for site-specific details of protein interactions (in terms of rules).

Or we can describe ligand-receptor and protein-protein binding actions with the mass action law and build models for signaling networks [ 22].

The intron evidence is then given to GeneID which then predicts the most likely gene structures given this evidence and its statistical models for signals and coding sequence.

When assessing performance of predictive models for signaling networks it is important to distinguish between afferent and efferent cascades from cues to intracellular signals and from intracellular signals to responses respectively.

Finally, we present a model for signal storage.