Sentence examples for distributed imaging from inspiring English sources

As we later discuss, most existing DCS algorithms for distributed imaging reconstruction rely fundamentally on sparse models to capture intra- and inter-signal correlations [5 8].

Armed with potentially limited communication and computational resources, designers of distributed imaging systems face increasing challenges in the quest to acquire, compress, and communicate ever richer and higher-resolution image ensembles.

This is due to its several significant advantages, such as simplicity of use, and the fact that it is easily combinable with portable and widely distributed imaging devices, resulting in friendly analytical procedures in many areas that demand out-of-lab applications for in situ and real-time monitoring.

Point-source inversion methods are an ideal way of localizing MEG activity for use in a DCM analysis, as they reduce the data down to a number of discrete regions rather than providing a distributed imaging solution that would have to be split artificially into regions of interest.

The ability to electronically distribute imaging studies in order to organise the radiologists' work more efficiently is also highly appreciated.

Let us now turn to the problem of distributed image compression for multi-view imaging.

Theoretically, this is the case of distributed coherent imaging where the resolution cell size and roughness are large relative to the illuminating wavelength, leading to the absolute phase variation over all scatterers in the cell being uniformly randomly distributed and the integrated in-phase and quadrature signals are therefore expected to be zero.

"The scallop distributes the imaging task," just as the Digital Window camera distributes its imaging job, said Peter Jones, president of Tenebraex.

The Company also manufactures and distributes thermal imaging equipment and dry thermal film products to a variety of markets including the screenprint, point of sale, signage and textile markets.

For CT, patients were distributed over more imaging profiles.

Robust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience.