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We have applied our algorithm to real scenes that include transparent objects and recovered the shapes and dimensions of the objects with high accuracy.
However, as long as the physical dimensions of the objects that light encounters (and the apertures through which it passes) are significantly greater than the wavelength of the electromagnetic wave, there is no need for the mathematical formalism of the wave model.
Many algorithms have been developed for the task of spatial clustering focusing on the spatial dimensions of the objects.
Many algorithms have been designed to perform spatial clustering with respect to spatial dimensions of the objects.
This type of matrix was chosen because dimensions of the objects that look like points are about six to seven pixels.
In general, previous works were performed to investigate different cases of nanofluid flow and heat transfer in channels with mounted objects by focusing on changing geometries, arrangement, and dimensions of the objects.
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A unit of magnification commonly used in microscopes and telescopes is the diameter, the magnification in diameters being equal to the number of times the linear dimensions of the object are increased.
The null hypothesis of interest is that the real surface of an object fits the theoretical shape and dimensions of the object.
We show that the Shannon sampling of diffraction intensities needed in CDI requires a coherence width about twice the lateral dimensions of the object, and that the linear number of detector pixels fixes the energy spread needed in the beam.
That means the Reynolds number (a critical parameter that reflects a fluid's density, velocity and viscosity as well as the dimensions of the object it flows around) has to be the same in a microengine as it is in a conventional motor.
Depending on the material and the dimensions of the object to be analysed, different approaches can be chosen.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com