In this section, we first recall the standard recovery conditions of sparse signals randomly sampled in a bounded orthonormal system.
Each area contains 500 sparse and randomly connected neurons described by the Izhikevich model.
If no hot spot is identified and the mitotic figures are sparse and randomly scattered, then the count should begin in a field containing a mitosis, then extended to immediately adjacent nonoverlapping high-power fields until a 1 mm area of tissue containing melanoma is assessed.
Surface particles of 3DD and 6DD were between 100 and 400 nm (Figure2, B5 and C5) and clustered, but they were sparse and distributed randomly (Figure2, B3, B4, C3, and C4).
Operationally, this is accomplished by randomly seeding sparse regressions that define candidate modules with genes.
We simulate 30 peak cluster centroids in the dense area and 20 in the sparse area, all picked randomly and uniformly distributed in the respective area.
Instead of capturing all data points, this technique records data randomly, drawing a sparse image.
There were sparse apoptotic cells that are randomly distributed in the cochlea of non-treated rats.
We captured 39 images with cracks randomly, and use sparse representation method to extract cracks.
To show the difference, we tested our solution on a second network, denoted as the sparse network, consisting of 100 randomly placed sensors.
