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A vector is considered sparse if a small number of its elements contain a large proportion of its energy.
To derive the adaptive step size, different from the traditional VSS-NLMS algorithm in [11], two practical problems are considered: sparse channel model and tractable independent assumptions [12].
bits with probability p to be 1 and probability 1−p to be 0. When p≠0.5, the source is considered sparse or compressible.
Minimization of the ℓ1-norm is one of the most logical methods for estimation of the sources as long as the signals can be considered sparse.
Due to the low occupancy of many communication systems, whose frequency support is much smaller than the band limit, the spectrum can be considered sparse and the uniform sampling becomes very redundant.
𝔼 is considered sparse if not all possible pairs exist in the input graph (∃ i, j∈𝕍: e ij ∉𝔼).
Similar(52)
We first consider sparse network topologies.
Then we consider sparse signal recovery problem with the random basis matrix in the presence of noise.
Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners.
Therefore, in this paper, we consider sparse signals with the prior knowledge of K-cluster-valued coefficients, either in the canonical (pixel) domain or in the wavelet domain.
In this paper, we consider sparse sediments under relatively slow, unidirectional water flow; we present a CA model and discuss the relation between bed states and sand flux.
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CEO of Professional Science Editing for Scientists @ prosciediting.com