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A vector is considered sparse if a small number of its elements contain a large proportion of its energy.
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.
𝔼 is considered sparse if not all possible pairs exist in the input graph (∃ i, j∈𝕍: e ij ∉𝔼).
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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].
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.
Each row in Figure 1c is considered as the sparse signal for our study.
Each row in Figure 3c is considered as a sparse signal in this article.
In "Generating sparse networks" section, the problem of making resulting networks sparse is considered.
However, the power transmitted by small cells in the sleep mode is assumed to be negligible, even when the DL signalling based scheme is considered, due to the sparse periodicity of DRS transmission times in that case.
x is considered to be K-sparse with respect to Ψ. Then Ψ is called a sparsifying matrix.
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