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It utilizes MATLAB sparse matrix representation for good performance and to reduce the amount of memory needed.
The unified matrix representation for precise merging is presented and the approximate merging curve is further derived based on matrix operation.
COurutational examethodare gisen which demonsaidto the accuracy and cost of the new scheme relative to existing schemes.
We present a matrix representation for the fast Gauss transform (FGT) originally proposed by Greengard and Strain.
They develop a matrix representation for the infinitesimal generator which on time scaling and matrix exponentiation delivers transition probability elements for the time scales involved.
In this section we introduce the matrix representation for the λ-Daehee numbers and polynomials of higher order given by Kim et al. [9].
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We present several matrix representations for fast computation within this recursion, using bases with controlled space spatial-frequency localization.
Moreover, new matrix representations for these numbers and polynomials are obtained.
As represented in Figure5, h m 1r 1 is not required at the RLS input, therefore is not necessary in the following matrix representation of the signals for modeling the interference cancellation process.
Furthermore, we introduce the matrix representation of some results for higher-order Daehee numbers and polynomials obtained by Kim et al. [8] in terms of Stirling numbers, Nörlund numbers, and Bernoulli numbers of higher order and give simple and short proofs of these results.
The matrix representation of (5) is useful for understanding of the receiver depicted in Figure 3. Figure 3 Basic block diagram of the GFDM receiver.
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