Sentence examples for the array dimension from inspiring English sources

In practice, the array dimension and the effective window length are both finite.

Interestingly, in Theorem 1 the window length and the array dimension jointly tend to infinity where lim M, N → ∞ M N = c > 0.

We emphasize that (6) and (7) give the exact distribution in the asymptotic regime as M,N → ∞ with M N → c > 0. Since in practice, the array dimension and/or sample size are usually finite numbers, this method gives a deterministic approximation for the actual sample eigenvalue distribution.

Consider a window w i >0 of length N and denote W N = diag ( w 1, …, w N ) ∈ R N × N with a converging distribution, i.e., lim N → ∞ F W N = F W. We assume that the sample size N is much larger than the array dimension M, i.e., c is small.

For c < 1, i.e., where the length of the window is more than the array dimension, lim m → ± ∞ z ( m ) = 0 ±, thus as Figure 1 shows, dz ( m ) dm = 0 has at least two solutions which we denote them m u ∈ − 1 c σ 2 max ( w i ), 0 and m l ∈ 0,∞).

The new techniques allow electrode and the array dimensions to be customized to the intended application.

No limits are placed on the array dimensions in this library, so the number of atoms that can be displayed is technically only limited by available memory.

Hu et al. can use parts of arrays in the assignment to the memory layers [23]: their illustrative example suggests cuts along one of the array dimensions as the main partitioning heuristic.

The invention of the multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant techniques (ESPRIT) algorithms enabled performance exceeding the resolution limit prescribed by the array dimensions (so-called "super-resolution").

It can be seen that as c 0 decreases (i.e., as the forgetting factor p increases for a fixed value of array dimension M) the spectral distribution tends to concentrate around the true eigenvalues.

Defining the effective length of a window, we have approximated the distribution of the eigenvalues in the weighted window case with that of a Wishart matrix, when the number of samples are much more than array dimension.