For watermarking one bit, the computational load is O(3 × L), but in [12], the cost for watermarking one bit is O(3 × L × M), M (which is much bigger than 1) reflecting the times of iterative computation.
If the sum of H T (m - τ, l) with any τ where 0 ≤ m ≤ M - 1 is bigger than zero, the system skips the τ while searching the pitch period because some of x(m - τ, l) with the τ may contain transient noise component.
For example, in the case that a 1 performs better than a 2 and thus, Q m (a 1) is definitely bigger than Q m (a 2), which results in Q m (a 1)−Q m (a 2)>0.
Note that (M) must be bigger than (N) and error estimation is found using the residual function (R_{N} left( x right)).
For the cDNA arrays, we select genes where the M-values was bigger than 0.3 in at least 4 arrays and in Affymetrix experiment we keep genes whose expression profile had range bigger than 0.5.
Note that typically the number of vertices in the maximal cliques will be at most ms, but some of them can be slightly bigger than ms. Thus, as with DACTAL, ms is a target maximal value, and not a strict upper bound on the size of any subset we analyze.
But one could also consider the set ℘(M) of all subsets of M, and by Cantor's theorem the cardinality of ℘(M) ought to be strictly bigger than the purported maximum m: contradiction.
However, if m is big, we have to offer a big C which is bigger than m.
For spans bigger than 1000 m, the error tends to stabilize, this is due to the fact that straight cable approximation follows the "secant" approach of the real cable deformation.
