Recently the computation of the mutual information for regression tasks has been proposed so this paper presents a new application of the concept of mutual information not to select the variables but to decide which prototypes should belong to the training data set in regression problems.
The approach is illustrated by the computation of the execution time of a task graph.
The major part of the CPU cost (N equivalent flow solutions) is for the computation of the gradient but, fortunately, this task is directly amenable to parallelization.
When the matrix A is non-normal, however, the computation of the pseudospectrum becomes a very expensive computational task.
The computation of the correlator outputs is the most demanding task of a GNSS receiver.
(2) In this work, the most time consuming task was, by far, the computation of the generalized co-occurrence matrix, which mainly depends on spectral vector-pairs distances and a large numbers of spectral bands.
Some of them also exploit SIMD parallelism to further accelerate the computation of a single task (i.e., a read) using SSE instructions.
The computation time of the task increases exponentially with n.
That is, we simply have to consider the count of flops of the computation of Ψ and the search task in the coarse CFO estimation, and the computational complexity for the proposed scheme is thus reduced to: text{Flop}_{text{Proposed}}approx N^{2}N_{r} + mu Nleft[1 +N(1 +N_{r})right].
Also, a design methodology was devised for the nonpreemptive EDF scheduling, based on the computation of bounds on the periodic task durations.
