Sentence examples for sparsity values from inspiring English sources

Furthermore, a local measure sparsity is shown to render meaningful information about the variation of a signal along time, by generating a set of local sparsity values which is much smaller than the dimension of the signal.

To verify our analysis, we compared the rate of convergence with varying sparsity values k and Φ t); the numerical results are shown in Fig. 2. Fig. 2 Comparison of the rates of convergence for different dimensions of measurement matrices (M = 128, N = 256, ρ = 0.8).

Our experiments and comparisons were implemented in Matlab R2010b on an Intel i7 4600 laptop with 8 GB of memory, running Windows 8. To compare the performance with the matrices Φ t), we measured the rate of convergence and the probability of an exact reconstruction for different sparsity values k and for different numbers of measurements.

The different DTI scanning parameters (higher number of diffusion directions and better spatial resolution for Group 2) for both groups resulted in Group 2 showing higher Sigma values for the sweep range than Group 1 for comparable sparsity values.

For a different sparsity value k, the curves of probability of exact reconstruction are shown in Fig. 1.

To measure the probability of exact reconstruction, we performed 500 trials for a single sparsity value of k.

The optimal wavelet packet node can be selected by visually inspecting the largest sparsity value of the wavelet packet coefficients obtained from all wavelet packet nodes.

Whereas the sparsity value k is relatively small, less iteration are needed, and the rate of convergence is still fast, despite reducing the dimensions in the measurement matrix.

If the original sparse vector x has a comparably large sparsity value k, it needs more iterations to derive the sparse solutions.

Furthermore, by contrasting frames (a) and (b), we can see that when sparsity value k approaches the limit value of M/2, (a) still has a higher probability of reconstruction than (b).

As shown in Fig. 1, when the sparsity value k is relatively small, namely k ≤ 20, the probability of an exact reconstruction remains almost 100%, regardless of whether the dimensions of the measurement matrix are reduced.