Small weights are assigned to the pixels where ghost artifacts occur to exclude the ghosting regions from the reconstruction process.
Then, in the correction phase, the weight of each particle is reevaluated based on the latest sensory information available, so that particles with small weights are eliminated (resampling process).
The basic idea behind the proposed WLS approaches is that smaller weights are given to the measurements which are likely to be biased (based on the MPC information), as opposed to variance-based WLS techniques in the literature.
When very large or very small weights are obtained for some observations, the corresponding variance of the estimates increase, and thus, cautionary measures should be used in interpreting the data.
This is consistent with the methodology of the iterative reweighted ℓ1 minimization, i.e., large weights are assigned to the entries whose indices are more likely to be outside of the row support, whereas small weights are assigned to the entries whose indices are inside of the row support [12, 14, 16].
To increase the reliability of the color distribution when boundary pixels resembles the background of the occluded, smaller weights are assigned to the pixels that are further away from the region center by employing a weighting function: k(r)=left{begin{array}{l}1-{r}^2kern1em :kern1emathrm{otherwise}end{array}right{otherwise}end{array}right.
For such interactions, smaller weights are given to domain pairs involved.
Then for a protein contained in I1 and an interaction centering on the protein, smaller weights are given to domain pairs involved in the interaction.
The smallest weight is 0.67 and the largest is 1.26, but in most cases the weights are close to 1, and the calculated marginal effects are very similar to those reported in Table 3 using equal weighting.
Initially, small weights were also used to straighten wires (Fig. 8). Figure 9 shows the wiring of a mobile system, Fig. 10 for a dental system.
