Sentence examples for distribution of tags from inspiring English sources

For large values of, the results obtained are similar to that obtained for a uniform distribution of tags.

We begin by analyzing the effect of overlap factor for a uniform distribution of tags in the area.

The geometric distribution parameter is varied from to, simulating conditions varying from a highly clustered cell to a uniform distribution of tags in all cells.

Next we consider the case where instead of varying overlap factor, the distribution of tags varies and is not a uniform distribution anymore but a geometric distribution.

The value of depends on the overlap between readers in terms of tags as well as the distribution of tags in the area.

Finally, we plotted the distribution of tags' reuse occurrences per number of tags (see Fig. 7), as well as the distribution of tags reuse occurrences per number of users (see Fig. 8). Figure 7 demonstrates a long-tail scheme, namely there are many tags which have been reused few times but only a small set of tags which have been reused many times.

A previous study [21] showed that AQS requires lighter tag specifications although its performance may be affected by the distribution of tag IDs.

The ABS may have a shorter identification delay compared to AQS, in which performance depends on the distribution of tag IDs [21].

In this article, we analytically investigate this problem and propose a low cost enhancement to ensure the full-range distribution of tag values for each data, hence effectively removing the vulnerability of the original design.

These works mainly concern about how to minimize the posterior expected value of a risk function (i.e., the posterior expected risk), such as using Bayesian rule to update the probability distribution of tag quantity and adjusting the frame size.

Thus, if the probability distribution of tag population, i.e., P ( n → ) is known, the improvement achieved by our scheduler over the reference scheduler is Δ = ∑ ∀ n → ( Φ n → ( n → ) - Φ n ∗ → ( n → ) ) P ( n → ).