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We consider a set of transmitters broadcasting simultaneously on the same frequency under the SINR model. Transmission power may vary from one transmitter to another, and a transmitters signal strength at a given point is modeled by the transmitters power divided by some constant power $alpha$ of the distance it traveled. Roughly, a receiver at a given location can hear a specific transmitter only if the transmitters signal is stronger by a specified ratio than the signals of all other transmitters combined. An SINR query is to determine whether a receiver at a given location can hear any transmitter, and if yes, which one. An approximate answer to an SINR query is such that one gets a definite YES or definite NO, when the ratio between the strongest signal and all other signals combined is well above or well below the reception threshold, while the answer in the intermediate range is allowed to be either YES or NO. We describe compact data structures that support approximate SINR queries in the plane in a dynamic context, i.e., where transmitters may be inserted and deleted over time. We distinguish between two main variants --- uniform power and non-uniform power. In both variants the preprocessing time is $O(n mathop{textrm{polylog}} n)$ and the amortized update time is $O(mathop{textrm{polylog}} n)$, while the query time is $O(mathop{textrm{polylog}} n)$ for uniform power, and randomized time $O(sqrt{n} mathop{textrm{polylog}} n)$ with high probability for non-uniform power. Finally, we observe that in the static context the latter data structure can be implemented differently, so that the query time is also $O(mathop{textrm{polylog}} n)$, thus significantly improving all previous results for this problem.
The Frechet distance is a popular similarity measure between curves. For some applications, it is desirable to match the curves under translation before computing the Frechet distance between them. This variant is called the Translation Invariant Fre
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Let $mathcal{P}$ be a polygonal domain of $h$ holes and $n$ vertices. We study the problem of constructing a data structure that can compute a shortest path between $s$ and $t$ in $mathcal{P}$ under the $L_1$ metric for any two query points $s$ and $
We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $mathbb{R}^d$, where the distance between two curves is measured using the discrete Frechet distance. We design data structures in an e