Indoor positioning systems is an actively researched area of computer science. Various techniques, methods and applications were developed in the last decade. This paper gives a brief comparison of trilateriation and fingerprinting techniques. Received signal strength of WiFi access points were used during the tests. Standard, genetic algorithm and simulated annealing based trilateriation metho...

Finding the closest point in a high-dimensional space is a problem that often occurs in pattern recognition. Unfortunately, the complexity of most known search algorithms grows exponentially with dimension, which makes them unsuitable for high dimensions. However, for most applications, the closest point is of interest only if it is closer than some pre-defined distance. In the article ”A Simpl...

A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose ...

Reverse nearest neighbor queries are defined as follows: Given an input point-set P , and a query point q, find all the points p in P whose nearest point in P ∪ {q} \ {p} is q. We give a data structure to answer reverse nearest neighbor queries in fixed-dimensional Euclidean space. Our data structure uses O(n) space, its preprocessing time is O(n log n), and its query time is O(log n).

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

Reverse nearest neighbor queries are defined as follows: Given an input point-set P , and a query point q, find all the points p in P whose nearest point in P ∪ {q} \ {p} is q. We give a data structure to answer reverse nearest neighbor queries in fixed-dimensional Euclidean space. Our data structure uses O(n) space, its preprocessing time is O(n log n), and its query time is O(log n).

For an arbitrary point of a homogeneous Poisson point process in a d-dimen-siona! Euclidean space, consider the number of Poisson points that have that given point as their r-th nearest neighbor (r = 1,2,...). It is shown that as d tends to infinity, these nearest neighbor counts (r = 1,2,...) are tid asymptotically Poisson with mean 1. The proof relies on Renyi's characterization of Poisson pr...

This contribution focuses on the matching of local features between images. Given a set of query descriptors and a database of candidate descriptors, the goal is to decide which ones should be matched. This is a crucial issue, since the matching procedure is often a preliminary step for object detection, scene identification or image matching. In practice, this matching step is often reduced to...

We suggest a novel distance-based method for the determination of phylogenetic trees. It is based on multidimensional scaling and Euclidean Steiner trees in high-dimensional spaces. Preliminary computational experience shows that the use of Euclidean Steiner trees for finding phylogenetic trees is a viable approach. Experiments also indicate that the new method is comparable with results produc...