We show that the complexity of the recently introduced medoid-shift algorithm in clustering N points is O(N), with a small constant, if the underlying distance is Euclidean. This makes medoid shift considerably faster than mean shift, contrarily to what previously believed. We then exploit kernel methods to extend both mean shift and the improved medoid shift to a large family of distances, wit...

A network is designed to have maximum area coverage with less energy consumption. Energy and coverage both are the current issue of wireless sensor network. The performance of wireless sensor network is affected by these two issues. Several methods are developed to make a network efficient. In this paper PSO Algorithm has been reviewed to resolve the challenges and issues of wireless sensor net...

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had been proposed as a clustering algorithm. ...

Wireless sensor networks (WSNs) usually employ different ranging techniques to measure the distance between an unknown node and its neighboring anchor nodes, and based on the measured distance to estimate the location of the unknown node. In its operation, a range-based localization scheme uses either trilateration or multilateration algorithms to obtain such range information. To trim down the...

The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to av...

A method for shoreline extraction from integrated LiDAR point cloud data and aerial orthophotos is presented. First, a Mean Shift Algorithm is used for LiDAR point segmentation. The horizontal position and elevation of the LiDAR point plus color information obtained from the corresponding orthophoto are used as the point features in the Mean Shift Algorithm. Due to the homogenous nature of the ...

The accuracy of the multiplication depends on the precision of the multiplier. The variable precision floating point multiplier will have more accuracy when compared with the fixed precision multiplier. In this paper a variable precision floating point multiplier is considered. An effective BIST test pattern generator for variable precision floating point multiplier is proposed. A BIST TPG cons...

In this paper, we present some major algorithmic improvements to fast correlation attacks. In previous articles about fast correlations, algorithmics never was the main topic. Instead, the authors of these articles were usually addressing theoretical issues in order to get better attacks. This viewpoint has produced a long sequence of increasingly successful attacks against stream ciphers, whic...

This paper presents a new effective Built-In Self Test (BIST) scheme that achieves 100% fault coverage with low area overhead, and without any modification of the circuit under test (CUT), i.e., no test point insertion. The set of patterns generated by a pseudo-random pattern generator, e.g. a Linear Feedback Shift Register (LFSR), is transformed into a new set of patterns that provides the des...

This paper focuses on covariant point-shifts of point processes on topological groups, which map points of a point process to other points of the point process in a translation invariant way. Foliations and connected components generated by point-shifts are studied, and the cardinality classification of connected components, previously known on Euclidean space, is generalized to unimodular grou...