Abstract: The relative neighborhood graph of a collection of objects assigns an edge to a pair of objects (A, B), provided that no other object is closer to both A and B, than A and B are to each other. This graph was originally proposed for the purpose of extracting the low-level visual perceptual structure of two-dimensional dot patterns. During the past thirty-four years the relative neighborhood graph has been applied to a multiplicity of different disciplines, and sometimes to several problems within a single discipline. This paper provides a review of some of these applications, including: wireless network communications, archaeological network analysis, grid typification in cartography, data mining for geographic information systems, shape analysis, image morphology, polygon decomposition, the extraction of primal sketches in computer vision, the reduction of the size of the training set in instance-based machine learning, the design of non-parametric decision rules, support-vector machin

• Publication Date: 02-Jun-2014
• DOI: 10.15224/978-1-63248-010-1-07
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Abstract: In this work, four different sets of data traffic have been generated from fractional brownian motion (fBm) to estimate true values of Hurst exponent boundaries in order to determine the degree of self-similarity in terms of long range dependence (LRD). One-dimensional multilevel wavelet decomposition and filtering algorithm is applied to filter fractional Gaussian noise (fGn) in the fBm generated. Autocorrelation function (ACF) and fast Fourier transform (FFT) energy spectrum is used to validate the result of the filtering effect. The result of the filtering process revealed that fGn in the fBm is de-noised successfully as the coefficient of ACF grow above zero and energy rate in the FFT- spectrum increases tremendously.

• Publication Date: 02-Jun-2014
• DOI: 10.15224/978-1-63248-010-1-08
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