Feature extraction using wavelet and fractal
Pattern Recognition Letters
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Web-Based Application for Traffic Anomaly Detection Algorithm
ICIW '07 Proceedings of the Second International Conference on Internet and Web Applications and Services
Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Neural Network Ensembles Using Clustering Ensemble and Genetic Algorithm
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Customizable FPGA IP core implementation of a general-purpose genetic algorithm engine
IEEE Transactions on Evolutionary Computation
A nonlinear time-frequency analysis method
IEEE Transactions on Signal Processing
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Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varri@?s and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm.