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References 1. Anand, A., Ramadurai, G. and Vanajakshi, L. (2013). Data Fusion Based Traffic Density Estimation and Prediction, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, accepted author version, DOI: 10.1080/15472450.2013.806844 2. Bachmann, C. (2011). Multi-Sensor Data Fusion for Traffic Speed and Travel Time Estimation , MSc Thesis, Toronto 3. Böker, G., Lunze, J. (2002). Stability and performance of switching Kalman filters, International Journal of Control , 75(16/17): 1269-1281 4. Claudel, C. G., Bayen, A. M

Control Scheme Based on the Interacting Multiple Model (IMM) Estimation. Journal of Mechanical Science & Technology, 2016, 30 (6):2759-2767. 21. Jin B., Jiu B., Su T., Switched Kalman Filter-Interacting Multiple Model Algorithm Based on Optimal Autoregressive Model for Manoeuvring Target Tracking. IET Radar Sonar and Navigation, 2015, 9(2): 199-209. 22. Yousef, M. T., Ali, H. E. I., Habashy, S. M., Adaptive Controller based PSO with Virtual Sensor for Obstacle Avoidance in Dynamic Environments, Radio Science Conference, 2014, 228-235. 23. Liu Y. Ch., Bucknall R., Path

, A. and Terzopoulos, D. (1998). Snakes. Active contour models, International Journal of Computer Vision   1 (4): 321-331. Marnik, J. (2003). The recognition of characters from the Polish finger alphabet, Technical report , StatSoft Polska, Cracow Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning , Ph.D. thesis, University of California, Berkeley, CA. Murphy, K. P. (1998). Switching Kalman filters, Technical report , DEC/Compaq Cambridge Research Labs, Cambridge

signals by using switching Kalman filters. Elhaj et al . [ 24 ] reduced features of ECG signals by exploiting principal component and independent component analysis. Arrhythmia classification is made by exploiting SVM and NNs. In this paper, an efficient approach is proposed in order to perform ECG arrhythmia classification using linear and nonlinear feature extraction methods and an entropy-based feature selection method. Since ECG beat classification strongly depends on feature extraction stage, DWT, as an efficient tool for analyzing nonstationary signals, is used