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Simulation and Analysis of Particle Filter Based Slam System

.: Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain, 2005, pp. 2432-2437. [8] Hartmann J., Klussendorff J., Maehle E.: A comparison of feature descriptors for visual SLAM, European Conference on Mobile Robots 2013. [9] Howard A.: Multi-robot Simultaneous Localization and Mapping using Particle Filters. Proceedings of the 2005 IEEE International Conference on Robotics and Automation. [10] Leonard J

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Indoor Navigation Using Particle Filter and Sensor Fusion

References [1] Deinzer F., Derichs C., Niemann H., Denzler J., A Framework for Actively Selecting Viewpoints in Object Recognition, International Journal of Pattern Recognition and Artificial Intelligence, 2009, Vol. 23, No. 4, pp. 765-799. [2] Doucet A., Johansen A. M., A tutorial on particle filtering and smoothing: Fifteen years later, Hand-book of Nonlinear Filtering, D. Crisan and B. Rozovsky eds. Oxford, UK, Oxford University Press, 2009. [3] Evennou F., Marx F., Novakov E., Map-aided indoor mobile

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Nonlinear Vibration Signal Tracking of Large Offshore Bridge Stayed Cable Based on Particle Filter

Distributed Hydrological Model Using the Particle Filter. Remote Sensing, Vol.5, No.11, pp.5825-5850, 2013. 4. Han Gao and Jingwen Li . “Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter- Based Track-Before-Detect Algorithm”. Sensors, Vol.14, No.6, pp. 10829-10845, 2014. 5. Hong weiming, Gao lili, “Vibration Analysis of Nonlinear Coupling System of cable-stayed beam”, Journal of Vibration Engineering, Vol.21, No.2, pp.115-119, 2008. 6. K.B.Waghulde, Dr. Bimlesh Kumar, “Vibration Analysis of

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Comparison of Estimation Accuracy of EKF, UKF and PF Filters

References [1] Arulampalam S., Gordon N., Ristic B., Beyond the Kalman Filter. Particie Fliters for tracking applications, Artech House, London 2004. [2] Cappe O., Douc R., Moulines E., Comparison of resampling schemes for particle filtering, 4th International Symposium on Image and Signal Processing and Anlysis, 2005. [3] Doucet A., de Freitas N., Van der Merwe R., Wan E. A., The unscented particle filter, Cambridge University Engineering Department, Cambridge 2000. [4] Doucet A., Gordon N. J

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The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

). Inference in Hidden Markov Models. Springer. [7] Fraser, A.M. (2008). Hidden Markov Models and Dynamical Systems (1st ed.). Society for Industrial and Applied Mathematics. [8] Doucet, A., de Freitas, N., Gordon, N. (2001).Sequential Monte Carlo Methods in Practice.Springer. [9] Douc, R., Cappé, O., Moulines, E. (2005). Comparison of resampling schemes for particle filtering. In Image and Signal Processing and Analysis : 4th International Symposium (ISPA 2005), 15-17 September 2005.IEEE, 64-69. [10] Daum, F

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Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

References [1] FLUERASU, A.—JARDAK, N.—VERVISCH-PICOIS, A.— SAMAMA, N.: Gnss Repeater based Approach for Indoor Positioning: Current Status, in European Navigation Conference, Global Navigation Satellite Systems, 2009. [2] KEUNHO, Y.—DAIJIN, K.: Robust Location Tracking using a Dual Layer Particle Filter, Pervasive and Mobile Computing 3 (03 2007), 209–232. [3] SAVARESE, C.—RABAEY, J. M.—BEUTEL, J.: Location in Distributed ad-hoc Wireless Sensor Networks, in Proc. IEEE ICASSP ‘01, vol. 4, 2001, pp. 2037–2040. [4] PATWARI, N.—HERO, A. O.—PERKINS, M

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Structural changes in the Czech, Slovak and euro area economies during the Great Recession


The goal of this paper is to identify and compare the most important changes in the structure of the Czech economy, as a small open economy with independent monetary policy, the Slovak economy, as a small open economy that entered monetary union, and the economy of the euro area, which has a common monetary policy, during the turbulent period of the Great Recession, the subsequent anaemic recovery and recent disinflationary period. Structural changes are identified with the help of nonlinear dynamic stochastic models of general equilibrium with time-varying parameters. The model parameters are estimated using Bayesian methods and a nonlinear particle filter. The results confirm the similarity of the Czech and Slovak economies and show that in certain respects the structure of the Czech economy might be closer to that of the euro area than that of Slovakia. The time-varying estimates reveal many similarities between the parameter changes in the Czech economy and those in the euro area. In Slovakia, the situation during the Great Recession was dominated by the country’s adoption of the euro, which caused large deviations in its Calvo parameters.

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PN-Emissions with Increased Lube Oil Consumption of GDI Car with/without GPF

particle filters appropriate measures? Proceedings of the 16th ETH Conference on Combustion Generated Nanoparticles 2012. [8] Buchholz, B. A., Dibble R. W., Rich, D., Cheng, A. S. (ed)., Quantifying the contribution of lubrication oil carbon to particulate emissions from a diesel engine, SAE Technical Paper 2003-01-1987. [9] Sonntag, D. B., Bailey, Ch. R., Fulper, C. R., Baldauf, R. W., Contribution of Lubricating Oil to Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City , Environment Science & Technology, 27, 2012. [10

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Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf

-45 7. Li Zhongbing,Zhang Huanren,2011. Extended Kalman Filter Enhanced Ship Electrical Propulsion System, Navigation of China, 34, 45-50 8. Lu Wenbin Yao Wenxi Lu Zhengyu,2013. Speed Sensorless Vector Control with Improved Closed-Loop Flux Observer for Induction Machines. Transactions of China Electro Technical Society,28, 148-153. 9. R.V.Merwe,A.Douvet,N.De.Freitas et al,2000. The unscented particle filter.Technical Report CUED/F@ INPENG/ TR380:Cambridge University Engi -neering Department. 10. Sui Shu-lin,Yao Wen

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Hand gesture recognition based on free-form contours and probabilistic inference

References Arulampalam, M. S., Maskell, S. and Gordon, N. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing   50 (2): 174-188. Baum, L., Petrie, T., Soules, G. and Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Annal Mathematics Statistics   41 (1): 164-171. Emambakhsh, M., Ebrahimnezhad, H. and Sedaaghi, M. H. (2010

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