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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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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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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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Method for Introducing Zeolites and MCM-41 into Polypropylene Melt-Blown Nonwovens

] Gregis, G., Schaefer, S., Sanchez, J. B., Fierro, V., Berger, F., Bezverkhy, I., Weber, G., Bellat, J. P., Celzard, A. (2017). Characterization of materials toward toluene traces detection for air quality monitoring and lung cancer diagnosis. Materials Chemistry and Physics, 192, 374-382. [32] EN 13274-3: 2008 Respiratory protective devices. Methods of tests. Determination of breathing resistance. [33] EN 13274-7: 2008 Respiratory protective devices. Methods of tests. Determination of particle filter penetration. [34] EN 14387: 2004+AC:2004

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The effect of dynamic operating conditions on nano-particle emissions from a light-duty diesel engine applicable to prime and auxiliary machines on marine vessels


This study presents the nano-sized particle emission characteristics from a small turbocharged common rail diesel engine applicable to prime and auxiliary machines on marine vessels. The experiments were conducted under dynamic engine operating conditions, such as steady-state, cold start, and transient conditions. The particle number and size distributions were analyzed with a high resolution PM analyzer. The diesel oxidation catalyst (DOC) had an insignificant effect on the reduction in particle number, but particle number emissions were drastically reduced by 3 to 4 orders of magnitude downstream of the diesel particulate filter (DPF) at various steady conditions. Under high speed and load conditions, the particle filtering efficiency was decreased by the partial combustion of trapped particles inside the DPF because of the high exhaust temperature caused by the increased particle number concentration. Retarded fuel injection timing and higher EGR rates led to increased particle number emissions. As the temperature inside the DPF increased from 25 °C to 300 °C, the peak particle number level was reduced by 70% compared to cold start conditions. High levels of nucleation mode particle generation were found in the deceleration phases during the transient tests.

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Synthesizing of models for identification of teletraffic Markov chains by artificial neural networks and decision tree method

Mining , Sydney, NSW, pp.334-343, 2010. [6] D. Lowd and J. Davis, “Improving Markov Network Structure Learning Using Decision Trees”, Journal of Machine Learning Research (JMLR) , no.15, pp.501-532, 2014. [7] B. Lakshminarayanan, D. Roy and Y. The, “Top-Down Particle Filtering for Bayesian Decision Trees”, Proceedings of the 30th International Conference on Machine Learning , Atlanta, Georgia, USA, JMLR: W&CP, vol.28, pp.1-9, 2013. [8] D. Bacciu, “Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data”, Proceedings of the IEEE

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Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software

University. [10] Kuryło, P., Cyganiuk, J., Tertel, E., Frankovský, P. (2016). Machine vision investigate the trajectory of the motion human body – review of the methods. Acta Mechatronica , 1 (2), 7–13. [11] Deutscher, J., Blake, A., Reid, I. (2000). Articulated body motion capture by annealed particle filtering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 126–133. [12] Schmidt, J., Fritsch, J., Kwolek, B. (2006). Kernel particle filter for real-time 3D body tracking in monocular color images. In Proceedings of

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