Modelling complexity in the upper atmosphere using GPS data Chris Budd, Cathryn Mitchell, Paul Spencer Bath Institute for Complex Systems, University of.
P.Colas D.Attié, W.Wang, I.Giomataris CEA/Saclay, France K.Fujii T.Fusayasu, K.Kato, M. Kobayashi, T. Matsuda, O. Nitoh, A Sugiyama, T. Watanabe KEK/IPNS,
Ekaterina Klimova Ekaterina TECHNIQUE OF DATA ASSIMILATION ON THE BASIS OF THE KALMAN FILTER Institute of Computational Technologies SB RAS [email protected].
Hanoi Regional Forecasting Support Centre SUSTAINING NATIONAL METEOROLOGICAL SERVICES – STRENGTHENING WMO REGIONAL AND GLOBAL CENTERS Presented by Pham.
Computing in High Energy Physics John Apostolakis SoFTware for Physics Group, PH Dep, CERN v0.98.3 2014.03.10 [email protected].
RAINFALL PREDICTION USING STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED REGION IN INDONESIA INTERNATIONAL WORKSHOP ON IMPLEMENTATION OF DIGITIZATION HISTORICAL.
Caught in Motion By: Eric Hunt-Schroeder EE275 – Final Project - Spring 2012.
Object Inter-Camera Tracking with non- overlapping views: A new dynamic approach Trevor Montcalm Bubaker Boufama.
Sajad Saeedi G. University of new Brunswick SUMMER 2010 An Introduction to the Kalman Filter.
TOWARD DYNAMIC GRASP ACQUISITION: THE G-SLAM PROBLEM Li (Emma) Zhang and Jeff Trinkle Department of Computer Science, Rensselaer Polytechnic Institute.
Communications & Multimedia Signal Processing Formant Based Synthesizer Qin Yan Communication & Multimedia Signal Processing Group Dept of Electronic.
Tracking using the Kalman Filter. Point Tracking Estimate the location of a given point along a sequence of images. (x 0,y 0 ) (x n,y n )