PPT-Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors

August 31 2017 G Cerati 4 P Elmer 3 S Krutelyov 1 S Lantz 2 M Lefebvre 3 M Masciovecchio 1 K McDermott 2 D Riley 2 M Tadel 1 P Wittich 2 . 10 req SMPTE timecode Time of day BWAV stamp but no sync in Slave and freewheel Adjustable Playback SpeedTrainer Yes Yes Yes Yes Yes Yes Effects Reverb Noise reduction Reverb Guitar amp mod els effects reverb Stereo Microphone Pattern AB Omni XY or A Kalman Filter. & LADAR Scans. State Space Representation. Continuous State Space Model. Commonly written . . Discrete . State Space Model. Commonly . written . .  . Discrete State Space Observer or Estimator. Pieter . Abbeel. UC Berkeley EECS. Many . slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . 1.1 What Is "Probability"?. 1.2 The Additive Law. 1.3 Conditional Probability and Independence. 1.4 Permutations and Combinations. 1.5 Continuous Random Variables. 1.6 . Countability. and Measure Theory. Filter Example. Rudolf E. Kalman. b. 1930. Hungary. Kalman Filter. NASA Ames. 1960. National Medal of Science (2009). Actions and Observations . Through Time. Belief(x. t. ). (using all evidence to date). Architecture for creating partially reconfigurable embedded systems. Module communication. Processor. – Fast Simplex Links (FSL). Intermodule. – MACS Network on Chip. Highly parametric. Number of PR regions. obot. ics. B. ay. e. s. . Fil. t. er Im. p. lemen. t. a. t. i. o. ns. Gaussian fil. t. ers. Markov . . . Kalman. . Fil. t. er. . L. ocaliza. t. ion. Mark. o. v. . lo. ca. liz. at. io. n. localization starting . By . Kamran. Arjomand. Outline. Background. Holographic Imaging. Acquire Hologram. Preprocessing. Numerical Reconstruction. Particle Extraction. Velocity Extraction. Turbulence. Minimum . Goals. Holographic Simulation with Gaussian Blur. Filter. Presenter: . Yufan. Liu. yliu33@kent.edu. November 17th, 2011. 1. Outline. Background. Definition. Applications. Processes. Example. Conclusion. 2. Low and high pass filters. Low pass filter allows passing low frequency signals. It can be used to filter out the gravity. . Contact MBM now for constructing Cycling tracks in Dubai, UAE at your location at the best price . http://mbmauh.ae/ Contact MBM now for constructing Cycling tracks in Dubai, UAE at your location at the best price . http://mbmauh.ae/ and. Optimal Adaptation To A Changing Body. (. Koerding. , Tenenbaum, . Shadmehr. ). Tracking. {Cars, people} in {video images, GPS}. Observations via sensors are noisy. Recover true position. Temporal task. Overview. Introduction. Purpose. Implementation. Simple Example Problem. Extended . Kalman. Filters. Conclusion. Real World Examples. Introduction. Optimal Estimator. Recursive Computation. Good when noise follows Gaussian distribution. Max Feng. Amit . Bashyal. 12/5/16. Kalman Filter and Particle Filter. 1. Kalman. Filter. 12/5/16. Kalman Filter and Particle Filter. 2. When Can . Kalman. Filters Help?. You can get measurements of a situation at a constant rate..