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

Author : kittie-lecroy | Published Date : 2018-03-12

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

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

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 . edu Kalman and Extended Kalman Filtering brPage 2br Kalman Filter Introduction Recursive LS RLS was for static data estimate the signal better and better as more and more data comes in eg estimating the mean intensity of an object from a video sequen vdveldtuvanl Rob van Nieuwpoort Vrije Universiteit Amsterdam The Netherlands rvvannieuwpoortvunl Ana Lucia Varbanescu Technische Universiteit Delft The Netherlands alvarbanescutudelftnl Chris Jesshope Universiteit van Amsterdam The Netherlands crjess Kalman Filters. Slide credits: Wolfram Burgard, Dieter Fox, Cyrill Stachniss, Giorgio Grisetti, Maren Bennewitz, Christian Plagemann, Dirk Haehnel, Mike Montemerlo, Nick Roy, Kai Arras, Patrick Pfaff and others. 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. Université. . Libre. de . Bruxelles. Kalman. Filter in . MarlinTPC. MarlinTPC. installation. MarlinTPC. is based on the Marlin framework:. Based on LCIO data format. Usage of successive « processors ». Justin . Manweiler. , . Ashutosh. Dhekne, . Romit. Roy Choudhury, Justin Weisz. 1. Tracking Drone Orientation with Multiple . GPS Receivers. 2. Tremendous excitement with drones . Despite the excitement …. 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. Kalman. filter. Part I: The Big Idea. Alison Fowler. Intensive course on advanced data-assimilation methods. 3-4. th. March 2016, University of Reading. Recap of problem we wish to solve. Given . prior knowledge . 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 . 3.2 . Faddeev’s. algorithm mapped onto Systolic. array [8]. 2.4 Reconfigurable Architectures. During . run-time the system model or requirements may change due to . sensor/actuator failure. , environment changes, or at scheduled times. . 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. . Introduction to INS. INS is a . navigation aid that uses a . computer,. motion sensors . and . rotation . sensors.. The motion sensors such as accelerometers.. The rotational sensors such as gyroscopes.. Aditya Chaudhry, Chris Shih, Alex Skillin, . Derek Witcpalek. EECS 373 Project Presentation Nov 12, 2018. Outline. Where IMUs . are . used. What makes up an IMU. How to choose one. How to get useful data. 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..

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