dc.creator | Koziri M.G., Loukopoulos T., Adam M., Assimakis N., Tzialas G. | en |
dc.date.accessioned | 2023-01-31T08:46:54Z | |
dc.date.available | 2023-01-31T08:46:54Z | |
dc.date.issued | 2017 | |
dc.identifier | 10.1007/978-3-319-56541-5_44 | |
dc.identifier.isbn | 9783319565408 | |
dc.identifier.issn | 21945357 | |
dc.identifier.uri | http://hdl.handle.net/11615/75490 | |
dc.description.abstract | Kalman filters have many practical applications in various fields such as sensor networks, image and video processing. Therefore, their fast computation is of paramount importance. In this paper distributed implementations for the steady state Kalman filter are proposed. The distributed algorithms are based on partitioning the measurement vector, the state vector or both of them. The number of processors is determined a priori. The optimal distribution of measurements/ states into parallel processors minimizing the computation time is also a priori determined. © Springer International Publishing AG 2017. | en |
dc.language.iso | en | en |
dc.source | Advances in Intelligent Systems and Computing | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018615021&doi=10.1007%2f978-3-319-56541-5_44&partnerID=40&md5=9b0bcc7089a8abb7f21908866f10498c | |
dc.subject | Bandpass filters | en |
dc.subject | Distributed computer systems | en |
dc.subject | Information systems | en |
dc.subject | Parallel algorithms | en |
dc.subject | Parallel processing systems | en |
dc.subject | Sensor networks | en |
dc.subject | Video signal processing | en |
dc.subject | Distributed implementation | en |
dc.subject | Image and video processing | en |
dc.subject | Optimal distributions | en |
dc.subject | Parallel and distributed systems | en |
dc.subject | Parallel processor | en |
dc.subject | Processor assignments | en |
dc.subject | Steady sate | en |
dc.subject | Steady-state Kalman filters | en |
dc.subject | Kalman filters | en |
dc.subject | Springer Verlag | en |
dc.title | On the optimal processor assignment for computing the steady state kalman filter in parallel and distributed systems | en |
dc.type | conferenceItem | en |