<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Compressed Motion Sensing and Dynamic Tomography"^^ . "Compressed sensing is a new sampling paradigm of mathematical signal processing\r\nwhich, under certain assumptions, allows signal recovery from highly undersampled\r\nmeasurements. The extension of the mathematical theory and the analysis and development\r\nof new applications in many fields are the subject of numerous international\r\nresearch activities.\r\nIn this thesis an industrial problem from experimental fluid dynamics is consider,\r\nexemplarily. The current state of the art methodology solves the problem in two\r\nindependent stages: First it recovers particle images by nonstandard tomography,\r\nand secondly it estimates the motion between two given time points. This motivates\r\nthe problem of joint signal and motion estimation while raising theoretical\r\nquestions in compressed sensing related to the recovery of sparse time-varying signals.\r\nIn particular, two different approaches are presented for recovering a time-varying\r\nsignal and its motion from undersampled linear measurements taken at two different\r\npoints in time. The first approach formulates a problem at hand as optimal transport\r\nbetween two indirectly observed densities with a physical constraint. Several methods\r\nare proposed to integrate the projection constraints into the convex optimization\r\nframework of Benamou and Brenier.\r\nIn the second approach, the signal is modeled as if observed by the real sensor\r\nspecified by the experimental setup and an additional virtual sensor due to motion.\r\nThe combination of these two sensors is called compressed motion sensor and its\r\nproperties are examined from the viewpoint of compressed sensing. It is shown that\r\nin compressed motion sensing (CMS), besides sparsity, a sufficient change of signal\r\nleads to recovery guarantees and it is demonstrated that the compressed motion\r\nsensor at least doubles the performance of the real sensor. Moreover, for certain\r\nsparsity levels the signal motion can be established, too."^^ . "2018" . . . . . . . "Robert"^^ . "Breckner"^^ . "Robert Breckner"^^ . . . . . . "Compressed Motion Sensing and Dynamic Tomography (PDF)"^^ . . . "Dissertation_Robert_Breckner.pdf"^^ . . . "Compressed Motion Sensing and Dynamic Tomography (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Compressed Motion Sensing and Dynamic Tomography (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Compressed Motion Sensing and Dynamic Tomography (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Compressed Motion Sensing and Dynamic Tomography (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Compressed Motion Sensing and Dynamic Tomography (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #24505 \n\nCompressed Motion Sensing and Dynamic Tomography\n\n" . "text/html" . . . "510 Mathematik"@de . "510 Mathematics"@en . .