{"paper":{"title":"Application of Compressive Sensing Theory for the Reconstruction of Signals in Plastic Scintillators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.ins-det"],"primary_cat":"physics.med-ph","authors_text":"A. Kochanowski, A. S{\\l}omski, A. Strzelecki, E. Czerwi\\'nski, G. Korcyl, J. Kowal, J. Smyrski, {\\L}. Kap{\\l}on, L. Raczy\\'nski, M. Molenda, M. Pa{\\l}ka, M. Pawlik, M. Silarski, M. Zieli\\'nski, N.G. Sharma, P. Bia{\\l}as, P. Kowalski, P. Moskal, P. Salabura, Sz. Nied\\'zwiecki, T. Bednarski, T. Kozik, W. Krzemie\\'n, W. Wi\\'slicki, Z. Rudy","submitted_at":"2013-10-06T18:31:08Z","abstract_excerpt":"Compressive Sensing theory says that it is possible to reconstruct a measured signal if an enough sparse representation of this signal exists in comparison to the number of random measurements. This theory was applied to reconstruct signals from measurements of plastic scintillators. Sparse representation of obtained signals was found using SVD transform."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.1612","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}