{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wave-machine-learning-for-full-waveform-time","title":"WAVE: Machine Learning for Full-Waveform Time-Of-Flight Detectors","arxiv_id":"1811.05875","date":"2018-11-14","proceeding":null,"authors":["Glenn R. Jocher","Kurtis Nishimura","John Koblanski","Viacheslav A. Li"],"abstract":"We propose a WAveform Vector Exploitation (WAVE) deep neural network for full-waveform Time-Of-Flight (TOF) physics detectors, and evaluate its performance against traditional reconstruction techniques via Monte Carlo study of a small plastic-scintillator scatter camera. Ultralytics LLC (www.ultralytics.com) provides WAVE freely under the open source GPL-3.0 license at https://github.com/ultralytics/wave.","url_abs":"http://arxiv.org/abs/1811.05875v1","url_pdf":"http://arxiv.org/pdf/1811.05875v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"wave-machine-learning-for-full-waveform-time","repo_url":"https://github.com/ultralytics/wave","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"wave-machine-learning-for-full-waveform-time","repo_url":"https://github.com/ultralytics/sandd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}