Event Signal Processing Based on State-Transition Models

Event Signal Processing Based on State-Transition Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 November 2025 V1 Latest version Share on Event Signal Processing Based on State-Transition Models Author : Paul Park 0009-0002-1366-9822 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176222170.03120891/v1 166 views 74 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract A novel event signal processing technique is reported based on state-transition models for the event-based vision sensor. Conventional method needs lots of memories to support timestamp map-based digital signal processing by extracting timestamp information from an event signal. This paper proposes and demonstrates anti-flicker, noise filter, and dead pixel correction methods based on single-bit state registers. The results show that the proposed technique could reduce the required memory size by 16 times than the previous methods while maintaining the image quality. Thus, it can be envisioned that the proposed technique can play a critical role in the development of event-based sensor chip by minimizing the required memory size. Supplementary Material File (event signal processing paul kj park.docx) Download 3.01 MB Information & Authors Information Version history V1 Version 1 04 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords digital filters image processing noise signal processing Authors Affiliations Paul Park 0009-0002-1366-9822 [email protected] Gachon University View all articles by this author Metrics & Citations Metrics Article Usage 166 views 74 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paul Park. Event Signal Processing Based on State-Transition Models. Authorea . 04 November 2025. DOI: https://doi.org/10.22541/au.176222170.03120891/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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