Data-driven modeling is a powerful way to handle errors in hardware by taking advantage of machine learning algorithms. We propose a error-aware models that handle high rate of errors in arrhythmia and seizure detection applications with nearly no extra cost in hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data-driven modeling is a powerful way to handle errors in hardware by taking advantage of machine learning algorithms. We propose a error-aware models that handle high rate of errors in arrhythmia and seizure detection applications with nearly no extra cost in hardware.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 IEEE. Personal use of this material is permitted. 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