{"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/dealing-with-unknown-unknowns-identification","title":"Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs","arxiv_id":"1803.04866","date":"2018-03-10","proceeding":null,"authors":["Gaurav Gupta","Sergio Pequito","Paul Bogdan"],"abstract":"This paper focuses on analysis and design of time-varying complex networks\nhaving fractional order dynamics. These systems are key in modeling the complex\ndynamical processes arising in several natural and man made systems. Notably,\nexamples include neurophysiological signals such as electroencephalogram (EEG)\nthat captures the variation in potential fields, and blood oxygenation level\ndependent (BOLD) signal, which serves as a proxy for neuronal activity.\nNotwithstanding, the complex networks originated by locally measuring EEG and\nBOLD are often treated as isolated networks and do not capture the dependency\nfrom external stimuli, e.g., originated in subcortical structures such as the\nthalamus and the brain stem. Therefore, we propose a paradigm-shift towards the\nanalysis of such complex networks under unknown unknowns (i.e., excitations).\nConsequently, the main contributions of the present paper are threefold: (i) we\npresent an alternating scheme that enables to determine the best estimate of\nthe model parameters and unknown stimuli; (ii) we provide necessary and\nsufficient conditions to ensure that it is possible to retrieve the state and\nunknown stimuli; and (iii) upon these conditions we determine a small subset of\nvariables that need to be measured to ensure that both state and input can be\nrecovered, while establishing sub-optimality guarantees with respect to the\nsmallest possible subset. Finally, we present several pedagogical examples of\nthe main results using real data collected from an EEG wearable device.","url_abs":"http://arxiv.org/abs/1803.04866v2","url_pdf":"http://arxiv.org/pdf/1803.04866v2.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":"abstracts"},"code_links":[{"paper_slug":"dealing-with-unknown-unknowns-identification","repo_url":"https://github.com/gaurav71531/UUknowns","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}