{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117535"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117535","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Toward a platform for programmable digital olfactory processing","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Wezelis, Abigail"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kumar, Rakesh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Digital Olfaction","E-nose","Odor Processing","Digital Scent Technology","Programmable Odor Platform"],"languages":["en","eng"],"rights":["Copyright 2022 Abigail Wezelis"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117535","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Rakesh"]},{"key":"dc:creator","label":"Author","values":["Wezelis, Abigail"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-10-06"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Digital Olfaction","E-nose","Odor Processing","Digital Scent Technology","Programmable Odor Platform"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Abigail Wezelis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117535"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Abigail Wezelis, accepted the attached license on 2022-10-03 at 21:23.","The student, Abigail Wezelis, submitted this Thesis for approval on 2022-10-03 at 21:35.","This Thesis was approved for publication on 2022-10-06 at 14:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18510 on 2023-04-12 at 11:34:38","Digital olfactory processing comprises a set of solutions that use odor detection and synthesis for solving problems. With recent advances in electronic nose (e-nose) and odor synthesis technologies, it may be time to consider the development of a programmable platform for digital olfactory processing. In this work, we identify a number of odor processing tasks that should be supported on such a platform in wearable and AR/VR devices: odor localization, e-nose classification, odor authentication, odor similarity, active odor cancellation, odor pleasantness estimation, and odor demixing. We then collate a list of commonly used algorithms for these tasks: particle filtering (PF), Infotaxis, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), artificial neural network (ANN), k-means clustering analysis (CA), angle distance of vector sums (ADVS), convex optimization (CVX), random forest (RF), and orthogonal matching pursuit (OMP). Benchmarking is then performed in order to learn about the characteristics of these algorithms. Common algorithmic characteristics across the selected odor processing tasks, such as the frequency of non-linear floating point operations and the vectorizability of linear floating point operations, will be able to drive support for effective specialization in future programmable platforms for digital olfactory processing."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Toward a platform for programmable digital olfactory processing"]}]}],"canonical_facts":{"dc:contributor":["Kumar, Rakesh"],"dc:creator":["Wezelis, Abigail"],"dc:date":["2022-12","2022-10-06"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Abigail Wezelis, accepted the attached license on 2022-10-03 at 21:23.","The student, Abigail Wezelis, submitted this Thesis for approval on 2022-10-03 at 21:35.","This Thesis was approved for publication on 2022-10-06 at 14:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18510 on 2023-04-12 at 11:34:38","Digital olfactory processing comprises a set of solutions that use odor detection and synthesis for solving problems. With recent advances in electronic nose (e-nose) and odor synthesis technologies, it may be time to consider the development of a programmable platform for digital olfactory processing. In this work, we identify a number of odor processing tasks that should be supported on such a platform in wearable and AR/VR devices: odor localization, e-nose classification, odor authentication, odor similarity, active odor cancellation, odor pleasantness estimation, and odor demixing. We then collate a list of commonly used algorithms for these tasks: particle filtering (PF), Infotaxis, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), artificial neural network (ANN), k-means clustering analysis (CA), angle distance of vector sums (ADVS), convex optimization (CVX), random forest (RF), and orthogonal matching pursuit (OMP). Benchmarking is then performed in order to learn about the characteristics of these algorithms. Common algorithmic characteristics across the selected odor processing tasks, such as the frequency of non-linear floating point operations and the vectorizability of linear floating point operations, will be able to drive support for effective specialization in future programmable platforms for digital olfactory processing."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117535"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Abigail Wezelis"],"dc:subject":["Digital Olfaction","E-nose","Odor Processing","Digital Scent Technology","Programmable Odor Platform"],"dc:title":["Toward a platform for programmable digital olfactory processing"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}