{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129844"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129844","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Patronus: multi-modal sensing, analytics, and localization assistance for heterogeneous working environments","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Tian, Beitong"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Nahrstedt, Klara","Caesar, Matthew","Soltanaghai, Elahe","Shenoy, Prashant"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-08","date_published":"2025-07-08","updated_at":"2026-07-22T22:25:05Z","subjects":["Internet Of Things","Smart Glasses","Multimodal Sensing","Condition Monitoring","Environmental Monitoring","Visual Localization","Intelligent Environments"],"languages":["en","eng"],"rights":["Copyright © 2025 Beitong Tian. All rights reserved."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129844","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nahrstedt, Klara","Caesar, Matthew","Soltanaghai, Elahe","Shenoy, Prashant"]},{"key":"dc:creator","label":"Author","values":["Tian, Beitong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-08","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Internet Of Things","Smart Glasses","Multimodal Sensing","Condition Monitoring","Environmental Monitoring","Visual Localization","Intelligent Environments"]}]},{"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 © 2025 Beitong Tian. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129844"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Beitong Tian, accepted the attached license on 2025-07-08 at 13:45.","The student, Beitong Tian, submitted this Dissertation for approval on 2025-07-08 at 14:09.","This Dissertation was approved for publication on 2025-07-08 at 15:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22441 on 2025-10-20 at 16:57:30","Today’s “Jarvis-like” AI assistants excel at generic, consumer-oriented tasks, yet they remain ill-suited for heterogeneous working environments—dynamic laboratories and industrial facilities where hands-on professionals must juggle safety-critical processes, rapidly changing context, and a deluge of multimodal data. This thesis argues that effective assistance in these settings hinges on the tight co-design of three pillars: (i) a low-cost, scalable, and evolvable sensing infrastructure, (ii) a trustworthy real-time analytics pipeline, and (iii) an accurate, practical localization layer that enables context-aware humandata interaction. In this thesis, we introduce Patronus, a modular framework composed of five interoperable systems that collectively satisfy these requirements. SENSELET++ deploys a plug-and-play sensor network and anomaly analytics for scalable environmental monitoring. MachineStethoscope enables on-device, unsupervised health monitoring for legacy rotating machinery. WeldMon fuses heterogeneous signals and introduces synthetic fault augmentation to improve failure prediction in ultrasonic welding. GaugeTracker digitizes analog gauges entirely on low-cost IoT hardware, leveraging multiple vision and vision language models for robust transcription. Finally, AnyLoc provides energy-efficient visual localization that operates under low-resolution and low-light conditions in cluttered indoor scenes. Together, these systems power MAINTGlasses, a hands-free smart-glasses interface that delivers spatially relevant insights to professionals in real time. Deployments across cleanrooms, nanofabrication labs, and server rooms demonstrate that Patronus fosters safer, faster, and more efficient workflows. By unifying sensing, analytics, and localization, this work charts a practical path toward intelligent environments that actively understand and support complex human work."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Patronus: multi-modal sensing, analytics, and localization assistance for heterogeneous working environments"]}]}],"canonical_facts":{"dc:contributor":["Nahrstedt, Klara","Caesar, Matthew","Soltanaghai, Elahe","Shenoy, Prashant"],"dc:creator":["Tian, Beitong"],"dc:date":["2025-07-08","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Beitong Tian, accepted the attached license on 2025-07-08 at 13:45.","The student, Beitong Tian, submitted this Dissertation for approval on 2025-07-08 at 14:09.","This Dissertation was approved for publication on 2025-07-08 at 15:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22441 on 2025-10-20 at 16:57:30","Today’s “Jarvis-like” AI assistants excel at generic, consumer-oriented tasks, yet they remain ill-suited for heterogeneous working environments—dynamic laboratories and industrial facilities where hands-on professionals must juggle safety-critical processes, rapidly changing context, and a deluge of multimodal data. This thesis argues that effective assistance in these settings hinges on the tight co-design of three pillars: (i) a low-cost, scalable, and evolvable sensing infrastructure, (ii) a trustworthy real-time analytics pipeline, and (iii) an accurate, practical localization layer that enables context-aware humandata interaction. In this thesis, we introduce Patronus, a modular framework composed of five interoperable systems that collectively satisfy these requirements. SENSELET++ deploys a plug-and-play sensor network and anomaly analytics for scalable environmental monitoring. MachineStethoscope enables on-device, unsupervised health monitoring for legacy rotating machinery. WeldMon fuses heterogeneous signals and introduces synthetic fault augmentation to improve failure prediction in ultrasonic welding. GaugeTracker digitizes analog gauges entirely on low-cost IoT hardware, leveraging multiple vision and vision language models for robust transcription. Finally, AnyLoc provides energy-efficient visual localization that operates under low-resolution and low-light conditions in cluttered indoor scenes. Together, these systems power MAINTGlasses, a hands-free smart-glasses interface that delivers spatially relevant insights to professionals in real time. Deployments across cleanrooms, nanofabrication labs, and server rooms demonstrate that Patronus fosters safer, faster, and more efficient workflows. By unifying sensing, analytics, and localization, this work charts a practical path toward intelligent environments that actively understand and support complex human work."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129844"],"dc:language":["en","eng"],"dc:rights":["Copyright © 2025 Beitong Tian. All rights reserved."],"dc:subject":["Internet Of Things","Smart Glasses","Multimodal Sensing","Condition Monitoring","Environmental Monitoring","Visual Localization","Intelligent Environments"],"dc:title":["Patronus: multi-modal sensing, analytics, and localization assistance for heterogeneous working environments"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}