{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122264"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122264","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"NetSketch: Automated network configuration from hand-drawn topologies","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Lu, Yuantao"],"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":["Caesar, Matthew Chapman"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Hand-drawn Diagrams","Computer Vision","Network Synthesis"],"languages":["en","eng"],"rights":["Copyright 2023 Yuantao Lu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122264","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Caesar, Matthew Chapman"]},{"key":"dc:creator","label":"Author","values":["Lu, Yuantao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Hand-drawn Diagrams","Computer Vision","Network Synthesis"]}]},{"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 2023 Yuantao Lu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122264"]}]},{"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 2025-12-01","The student, Yuantao Lu, accepted the attached license on 2023-12-01 at 10:59.","The student, Yuantao Lu, submitted this Thesis for approval on 2023-12-01 at 11:07.","This Thesis was approved for publication on 2023-12-05 at 08:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20102 on 2024-03-01 at 13:56:58","Designing and configuring computer networks can be a complex and time-consuming process, often requiring expert knowledge and specialized tools. With the growing demand for more intuitive and efficient network design methodologies, this thesis explores the possibilities of computer vision technology as a solution. Specifically, this research seeks to address the following question: Can we simplify the network design process by utilizing computer vision technology to recognize hand-drawn computer networks and configure them automatically? We propose NetSketch, a novel system that integrates modern object detection techniques with classic image gradient-based computer vision algorithms. This system identifies network components from hand-drawn diagrams and employs network synthesis algorithms to automatically generate correct and resilient network models. Based on our evaluation, our network vision framework demonstrates over 99.5% accuracy in detecting network components and network links from hand-drawn diagrams. In addition, our vision synthesis component always generates configurations that meet network design requirements, leading to more efficient network planning and management. This work has the potential to improve network design processes, making them more intuitive and efficient, with applications in network management, education, and collaboration."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["NetSketch: Automated network configuration from hand-drawn topologies"]}]}],"canonical_facts":{"dc:contributor":["Caesar, Matthew Chapman"],"dc:creator":["Lu, Yuantao"],"dc:date":["2023-12","2023-12-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","The student, Yuantao Lu, accepted the attached license on 2023-12-01 at 10:59.","The student, Yuantao Lu, submitted this Thesis for approval on 2023-12-01 at 11:07.","This Thesis was approved for publication on 2023-12-05 at 08:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20102 on 2024-03-01 at 13:56:58","Designing and configuring computer networks can be a complex and time-consuming process, often requiring expert knowledge and specialized tools. With the growing demand for more intuitive and efficient network design methodologies, this thesis explores the possibilities of computer vision technology as a solution. Specifically, this research seeks to address the following question: Can we simplify the network design process by utilizing computer vision technology to recognize hand-drawn computer networks and configure them automatically? We propose NetSketch, a novel system that integrates modern object detection techniques with classic image gradient-based computer vision algorithms. This system identifies network components from hand-drawn diagrams and employs network synthesis algorithms to automatically generate correct and resilient network models. Based on our evaluation, our network vision framework demonstrates over 99.5% accuracy in detecting network components and network links from hand-drawn diagrams. In addition, our vision synthesis component always generates configurations that meet network design requirements, leading to more efficient network planning and management. This work has the potential to improve network design processes, making them more intuitive and efficient, with applications in network management, education, and collaboration."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122264"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Yuantao Lu"],"dc:subject":["Hand-drawn Diagrams","Computer Vision","Network Synthesis"],"dc:title":["NetSketch: Automated network configuration from hand-drawn topologies"],"dc:type":["text"],"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":"2026-07-22T22:25:00Z"}