{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/29690"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/29690","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Modeling and algorithms for optimizing beam steering optical crossconnects","abstract":"One of the most significant applications of Micro-Electromechanical Systems (MEMS) technology in optical communications today is in building large non-blocking optical crossconnects based on arrays of tiltable micro-mirrors. The complexity for these crossconnects to make all possible connections lies in the calibration or fine-tuning of the mirror tilt angles to optimize the transmissivity through each possible input/output pair. The result from the fine-tuning process that produces optimization at one point in time, however, does not guarantee optimization for future attempts. This thesis models the transmissivity as a function of control variables in the vicinity of an optimal point and uses this model to re-optimize the connections quickly when a connection is reestablished. The re-optimization algorithm achieves the goal of optimizing quickly by requiring that some prior knowledge about each connection is already known. Scalable methods for representing the per-connection transmissivity model are also studied. Experimental results of the algorithm performance on real crossconnect systems are reported, including connection setup in under 50 milliseconds.","abstract_html":"One of the most significant applications of Micro-Electromechanical Systems (MEMS) technology in optical communications today is in building large non-blocking optical crossconnects based on arrays of tiltable micro-mirrors. The complexity for these crossconnects to make all possible connections lies in the calibration or fine-tuning of the mirror tilt angles to optimize the transmissivity through each possible input/output pair. The result from the fine-tuning process that produces optimization at one point in time, however, does not guarantee optimization for future attempts. This thesis models the transmissivity as a function of control variables in the vicinity of an optimal point and uses this model to re-optimize the connections quickly when a connection is reestablished. The re-optimization algorithm achieves the goal of optimizing quickly by requiring that some prior knowledge about each connection is already known. Scalable methods for representing the per-connection transmissivity model are also studied. Experimental results of the algorithm performance on real crossconnect systems are reported, including connection setup in under 50 milliseconds.","abstract_has_math":false,"creators":["Phanaphat, Piyajit, 1980-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Jungsang Kim, Carl J. Nuzman and Vincent W.S. Chan."],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003","date_published":"2003","updated_at":"2026-07-22T22:21:00Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/29690","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Jungsang Kim, Carl J. Nuzman and Vincent W.S. Chan."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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The complexity for these crossconnects to make all possible connections lies in the calibration or fine-tuning of the mirror tilt angles to optimize the transmissivity through each possible input/output pair. The result from the fine-tuning process that produces optimization at one point in time, however, does not guarantee optimization for future attempts. This thesis models the transmissivity as a function of control variables in the vicinity of an optimal point and uses this model to re-optimize the connections quickly when a connection is reestablished. The re-optimization algorithm achieves the goal of optimizing quickly by requiring that some prior knowledge about each connection is already known. Scalable methods for representing the per-connection transmissivity model are also studied. 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Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Phanaphat, Piyajit, 1980-"],"dc:date.accessioned":["2006-03-24T16:15:35Z"],"dc:date.available":["2006-03-24T16:15:35Z"],"dc:date.issued":["2003"],"dc:description":["Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.","Includes bibliographical references (p. 81)."],"dc:description.abstract":["One of the most significant applications of Micro-Electromechanical Systems (MEMS) technology in optical communications today is in building large non-blocking optical crossconnects based on arrays of tiltable micro-mirrors. The complexity for these crossconnects to make all possible connections lies in the calibration or fine-tuning of the mirror tilt angles to optimize the transmissivity through each possible input/output pair. The result from the fine-tuning process that produces optimization at one point in time, however, does not guarantee optimization for future attempts. This thesis models the transmissivity as a function of control variables in the vicinity of an optimal point and uses this model to re-optimize the connections quickly when a connection is reestablished. The re-optimization algorithm achieves the goal of optimizing quickly by requiring that some prior knowledge about each connection is already known. Scalable methods for representing the per-connection transmissivity model are also studied. 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