{"id":{"repo_id":"windsor","oai_identifier":"oai:uwindsor.scholaris.ca:20.500.14776/9459"},"canonical_url":"https://search.dev.ndltd.org/etd/windsor/oai:uwindsor.scholaris.ca:20.500.14776/9459","repository":{"repo_id":"windsor","name":"University of Windsor","base_url":"https://uwindsor.scholaris.ca/server/oai/request"},"display":{"title":"Adaptive noise cancellation using multichannel lattice structure.","abstract":"This thesis presents a multichannel adaptive noise cancellation technique (MCLS) for cancelling the noise over nonlinear transmission channel. The technique applies to the situation in which the reference signal and noisy primary signal are collected simultaneously. The coefficients of the multichannel multiple regression transversal filter are modified adaptively according to the backward prediction error vector generated from the multichannel adaptive lattice predictor. This multichannel adaptive noise cancellation procedure involves the NLMS adaptive algorithm. The performance of the new technique using different types of transmission channels, different types of reference inputs and different types of noise-free primary inputs are examined analytically. The new approach is experimentally shown to have better noise cancellation performance than the existing single-channel adaptive lattice noise cancellation algorithm (SCLS) over nonlinear transmission channel case, especially in low input SNR situation.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .X54. Source: Masters Abstracts International, Volume: 43-01, page: 0288. Adviser: H. K. Kwan. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.","abstract_html":"This thesis presents a multichannel adaptive noise cancellation technique (MCLS) for cancelling the noise over nonlinear transmission channel. The technique applies to the situation in which the reference signal and noisy primary signal are collected simultaneously. The coefficients of the multichannel multiple regression transversal filter are modified adaptively according to the backward prediction error vector generated from the multichannel adaptive lattice predictor. This multichannel adaptive noise cancellation procedure involves the NLMS adaptive algorithm. The performance of the new technique using different types of transmission channels, different types of reference inputs and different types of noise-free primary inputs are examined analytically. The new approach is experimentally shown to have better noise cancellation performance than the existing single-channel adaptive lattice noise cancellation algorithm (SCLS) over nonlinear transmission channel case, especially in low input SNR situation.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .X54. Source: Masters Abstracts International, Volume: 43-01, page: 0288. Adviser: H. K. Kwan. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.","abstract_has_math":false,"creators":["Xie, Qing"],"institution":"University of Windsor","degree_name":"M.A.Sc.","degree_level":"Masters","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["scholarship@uwindsor.ca"],"advisors":["Kwan, H. 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The technique applies to the situation in which the reference signal and noisy primary signal are collected simultaneously. The coefficients of the multichannel multiple regression transversal filter are modified adaptively according to the backward prediction error vector generated from the multichannel adaptive lattice predictor. This multichannel adaptive noise cancellation procedure involves the NLMS adaptive algorithm. The performance of the new technique using different types of transmission channels, different types of reference inputs and different types of noise-free primary inputs are examined analytically. The new approach is experimentally shown to have better noise cancellation performance than the existing single-channel adaptive lattice noise cancellation algorithm (SCLS) over nonlinear transmission channel case, especially in low input SNR situation.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .X54. Source: Masters Abstracts International, Volume: 43-01, page: 0288. Adviser: H. K. Kwan. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004."]},{"key":"dc:title","label":"Title","values":["Adaptive noise cancellation using multichannel lattice structure."]}]}],"canonical_facts":{"dc:contributor":["scholarship@uwindsor.ca"],"dc:contributor.advisor":["Kwan, H. K."],"dc:creator":["Xie, Qing"],"dc:date.accessioned":["2025-07-03 14:26"],"dc:date.available":["2013-03-15 16:27","2025-07-03T18:26:46Z"],"dc:date.issued":["2004-01-01"],"dc:description.abstract":["This thesis presents a multichannel adaptive noise cancellation technique (MCLS) for cancelling the noise over nonlinear transmission channel. The technique applies to the situation in which the reference signal and noisy primary signal are collected simultaneously. The coefficients of the multichannel multiple regression transversal filter are modified adaptively according to the backward prediction error vector generated from the multichannel adaptive lattice predictor. This multichannel adaptive noise cancellation procedure involves the NLMS adaptive algorithm. The performance of the new technique using different types of transmission channels, different types of reference inputs and different types of noise-free primary inputs are examined analytically. The new approach is experimentally shown to have better noise cancellation performance than the existing single-channel adaptive lattice noise cancellation algorithm (SCLS) over nonlinear transmission channel case, especially in low input SNR situation.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .X54. Source: Masters Abstracts International, Volume: 43-01, page: 0288. Adviser: H. K. Kwan. 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