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http://hdl.handle.net/10603/470796
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DC Field | Value | Language |
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dc.coverage.spatial | Investigations on performance enhancement of proportionate affine projection algorithms for sparse system identification | |
dc.date.accessioned | 2023-03-20T05:14:51Z | - |
dc.date.available | 2023-03-20T05:14:51Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/470796 | - |
dc.description.abstract | Cluster-sparse or block-sparse system identification has gained newlinemuch attention in the domain of adaptive signal processing and the adaptive newlinefilters that improve the performance of identifying cluster-sparse systems are newlinebeing studied intensively. As sparse systems have only a few significant newlinecoefficients amidst large number of insignificant coefficients, the newlineproportionate algorithms exploit sparsity by assigning step-sizes that are newlineproportional to the individual coefficients. The objective of this research is to newlinepropose new algorithms for cluster-sparse or block-sparse system newlineidentification that exhibit improvement in terms of normalised misalignment, newlinetracking, and convergence rate over the existing algorithms in literature. newlineFirstly, a set of algorithms for the Cluster-Sparse Proportionate newlineAffine Projection Algorithm (CS-PAPA) or Cluster-Sparse Improved newlineProportionate Affine Projection Algorithm (CS-IPAPA) is proposed in the newlinecontext of echo cancellation. The algorithms are named Memory Cluster- newlineSparse Proportionate Affine Projection Algorithm (MCS-PAPA) and Memory newlineCluster-Sparse Improved Proportionate Affine Projection Algorithm (MCSIPAPA), newlinewhich incorporate the history of proportionate factors into the CSPAPA newlineand CS-IPAPA, respectively. The update equation is derived from the newlinecost function of the CS-PAPA or CS-IPAPA. Based on the energy newlineconservation arguments, a rigorous performance analysis of the proposed newlineMCS-PAPA is presented. Experimental results for the steady-state mean newlinesquare error corroborate with the theoretical expressions. Moreover, the newlinecondition for the mean stability is derived. Through simulations, the transient newlineperformance of MCS-PAPA is analyzed. newline | |
dc.format.extent | xxii,162p. | |
dc.language | English | |
dc.relation | p.152-161 | |
dc.rights | university | |
dc.title | Investigations on performance enhancement of proportionate affine projection algorithms for sparse system identification | |
dc.title.alternative | ||
dc.creator.researcher | Senthil MUrugan, B | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Library Information and Science | |
dc.subject.keyword | Proportionate | |
dc.subject.keyword | Affine Projection | |
dc.subject.keyword | Sparse system | |
dc.description.note | ||
dc.contributor.guide | Swarnalatha, A | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Information and Communication Engineering | |
dc.date.registered | ||
dc.date.completed | 2022 | |
dc.date.awarded | 2022 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 179.95 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.08 MB | Adobe PDF | View/Open | |
03_content.pdf | 327.54 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 235.95 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 872.4 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 668.73 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.06 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 706.83 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.06 MB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 997.73 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 82.35 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 400.58 kB | Adobe PDF | View/Open |
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