Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93910
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | - |
dc.creator | Feng, ZG | en_US |
dc.creator | Yiu, KFC | en_US |
dc.creator | Wu, SY | en_US |
dc.date.accessioned | 2022-08-03T01:24:10Z | - |
dc.date.available | 2022-08-03T01:24:10Z | - |
dc.identifier.issn | 0278-081X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93910 | - |
dc.language.iso | en | en_US |
dc.publisher | Birkhäuser | en_US |
dc.rights | © Springer Science+Business Media, LLC, part of Springer Nature 2018 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00034-018-0758-z | en_US |
dc.subject | Discrete search method | en_US |
dc.subject | Filled function | en_US |
dc.subject | Sparse filter design | en_US |
dc.title | Design of sparse filters by a discrete filled function technique | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 4279 | en_US |
dc.identifier.epage | 4294 | en_US |
dc.identifier.volume | 37 | en_US |
dc.identifier.issue | 10 | en_US |
dc.identifier.doi | 10.1007/s00034-018-0758-z | en_US |
dcterms.abstract | In this paper, we consider the sparse filter design problem where some of the coefficients can be reduced to zeroes in order to lower implementation complexity. The objective is to choose the fewest number of nonzero filter coefficients to meet a given performance requirement. We formulate a discrete optimization problem to minimize the number of nonzero terms and develop a discrete search method to find the minimal nonzero terms. In each step, we need to consider a subproblem to design the filter coefficients with a given set of nonzero terms. We formulate this subproblem as a linear programming problem and apply an exchange algorithm to find the optimal coefficients. For illustration, we compare the proposed algorithm with existing methods and show that the proposed method gives better results in all our test cases. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Circuits, systems and signal processing, Oct. 2018, v. 37, no. 10, p. 4279-4294 | en_US |
dcterms.isPartOf | Circuits, systems and signal processing | en_US |
dcterms.issued | 2018-10 | - |
dc.identifier.scopus | 2-s2.0-85053041214 | - |
dc.identifier.eissn | 1531-5878 | en_US |
dc.description.validate | 202208 bcfc | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0410 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | PolyU | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 24336747 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yiu_Design_Sparse_Filters.pdf | Pre-Published version | 879.41 kB | Adobe PDF | View/Open |
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