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dc.contributor.authorZhang, Yuzhen
dc.contributor.authorZhao, Wenshan
dc.contributor.authorSun, Yichuang
dc.date.accessioned2021-04-30T13:55:51Z
dc.date.available2021-04-30T13:55:51Z
dc.date.issued2021-04-21
dc.identifier.citationZhang , Y , Zhao , W & Sun , Y 2021 , ' Design of Programmable Gaussian-Derived Wavelet Filter for Wearable Biomedical Sensor ' , International Journal of Circuit Theory and Applications . https://doi.org/10.1002/cta.3032
dc.identifier.issn0098-9886
dc.identifier.otherPURE: 25003072
dc.identifier.otherPURE UUID: b27293e3-b472-40a7-b998-99f168c1c14c
dc.identifier.otherScopus: 85104654450
dc.identifier.urihttp://hdl.handle.net/2299/24359
dc.description© 2021 John Wiley & Sons, Ltd. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1002/cta.3032
dc.description.abstractTo provide multiple options for specific application in bio-signal processing, the programmable Gaussian-derived Gm-C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow-the-leader feedback Gm-C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh-order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a=0.1, with dynamic range of 42.6 dB and figure-of-merit of 2.05×10-13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.en
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofInternational Journal of Circuit Theory and Applications
dc.titleDesign of Programmable Gaussian-Derived Wavelet Filter for Wearable Biomedical Sensoren
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.date.embargoedUntil2022-04-21
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dcterms.dateAccepted2021-04-21
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1002/cta.3032
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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