Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17771
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dc.contributor.authorAdibhatla, VA-
dc.contributor.authorShieh, JS-
dc.contributor.authorAbbod, MF-
dc.contributor.authorChih, HC-
dc.contributor.authorHsu, CC-
dc.contributor.authorCheng, J-
dc.coverage.spatialTaipei, Taiwan, Taiwan-
dc.date.accessioned2019-03-25T11:30:10Z-
dc.date.available2019-01-24-
dc.date.available2019-03-25T11:30:10Z-
dc.date.issued2019-
dc.identifier.citationProceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT, 2019, 2018-October pp. 202 - 205en_US
dc.identifier.issn2150-5934-
dc.identifier.issnhttp://dx.doi.org/10.1109/IMPACT.2018.8625828-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/17771-
dc.format.extent202 - 205-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.source13th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)-
dc.source13th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)-
dc.subjectConvolutionen_US
dc.subjectDeep learningen_US
dc.subjectTrainingen_US
dc.subjectInspectionen_US
dc.titleDetecting defects in PCB using deep learning via convolution neural networksen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1109/IMPACT.2018.8625828-
dc.relation.isPartOfProceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT-
pubs.finish-date2018-10-26-
pubs.finish-date2018-10-26-
pubs.publication-statusPublished-
pubs.start-date2018-10-24-
pubs.start-date2018-10-24-
pubs.volume2018-October-
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