dc.contributor.authorHuang, Xiwei
dc.contributor.authorWang, Xiaolong
dc.contributor.authorYan, Mei
dc.contributor.authorYu, Hao
dc.identifier.citationHuang, X., Wang, Xi., Yan, M., & Yu, H. (2014). A robust recognition error recovery for micro-flow cytometer by machine-learning enhanced single-frame super-resolution processing. Integration, the VLSI journal, 51, 208-218.en_US
dc.description.abstractWith the recent advancement in microfluidics based lab-on-a-chip technology, lensless imaging system integrating microfluidic channel with CMOS image sensor has become a promising solution for the system minimization of flow cytometer. The design challenge for such an imaging-based micro-flow cytometer under poor resolution is how to recover cell recognition error under various flow rates. A microfluidic lensless imaging system is developed in this paper using extreme-learning-machine enhanced single-frame super-resolution processing, which can effectively recover the recognition error when increasing flow rate for throughput. As shown in the experiments, with mixed flowing HepG2 and Huh7 cells as inputs, the developed scheme shows that 23% better recognition accuracy can be achieved compared to the one without error recovery. Meanwhile, it also achieves an average of 98.5% resource saving compared to the previous multi-frame super-resolution processing.en_US
dc.relation.ispartofseriesIntegration, the VLSI journalen_US
dc.rights© 2014 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Integration, the VLSI Journal, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.vlsi.2014.07.004].en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
dc.titleA robust recognition error recovery for micro-flow cytometer by machine-learning enhanced single-frame super-resolution processingen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionAccepted versionen_US

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