Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/14442
Authors: Kumar, Satendra
Keywords: Microfuidics Biochips;DNA Analysis;K-Mixer Scheduling (KMS);K-Droplet Rotary Mixer
Issue Date: 2016
Publisher: Department of Computer Science and Engineering,IITR.
Abstract: Micro uidics biochips are revolutionary devices in the eld of clinical diagnostics, DNA analysis and molecular biology. Biochips involve various elds of science and engineering i.e., physics, chemistry, biochemistry, nanotechnology, fabrication tech- nology and computer science. It uses small volume of uids on scale of micro to nano liter for automatically carrying out the reactions needed for some biochemical assays. In this report I have covered basic overview of biochips, existing algorithms for sample preparation speci cally for dilution, mixing, and multiple droplet of sin- gle target. Some application requires perticular sample repeatedy at the time of asssy execution. So fast and e cient sample preparation process is require to gen- erate multiple droplets of sample. Three approaches are presented in this report to reduce the time to generate stream of droplets for bioassay having high demand of sample at the time of assay execution. Simulation results show that new approach is quite promising as compare to existing MMS and SRS algorithms to reduce the time taken to prepare sample for such high demand. First algorithm proposed, called KMS or K-Mixer Scheduling, utilizes K-Droplet Mixer[3] to schedule mixing tree for multiple demand of single target generation. This algorithm reduces total mix-split steps, by 74:6% than MMS and SRS but compromises with storage requirement by 18:5% than SRS but still good by 25:1% than MMS. To reduce the storage requirement modi ed KMS (m-KMS) schedules mixing tree more then once with fraction f of total required demand D, fraction f used to balance the total storage requirement U and total mixing operation Tms. This new modi ed algorithm reduces total mix-split steps, by 79% and storage requirement by 80:2%; 67:7% than MMS, SRS respectively. And nally we presented KMS for mixing graph which used to schedule mixing graph. This algorithm reduces total mix-split steps by 76% than MMS and SRS, storage requirement by 79:6%, 66:5% than MMS and SRS respectively.
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Appears in Collections:DOCTORAL THESES (E & C)

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