Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs
Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, m...
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| Veröffentlicht in: | Proceedings - IEEE International Conference on Computer Design S. 422 - 428 |
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01.11.2019
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| Abstract | Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient. |
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| AbstractList | Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient. |
| Author | Song, Ling-Yen Lei, Yung-Chun Chen, Yi-Ling Huang, Juinn-Dar |
| Author_xml | – sequence: 1 givenname: Ling-Yen surname: Song fullname: Song, Ling-Yen organization: National Chiao Tung University – sequence: 2 givenname: Yi-Ling surname: Chen fullname: Chen, Yi-Ling organization: National Chiao Tung University – sequence: 3 givenname: Yung-Chun surname: Lei fullname: Lei, Yung-Chun organization: National Chiao Tung University – sequence: 4 givenname: Juinn-Dar surname: Huang fullname: Huang, Juinn-Dar organization: National Chiao Tung University |
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| Snippet | Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to... |
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| SubjectTerms | correction on demand digital microfluidic biochip Digital microfluidic biochips Electrodes forecast-based correction Hardware Optimization probability-based forecast Reservoirs sample preparation Solid modeling System-on-chip unbalanced splitting |
| Title | Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs |
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