Browsing by Author "Chen, Yi-Ling"

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Color Optimization Model for High Dynamic Range LCDs with RGB Color Backlights
(2009)
Chen, Yi-Ling
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Cheng, Yu-Kuo
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Huang, Yi-Pai
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Shieh, Han-Ping D.
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Yeh, Szu-Che
The high dynamic range liquid crystal display (TIDR-LCD) with the color controlled backlight can achieve low power consumption, high color saturation and high contrast ratio. A modified color model for the RGB LEDs backlight system was proposed to optimize the color appearance in this paper. By applying the model, the color accuracy of the HDR-image could be much improved with the clear image details.
Concentration-Resilient Mixture Preparation with Digital Microfluidic Lab-on-Chip
(2018-04-01)
Bhattacharjee, Sukanta
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Chen, Yi-Ling
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Huang, Juinn-Dar
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Bhattacharya, Bhargab B.
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電子工程學系及電子研究所
Sample preparation plays a crucial role in almost all biochemical applications, since a predominant portion of biochemical analysis time is associated with sample collection, transportation, and preparation. Many sample-preparation algorithms are proposed in the literature that are suitable for execution on programmable digital microfluidic (DMF) platforms. In most of the existing DMF-based sample-preparation algorithms, a fixed target ratio is provided as input, and the corresponding mixing tree is generated as output. However, in many biochemical applications, target mixtures with exact component proportions may not be needed. From a biochemical perspective, it may be sufficient to prepare a mixture in which the input reagents may lie within a range of concentration factors. The choice of a particular valid ratio, however, strongly impacts solution-preparation cost and time. To address this problem, we propose a concentration-resilient ratioselection method from the input ratio space so that the reactant cost is minimized. We propose an integer linear programming-based method that terminates very fast while producing the optimum solution, considering both uniform and weighted cost of reagents. Experimental results reveal that the proposed method can be used conveniently in tandem with several existing sample-preparation algorithms for improving their performance.
Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs
(2019-01-01)
Song, Ling-Yen
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Chen, Yi-Ling
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Lei, Yung-Chun
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Huang, Juinn-Dar
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電子工程學系及電子研究所
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.
Reactant Cost Minimization through Target Concentration Selection on Microfluidic Biochips
(2016-01-01)
Lei, Yung-Chun
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Chen, Yi-Ling
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Huang, Juinn-Dar
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Nowadays, lab-on-a-chip is one of the most sophisticated technologies, while sample preparation, one of the essential steps in bioassays, is the process that mixes buffer and reactant to achieve the desired target concentration. Since the reactant consumption generally takes a notable part of the total cost in a bioassay, minimizing its usage is generally preferable. Existing techniques only consider one fixed target concentration, whereas a range of values is acceptable in real applications. In this paper, we propose two algorithms that can select a feasible concentration value within a given range for cost minimization. Experimental results show that the proposed methods can further reduce the reactant cost up to 20%.