2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015
This paper presents a non-contact electrocardiogram (ECG) measurement platform that compensates f... more This paper presents a non-contact electrocardiogram (ECG) measurement platform that compensates for motion-induced impedance changes via interdigitated electrode channels in concert with software reconstruction algorithms. Specifically, the impedance of the non-contact electrode is non-invasively acquired in real-time by exploiting a custom electrode designed with two independent channels featuring independent transfer functions that are used to reconstruct motion-compensated ECG waveforms. The developed platform is validated on human subjects, illustrating up to a 76.3% improvement over conventional approaches, paving the path towards comfortable, convenient, and robust non-contact electrophysiological sensing.
2013 International Symposium onVLSI Design, Automation, and Test (VLSI-DAT), 2013
Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to ac... more Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to achieve given target concentrations in sample preparation. Since some reactants like costly reagents and infant's blood are valuable, their usage should be minimized during dilution. In this paper, we propose an optimal reactant minimization algorithm, GORMA, for sample preparation on digital microfluidic biochips. GORMA adopts a systematic method to exhaustively check all possible dilution solutions and then identifies the one with minimal reactant usage and waste through maximal droplet sharing. Experimental results show that GORMA outperforms all the existing methods in reactant usage. Meanwhile, the waste amount is reduced up to 30% as compared with existing waste minimization methods. Moreover, GORMA requires only 0.6% more operations on average when compared with an operation-minimal dilution method. I.
Digital microfluidic biochip (DMFB) is a latest development in biomedical electronics. DMFBs can ... more Digital microfluidic biochip (DMFB) is a latest development in biomedical electronics. DMFBs can replace traditional bench-top equipments, which are generally costly and bulky, to accelerate processes and save the costs of biochemical experiments. However, synthesis of various reactions on a biochip is a complicated work and thus needs the help of design automation tools. One of the major optimization goals of DMFB synthesis is latency minimization. To minimize the assay latency, module selection must be considered in synthesis flow. Most of current approaches with module selection capability adopt non-deterministic methods, such as genetic algorithms or Tabu searches. These methods may consume lots of runtime and thus make online (real-time) synthesis impossible. In this thesis, I propose an efficient latency-optimization synthesis with module selection ability, named LOSMOS. It minimizes assay latency by storage minimization and latency-driven iterative rebinding. Experimental results show that LOSMOS outperforms all the previous works, including the state-of-the-art Path-scheduler by 18.22% in terms of latency reduction; and even does better than an optimal ILP-based scheduler without module selection in most cases with very little runtime.
Sample preparation is an essential processing step in most biochemical applications. Various reac... more Sample preparation is an essential processing step in most biochemical applications. Various reactants are mixed together to produce a solution with the target concentration. Since reactants generally take a notable part of the cost in a bioassay, their usage should be minimized whenever possible. In this paper, we propose an algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles in the matrix, where each rectangle indicates an opportunity of dilution operation sharing for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 27% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-the-art algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.
Digital microfluidic biochip (DMFB) is one of the latest developments in bioelectronics. It can r... more Digital microfluidic biochip (DMFB) is one of the latest developments in bioelectronics. It can replace traditional bulky equipment to facilitate biochemical assays and save the labor cost. Latency optimization is a common optimization objective of synthesis on DMFBs for assay time reduction. It is helpful for latency minimization if an operation can freely select one from a set of functional modules with various area-latency tradeoffs. In this paper, we propose an efficient latency-optimization synthesis algorithm with module selection capability, LOSMOS, for DMFBs. Experimental results show that LOSMOS outperforms several existing state-of-the-art synthesis algorithms and an ILP-based method without module selection.
Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to ac... more Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to achieve given target concentrations in sample preparation. Since some reactants like costly reagents and infant's blood are valuable, their usage should be minimized during dilution. In this paper, we propose an optimal reactant minimization algorithm, GORMA, for sample preparation on digital microfluidic biochips. GORMA adopts a systematic method to exhaustively check all possible dilution solutions and then identifies the one with minimal reactant usage and waste through maximal droplet sharing. Experimental results show that GORMA outperforms all the existing methods in reactant usage. Meanwhile, the waste amount is reduced up to 30% as compared with existing waste minimization methods. Moreover, GORMA requires only 0.6% more operations on average when compared with an operation-minimal dilution method.
Sample preparation is an indispensable process to biochemical reactions. Original reactants are u... more Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.
Sample preparation is one of essential processes in biochemical reactions. Raw reactants are dilu... more Sample preparation is one of essential processes in biochemical reactions. Raw reactants are diluted in this process to achieve given target concentrations. A bioassay may require several different target concentrations of a reactant. Both the dilution operation count and the reactant usage can be minimized if multiple target concentrations are considered simultaneously during sample preparation. Hence, in this paper, we propose a multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips. Experimental results show that our waste recycling algorithm can reduce the waste and operation count by 48% and 37%, respectively, as compared to an existing state-of-the-art multitarget sample preparation method if the number of target concentrations is ten. The reduction can be up to 97% and 73% when the number of target concentrations goes even higher.
2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015
This paper presents a non-contact electrocardiogram (ECG) measurement platform that compensates f... more This paper presents a non-contact electrocardiogram (ECG) measurement platform that compensates for motion-induced impedance changes via interdigitated electrode channels in concert with software reconstruction algorithms. Specifically, the impedance of the non-contact electrode is non-invasively acquired in real-time by exploiting a custom electrode designed with two independent channels featuring independent transfer functions that are used to reconstruct motion-compensated ECG waveforms. The developed platform is validated on human subjects, illustrating up to a 76.3% improvement over conventional approaches, paving the path towards comfortable, convenient, and robust non-contact electrophysiological sensing.
2013 International Symposium onVLSI Design, Automation, and Test (VLSI-DAT), 2013
Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to ac... more Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to achieve given target concentrations in sample preparation. Since some reactants like costly reagents and infant's blood are valuable, their usage should be minimized during dilution. In this paper, we propose an optimal reactant minimization algorithm, GORMA, for sample preparation on digital microfluidic biochips. GORMA adopts a systematic method to exhaustively check all possible dilution solutions and then identifies the one with minimal reactant usage and waste through maximal droplet sharing. Experimental results show that GORMA outperforms all the existing methods in reactant usage. Meanwhile, the waste amount is reduced up to 30% as compared with existing waste minimization methods. Moreover, GORMA requires only 0.6% more operations on average when compared with an operation-minimal dilution method. I.
Digital microfluidic biochip (DMFB) is a latest development in biomedical electronics. DMFBs can ... more Digital microfluidic biochip (DMFB) is a latest development in biomedical electronics. DMFBs can replace traditional bench-top equipments, which are generally costly and bulky, to accelerate processes and save the costs of biochemical experiments. However, synthesis of various reactions on a biochip is a complicated work and thus needs the help of design automation tools. One of the major optimization goals of DMFB synthesis is latency minimization. To minimize the assay latency, module selection must be considered in synthesis flow. Most of current approaches with module selection capability adopt non-deterministic methods, such as genetic algorithms or Tabu searches. These methods may consume lots of runtime and thus make online (real-time) synthesis impossible. In this thesis, I propose an efficient latency-optimization synthesis with module selection ability, named LOSMOS. It minimizes assay latency by storage minimization and latency-driven iterative rebinding. Experimental results show that LOSMOS outperforms all the previous works, including the state-of-the-art Path-scheduler by 18.22% in terms of latency reduction; and even does better than an optimal ILP-based scheduler without module selection in most cases with very little runtime.
Sample preparation is an essential processing step in most biochemical applications. Various reac... more Sample preparation is an essential processing step in most biochemical applications. Various reactants are mixed together to produce a solution with the target concentration. Since reactants generally take a notable part of the cost in a bioassay, their usage should be minimized whenever possible. In this paper, we propose an algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles in the matrix, where each rectangle indicates an opportunity of dilution operation sharing for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 27% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-the-art algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.
Digital microfluidic biochip (DMFB) is one of the latest developments in bioelectronics. It can r... more Digital microfluidic biochip (DMFB) is one of the latest developments in bioelectronics. It can replace traditional bulky equipment to facilitate biochemical assays and save the labor cost. Latency optimization is a common optimization objective of synthesis on DMFBs for assay time reduction. It is helpful for latency minimization if an operation can freely select one from a set of functional modules with various area-latency tradeoffs. In this paper, we propose an efficient latency-optimization synthesis algorithm with module selection capability, LOSMOS, for DMFBs. Experimental results show that LOSMOS outperforms several existing state-of-the-art synthesis algorithms and an ILP-based method without module selection.
Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to ac... more Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to achieve given target concentrations in sample preparation. Since some reactants like costly reagents and infant's blood are valuable, their usage should be minimized during dilution. In this paper, we propose an optimal reactant minimization algorithm, GORMA, for sample preparation on digital microfluidic biochips. GORMA adopts a systematic method to exhaustively check all possible dilution solutions and then identifies the one with minimal reactant usage and waste through maximal droplet sharing. Experimental results show that GORMA outperforms all the existing methods in reactant usage. Meanwhile, the waste amount is reduced up to 30% as compared with existing waste minimization methods. Moreover, GORMA requires only 0.6% more operations on average when compared with an operation-minimal dilution method.
Sample preparation is an indispensable process to biochemical reactions. Original reactants are u... more Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.
Sample preparation is one of essential processes in biochemical reactions. Raw reactants are dilu... more Sample preparation is one of essential processes in biochemical reactions. Raw reactants are diluted in this process to achieve given target concentrations. A bioassay may require several different target concentrations of a reactant. Both the dilution operation count and the reactant usage can be minimized if multiple target concentrations are considered simultaneously during sample preparation. Hence, in this paper, we propose a multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips. Experimental results show that our waste recycling algorithm can reduce the waste and operation count by 48% and 37%, respectively, as compared to an existing state-of-the-art multitarget sample preparation method if the number of target concentrations is ten. The reduction can be up to 97% and 73% when the number of target concentrations goes even higher.
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Papers by Chia-Hung Liu