Precise reagent mixing in user-defined ratios is a fundamental requirement in many microfluidic applications, including diagnostics, chemical synthesis, and biological assays. However, existing solutions for ratio-specific mixing often rely on complex active components, such as multiple pressure sources, flow controllers, or on-chip valves, making them costly, bulky, and unsuitable for portable or low-resource settings. In this work, we present a mixer design and a synthesis method for generating 3D-printable microfluidic devices that achieve ratiospecific mixing using only a single constant pressure source. Our method decomposes the desired mixing ratio into additive subcomponents, each represented by a dedicated inlet channel with a tailored length to enforce the correct hydraulic resistance. The method outputs a complete microfluidic layout, ready for direct fabrication via 3D printers. We validate our approach through numerical simulations and physical prototyping across eight diverse mixing scenarios. Results show that the achieved mixing ratios closely resemble the target, demonstrating the method’s accuracy and robustness. This work enables low-cost, portable, and accessible microfluidic devices for ratio-specific solution delivery, broadening the scope of microfluidics in settings where simplicity, reproducibility, and affordability are critical.

Microfluidic biochips play an important role in point-of-care diagnosis. Advanced microfluidic biochips can efficiently perform various fluidic operations and robustly execute bioassays like protein synthesis, drug discovery, and many others. Micro-electrode-dot-array (MEDA) digital microfluidic biochips are one of the promising new generation microfluidic biochips consisting of an array of micro-electrodes with dedicated sensors on each electrode. Its ability to manipulate discrete droplets of different volumes and route them in any direction makes it an advanced microfluidic technology. However, the working principle of MEDA and conventional digital microfluidic biochips is based on the same phenomenon called electro-wetting-on-dielectric. In spite of all the advantages, MEDA still suffers from electrode malfunctioning due to dielectric breakdown. This kind of electrode malfunction happens primarily due to repeated actuation or prolonged actuation. Therefore, it is important to develop new bioassay synthesis methods that ensure reliability and guarantee error-free operations on MEDA. Since repeated and prolonged electrode actuations primarily occur within the modules, the reliability of MEDA biochips can be ensured with an appropriate placement strategy. As fluid routing is an inherent phase to complete the synthesis of any bioassay, we also propose a simple collision avoidance routing algorithm for MEDA. In this article, we propose a two-phase synthesis technique for MEDA biochips to improve the reliability of the biochips. First, a reinforcement learning-based placement method ( RLPM ) is designed for obtaining the reliability-aware placement of rectilinear-shaped microfluidic modules. Then, we propose a heuristic-based approach called collision avoidance MEDA routing ( CAMR ) that determines the collision-free routes for on-chip transportation of droplets of different volumes. RLPM utilizes the power of reinforcement learning and aims to minimize the utilization area of the chip while increasing its reliability. Whereas, RLPM together with CAMR minimizes the total completion time of the bioassay. Simulation results justify that the proposed synthesis technique can reduce the total chip area utilization by 28.6% (on average) without compromising the chip reliability compared to the state-of-the-art techniques.

Real-time execution of bioassays on microelectrode dot array (MEDA) biochips can revolutionize point-of-care diagnostics. Despite numerous design automation efforts for digital microfluidic biochips (DMFBs), there is no online synthesis framework for their advanced counterpart, MEDA biochips, which have various operational advantages. The DMFB-specific virtual topology [Grissom et al., TCAD, 2014] is inadequate for MEDA synthesis as it suffers from significant chip-area fragmentation issues. In this article, we propose a virtual topology that provides an abstraction of the physical geometry of MEDA biochips, thereby reducing the algorithmic runtime for MEDA synthesis by reducing the search space for optimization problems. On top of this, we propose an A rea-efficient O nline P lacer ( AOP ) to achieve placement results with minimum chip-area. However, due to the overuse of some microelectrodes, they may be degraded, which may result in unreliable outcomes of the bioassays. P lacement R eliability E nhancer ( PRE ) is proposed to improve the reliability of MEDA chips. Finally, a F ast O nline S ynthesis ( FOS ) is proposed for MEDA based on the virtual topology and these two methods, which always outperform the existing online synthesis in the average assay completion time and the number of routing sub-problems. Simulations confirm that AOP significantly reduces both occupied area and algorithmic runtime compared with state-of-the-art WLSM and RLPM , albeit at the expense of reliability. By integrating PRE , AOP+PRE achieves reliability similar to WLSM and RLPM , but requires additional chip area compared with AOP while maintaining similar runtime efficiency. Hence, AOP+PRE enables efficient and reliable online synthesis for MEDA biochips.

The 2-D architecture of fully programmable valve arrays (FPVAs) is designed as a crossbar consisting of reaction chambers and microvalves, functioning as a versatile, flow-based microfluidic lab-on-chip for implementing biochemical protocols. While an FPVA enables efficient execution of various fluidic operations—such as mixing, loading, and storage, transporting fluids between chambers remains a challenging task. Furthermore, mapping a general mixing tree (representing a sequence of mixing steps) onto an FPVA is complex. It requires careful placement of reagents into specific chambers and the scheduling of subsequent mixing operations. Most sample preparation algorithms aim to generate a minimum-depth mixing tree to achieve the target mixing ratio. However, due to constraints on fluid transportation and scheduling, such a tree may not be the most practical for FPVA implementation. In this article, we harness the power of a satisfiability solver to derive a skewed mixing tree/graph that can be efficiently mapped onto an FPVA using a single mixer. This approach localizes most fluidic operations to a small region of the crossbar. Simulation results show that, for most mixing ratios, a skewed mixing tree can be found which not only reduces fluid-transportation distance and scheduling complexities but also the number of loading cycles, reagent volumes, and waste production in sample preparation, when compared to the approach based on the minimum-depth mixing tree.

promising new generation microfluidic biochips consisting of a sea-of-micro-electrodes with dedicated detection circuit for each microelectrode. Moreover, the ability to manipulate discrete droplets of different volumes and to route them in any direction presents MEDA biochips as an advanced microfluidic technology. Due to similarity in the working principles, the reliability issues of both MEDA biochips and digital microfluidic biochips are similar. In this paper, we propose a module placement technique for MEDA biochips to improve the reliability of biochips. Reinforcement learning based placement method (RLPM) is designed for obtaining the reliability-aware placement of rectilinear shaped microfluidic modules. RLPM aims to minimize the area of a biochip while increasing its reliability. Simulation results confirm that on average RLPM minimizes the chip utilization area by 28.6% while enhancing the reliability of MEDA biochips compared to the state-of-the-art method.

Sample preparation is an essential step in almost all bioprotocols, which can be efficiently achieved via a sequence of mixing steps called mixing graph. In the literature, several techniques have been reported to determine a mixing graph with the minimal number of mixing steps, the minimal usage of reagent fluids, the minimal wastage, or sometimes a combination of them. The retention time of a flow-based microfluidic biochip (FMB) is defined as the maximum duration for which a fluid can be stored within a microchannel without any fluid leakage. However, the retention time has not yet been considered as a scheduling constraint during the automation of the sample preparation using an FMB in order to obtain the scheduled mixing graphs. In this article, we propose a retention time-aware scheduling method called time-aware list scheduling (TALS), which can be used with the state-of-the-art methods, and a new satisfiability-based mixing algorithm called time-aware sample preparation (TASP) to obtain the scheduled mixing graph for a target ratio satisfying the retention time constraint and the number of available on-chip mixers in an FMB. Simulation results suggest that on an average TALS always outperforms a baseline scheduling method while scheduling any mixing graph, whereas TASP can determine the optimal and scheduled mixing graphs compared to the existing mixing methods combined with TALS.

Sample preparation is an inherent procedure of many biochemical applications, and digital microfluidic biochips (DMBs) have proved to be very effective in performing such a procedure. In a single mixing step, conventional DMBs can mix two droplets in a 1:1 ratio only. Due to this limitation, DMBs suffer from heavy fluid wastage and often require a lot of mixing steps. However, the next-generation DMBs, i.e., micro-electrode-dot-array (MEDA) biochips, can realize multiple mixing ratios, which in general helps in minimizing the number of mixing operations. In this article, we present a heuristic-based sample preparation algorithm, specifically a mixing algorithm calledDivision by Factor Method for MEDAthat exploits the mixing models of MEDA biochips. We propose another mixing algorithm for MEDA biochips calledSingle Target Waste Minimization(STWM), which minimizes the wastage of fluids and determines an efficient mixing graph. We also propose an advanced methodology for multiple target reagent mixing problems calledMulti-target Waste Minimization(MTWM), which determines efficient mixing graphs for different target ratios by maximizing the sharing of fluids and minimizing the fluid wastage. Simulation results suggest that the proposedSTWMandMTWMmethods outperform the state-of-the-art methods in terms of minimizing the amount of fluid wastage, reducing the total usage of reagent fluids, and minimizing the number of mixing operations.

Sample preparation is an inherent procedure of many biochemical applications, and digital microfluidic biochips (DMBs) proved to be very effective in performing such a procedure. In a single mixing step, conventional DMBs can mix two droplets in 1:1 ratio only. Due to this limitation, DMBs suffer from heavy fluid wastage and large number of mixing steps. However, the next generation DMBs, i.e., micro-electrode-dot-array (MEDA) biochips can realize multiple mixing ratios and are able to overcome a lot of those limitations. In this paper, we present a heuristic-based sample preparation algorithm, specifically a mixing algorithm called Division by Factor Method for Mixing that exploits the mixing models of MEDA biochips. We propose another mixing algorithm for MEDA biochips called Single Target Waste Minimization (STWM), which minimizes the wastage of fluids and determines an optimized mixing graph. Simulation results confirm that the proposed STWM method outperforms the state-of-the-art method in terms of minimizing the number of waste fluids, reducing the total reagent usage, and minimizing the number of mixing operations.

Microfluidic biochips have recently emerged with significant promise and versatility in automating a variety of biochemical protocols on a tiny chip. Sample preparation, which involves the mixing of fluids with a specified target ratio in the minuscule scale, is an essential component of these protocols. Algorithms that optimize on-chip sample-preparation cost and time are closely intertwined with the underlying mixing model, mixing sequence, and fluidic architecture. Although numerous mixing models have been studied in the literature, their impact on the dynamics of mixing steps is hitherto not fully understood. In this article, we show that various mixing models can be envisaged in the light of prime factorization of integers thus establishing a connection among mixing algorithms, chip architectures, and performance. This insight has led to the development of the proposed factorization-based dilution algorithm (FacDA) considering a generalized mixing model suitable for micro-electrode-dot-array (MEDA) biochips. It further leads to target volume oriented dilution algorithm (TVODA) to cater to user’s demand for an output with a given volume. We formulate the optimization problem on the fabric of the satisfiability modulo theory (SMT) while determining mixing sequences. Simulation results on a large number of test-cases reveal thatFacDAandTVODAoutperform the state-of-the-art dilution algorithms for MEDA biochips with respect to reactant cost, mixing time, and waste production.

Among recent technological advances, microfluidic biochips have been leading a prominent solution for healthcare and miniaturized bio-laboratories with the assurance of high sensitivity and reconfigurability. On increasing more unreliable communication networks day-by-day, technological shifts in the fields of communication and security are now converging. In today's cyber threat landscape, these microfluidic biochips are ripe targets of powerful cyber-attacks from different hackers or cyber-criminals. Hence, securing such systems is of paramount importance. This paper presents the security aspects of digital microfluidic biochip layout to protect the confidentiality of layout data from unscrupulous people and man-in-the-middle attacks. We propose an authentication mechanism with an error control mechanism that provides reliability, authentication, trustworthy and safety for both storage and communication of GDS, i.e., Graphical Design System, file generally used for digital microfluidic biochip layouts. Simulation results articulate the efficacy of the proposed security model without the overhead of the bioprotocol completion time. The proposed scheme, which used AES as an encryption algorithm with a 256-bit encryption key, has also shown a speedup of 6.0 (with 85% efficiency) faster than the prior efficient scheme. We hope to develop a secure layout design flow for biochips to achieve better resistance to any attack.

Microfluidic lab-on-a-chip has emerged as a new technology for implementing biochemical protocols on small-sized portable devices targeting low-cost medical diagnostics. Among various efforts of fabrication of such chips, programmable microfluidic device (PMD) is a relatively new technology for implementation of flow-based lab-on-a-chips. A PMD chip is suitable for automation due to its symmetric nature. In order to implement a bioprotocol on such a reconfigurable device, it is crucial to automate sample preparation on a chip as well. Sample preparation, which is a front-end process to produce the desired target concentrations of the input reagent fluid, plays a pivotal role in every bioassay or bioprotocol. In this paper, first, a method referred as dilution algorithm in two steps (DATS) is proposed, which needs only two diluting operations for any target concentration to achieve. Then, we present another method called as dilution algorithm on a small dilution area (DASDA), which needs less area compared to that by DATS. Finally, we propose the heuristic for efficient dilution of biochemical fluids using a PMD chip referred as dilution algorithm in a restricted dilution area (DARDA) that produces more accurate (with less error) target concentration value on a restricted area of the PMD chip in a shorter mixing time. Simulation results reveal that DARDA outperforms a start-of-the-art dilution algorithm applicable for PMD chips in terms of three performance parameters namely mixing time, mixing area and error in target concentration.

Recently, Programmable Microfluidic Device (PMD) has got an attention of the design automation communities as a new type of microfluidic biochips. For the design of PMD chips, one of the important tasks is to minimize the number of flows for loading the reactant fluids into specific cells (by creating some flows of the fluids) before the bio-protocol is executed. Nevertheless of the importance of the problem, there has been almost no work to study this problem. Thus, in this paper, we intensively study this fluid loading problem in PMD chips. First, we successfully formulate the problem as a constraint satisfaction problem (CSP) to solve the problem optimally for the first time. Then, we also propose an efficient heuristic called Determining Flows from the Last (DFL) method for larger problem instances. DFL is based on a novel idea that it is better to determine the flows from the last flow unlike the state-of-the-art method Fluid Loading Algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Simulation results confirm that the exact method can find the optimal solutions for practical test cases, whereas our heuristic can find near-optimal solutions, which are better than those obtained by FLAP.

Microfluidic fully programmable valve array (FPVA) biochips have emerged as general-purpose flow-based microfluidic lab-on-chips (LoCs). An FPVA supports highly re-configurable on-chip components (modules) in the two-dimensional grid-like structure controlled by some software programs, unlike application-specific flow-based LoCs. Fluids can be loaded into or washed from a cell with the help of flows from the inlet to outlet of an FPVA, whereas cell-to-cell transportation of discrete fluid segment(s) is not precisely possible. The simplest mixing module to realize on an FPVA-based LoC is a four-way mixer consisting of a 2 × 2 array of cells working as a ring-like mixer having four valves. In this paper, we propose a design automation method for sample preparation that finds suitable placements of mixing operations of a mixing tree using four-way mixers without requiring any transportation of fluid(s) between modules. We also propose a heuristic that modifies the mixing tree to reduce the sample preparation time. We have performed an extensive simulation and examined several parameters to determine the performance of the proposed solution.

The estimation of crop area in advance takes us a step closer towards the intelligent farming as it is beneficial in both pre and post harvesting scenarios for better utilization of resources and higher production at a reasonable cost. There are many challenges in the estimation of crop area in freely available low resolution satellite images due to mixed pixels, especially in the boundaries of crop classes. A neural network has the ability to learn from unknown patterns in the satellite images and then take a decision based on their learning. Fuzzy logic is used together with a neural network that can explain partial membership of each class. Hence, in this paper, we integrate these two models and found it useful to perform accurate estimation of area for each crop class. A quantitative analysis is performed with the help of reference data created by drone images and global positioning system field survey. This study indicates that the proposed method improves the accuracy of area estimation for the crop classes.

Earthquakes, floods, tsunami, and other natural disasters are appearing as worldwide threats because of their widespread destruction that results in thousands of human and economic losses. It is vital for first responders to know the root cause of damages in a region so that the emergency response activities can be planned accordingly and more effectively. We proposed a framework for the detection and recognition of natural disasters from satellite images. In this work, satellite images of six different types of disasters are considered, namely earthquake, volcanic eruption, flood, tsunami, and hurricane. The framework relies on the fusion of wavelet image scattering features and local binary pattern features for constructing the final feature vector. We also compared the accuracy of our framework with the existing state-of-the-art hand-crafted and machine learning models. Simulation results confirm that the proposed framework is able to recognize the type of natural disaster from the satellite images with an accuracy of 99.59% (Kappa coefficient 98.54% and F-Score 99.40%). The proposed approach results in less computational cost while achieving better accuracy compared to the deep convolutional neural network. We believe that the proposed model can be integrated with satellite imagery for locating the geographical regions affected by the multiple natural disaster events at the same time or at short intervals.

Reliability of the prepared fluidic samples is a major concern for automated sample preparation using microfluidic biochips, where induced errors in the resultant concentration values severely affect the assay outcome. However, the existing design automation techniques have not thoroughly considered the reliability model to reduce the induced concentration errors during sample preparation. This article proposes a fast reliability-aware sample preparation (RASP) method for determining the optimized sequence of mixing steps (mixing process) with the enhanced reliability. In RASP, a probabilistic concentration prediction model is proposed for analyzing the reliability of a given mixing process. Based on this probabilistic model, a lookup table construction algorithm along with the table query method is proposed to obtain the optimized mixing process. The simulation results show that for any user-specified target concentration, RASP can effectively determine the optimized mixing process, which generates the droplets with target concentration within the error tolerance of 0.1%. Compared with the state-of-the-art sample preparation algorithm, RASP improves the reliability-related accuracy by 91.4% on average via 2048 testcases.

Microfluidic technologies enable replacement of time-consuming and complex steps of biochemical laboratory protocols with a tiny chip. Sample preparation (i.e., dilution or mixing of fluids) is one of the primary tasks of any bioprotocol. In real-life applications where several assays need to be executed for different diagnostic purposes, the same sample fluid is often required with different target concentration factors ( CF s). Although several multi-target dilution algorithms have been developed for digital microfluidic biochips, they are not efficient for implementation with continuous-flow-based microfluidic chips, which are preferred in the laboratories. In this article, we present a multi-target dilution algorithm ( MTDA ) for continuous-flow-based microfluidic biochips, which to the best of our knowledge is the first of its kind. We design a flow-based rotary mixer with a suitable number of segments depending on the target- CF profile, error tolerance, and optimization criteria. To schedule several intermediate fluid-mixing tasks, we develop a multi-target scheduling algorithm ( MTSA ) aiming to minimize the usage of storage units while producing dilutions with multiple CF s. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.

Sample preparation, an essential preprocessing step for biochemical protocols, is concerned with the generation of fluids satisfying specific target ratios and error-tolerance. Recent micro-electrode-dot-array (MEDA)-based DMF biochips provide the advantage of supporting both discrete and dynamic mixing models, the power of which has not yet been fully harnessed for implementing on-chip dilution and mixing of fluids. In this paper, we propose a novel factorization-based algorithm called FacDA for efficient and accurate dilution of sample fluid on a MEDA chip. Simulation results reveal that over a large number of test-cases with the mixing volume constraint in the range of 4--10 units, FacDA requires around 38% fewer mixing steps, 52% less sample units, and generates approximately 23% less wastage, all on average, compared to two prior dilution algorithms used for MEDA chips.

In the response phase of disaster management, immediate actions are taken by various organizations to fulfill humanitarian needs. In a post-disaster scenario, the aim of disaster relief team is to serve maximum people within a time constraint, but by increasing the cost of supply by a small amount if lives of more people can be saved, then increase in cost can be taken positively. This paper presents a web-based tool, which finds the relief vehicle routes among real locations and visualizes it on the map in a hassle-free way. In addition, to tackle the disaster scenario, two algorithms are proposed, namely priority-based route find (PBRF) and hybrid route find (HRF), for the post-disaster relief supply. PBRF is location priority-based algorithm, which is efficient in terms of serving the maximum number of people in a single trip, but it takes a longer time. HRF is a hybrid algorithm of PBRF and distance-based route find (DBRF), i.e., a traditional algorithm of solving the travelling salesman problem (TSP). Through various simulations, all three algorithms are compared in terms of time and number of people served. Results show that HRF is efficient compared to PBRF and DBRF.

With the increased occurrences of crowd disasters like human stampedes, crowd management and their safety during mass gathering events like concerts, congregation or political rally, etc., are vital tasks for the security personnel. In this paper, we propose a framework named as CrowdVAS-Net for crowd-motion analysis that considers velocity, acceleration and saliency features in the video frames of a moving crowd. CrowdVAS-Net relies on a deep convolutional neural network (DCNN) for extracting motion and appearance feature representations from the video frames that help us in classifying the crowd-motion behavior as abnormal or normal from a short video clip. These feature representations are then trained with a random forest classifier. Furthermore, a dataset having 704 video clips having dense crowded scenes have been created for performance evaluation of the proposed method. Simulation results confirm that the proposed CrowdVAS-Net achieves the classification accuracy of 77.8% outperforming the state-of-the-art machine learning models. Moreover, this framework can reduce the video processing and analyzing time up to 96.8% compared to state-of-the-art techniques on the larger dataset. Based on our results, we believe that our work will help security personnel and crowd managers in ensuring the public safety during mass gatherings with better accuracy.

With the evolution of the technology, digital microfluidic (DMF) biochips have become a vital part of biochemical research. Hence, it is required to consider the reliability of the different fluidic operations performed on a biochip. Sample preparation is an important process of any real-life bioprotocol implementation on a DMF biochip. In this process, sequence of mixing and dilution steps are determined to get the desired target concentrations. The mixers used for performing mix-split steps may incorporate some noise during mixing and can result in the erroneous concentrations of the reagents. Thus, the reliability analysis of the resultant target concentration is necessary and methods are required to be developed to reduce these concentration errors. In this paper, the error and reliability models are discussed to compare the reliabilities of the existing mixing algorithms. Simulation results show that for a given target ratio, reliability of common dilution operation sharing (Liuet al., ICCAD-2013) is higher. We also discuss the mixer assignment techniques and the heuristic approach is found to quickly provide the better order of mixer assignment in order to achieve highly reliable mixture after sample preparation.

Microfluidic lab-on-a-chip has emerged as a new technology for implementing biochemical protocols on small-sized portable devices targeting low-cost medical diagnostics. Among various efforts of fabrication of such chips, a relatively new technology is a programmable microfluidic device (PMD) for implementation of flow-based lab-on-a-chip. A PMD chip is suitable for automation due to its symmetric nature. In order to implement a bioprotocol on such a reconfigurable device, it is crucial to automate a sample preparation on-chip as well. In this article, we propose a dilution PMD algorithm (namely DPMD ) and its architectural mapping scheme (namely generalized architectural mapping algorithm ( GAMA )) for addressing fluidic cells of such a device to perform dilution of a reagent fluid on-chip. We used an optimization function that first minimizes the number of mixing steps and then reduces the waste generation and further reagent requirement. Simulation results show that the proposed DPMD scheme is comparative to the existing state-of-the-art dilution algorithm. The proposed design automation using the architectural mapping scheme reduces the required chip area and, hence, minimizes the valve switching that, in turn, increases the life span of the PMD-chip.

In recent years, the design of all-optical circuits has received a great attention among the researchers due to high-speed and low-power characteristics and compatibility with CMOS technology. Some combinational logic circuits like adders, subtractors, multipliers, multiplexers, which are useful in optical communication network in data centers and high-perform computers, have been designed using optical components. There are two different design styles, called as Design1 (based on conventional truth-table based approach) and Design2 (based on binary decision diagram). In this paper, four different all-optical multipliers have been explored for array multiplier and carry save adder (CSA)-based multiplier based on these two design styles, using semiconductor optical amplifier (SOA) based Mach-Zender interferometers (MZIs). Simulation results confirm that MZI-based CSA multiplier Design1) has the lowest optical cost and delay compared to those of other three multiplier designs (CSA multiplier - Design2, array multiplier - Design1, array multiplier - Design2) with a precision of 2 or more bits. Further, the proposed all-optical CSA multiplier designs outperform in terms of both optical cost and delay compared to the state-of-the-art designs of all-optical multipliers.

Recent studies in algorithmic microfluidics have led to the development of several techniques for automated solution preparation using droplet-based digital microfluidic (DMF) biochips. A major challenge in this direction is to produce a mixture of several reactants with a desired ratio while optimizing reactant cost and preparation time. The sequence of mix-split operations that are to be performed on the droplets is usually represented as a mixing tree (or graph). In this article, we present an efficient mixing algorithm, namely, Mixing Tree with Common Subtrees ( MTCS ), for preparing single-target mixtures. MTCS attempts to best utilize intermediate droplets, which were otherwise wasted, and uses morphing based on permutation of leaf nodes to further reduce the graph size. The technique can be generalized to produce multitarget ratios, and we present another algorithm, namely, Multiple Target Ratios ( MTR ). Additionally, in order to enhance the output load, we also propose an algorithm for droplet streaming called Multitarget Multidemand ( MTMD ). Simulation results on a large set of target ratios show that MTCS can reduce the mean values of the total number of mix-split steps ( T ms ) and waste droplets ( W ) by 16% and 29% over Min-Mix (Thies et al. 2008) and by 22% and 34% over RMA (Roy et al. 2015), respectively. Experimental results also suggest that MTR can reduce the average values of T ms and W by 23% and 44% over the repeated version of Min-Mix , by 30% and 49% over the repeated version of RMA , and by 9% and 22% over the repeated-version of MTCS , respectively. It is observed that MTMD can reduce the mean values of T ms and W by 64% and 85%, respectively, over MTR . Thus, the proposed multitarget techniques MTR and MTMD provide efficient solutions to multidemand, multitarget mixture preparationon a DMF platform.

In recent years, digital microfluidic biochips are being dominantly used for implementing a wide range of biochemical laboratory protocols (bioprotocols) on hand-held devices. Accurate preparation of fluid-samples is a fundamental preprocessing step that is needed in many bioprotocols. Oftentimes, the number of reservoirs built on-chip may be far less than that of the reactant fluids to be mixed. Hence, during the execution of an assay, several fluids are to be unloaded from the reservoirs to make room for loading new fluids stored off-line. Such unload-wash-load steps (switching) may be required several times, and these steps, being manual, significantly impact assay-completion time. In this paper, we propose a new scheduling scheme namely Reservoir and Mixer constrained Scheduling (RMS) that can schedule a mixing tree obtained by a mixing algorithm, while minimizing the number of switching such that the total completion time can be minimized. Simulation results over a large number of target ratios show that given the mixing trees obtained by standard mixing algorithms such as MinMix/RMA/CoDOS, RMS reduces switching steps (on average by 40.3%/41.9%/33%) at the cost of increasing mixing time (by only 3.5%/6.2%/4.8%), compared to an existing scheduling scheme invoked with reservoir constraints.

Synthetic biologists design genetic logic circuit using living cells. A challenge in this task is the difficulty in constructing bigger logic circuits with several living cells due to the crosstalk effect among the biological cells. In order to remove the crosstalk effect, current practice is to use separate chambers on a flow-based microfluidic biochip to isolate each reaction zone. The state-of-the-art technique assumes different reaction times for each gates in a genetic logic circuit. This assumption is pessimistic as each gate has different reaction rate from others. Hence, it will cause unnecessary waiting time for faster gates and this may in turn increase the total experiment completion time significantly. In this paper, we propose a genetic logic circuit synthesis technique for flow-based microfluidic biochip considering different reaction time of each logic gate. Simulation results show that the proposed scheme reduces the total experiment completion time. We further minimize the number of control valves and optimize the routing of flow and control layers in the chip layout, which in turn reduces the design cost.

Microfluidic-based lab-on-a-chips have emerged as a popular technology for implementation of different biochemical test protocols used in medical diagnostics. However, in the manufacturing process or during operation of such chips, some faults may occur that leads to damage of the chip, which in turn results in wastage of expensive reagent fluids. In order to make the chip fault-tolerant, the state-of-the-art technique adopts simulated annealing (SA) based approach to synthesize a fault-tolerant architecture. However, the SA method is time consuming and non-deterministic with over-simplified model that usually derive sub-optimal results. Thus, we propose a progressive optimization procedure for the synthesis of fault-tolerant flow-based microfluidic biochips. Simulation results demonstrate that proposed method is efficient compared to the state-of-the-art techniques and can provide effective solutions in 88% (on average) less CPU time compared to state-of-the-art technique over three benchmark bioprotocols.

Albeit sample preparation is well-studied for digital microfluidic biochips, very few prior work addressed this problem in the context of continuous-flow microfluidics from an algorithmic perspective. In the latter class of chips, microvalves and micropumps are used to manipulate on-chip fluid flow through microchannels in order to execute a biochemical protocol. Dilution of a sample fluid is a special case of sample preparation, where only two input reagents (commonly known as sample and buffer) are mixed in a desired volumetric ratio. In this paper, we propose a satisfiability-based dilution algorithm assuming the generalized mixing models supported by an N-segment, continuous-flow, rotary mixer. Given a target concentration and an error limit, the proposed algorithm first minimizes the number of mixing operations, and subsequently, reduces reagent-usage. Simulation results demonstrate that the proposed method outperforms existing dilution algorithms in terms of mixing steps (assay time) and waste production, and compares favorably with respect to reagent-usage (cost) when 4- and 8-segment rotary mixers are used. Next, we propose two variants of an algorithm for handling the open problem of k-reagent mixture-preparation (k ≥ 3) with an N-segment continuous-flow rotary mixer, and report experimental results to evaluate their performance. A software tool called flow-based sample preparation algorithm has also been developed that can be readily used for running the proposed algorithms.

As the complexity of vehicular distributed systems increases rapidly, several hundreds of devices are being placed in a modern automotive system. With the increase in wiring cables connecting these devices, the weight of a vehicle increases significantly and degrades the fuel efficiency during driving. In order to reduce the wiring weight, wireless communication has been introduced to replace wiring cables between some devices. However, the extra energy consumption and the transmission delay due to wireless communication need to be considered because they may result in frequent maintenance (e.g., recharging of batteries) and deadline violation, respectively. In this paper, we propose an intravehicle network routing algorithm to simultaneously minimize the wiring weight and the wireless transmit power while considering the transmission delay in automotive systems. Experimental results show that the proposed method can effectively minimize the wiring weight and the wireless transmit power and satisfy other design constraints.

In many biochemical protocols, a mixture of several fluids in a certain ratio is repeatedly required, and hence a sufficient quantity of the mixture must be supplied for on-chip bioassay completion. For example, in polymerase chain reaction (PCR) a premixed and ready-to-use solution, called PCR master mix containing different fluids at optimal concentrations, is used for efficient amplification of DNA templates by PCR. Existing scheduling scheme, namely SRS (storage reduced scheduling), finds a trade-off between time of completion and storage unit requirement depending on the demand while designing a mixing engine using the (1 : 1) mixing model on a digital microfluidic (DMF) biochip. In this paper, we present a scheduling scheme, namely KMS, for optimizing time and storage requirements in multiple-demand mixture preparation using a DMF rotary mixer. The scheduling scheme has been combined with all existing mixing algorithms, namely MinMix, RMA, MTCS and CoDOS to analyze its performance. Simulation results show significant reduction in latency (75%) and storage (48%) requirements by KMS compared with SRS for multiple-demand mixture preparation.

As finite energy resources are being consumed at faster rate than they can be replaced, renewable energy resources have drawn extensive attention. Wind power development is one such example growing significantly throughout the world. The main difficulty in wind power development is that wind turbines interfere with each other. The produced turbulence—wake effect—directly reduces the power generation. In addition, wirelength of the collection network among wind turbines is not merely an economic factor; it also decides power loss on the wind farm. Moreover, in reality, obstacles (buildings, lakes, etc.) exist on the wind farm, which are unavoidable. Nevertheless, to the best of our knowledge, none of the existing works consider wake effect, wirelength, and avoidance of obstacles all together in the wind turbine placement problem. In this article, we propose an analytical method to obtain the obstacle-avoiding placement of wind turbines, thus minimizing both power loss and wake effect. We also propose a postprocessing method to fine-tune the solution obtained from the analytical method to find a better solution. Simulation results show that our tool is 12x faster than the state-of-the-art industrial tool AWS OpenWind and 203x faster than the state-of-the-art academic tool TDA with almost the same produced power.

As the complexity of vehicular distributed systems increases rapidly, several hundreds of devices (sensors, actuators, etc.) are being placed in a modern automotive system. With the increase in wiring cables connecting these devices, the weight of a car increases significantly, which degrades the fuel efficiency in driving. In order to reduce the weight of a car, wireless communication has been introduced to replace wiring cables between some devices. However, the extra energy consumption for packet transmissions by wireless devices requires frequent maintenance, e.g., recharging of batteries. In this paper, we propose an intra-vehicle network routing algorithm to simultaneously minimize the wiring weight and the transmit power for wireless communication. Experimental results show that the proposed method can effectively minimize the wiring weight and the transmit power for wireless communication.

As finite energy resources are being consumed at fast rate than they can be replaced, renewable energy resources have drawn an extensive attention. Wind power development is one such example, which is growing significantly throughout the world. The main difficulty in wind power development is that wind turbines interfere with each other. The produced turbulence, known as wake effect, directly reduces the power generation. In addition, wirelength among wind turbines is not merely an economic factor, but also it decides power loss in the wind farm. Moreover, in reality, obstacles exist in the wind farm which is unavoidable, e.g., private land, lake and so on. Nevertheless, to the best of our knowledge, none of the existing works consider wake effect, wirelength and obstacle-avoiding at the same time in the wind turbine placement problem. In this paper, we propose an analytical method to obtain the obstacle-avoiding placement of wind turbines optimizing both power loss and wake effect. Simulation results show that the wind power produced by our tool is similar to that by the industrial tool AWS OpenWind. Besides, our algorithm can reduce the wirelength and avoid obstacles successfully while finding the locations of wind turbines at the same time.

The recent proliferation of digital microfluidic (DMF) biochips has enabled rapid on-chip implementation of many biochemical laboratory assays or protocols. Sample preprocessing, which includes dilution and mixing of reagents, plays an important role in the preparation of assays. The automation of sample preparation on a digital microfluidic platform often mandates the execution of a mixing algorithm, which determines a sequence of droplet mix-split steps (usually represented as a mixing graph). However, the overall cost and performance of on-chip mixture preparation not only depends on the mixing graph but also on the resource allocation and scheduling strategy, for instance, the placement of boundary reservoirs or dispensers, mixer modules, storage units, and physical design of droplet-routing pathways. In this article, we first present a new mixing algorithm based on a number-partitioning technique that determines a layout-aware mixing tree corresponding to a given target ratio of a number of fluids. The mixing graph produced by the proposed method can be implemented on a chip with a fewer number of crossovers among droplet-routing paths as well as with a reduced reservoir-to-mixer transportation distance. Second, we propose a routing-aware resource-allocation scheme that can be used to improve the performance of a given mixing algorithm on a chip layout. The design methodology is evaluated on various test cases to demonstrate its effectiveness in mixture preparation with the help of two representative mixing algorithms. Simulation results show that on average, the proposed scheme can reduce the number of crossovers among droplet-routing paths by 89.7% when used in conjunction with the new mixing algorithm, and by 75.4% when an earlier algorithm [Thies et al. 2008] is used.

In many biochemical protocols, such as polymerase chain reaction, a mixture of fluids in a certain ratio is repeatedly required, and hence a sufficient quantity of the mixture must be supplied for assay completion. Existing sample-preparation algorithms based on digital microfluidics (DMF) emit two target droplets in one pass, and costly multiple passes are required to sustain the emission of the mixture droplet. To alleviate this problem, we design a streaming engine on a DMF biochip, which optimizes droplet emission depending on the demand and available storage. Simulation results show significant reduction in latency and reactant usage for mixture preparation.

Digital microfluidic (DMF) biochips are recently being advocated for fast on-chip implementation of biochemical laboratory assays or protocols, and several algorithms for diluting and mixing of reagents have been reported. However, all methods for such automatic sample preparation suffer from a drawback that they assume the availability of input fluids in pure form, that is, each with an extreme concentration factor ( CF ) of 100%. In many real-life scenarios, the stock solutions consist of samples/reagents with multiple CF s. No algorithm is yet known for preparing a target mixture of fluids with a given ratio when its constituents are supplied with random concentrations. An intriguing question is whether or not a given target ratio is feasible to produce from such a general input condition. In this article, we first study the feasibility properties for the generalized mixing problem under the (1:1) mix-split model with an allowable error in the target CF s not exceeding 1 2d, where the integer d is user specified and denotes the desired accuracy level of CF . Next, an algorithm is proposed which produces the desired target ratio of N reagents in ONd mix-split steps, where N ( ≥ 3) denotes the number of constituent fluids in the mixture. The feasibility analysis also leads to the characterization of the total space of input stock solutions from which a given target mixture can be derived, and conversely, the space of all target ratios, which are derivable from a given set of input reagents with arbitrary CF s. Finally, we present a generalized algorithm for diluting a sample S in minimum (1:1) mix-split steps when two or more arbitrary concentrations of S (diluted with the same buffer) are supplied as inputs. These results settle several open questions in droplet-based algorithmic microfluidics and offer efficient solutions for a wider class of on-chip sample preparation problems.

In many biochemical protocols, sample preparation is an extremely important step for mixing multiple reagents in a given ratio. Dilution of a biochemical sample/reagent is the special case of mixing or solution preparation where only two fluids (sample and buffer) are mixed at a certain ratio corresponding to the desired concentration factor. Many bioassays often require multiple concentration values of the same sample/reagent, and implementing them efficiently on a digital microfluidic biochip is a challenge. In this paper, we present an algorithmic solution for the problem of producing a set of different target droplets in a minimum number of mix-split steps, and satisfying a given upper bound in concentration error. Unlike prior methods, this approach does not require any intermediate storage. We represent the underlying search space using a binary de Brujin graph and show that a shortest mix-split sequence can be obtained by solving an asymmetric traveling salesman problem therein. Simulation results over a large data set reveal that the proposed technique outperforms existing methods in terms of the number of mix-split steps, waste droplets, and reactant usage. The method is applicable in general scenarios of either one mixer or more mixers on the chip. A digital microfluidic platform can be easily designed to implement such a technique for rapid on-chip sample preparation.

Digital microfluidic biochips are recently being developed for on-chip implementation of biochemical laboratory assays. Existing mixing algorithms determine the mixing tree or mixing graph from a given target ratio of several biochemical fluids for on-chip mixture preparation. We present an algorithm to determine a reduced mixing tree by sharing the common subtrees within itself. We observe two transformations that preserve the semantics of the tree: (a) permutation of leaf nodes (input fluids/reagents) within the same level of a mixing tree, and (b) level-shifting of a leaf node to the next lower level by duplicating its appearance. The proposed algorithm utilizes both the intermediate droplets obtained after a split operation when a pair of identical subtrees are identified under permutation of leaf nodes at the same level. Simulation results for a large set of target ratios show that our algorithm reduces the mean values of the total number of mix-split steps, waste droplets and the number of mixer modules required for earliest completion by 16%, 29% and 12% over Min-Mix and by 22%, 34% and 20% over RMA, respectively. Moreover, it reduces the number of checkpoint insertions required for dynamic error recovery against incorrect mix-split steps during mixture preparation.

Potential applications of digital microfluidic (DMF) biochips now include several areas of real-life applications like environmental monitoring, water and air pollutant detection, and food processing to name a few. In order to achieve sufficiently high throughput for these applications, several instances of the same bioassay may be required to be executed concurrently on different samples. As a straightforward implementation, several identical biochips can be integrated on a single substrate as a multichip to execute the assay for various samples concurrently. Controlling individual electrodes of such a chip by independent pins may not be acceptable since it increases the cost of fabrication. Thus, in order to keep the overall pin-count within an acceptable bound, all the respective electrodes of these individual pieces are connected internally underneath the chip so that they can be controlled with a single external control pin. In this article, we present an orientation strategy for layout of a multichip that reduces routing congestion and consequently facilitates wire routing for the electrode array. The electrode structure of the individual pieces of the multichip may be either direct-addressable or pin-constrained. The method also supports a hierarchical approach to wire routing that ensures scalability. In this scheme, the size of the biochip in terms of the total number of electrodes may be increased by a factor of four by increasing the number of routing layers by only one. In general, for a multichip with 4 n identical blocks, ( n + 1) layers are sufficient for wire routing.

A key challenge in design automation of digital microfluidic biochips is to carry out on-chip dilution/mixing of biochemical samples/reagents for achieving a desired concentration factor (CF). In a bioassay, reducing the waste is crucial because the waste droplet handling is cumbersome and the number of waste reservoirs on-chip needs to be minimized to use limited volume of sample and expensive reagents and hence to reduce the cost of a biochip. The existing dilution algorithms attempt to reduce the number of mix/split steps required in the process but focus little on minimization of sample requirement or waste droplets. In this work, we characterize the underlying combinatorial properties of waste generation and identify the inherent limitations of two earlier mixing algorithms (BS algorithm by Thies et al., Natural Computing 2008; DMRW algorithm by Roy et al., IEEE TCAD 2010) in addressing this issue. Based on these properties, we design an improved dilution/mixing algorithm (IDMA) that optimizes the usage of intermediate droplets generated during the dilution process, which in turn, reduces the demand of sample/reagent and production of waste. The algorithm terminates in O(n) steps for producing a target CF with a precision of 1/2n. Based on simulation results for all CF values ranging from 1/1024 to 1023/1024 using a sample (100% concentration) and a buffer solution (0% concentration), we present an integrated scheme of choosing the best waste-aware dilution algorithm among BS, DMRW, and IDMA for any given value of CF. Finally, an architectural layout of a DMF biochip that supports the proposed scheme is designed.

The recent emergence of lab-on-a-chip (LoC) technology has led to a paradigm shift in many healthcare-related application areas, e.g., point-of-care clinical diagnostics, high-throughput sequencing, and proteomics. A promising category of LoCs is digital microfluidic (DMF)-based biochips, in which nanoliter-volume fluid droplets are manipulated on a 2-D electrode array. A key challenge in designing such chips and mapping lab-bench protocols to a LoC is to carry out the dilution process of biochemical samples efficiently. As an optimization and automation technique, we present a dilution/mixing algorithm that significantly reduces the production of waste droplets. This algorithm takesO(n) time to compute at mostnsequential mix/split operations required to achieve any given target concentration with an error in concentration factor less than [1/(2n)]. To implement the algorithm, we design an architectural layout of a DMF-based LoC consisting of twoO(n)-size rotary mixers andO(n) storage electrodes. Simulation results show that the proposed technique always yields nonnegative savings in the number of waste droplets and also in the total number of input droplets compared to earlier methods.

As the fabrication process technology is moving from submicron region to deep submicron or nanometer region, the impact of process parameter variations are becoming more and more dominant, increasing the loss in yield due to variations in leakage power and delay. As a consequence, parametric yield loss has become a serious concern of the fabrication houses. This has opened up a challenge to the designers' community to design circuits that are tolerant to process parameter variations, thereby increasing the parametric yield. In this paper we have studied the impact of process parameter variations on the representative static approaches of runtime leakage power reduction and compared them with a proposed approach using Monte-Carlo simulation. The simulation results indicate that the proposed approach provides higher parametric yield compared to the existing representative approaches with comparable reduction in total and leakage power.