22 research outputs found

    Towards Microfluidic Design Automation

    Get PDF
    Microfluidic chips, lab-on-a-chip devices that have channels transporting liquids instead of wires carrying electrons, have attracted considerable attention recently from the bio-medical industry because of their application in testing assay and large-scale chemical reaction automation. These chips promise dramatic reduction in the cost of large-scale reactions and bio-chemical sensors. Just like in traditional chip design, there is an acute need for automation tools that can assist with design, testing and verification of microfluidics chips. We propose a design methodology and tool to design microfluidic chips based on SMT solvers. The design of these chips is expressed using the language of partial differential equations (PDEs) and non-linear multi-variate polynomials over the reals. We convert such designs into SMT2 format through appropriate approximations, and invoke Z3 and dReal solver on them. Through our experiments we show that using SMT solvers is a not only a viable strategy to address the microfluidics design problem, but likely will be key component of any future development environment. In addition to analysis of Microfluidic Chip design, we discuss the new area of Microhydraulics; a new technology being developed for the purposes of macking dynamic molds and dies for manufacturing. By contrast, Microhydraulics is more concerned on creating designs that will satisfy the dynamic requirements of manufacturers, as opposed to microfludics which is more concerned about the chemical reactions taking place in a chip. We develop the background of the technology as well as the models required for SMT solvers such as Z3 and dReal to solve them

    Faculty Publications & Presentations, 2003-2004

    Get PDF

    Faculty Publications & Presentations, 2003-2004

    Get PDF

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

    Get PDF
    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Virginia Commonwealth University Courses

    Get PDF
    Listing of courses for the 2019-2020 year

    Virginia Commonwealth University Courses

    Get PDF
    Listing of courses for the 2018-2019 year

    Virginia Commonwealth University Courses

    Get PDF
    Listing of courses for the 2022-2023 year
    corecore