Microfluidic biochips have gained prominence due to their versatile applications to biochemistry and health-care domains such as point-of-care clinical diagnosis of tropical and cardiovascular diseases, cancer, diabetes, toxicity analysis, and for the mitigation of the global HIV crisis, among others. Microfluidic Lab-on-Chips (LoCs) offer a convenient platform for emulating various fluidic operations in an automated fashion. However, because of the inherent uncertainty of fluidic operations, the outcome of biochemical experiments performed on-chip can be erroneous even if the chip is tested a priori and deemed to be defect-free. This book focuses on the issues encountered in reliable sample preparation with digital microfluidic biochips (DMFBs), particularly in an error-prone environment. It presents state-of-the-art error management techniques and underlying algorithmic challenges along with their comparative discussions. Describes a comprehensive framework for designing a robust and error-tolerant biomedical system which will help in migrating from cumbersome medical laboratory tasks to small-sized LOC-based systems Presents a comparative study on current error-tolerant strategies for robust sample preparation using DMFBs and reports on efficient algorithms for error-tolerant sample dilution using these devices Illustrates how algorithmic engineering, cyber-physical tools, and software techniques are helpful in implementing fault tolerance Covers the challenges associated with design automation for biochemical sample preparation Teaches how to implement biochemical protocols using software-controlled microfluidic biochips Interdisciplinary in its coverage, this reference is written for practitioners and researchers in biochemical, biomedical, electrical, computer, and mechanical engineering, especially those involved in LOC or bio-MEMS design.

Recent microfluidic technologies have brought a complete paradigm shift in automating biochemical processing on a tiny lab-on-chip (a.k.a. biochip) that replaces expensive and bulky instruments traditionally used in implementing bench-top laboratory protocols. Biochips have already made a profound impact on various application domains such as clinical diagnostics, DNA analysis, genetic engineering, and drug discovery, among others. They are capable of precisely manipulating micro-/pico-liter quantities of fluids, and provide integrated support for mixing, storage, transportation, and sensing, on-chip. In almost all bioprotocols, sample preparation plays an important role, which includes dilution and mixing of several fluids satisfying certain volumetric ratios. However, designing algorithms that minimize reactant-cost and sample-preparation time suited for microfluidic chips poses a great challenge from the perspective of protocol mapping, scheduling, and physical design. Algorithms for Sample Preparation with Microfluidic Lab-on-Chip attempts to bridge the widening gap between biologists and engineers by introducing, from the fundamentals, several state-of-the-art computer-aided-design (CAD) algorithms for sample preparation with digital and flow-based microfluidic biochips. Technical topics discussed in the book include: - Basics of digital and flow-based microfluidic lab-on-chip - Comprehensive review of state-of-the-art sample preparation algorithms - Sample-preparation algorithms for digital microfluidic lab-on-chip - Sample-preparation algorithms for flow-based microfluidic lab-on-chip

This volume constitutes the refereed proceedings of the 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015, held in Kolkata, India, in November 2015. The 24 revised full papers and 2 invited papers presented were carefully reviewed and selected from numerous submissions. The workshop provides theoretical foundations and methods for solving problems from various areas of human practice. In contrast to traditional approaches to image analysis which implement continuous models, float arithmetic and rounding, combinatorial image analysis features discrete modelsusing integer arithmetic. The developed algorithms are based on studying combinatorial properties of classes of digital images, and often appear to be more efficient and accurate than those based on continuous models.