Krishnendu Chakrabarty

Bio: Krishnendu Chakrabarty is an academic researcher from Duke University. The author has contributed to research in topics: Biochip & Automatic test pattern generation. The author has an hindex of 79, co-authored 996 publications receiving 27583 citations. Previous affiliations of Krishnendu Chakrabarty include Huawei & Wake Forest University.

Droplet Routing in the Synthesis of Digital Microfluidic Biochips

Abstract: Recent advances in microfluidics are expected to lead to sensor systems for high-throughput biochemical analysis. CAD tools are needed to handle increased design complexity for such systems. Analogous to classical VLSI synthesis, a top-down design automation approach can shorten the design cycle and reduce human effort. We focus here on the droplet routing problem, which is a key issue in biochip physical design automation. We develop the first systematic droplet routing method that can be integrated with biochip synthesis. The proposed approach minimizes the number of cells used for droplet routing, while satisfying constraints imposed by throughput considerations and fluidic properties. A real-life biochemical application is used to evaluate the proposed method.

Frequency-directed run-length (FDR) codes with application to system-on-a-chip test data compression

Abstract: We showed recently that Golomb codes can be used for efficiently compressing system-on-a-chip test data. We now present a new class of variable-to-variable-length compression codes that are designed using the distributions of the runs of 0s in typical test sequences. We refer to these as frequency-directed run-length (FDR) codes. We present experimental results for the ISCAS 89 benchmark circuits to show that FDR codes outperform Golomb codes for test data compression. We also present a decompression architecture for FDR codes, and an analytical characterization of the amount of compression that can be expected using these codes. Analytical results show that FDR codes are robust, i.e. they are insensitive to variations in the input data stream.

Nonlinear modeling and bifurcations in the boost converter

Abstract: The occurrence of nonlinear phenomena like subharmonics and chaos in power electronic circuits has been reported recently. In this paper, the authors investigate these phenomena in the current-mode-controlled boost power converter. A nonlinear model in the form of a mapping from one point of observation to the next has been derived. The map has a closed form even when the parasitic elements are included. The bifurcation behavior of the boost power converter has been investigated with the help of this discrete model.

Test scheduling for core-based systems using mixed-integer linear programming

Abstract: We present optimal solutions to the test scheduling problem for core-based systems. Given a set of tasks (test sets for the cores), a set of test resources (e.g., test buses, BIST hardware) and a test access architecture, we determine start times for the tasks such that the total test application time is minimized. We show that the test scheduling decision problem is equivalent to the m-processor open shop scheduling problem and is therefore NP-complete. However a commonly encountered instance of this problem (m=2) can be solved in polynomial time. For the general case (m>2), we present a mixed-integer linear programming (MILP) model for optimal scheduling and apply it to a representative core-based system using an MILP solver available in the public domain. We also extend the MILP model to allow optimal test set selection from a set of alternatives. Finally, we present an efficient heuristic algorithm for handling larger systems for which the MILP model may be infeasible.

Cooling of integrated circuits using droplet-based microfluidics

Abstract: Decreasing feature sizes and increasing package densities are making thermal issues extremely important in IC design. Uneven thermal maps and hot spots in ICs cause physical stress and performance degradation. Many MEMS and microfluidics-based solutions were proposed in the past. We present a cooling method based on high-speed electrowetting manipulation of discrete sub-microliter droplets under voltage control with volume flow rates in excess of 10 mL/min. We also propose a flow-rate feedback control where the hot areas get increased supply of droplets without the need for external sensors and electrothermocapillary control where hot areas attract droplets due to thermocapillarity and are returned to their reservoirs by electrowetting resulting in a self-contained and a self-regulated system.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.