Computationally efficient characterization of standard cells for statistical static timing analysis

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 44-45).We propose a computationally efficient statistical static timing analysis (SSTA) technique that addresses intra-die variations at near-threshold to sub-threshold supply voltage, simulated on a scaled 32nm CMOS standard cell library. This technique would characterize the propagation delay and output slew of an individual cell for subsequent timing path analyses. Its efficiency stems from the fact that it only needs to find the delay or output slew in the vicinity of the ?- sigma operating point (where ? = 0 to 3) rather than the entire probability density function of the delay or output slew, as in conventional Monte-Carlo simulations. The algorithm is simulated on combinational logic gates that include inverters, NANDs, and NORs of different sizes. The delay and output slew estimates in most cases differ from the Monte-Carlo results by less than 5%. Higher supply voltage, larger transistor widths, and slower input slews tend to improve delay and output slew estimates. Transistor stacking is found to be the only major source of under-prediction by the SSTA technique. Overall, the cell characterization approach has a substantial computational advantage compared to SPICE-based Monte-Carlo analysis.by Sharon H. Chou.M.Eng

Concepts for on-board satellite image registration, volume 1

The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite

An analysis of spending behaviour under liquidity constraints with an application to financial hedging

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Acceleration Methods for MRI

Acceleration methods are a critical area of research for MRI. Two of the most important acceleration techniques involve parallel imaging and compressed sensing. These advanced signal processing techniques have the potential to drastically reduce scan times and provide radiologists with new information for diagnosing disease. However, many of these new techniques require solving difficult optimization problems, which motivates the development of more advanced algorithms to solve them. In addition, acceleration methods have not reached maturity in some applications, which motivates the development of new models tailored to these applications. This dissertation makes advances in three different areas of accelerations. The first is the development of a new algorithm (called B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithm or BARISTA), that solves a parallel MRI optimization problem with compressed sensing assumptions. BARISTA is shown to be 2-3 times faster and more robust to parameter selection than current state-of-the-art variable splitting methods. The second contribution is the extension of BARISTA ideas to non-Cartesian trajectories that also leads to a 2-3 times acceleration over previous methods. The third contribution is the development of a new model for functional MRI that enables a 3-4 factor of acceleration of effective temporal resolution in functional MRI scans. Several variations of the new model are proposed, with an ROC curve analysis showing that a combination low-rank/sparsity model giving the best performance in identifying the resting-state motor network.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120841/1/mmuckley_1.pd