Symbolic execution is a key component of precise binary program analysis tools. We discuss how to automatically boot-strap the construction of a symbolic execution engine for a processor instruction set such as x86, x64 or ARM. We show how to automatically synthesize symbolic representations

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We present a new scheme for performing binary translation that produces code comparable to or better than existing binary translators with much less engineering effort. Instead of hand-coding the translation from one instruction set to another, our approach automatically learns translation rules

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The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement

-case design strategy and is shown to be advantageous in many aspects. We provide a thorough analysis showing in which situations each of the methods is preferable and propose a non-parametric method for approximating the estimators from a set of examples. Finally, we demonstrate our approach in the context

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Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send