Browsing by Author "Boufounos, Petros T."

Now showing 1 - 5 of 5
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    Method and apparatus for automatic gain control for nonzero saturation rates
    (2013-07-16) Baraniuk, Richard G.; Laska, Jason N.; Boufounos, Petros T.; Davenport, Mark A.; Rice University; United States Patent and Trademark Office
    A method for automatic gain control comprising the steps of measuring a signal using compressed sensing to produce a sequence of blocks of measurements, applying a gain to one of the blocks of measurements, adjusting the gain based upon a deviation of a saturation rate of the one of the blocks of measurements from a predetermined nonzero saturation rate and applying the adjusted gain to a second of the blocks of measurements. Alternatively, a method for automatic gain control comprising the steps of applying a gain to a signal, computing a saturation rate of the signal and adjusting the gain based upon a difference between the saturation rate of the signal and a predetermined nonzero saturation rate.
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    Method and apparatus for compressive domain filtering and interference cancellation
    (2014-05-13) Davenport, Mark A.; Boufounos, Petros T.; Baraniuk, Richard G.; Rice University; United States Patent and Trademark Office
    A method for compressive domain filtering and interference cancelation processes compressive measurements to eliminate or attenuate interference while preserving the information or geometry of the set of possible signals of interest. A signal processing apparatus assumes that the interfering signal lives in or near a known subspace that is partially or substantially orthogonal to the signal of interest, and then projects the compressive measurements into an orthogonal subspace and thus eliminate or attenuate the interference. This apparatus yields a modified set of measurements that can provide a stable embedding of the set of signals of interest, in which case it is guaranteed that the processed measurements retain sufficient information to enable the direct recovery of this signal of interest, or alternatively to enable the use of efficient compressive-domain algorithms for further processing. The method and apparatus operate directly on the compressive measurements to remove or attenuate unwanted signal components.
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    Method and apparatus for compressive parameter estimation and tracking
    (2013-10-22) Baraniuk, Richard G.; Boufounos, Petros T.; Schnelle, Stephen R.; Davenport, Mark A.; Laska, Jason N.; Rice University; United States Patent and Trademark Office
    A method for estimating and tracking locally oscillating signals. The method comprises the steps of taking measurements of an input signal that approximately preserve the inner products among signals in a class of signals of interest and computing an estimate of parameters of the input signal from its inner products with other signals. The step of taking measurements may be linear and approximately preserve inner products, or may be non-linear and approximately preserves inner products. Further, the step of taking measurements is nonadaptive and may comprise compressive sensing. In turn, the compressive sensing may comprise projection using one of a random matrix, a pseudorandom matrix, a sparse matrix and a code matrix. The step of tracking said signal of interest with a phase-locked loop may comprise, for example, operating on compressively sampled data or by operating on compressively sampled frequency modulated data, tracking phase and frequency.
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    Method and apparatus for signal reconstruction from saturated measurements
    (2013-06-04) Baraniuk, Richard G.; Laska, Jason N.; Boufounos, Petros T.; Davenport, Mark A.; Rice University; United States Patent and Trademark Office
    A method for recovering a signal by measuring the signal to produce a plurality of compressive sensing measurements, discarding saturated measurements from the plurality of compressive sensing measurements and reconstructing the signal from remaining measurements from the plurality of compressive sensing measurements. Alternatively, a method for recovering a signal comprising the steps of measuring a signal to produce a plurality of compressive sensing measurements, identifying saturated measurements in the plurality of compressive sensing measurements and reconstructing the signal from the plurality of compressive sensing measurements, wherein the recovered signal is constrained such that magnitudes of values corresponding to the identified saturated measurements are greater than a predetermined value.
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    Quantization of Sparse Representations
    (2007-01-16) Boufounos, Petros T.; Baraniuk, Richard G.
    Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with randomized basis functions; the signal is then recovered by a convex optimization. Random CS measurements are universal in the sense that the same acquisition system is sufficient for signals sparse in any representation. This paper examines the effect of quanitization of CS measurements. A careful study of stictly sparse, power-limited signals concludes that CS with scalar quantization does not use its allocated rate efficiently. The inefficiency, which is quantified, can be interpreted as the price that must be paid for the universality of the encoding system. The results in this paper complement and extend recent results on the quantization of compressive sensing measurements of compressible signals.