Abstract: Methods, systems, and computer-readable media are provided to suggest a medical course of action for a patient. Based on a diagnosis, a medical treatment decision tree is identified and integrated into an electronic medical record (EMR) of the patient. A series of branching nodes in the medical treatment decision tree are processed until a node with a suggested medical course of action for the patient is satisfied. The suggested course of action is exported. A medical procedure based on the suggested course of action is ordered and integrated into the medical treatment decision tree located in the EMR of the patient. It is then documented that the medical procedure was ordered in the treatment decision tree located in the EMR of the patient.

Abstract: 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.

Abstract: A typical data acquisition system takes periodic samples of a signal, image, or other data, often at the so-called Nyquist/Shannon sampling rate of two times the data bandwidth in order to ensure that no information is lost. In applications involving wideband signals, the Nyquist/Shannon sampling rate is very high, even though the signals may have a simple underlying structure. Recent developments in mathematics and signal processing have uncovered a solution to this Nyquist/Shannon sampling rate bottlenck for signals that are sparse or compressible in some representation. We demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals. One embodiment of our invention is a low-rate analog-to-information converter that can replace the high-rate analog-to-digital converter in certain applications involving wideband signals.

Abstract: 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.

Abstract: 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.

Abstract: The recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of “compressive” linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal acquisition, storage, and processing. However, the current CS framework considers only a certain task (signal recovery) and only in a certain model setting (sparsity). We show that compressive measurements are in fact information scalable, allowing one to answer a broad spectrum of questions about a signal when provided only with a reduced set of compressive measurements. These questions range from complete signal recovery at one extreme down to a simple binary detection decision at the other. (Questions in between include, for example, estimation and classification.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: We have developed a new method and apparatus for tracking and estimating parameters of locally oscillating signals from measurements that approximately preserve the inner product among signals in a class of signals of interest. Random demodulation, random sampling, and coset sampling are three prime examples of these techniques. One example of this is a compressive phase locked loops (PLL), which has a wide variety of applications, including but not limited to communications, phase tracking, robust control, sensing, and frequency modulation (FM) demodulation. The design modifies classical PLL designs to operate with CS-based sampling systems. By introducing a compressive sampler at the output of the oscillator and by appropriately adjusting the phase difference estimator we enable the use of PLLs with modern CS sampling technology. Other modifications can be made to reduce concerns such as normalization of the measurements, for example using the QCS-PLL.

Abstract: A typical data acquisition system takes periodic samples of a signal, image, or other data, often at the so-called Nyquist/Shannon sampling rate of two times the data bandwidth in order to ensure that no information is lost. In applications involving wideband signals, the Nyquist/Shannon sampling rate is very high, even though the signals may have a simple underlying structure. Recent developments in mathematics and signal processing have uncovered a solution to this Nyquist/Shannon sampling rate bottlenck for signals that are sparse or compressible in some representation. We demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals. One embodiment of our invention is a low-rate analog-to-information converter that can replace the high-rate analog-to-digital converter in certain applications involving wideband signals.

Abstract: The recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of “compressive” linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal acquisition, storage, and processing. However, the current CS framework considers only a certain task (signal recovery) and only in a certain model setting (sparsity). We show that compressive measurements are in fact information scalable, allowing one to answer a broad spectrum of questions about a signal when provided only with a reduced set of compressive measurements. These questions range from complete signal recovery at one extreme down to a simple binary detection decision at the other. (Questions in between include, for example, estimation and classification.