Abstract: One or more signal are embedded by producing complex-valued measurements of the signal by measuring the signal using a complex measurement matrix. Then, only a phase of the complex-valued measurements are retinas, such that angles of the signal are preserved. Subsequently, the phases, which can be quantized and stored in a database, can be searched to locate similar signals based only on their phase angles.

Abstract: Signals received by an array of sensing elements are processed by first positioning the sensing elements in a uniform grid of L locations, wherein each location to include or not to include a sensing element is selected during a design phase. The sensing elements are selected and grouped into subsets, wherein each subset contains one or more sensing elements, and each sensing element is a member of one or more subsets. The signals in each subset are linearly combined to produce a combined signal, which is then sampled to form an output channel, which can detect objects.

Abstract: Distances between data are encoded by performing a random projection, followed by dithering and scaling, with a fixed scaling for all values. The resulting dithered and scaled projection is quantized using a non-monotonic 1-bit quantizer to form a vector of bits representing the signal. The distance between signals can be approximately calculated from the corresponding vectors of bits by computing the hamming distance of the two vectors of bits. The computation is approximately correct up to a specific distance, determined by the scaling, and not beyond that.

Abstract: A scene is reconstructed by transmitting pulses into a scene from an array of transmitters so that only one pulse is transmitted by one transmitter at any one time. The one pulse is reflection by the scene and received as a set of signals. Each signal is sampled and decomposed to produce frequency coefficients stacked in a set of linear systems modeling a reflectivity of the scene. Then, a reconstruction method is applied to the set of linear systems. The reconstruction method solves each linear system separately to obtain corresponding solutions, which are shared and combined to reconstruct the scene.

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 Pan-sharpens a single panchromatic (Pan) image and a single multispectral (MS) image. A wavelet transform is applied to the Pan image and the MS image to obtain a wavelet transformed Pan image and a wavelet transformed MS image. Features, in the form of vectors, are extracted from the wavelet transformed Pan image and the wavelet transformed MS image. The features are separated into features without missing values and features with missing values. A dictionary is learned from features without missing values and used to predict the values for the features with the missing values. After the predicting, the features of the low frequency wavelet coefficients and the high frequency coefficients to form a fused wavelet coefficient map, and an inverse wavelet transform is applied to the fused wavelet coefficient map to obtain a fused MS image.

Abstract: A single panchromatic (Pan) image and a single multispectral (MS) image are Pan-sharpened by extracting features from the Pan image and the MS image. The features are decomposed into features without missing values and features with missing values. A dictionary is learned from the features without missing values. The values for the features with the missing values are learned using the dictionary. The MS image is merged with the Pan image including the predicted values into a merged image, and the merged image is then Pan sharpened.

Abstract: Scale-invariant features are extracted from an image. The features are projected to a lower dimensional random projection matrix by multiplying the features by a matrix of random entries. The matrix of random projections is quantized to produce a matrix of quantization indices, which form a query vector for searching a database of images to retrieve metadata related to the image.

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: Reflectors in a scene are reconstructed using reflected signals. First, signals are transmitted to the scene, and the reflected signals are received by receivers are arranged in an array. The reflected signals are then processed to reconstruct the reflectors in the scene, wherein the processing enforces a model that a reflectivity of the scene in front and behind any reflector is equal to zero.

Abstract: A saturated input signal acquired by a synthetic aperture radar (SAR) system is processed by estimating a reconstruction that generated the input signal, reproducing an input signal from an estimated reconstruction to generate a reproduced signal, comparing the reproduced signal with the input signal; adjusting an estimated reconstruction based on the comparison; and iterating from the reproducing step until a termination condition is reached.

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: 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 Pan-sharpens a single panchromatic (Pan) image and a single multispectral (MS) image. A wavelet transform is applied to the Pan image and the MS image to obtain a wavelet transformed Pan image and a wavelet transformed MS image. Features, in the form of vectors, are extracted from the wavelet transformed Pan image and the wavelet transformed MS image. The features are separated into features without missing values and features with missing values. A dictionary is learned from features without missing values and used to predict the values for the features with the missing values. After the predicting, the features of the low frequency wavelet coefficients and the high frequency coefficients to form a fused wavelet coefficient map, and an inverse wavelet transform is applied to the fused wavelet coefficient map to obtain a fused MS image.

Abstract: A single panchromatic (Pan) image and a single multispectral (MS) image are Pan-sharpened by extracting features from the Pan image and the MS image. The features are decomposed into features without missing values and features with missing values. A dictionary is learned from the features without missing values. The values for the features with the missing values are learned using the dictionary. The MS image is merged with the Pan image including the predicted values into a merged image, and the merged image is then Pan sharpened.

Abstract: A hash of signal is determining by dithering and scaling random projections of the signal. Then, the dithered and scaled random projections are quantized using a non-monotonic scalar quantizer to form the hash, and a privacy of the signal is preserved as long as parameters of the scaling, dithering and projections are only known by the determining and quantizing steps.

Abstract: A method for encoding a source image, wherein the source image includes a set of bitplanes of pixels, is disclosed. For each bitplane in a most to least significant order, the method include obtaining a list of significant pixels (LSP), a list of insignificant pixels (LIP), and a list of insignificant sets (LIS) according to a hierarchical ordering of the source image pixels; synchronizing the LSP, LIP and LIS of the source image with the LSP, LIP and LIS of a key image; constructing a temporary list of insignificant sets (TLIS) for the source image; and applying syndrome encoding to the LSP, LIP, and TLIS of the source image to obtain syndromes corresponding to magnitudes and signs of pixels in the source image, wherein the steps are performed in a processor.

Abstract: Pulses are transmitted into a scene by an array of the transducers, wherein each pulse has wideband ultrasound frequency, wherein the pulses are transmitted simultaneously, and wherein a pattern of the wideband ultrasound frequencies in each pulse is unique with respect to the patterns of each other pulse. The pulses as received when the pulse are reflected by the scene and objects in the scene. Each received pulse is sampled and decomposed using a Fourier transform to produce frequency coefficients, which are stacked to produce a linear system modeling a reflectivity of the scene and the objects. Then, a recovery method is applied to the linear system to recover the reflectivity of the scene and the objects.

Abstract: A method for encoding a source image, wherein the source image includes a set of bitplanes of pixels, is disclosed. For each bitplane in a most to least significant order, the method include obtaining a list of significant pixels (LSP), a list of insignificant pixels (LIP), and a list of insignificant sets (LIS) according to a hierarchical ordering of the source image pixels; synchronizing the LSP, LIP and LIS of the source image with the LSP, LIP and LIS of a key image; constructing a temporary list of insignificant sets (TLIS) for the source image; and applying syndrome encoding to the LSP, LIP, and TLIS of the source image to obtain syndromes corresponding to magnitudes and signs of pixels in the source image, wherein the steps are performed in a processor.

Abstract: A saturated input signal acquired by a synthetic aperture radar (SAR) system is processed by estimating a reconstruction that generated the input signal, reproducing an input signal from an estimated reconstruction to generate a reproduced signal, comparing the reproduced signal with the input signal; adjusting an estimated reconstruction based on the comparison; and iterating from the reproducing step until a termination condition is reached.