Abstract: A lens-free imaging system for generating an image of a scene includes an electromagnetic (EM) radiation sensor; a mask disposed between the EM radiation sensor and the scene; an image processor that obtains signals from the EM radiation sensor while the EM radiation sensor is exposed to the scene; and estimates the image of the scene based on, at least in part, the signals and a transfer function between the scene and the EM radiation sensor.

Abstract: In one aspect, embodiments disclosed herein relate to a lens-free imaging system. The lens-free imaging system includes: an image sampler, a radiation source, a mask disposed between the image sampler and a scene, and an image sampler processor. The image sampler processor obtains signals from the image sampler that is exposed, through the mask, to radiation scattered by the scene which is illuminated by the radiation source. The image sampler processor then estimates an image of the scene based on the signals from the image sampler, processed using a transfer function that relates the signals and the scene.

Abstract: In one aspect, embodiments disclosed herein relate to a lens-free imaging system. The lens-free imaging system includes: an image sampler, a radiation source, a mask disposed between the image sampler and a scene, and an image sampler processor. The image sampler processor obtains signals from the image sampler that is exposed, through the mask, to radiation scattered by the scene which is illuminated by the radiation source. The image sampler processor then estimates an image of the scene based on the signals from the image sampler, processed using a transfer function that relates the signals and the scene.

Abstract: A lens-free imaging system for generating an image of a scene includes an electromagnetic (EM) radiation sensor; a mask disposed between the EM radiation sensor and the scene; an image processor that obtains signals from the EM radiation sensor while the EM radiation sensor is exposed to the scene; and estimates the image of the scene based on, at least in part, the signals and a transfer function between the scene and the EM radiation sensor.

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.

Abstract: A method for approximating a plurality of digital signals or images using compressed sensing. In a scheme where a common component xc of said plurality of digital signals or images an innovative component xi of each of said plurality of digital signals each are represented as a vector with m entries, the method comprises the steps of making a measurement yc, where yc comprises a vector with only ni entries, where ni is less than m, making a measurement yi for each of said correlated digital signals, where yi comprises a vector with only ni entries, where ni is less than m, and from each said innovation components yi, producing an approximate reconstruction of each m-vector xi using said common component yc and said innovative component yi.

Abstract: A new digital image/video camera that directly acquires random projections of the incident light field without first collecting the pixels/voxels. In one preferred embodiment, the camera employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with only a single detection element while measuring the image/video fewer times than the number of pixels or voxels—this can significantly reduce the computation required for image/video acquisition/encoding. Since the system features a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.

Abstract: A communication method comprising characterizing a communications channel, determining a data rate and optionally a power allocation strategy that maximizes channel throughput, and configuring a transmitter to send a transmit signal with said data rate and said optional power allocation strategy.