Abstract: An apparatus (20) for sampling signals includes a modulo circuit (24), an Analog to Digital Converter (ADC—28) and processing circuitry (32). The modulo circuit is configured to receive an analog signal (40), and to derive from the analog signal (i) an analog folded signal (48) whose amplitude is limited to a predefined dynamic range, and (ii) side information (36) indicative of instances in which the analog signal transitions between levels corresponding to odd integer multiples of boundaries specifying the dynamic range. The ADC is configured to sample the analog folded signal to produce a digital folded signal (52). The processing circuitry is configured to generate a digital unfolded signal (44), which represents the analog signal both within and outside the dynamic range, based on the digital folded signal and on the side information, and to output the digital unfolded signal.

Abstract: An ultrasound scanning system includes an ultrasound scanner, a trained spare recovery neural network and a super resolution image generator. The ultrasound scanner views a patient after intravenous injection of microbubbles and then generates a sequence of ultrasound images. The sequence of ultrasound images is formed of a sequence of B-mode ultrasound images and a sequence of contrast enhanced ultrasound images corresponding to the B-mode ultrasound images. The trained sparse recovery neural network is trained on synthesized images of point sources and generates, for at least one image of the sequence of contrast-enhanced ultrasound images, a super-resolved image of locations of the microbubbles in that image. The super resolution image generator receives an output of the trained sparse recovery neural network and generates therefrom a super-resolved image of a microvasculature through which the microbubbles flowed.

Abstract: Some embodiments relates to the technique, including systems and methods, for use in analog-to-digital conversion (ADC). A sampling system is presented for sampling an input analog signal including a train of pulses of a predetermined shape and allowing a recovery of the degrees of freedom of the signal. The sampling system includes: a kernel for selectively passing components of an input signal, and an integrate and fire time encoding machine (IF-TEM), wherein the kernel has a size of kernel support set being in a predetermined relation with a number of degrees of freedom in the finite rate of innovation signal.

Abstract: Some embodiments are directed towards a multiple-input multiple-output (MIMO) receiver system for use with N antennas. The receiver system includes: an analog combiner assembly comprising a matrix of vector modulators applying gain and phase shift to each of N signals being received from the respective antenna and combine said N signals into P corresponding signals, P<N; an array of ADC units, each performing quantization of a respective one of said P corresponding signals with a predetermined bit constrain; and a signal recovery system. The signal recovery system can include a digital signal processor performing task-specific recovery of selected K signals, arriving on the antennas in predetermined input directions, from quantized and filtered digital representation of the N signals being received by the analog combiner assembly; and a control unit. The control unit is adapted to utilize optimized operational data to operate the analog combiner and the digital signal processor.

Abstract: A system for recovering physical properties from a non-linear medium includes a wave field modeler, a properties adjuster and a medium properties recoverer. The wave field modeler models a wave field generated by a transmitted pulse traveling in the non-linear medium and generates a predicted transducer output from the modeled wave field. The wave field modeler is a neural network having a neural network representation of a non-linear wave function of the physical properties of the wave field. The properties adjuster optimizes a loss function between the predicted transducer output and a measured transducer output and generates an improved set of the physical properties. The properties adjuster operates a backpropagator using the neural network representation and activates the wave field modeler with the improved set of the physical properties. The medium properties recoverer outputs a current improved set of the physical properties once the properties adjuster finishes operation.

Abstract: A monitoring system is presented for monitoring vital signs of subject(s). The monitoring system includes a control system configured for signal communication with a frequency modulated continuous wave (FMCW) radar to process measured data which is received from a single-channel front end of receiver of said FMCW radar and which is in the form of data matrix indicative of consecutive beat signals. The control system comprises a data processing utility comprising: a localization module configured and operable to process the measured data indicative of said data matrix and provide support recovery data indicative of the received signals originated at localized one or more subjects; and a vital signs monitoring module configured and operable to analyze the support recovery data and monitor vital signs of said localized one or more subjects.

Abstract: A way to design a codebook for estimating the type of a molecule at a particular location in a fluorescence microscopy image makes use of one or both of (1) knowledge of the non-uniform prior distribution of molecule types (i.e., some types are known a priori to occur more frequently than others) and/or knowledge of co-occurrence of molecule types at close locations (e.g., in a same cell); and (2) knowledge of a model of the (e.g., random) process that yields the intensities that are expected at a location when a molecule with a particular subset of markers (i.e., a molecule of a type that has been assigned a codeword that defines that subset) is present at that location. The codebook design may provide experimental efficiency by reducing the number of images that need to be acquired and/or improve classification or detection accuracy by making the codewords for different molecule types more distinctive.

Abstract: An ultrasound scanning system includes an ultrasound scanner, a trained spare recovery neural network and a super resolution image generator. The ultrasound scanner views a patient after intravenous injection of microbubbles and then generates a sequence of ultrasound images. The sequence of ultrasound images is formed of a sequence of B-mode ultrasound images and a sequence of contrast enhanced ultrasound images corresponding to the B-mode ultrasound images. The trained sparse recovery neural network is trained on synthesized images of point sources and generates, for at least one image of the sequence of contrast-enhanced ultrasound images, a super-resolved image of locations of the microbubbles in that image. The super resolution image generator receives an output of the trained sparse recovery neural network and generates therefrom a super-resolved image of a microvasculature through which the microbubbles flowed.

Abstract: A way to design a codebook for estimating the type of a molecule at a particular location in a fluorescence microscopy image makes use of one or both of (1) knowledge of the non-uniform prior distribution of molecule types (i.e., some types are known a priori to occur more frequently than others) and/or knowledge of co-occurrence of molecule types at close locations (e.g., in a same cell); and (2) knowledge of a model of the (e.g., random) process that yields the intensities that are expected at a location when a molecule with a particular subset of markers (i.e., a molecule of a type that has been assigned a codeword that defines that subset) is present at that location. The codebook design may provide experimental efficiency by reducing the number of images that need to be acquired and/or improve classification or detection accuracy by making the codewords for different molecule types more distinctive.

Abstract: An imaging apparatus (20) includes an interface (50) and a processor (30). The interface is configured to receive a sequence of images of an object (38) acquired at respective acquisition times, the object is labeled with fluorescent emitters. The images depict intensity fluctuations of emitters on an acquisition grid, and the intensity fluctuations being uncorrelated among the emitters and during an overall time over which the images in the sequence are acquired. The a processor is configured to calculate a temporal correlation function of the intensity fluctuations over the sequence of images, and to estimate locations of the emitters on a super-resolution grid having a higher resolution than the acquisition grid, by applying to the temporal correlation function a recovery process in which the locations on the super-resolution grid are assumed to be sparse or compressible in a predefined domain. The recovery process includes a sparse-recovery process or a regularized process.

Abstract: An imaging apparatus (20) includes an interface (50) and a processor (30). The interface is configured to receive a sequence of images of an object (38) acquired at respective acquisition times, the object is labeled with fluorescent emitters. The images depict intensity fluctuations of emitters on an acquisition grid, and the intensity fluctuations being uncorrelated among the emitters and during an overall time over which the images in the sequence are acquired. The a processor is configured to calculate a temporal correlation function of the intensity fluctuations over the sequence of images, and to estimate locations of the emitters on a super-resolution grid having a higher resolution than the acquisition grid, by applying to the temporal correlation function a recovery process in which the locations on the super-resolution grid are assumed to be sparse or compressible in a predefined domain. The recovery process includes a sparse-recovery process or a regularized process.

Abstract: An apparatus (20) for imaging includes an input interface (54) and a processor (50). The input interface receives a sequence of input images of a target. Each input image includes a grid of pixels representing reflections of a transmitted signal from reflectors or scatterers in the target. A resolution of the input images is degraded by a measurement process of capturing the input images in the sequence. The processor derives, from the sequence of input images, an aggregated image in which each pixel comprises a statistical moment calculated over corresponding pixels of the input images, and converts the aggregated image into a super-resolution image of the target, having a higher resolution than the input images, by applying to the aggregated image a recovery function, which outputs the super-resolution image as a solution to the recovery function, provided that the reflectors or scatterers are sparse or compressible in a predefined transform domain.

Abstract: A method includes receiving a signal, which includes reflections of multiple pulses from one or more targets (24). A Doppler focusing function, in which the reflections of the multiple pulses from each target accumulate in-phase to produce a respective peak associated with a respective delay and a respective Doppler frequency of the target, is evaluated based on the received signal. Respective delays and Doppler frequencies of the targets are estimated based on the Doppler focusing function.

Abstract: A method includes receiving from multiple transducers respective signals including reflections of a transmitted signal from a target. An image of the target is produced irrespective of sparsity of the received signals, by computing transducer-specific frequency-domain coefficients for each of the received signals, deriving, from the transducer-specific frequency-domain coefficients, beamforming frequency-domain coefficients of a beamformed signal in which the reflections received from a selected direction relative to the transducers are emphasized, and reconstructing the image of the target at the selected direction based on the beamforming frequency-domain coefficients.

Abstract: A method for reconstructing high signal-to-noise ratio (SNR) magnetic resonance imaging (MRI) slices, including: receiving a thick MRI slice of bodily tissue acquired using a single MRI scan, wherein the thick slice has a high SNR; receiving two thin MRI slices of the bodily tissue acquired using a single MRI scan, wherein each of the two thin MRI slices has a low SNR; and reconstructing multiple high SNR thin slices of the bodily tissue using the thick slice and the two thin slices.

Abstract: A method for reconstructing high signal-to-noise ratio (SNR) magnetic resonance imaging (MRI) slices, including: receiving a thick MRI slice of bodily tissue acquired using a single MRI scan, wherein the thick slice has a high SNR; receiving two thin MRI slices of the bodily tissue acquired using a single MRI scan, wherein each of the two thin MRI slices has a low SNR; and reconstructing multiple high SNR thin slices of the bodily tissue using the thick slice and the two thin slices.

Abstract: A method and system are presented for reconstructing an input field where the latter is sensed by a measurement system. The method comprises: providing measured data corresponding to output field of said measurement system; providing data about sparsity of the input field, and data about effective response function of the measurement system; and processing the measured data based on said known data, the processing comprising: determining a sparse vector as a function of said measured data, said data about the sparsity of the input field, and said data about the effective response function; and using the sparse vector for reconstructing the input information. The invention allows for sub-wavelength resolution in imaging applications, and allows for detection of very short pulses by slow detectors in some other applications.

Abstract: A method includes receiving a signal, which includes reflections of multiple pulses from one or more targets (24). A Doppler focusing function, in which the reflections of the multiple pulses from each target accumulate in-phase to produce a respective peak associated with a respective delay and a respective Doppler frequency of the target, is evaluated based on the received signal. Respective delays and Doppler frequencies of the targets are estimated based on the Doppler focusing function.

Abstract: A method includes accepting an analog input signal that includes a sequence of pulses. The analog input signal is filtered so as to produce a filter output, using a filter whose time-domain response is confined to a finite time period and whose frequency-domain response is non-zero at a finite set of integer multiples of a frequency shift ??, and is zero at all other integer multiples of ??. The filter output is sampled so as to produce digital samples. Respective amplitudes and time positions of the pulses in the sequence are calculated based on the digital samples.

Abstract: A method for signal processing includes accepting an analog signal, which consists of a sequence of pulses confined to a finite time interval. The analog signal is sampled at a sampling rate that is lower than a Nyquist rate of the analog signal and with samples taken at sample times that are independent of respective pulse shapes of the pulses and respective time positions of the pulses in the time interval. The sampled analog signal is processed.