Abstract: A computer-implemented method for estimating a vital sign of a subject comprises receiving a sequence of imaging photoplethysmography (iPPG) signals measured from different regions of a skin of the subject and executing a cross-domain unrolling optimization iteratively minimizing a difference between the received iPPG signals and reconstructed iPPG signals followed by processing the reconstructed iPPG signals with an iPPG neural network trained in the frequency domain with machine learning to enforce a learned structure on the frequency coefficients of the reconstructed iPPG signals. The method further comprises determining the vital sign signal of the subject from the frequency coefficients of the reconstructed iPPG signals upon reaching a termination condition of the cross-domain unrolling optimization and outputting the vital sign signal corresponding to the reconstructed iPPG signals.

Abstract: In an embodiment, a computer-implemented data processing method comprises, in a collaborative electronic information management system that is hosted using a collaboration computer, establishing operation of a plurality of event listener units, wherein each of the event listener units is configured to communicate with or listen for a plurality of application events that have been generated by a particular corresponding external application; determining that one or more of the application events relate to a particular content item from among a plurality of content items that are managed in the system; generating and causing displaying, as a part of a graphical user interface of the system for a user account associated with a user computer, a set of notifications comprising one or more first notifications generated from the system and one or more second notifications that are based upon the application events, in association with data identifying the particular content item.

Abstract: The present disclosure discloses a device and a method for controlling an operation of a system to perform a task. The system is communicatively coupled to a digital twin configured to concurrently simulate the operation of the system. The method includes collecting a sequence of control inputs for controlling the system to change states of the system according to the task. The method further includes collecting a sequence of outputs of the system caused by the corresponding sequence of control inputs. The method further includes estimating a current internal state of the digital twin using a neural network trained to estimate a sequence of internal states of the digital twin mapping the sequence of control inputs to the sequence of outputs. The method further includes performing the task using the current internal state of the digital twin.

Abstract: A distance estimation method comprises transmitting a wave of radiation modulated in frequency domain by an emitter to a scene, receiving a reflection of the transmitted wave from the scene, and interfering a copy of the transmitted wave with the received reflection to generate a sequence of samples of the beat signal with wrapped phases in a time domain. The method also comprises estimating a frequency of the beat signal in the time domain in an iterative manner until a termination condition is met. The iterative estimation of the frequency of the beat signal is based on phase unwrapping of the samples of the beat signal subject to correlated phase error derived from phase noise statistics of the emitter and a linear regression fitting the frequency of the beat signal into the unwrapped phases of the beat signal.

Abstract: A tomographic imaging system is provided. The system receives measurements at frequencies of a wave-field scattered by an internal structure of an object, recursively reconstructs an image of the internal structure of the object until a termination condition is met, and renders the reconstructed image. For a current iteration, a frequency is added to previous frequencies used during a previous iteration to produce current frequencies, such that the added frequency is higher than frequencies present in the previous frequencies, and reconstructs a current image of the internal structure of the object that minimizes a difference between the scattered wave-field measured at the current frequencies and a wave-field synthetized from the current image. The wave-field synthesized from the current image is generated by a neural network operator. A previous image determined during the previous iteration initializes the reconstruction of the current image.

Abstract: A system and method are provided for training neural network for controlling operation of system having non-linear dynamics represented by partial differential equations (PDEs). The method comprises collecting digital representation of time series data indicative of instances of function space of the system and measurements of state of the operation of the system. Collocation points corresponding to solutions of the PDE are generated. The neural network is trained using training data including the collected time series data and the collocation points to train parameters of non-linear operator. The neural network has autoencoder architecture including encoder to encode each instance of the training data into latent space, the non-linear operator to propagate the encoded instances into the latent space with transformation determined by parameters of the non-linear operator, and decoder to decode the transformed encoded instances of the training data to minimize a hybrid loss function.

Abstract: A remote photoplethysmography (RPPG) system for estimating vital signs of a person is provided. The RPPG system is configured to receive a set of imaging photoplethysmography (iPPG) signals measured from different regions of a skin of a person. The RPPG system is further configured to determine frequency coefficients at the frequency bins of the quantized frequency spectrum of the measured iPPG signals by minimizing a distance between the measured iPPG signals and corresponding iPPG signals reconstructed from the determined frequency coefficients, while enforcing joint sparsity of the determined frequency coefficients subject to the sparsity level constraint, such that the determined frequency coefficients of different iPPG signals have the non-zero values at the same frequency bins; and output one or a combination of the determined frequency coefficients, the iPPG signals reconstructed from the determined frequency coefficients, and a vital sign signal corresponding to the reconstructed iPPG signals.

Abstract: Embodiments of the present disclosure provide a method of training a neural network model for controlling an operation of a system represented by partial differential equations (PDEs). The method comprises collecting digital representation of time series data indicative of measurements of the operation of the system at different instances of time. The method further comprises training the neural network model having an autoencoder architecture including an encoder to encode the digital representation into a latent space, a linear predictor to propagate the digital representation into the latent space, and a decoder to decode the digital representation to minimize a loss function including a prediction error between outputs of the neural network model decoding measurements of the operation at an instant of time and measurements of the operation collected at a subsequent instance of time, and a residual factor of the PDE having eigenvalues dependent on parameters of the linear predictor.

Abstract: Embodiment of the present disclosure disclose a tomographic imaging system for reconstructing an image of an internal structure of an object. An incident wavefield is transmitted into the object occupying a background domain embedding the object. The incident wavefield is scattered into multiple scattered wavefield by the object. The incident and scattered wavefields are measured as a total wavefield. The total wavefield propagates through a computational domain and a residual domain in the background domain that are defined by cross-domain and residual measurement operators. The total wavefield is used for the image reconstruction. The image is reconstructed by solving an optimization problem corresponding to the computational domain. The optimization problem is solved iteratively to minimize a difference between the total wavefield and a wavefield synthesized using a measurement operator and a Green's function operator from the reconstructed image.

Abstract: An optical coherence tomography (OCT) system comprises an interferometer configured to split incident light into a reference beam and a test beam, and to interfere the test beam reflected from the specimen with the reference beam reflected from a reference mirror to produce an interference pattern. The OCT system also comprises a spectrometer configured to analyze spectral components of the interference pattern at non-uniformly sampled wavenumbers. A computer-readable memory of the OCT system is configured to store a measurement model with elements connecting different depth values with different non-uniformly sampled wavenumbers and weighted with weights derived from a power spectral density (PSD) of the incident light for corresponding wavenumbers. The OCT system further comprises a processor configured to determine the profilometry measurements of the specimen as a maximum likelihood estimate of the specimen surface depth by back-projection of the measured intensities with the measurement model.

Abstract: Embodiments of the present disclosure provide a wire electric discharge machine (EDM) including a delivery system with one or combination of a wire electrode and a workpiece for delivering the wire electrode and the workpiece into proximity of each other and an energy source for creating electric discharge between the wire electrode and the workpiece. The wire EDM includes a wire electrode position measurement unit including light source to illuminate the wire electrode with encoded illumination pattern and a camera for acquiring a set of images of the wire electrode illuminated by the encoded illumination pattern. The wire EDM includes a processor to reconstruct positions of a segment of the wire electrode at a reconstruction rate greater than an acquisition rate of the camera by utilization of compressive sensing with sparse reconstruction technique and a controller to control the delivery system and the energy source based on the reconstructed positions.

Abstract: A tracking system for tracking an expanded state of an object is provided. The tracking system comprises at least one processor and a memory having instructions stored thereon that, when executed by the at least one processor, cause the tracking system to execute a probabilistic filter that iteratively tracks a belief on the expanded state of the object, wherein the belief is predicted using a motion model of the object and is further updated using a compound measurement model of the object. The compound measurement model includes multiple probabilistic distributions constrained to lie on a contour of the object with a predetermined relative geometrical mapping to the center of the object. Further, the tracking system tracks the expanded state of the object based on the updated belief on the expanded state.

Abstract: A system and a method for tracking an expanded state of an object including a kinematic state indicative of a position of the object and an extended state indicative of one or combination of a dimension and an orientation of the object is provided herein. The system comprises at least one sensor configured to probe a scene including a moving object with one or multiple signal transmissions to produce one or multiple measurements of the object per the transmission, and a processor configured to execute a probabilistic filter tracking a joint probability of the expanded state of the object estimated by a motion model of the object and a measurement model of the object, wherein the measurement model includes a center-truncated distribution having predetermined truncation intervals. The system further comprises an output interface configured to output the expanded state of the object.

Abstract: Embodiment of the present disclosure disclose a tomographic imaging system for reconstructing an image of an internal structure of an object. An incident wavefield is transmitted into the object occupying a background domain embedding the object. The incident wavefield is scattered into multiple scattered wavefield by the object. The incident and scattered wavefields are measured as a total wavefield. The total wavefield propagates through a computational domain and a residual domain in the background domain that are defined by cross-domain and residual measurement operators. The total wavefield is used for the image reconstruction. The image is reconstructed by solving an optimization problem corresponding to the computational domain. The optimization problem is solved iteratively to minimize a difference between the total wavefield and a wavefield synthesized using a measurement operator and a Green's function operator from the reconstructed image.

Abstract: A tracking system for tracking an expanded state of an object is provided. The tracking system executes, for a predetermined time period, a probabilistic filter that iteratively tracks a belief on the expanded state of the object, wherein the belief is predicted using a motion model of the object and is further updated using a compound measurement model of the object. After the predetermined time period, the updated beliefs are smoothed to generate a state-decoupled online batch of training data. The compound measurement model includes multiple probabilistic distributions constrained to lie around a contour of the object with a predetermined relative geometrical mapping to the center of the object. The compound measurement model is updated using the online batch of training data. Further, the tracking system tracks the expanded state of the object based on the updated compound measurement model.

Abstract: The present disclosure provides a system and a method for generating a radar image of a scene. The method comprises receiving radar measurements of a scene collected from a set of antennas, wherein the set of antennas are under uncertainties caused by one or a combination of position ambiguities and clock ambiguities of each of the antennas. The method further comprises generating the radar image of the scene by solving a sparse recovery problem. The sparse recovery problem determines, until a termination condition is met, a set of image shifts of the radar image corresponding to different uncertainties of the antennas and updates an estimate of the radar image, based on the determined set of image shifts of the radar image. The sparse recovery problem is solved with a neural network denoiser that denoises a filtering of the estimate of the radar image.

Abstract: A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

Abstract: A system and a method for tracking an expanded state of an object including a kinematic state indicative of a position of the object and an extended state indicative of one or combination of a dimension and an orientation of the object is provided herein. The system comprises at least one sensor configured to probe a scene including a moving object with one or multiple signal transmissions to produce one or multiple measurements of the object per the transmission, and a processor configured to execute a probabilistic filter tracking a joint probability of the expanded state of the object estimated by a motion model of the object and a measurement model of the object, wherein the measurement model includes a center-truncated distribution having truncation intervals. The system further comprises an output interface configured to output the expanded state of the object.

Abstract: An imaging photoplethysmography (iPPG) system is provided. The iPPG system receives a sequence of images of different regions of the skin of the person, where each region including pixels of different intensities indicative of variation of coloration of the skin. The iPPG system further transforms the sequence of images into a multidimensional time-series signal, each dimension corresponding to a different region from the different regions of the skin. The iPPG system further processes the multidimensional time-series signal with a time-series U-Net neural network wherein the pass-through layers include a recurrent neural network (RNN) to generate a PPG waveform, where the vital sign of the person is estimated based on the PPG waveform, and the iPPG system further renders the estimated vital sign of the person.

Abstract: A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.