Abstract: A motor comprising a shaft, an array of stator assemblies rigidly attached to the shaft, each stator assembly includes a stator yoke having a toroid shape fixed around the shaft and having a number of slots at radial and axis directional faces with windings within the slots of the stator yoke, and a rotor assembly rotatively attached to the shaft to enclose the array of stator assemblies, the rotor assembly has a rotor drum with sections, each section embraces one stator assembly, each section has two axial-flux permanent magnet arrays attached on axial-directional inner surfaces of the section and has one radial-flux permanent magnet array attached on a radial-directional inner surface of the section furthest from the shaft, wherein the axial-flux and the radial-flux permanent magnet arrays with the number of pole pairs equals the number of the stator slots plus or minus the number of stator winding pole pair.

Abstract: A system for detecting faults of an electric machine is provided. The system includes an interface, a memory to store computer-implemented programs including a signal sampling program, a matrix formation program, an optimization formation program, a matrix pencil program, optimization solvers and lookup data including predetermined system parameters related to the faults, and a processer.

Abstract: A system for estimating a severity of a bearing fault in an induction motor, uses a set of filters and a set of quantitative models designed for a set of fault frequencies. The system, upon receiving the measurements of the stator current, extracts the first fault current from the stator current using the first filter, determine the first mutual inductance variation from the first fault current using the first quantitative model, and classify the first mutual inductance variation with the fault severity classifier to determine the severity of a first type of fault in the induction motor. Similarly, the system classifies a second type of fault using the second filter and the second quantitative model. The system outputs one or combination of the severity of the first type of fault in the induction motor and the severity of the second type of fault in the induction motor.

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: Systems and methods for multiple image registration of images of a scene or an object. Receiving image data, the image data includes images collected from different measurements of a single modality or multiple modalities, either at different rotation angles, horizontal shifts, or vertical shifts, of the scene or the object. Estimating registration parameters, using pairs of images, each pair of images includes a reference image and a floating image. Generating parameter matrices corresponding to registration parameters using an image registration process for all pairs of images. Decomposing each parameter matrix into a low-rank matrix of updated registration parameters and a sparse matrix corresponding to the registration parameter errors for each low-rank matrix, to obtain updated registration parameters for robust registration. Using the updated registration parameters to form a transformation matrix to register the images with at least one reference image, resulting in robust registration of the images.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution (SR) image from multispectral (MS) images. A processor to iteratively, fuse a MS image with an associated PAN image of the scene. Each iteration includes using a gradient descent (GD) approach with a learned forward operator, to generate an intermediate high-resolution multispectral (IHRMS) image with an increased spatial resolution and a smaller error to the DSRMS image compared to the stored MS image. Project the IHRMS image using a trained convolutional neural network (CNN) to obtain an estimated synthesized high-resolution multispectral (ESHRMS) image, for a first iteration. Use the ESHRMS image and the PAN image, as an input to the GD approach for following iterations. The updated IHRMS image is an input to another trained CNN for the following iterations. After predetermined number of iterations, output the fused high-spatial and high-spectral resolution MS image.

Abstract: Systems and methods for multiple image registration of images of a scene or an object. Receiving image data, the image data includes images collected from different measurements of a single modality or multiple modalities, either at different rotation angles, horizontal shifts, or vertical shifts, of the scene or the object. Estimating registration parameters, using pairs of images, each pair of images includes a reference image and a floating image. Generating parameter matrices corresponding to registration parameters using an image registration process for all pairs of images. Decomposing each parameter matrix into a low-rank matrix of updated registration parameters and a sparse matrix corresponding to the registration parameter errors for each low-rank matrix, to obtain updated registration parameters for robust registration. Using the updated registration parameters to form a transformation matrix to register the images with at least one reference image, resulting in robust registration of the images.

Abstract: A system for estimating a severity of a bearing fault in an induction motor, uses a set of filters and a set of quantitative models designed for a set of fault frequencies. The system, upon receiving the measurements of the stator current, extracts the first fault current from the stator current using the first filter, determine the first mutual inductance variation from the first fault current using the first quantitative model, and classify the first mutual inductance variation with the fault severity classifier to determine the severity of a first type of fault in the induction motor. Similarly, the system classifies a second type of fault using the second filter and the second quantitative model. The system outputs one or combination of the severity of the first type of fault in the induction motor and the severity of the second type of fault in the induction motor.

Abstract: Systems and methods for radar systems to produce a radar image of a region of interest (ROI with targets. Sensors to transmit source signals to the ROI and to measure echoes reflected back from the targets corresponding to the transmitted source signals. A processor to calculate an estimate of a noisy and a partial Euclidean Distance Matrix (EDM) of the sensors and the targets. Decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process. The low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations. Implement an inverse imaging process using the estimated actual sensor locations and the received data, to produce the radar image to output to a communication channel.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution (SR) image from multispectral (MS) images. A processor to iteratively, fuse a MS image with an associated PAN image of the scene. Each iteration includes using a gradient descent (GD) approach with a learned forward operator, to generate an intermediate high-resolution multispectral (IHRMS) image with an increased spatial resolution and a smaller error to the DSRMS image compared to the stored MS image. Project the IHRMS image using a trained convolutional neural network (CNN) to obtain an estimated synthesized high-resolution multispectral (ESHRMS) image, for a first iteration. Use the ESHRMS image and the PAN image, as an input to the GD approach for following iterations. The updated IHRMS image is an input to another trained CNN for the following iterations. After predetermined number of iterations, output the fused high-spatial and high-spectral resolution MS image.

Abstract: A tomographic imaging system receives measurements at a set of frequencies of a wavefield 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, the system adds a frequency to a previous set of frequencies used during a previous iteration to produce a current set of frequencies, such that the added frequency is higher than any frequency in the previous set of frequencies, and reconstructs a current image of the internal structure of the object that minimizes a difference between a portion of the scattered wavefield measured at the current set of frequencies and a wavefield synthetized from the current image. A previous image determined during the previous iteration initializes the reconstruction of the current image.

Abstract: Systems and methods for a radar system to produce a radar image of a region of interest (ROI). A set of antennas to transmit radar pulses to the ROI and to measure a set of reflections from the ROI corresponding to the transmitted radar pulses. A processor acquires an estimate of the radar image, by matching the reflections of the ROI measurements for each antenna. Determine a set of shifts of the radar image. Wherein each shift corresponds to an antenna, and is caused by an uncertainty in a position of the antenna. Update the estimate of the radar image, based on the determined set of shifts of the radar image. Wherein for each antenna, the estimate of the radar image is shifted by the determined shift of the radar image corresponding to the antenna, that fits the reflections of the ROI measurements of the antenna.

Abstract: Systems and methods for a computer implemented image reconstruction system that includes an input interface to receive measurements of a scene. A memory to store a sparsity enforcing neural network (SENN) formed by layers of nodes propagating messages through the layers. Wherein at least one node of the SENN modifies an incoming message with a non-linear function to produce an outgoing message and propagates the outgoing message to another node of the SENN. Wherein the non-linear function is a dual-projection function that limits the incoming message if the incoming message exceeds a threshold. Such that, the SENN is trained to reconstruct an image of the scene from the measurements of the scene. A processor to process the measurements with the SENN to reconstruct the image of the scene. Finally, an output interface to render the reconstructed image of the scene.

Abstract: A method propagates a pulse of wave through the material to receive a set of echoes resulted from scattering the pulse by different portions of the material and simulates a propagation of the pulse in the material using a neural network to determine a simulated set of echoes. Each node in a layer of the neural network corresponds to a portion of the material and assigned a value the permittivity of the portion of the material, such that the values of the nodes at locations of the portions form the image of the distribution of the permittivity of the material. The connection between two layers in the neural network models a scattering event. The method updates the values of the nodes by reducing an error between the received set of echoes and the simulated set of echoes to produce an image of the distribution of the permittivity of the material.

Abstract: Systems and methods for fusing a radar image in response to radar pulses transmitted to a region of interest (ROI). The method including receiving a set of reflections from a target located in the ROI. Each reflection is recorded by a receiver at a corresponding time and at a corresponding coarse location. Aligning the set of reflections on a time scale using the corresponding coarse locations of the set of distributed receivers to produce a time projection of the set of reflections for the target. Fitting a line into data points formed from radar pulses in the set of reflections. Determining a distance between the fitted line and each data point. Adjusting the coarse position of the set of distributed receivers using the corresponding distance between the fitted line and each data point. Fusing the radar image using the set of reflections received at the adjusted coarse position.

Abstract: Systems and methods for radar systems to produce a radar image of a region of interest (ROI with targets. Sensors to transmit source signals to the ROI and to measure echoes reflected back from the targets corresponding to the transmitted source signals. A processor to calculate an estimate of a noisy and a partial Euclidean Distance Matrix (EDM) of the sensors and the targets. Decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process. The low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations. Implement an inverse imaging process using the estimated actual sensor locations and the received data, to produce the radar image to output to a communication channel.

Abstract: Systems and methods for image processing for increasing resolution of a multi-spectral image. Accept a multi-spectral image including a set of images of a scene. A memory to store a set of dictionaries trained for different channels, and a set of filters trained for the different channels. A hardware processor is to process the set of images of the different channels with the set of filters, and to fuse, for each channel, the set of structures, to produce a set of fused structures. Wherein a fused structure of the channel is fused as a weighted combination of the set of structures using weights corresponding to the channel, such that the fused structures of different channels are combined with different weights. To process the set of fused structures with corresponding dictionaries from the set of dictionaries, to produce a super-resolution multi-spectral image. An output interface to render the super-resolution multi-spectral image.

Abstract: Systems and methods for image processing for increasing resolution of a multi-spectral image. Accept a multi-spectral image including a set of images of a scene. A memory to store a set of dictionaries trained for different channels, and a set of filters trained for the different channels. A hardware processor is to process the set of images of the different channels with the set of filters, and to fuse, for each channel, the set of structures, to produce a set of fused structures. Wherein a fused structure of the channel is fused as a weighted combination of the set of structures using weights corresponding to the channel, such that the fused structures of different channels are combined with different weights. To process the set of fused structures with corresponding dictionaries from the set of dictionaries, to produce a super-resolution multi-spectral image. An output interface to render the super-resolution multi-spectral image.

Abstract: Systems and methods for an imaging system including a first sensor to acquire a sequence of images of a first modality. A memory to store a first convolutional memory matrix. Wherein each element of the first convolutional memory matrix is a convolutional function of correspondingly located elements of coefficient matrices of convolutional representation of the images of the first modality, and to store a first dictionary matrix including atoms of the images of the first modality. A processor to transform a first image of a scene acquired by the first sensor as a convolution of the first dictionary matrix and a first coefficient matrix, to update the elements of the first convolutional memory matrix with the convolutional function of correspondingly located non-zero elements of the first coefficient matrix, and to update the dictionary matrix using the updated first convolutional memory matrix.

Abstract: Systems and methods for a radar system to produce a radar image of a region of interest (ROI). A set of antennas to transmit radar pulses to the ROI and to measure a set of reflections from the ROI corresponding to the transmitted radar pulses. A processor acquires an estimate of the radar image, by matching the reflections of the ROI measurements for each antenna. Determine a set of shifts of the radar image. Wherein each shift corresponds to an antenna, and is caused by an uncertainty in a position of the antenna. Update the estimate of the radar image, based on the determined set of shifts of the radar image. Wherein for each antenna, the estimate of the radar image is shifted by the determined shift of the radar image corresponding to the antenna, that fits the reflections of the ROI measurements of the antenna.