Motor current signature analysis (MCSA) has been widely used in motor fault diagnosis by extracting characteristic frequency components in the spectrum of the stator current. However, fault signatures in the motor current are generally weak and easily influenced by noise and spectrum distortion caused by varying loads, especially in the early stage of motor faults. In this paper, we develop a sparsity-driven joint blind deconvolution-demodulation approach to extract small fault signatures of motors operating at a varying load. Results on experimental data demonstrate that our approach can effectively extract fault signatures from real noisy measurements of different load variation patterns.

Pulse-width-modulation (PWM) inverters generate electromagnetic interference (EMI) due to large dv/dt and di/dt of switching operations. When a periodic carrier is used to generate the PWM control signal, the corresponding EMI spectrum contains strong harmonic components of the carrier frequency. Research has shown that the carrier harmonic spectrum can be mitigated by modulating the carrier frequency. In this paper, we proposed a learning-based method to design the carrier frequency modulation of the inverter by solving an optimization problem, given EMI spectra of the PWM inverter under different carriers as learning data. As a result, a desired EMI spectrum envelop can be achieved according to the EMC regulation. Synthesized EMI results show that our learning-based method generalize the frequency modulation carrier design for EMI mitigation. Experiments provide promising results on EMI reduction.

Isolated converters are the ideal candidate for high-gain DC/DC converter applications. Although the design of converters considers safety margins and derating requirements to ensure acceptable performance until the end of product life, it does not completely rule out any early component failure. It is inevitable that the post-deployment stresses result in component aging and subsequent failure before the end of its useful life. To minimize downtime, early failure signatures need be detected for predictive maintenance, which requires continuous/periodic monitoring of critical components. This paper presents an Extended Kalman Filter (EKF)-based predictive maintenance algorithm of a 2kW, 270V/28V DC/DC converter. The detailed mathematical framework and observability analysis highlighting the feasibility of state/parameter estimation are presented. Based on observability analysis, the converter operating condition is devised such that accurate parameter estimation is achievable at a lower sensor sampling rate (equal to the switching time period). The significance of module-exclusive measurements on the observability and estimation of parameters is also discussed for a two-module system. The analyses presented are validated with detailed simulation results for single (2kW) and double ($2\times 2\text{kW}$) modules to estimate parameters of the filter inductor and the output capacitor.

Deep Neural Networks (DNNs) have achieved remarkable success in various Artificial Intelligence applications. Quantization is a critical step in DNNs compression and acceleration for deployment. To further boost DNN execution efficiency, many works explore to leverage the input-dependent redundancy with dynamic quantization for different regions. However, the sensitive regions in the feature map are irregularly distributed, which restricts the real speed up for existing accelerators. To this end, we propose an algorithm-architecture co-design, named Structured Dynamic Precision (SDP). Specifically, we propose a quantization scheme in which the high-order bit part and the low-order bit part of data can be masked independently. And a fixed number of term parts are dynamically selected for computation based on the importance of each term in the group. We also present a hardware design to enable the algorithm efficiently with small overheads, whose inference time mainly scales with the precision proportionally. Evaluation experiments on extensive networks demonstrate that compared to the state-of-the-art dynamic quantization accelerator DRQ, our SDP can achieve 29% performance gain and 51% energy reduction for the same level of model accuracy.

Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this article, we propose a fast approach to blind pansharpening and achieve the state-of-the-art image reconstruction quality. Typical blind pansharpening algorithms are often computationally intensive since the blur kernel and the target HRMS image are often computed using iterative solvers and in an alternating fashion. To achieve fast blind pansharpening, we decouple the solution of the blur kernel and of the HRMS image. First, we estimate the blur kernel by computing the kernel coefficients with minimum total generalized variation that blur a downsampled version of the PAN image to approximate a linear combination of the LRMS image channels. Then, we estimate each channel of the HRMS image using local Laplacian prior (LLP) to regularize the relationship between each HRMS channel and the PAN image. Solving the HRMS image is accelerated by both parallelizing across the channels and by fast numerical algorithms for each channel. Due to the fast scheme and the powerful priors we used on the blur kernel coefficients (total generalized variation) and on the cross-channel relationship (LLP), numerical experiments demonstrate that our algorithm outperforms the state-of-the-art model-based counterparts in terms of both computational time and reconstruction quality of the HRMS images.

We address the problem of enabling two-dimensional digital image correlation (DIC) for strain measurement on large three-dimensional objects with curved surfaces. It is challenging to acquire full-field qualified images of the surface required by DIC due to geometric distortion and the narrow visual field of the surface that a single image can cover. To overcome this issue, we propose an end-to-end DIC framework incorporating the image fusion principle to achieve full-field strain measurement over the curved surface. With a sequence of blurry images as inputs, we first recover sharp images using blind deconvolution, then project recovered sharp images to the curved surface using camera poses estimated by our proposed perspective-n-point (PnP) method called RRWLM. Images on the curved surface are stitched and then unfolded for strain analysis using DIC. Numerical experiments are conducted to validate our framework using RRWLM with comparisons to existing methods.

Camera pose estimation plays a crucial role in stitching overlapped images captured by a camera to achieve a broad view of interest. In this paper, we propose a robust camera pose estimation approach to stitching images of a large 3D surface with known geometry. In particular, given a collection of images, we first construct a relative pose matrix estimation of all image pairs from the collection, where each entry of the matrix is calculated by solving a perspective-n-point (PnP) problem over the corresponding pair of images. To continue, we jointly estimate all camera poses by solving an optimization problem that exploits the underlying rank-2 relative pose matrices and the joint sparsity of camera pose errors. Finally, images are projected on to the 3D surface of interest based on estimated camera poses for the subsequent stitching process. Numerical experiments demonstrate that our proposed method outperforms existing methods in terms of both camera pose estimation and image stitching quality.

We study the problem of reconstructing an object from phaseless measurements in the context of inverse multiple scattering. Our formulation explicitly decouples the variables that represent the unknown object image and the unknown phase, respectively, in the forward model. This enables us to simultaneously optimize over both unknowns with appropriate regularization for each. The resulting optimization problem is nonconvex due to the nonlinear propagation model for multiple scattering and the nonconvex regularization of the phase variables. Nevertheless, we demonstrate experimentally that we can solve the optimization problem using a variation of the fast iterative shrinkage-thresholding algorithm (FISTA)-a convex algorithm, popular for its speed and simplicity-that converges well in our experiments. Numerical results with both simulated and experimentally measured data show that the proposed method outperforms the state-of-the-art phaseless inverse scattering method.

Parameter estimation of damped exponential signals has wide applications including fault detection and system parameter identification, etc. However, existing methods for estimating parameters of damped exponentials are either sensitive to noise or restricted to dealing with a certain type of noise such as Gaussian noise. In this paper we aim to estimate parameters of damped exponentials from contaminated signal, i.e., a mixture of damped exponentials, random Gaussian noise, and spike interference. We propose two robust approaches, a convex one solved by the alternating direction method of multipliers (ADMM) and a non-convex one solved by coordinate descent, to recovering a low-rank Hankel matrix of damped exponentials from noisy measurements for further parameter estimation using the matrix pencil technique. Numerical experiments show that our proposed methods outperform classical ones in detecting small damped fault signatures from noisy measurements. While the convex approach is amenable to theoretical analysis and global convergence guarantees, the non-convex one exhibits more robustness and computational efficiency.

We address the problem of sharpening low spatial-resolution multi-spectral (MS) images with their associated misaligned high spatial-resolution panchromatic (PAN) image, based on priors on the spatial blur kernel and on the cross-channel relationship. In particular, we formulate the blind pan-sharpening problem within a multi-convex optimization framework using total generalized variation for the blur kernel and local Laplacian prior for the cross-channel relationship. The problem is solved by the alternating direction method of multipliers (ADMM), which alternately updates the blur kernel and sharpens intermediate MS images. Numerical experiments demonstrate that our approach is more robust to large misalignment errors and yields better super resolved MS images compared to state-of-the-art optimization-based and deep-learning-based algorithms.

The performance of synthetic aperture radar degrades when its moving platform is perturbed with unknown position errors or received signals are interfered by strong random noise. Therefore, it is desirable to perform robust imaging with noisy radar echoes even under large position perturbations. In this paper, we propose a graph-based denoising method, which regularizes both the smoothness in the graph domain and the sparse gradients in the time domain. Different from previous GSP-based methods, our graph model is built in the radar signal domain instead of the image domain, so that we can jointly estimate position perturbations of the radar platform and denoise the received signals, providing focused imaging results. Simulation results demonstrate that our method improves the radar imaging quality from 13.3dB provided by coherence analysis to 21.6dB in terms of PSNR.

Reflection tomography is an inverse scattering technique that estimates the spatial distribution of an object's permittivity by illuminating it with a probing pulse and measuring the scattered wavefields by receivers located on the same side as the transmitter. Unlike conventional transmission tomography, the reflection regime is severely ill-posed since the measured wavefields contain far less spatial frequency information about the object. In this paper, we propose an incremental frequency inversion framework that requires no initial target model, and that leverages spatial regularization to reconstruct the permittivity distribution of highly scattering objects. Our framework solves a wave-equation constrained, total-variation (TV) regularized nonlinear least squares problem that solves a sequence of subproblems that incrementally enhance the resolution of the estimated object model. With each subproblem, higher frequency wavefield components are incorporated in the inversion to improve the recovered model resolution. We validate the performance of our approach using synthetically generated data for retrieving high-contrast material such as water in an underground radar imaging setup.

In this paper, we consider the problem of fusing low spatial resolution multi-spectral (MS) aerial images with their associated high spatial resolution panchromatic image. To solve this problem, various methods have been proposed, using either model-based or model-agnostic algorithms such as deep learning techniques. In this paper, we aim to utilize more interpretable architectures to solve the MS fusion problem by integrating existing ideas from image processing with deep learning. In particular, we develop a signal processing-inspired learning solution, where we unroll the iterations of the projected gradient descent (PGD) algorithm, and each iteration contains a projection operation carried out by a deep convolutional neural network. We observe that our proposed method provides a new perspective on existing deep-learning solutions, and under certain circumstance it reduces to current black-box deep learning methods. Our extensive experimental results show significant improvements of the proposed approach over several baselines.

Image registration is of crucial importance in image fusion such as pan-sharpening. Mutual information (MI)-based methods have been widely used and demonstrated effectiveness in registering multi-spectral or multi-modal images. However, MI-based methods may fail to converge in searching registration parameters, resulting mis-registration. In this paper, we propose an outlier robust method to improve the robustness of MI-based registration for multiple rigid transformed images. In particular, we first generate registration parameter matrices using a MI-based approach, then we decompose each parameter matrix into a low-rank matrix of inlier registration parameters and a sparse matrix corresponding to outlier parameter errors. Results of registering multi-spectral images with random rigid transformations show significant improvement and robustness of our method.

The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formulations that can account for multiple light scattering. In this paper, we propose an image reconstruction method, called CISOR, for nonlinear diffractive imaging, based on our new variant of fast iterative shrinkage/thresholding algorithm (FISTA) and total variation (TV) regularization. We prove that CISOR reliably converges for our nonconvex optimization problem, and systematically compare our method with other state-of-the-art methods on simulated as well as experimentally measured data.

The radar autofocus problem arises in situations where radar measurements are acquired of a scene using antennas that suffer from position ambiguity. Current techniques model the antenna ambiguity as a global phase error affecting the received radar measurement at every antenna. However, the phase error signal model is only valid in the far field regime where the position error can be approximated by a one dimensional shift in the down-range direction. We propose in this paper an alternate formulation where the antenna position error is modeled using a two-dimensional shift operator in the image-domain. The radar autofocus problem then becomes a multichannel two-dimensional blind deconvolution problem where the static radar image is convolved with a two dimensional shift kernel for each antenna measurement. We develop an alternating minimization framework that leverages the sparsity and piece-wise smoothness of the radar scene, as well as the one-sparse property of the two dimensional shift kernels.

Multi-spectral (MS) image super-resolution aims to reconstruct super-resolved multi-channel images from their low-resolution images by regularizing the image to be reconstructed. Recently data-driven regularization techniques based on sparse modeling and deep learning have achieved substantial improvements in single image reconstruction problems. Inspired by these data-driven methods, we develop a novel coupled analysis and synthesis dictionary (CASD) model for MS image super-resolution, by exploiting a regularizer that operates within, as well as across, multiple spectral channels using convolutional dictionaries. To learn the CASD model parameters, we propose a deep dictionary learning framework, named DeepCASD, by unfolding and training an end-to-end CASD based reconstruction network over an image data set. Experimental results show that the DeepCASD framework exhibits improved performance on multi-spectral image super-resolution compared to state-of-the-art learning based super-resolution algorithms.

In remote sensing systems, exact knowledge of the sensor locations is critical for generating focused images. In order to accurately locate misplaced or perturbed sensors from their received signal data, we proposed a robust sensor localization method based on low-rank Euclidean distance matrix (EDM) reconstruction. To this end, an EDM of sensors and objects under detection is defined and partially initialized by computing distances between the inaccurate sensor locations and distances from the sensors to the objects using signal coherence analysis. We then decompose the noisy EDM with missing entries into a low-rank EDM corresponding to true sensor locations and a sparse matrix of distance errors by solving a constrained optimization problem using the alternating direction method of multipliers (ADMM). We verify our method with simulations on a uniform linear array with unknown perturbations up to several wavelengths.

We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement model that can account for the multiple scattering phenomenon, which makes the method preferable in applications where linear measurement models are inaccurate. We construct the measurement model by expanding the scattered wave-field with an accelerated-gradient method, which is guaranteed to converge and is suitable for large-scale problems. We provide explicit formulas for computing the gradient of our measurement model with respect to the unknown image, which enables image formation with a sparsity-driven numerical optimization algorithm. We validate the method both analytically and with numerical simulations.

Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this paper, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with total variation (TV) regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications. We illustrate the benefit of our approach in the context of joint intensity-depth imaging.

We consider the problem of fusing multiple cloud-contaminated aerial images of a 3D scene to generate a cloud-free image, where the images are captured from multiple unknown view angles. In order to fuse these images, we propose an end-to-end framework incorporating epipolar geometry and low-rank matrix completion. In particular, we first warp the multi-angular images to single-angle ones based on the estimated fundamental matrices that relate the multi-angular images according to their projective relations to the 3D scene. Then we formulate the fusion process of the warpped images as a low-rank matrix completion problem where each column of the matrix corresponds to a vectorized image with missing entries corresponding to cloud or occluded areas. Results using DigitalGlobe high spatial resolution images demonstrate that our algorithm outperforms existing approaches.

Tensor robust principal component analysis (PCA) approaches have drawn considerable interests in many applications such as background subtraction, denoising, and outlier detection, etc. In this paper we propose an online tensor robust PCA where the multidimensional data (tensor) is revealed sequentially in online mode, and tensor PCA is updated based on the latest estimation and the newly collected data. Compared to the tensor robust PCA in batch mode, we significantly reduce the required memory and improve the computation efficiency. Application on fusing cloud-contaminated satellite images demonstrates that the proposed method shows superiority in both convergence speed and performance compared to the state-of-the-art approaches.

The iterative Born approximation (IBA) is a well-known method for describing waves scattered by semitransparent objects. In this letter, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation regularizer. The proposed method is obtained by relating iterations of IBA to layers of an artificial multilayer neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.

We consider a 3D configuration where a coherent cross-track MIMO radar with uniform spaced transmit-receive antennas is mounted on a moving platform. Given a restricted number of transmit channels and known motion errors, we propose to randomize the transmit channels and employ a com-pressive sensing (CS)-based imaging approach to reconstruct 3D images by exploiting sparsity of the scene to be reconstructed. Results on simulated data demonstrate that our proposed moving MIMO platform performs much better than that with the same number but fixed transmit channels in reconstructing 3D images, and close to that using full channel MIMO operation but with much fewer transmit channels.

In this paper, we fundamentally re-examine 3D SAR imaging and propose a CS-based approach aiming to reduce the data collection cost and increase the elevation resolution. In particular, our approach significantly reduces the number of baselines required to acquire the scene of interest, as well as the pulsing rate in each baseline. The baselines are collected using multiple passes of a single or multiple SAR platforms such that their elevations are randomly distributed in the available elevation space. Each baseline uses a fixed pulse repetition frequency (PRF) which can be different from the PRFs used in other baselines. Using the collected multi-baseline data in its entirety we generate a high resolution 3D reflectivity map, using a CS-based iterative imaging algorithm. Our simulation results demonstrate that the proposed method can improve elevation resolution significantly by fusing data from multiple platforms due to the very large virtual elevation aperture even with a small number of baselines.

In classical spotlight-mode synthetic aperture radar (SAR), a mobile sensor array is steered to always focus on the same area (spot) as it moves, transmitting pulses to illuminate the spot and receiving and processing their reflections. The result is a high resolution image, covering a relatively small area. In this paper, we propose using a randomly steerable sensor array for synthetic aperture imaging, aiming to increase the coverage area without sacrificing the imaging resolution. This is realized by steering the beam of the array such that each transmitted pulse illuminates one of two or more spots, randomly selected with equal probability. Each of those spots has the same size as a single spot in a classical array, effectively doubling (or more) the total area illuminated. Using principles from compressive sensing (CS) we demonstrate that it is possible to reconstruct the images of all illuminated areas by exploiting the structure of the reconstructed images. Our experimental results demonstrate that our random steerable array can double coverage with almost the same imaging resolution.

In this paper, we develop a new approach to synthetic aperture imaging inspired by recently developed compressive sensing (CS) methods. Our approach modifies the beam steering pattern of conventional sliding spotlight-mode systems and randomizes it such that with each pulse the beam illuminates a different, randomly chosen, part of the imaged area. The randomization allows the acquisition of the area of interest with a significantly larger effective aperture compared to the conventional sliding spotlight mode and, therefore, with significantly larger resolution. The reconstruction estimates the signal using a model that combines a sparse and a dense component. This model captures the structure of SAR images better than conventional sparse models, typically used in CS, and provides superior reconstruction performance. Our experimental results demonstrate that the proposed randomly steered spotlight array can improve imaging resolution, as measured by the reconstruction SNR and the phase error, without compromising the covered area size.

Pan-sharpening is an image fusion process in which high resolution (HR) panchromatic (Pan) imagery is used to sharpen the corresponding low resolution (LR) multi-spectral (MS) imagery. Pan-sharpened MS images generally have high spatial resolutions, but exhibit color distortions. In this paper, we propose a dictionary learning based pan-sharpening process to reduce the color distortion caused by the interpolation of the MS imagery. Instead of interpolating the LR MS image before fusion, we generate an improved MS image which is sparse with respect to a dictionary learned from the image data. Our experiments on degraded QuickBird and IKONOS images demonstrate that the distortion in the MS images produced using our approach is significantly reduced.

In satellite image processing, pan-sharpening is the fusion process in which a low resolution (LR) multi-spectral (MS) image is sharpened using the corresponding high resolution (HR) panchromatic (Pan) image to obtain a HR MS image. In this paper we propose a novel pan-sharpening method which combines the ideas of classical wavelet-based pan-sharpening with recently developed dictionary learning (DL) methods. The HR MS image is generated using wavelet-based pan-sharpening, regulated by promoting sparsity with respect to a dictionary. The dictionary is obtained using DL on the multi-scale wavelet tree vectors of the image to be pan-sharpened. A significant advantage of our approach compared to most DL-based approaches is that it does not require a large database of images on which to train the dictionary. Experiments on degraded satellite images demonstrate that our method significantly reduces color distortions and wavelet artifacts compared to the state of the art.

Synthetic Aperture Radar (SAR) is a fundamental technology with significant impact in remote sensing applications. SAR relies on the motion of the radar platform to synthesize a large aperture, and achieve high resolution imaging of a large area. However, current strip-map SAR designs, relying on uniform pulsing, suffer from a fundamental trade-off between the azimuth resolution and the range coverage length. In this paper we overcome this trade-off using a randomized pulsing scheme combined with non-linear compressive sensing (CS) reconstruction. Our experimental results demonstrate significant improvement in the azimuth resolution using the proposed approach, without compromise on the range length of the imaged area.

We consider problems for which one has incomplete binary matrices that evolve with time (e.g., the votes of legislators on particular legislation, with each year characterized by a different such matrix). An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix. In addition, it is assumed that documents are available for the entities associated with at least one of the matrix axes. By jointly analyzing the matrices and documents, one may be used to inform the other within the analysis, and the model offers the opportunity to predict matrix values (e.g., votes) based only on an associated document (e.g., legislation). The research presented here merges two areas of machine-learning that have previously been investigated separately: incomplete-matrix analysis and topic modeling. The analysis is performed from a Bayesian perspective, with efficient inference constituted via Gibbs sampling. The framework is demonstrated by considering all voting data and available documents (legislation) during the 220-year lifetime of the United States Senate and House of Representatives.

Compressive sensing (CS) is an emerging £eld that, under appropriate conditions, can signi£cantly reduce the number of measurements required for a given signal. In many applications, one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multitask compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a principled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters.

We propose two new methods for wideband array signal imaging for targets situated in unknown random media. First, a normalized coherent interferometric (N-CINT) imaging algorithm is developed based on coherent interferometric (CINT) imaging theory, yielding improved imaging performance with experimental data. Second, a phase-difference analysis (PDA) method is proposed to significantly reduce computation time and to improve imaging quality. The parameters in the two methods are determined adaptively by optimizing an objective function. Experiments are carried out for electromagnetic scattering using a linear antenna array, providing a demonstration of these methods.

An experimental study is performed on imaging targets that are situated in a highly scattering environment, employing electromagnetic time-reversal methods. A particular focus is placed on performance when the electrical properties of the background environment (medium) are uncertain. It is assumed that the (unknown) medium characteristic of the scattered fields represents one sample from an underlying random process, with this random process representing our uncertainty in the media properties associated with the scattering measurement. While the specific Green's function associated with the scattered fields is unknown, we assume access to an ensemble of Green's functions sampled from the aforementioned distribution. This ensemble of Green's functions may be used in several ways to mitigate uncertainty in the true Green's function. Specifically, when performing time-reversal imaging, we consider a Green's function as a representative of the average of the ensemble, as well as Green's functions based on a principal components analysis of the ensemble. We also develop a wideband minimum-variance beamformer with environment perturbation constraints, in which the unknown Green's function is constrained to reside in a subspace spanned by the Green's function ensemble. These algorithms are examined using electromagnetic scattering data measured in a canonical set of laboratory experiments. The qualitative performance of the different techniques is presented in the form of images, with quantitative results presented in the form of receiver operating characteristic performance

A method to detect airports in large aerial optical imagery is considered. Combining texture segmentation and shape detection, this method shows advantages in analyzing large aerial imagery. First, large aerial images are segmented and interpreted according to textural features using a fast kernel matching pursuits (KMP) algorithm. As a result, attention is then paid to small regions of interest, extracted from the large images. Second, for each region of interest, a corresponding binary image is generated via the Canny edge operator, yielding a modified Hough transform image with which we search for elongated rectangles with desired dimensions (characteristic of runways). Those detected rectangles are declared as runways and the corresponding region of interest as an airport. Application on a dozen aerial images from southern California, demonstrates the effectiveness of the algorithm.