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Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Compressive sensing is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient stellar events. This architecture recon-structs a differenced image itself, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of critical importance as the information required for generating a time-series photometric light curve is obtained only from a differenced image. Hence, reconstructing a crowded star spatial image, followed by differencing is wasteful. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) Reconstruct a dense field, and then apply differencing on the image to obtain the image of critical value. We study the case of microlensing to depict a star source experiencing magnification in time. Our results show that this architecture is able to reconstruct star source magnitudes with magnification factors greater than 1 for clean images with error less than 2% using only 10% of the Nyquist rate samples.

Asmita Korde-patel↗
DOE OSTI2022

$\mathrm{SIM}$-$\mathrm{STEM}$ $\mathrm{Lab}$: Incorporating Compressed Sensing Theory for Fast STEM Simulation

We report recently it has been shown that precise dose control and an increase in the overall acquisition speed of atomic resolution scanning transmission electron microscope (STEM) images can be achieved by acquiring only a small fraction of the pixels in the image experimentally and then reconstructing the full image using an inpainting algorithm. In this paper, we apply the same inpainting approach (a form of compressed sensing) to simulated, sub-sampled atomic resolution STEM images. We find that it is possible to significantly sub-sample the area that is simulated, the number of g-vectors contributing the image, and the number of frozen phonon configurations contributing to the final image while still producing an acceptable fit to a fully sampled simulation. Here we discuss the parameters that we use and how the resulting simulations can be quantifiably compared to the full simulations. As with any Compressed Sensing methodology, care must be taken to ensure that isolated events are not excluded from the process, but the observed increase in simulation speed provides significant opportunities for real time simulations, image classification and analytics to be performed as a supplement to experiments on a microscope to be developed in the future

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗
DOE OSTI2022

Self‐gated, dynamic contrast‐enhanced magnetic resonance imaging with compressed‐sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice

High‐risk atherosclerotic plaques are characterized by active inflammation and abundant leaky microvessels. We present a self‐gated, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) acquisition with compressed sensing reconstruction and apply it to assess longitudinal changes in endothelial permeability in the aortic root of Apoe −/− atherosclerotic mice during natural disease progression. Twenty‐four, 8‐week‐old, female Apoe −/− mice were divided into four groups (n = 6 each) and imaged with self‐gated DCE‐MRI at 4, 8, 12, and 16 weeks after high‐fat diet initiation, and then euthanized for CD68 immunohistochemistry for macrophages. Eight additional mice were kept on a high‐fat diet and imaged longitudinally at the same time points. Aortic‐root pseudo‐concentration curves were analyzed using a validated piecewise linear model. Contrast agent wash‐in and washout slopes ( b 1 and b 2 ) were measured as surrogates of aortic root endothelial permeability and compared with macrophage density by immunohistochemistry. b 2 , indicating contrast agent washout, was significantly higher in mice kept on an high‐fat diet for longer periods of time ( p = 0.03). Group comparison revealed significant differences between mice on a high‐fat diet for 4 versus 16 weeks ( p = 0.03). Macrophage density also significantly increased with diet duration ( p = 0.009). Spearman correlation between b 2 from DCE‐MRI and macrophage density indicated a weak relationship between the two parameters (r = 0.28, p = 0.20). Validated piecewise linear modeling of the DCE‐MRI data showed that the aortic root contrast agent washout rate is significantly different during disease progression. Further development of this technique from a single‐slice to a 3D acquisition may enable better investigation of the relationship between in vivo imaging of endothelial permeability and atherosclerotic plaques' genetic, molecular, and cellular makeup in this important model of disease.

Calcagno, Claudia↗
DOE OSTI2024

Thermal field reconstruction and compressive sensing using proper orthogonal decomposition

Model order reduction allows critical information about sensor placement and experiment design to be distilled from raw fluid mechanics simulation data. In many cases, sensed information in conjunction with reduced order models can also be used to regenerate full field variables. In this paper, a proper orthogonal decomposition (POD) inferencing method is extended to the modeling and compressive sensing of temperature, a scalar field variable. The method is applied to a simulated, critically stable, incompressible flow over a heated cylinder (Re = 1000) with Prandtl number varying between 0.001 and 50. The model is trained on pressure and temperature data from simulations. Field reconstructions are then generated using data from selected sensors and the POD model. Finally, the reconstruction error is evaluated across all Prandtl numbers for different numbers of retained modes and sensors. The predicted trend of increasing reconstruction accuracy with decreasing Prandtl number is confirmed and a Prandtl number/sensor count error matrix is presented.

Matulis, John↗
DOE OSTI2022

Impact of cyber attacks on distributed compressive sensing based state estimation in power distribution grids

Modern power distribution grids suffer from multiple vulnerabilities due to the tight integration between the physical system and the cyber infrastructure. Sophisticated and malicious cyber attacks continue to adversely impact the grid operation leading to performance degradation, service interruption, and grid failure. State estimation plays an essential role in grid monitoring and advancing cyber-attack situational awareness. In this regard, this paper first proposes a distributed compressive sensing (CS) state estimation approach for an unobservable distribution grid. Further, the proposed distributed CS approach divides the distribution grid into sub-areas to perform local state estimation. Then an alternating direction method of multipliers (ADMM) based iterative information exchange among neighboring areas is employed to complete the estimation process. In this estimation process, the impact of loss of measurement data, false data injection (FDI), replay, and neighborhood cyber-attacks is analyzed. Extensive simulations are performed on the IEEE 37-bus and IEEE 123-bus standard networks to demonstrate the algorithm’s robustness to the aforementioned cyber-attacks. A quantitative analysis of computational complexity and simulation time of the distributed CS based approach is also presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Accelerating Ultrafast Spectroscopy with Compressive Sensing

We report ultrafast spectroscopy is an important tool for studying photoinduced dynamical processes in atoms, molecules, and nanostructures. Typically, the time to perform these experiments ranges from several minutes to hours depending on the choice of spectroscopic method. It is desirable to reduce this time overhead not only to shorten time and laboratory resources, but also to make it possible to examine fragile specimens that quickly degrade during long experiments. In this article, we motivate using compressive sensing to significantly shorten data acquisition time by reducing the total number of measurements in ultrafast spectroscopy. We apply this technique to experimental data from ultrafast transient absorption spectroscopy and ultrafast terahertz spectroscopy and show that good estimates can be obtained with as low as 15% of the total measurements, implying a sixfold reduction in the data acquisition time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Recursive Dynamic Compressive Sensing in Smart Distribution Systems

With a transition to a smarter grid, we are witnessing a significant growth in smart metering infrastructure and sensor deployment in the distribution system. The underlying communication infrastructure is stressed due to the large volume of data that is generated by the smart meters/sensors. Furthermore, real time operations such as state estimation and control are impaired due to the lack of reliable aggregation of the data. In this paper, we exploit the underlying sparsity in grid data to implement two recursive dynamic compressive sensing (CS) approaches-streaming modified weighted-`1 CS and Kalman filtered CS. These approaches aim to reconstruct the sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Slow signal and support change is in distribution grid data is validated using Pecan Street data. Both the IEEE 34 node test feeder system and PecanStreet data are considered as two examples to validate the superior performance of the two recursive CS techniques relative to classic CS.

Karimi, Hazhar Sufi↗
DOE OSTI2025

Compressed sensing methods with applications to advanced air sampling

Environmental sampling methods developed by the Savannah River National Laboratory (SRNL) employ collectors with sorbent media tubes set at various locations to collect airborne emissions. Laboratory analyses of these tubes results in one-dimensional signals regarding what chemicals are being released and transported within the atmosphere. The analysis process is time consuming especially when analyzing a full year’s worth of tubes (hourly sample collection results in nearly 9,000 tubes per year). Using a signal processing method such as compressed sensing allows for recreation of the full signal while greatly reducing the number of analyzed samples required. Due to the sparsity of data retrieved from the air tubes, it is possible to use measurements a fraction of the size of the original data to gain much of the same information. This would improve the overall time and cost of analysis when modeling one-dimensional sampling signals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Compressed Sensing Methods with Applications to Advanced Air Sampling [Poster]

Environmental sampling methods developed by the Savannah River National Laboratory (SRNL) employ collectors with sorbent media tubes set at various locations to collect airborne emissions. Laboratory analyses of these tubes results in one-dimensional signals regarding what chemicals are being released and transported within the atmosphere. The analysis process is time consuming especially when analyzing a full year’s worth of tubes (hourly sample collection results in nearly 9,000 tubes per year). Using a signal processing method such as compressed sensing allows for recreation of the full signal while greatly reducing the number of analyzed samples required. Due to the sparsity of data retrieved from the air tubes, it is possible to use measurements a fraction of the size of the original data to gain much of the same information. This would improve the overall time and cost of analysis when modeling one-dimensional sampling signals.

Campbell, Cassidy [Savannah River National Laborat↗
NASA NTRS2021

Infrared Imaging using Sparse Microresonator Arrays and Compressed Sensing

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in high-temperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption at the expense of moderate post-processing time and increased complexity to recover the image. Three 128 by 128 sparse resonator arrays with the array densities of 10%, 25% and 37.5% are designed, and used in conjunction with the Compressed Sensing (CS) method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
DOE OSTI2023

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. Here, this paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully re- constructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

42 ENGINEERING↗
DOE OSTI2022

Enhanced detection of paramagnetic fluorine-19 magnetic resonance imaging agents using zero echo time sequence and compressed sensing

Fluorine-19 ( 19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density–weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T 1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T 2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a “zero” echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T 1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗