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Zhehui Wang

Publications and source records attributed to Zhehui Wang.

At least 55 records · Page 3Linked to original sources
arXiv2021

Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.

cs.DC↗
arXiv2021

Time-lens Photon Doppler Velocimetry (TL-PDV)

We describe a time lens to expand the dynamic range of photon Doppler velocimetry (PDV) systems. The principle and preliminary design of a time-lens PDV (TL-PDV) are explained and shown to be feasible through simulations. In a PDV system, an interferometer is used for measuring frequency shifts due to the Doppler effect from the target motion. However, the sampling rate of the electronics could limit the velocity range of a PDV system. A four-wave-mixing (FWM) time lens applies a quadratic temporal phase to an optical signal within a nonlinear FWM medium (such as an integrated photonic waveguide or highly nonlinear optical fiber). By spectrally isolating the mixing product, termed the idler, and with appropriate lengths of dispersion prior and after to this FWM time lens, a temporally magnified version of the input signal is generated. Therefore, the frequency shifts of PDV can be "slowed down" with the magnification factor $M$ of the time lens. $M=1$ corresponds to a regular PDV without a TL. $M=10$ has been shown to be feasible for a TL-PDV. Use of this effect for PDV can expand the velocity measurement range and allow the use of lower bandwidth electronics. TL-PDV will open up new avenues for various dynamic materials experiments.

physics.optics↗
arXiv2021

Neural Network for 3D ICF Shell Reconstruction from Single Radiographs

In inertial confinement fusion (ICF), X-ray radiography is a critical diagnostic for measuring implosion dynamics, which contains rich 3D information. Traditional methods for reconstructing 3D volumes from 2D radiographs, such as filtered backprojection, require radiographs from at least two different angles or lines of sight (LOS). In ICF experiments, space for diagnostics is limited and cameras that can operate on the fast timescales are expensive to implement, limiting the number of projections that can be acquired. To improve the imaging quality as a result of this limitation, convolutional neural networks (CNN) have recently been shown to be capable of producing 3D models from visible light images or medical X-ray images rendered by volumetric computed tomography LOS (SLOS). We propose a CNN to reconstruct 3D ICF spherical shells from single radiographs. We also examine sensitivity of the 3D reconstruction to different illumination models using preprocessing techniques such as pseudo-flat fielding. To resolve the issue of the lack of 3D supervision, we show that training the CNN utilizing synthetic radiographs produced by known simulation methods allows for reconstruction of experimental data as long as the experimental data is similar to the synthetic data. We also show that the CNN allows for 3D reconstruction of shells that possess low mode asymmetries. Further comparisons of the 3D reconstructions with direct multiple LOS measurements are justified.

physics.data-an↗
arXiv2021

Billion-pixel X-ray camera (BiPC-X)

The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 micron) motivate billion-pixel X-ray cameras (BiPC-X) based on commercial CMOS imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy X-rays ($<$ 10 keV) and indirect detection of higher energies using scintillators. Modified Birks's law is proposed for light-yield nonproportionality in scintillators as a function of X-ray energy. Single X-ray sensitivity and spatial resolution have been validated experimentally using laboratory X-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view (FOV) or large X-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, X-ray Free Electron Laser (XFEL), and pulsed power facilities.

physics.ins-det↗
arXiv2020

Strong scattering and parallel guiding of ultracold neutrons

For ultracold neutrons with a kinetic energy below 10 neV, strong scattering, characterized by $2πl_{c} / λ\leq 1$, can be obtained in metamaterials of C and $^7$Li. Here $l_{c}$ and $λ$ are the coherent scattering mean free path and the neutron wavelength, respectively. UCN interferometry and high-resolution spectroscopy (nano-electronvolt to pico-electronvolt resolution) in parallel waveguide arrays of neutronic metamaterials are given as examples of new experimental possibilities.

physics.ins-det↗
arXiv2020

Monte Carlo Modeling and Design of Photon Energy Attenuation Layers (PALs) for 10-30x Quantum Yield Enhancement in Si-based Hard X-ray Detectors

High-energy (>20keV) X-ray photon detection at high quantum yield, high spatial resolution and short response time has long been an important area of study in physics. Scintillation is a prevalent method but limited in various ways. Directly detecting high-energy X-ray photons has been a challenge to this day, mainly due to low photon-to-photoelectron conversion efficiencies. Commercially available state-of-the-art Si direct detection products such as the Si charge-coupled device (CCD) are inefficient for >10keV photons. Here, we present Monte Carlo simulation results and analyses to introduce a highly effective yet simple high-energy X-ray detection concept with significantly enhanced photon-to-electron conversion efficiencies composed of two layers: a top high-Z photon energy attenuator layer (PAL) and a bottom Si detector. We use the principle of photon energy down conversion, where high-energy X-ray photon energies are attenuated down to and below 10keV via inelastic scattering suitable for efficient photoelectric absorption by Si. Our Monte Carlo simulation results demonstrate that 10-30x increase in quantum yield can be achieved using PbTe PAL on Si, potentially advancing high-resolution, high-efficiency X-ray detection using PAL-enhanced Si CMOS image sensors.

physics.ins-det↗
arXiv2020

Ultrafast X-ray imaging of pulsed plasmas in water

Pulsed plasmas in liquids exhibit complex interaction between three phases of matter (liquids, gas, plasmas) and are currently used in a wide range of applications across several fields, however significant knowledge gaps in our understanding of plasma initiation in liquids hinder additional application and control; this area of research currently lacks a comprehensive predictive model. To aid progress in this area experimentally, here we present the first-known ultrafast (50 ps) X-ray images of pulsed plasma initiation processes in water (+25 kV, 10 ns, 5 mJ), courtesy of the X-ray imaging techniques available at Argonne National Laboratory's Advanced Photon Source (APS), with supporting nanosecond optical imaging and a computational X-ray diffraction model. These results clearly resolve narrow (~10 micron) low-density plasma channels during initiation timescales typically obscured by optical emission (<100 ns), a well-known and difficult problem to plasma experiments without access to state-of-the-art X-ray sources such as the APS synchrotron. Images presented in this work speak to several of the prevailing plasma initiation hypotheses, supporting electrostriction and bubble deformation as dominant initiation phenomena. We also demonstrate the plasma setup used in this work as a cheap ($<$US\$100k), compact, and repeatable benchmark imaging target (29.1 km/s, 1 TW/cm$^2$) useful for the development of next-generation ultrafast imaging of high-energy-density physics (HEDP), as well as easier integration of HEDP research into synchrotron-enabled facilities.

physics.plasm-ph↗
arXiv2020

EDCompress: Energy-Aware Model Compression for Dataflows

Edge devices demand low energy consumption, cost and small form factor. To efficiently deploy convolutional neural network (CNN) models on edge device, energy-aware model compression becomes extremely important. However, existing work did not study this problem well because the lack of considering the diversity of dataflow types in hardware architectures. In this paper, we propose EDCompress, an Energy-aware model compression method for various Dataflows. It can effectively reduce the energy consumption of various edge devices, with different dataflow types. Considering the very nature of model compression procedures, we recast the optimization process to a multi-step problem, and solve it by reinforcement learning algorithms. Experiments show that EDCompress could improve 20X, 17X, 37X energy efficiency in VGG-16, MobileNet, LeNet-5 networks, respectively, with negligible loss of accuracy. EDCompress could also find the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN models on hardware systems.

cs.LG↗
arXiv2020

Two-photon X-ray Ghost Microscope

X-ray imaging allows for a non-invasive image of the internal structure of an object. The most common form of X-ray imaging, projectional radiography, is simply a projection or "shadow" of the object rather than a point-to-point image possible with a lens. This technique fails to take advantage of the resolving capabilities of short-wavelength X rays. Various X-ray microscopes, typically operating with soft X rays (< 10 keV), use focusing X-ray optics to obtain higher resolution images of the internal structure of an object. Due to the short focal length of focusing X-ray optics, it becomes difficult to focus on the internal structure of larger objects in such a way to provide significant magnification to be resolvable. Here we present an imaging mechanism that utilizes two-photon X-ray ghost imaging to produce a true point-to-point image of the internal structure of an object, with the potential to introduce focusing X-ray optics or a scintillator-lens pairing to produce a magnified secondary ghost image. The focusing X-ray optics would image the primary ghost image (which has no physical structure to it) allowing the imaging of internal structures deeper than a standard X-ray microscope would allow. In principle, once some experimental barriers are overcome, this X-ray "ghost microscope" may achieve nanometer spatial resolution and open up new capabilities that would be of interest to the fields of physics, material science, and medical imaging.

physics.optics↗
arXiv2020

High-Speed Imaging of Transition from Fluid Breakup to Phase Explosion in Electric Explosion of Tungsten Wires in Air

High-speed visible imaging of sub-microsecond electric explosion of wires at the low specific energy deposition threshold reveals three distinct modes of wire failure as capacitor charge voltage and energy deposition are increased. For 100 micron diameter gold-plated tungsten wires of 2 cm length, deposited energies of 1.9 eV/atom produces a liquid column that undergoes hydrodynamic breakup into droplets with radii of order of wire diameter on timescales of 200 microseconds. Instability growth, column breakup, and droplet coalescence follow classic Rayleigh-Plateau predictions for instability of fluid column. Above 3.2 eV/atom of deposited energy, wires are seen to abruptly transition to an expanding mixture of micron scale liquid-droplets and vapor within one frame (less than 3.33 microseconds), which has been termed phase explosion in literature. Between these two limits, at 2.5 eV/atom of deposited energy, wire radius is unchanged for the first 10 microseconds before the onset of a rapid expansion and disintegration that resembles homogeneous nucleation of mechanically unstable bubbles. Thermodynamic calculations are presented that separate cases by temperature obtained during heating: below boiling point, near boiling point, and exceeding boiling point.

physics.app-ph↗
arXiv2019

Microparticle cloud imaging and tracking for data-driven plasma science

Large data sets give rise to the `fourth paradigm' of scientific discovery and technology development, extending other approaches based on human intuition, fundamental laws of physics, statistics and intense computation. Both experimental and simulation data are growing explosively in plasma science and technology, motivating data-driven discoveries and inventions, which are currently in infancy. Here we describe recent progress in microparticle cloud imaging and tracking (mCIT, $μ$CIT) for laboratory plasma experiments. Three types of microparticle clouds are described: from exploding wires, in dusty plasmas and in atmospheric plasmas. The experimental data sets are obtained with one or more imaging cameras at a rate up to 100k frames per second (fps). A physics-constrained motion tracker, a Kohonen neural network (KNN) or self-organizing map (SOM), the feature tracking kit (FTK), and U-Net are described and compared with each other for particle tracking using the datasets. Particle density and signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform (FFT) is used to reveal how U-Net, a deep convolutional neural network (CNN) developed for non-plasma applications, achieves the improvements for noisy scenes. The fitting parameters for a simple polynomial track model are found to group into clusters that reveal the geometry information about the camera setup. The mCIT or $μ$CIT techniques, when enhanced with data models, can be used to study the microparticle- or Debye-length scale plasma physics. The datasets are also available for ML code development and comparisons of algorithms.

physics.plasm-ph↗
arXiv2019

Hollow pellet injection for magnetic fusion

Precise delivery of mass to burning plasmas is a problem of growing interest in magnetic fusion. The answers to how much mass is necessary and sufficient can vary depending on parameters such as the type of atoms involved, the type of applications, plasma conditions, mass injector, and injection timing. Motivated by edge localized mode (ELM) control in H-mode plasmas, disruption mitigation and other applications in magnetic fusion, we report progress and new possibilities in mass delivery based on hollow pellets. Here, a hollow pellet refers to a spherical shell mass structure with a hollow core. Based on an empirical model of pellet ablation, coupled with BOUT++ simulations of ELM triggering threshold, hollow pellets are found to be attractive in comparison with solid spheres for ELM control. By using hollow pellets, it is possible to tailor mass delivery to certain regions of edge plasmas while minimizing core contamination and reducing the total amount of mass needed. We also include experimental progress in mass delivery experiments, in-situ diagnostics and hollow pellet fabrication, and emphasize new experimental possibilities for ELM control based on hollow pellets. A related application is the disruption mitigation scheme using powder encapsulated inside hollow shells. Further experiments will also help to resolve known discrepancies between theoretical predictions and experiments in using mass injection for ELM control and lead to better predictive models for ELM stability and triggering.

physics.plasm-ph↗
arXiv2019

The ISTI Rapid Response on Exploring Cloud Computing 2018

This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories. These demonstrations ranged from deploying proprietary software in a cloud environment to leveraging established cloud-based analytics workflows for processing scientific datasets. By and large, the projects were successful and collectively they suggest that cloud computing can be a valuable computational resource for scientific computation at national laboratories.

cs.DC↗
arXiv2018

Measurement of high-temperature microparticle acceleration through imaging

Microparticles ranging from sub-microns to millimeter in size are a common form of matter in magnetic fusion environment, and they are highly mobile due to their small mass. Different forces in addition to gravity can affect their motion both inside and outside the plasmas. Several recent advances open up new diagnostic possibilities to characterize the particle motion and their forces: high-speed imaging camera technology, microparticle injection techniques developed for fusion, and image processing software. Extending our earlier work on high-temperature 4D microparticle tracking using exploding wires, we report latest results on time-resolved microparticle acceleration measurement. New particle tracking algorithm is found to be effective in particle tracking even when there are a large number of particles close to each other. Epipolar constraint is used for track-pairing from two-camera views. Error field based on epi-geometry model is characterized based on a large set of 2D track data and 3D track reconstructions. Accelerations based on individual reconstructed 3D tracks are obtained. Force sensitivity on the order of ten gravitational acceleration has achieved. High-speed imaging is a useful diagnostic tool for microparticle physics, computer model validation and mass injection technology development for magnetic fusion.

physics.ins-det↗
arXiv2018

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.

quant-ph↗
arXiv2017

Random on-board pixel sampling (ROPS) X-ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.

physics.ins-det↗
arXiv2016

Position-sensitive detection of ultracold neutrons with an imaging camera and its implications to spectroscopy

Position-sensitive detection of ultracold neutrons (UCNs) is demonstrated using an imaging charge-coupled device (CCD) camera. A spatial resolution less than 15 $μ$m has been achieved, which is equivalent to an UCN energy resolution below 2 pico-electron-volts through the relation $δE = m_0g δx$. Here, the symbols $δE$, $δx$, $m_0$ and $g$ are the energy resolution, the spatial resolution, the neutron rest mass and the gravitational acceleration, respectively. A multilayer surface convertor described previously is used to capture UCNs and then emits visible light for CCD imaging. Particle identification and noise rejection are discussed through the use of light intensity profile analysis. This method allows different types of UCN spectroscopy and other applications.

physics.ins-det↗