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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

LoTToR: An Algorithm for Missing-Wedge Correction of the Low-Tilt Tomographic 3D Reconstruction of a Single-Molecule Structure

A single-molecule three-dimensional (3D) structure is essential for understanding the thermal vibrations and dynamics as well as the conformational changes during the chemical reaction of macromolecules. Individual-particle electron tomography (IPET) is an approach for obtaining a snap-shot 3D structure of an individual macromolecule particle by aligning the tilt series of electron tomographic (ET) images of a targeted particle through a focused iterative 3D reconstruction method. The method can reduce the influence on the 3D reconstruction from large-scale image distortion and deformation. Due to the mechanical tilt limitation, 3D reconstruction often contains missing-wedge artifacts, presented as elongation and an anisotropic resolution. Here, we report a post-processing method to correct the missing-wedge artifact. This low-tilt tomographic reconstruction (LoTToR) method contains a model-free iteration process under a set of constraints in real and reciprocal spaces. A proof of concept is conducted by using the LoTToR on a phantom, i.e., a simulated 3D reconstruction from a low-tilt series of images, including that within a tilt range of ±15°. The method is validated by using both negative-staining (NS) and cryo-electron tomography (cryo-ET) experimental data. A significantly reduced missing-wedge artifact verifies the capability of LoTToR, suggesting a new tool to support the future study of macromolecular dynamics, fluctuation and chemical activity from the viewpoint of single-molecule 3D structure determination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

LoTToR: An Algorithm for Missing-Wedge Correction of the Low-Tilt Tomographic 3D Reconstruction of a Single-Molecule Structure (LoTToR) v1.0

The low-tilt tomographic reconstruction (LoTToR) method is a post-processing method to correct the missing-wedge artifact, which contains a model-free iteration process under a set of constraints in real and reciprocal spaces. A proof of concept is conducted by using the LoTToR on a phantom, i.e., a simulated 3D reconstruction from a low-tilt series of images, including that within a tilt range of ±15°. The method is validated by using both negative-staining (NS) and cryo-electron tomography (cryo-ET) experimental data. A significantly reduced missing-wedge artifact verifies the capability of LoTToR, suggesting a new tool to support the future study of macromolecular dynamics, fluctuation and chemical activity from the viewpoint of single-molecule 3D structure determination. Moreover, the software can be used for hospital computer tomography (CT) to reduce the radiation damage to patients without reducing the CT image quality.

Ren, Gang↗
NASA NTRS2016

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
DOE OSTI2022

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗
DOE OSTI2023

Measurement of the nuclear modification factor of $b$-jets in 5.02 TeV Pb+Pb collisions with the ATLAS detector

This paper presents a measurement of b-jet production in Pb+Pb and pp collisions at $\sqrt{sNN}$ = 5.02 TeV with the ATLAS detector at the LHC. The measurement uses 260 pb –1 of pp collisions collected in 2017 and 1.4 nb –1 of Pb+Pb collisions collected in 2018. In both collision systems, jets are reconstructed via the anti-$k_t$ algorithm. The b-jets are identified from a sample of jets containing muons from the semileptonic decay of b-quarks using template fits of the muon momentum relative to the jet axis. In pp collisions, b-jets are reconstructed for radius parameters R = 0.2 and R = 0.4, and only R = 0.2 jets are used in Pb+Pb collisions. For comparison, inclusive R = 0.2 jets are also measured using 1.7 nb –1 of Pb+Pb collisions collected in 2018 and the same pp collision data as the b-jet measurement. The nuclear modification factor, $R_{AA}$, is calculated for both b-jets and inclusive jets with R = 0.2 over the transverse momentum range of 80–290 GeV. The nuclear modification factor for b-jets decreases from peripheral to central collisions. The ratio of the b-jet $R_{AA}$ to inclusive jet $R_{AA}$ is also presented and suggests that the $R_{AA}$ for b-jets is larger than that for inclusive jets in central Pb+Pb collisions. The measurements are compared with theoretical calculations and suggest a role for mass and colour-charge effects in partonic energy loss in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Neutron-antineutron oscillation sensitivity study at DUNE

The Deep Underground Neutrino Experiment (DUNE) aims to measure neutrino oscillations as well as search for beyond the standard model physics such as baryon number violating (BNV) processes. DUNE will use a 70 kt Liquid Argon Time Projection Chamber (LArTPC) located more than 1 km underground. A promising BNV process is neutron-antineutron oscillation ($n \rightarrow \bar{n}$) which, if discovered, would offer unique insight into the baryon asymmetry of the universe. We are developing a classification algorithm that separates $n \rightarrow \bar{n}$ events from major background atmospheric neutrino interactions using DUNE far detector simulations. We will perform the classification of signals and backgrounds by analyzing key features such as the multiplicity, isotropy, and kinematics of the reconstructed events. In the future, this algorithm can be used to obtain the sensitivity of the DUNE detectors to the neutron-antineutron oscillation lifetime.

Wheeler, Justin↗
DOE OSTI2024

Signal discrimination for neutron-antineutron oscillation sensitivity study at DUNE

The Deep Underground Neutrino Experiment (DUNE) aims to measure neutrino oscillations as well as search for beyond the standard model physics such as baryon number violating (BNV) processes. DUNE will use a 70 kt Liquid Argon Time Projection Chamber (LArTPC) located more than 1 km underground. A promising BNV process is neutron-antineutron oscillation $\left(n\rightarrow\bar{n}\right)$ which, if discovered, would offer unique insight into the baryon asymmetry of the universe. We are developing a classification algorithm that separates $n\rightarrow\bar{n}$ events from major background atmospheric neutrino interactions using DUNE far detector simulations. We will perform the classification of signals and backgrounds by analyzing key features such as the multiplicity, isotropy, and kinematics of the reconstructed events. In the future, this algorithm can be used to obtain the sensitivity of the DUNE detectors to the neutron-antineutron oscillation lifetime.

Wheeler, Justin↗
DOE OSTI2021

An automated procedure built on MTEX for reconstructing deformation twin hierarchies from electron backscattered diffraction datasets of heavily twinned microstructures

Here we present a set of algorithms built on the MTEX and MATLAB graph toolboxes for automatic reconstruction of deformation twin hierarchies from Electron Backscatter Diffraction (EBSD) datasets with a focus on developing methods for heavily twinned microstructures (twin fractions >0.5). The algorithms address key issues arising at large strains, mainly: missing twin relationships, grouping of heavily deformed grain fragments into families of similar orientation originating from a single initial grain, identification of parent fragments for large twin volume fractions, and classification of families having twin relationships with multiple families. To facilitate the development of these algorithms, large-grained ultra-high purity α-Ti deformed in compression along two directions is investigated. Graphs are utilized to handle non-local geometric merging and to represent relationships throughout the reconstruction process. When determining if a grain fragment is from the undeformed microstructure, the combined metrics of the fragment's orientation volume fraction in the initial texture and the directed graph centrality measure of out-closeness (the number of nodes reached in a graph from a given node) are essential. To address automation in reconstructing the sequence of twinning and relating fragments originating from a single grain in the initial microstructure, the twin family tree is formulated as a minimum spanning tree emanating from the initial grain family. A scheme constructing the distances associated with twin relationship comprising the spanning tree is developed, and a novel quasi-directional Prim spanning tree algorithm is used to determine the twin family tree. The procedure is demonstrated to significantly improve the level of automation in reconstructing twin hierarchies in heavily twinned microstructure compared to other methodologies in literature. The procedure can readily be applied to analyses of twinning in metals, as well as provide an approach for routinely extracting twin statistics at larger deformation levels than previously possible. Significantly, the procedure is demonstrated to be capable of identifying third generation twinning in α-Ti microstructures.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Algorithmic problems of nontransitive (SSB) utilities

The standard utility theory is based on several natural axioms including transitivity of preference; however, real preference is often not transitive. To describe such preferences, Fishburn (1988) introduced a new formalism (SSB-utilities), in which preference is described by a skew-symmetric function F:M x M - R, where M is the set of all alternatives. He also showed that it is in principle possible to reconstruct this function F by asking the person to compare different alternatives and lotteries. In the present paper we propose a new algorithm for reconstructing F that is asymptotically optimal in the sense that the number of binary (yes-no) questions that one has to ask to determine the values of F with given precision is of minimal possible order.

Kosheleva, O. M.↗
NASA NTRS1972

Adaptive line by line encoder for image transmission.

A simple adaptive algorithm is applied to the line by line encoding of images. Among its desirable properties the algorithm includes a control of encoding errors independently of the image statistics. This block transform encoder, because of its adaptive nature gives rise to nonstationary errors and end of block effects. Two methods are proposed to reduce these effects and thus improve the subjective quality of reconstructed images. The performance of the algorithm is image dependent and several illustrative examples are given.

Algazi, V. R.↗
DOE OSTI2026

Electromagnetic Shower Reconstruction and Identification in FASER's Emulsion Detector for LHC Forward Neutrino Measurements

We present methods for electromagnetic shower reconstruction and identification in the FASERnu emulsion detector using 100 GeV and 200 GeV electron test-beam data from the CERN SPS H4 beamline. The reconstruction employs a clustering-based algorithm without energy-dependent tuning to determine shower axes. A multi-level identification chain comprising track pre-selection, a cut-based selection, and a BDT classifier achieves combined background rejection rates of 99.99% (100 GeV) and 99.94% (200 GeV). The method reaches total reconstruction and identification efficiencies of 58.9% (100 GeV) and 70.8% (200 GeV) evaluated from simulated samples. Energy reconstruction using the total number of reconstructed segments as the calorimetric estimator yields relative biases of +0.6% (100 GeV) and -0.8% (200 GeV), with resolutions of 25.4% and 22.6%, respectively. Systematic uncertainties on the energy reconstruction are dominated by variations in emulsion film detection efficiency, contributing (+10.9%/-8.2%) at 100 GeV and (+10.3%/-6.9%) at 200 GeV. The methodology provides a validated framework for electron neutrino identification with the FASERnu detector at the LHC.

Mammen Abraham, Roshan [UC, Irvine]↗
DOE OSTI2022

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC

We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.

Instruments & Instrumentation↗
NASA NTRS1995

Category 5 problem solution using an unstructured finite volume algorithm

For the simulation of flows with complex geometries, unstructured finite volume methods have proven to be very popular, and simulations of a large number of flows have been done with good results using this approach. Since most of the simulations to date were done for steady flows, it is not clear that present unstructured finite volume algorithms can accurately track the unsteady propagation of acoustic waves in a computation. Therefore, there is a need to assess the accuracy of these methods for acoustic calculations. In this paper, we perform the numerical simulation of a very small amplitude acoustic wave incident on the non-uniform steady flow in a quasi- 1 D convergent-divergent nozzle using an unstructured finite volume algorithm with piece-wise linear, least square reconstruction, Roe flux difference splitting, and second-order MacCormack time marching. First, the spatial accuracy of the algorithm is evaluated for the steady flow by running the simulation with a sequence of successively finer meshes. Then the unsteady numerical solution with the acoustic perturbation is presented.

Bui, Trong T.↗
DOE OSTI2024

Capturing thin structures in VOF simulations with two-plane reconstruction

A novel interface reconstruction strategy for volume of fluid (VOF) methods is introduced that represents the liquid-gas interface as two planes that co-exist within a single computational cell. In comparison to the piecewise linear interface calculation (PLIC), this new algorithm greatly improves the accuracy of the reconstruction, in particular when dealing with thin structures such as films. The placement of the two planes requires the solution of a non-linear optimization problem in six dimensions, which has the potential to be overly expensive. Further, an efficient solution to this optimization problem is presented here that exploits two key ideas: an algorithm for extracting multiple plane orientations from transported surface data, and an efficient and mass-conserving distance-finding algorithm that accounts for two planes with arbitrary orientation. Additionally, a simple and robust strategy is presented to accurately represent the surface tension forces produced at the interface of subgrid-thickness films. The performance of this new VOF reconstruction is demonstrated on several test cases that illustrate the capability to handle arbitrarily thin films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗