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All sources NASA NTRS DOE OSTI

DOE OSTI2016

Initial study of Schroedinger eigenmaps for spectral target detection

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. Inscene pixel and spectral signature detection approaches are presented. Finally, the HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Fiber-Coupled Substrate-Integrated Hollow Waveguides: An Innovative Approach to Mid-infrared Remote Gas Sensors

Here, in this study, an innovative approach based on fiberoptically coupled substrate-integrated hollow waveguide (iHWG) gas cells for the analysis of low sample volumes suitable for remote broad- and narrow-band mid-infrared (MIR; 2.5–20 μm) sensing applications is reported. The feasibility of remotely addressing iHWG gas cells, configured in a double-pass geometry via a reflector, by direct coupling to a 7-around-1 mid-infrared fiber bundle is demonstrated, facilitating low-level hydrocarbon gas analysis. For comparison studies, two iHWGs with substrate dimensions of 50 × 50 × 12 mm (L × W × H) and geometric channel lengths of 138 and 58.5 mm, serving as miniature light-guiding gas cells, were fiber-coupled to a Fourier transform infrared spectrometer enabling broadband MIR sensing. In addition to the fundamental feasibility of this concept, the achievable sensitivity toward several gaseous hydrocarbons and the reproducibility of assembling the fiber-iHWG interface were investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1989

Implementation of an algorithm for cylindrical object identification using range data

One of the problems in 3-D object identification and localization is addressed. In robotic and navigation applications the vision system must be able to distinguish cylindrical or spherical objects as well as those of other geometric shapes. An algorithm was developed to identify cylindrical objects in an image when range data is used. The algorithm incorporates the Hough transform for line detection using edge points which emerge from a Sobel mask. Slices of the data are examined to locate arcs of circles using the normal equations of an over-determined linear system. Current efforts are devoted to testing the computer implementation of the algorithm. Refinements are expected to continue in order to accommodate cylinders in various positions. A technique is sought which is robust in the presence of noise and partial occlusions.

Bozeman, Sylvia T.↗

NASA NTRS1992

A multiblock grid generation technique applied to a jet engine configuration

Techniques are presented for quickly finding a multiblock grid for a 2D geometrically complex domain from geometrical boundary data. An automated technique for determining a block decomposition of the domain is explained. Techniques for representing this domain decomposition and transforming it are also presented. Further, a linear optimization method may be used to solve the equations which determine grid dimensions within the block decomposition. These algorithms automate many stages in the domain decomposition and grid formation process and limit the need for human intervention and inputs. They are demonstrated for the meridional or throughflow geometry of a bladed jet engine configuration.

Stewart, Mark E. M.↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗

NASA NTRS1974

Evaluation of digitally corrected ERTS images

The discussion is limited to processes by which a set of data samples collected on a geometrically distorted grid can be converted to samples on a different precisely specified grid. It is assumed that a mathematical expression for the desired transformation already exists. Concern will be the effects on image quality (specifically loss of resolution, photometric accuracy, and miscellaneous aberations) that result from various techniques used in accomplishing the resampling. The resampling techniques evaluated have included nearest neighbor, bilinear, cubic convolution, and various truncated versions of sinc (x). The conclusion is that cubic convolution yields substantially higher quality output data for scenes typically encountered and can, in some instances, substantially enhance one's ability to abstract information.

Taber, J. E.↗

DOE OSTI2022

Hyperbolic string vertices

The string vertices of closed string field theory are subsets of the moduli spaces of punctured Riemann surfaces that satisfy a geometric version of the Batalin-Vilkovisky master equation. We present a homological proof of existence of string vertices and their uniqueness up to canonical transformations. Using hyperbolic metrics on surfaces with geodesic boundaries we give an exact construction of string vertices as sets of surfaces with systole greater than or equal to L with L ≤ 2 arcsinh 1. Intrinsic hyperbolic collars prevent the appearance of short geodesics upon sewing. The surfaces generated by Feynman diagrams are naturally endowed with Thurston metrics: hyperbolic on the vertices and flat on the propagators. For the classical theory the length L is arbitrary and, as L → ∞hyperbolic vertices become the minimal-area vertices of closed string theory

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Versatile laser-free trapped-ion entangling gates

We present a general theory for laser-free entangling gates with trapped-ion hyperfine qubits, using either static or oscillating magnetic-field gradients combined with a pair of uniform microwave fields symmetrically detuned about the qubit frequency. By transforming into a 'bichromatic' interaction picture, we show that either ${\hat{\sigma }}_{\phi }\otimes {\hat{\sigma }}_{\phi }$ or ${\hat{\sigma }}_{z}\otimes {\hat{\sigma }}_{z}$ geometric phase gates can be performed. The gate basis is determined by selecting the microwave detuning. The driving parameters can be tuned to provide intrinsic dynamical decoupling from qubit frequency fluctuations. The ${\hat{\sigma }}_{z}\otimes {\hat{\sigma }}_{z}$ gates can be implemented in a novel manner which eases experimental constraints. Here, we present numerical simulations of gate fidelities assuming realistic parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗

NASA NTRS1972

Effects of gravity-gradient torque on the rotational motion of a triaxial satellite in a precessing elliptic orbit.

A method of general perturbations, based on the use of Lie series to generate approximate canonical transformations, is applied to study the effects of gravity-gradient torque on the rotational motion of a triaxial, rigid satellite. The center of mass of the satellite is constrained to move in an elliptic orbit about an attracting point mass. The orbit, which has a constant inclination, is free to precess and spin. The method of general perturbations is used to obtain the Hamiltonian for the nonresonant secular and long-period rotational motion of the satellite. The differential equations derivable from the transformed Hamiltonian are integrable, and the solution for the long-term motion may be expressed in terms of Jacobian elliptic functions and elliptic integrals. Geometrical aspects of the long-term rotational motion are discussed, and a comparison of theoretical results with observations is made.

Cochran, J. E.↗

NASA NTRS1973

Long-term motion in a restricted problem of rotational motion

A method of general perturbations, based on the use of Lie series to generate approximate canonical transformations, is applied to study the long-term effects of gravity-gradient torque and orbital evolution on the rotational motion of a triaxial, rigid satellite. The center of mass of the satellite is constrained to move in an elliptic orbit about an attracting point mass. The orbit, which has a constant inclination, is constrained to precess and spin with constant rates. The method of general perturbations is used to obtain the Hamiltonian for the nonresonant secular and long-period rotational motion of the satellite to second order in n/omega sub 0, where n is the orbital mean motion of the center of mass and omega sub 0 is a reference value of the magnitude of the satellite's rotational angular velocity. The differential equations derivable from the transformed Hamiltonian are integrable, and the solution for the long-term motion may be expressed in terms of Jacobian elliptic functions and elliptic integrals. Geometrical aspects of the long-term rotational motion are discussed, and a comparison of theoretical results with observations is made.

Cochran, J. E.↗

NASA NTRS1982

Seasat wind and wave observations of northeast Pacific hurricane Iva, August 13, 1978

The paper examines Seasat wind and wave observations collected in the eye of hurricane Iva on August 13, 1978 in the northeast Pacific. A maximum wind speed of 25-30 m/s is observed, along with a banana-shape high-wind-speed distribution. Two-dimensional Fourier transforms of selected SAR scenes show a dominant wavefield characterized by wavelengths of 166-211 m, with a fanlike distribution of propagation directions. A simple geometric model is proposed to explain the dominant wave field in terms of prior storm intensity and location, which is combined with the Ross parametric hurricane wave model and Seasat wind and wave data to estimate a value of 43 km for the effective radial distance from the storm center to the region of maximum winds.

Gonzalez, F. I.↗

NASA NTRS1986

Modified Denavit-Hartenberg parameters for better location of joint axis systems in robot arms

The Denavit-Hartenberg parameters define the relative location of successive joint axis systems in a robot arm. A recent justifiable criticism is that one of these parameters becomes extremely large when two successive joints have near-parallel rotational axes. Geometrically, this parameter then locates a joint axis system at an excessive distance from the robot arm and, computationally, leads to an ill-conditioned transformation matrix. In this paper, a simple modification (which results from constraining a transverse vector between successive joint rotational axes to be normal to one of the rotational axes, instead of both) overcomes this criticism and favorably locates the joint axis system. An example is given for near-parallel rotational axes of the elbow and shoulder joints in a robot arm. The regular and modified parameters are extracted by an algebraic method with simulated measurement data. Unlike the modified parameters, extracted values of the regular parameters are very sensitive to measurement accuracy.

Barker, L. K.↗

NASA NTRS2001

Robust Matching of Wavelet Features for Sub-Pixel Registration of Landsat Data

For many Earth and Space Science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a generalized Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C. area are presented.

LeMoigne, Jacqueline↗

DOE OSTI2015

Human machine interface to manually drive rhombic like vehicles such as transport casks in ITER

The Cask and Plug Remote Handling System (CPRHS) and the respective Cask Transfer System (CTS) are designed to transport activated components between the reactor and the hot cell buildings of ITER during maintenance operations. In nominal operation, the CPRHS/CTS shall operate autonomously under human supervision. However, in some unexpected situations, the automatic mode must be overridden and the vehicle must be remotely guided by a human operator due to the harsh conditions of the environment. The CPRHS/CTS is a rhombic-like vehicle with two independent steerable and drivable wheels along its longitudinal axis, giving it omni-directional capabilities. During manual guidance, the human operator has to deal with four degrees of freedom, namely the orientations and speeds of two wheels. This work proposes a Human Machine Interface (HMI) to manage the degrees of freedom and to remotely guide the CPRHS/CTS in ITER taking the most advantages of rhombic like capabilities. Previous work was done to drive each wheel independently, i.e., control the orientation and speed of each wheel independently. The results have shown that the proposed solution is inefficient. The attention of the human operator becomes focused in a single wheel. In addition, the proposed solution cannot assure that the commands accomplish the physical constrains of the vehicle, resulting in slippage or even in clashes. This work proposes a solution that consists in the control of the vehicle looking at the position of its center of mass and its heading in the world frame. The solution is implemented using a rotational disk to control the vehicle heading and a common analogue joystick to control the vector speed of the center of the mass of the vehicle. The number of degrees of freedom reduces to three, i.e., two angles (vehicle heading and the orientation of the vector speed) and a scalar (the magnitude of the speed vector). This is possible using a kinematic model based on the vehicle Instantaneous Center of Rotation (ICR): a geometric approach where, at each time instant, the vehicle describes a circumference (either with a finite or infinite radius). The inverse of the kinematic model transforms the three input parameters of the center of mass into the four parameters for the wheels, preserving the omni-directional capabilities. The solution is implemented and tested using a HMI with a control disk and an analog joystick with two axis. The control disk was specially designed for this solution and implemented using a programmable micro-controller. In the first set of experiments, the HMI communicates with a computer running a simulator of the CPRHS/CTS, with the vehicle kinematics and dynamics, moving in a map of the ITER buildings. In the second set of experiments, the HMI communicates with a scaled prototype of the CPRHS running in a mock-up scenario to obtain more realistic results. Several type of tests were performed to evaluate the usability of the HMI. Different human operators without knowledge neither experience with this interface were invited to test the HMI. The operators had to drive the vehicle from an initial place to a final destination under the following conditions: with a pre-computed path to help guidance, without any path, with the information of the closest obstacles and without any help. The performance was evaluated using the time duration of the operation, the energy required to perform the described path, the risk of collision and, in case of a pre-computed path, the comparison between paths. In addition, each operator tested the HMI several times to evaluate the performance along consecutive trials. (authors)

42 ENGINEERING↗

DOE OSTI2020

Kirigami Engineering—Nanoscale Structures Exhibiting a Range of Controllable 3D Configurations

Abstract Kirigami structures provide a promising approach to transform flat films into 3D complex structures that are difficult to achieve by conventional fabrication approaches. By designing the cutting geometry, it is shown that distinct buckling‐induced out‐of‐plane configurations can be obtained, separated by a sharp transition characterized by a critical geometric dimension of the structures. In situ electron microscopy experiments reveal the effect of the ratio between the in‐plane cut size and film thickness on out‐of‐plane configurations. Moreover, geometrically nonlinear finite element analyses (FEA) accurately predict the out‐of‐plane modes measured experimentally, their transition as a function of cut geometry, and provide the stress–strain response of the kirigami structures. The combined computational–experimental approach and results reported here represent a step forward in the characterization of thin films experiencing buckling‐induced out‐of‐plane shape transformations and provide a path to control 3D configurations of micro‐ and nanoscale buckling‐induced kirigami structures. The out‐of‐plane configurations promise great utility in the creation of micro‐ and nanoscale systems that can harness such structural behavior, such as optical scanning micromirrors, novel actuators, and nanorobotics. This work is of particular significance as the kirigami dimensions approach the sub‐micrometer scale which is challenging to achieve with conventional micro‐electromechanical system technologies.

Zhang, Xu↗