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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast-enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. Here, the analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1978

A study of 'onboard' data processing for typical astrophysics payloads

The data, signal flow, and ground processing requirements for a typical set of X-ray and gamma-ray experiments were examined. A set of algorithms for ground processing were derived and these were taken as requirements imposed on 'onboard' spacecraft computers. Five types of CPU microprocessors were evaluated and compared with one another to determine the degree to which the data processing could be performed onboard the spacecraft. These included the NASA Standard Spacecraft Computer (NSSC-1), the Z-80, a Z-80 augmented with an auxiliary fast multiply chip, and an advanced augmented Z-80 with virtually unlimited memory. These CPU were investigated when used in a centralized mode, an experiment dedicated mode, and a hybrid of the first two modes. It was found that the use of onboard processing can significantly reduce downlink data rates for typical payloads, and may lead to greater efficiency of operation. Quantitative data for gamma-ray and X-ray payloads will be presented, and the results of the analysis discussed.

Stout, C. M.↗
NASA NTRS2010

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

Racette, Paul↗
NASA NTRS2020

Satellite-To-Satellite Imaging in Support of LEO Optical Navigation, Using the ASTERIA Cubesat

The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) was a 6-unit CubeSat technology demonstration mission that was built at NASA’s Jet Propulsion Laboratory (JPL) and deployed from the International Space Station (ISS) on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations alongside science until end of mission in December 2019. At the end of its lifetime it was being used as a demonstration platform for several experiments, including low earth orbit (LEO) optical navigation operations. With its visible light astrometric camera and stable attitude control system, the ASTERIA spacecraft showed itself to be a capable platform for the imaging of geosynchronous satellites from LEO. This paper will describe the imagery attained in flight and also the image processing algorithms that were developed to render that imagery into navigation quality data. These algorithms dealt with hot pixel filtering, noise modeling, attitude registration, star signal rejection and satellite signal identification. Brightness prediction algorithms used for target selection will also be discussed.

Fesq, Lorraine↗
DOE OSTI2015

Robotic real-time translational and rotational head motion correction during frameless stereotactic radiosurgery

Purpose: To develop a control system to correct both translational and rotational head motion deviations in real-time during frameless stereotactic radiosurgery (SRS). Methods: A novel feedback control with a feed-forward algorithm was utilized to correct for the coupling of translation and rotation present in serial kinematic robotic systems. Input parameters for the algorithm include the real-time 6DOF target position, the frame pitch pivot point to target distance constant, and the translational and angular Linac beam off (gating) tolerance constants for patient safety. Testing of the algorithm was done using a 4D (XY Z + pitch) robotic stage, an infrared head position sensing unit and a control computer. The measured head position signal was processed and a resulting command was sent to the interface of a four-axis motor controller, through which four stepper motors were driven to perform motion compensation. Results: The control of the translation of a brain target was decoupled with the control of the rotation. For a phantom study, the corrected position was within a translational displacement of 0.35 mm and a pitch displacement of 0.15° 100% of the time. For a volunteer study, the corrected position was within displacements of 0.4 mm and 0.2° over 98.5% of the time, while it was 10.7% without correction. Conclusions: The authors report a control design approach for both translational and rotational head motion correction. The experiments demonstrated that control performance of the 4D robotic stage meets the submillimeter and subdegree accuracy required by SRS.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Application of Reconfigurable Computing Technology to Multi-KiloHertz Micro-Laser Altimeter (MMLA) Data Processing

The Multi-KiloHertz Micro-Laser Altimeter (MMLA) is an aircraft based instrument developed by NASA Goddard Space Flight Center with several potential spaceflight applications. This presentation describes how reconfigurable computing technology was employed to perform MMLA signal extraction in real-time under realistic operating constraints. The MMLA is a "single-photon-counting" airborne laser altimeter that is used to measure land surface features such as topography and vegetation canopy height. This instrument has to date flown a number of times aboard the NASA P3 aircraft acquiring data at a number of sites in the Mid-Atlantic region. This instrument pulses a relatively low-powered laser at a very high rate (10 kHz) and then measures the time-of-flight of discrete returns from the target surface. The instrument then bins these measurements into a two-dimensional array (vertical height vs. horizontal ground track) and selects the most likely signal path through the array. Return data that does not correspond to the selected signal path are classified as noise returns and are then discarded. The MMLA signal extraction algorithm is very compute intensive in that a score must be computed for every possible path through the two dimensional array in order to select the most likely signal path. Given a typical array size with 50 x 6, up to 33 arrays must be processed per second. And for each of these arrays, roughly 12,000 individual paths must be scored. Furthermore, the number of paths increases exponentially with the horizontal size of the array, and linearly with the vertical size. Yet, increasing the horizontal and vertical sizes of the array offer science advantages such as improved range, resolution, and noise rejection. Due to the volume of return data and the compute intensive signal extraction algorithm, the existing PC-based MMLA data system has been unable to perform signal extraction in real-time unless the array is limited in size to one column, This limits the ability of the MMLA to operate in environments with sparse signal returns and a high number of noise return. However, under an IR&D project, an FPGA-based, reconfigurable computing data system has been developed that has been demonstrated to perform real-time signal extraction under realistic operating constraints. This reconfigurable data system is based on the commercially available Firebird Board from Annapolis Microsystems. This PCI board consists of a Xilinx Virtex 2000E FPGA along with 36 MB of SRAM arranged in five separately addressable banks. This board is housed in a rackmount PC with dual 850MHz Pentium processors running the Windows 2000 operating system. This data system performs all signal extraction in hardware on the Firebird, but also runs the existing "software based" signal extraction in tandem for comparison purposes. Using a relatively small amount of the Virtex XCV2000E resources, the reconfigurable data system has demonstrated to improve performance improvement over the existing software based data system by an order of magnitude. Performance could be further improved by employing parallelism. Ground testing and a preliminary engineering test flight aboard the NASA P3 has been performed, during which the reconfigurable data system has been demonstrated to match the results of the existing data system.

Powell, Wesley↗
NASA NTRS2000

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

NASA Discipline Environmental Health↗
DOE OSTI2018

Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. In this paper, we present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. Finally, with ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

58 GEOSCIENCES↗
NASA NTRS2016

The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by approximately equal to 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of approximately equal to 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.

methods: analytical – methods: data analysis –↗
DOE OSTI2019

Postprocessing for Improved Accuracy and Resolution of Spread Spectrum Time-Domain Reflectometry

Reflectometry, which is commonly used for locating faults on electrical wires, produces sampled time domain signatures with peaks that are often missed due to this sampling. Resultant errors in these sampled peaks translate to errors in calculating the impedance and location of the fault. Typical signal processing methods to improve the accuracy of these sampled peaks have complexity on the order of O(N 2 ). For embedded fault location applications, algorithms with lower complexity are desired. In this article, we introduce three algorithms for improving the accuracy of the peak with a complexity of O(N). Finally, we evaluate these algorithms on the practical case of calculating the velocity of propagation and the characteristic impedance of a photovoltaic (PV) cable using spread spectrum time-domain reflectometry.

47 OTHER INSTRUMENTATION↗
NASA NTRS1989

Application of an optimal filter for inflatable sphere data processing

The improvement in the sphere data processing, concerning the signal to noise ratio, is discussed. Frequency analysis of the radar data is effectuated. It reveals a specific frequency component in the radar angle error, which may originate from the tracking radar mechanism itself. An optimal (Wiener) filter is applied to the radar data in order to suppress the systematic angular error components selectively. Using this technique, a significant improvement in the signal to noise ratio is achieved. The resolution of sphere measurements, previously limited by the length of the polynomial filter in the sphere data processing algorithm, is improved.

Lee, H. S.↗
NASA NTRS1991

HYMOSS signal processing for pushbroom spectral imaging

The objective of the Pushbroom Spectral Imaging Program was to develop on-focal plane electronics which compensate for detector array non-uniformities. The approach taken was to implement a simple two point calibration algorithm on focal plane which allows for offset and linear gain correction. The key on focal plane features which made this technique feasible was the use of a high quality transimpedance amplifier (TIA) and an analog-to-digital converter for each detector channel. Gain compensation is accomplished by varying the feedback capacitance of the integrate and dump TIA. Offset correction is performed by storing offsets in a special on focal plane offset register and digitally subtracting the offsets from the readout data during the multiplexing operation. A custom integrated circuit was designed, fabricated, and tested on this program which proved that nonuniformity compensated, analog-to-digital converting circuits may be used to read out infrared detectors. Irvine Sensors Corporation (ISC) successfully demonstrated the following innovative on-focal-plane functions that allow for correction of detector non-uniformities. Most of the circuit functions demonstrated on this program are finding their way onto future IC's because of their impact on reduced downstream processing, increased focal plane performance, simplified focal plane control, reduced number of dewar connections, as well as the noise immunity of a digital interface dewar. The potential commercial applications for this integrated circuit are primarily in imaging systems. These imaging systems may be used for: security monitoring systems, manufacturing process monitoring, robotics, and for spectral imaging when used in analytical instrumentation.

Ludwig, David E.↗
NASA NTRS1999

Single-shot diffusion measurement in laser-polarized Gas

A single-shot pulsed gradient stimulated echo sequence is introduced to address the challenges of diffusion measurements of laser polarized 3He and 129Xe gas. Laser polarization enhances the NMR sensitivity of these noble gases by >10(3), but creates an unstable, nonthermal polarization that is not readily renewable. A new method is presented which permits parallel acquisition of the several measurements required to determine a diffusive attenuation curve. The NMR characterization of a sample's diffusion behavior can be accomplished in a single measurement, using only a single polarization step. As a demonstration, the diffusion coefficient of a sample of laser-polarized 129Xe gas is measured via this method. Copyright 1999 Academic Press.

Non-NASA Center↗
NASA NTRS2010

Computation of Earth Science Products on Spaceborne Platforms

Spaceborne sensors like NASA's Hyperion hyperspectral imager generate huge data volumes, and several near-term trends indicate that data volumes will only increase. Next-generation hyperspectral missions, such as NASA's Hyperspectral Infrared Imager (HyspIRI), will operate at higher duty cycles and higher data rates, and their users will expect products to be generated from the data in near real time [1]. Barring a sudden advance in satellite downlink capacity, these trends point to a need to process data and generate products onboard the spacecraft. Rather than downlink an entire hyperspectral image cube, onboard processing enables satellites to downlink partial or completed scientific data products, which are often one to two orders of magnitude smaller than the original image. In addition, a satellite with onboard data processing resources and direct broadcast transmission equipment could send data products directly to first responders, research scientists or other users on the ground. Next-generation space-capable data processors will have a combination of reconfigurable gate arrays, digital signal processors and general-purpose CPUs. Correctly programmed and configured, these resources are sufficient to run sophisticated data analysis programs, including hyperspectral image processing algorithms that commonly run on desktop computers [2]. This paper describes how we implemented one such program, the HSEG hierarchical image segmentation algorithm, software commonly used on desktop and parallel processors, on a hardware platform designed to mimic a next-generation space-capable data processor [3]. We also describe our approach to porting the algorithm to and optimizing it for the new platform, and determine the expected performance gains enabled by our design. This extended abstract will describe the HSEG algorithm and hardware platform in greater detail, provide an analysis of the key function within the algorithm that required hardware acceleration, and describe our implementation of that function in hardware.

Fisher, Kevin↗
DOE OSTI2018

Spatially dependent regularization parameter selection for total generalized variation-based image denoising

We propose a novel image denoising model based on the total generalized variation (TGV) regularization. In the model, a spatially dependent regularization parameter is utilized to adaptively fit the local image features, resulting in further exploitation of the denoising potential of the TGV regularization. The proposed model is formulated under a joint optimization framework, by which the estimations of the restored image and the regularization parameter are achieved simultaneously. Furthermore, the model is general purpose that can handle various types of noise occurring in image processing. An alternating minimization-based numerical scheme is especially developed, which leads to an efficient algorithmic solution to the nonconvex optimization problem. Numerical experiments are reported to illustrate the effectiveness of our model in terms of both peak signal-to-noise ratio and visual perception.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Synthetic aperture radar signal data compression using block adaptive quantization

This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.

Kuduvalli, Gopinath↗
DOE OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗