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NASA NTRS1980

Composite model of a 3-D image

This paper presents a composite model of a moving (3-D) image especially useful for the sequential image processing and encoding. A non-linear predictor based on the composite model is described. The performance of this predictor is used as a measure of the validity of the model for a real image source. The minimization of a total mean square prediction error provides an inequality which determines a condition for the profitable use of the composite model and can serve as a decision device for the selection of the number of subsources within the model. The paper also describes statistical properties of the prediction error and contains results of computer simulation of two non-linear predictors in the case of perfect classification between subsources.

Dukhovich, I. J.↗

NASA NTRS1989

Effects of the ephemeris error on effective pointing for a spaceborne SAR

Both Magellan SAR data acquisition and image processing require the knowledge of both the ephemeris (spacecraft position and velocity) and the radar pointing direction. Error in the knowledge of the radar pointing direction results in a loss of SNR in the image product. An error in the ephemeris data has a similar effect. To facilitate SNR performance analysis, an effective pointing error is defined to characterize the effect of the ephemeris error. A systematic approach to relate the ephemeris error to the effective pointing errors is described. Result of this analysis has led to a formal accuracy requirement levied on the Magellan navigation system.

Jin, Michael Y.↗

NASA NTRS1993

Imaging Genetic Molecules At Atomic Resolution

Proposed method of imaging informational polymeric biological molecules at atomic resolution enables determination of sequences of component monomers about 10 to the 3rd power to 10 to the 4th power times as fast as conventional methods do. Accelerates research on genetic structures of animals and plants. Also contributes significantly to imaging processes like scanning electron microscopy (SEM), atomic-force microscopy (AFM), and scanning tunneling microscopy (STM) in cases in which necessary to locate or identify small specimens on relatively large backgrounds and subtract background images to obtain images of specimens in isolation. V-grooves on silicon wafer laid out in square pattern, intersections of which marked to identify coordinates. Specimen molecules held in grooves for reproducible positioning and scanning by AFM or STM.

Coles, L. Stephen↗

NASA NTRS1993

A CCD antiblooming technique for use in photometry

A new antiblooming technique was used to collect images for a BL Lac monitoring project. The antiblooming was accomplished by clocking two of the collection phases (in a three-phase chip) back and forth during the integration of an image. Calibrations were done using VRI standard stars to insure that the technique did not affect normal photon collection and photometric data. New sources of noise were identified which must be subtracted from the processed imaging. In contrast, the technique reduced dark current noise, which is of benefit with a thermoelectric-cooled camera. The technique eliminated blooming across the stars of interest, while preserving photon-collection efficiency. It also reduced the total noise of the exposures.

Neely, A. William↗

NASA NTRS2013

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Cloud Computing↗

DOE OSTI2014

WE-E-18A-07: MAGIC: Multi-Acquisition Gain Image Correction for Mobile X-Ray Systems with Intrinsic Localization Crosshairs

Purpose: Gain calibration for X-ray imaging systems with movable flat panel detectors (FPD) and intrinsic crosshairs is a challenge due to the geometry dependence of the heel effect and crosshair artifact. This study aims to develop a gain correction method for such systems by implementing the multi-acquisition gain image correction (MAGIC) technique. Methods: Raw flat-field images containing crosshair shadows and heel effect were acquired in 4 different FPD positions with fixed exposure parameters. The crosshair region was automatically detected and substituted with interpolated values from nearby exposed regions, generating a conventional single-image gain-map for each FPD position. Large kernel-based correction was applied to these images to correct the heel effect. A mask filter was used to invalidate the original cross-hair regions previously filled with the interpolated values. A final, seamless gain-map was created from the processed images by either the sequential filling (SF) or selective averaging (SA) techniques developed in this study. Quantitative evaluation was performed based on detective quantum efficiency improvement factor (DQEIF) for gain-corrected images using the conventional and proposed techniques. Results: Qualitatively, the MAGIC technique was found to be more effective in eliminating crosshair artifacts compared to the conventional single-image method. The mean DQEIF over the range of frequencies from 0.5 to 3.5 mm-1 were 1.09±0.06, 2.46±0.32, and 3.34±0.36 in the crosshair-artifact region and 2.35±0.31, 2.33±0.31, and 3.09±0.34 in the normal region, for the conventional, MAGIC-SF, and MAGIC-SA techniques, respectively. Conclusion: The introduced MAGIC technique is appropriate for gain calibration of an imaging system associated with a moving FPD and an intrinsic crosshair. The technique showed advantages over a conventional single image-based technique by successfully reducing residual crosshair artifacts, and higher image quality with respect to DQE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

A Quantum Leap for Dynamic Radiography

Dynamic radiography techniques have been instrumental in advancing the National Nuclear Security Administration's (NNSA) mission since WWII. This paper explores the transformative potential of quantum information science (QIS) to revolutionize dynamic radiography through enhanced image processing, tomographic reconstruction, statistics and uncertainty quantification (UQ), and artificial intelligence integration. By leveraging quantum algorithms to extract previously inaccessible information from existing datasets, this interdisciplinary approach promises unprecedented insights at the intersection of dynamic imaging, artificial intelligence, and quantum information technologies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

NASA NTRS1986

Mapping the Structure of Heterogeneous Materials

Image-processing microdensitometer/Fourier analyzer yields statistics of subcomponent distribution. Nondestructive method for studying structure heterogeneous materials uses energy-dispersive X-ray analysis in scanning electron microscope. Scanning microdensitometer/Fourier analyzer (SMFA) is applied to SEM images to obtain statistics about sample structure. Method originally developed for studying effect on combustion of fine structure of composite solid propellants.

Strand, L. D.↗

NASA NTRS2001

Autonomous Sciencecraft Constellation science study report

The image processing and scientific analysis techniques described in this study report are not confined to radar date of Earth. The techniques have widespread applications, theoretically to any instrument at any wavelength (such as visible and infrared imagers). We illustrate how the same techniques demonstrated by ASC can be used to search for change, and identify features, on Mars and the Jovian satelites, Europa and IO.

Constellation ASC↗

NASA NTRS1993

A pattern recognition system for locating small volvanoes in Magellan SAR images of Venus

The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

Burl, M. C.↗

NASA NTRS2019

An Automatic Light Rain Detection Algorithm on NASA MPLNET Lidar Observations in the Frame of WMO GALION Project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Simone Lolli↗

NASA NTRS2021

Features and Characteristics of the New NASA MicroPuLse NETwork (MPLNET) Automatic Rain Detection Algorithm

S. Lolli↗

DOE OSTI2020

Impacts of advanced diesel combustion operation and fuel formulation on soot nanostructure and reactivity

Advanced diesel combustion, accomplished via a single pulse fuel injection and high levels of exhaust gas recirculation (referred to as “PCCI”, partially-premixed charge compression ignition), is shown to be a path to reduce oxides of nitrogen and particulate matter simultaneously. This is well established in the literature. Less established is the extent to which such dilute combustion processes influence soot formation and affect soot that is emitted from diesel engines under such combustion modes. This work focuses on characterization of the nanostructure and oxidative reactivity of soot generated by a light-duty turbodiesel engine operating under a PCCI combustion mode, a dilute, low-temperature combustion process. Previous work on a type of PCCI combustion, referred to as high-efficiency clean combustion (HECC), showed soot samples having a fullerenic nanostructure, characterized by high levels of tortuosity of the fringe layers as seen in transmission electron micrograph images and as quantified using an image processing algorithm. Thermogravimetric analysis of the HECC mode soot samples showed that they displayed higher rates of oxidation than soot samples from a conventional diesel combustion mode. The present work returns to PCCI combustion, considering the timing of the main fuel injection, and the effects of operating on fuels rich in n-alkanes, particularly a fuel produced from a low temperature Fischer-Tropsch process (LTFT) and a renewable diesel fuel (RD) produced via hydrodeoxygenation of a plant oil. PCCI combustion conditions yield soot that shows higher reactivity compared to soot from the conventional combustion mode regardless of fuel type. LTFT and RD fuels produce soots with lower reactivity compared to ULSD. Soots produced from PCCI combustion have higher surface oxygen concentration and higher proportion of amorphous carbon. In addition, TEM images show that PCCI soots from all three fuels have smaller primary particle and particle aggregate sizes, and smaller graphene layers. These properties explain the higher reactivity of soot from PCCI combustion. The less reactive soots, which are produced from LTFT and RD fuel under conventional combustion, show internal burning during oxidation. However, soots with higher reactivity which are produced from late injection PCCI combustion and ULSD show shrinking core oxidation, most likely because of their overall amorphous structure.

42 ENGINEERING↗

DOE OSTI2018

A sub-sampled approach to extremely low-dose STEM

The inpainting of deliberately and randomly sub-sampled images offers a potential means to image specimens at a high resolution and under extremely low-dose conditions (≤1 e - /Å 2 ) using a scanning transmission electron microscope. We show that deliberate sub-sampling acquires images at least an order of magnitude faster than conventional low-dose methods for an equivalent electron dose. More importantly, when adaptive sub-sampling is implemented to acquire the images, there is a significant increase in the resolution and sensitivity which accompanies the increase in imaging speed. Lastly, we demonstrate the potential of this method for beam sensitive materials and in-situ observations by experimentally imaging the node distribution in a metal-organic framework.

47 OTHER INSTRUMENTATION↗

NASA NTRS1979

On-board multispectral classification study

The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.

Ewalt, D.↗

NASA NTRS1980

Data compression for National Oceanic and Atmospheric Administration /NOAA/ weather satellite systems

The National Oceanic and Atmospheric Administration (NOAA) receives high quality infrared weather images from each of its two geostationary weather satellites at an average data rate of 57 kilobits/second. These images are currently distributed to field stations over 3 kilohertz analog phone lines. The resulting loss in image quality renders the images unacceptable for proposed digital image processing. This paper documents the study leading to a current effort to implement a microprocessor-based universal noiseless coder/decoder to satisfy NOAA's requirements of high quality, good coverage and timely transmission of its infrared images.

Rice, R. F.↗

NASA NTRS1986

Calculation of day and night emittance values

In July 1983, the Thermal Infrared Multispectral Scanner (TIMS) was flown over Death Valley, California on both a midday and predawn flight within a two-day period. The availability of calibrated digital data permitted the calculation of day and night surface temperature and surface spectral emittance. Image processing of the data included panorama correction and calibration to radiance using the on-board black bodies and the measured spectral response of each channel. Scene-dependent isolated-point noise due to bit drops, was located by its relatively discontinuous values and replaced by the average of the surrounding data values. A method was developed in order to separate the spectral and temperature information contained in the TIMS data. Night and day data sets were processed. The TIMS is unique in allowing collection of both spectral emittance and thermal information in digital format with the same airborne scanner. For the first time it was possible to produce day and night emittance images of the same area, coregistered. These data add to an understanding of the physical basis for the discrimination of difference in surface materials afforded by TIMS.

Kahle, Anne B.↗