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DOE OSTI2021

Novel compound heterozygous CPLANE1 variants identified in a Chinese family with Joubert syndrome

Abstract Joubert syndrome (JS) and JS‐related disorders (JSRD) are a group of neurodevelopmental diseases that share the “molar tooth sign” on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype–phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six‐person family, and the pathogenic variants were detected by whole‐exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1 : c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1 , which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.

Zhang, Cheng↗

NASA NTRS2004

Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets

A proposed optoelectronic instrument would identify targets rapidly, without need to radiate an interrogating signal, apply identifying marks to the targets, or equip the targets with transponders. The instrument was conceived as an identification, friend or foe (IFF) system in a battlefield setting, where it would be part of a targeting system for weapons, by providing rapid identification for aimed weapons to help in deciding whether and when to trigger them. The instrument could also be adapted to law-enforcement and industrial applications in which it is necessary to rapidly identify objects in view. The instrument would comprise mainly an optical correlator and a neural processor (see figure). The inherent parallel-processing speed and capability of the optical correlator would be exploited to obtain rapid identification of a set of probable targets within a scene of interest and to define regions within the scene for the neural processor to analyze. The neural processor would then concentrate on each region selected by the optical correlator in an effort to identify the target. Depending on whether or not a target was recognized by comparison of its image data with data in an internal database on which the neural processor was trained, the processor would generate an identifying signal (typically, friend or foe ). The time taken for this identification process would be less than the time needed by a human or robotic gunner to acquire a view of, and aim at, a target. An optical correlator that has been under development for several years and that has been demonstrated to be capable of tracking a cruise missile might be considered a prototype of the optical correlator in the proposed IFF instrument. This optical correlator features a 512-by-512-pixel input image frame and operates at an input frame rate of 60 Hz. It includes a spatial light modulator (SLM) for video-to-optical image conversion, a pair of precise lenses to effect Fourier transforms, a filter SLM for digital-to-optical correlation-filter data conversion, and a charge-coupled device (CCD) for detection of correlation peaks. In operation, the input scene grabbed by a video sensor is streamed into the input SLM. Precomputed correlation-filter data files representative of known targets are then downloaded and sequenced into the filter SLM at a rate of 1,000 Hz. When there occurs a match between the input target data and one of the known-target data files, the CCD detects a correlation peak at the location of the target. Distortion- invariant correlation filters from a bank of such filters are then sequenced through the optical correlator for each input frame. The net result is the rapid preliminary recognition of one or a few targets.

Moynihan, Philip↗

DOE OSTI2022

Automatic recognition system for document digitization in nuclear power plants

With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are required to accurately model and predict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. Here we evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2010

Mini-TES Observations of Comanche Carbonate and Its Distribution

The discovery by the Spirit rover of outcrops rich in Mg-Fe carbonate [Morris et al., 2010] represents another manifestation of a diverse aqueous history in Gusev crater. In 2005, observations by the Moessbauer spectrometer (MB) on outcrops dubbed Comanche provided initial indication of Fe-Mg carbonate that was subsequently supported by analysis of elemental data from the Alpha Particle X-ray Spectrometer (APXS). The recognition of a carbonate component in thermal infrared spectra measured by the Miniature Thermal Emission Spectrometer (Mini-TES) was significantly delayed due to dust contamination of the instrument's optics. With the implementation of a viable dust correction, the Comanche spectra were revisited and presented clear and compelling evidence for a Mg-Fe carbonate component that could be as much as a third of the total mineral abundance. The data from all three instruments in combination are best matched by Mg-Fe carbonate with an abundance of 16-34 wt%. Mini-TES spectra were acquired for 12 targets at various locations on the Comanche (4-5 m long) and Comanche Spur (1-2 m long) outcrops, the latter being the location of the MB and APXS measurements. The two outcrops are spectrally comparable and share similar morphology and texture based on color images from the Panoramic Camera (Pancam). The highest quality Mini-TES spectrum comes from the larger Comanche outcrop on a target named Saupitty. Linear least squares modeling of the Saupitty spectrum employed a library of laboratory spectra tailored for consistency with the APXS and MB data and included spectra representing Martian dust, a slope spectrum to account for any temperature determination errors, and a blackbody spectrum to account for differences in spectral contrast between the laboratory and Mini-TES spectrum. Successful modeling of the Comanche Saupitty spectrum required one or more carbonate phases to obtain a good fit. Excluding all carbonates from the full starting library more than doubled the root-mean-squared error of the model fit (0.147% vs. 0.299%). Because Mg-Fe carbonate and Ca-Mg carbonate (dolomite) are so spectrally similar over the range used for modeling, both provide a comparable fit. However, Carich carbonates like dolomite are precluded based on APXS data and are inconsistent with MB results. The Comanche carbonate rocks are stratigraphically above a set of olivine-rich volcaniclastic rocks known as Algonquin class that mantle the Haskin Ridge feature of the Columbia Hills. Based on ~50 Mini-TES observations, the Comanche outcrops are the only rocks that host abundant carbonate. However, a target at the base of the larger Comanche outcrop appears spectrally transitional between the carbonate and olivine units. This transitional spectral character applies to additional outcrops a few 10s of meters away from Comanche that also appear stratigraphically transitional. Additional work will attempt to establish whether we are seeing an alteration horizon or depositional unit associated with the emplacement Comanche carbonate.

Ruff, Steven W.↗

NASA NTRS2010

Multi-Stage System for Automatic Target Recognition

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Chao, Tien-Hsin↗

NASA NTRS2012

NASA Tech Briefs, January 2012

Contents of this issue are: (1) Energy-Based Tetrahedron Sensor for High-Temperature, High-Pressure Environments (2) Handheld Universal Diagnostic Sensor (3) Large-Area Vacuum Ultraviolet Sensors (4) Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures (5) Health-Enabled Smart Sensor Fusion Technology (6) Extended-Range Passive RFID and Sensor Tags (7) Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks (8) Self-Healing, Inflatable, Rigidizable Shelter (9) Improvements in Cold-Plate Fabrication (10) Technique for Radiometer and Antenna Array Calibration - TRAAC (11) Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment (12) Programmable Digital Controller (13) Use of CCSDS Packets Over SpaceWire to Control Hardware (14) Key Decision Record Creation and Approval Module (15) Enhanced Graphics for Extended Scale Range (16) Debris Examination Using Ballistic and Radar Integrated Software (17) Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) (18) Integration Manager (19) Eclipse-Free-Time Assessment Tool for IRIS (20) Automated and Manual Rocket Crater Measurement Software (21) MATLAB Stability and Control Toolbox Trim and Static Stability Module (22) Patched Conic Trajectory Code (23) Ring Image Analyzer (24) SureTrak Probability of Impact Display (25) Implementation of a Non-Metallic Barrier in an Electric Motor (26) Multi-Mission Radioisotope Thermoelectric Generator Heat Exchangers for the Mars Science Laboratory Rover (27) Uniform Dust Distributor for Testing Radiative Emittance of Dust-Coated Surfaces (28) MicroProbe Small Unmanned Aerial System (29) Highly Stable and Active Catalyst for Sabatier Reactions (30) Better Proton-Conducting Polymers for Fuel-Cell Membranes (31) CCD Camera Lens Interface for Real-Time Theodolite Alignment (32) Peregrine 100-km Sounding Rocket Project (33) SOFIA Closed- and Open-Door Aerodynamic Analyses (34) Sonic Thermometer for High-Altitude Balloons (35) Near-Infrared Photon-Counting Camera for High-Sensitivity Observations (36) Integrated Optics Achromatic Nuller for Stellar Interferometry (37) High-Speed Digital Interferometry (38) Ultra-Miniature Lidar Scanner for Launch Range Data Collection (39) Shape and Color Features for Object Recognition Search (40) Explanation Capabilities for Behavior-Based Robot Control (41) A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks (42) Quality Control Method for a Micro-Nano-Channel Microfabricated Device (43) Corner-Cube Retroreflector Instrument for Advanced Lunar Laser Ranging (44) Electrospray Collection of Lunar Dust (45) Fabrication of a Kilopixel Array of Superconducting Microcalorimeters with Microstripline Wiring Spacecraft Attitude Tracking and Maneuver Using Combined Magnetic Actuators (46) Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

Source record↗

NASA NTRS2008

NASA Tech Briefs, October 2008

Topics covered include: Control Architecture for Robotic Agent Command and Sensing; Algorithm for Wavefront Sensing Using an Extended Scene; CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO; Improved Airborne System for Sensing Wildfires; VHF Wide-Band, Dual-Polarization Microstrip-Patch Antenna; Onboard Data Processor for Change-Detection Radar Imaging; Using LDPC Code Constraints to Aid Recovery of Symbol Timing; System for Measuring Flexing of a Large Spaceborne Structure; Integrated Formation Optical Communication and Estimation System; Making Superconducting Welds between Superconducting Wires; Method for Thermal Spraying of Coatings Using Resonant-Pulsed Combustion; Coating Reduces Ice Adhesion; Hybrid Multifoil Aerogel Thermal Insulation; SHINE Virtual Machine Model for In-flight Updates of Critical Mission Software; Mars Image Collection Mosaic Builder; Providing Internet Access to High-Resolution Mars Images; Providing Internet Access to High-Resolution Lunar Images; Expressions Module for the Satellite Orbit Analysis Program Virtual Satellite; Small-Body Extensions for the Satellite Orbit Analysis Program (SOAP); Scripting Module for the Satellite Orbit Analysis Program (SOAP); XML-Based SHINE Knowledge Base Interchange Language; Core Technical Capability Laboratory Management System; MRO SOW Daily Script; Tool for Inspecting Alignment of Twinaxial Connectors; An ATP System for Deep-Space Optical Communication; Polar Traverse Rover Instrument; Expert System Control of Plant Growth in an Enclosed Space; Detecting Phycocyanin-Pigmented Microbes in Reflected Light; DMAC and NMP as Electrolyte Additives for Li-Ion Cells; Mass Spectrometer Containing Multiple Fixed Collectors; Waveguide Harmonic Generator for the SIM; Whispering Gallery Mode Resonator with Orthogonally Reconfigurable Filter Function; Stable Calibration of Raman Lidar Water-Vapor Measurements; Bimaterial Thermal Compensators for WGM Resonators; Root Source Analysis/ValuStream[Trade Mark] - A Methodology for Identifying and Managing Risks; Ensemble: an Architecture for Mission-Operations Software; Object Recognition Using Feature-and Color-Based Methods; On-Orbit Multi-Field Wavefront Control with a Kalman Filter; and The Interplanetary Overlay Networking Protocol Accelerator.

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NASA NTRS2012

Compact Autonomous Hemispheric Vision System

Solar System Exploration camera implementations to date have involved either single cameras with wide field-of-view (FOV) and consequently coarser spatial resolution, cameras on a movable mast, or single cameras necessitating rotation of the host vehicle to afford visibility outside a relatively narrow FOV. These cameras require detailed commanding from the ground or separate onboard computers to operate properly, and are incapable of making decisions based on image content that control pointing and downlink strategy. For color, a filter wheel having selectable positions was often added, which added moving parts, size, mass, power, and reduced reliability. A system was developed based on a general-purpose miniature visible-light camera using advanced CMOS (complementary metal oxide semiconductor) imager technology. The baseline camera has a 92 FOV and six cameras are arranged in an angled-up carousel fashion, with FOV overlaps such that the system has a 360 FOV (azimuth). A seventh camera, also with a FOV of 92 , is installed normal to the plane of the other 6 cameras giving the system a > 90 FOV in elevation and completing the hemispheric vision system. A central unit houses the common electronics box (CEB) controlling the system (power conversion, data processing, memory, and control software). Stereo is achieved by adding a second system on a baseline, and color is achieved by stacking two more systems (for a total of three, each system equipped with its own filter.) Two connectors on the bottom of the CEB provide a connection to a carrier (rover, spacecraft, balloon, etc.) for telemetry, commands, and power. This system has no moving parts. The system's onboard software (SW) supports autonomous operations such as pattern recognition and tracking.

Pingree, Paula J.↗

NASA NTRS2009

NASA Tech Briefs, August 2009

Topics covered include: Aligning a Receiving Antenna Array to Reduce Interference; Collecting Ground Samples for Balloon-Borne Instruments; Tethered Pyrotechnic Apparatus for Acquiring a Ground Sample; Enhanced Video-Oculography System; Joint Carrier-Phase Synchronization and LDPC Decoding; Dual-Polarization, Sideband-Separating, Balanced Receiver for 1.5 THz Modular Battery Charge Controller; Efficient Multiplexer FPGA Block Structures Based on G4FETs; VLSI Microsystem for Rapid Bioinformatic Pattern Recognition; Low-Noise Amplifier for 100 to 180 GHz; Improved Fabrication of Ceramic Matrix Composite/Foam Core Integrated Structures; Inert Welding/Brazing Gas Filters and Dryers; Fabricating Copper Nanotubes by Electrodeposition; Reducing Aerodynamic Drag on Empty Open Cargo Vehicles; Rotary Percussive Auto-Gopher for Deep Drilling and Sampling; More About Reconfigurable Exploratory Robotic Vehicles; Thermostatic Valves Containing Silicone-Oil Actuators; Improving Heat Flux Performance of Flat Surface in Spray-Cooling Systems; Treating Fibrous Insulation to Reduce Thermal Conductivity; Silica-Aerogel Composites Opacified with La(sub0.7)Sr(sub0.3)MnO3; Cyclic Oxidation Behavior of CuCrAl Cold-Sprayed Coatings for Reusable Launch Vehicles; Ceramic Fiber Structures for Cryogenic Load-Bearing Applications; Elastomer Reinforced with Carbon Nanotubes; Biologically Inspired Purification and Dispersion of SWCNTs; A Technique for Adjusting Eigenfrequencies of WGM Resonators; Low-Pressure, Field-Ionizing Mass Spectrometer; Modifying Operating Cycles to Increase Stability in a LITS; Chamber for Simulating Martian and Terrestrial Environments; Algorithm for Detecting a Bright Spot in an Image; Extreme Programming: Maestro Style; Adaptive Behavior for Mobile Robots; Protocol for Communication Networking for Formation Flying; Planning Complex Sequences Using Compressed Representations; and Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors.

Source record↗

NASA NTRS1992

Magellan stereo images and Venusian geology

Areas of Venus imaged by Magellan radar with multiple viewing conditions provide unique data that will contribute to the solution of venusian geologic problems and provide a basis for quantitative comparison of venusian landforms with those on other planetary bodies. Three sets of images with different viewing conditions have been acquired: (1) left-looking with variable incidence angles (cycle 1 profile), (2) right-looking with nearly constant incidence angles (cycle 2 profile), and (3) left-looking with variable incidence angles that are almost always smaller than those in (1) (cycle 3 profiles). The unique data provided by paired images of the same scene with different incidence angles arises from image displacements caused by the relief of individual landforms at scales comparable to the ground-range and azimuth resolutions of the images. There are two aspects of the data: (1) Stereopsis achieved by simultaneous viewing of paired left-looking images of the same scene permits three-dimensional perception and interpretation of the morphologies of landforms at resolutions much finer than the altimetry footprints. (2) Measurements of differences of image displacements (parallax) on paired images with known imaging geometries provide quantitative estimates of the relief and shapes of landforms. The potential scientific contributions of the data can be grouped into two interrelated classes: (A) geologic mapping, analysis, and interpretation and (B) topical studies that involve topographic measurements. Stereopsis, without quantitative measurements, enhances geologic mapping, analysis, and interpretation of the rock units of Venus to a degree that cannot be overestimated. In geologic mapping, assemblages of landforms, assessments of backscatter and variations in backscatter, and fine-scale topography are used to define and characterize geologic map units that represent laterally continuous deposits or rock units. Stereopsis adds the important dimension of local relief for characterization of geologic units at a scale that is not possible with Magellan altimetry or products derived from the altimetry. Relative ages of the geologic units are determined using the well-known principles of superposition and intersection. Here, the perception of relief is invaluable because superposition relations among the geological units are more readily and clearly established. The recognition of folds, faults, and fault systems, regardless of their orientations, is facilitated with stereopsis so that sequences of deformation of the geologic units can be determined and structural analyses vastly improved. Shapes of landforms are readily perceived so that they can be properly interpreted. The end result of the mapping, analyses, and interpretations is a geologic history of Venus that includes the sequences of formation and deformation of various geologic units. Measurements of relief at the finest scale possible are necessary for numerous topical studies. Standard altimetry will provide the necessary information on the relief of most large landforms, but it tends to underestimate the relief of small landforms and distorts their shapes. Although special processing of the altimeter echoes improves the estimates of the relief and shapes of some landforms, there are uncertainties in the interpretations of the echoes. Examples of topical studies requiring measurements of relief are given.

Moore, H. J.↗

DOE OSTI2025

Confidence-weighted integration of human and machine judgments for superior decision-making

Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members’ confidence is well calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated its effectiveness in both image classification and neuroscience forecasting tasks. Combining human judgments with one or more machines consistently improved overall team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

Neural network tracking and extension of positive tracking periods

Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

feature extraction↗

NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Rutishauser, David↗

NASA NTRS2013

Helicopter Field Testing of NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System fully Integrated with the Morpheus Vertical Test Bed Avionics

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project was chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with real-time terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. This is accomplished with the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN). The NASA plan for the ALHAT technology is to perform the TRL6 closed loop demonstration on the Morpheus Vertical Test Bed (VTB). The first Morpheus vehicle was lost in August of 2012 during free-flight testing at Kennedy Space Center (KSC), so the decision was made to perform a helicopter test of the integrated ALHAT System with the Morpheus avionics over the ALHAT planetary hazard field at KSC. The KSC helicopter tests included flight profiles approximating planetary approaches, with the entire ALHAT system interfaced with all appropriate Morpheus subsystems and operated in real-time. During these helicopter flights, the ALHAT system imaged the simulated lunar terrain constructed in FY2012 to support ALHAT/Morpheus testing at KSC. To the best of our knowledge, this represents the highest fidelity testing of a system of this kind to date. During this helicopter testing, two new Morpheus landers were under construction at the Johnson Space Center to support the objective of an integrated ALHAT/Morpheus free-flight demonstration. This paper provides an overview of this helicopter flight test activity, including results and lessons learned, and also provides an overview of recent integrated testing of ALHAT on the second Morpheus vehicle.

Epp, Chirold D.↗

DOE OSTI2020

Fireworks on Weather Radar and Camera

High-sensitivity weather radars easily detect nonmeteorological phenomena characterized by weak radar returns. Fireworks are the example presented here. To understand radar observations, an experiment was conducted in which the National Severe Storms Laboratory (NSSL)’s research (3-cm wavelength) dual-polarization radar and a video camera were located at 1 km from fireworks in Norman, Oklahoma. The fireworks from the 4 July 2017 celebration were recorded by both instruments. The experiment is described. Few bursts recorded by the camera are analyzed to obtain the height of the explosion, its maximum diameter, number of stars, and the duration of the visible image. Radar volume scans are examined to characterize the height of the observation, the maximum reflectivity, and its distribution with height. The fireworks location is close to the Terminal Doppler Weather Radar (TDWR) that operates in single polarization at a 5-cm wavelength and monitors hazardous weather over the Oklahoma City airport. A third radar with data from the event is the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman. It has a wavelength of 10 cm and supports technical developments at the Radar Operation Center. Reflectivity factors measured by the three radars are compared to infer the size of dominant scatterers. The polarimetric characteristics of fireworks returns are analyzed. Although these differ from those of precipitation, they are indistinguishable from insect returns. Radar observation of larger fireworks in Fort Worth, Texas, with a WSR-88D is included and compared with the observations of the smaller fireworks in Norman. We expect the detectability of explosions would be similar as of fireworks. Pinpointing locations would be useful to first responders, or air quality forecasters. We note a benefit of fireworks recognition in weather radar data is that it can prevent contamination of precipitation accumulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗

NASA NTRS1994

Design of a composite filter realizable on practical spatial light modulators

Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.

Rajan, P. K.↗

NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗