Results for “sensor network design” — Page 22

NASA NTRS2000

Predictive and Neural Predictive Control of Uncertain Systems

Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

Kelkar, Atul G.↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

DOE OSTI2025

Hybrid classical-quantum communication networks

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum–classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.

Fiber-optic communication↗

NASA NTRS1992

Real-time processing of radar return on a parallel computer

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

Aalfs, David D.↗

NASA NTRS2024

Development of a High Frequency Class-A Amplifier for High Temperature Wireless Microsystems based on a Silicon Carbide Static Induction Transistor

Next generation gas turbine engine health monitoring for high performance jet engine-powered aircraft will incorporate integrated microelectronic sensors specifically designed to enhance aircraft functionality, engine efficiency, and safety. Temperature, air flow and pressure associated with the gas path as well as emissions from the engine correlate to the state of the combustor and other engine health conditions. Monitoring these parameters accurately requires positioning electronic sensors in the hot zone of the gas turbine engine, which exposes them to temperatures in excess of 400ºC. At these temperatures, signal amplification is needed at the transducer level to distinguish the desired measured signal from the significant electronic noise generated by the harsh environment. In this presentation, we report on the simulation, development, fabrication, and testing on a high temperature Class-A amplifier which utilizes a 4H-SiC Static Induction Transistor (SIT) as the active device and input/output matching and DC bias networks comprised of thin-film spiral inductors, metal-insulator-metal capacitors, and thick film chip resistors. A small signal model that emulates the operation of the 4H-SiC SIT from 25 to 400ºC, with an emphasis on operation at 400οC, was utilized. Measurements were performed from 25 to 400ºC to generate current-voltage curves, capacitive transistor characteristics and high frequency scattering parameters (S-parameters). The measured data was used to extrapolate the transconductance, gm, as a function of temperature for model development. Circuit simulation tools were used to generate S-parameters, which were compared to the measured values. At 400οC, a maximum difference between measured and simulated S-parameters for frequencies from 20 to 100 MHz were 3.84%, 0.68%, 10.61%, and 3.26% for S21, S12, S11, and S22, respectively. The average transit frequency, ft, was calculated from measured values to be 197.8 MHz, while the simulated value from the model was found to be 200 MHz. The Amplifier’s S-parameters were recorded at 20-100 MHz over a temperature range of 25-400ºC and show a gain of approximately 15.8 and 5.80 dBm at 25 and 400ºC, respectively. The input and output reflection coefficients at 50 MHz and 400ºC were -18.5 and -15.2 dB, respectively. The noise figure and phase noise were measured over the temperature range of 25-400ºC and recorded. The noise figure increased 21% at 50 MHz over the temperature range, while the 1 kHz offset of the phase noise remained below -110 dB. The stability factor, K, calculated with measured and simulated data demonstrates unconditional stability over the frequency range at 400ºC. Lastly, the 1-dB compression point was measured at 50 MHz and 400ºC with an approximated output of 9.5 dBm.

SiC↗

NASA NTRS2015

Exploration Architecture with Quantum Inertial Gravimetry and In-Situ ChipSat Sensors

The Dual Exploration Architecture is a mission concept that combines remote sensing and in-situ observations into a single mission to answer planetary science questions that can only be answered with both types of data. Adoption of dual exploration architectures may short circuit the long, slow cycle of missions to inaccessible bodies by eliminating the need for separate precursor and follow-up missions. Additionally, the dual architecture possesses inherent flexibility that enables the design of adaptive, event-driven missions that are very different from traditional, largely pre-planned missions. Five key observations about the state and trends of planetary science exploration lead us to the dual architecture: increasing complexity of observations; scarcity of future mission opportunities; desire to capture transitory events; continued miniaturization of spacecraft components; and the Mars exploration cycle. Our goal in this study is to explore missions that can only happen using the dual architecture concept and find technology development needs that must be filled for those missions to compete. A survey of historical and current missions finds that opportunities for exploration are becoming less frequent, causing the flexibility and dual-nature elements of each mission to become more common. The dual exploration architecture takes these trends to their far conclusion, attempting to eliminate precursor and follow-up missions while still returning more scientific payoff. A study of the future of planetary science goals through the decadal survey reveals broad applicability of dual missions to solve mysteries that cannot be answered with a traditional mission architecture. These missions fall into three broad classes: choosing a local target from a global survey; dynamic/reactive science; and global in-situ networks. Two example missions of each class are notionally described. A deeper look at these dual architecture classes reveals four technology development needs that must be addressed for wide adoption of dual missions: passive landers; guided atmospheric probes; robust sensing packages; and small, precise orbital instruments. This study pursues a specific focus on two examples of such enabling technologies: the ChipSat and cold atom gravimetry. The ChipSat is a fully functional spacecraft-on-a-chip system that has broad versatility in the dual architecture mission space. Initial studies show that ChipSats could survive as passive impactor landers on bodies up to the size of Europa. Furthermore, COTS (Commercial Off-The-Shelf) components could provide an in-situ sensor suite that readily answers a number of pressing planetary science questions. Cold atom gravimetry uses inertial sensors based on light-pulse atom interferometry in a small form factor to map the gravity field of a body to precision equaling what would normally require two full spacecraft to achieve. The cold atom gravimeter provides an example of how advanced remote sensing capability can enable dual missions by providing greater returns in a significantly smaller package. Using the above two technologies, we study an example dual-architecture mission to both characterize and sample the subsurface oceans at Europa. The greatest scientific return in terms of detecting extraterrestrial life is in those regions where Europa’s ice crust is thin. The identification of regions with thin ice should therefore precede the selection of surface targets and dispatch of probes to those targets. This two-step process, if accomplished by separate flagship-scale missions, might take decades. As a result, a combined mission to both identify thin areas of Europa’s ice and follow up with surface observations at those regions is a good candidate for the dual-exploration architecture. This example mission consists of an orbiter spacecraft carrying a cold atom gravimeter capable of sensing or inferring the ice thickness on regional to local scales, along with a number of ChipSat probes capable of landing on the moon. The small size and weight of the ChipSats allows large numbers of them to be carried, ensuring that enough can be dropped to ensure survival of a minimum number of probes and potentially allowing for the in-situ sampling of multiple locations on the moon. The example missions and Europa case study show that amazing scientific return can obtained from dual-exploration architecture missions with a single launch by breaking the long timescales of planetary exploration and providing the flexibility to capturing transitory events and collect data across the local, regional, and global scales.

Exploration↗

NASA NTRS2011

The GOES-R Series Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Goodman, Steven J.↗

NASA NTRS2009

A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems

Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.

Balaban, Edward↗

NASA NTRS2017

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

Distributed engine control (DEC) systems alter aircraft engine design constraints because of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

Networked Systems↗

NASA NTRSDate not supplied

A Survey of Autonomous Navigation Techniques Applicable to Lunar Surface Exploration

As humanity returns to the Moon, and more and more attention is being paid to lunar surface operations, there is a greater need than ever for methods of surface navigation. These could be methods of computer-assisted orienteering for astronauts exploring on foot during an Extra-Vehicular Activity (EVA), or methods of solving the Lost-on-the-Moon problem to initialize a crewed or autonomous rover’s state estimate. It may also be necessary to process navigation data associated with surface samples or other surface operations a posteriori to better understand where that analysis occurred. Autonomous rover operation will also require Hazard Detection and Avoidance (HDA) and terrain-aware pathfinding. While navigation on the surface of the Moon will likely rely on Earth-based assets such as the Deep Space Network (DSN) or communication with other spacecraft (e.g., LunaNet, LCRNS, pre-deployed moon beacons, a nearby lander) it may be necessary to navigate in a loss-of-communication scenario. This paper analyzes the methods of surface navigation used on other celestial bodies, such as those used during the Apollo missions and autonomous exploration of Mars, as well as novel methods which have been studied but not yet implemented which may prove useful. It is shown that the navigator has myriad options when processing data from an Inertial Measurement Unit (IMU), a star tracker, (rover) wheel encoders, optical cameras, and LIght Detection and Ranging (LIDAR) sensors. The intention of this paper is to provide a broad overview of what has been done and what could be done, to aid those designing vehicles and/or missions to the lunar surface.

Paul D Mckee↗

NASA NTRSDate not supplied

A Survey of Autonomous Navigation Techniques Applicable to Lunar Surface Exploration

As humanity returns to the Moon, and more and more attention is being paid to lunar surface operations, there is a greater need than ever for methods of surface navigation. These could be methods of computer-assisted orienteering for astronauts exploring on foot during an Extra-Vehicular Activity (EVA), or methods of solving the Lost-on-the-Moon problem to initialize a crewed or autonomous rover’s state estimate. It may also be necessary to process navigation data associated with surface samples or other surface operations a posteriori to better understand where that analysis occurred. Autonomous rover operation will also require Hazard Detection and Avoidance (HDA) and terrain-aware pathfinding. While navigation on the surface of the Moon will likely rely on Earth-based assets such as the Deep Space Network (DSN) or communication with other spacecraft (e.g., LunaNet, LCRNS, pre-deployed moon beacons, a nearby lander) it may be necessary to navigate in a loss-of-communication scenario. This paper analyzes the methods of surface navigation used on other celestial bodies, such as those used during the Apollo missions and autonomous exploration of Mars, as well as novel methods which have been studied but not yet implemented which may prove useful. It is shown that the navigator has myriad options when processing data from an Inertial Measurement Unit (IMU), a star tracker, (rover) wheel encoders, optical cameras, and LIght Detection and Ranging (LIDAR) sensors. The intention of this paper is to provide a broad overview of what has been done and what could be done, to aid those designing vehicles and/or missions to the lunar surface.

Paul McKee↗

DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗

NASA NTRS2017

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

Distributed engine control (DEC) systems alter aircraft engine design constraints be- cause of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

Networked Systems↗

DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗

NASA NTRS2019

Design of Experiment for the Measurement of Aerosol Droplet Size Distribution of Temperature-Controlled Thermally Atomized Printed Electronic Inks

In-Space Manufacturing (ISM) centers around NASA’s growing need and ability to produce space technologies on demand in space. As the future of long term presence in space and deep space exploration approach, fundamental questions of our dependence on earth resupply to Low Earth Orbit (LEO) remain unanswered. ISM is leading various effort to evaluate the feasibility of producing essential spares and redundant parts on demand to enable a sustainable space-based supply chain model for part resupply. Among the parts and systems being considered, Avionics form the neural network of modern day aircraft and space vehicles providing a wealth of information ranging from Guidance, Navigation, and Control (GN&C) systems to on board Environmental Control and Life Support (ECLS) systems. Recent advances in the use of Aerosol Jet Technology to print Avionics components ranging from electrical traces on a circuit board to complex transistors and sensors raise the possibility of using such technology to reproduce or recreate electronic parts on demand with the help of custom electronics 3D printers. The challenge herein lies within the ability of such printers to generate and deposit an aerosol of electronic material utilizing processes independent of or enhanced by gravity to ensure controllably identical or improved behavior of the aerosol in an International Space Station (ISS) laboratory and on the ground. The behavior as well as the hazards and properties associated with such aerosols in a microgravity environment must be understood well in order to merit a feasible approach to utilizing them for manufacturing in space. In this report, we outline the experimental setup of a modified conventional vaping device to be used as the ideal gravity independent thermal atomization mechanism to generate aerosol. Our objective is to identify the ideal mass, density, and volume of our aerosolized droplets of ink to conclude that there exist a threshold of aerosolized ink droplet sizes that are indeed independent of the effects of gravity and remain stable after atomization. We use a Malvern Spraytec® Spray Particle Size Analyzer to perform real-time laser diffraction measurement of our ink droplets during atomization. The droplet size between conductive ink, dielectric ink and vegetable glycerin have been measured and contrasted. Furthermore, the mechanism of thermal atomization versus traditional pneumatic and ultrasonic atomization for operation in microgravity have been explored.

Wu, Chih-Hao↗

NASA NTRS2013

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

System & Software Health Management↗

NASA NTRS2012

Heading Toward Launch with the Integrated Multi-Satellite Retrievals for GPM (IMERG)

The Day-l algorithm for computing combined precipitation estimates in GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). We plan for the period of record to encompass both the TRMM and GPM eras, and the coverage to extend to fully global as experience is gained in the difficult high-latitude environment. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in the three groups that are contributing expertise: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; 2) the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following cloud motion; and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures. In this talk we summarize the major building blocks and important design issues driven by user needs and practical data issues. One concept being pioneered by the IMERG team is that the code system should produce estimates for the same time period but at different latencies to support the requirements of different groups of users. Another user requirement is that all these runs must be reprocessed as new IMERG versions are introduced. IMERG's status at meeting time will be summarized, and the processing scenario in the transition from TRMM to GPM will be laid out. Initially, IMERG will be run with TRMM-based calibration, and then a conversion to a GPM-based calibration will be employed after the GPM sensor products are validated. A complete reprocessing will be computed, which will complete the transition from TMPA.

Huffman, George J.↗