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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Development of a Computer Architecture to Support the Optical Plume Anomaly Detection (OPAD) System

The NASA OPAD spectrometer system relies heavily on extensive software which repetitively extracts spectral information from the engine plume and reports the amounts of metals which are present in the plume. The development of this software is at a sufficiently advanced stage where it can be used in actual engine tests to provide valuable data on engine operation and health. This activity will continue and, in addition, the OPAD system is planned to be used in flight aboard space vehicles. The two implementations, test-stand and in-flight, may have some differing requirements. For example, the data stored during a test-stand experiment are much more extensive than in the in-flight case. In both cases though, the majority of the requirements are similar. New data from the spectrograph is generated at a rate of once every 0.5 sec or faster. All processing must be completed within this period of time to maintain real-time performance. Every 0.5 sec, the OPAD system must report the amounts of specific metals within the engine plume, given the spectral data. At present, the software in the OPAD system performs this function by solving the inverse problem. It uses powerful physics-based computational models (the SPECTRA code), which receive amounts of metals as inputs to produce the spectral data that would have been observed, had the same metal amounts been present in the engine plume. During the experiment, for every spectrum that is observed, an initial approximation is performed using neural networks to establish an initial metal composition which approximates as accurately as possible the real one. Then, using optimization techniques, the SPECTRA code is repetitively used to produce a fit to the data, by adjusting the metal input amounts until the produced spectrum matches the observed one to within a given level of tolerance. This iterative solution to the original problem of determining the metal composition in the plume requires a relatively long period of time to execute the software in a modern single-processor workstation, and therefore real-time operation is currently not possible. A different number of iterations may be required to perform spectral data fitting per spectral sample. Yet, the OPAD system must be designed to maintain real-time performance in all cases. Although faster single-processor workstations are available for execution of the fitting and SPECTRA software, this option is unattractive due to the excessive cost associated with very fast workstations and also due to the fact that such hardware is not easily expandable to accommodate future versions of the software which may require more processing power. Initial research has already demonstrated that the OPAD software can take advantage of a parallel computer architecture to achieve the necessary speedup. Current work has improved the software by converting it into a form which is easily parallelizable. Timing experiments have been performed to establish the computational complexity and execution speed of major components of the software. This work provides the foundation of future work which will create a fully parallel version of the software executing in a shared-memory multiprocessor system.

Katsinis, Constantine↗
NASA NTRS1997

By Hand or Not By-Hand: A Case Study of Alternative Approaches to Parallelize CFD Applications

While parallel processing promises to speed up applications by several orders of magnitude, the performance achieved still depends upon several factors, including the multiprocessor architecture, system software, data distribution and alignment, as well as the methods used for partitioning the application and mapping its components onto the architecture. The existence of the Gorden Bell Prize given out at Supercomputing every year suggests that while good performance can be attained for real applications on general purpose multiprocessors, the large investment in man-power and time still has to be repeated for each application-machine combination. As applications and machine architectures become more complex, the cost and time-delays for obtaining performance by hand will become prohibitive. Computer users today can turn to three possible avenues for help: parallel libraries, parallel languages and compilers, interactive parallelization tools. The success of these methodologies, in turn, depends on proper application of data dependency analysis, program structure recognition and transformation, performance prediction as well as exploitation of user supplied knowledge. NASA has been developing multidisciplinary applications on highly parallel architectures under the High Performance Computing and Communications Program. Over the past six years, the transition of underlying hardware and system software have forced the scientists to spend a large effort to migrate and recede their applications. Various attempts to exploit software tools to automate the parallelization process have not produced favorable results. In this paper, we report our most recent experience with CAPTOOL, a package developed at Greenwich University. We have chosen CAPTOOL for three reasons: 1. CAPTOOL accepts a FORTRAN 77 program as input. This suggests its potential applicability to a large collection of legacy codes currently in use. 2. CAPTOOL employs domain decomposition to obtain parallelism. Although the fact that not all kinds of parallelism are handled may seem unappealing, many NASA applications in computational aerosciences as well as earth and space sciences are amenable to domain decomposition. 3. CAPTOOL generates code for a large variety of environments employed across NASA centers: MPI/PVM on network of workstations to the IBS/SP2 and CRAY/T3D.

Yan, Jerry C.↗
NASA NTRS2012

Applying Biomimetic Algorithms for Extra-Terrestrial Habitat Generation

The objective is to simulate and optimize distributed cooperation among a network of robots tasked with cooperative excavation on an extra-terrestrial surface. Additionally to examine the concept of directed Emergence among a group of limited artificially intelligent agents. Emergence is the concept of achieving complex results from very simple rules or interactions. For example, in a termite mound each individual termite does not carry a blueprint of how to make their home in a global sense, but their interactions based strictly on local desires create a complex superstructure. Leveraging this Emergence concept applied to a simulation of cooperative agents (robots) will allow an examination of the success of non-directed group strategy achieving specific results. Specifically the simulation will be a testbed to evaluate population based robotic exploration and cooperative strategies while leveraging the evolutionary teamwork approach in the face of uncertainty about the environment and partial loss of sensors. Checking against a cost function and 'social' constraints will optimize cooperation when excavating a simulated tunnel. Agents will act locally with non-local results. The rules by which the simulated robots interact will be optimized to the simplest possible for the desired result, leveraging Emergence. Sensor malfunction and line of sight issues will be incorporated into the simulation. This approach falls under Swarm Robotics, a subset of robot control concerned with finding ways to control large groups of robots. Swarm Robotics often contains biologically inspired approaches, research comes from social insect observation but also data from among groups of herding, schooling, and flocking animals. Biomimetic algorithms applied to manned space exploration is the method under consideration for further study.

Birge, Brian↗
NASA NTRSDate not supplied

Flight and Direct to Earth/Space Relay Communication System Architecture for GSFC CubeSat Missions

The CubeSat platform is finding increasing use in space science applications due to its low cost and comparative ease of launch. It is becoming a key scientific discovery tool in low Earth orbit (LEO) and beyond, including geosynchronous equatorial orbit (GEO), the Lagrange Points, Lunar missions, and more. The increasing complexity of these missions and their scientific goals must be supported by equal advancements in communications technology. Higher data rates and greater reliability are required every year. However, the reduced Size, Weight, and Power (SWaP) constraints of CubeSat platforms introduce unique challenges in the area of satellite communications. There is currently a lack of communication equipment tailored specifically to the CubeSat platform. This lack of standardized, tested equipment extends development time and reduces mission confidence. Furthermore, missions utilizing the CubeSat platform are often subject to more difficult design constraints. Antenna placement, size, and pointing are often subordinate to the requirements of the payload instruments and mission goals. Traditional link margin estimation techniques are insufficient in these cases, as they emphasize worst case scenarios. In reality the actual link parameters may vary widely even during a single pass. This presents new challenges in predicting communications performance and scheduling ground station contacts, but also new opportunities for improving efficiency. This paper presents the integration, testing, and validation process for a new software defined radio (SDR) designed for the CubeSat platform in conjunction with Vulcan Wireless, Inc. The SDR is planned for use on 5 upcoming CubeSat missions at NASAs Goddard Space Flight Center (GSFC) including a Geosynchronous Transfer Orbit (GTO) mission and it may also serve as a standard and well-tested option for future missions by enabling a standardized, rapid and low cost CubeSat communication system network integration process. Detailed simulations have been developed to estimate the communication performance of these missions, taking the unique antenna placements and attitude behavior of each satellite into account. These simulations allow a much more accurate analysis of the expected link margin, which varies considerably during each pass for the NASA Space Relay (SR) and Direct to Earth (DTE) network. The modelling procedures are outlined, and the results are used to predict communications performance of the missions.

Space Networks↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRS1996

Algorithms for Automatic Alignment of Arrays

Aggregate data objects (such as arrays) are distributed across the processor memories when compiling a data-parallel language for a distributed-memory machine. The mapping determines the amount of communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: an alignment that maps all the objects to an abstract template, followed by a distribution that maps the template to the processors. This paper describes algorithms for solving the various facets of the alignment problem: axis and stride alignment, static and mobile offset alignment, and replication labeling. We show that optimal axis and stride alignment is NP-complete for general program graphs, and give a heuristic method that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. We also show how local graph contractions can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. We show how to model the static offset alignment problem using linear programming, and we show that loop-dependent mobile offset alignment is sometimes necessary for optimum performance. We describe an algorithm with for determining mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself or can be used to improve performance. We describe an algorithm based on network flow that replicates objects so as to minimize the total amount of broadcast communication in replication.

Chatterjee, Siddhartha↗
NASA NTRS1993

Software to model AXAF image quality

This draft final report describes the work performed under this delivery order from May 1992 through June 1993. The purpose of this contract was to enhance and develop an integrated optical performance modeling software for complex x-ray optical systems such as AXAF. The GRAZTRACE program developed by the MSFC Optical Systems Branch for modeling VETA-I was used as the starting baseline program. The original program was a large single file program and, therefore, could not be modified very efficiently. The original source code has been reorganized, and a 'Make Utility' has been written to update the original program. The new version of the source code consists of 36 small source files to make it easier for the code developer to manage and modify the program. A user library has also been built and a 'Makelib' utility has been furnished to update the library. With the user library, the users can easily access the GRAZTRACE source files and build a custom library. A user manual for the new version of GRAZTRACE has been compiled. The plotting capability for the 3-D point spread functions and contour plots has been provided in the GRAZTRACE using the graphics package DISPLAY. The Graphics emulator over the network has been set up for programming the graphics routine. The point spread function and the contour plot routines have also been modified to display the plot centroid, and to allow the user to specify the plot range, and the viewing angle options. A Command Mode version of GRAZTRACE has also been developed. More than 60 commands have been implemented in a Code-V like format. The functions covered in this version include data manipulation, performance evaluation, and inquiry and setting of internal parameters. The user manual for these commands has been formatted as in Code-V, showing the command syntax, synopsis, and options. An interactive on-line help system for the command mode has also been accomplished to allow the user to find valid commands, command syntax, and command function. A translation program has been written to convert FEA output from structural analysis to GRAZTRACE surface deformation file (.dfm file). The program can accept standard output files and list files from COSMOS/M and NASTRAN finite analysis programs. Some interactive options are also provided, such as Cartesian or cylindrical coordinate transformation, coordinate shift and scale, and axial length change. A computerized database for technical documents relating to the AXAF project has been established. Over 5000 technical documents have been entered into the master database. A user can now rapidly retrieve the desired documents relating to the AXAF project. The summary of the work performed under this contract is shown.

Ahmad, Anees↗
NASA NTRS2021

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign is a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR will, for the firsttime ever, present a need to communicate with multiple surfaceassets that are co-located on Mars in a coordinated effort toaccomplish the unified objective of fetching, transporting, andreturning samples from Mars. Currently, Mars surface assetsrelay data to and from Earth using a number of orbiters inwhat’s known as the Mars Relay Network (MRN). This networkis characterized by a small number of surface assets distributedacross the Martian globe and a larger number of orbiters toprovide relay services. As of June 2020, there are two surfaceassets for which five orbiters are providing relay. During theMSR Campaign, there will be two rovers and a lander that allwill require relay communication from a small number of Marsorbiters to meet the aggressive MSR timeline. The inversion ofthe current MRN paradigm, a system of many surface assetsrequiring relay and few orbiters to provide relay, necessitatesthe unique challenge of optimally allocating relay passes tomaximize the operational capability of all assets. The allocationmust consider a large number of trade variables includingMars asset operational requirements and Earth ground systemconstraints, including staffing schedules, operations planningacross time zones, and more. To address these telecommunicationchallenges, the Mars Asset Relay Mission Link AllocationDesign Environment (MARMLADE) tool was developed. Itis a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets andquantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbitergeometry and telecommunication capabilities provided by JPL’sTelecom Orbit Analysis and Simulation Tool (TOAST) softwareto compute which asset should get each pass based on a seriesof heuristics and predictions of all assets’ states. WithinMARMLADE, the user can provide inputs including the optionfor time-based pass splitting, fixed FWD data rate capabilities,DFE communication capabilities, and link parameters allowingfor the assessment of complex operations and hardware tradesusing surface mission operational efficiency as a primary figureof merit. As the MSR mission concepts continue to mature,MARMLADE is being used to assess ability of all MSR elementsto meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.

Lee, Charles↗
NASA NTRSDate not supplied

Toward an IMU-Based Space Suit Motion Capture System

Spacesuits are complex engineering systems that sustain human health and enable performance outside Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA operations on the Lunar surface are expected to be more frequent and require higher physical workloads than previously during the ISS, Shuttle, and Apollo programs. To characterize the workloads and ergonomics needs a suit must support, the kinematics of the space suit must be measured during operationally-relevant tasks in ground analog environments. Kinematics capture of the suit is challenging for traditional optical motion capture (OMC) approaches due to marker occlusion, harsh lighting or environmental conditions, and tests with suit surrogates in outdoor field environments. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture method and inverse kinematics solver which relies solely on a network of wireless inertial measurement units (IMUs) attached to the major kinematic segments of the spacesuit. The ASIK modeling language allows for the simple inclusion of probabilistic priors such as suit size and shape or IMU poses. The ASIK system was tested in a 7-subject pilot study. Each subject donned NASA’s new prototype exploration spacesuit in the Active Response Gravity Offload System (ARGOS) facility at Johnson Space Center in Houston, TX. The suits were outfitted with 12 IMUs to estimate lower body and trunk kinematics. The suits were also outfitted with a set of reflective OMC markers, and traditional OMC data was collected and processed. Characterization of the ASIK-derived suit joint angles’ accuracy against an optical motion capture datum will be presented. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.

IMU↗
NASA NTRS2010

A Mesoscale Analysis of Column-Integrated Aerosol Properties in Northern India During the TIGERZ 2008 Pre-Monsoon Period and a Comparison to MODIS Retrievals

The Indo-Gangetic Plain (IGP) of the northern Indian subcontinent produces anthropogenic pollution from urban, industrial and rural combustion sources nearly continuously and is affected by convection-induced winds driving desert and alluvial dust into the atmosphere during the premonsoon period. Within the IGP, the NASA Aerosol Robotic Network (AERONET) project initiated the TIGERZ measurement campaign in May 2008 with an intensive operational period from May 1 to June 23, 2008. Mesoscale spatial variability of aerosol optical depth (AOD, tau) measurements at 500mn was assessed at sites around Kanpur, India, with averages ranging from 0.31 to 0.89 for spatial variability study (SVS) deployments. Sites located downwind from the city of Kanpur indicated slightly higher average aerosol optical depth (delta Tau(sub 500)=0.03-0.09). In addition, SVS AOD area-averages were compared to the long-tenn Kanpur AERONET site data: Four SVS area-averages were within +/- 1 cr of the climatological mean of the Kanpur site, while one SVS was within 2sigma below climatology. For a SVS case using AERONET inversions, the 440-870mn Angstrom exponent of approximately 0.38, the 440-870mn absorption Angstrom exponent (AAE) of 1.15-1.53, and the sphericity parameter near zero suggested the occurrence of large, strongly absorbing, non-spherical aerosols over Kanpur (e.g., mixed black carbon and dust) as well as stronger absorption downwind of Kanpur. Furthermore, the 3km and lOkm Terra and Aqua MODIS C005 aerosol retrieval algorithms at tau(sub 550) were compared to the TIGERZ data set. Although MODIS retrievals at higher quality levels were comparable to the MODIS retrieval uncertainty, the total number of MODIS matchups (N) were reduced with subsequent quality levels (N=25, QA>=0; N=9,QA>=l; N=6, QA>=2; N=1, QA=3) over Kanpur during the premonsoon primarily due to the semi-bright surface, complex aerosol mixture and cloud-contaminated pixels. The TIGERZ 2008 data set provided a unique opportunity to measure the spatial and temporal variations of aerosol loading in the IGP. The strong aerosol absorption derived from ground-based sun/sky radiometer measurements suggested the presence of a predominately black carbon and dust mixture during the pre-monsoon period. Consistent with the elevated heat-pump hypothesis, these absorbing aerosols found across Kanpur and the greater IGP region during the pre-monsoon period likely induced regional atmospheric warming, which lead to a more rapid advance of the southwest Asian monsoon and above normal precipitation over northern India in June 2008.

Giles, D. M.↗
NASA NTRS2019

Inversion of Multiangular Polarimetric Measurements Over Open and Coastal Ocean Waters: A Joint Retrieval Algorithm for Aerosol and Water-Leaving Radiance Properties

Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.

Gao, Meng↗
NASA NTRS2016

Benchmarking Model Variants in Development of a Hardware-in-the-Loop Simulation System

Distributed engine control architecture presents a significant increase in complexity over traditional implementations when viewed from the perspective of system simulation and hardware design and test. Even if the overall function of the control scheme remains the same, the hardware implementation can have a significant effect on the overall system performance due to differences in the creation and flow of data between control elements. A Hardware-in-the-Loop (HIL) simulation system is under development at NASA Glenn Research Center that enables the exploration of these hardware dependent issues. The system is based on, but not limited to, the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k). This paper describes the step-by-step conversion from the self-contained baseline model to the hardware in the loop model, and the validation of each step. As the control model hardware fidelity was improved during HIL system development, benchmarking simulations were performed to verify that engine system performance characteristics remained the same. The results demonstrate the goal of the effort; the new HIL configurations have similar functionality and performance compared to the baseline C-MAPSS40k system.

Networked Systems↗
NASA NTRS2018

Toward a NASA Deep Space Optical Communications System

As discussed at SpaceOps in 2016, we expect the data rates from deep space missions to increase approximately one order of magnitude per decade for the next 50 years. The first order of magnitude improvement will come from existing plans for radio frequency (RF) communications including enhancements to both spacecraft and Deep Space Network (DSN) facilities. The next two orders of magnitude are predicted to come from the introduction of deep space optical communications. Studies indicate that optical receive apertures of between 8m12m are desired. The large cost of dedicated receive telescopes makes this method unrealistic – at least in the near-term. The cost of large optical ground terminals is driven primarily by the cost of the optics and by the cost of a stable structure for the telescope. We propose a novel hybrid design in which existing DSN 34m beam waveguide (BWG) radio antennas can be modified to include an 8m equivalent optical primary. By utilizing a low-cost segmented spherical mirror optical design, pioneered by the optical astronomical community, and by exploiting the already existing extremely stable large radio aperture structures in the DSN, we can minimize both of these cost drivers for implementing large optical communications ground terminals. Two collocated hybrid RF/optical antennas could be arrayed to synthesize the performance of an 11.3m receive aperture to support more capable or more distant space missions or used separately to communicate with two optical spacecraft simultaneously. NASA is in the midst of building six new 34m BWG antennas in the DSN. The final two are planned to be built at the DSN Goldstone, California and Canberra complexes. We are now investigating building these last two antennas as RF/optical hybrids. By delaying their operational dates by two years, we would be able to add the 8m optical receive capability for these two antennas while fitting within existing budgetary constraints. This paper describes the hybrid antenna design, the technical challenges being addressed, and plan for using this concept, together with ongoing work on optical flight terminals, to infuse operation optical communications into deep space missions.

Cornwell, Donald M.↗
NASA NTRS1995

3-D Reconstruction of Macular Type II Cell Innervation Patterns in Space-Flight and Control Rats

A semiautomated method for reconstructing objects from serial thin sections has been developed in the Biocomputation Center. The method is being used to completely, for the first time, type II hair cells and their innervations. The purposes are to learn more about the fundamental circuitry of the macula on Earth and to determine whether changes in connectivities occur under space flight conditions. Data captured directly from a transmission electron microscope via a video camera are sent to a graphics workstation. There, the digitized micrographs are mosaicked into sections and contours are traced, registered and displayed by semiautomated methods. Current reconstructions are of type II cells from the medial part of rat maculas collected in-flight on the Space Life Sciences-2 mission, 4.5 hrs post-flight, and from a ground control. Results show that typical type II cells receive processes from tip to six nearby calyces or afferents. Nearly all processes are elongated and have bouton-like enlargements; some have numerous vesicles. Multiple (2 to 4) processes from a single calyx to a type II cell are common, and approximately 1/3 of the processes innervale 2 or 3 type II cells or a neighboring cluster. From 2% to 6% of the cells resemble type I cells morphologically but have demi-calyces. Thus far, increments in synaptic number in type II cells of flight rats are prominent along processes that supply two hair cells. It is clear that reconstruction methods provide insights into details of macular circuitry not obtainable by other techniques. The results demonstrate a morphological basis for interactions between adjacent receptive fields through feed back-feed forward connections, and for dynamic alterations in receptive field range and activity during preprocessing of linear acceleratory information by the maculas. The reconstruction method we have developed will find further applications in the study of the details of neuronal architecture of more complex systems, to seek out shared organizational properties or neuronal networks and to understand better localization of synaptic changes in altered environments.

Ross, Muriel Dorothy↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2020

Learning instrument invariant characteristics for generating high-resolution global coral reef maps

Coral reefs are one of the most biologically complex and diverse ecosystems within the shallow marine environment. Unfortunately, these underwater ecosystems are threatened by a number of anthropogenic challenges, including ocean acidification and warming, overfishing, and the continued increase of marine debris in oceans. This requires a comprehensive assessment of the world's coastal environments, including a quantitative analysis on the health and extent of coral reefs and other associated marine species, as a vital Earth Science measurement. However, limitations in observational and technological capabilities inhibit global sustained imaging of the marine environment. Harmonizing multimodal data sets acquired using different remote sensing instruments presents additional challenges, thereby limiting the availability of good quality labeled data for analysis. In this work, we develop a deep learning model for extracting domain invariant features from multimodal remote sensing imagery and creating high-resolution global maps of coral reefs by combining various sources of imagery and limited hand-labeled data available for certain regions. This framework allows us to generate, for the first time, coral reef segmentation maps at 2-meter resolution, which is a significant improvement over the kilometer-scale state-of-the-art maps. Additionally, this framework doubles accuracy and IoU metrics over baselines that do not account for domain invariance.

Domain Adaptation↗
NASA NTRS2016

Benchmarking Model Variants in Development of a Hardware-in-the-Loop Simulation System

Distributed engine control architecture presents a significant increase in complexity over traditional implementations when viewed from the perspective of system simulation and hardware design and test. Even if the overall function of the control scheme remains the same, the hardware implementation can have a significant effect on the overall system performance due to differences in the creation and flow of data between control elements. A Hardware-in-the-Loop (HIL) simulation system is under development at NASA Glenn Research Center that enables the exploration of these hardware dependent issues. The system is based on, but not limited to, the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k). This paper describes the step-by-step conversion from the self-contained baseline model to the hardware in the loop model, and the validation of each step. As the control model hardware fidelity was improved during HIL system development, benchmarking simulations were performed to verify that engine system performance characteristics remained the same. The results demonstrate the goal of the effort; the new HIL configurations have similar functionality and performance compared to the baseline C-MAPSS40k system.

Propulsion Simulation↗