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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗
NASA NTRS2014

Cultivating an Emergent Earth Observation Analytics Ecosystem in the Cloud

A diverse set of data analytics systems for Earth Observations are sprouting up in the Earth Science community, with a wealth of processing algorithms and analysis methods. There is a similar wealth of data resources available via myriad data providers and clearinghouses, including large institutional systems like the Earth Observing System Data and Information System, Comprehensive Large Scale Array-data Stewardship System, and Federated Earth Observation Missions gateway. With Earth system science driving a need to work with more datasets together, and the community developing more analysis tools (some of them dataset-specific), how can we develop analysis workflows that incorporate far-flung datasets and leverage analysis resources from multiple organizations? Cloud computing points the way toward a solution in two different respects. Firstly, the access to and abstraction of virtually unlimited storage and computing power provides an environment that enables more straightforward means of pulling datasets and analysis resources together. Just as importantly, however, cloud computing serves as an example of an "ecosystem" of interoperating services, since the essence of cloud computing is the presentation of all resources as a service, from hardware to infrastructure to platform to software. This enables the combination of off-the-shelf, diverse services to construct entire systems that emerge out of an equally diverse community of architects and developers. This approach can be similarly applied to the data and analysis resources in the Earth Observation community. By exposing these resources via well understood services, and consuming resources in the same way, different organizations can construct bespoke analysis workflows and systems for their own purposes. The key leap the community needs to make is to develop analysis systems in components that interact with other components via services. The result would be a rich ecosystem of analytics components that can be combined to analyze datasets at scale and in conjunction with other datasets from other sources.

chaos↗
NASA NTRS1972

Earth resources data processor

The recent development of manned and unmanned space vehicles has brought about an almost unprecedented advance in studies concerned with remotely sensed earth observations. With this advance comes an unprecedented amount of data. The problem arises of how to efficiently analyze and compress unmanageable amounts of data into manageable amounts of useful information. A recently developed computer program is proposed as a partial solution to the above problem. The computer program is designed to determine the ground scene location and distribution of features extracted from remotely sensed earth observation data without human involvement in the data processing or a priori knowledge of ground truth. Human involvement and judgement are reserved for identification of the features presented in the compressed data.

Phillips, M. R.↗
NASA NTRS1980

Distributed microprocessors in a tactical universal modem

The distributed microprocessor system associated with a wideband signal conversion unit (WBSCU) is described. Multiple embedded 8086 and 2901 microprocessors, supported by dedicated hardware modules, perform the required real time operations for both transmit and receive functions. Commands from a host computer determine the configuration of the WBSCU via the IEEE 488 bus. Each of the four WBSCU channels is assigned to process a specified IF waveform; each channel configures its own resources and, in some cases, borrows resources from other channels. The processed waveform data is communicated from individual channels to redundant global memories. Data flow between the user community and global memories occurs via redundant 1553 buses through intelligent Bus Interface Units. Each WBSCU channel contains one 2901 bit slice machine and one 8086 microprocessor. The 2901 provides high speed processing capability for the most time critical operations. The 8086 is used for lower speed processing tasks where its high level language capability can be better exploited. Each 8086 has a global bus for wideband interprocessor communication, and a local bus for 8086/2901, master/slave communication. Software architecture consists of a control and communications structure governing mode dependent signal processing tasks.

Gray, D. M.↗
NASA NTRS2003

Experiences with Testing the Largest Ground System NASA Has Ever Built

In the 1980s, the National Aeronautics and Space Administration (NASA) embarked upon a major Earth-focused program called Mission to Planet Earth. The Goddard Space Flight Center (GSFC) was selected to manage and develop a key component - the Earth Observing System (EOS). The EOS consisted of four major missions designed to monitor the Earth. The missions included 4 spacecraft. Terra (launched December 1999), Aqua (launched May 2002), ICESat (Ice, Cloud, and Land Elevation Satellite, launched January 2003), and Aura (scheduled for launch January 2004). The purpose of these missions was to provide support for NASA s long-term research effort for determining how human-induced and natural changes affect our global environment. The EOS Data and Information System (EOSDIS), a globally distributed, large-scale scientific system, was built to support EOS. Its primary function is to capture, collect, process, and distribute the most voluminous set of remotely sensed scientific data to date estimated to be 350 Gbytes per day. The EOSDIS is composed of a diverse set of elements with functional capabilities that require the implementation of a complex set of computers, high-speed networks, mission-unique equipment, and associated Information Technology (IT) software along with mission-specific software. All missions are constrained by schedule, budget, and staffing resources, and rigorous testing has been shown to be critical to the success of each mission. This paper addresses the challenges associated with the planning, test definition. resource scheduling, execution, and discrepancy reporting involved in the mission readiness testing of a ground system on the scale of EOSDIS. The size and complexity of the mission systems supporting the Aqua flight operations, for example, combined with the limited resources available, prompted the project to challenge the prevailing testing culture. The resulting success of the Aqua Mission Readiness Testing (MRT) program was due in no small measure to re-structuring the traditional programmatic and technical approach to a more efficient and robust program. Programmatically, it meant gaining the endorsement, commitment, and cooperation of the numerous subsystem element managers and other stakeholder organizations. Technically, it required an MRT program that was agile, could rapidly adapt to requirements changes, and was flexible in its overall approach. Furthermore, this paper addresses the following questions: 1. What are the key ingredients (e.g., test tools, organization) needed to conduct a successful MRT program? 2. What distinguishes EOS MRT from the traditional system testing approach? 3. Where should the focus of testing be since it is infeasible to test every element or subsystem? 4. How can MRT be applied effectively to other systems or missions? To provide answers to these questions, this paper relies heavily on real-life, hands-on experiences ("lessons learned") gained during mission readiness testing of the Terra ground system and, most recently, the Aqua and ICESat missions. Moreover, this paper explores how lessons learned were turned into lessons applied for the upcoming Aura mission. Although derived from the EOS missions, MRT techniques and strategies can be applied to enhance the testing of other missions.

Lehtonen, Ken↗
NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗
NASA NTRS2001

Challenges Facing Design and Analysis Tools

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

Knight, Norman F., Jr.↗
NASA NTRS2011

Evaluating the Performance of Single and Double Moment Microphysics Schemes During a Synoptic-Scale Snowfall Event

Increases in computing resources have allowed for the utilization of high-resolution weather forecast models capable of resolving cloud microphysical and precipitation processes among varying numbers of hydrometeor categories. Several microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, ranging from single-moment predictions of precipitation content to double-moment predictions that include a prediction of particle number concentrations. Each scheme incorporates several assumptions related to the size distribution, shape, and fall speed relationships of ice crystals in order to simulate cold-cloud processes and resulting precipitation. Field campaign data offer a means of evaluating the assumptions present within each scheme. The Canadian CloudSat/CALIPSO Validation Project (C3VP) represented collaboration among the CloudSat, CALIPSO, and NASA Global Precipitation Measurement mission communities, to observe cold season precipitation processes relevant to forecast model evaluation and the eventual development of satellite retrievals of cloud properties and precipitation rates. During the C3VP campaign, widespread snowfall occurred on 22 January 2007, sampled by aircraft and surface instrumentation that provided particle size distributions, ice water content, and fall speed estimations along with traditional surface measurements of temperature and precipitation. In this study, four single-moment and two double-moment microphysics schemes were utilized to generate hypothetical WRF forecasts of the event, with C3VP data used in evaluation of their varying assumptions. Schemes that incorporate flexibility in size distribution parameters and density assumptions are shown to be preferable to fixed constants, and that a double-moment representation of the snow category may be beneficial when representing the effects of aggregation. These results may guide forecast centers in optimal configurations of their forecast models for winter weather and identify best practices present within these various schemes.

Molthan, Andrew L.↗
NASA NTRS2021

An Innovative Approach to Modeling VIPER Rover Software Life Cycle Cost

NASA’s “Volatiles Investigating Polar Exploration Rover” (VIPER) will be the first robotic mission to prospect for water ice near the south pole of the Moon in late 2023 on a 100-Earth-day mission. The information that the VIPER rover provides will help improve understanding of the composition, distribution, and accessibility of Lunar polar volatiles and will help determine how the Moon’s resources can support future human space exploration. VIPER, however, represents a radical departure from the way that NASA has traditionally developed planetary robotic missions. A key consequence of these differences is that estimating the cost of VIPER’s rover software is challenging and complex.For example, VIPER is being developed using management procedures typically applied to NASA research and technology projects, rather than space flight programs. In addition, key portions of the rover’s software are being designed as ground software to run on mission control computers (rather than on-board the rover as flight software as with prior planetary missions) taking advantage of continuous, interactive data communications between the Moon and Earth and higher performance computing available on the ground. Moreover, the rover’s software is being engineered using Agile software development practices and incorporates a significant amount of open-source, rather than following traditional (spiral, waterfall, etc.) development methods and in-house code. In this paper, we present an innovative process to estimate the life cycle cost of VIPER’s rover software. We first describe how we modeled the architecture and code counts for three software elements: Rover Flight Software (RFSW), Rover Ground Software (RGSW), and Rover Simulation Software (RSIM). We then discuss key challenges and unique aspects of our approach, such as the lack of Lunar rover analogies, the need to integrate and test large open source software, and the strategies developed to account for use of non-space flight management practices and the impact of the COVID-19 pandemic. We conclude with a summary of our results, including cumulative distribution, nearest neighbors and cluster analysis, as well as heuristics used to confirm the reasonableness of the cost estimate.

Utz, Hans↗
NASA NTRS1993

Global Magellan-image map of Venus at full resolution

During its first 243-day mapping cycle, the Magellan spacecraft succeeded in imaging 84 percent of the surface of Venus at resolutions on the order of 100 meters; subsequent cycles have increased the total coverage to over 97 percent and provided redundant coverage of much of the planet with differing viewing geometries. Unfortunately, this full-resolution global dataset is in the form of thousands of individual orbit tracks (F-BIDR's) whose length-to-width ratio of nearly 1000:1 makes them minimally useful unless mosaicked. The Magellan project produced full-resolution mosaics (F-MIDR's) only for selected regions on the planet, whereas a global set of mosaics was made only at threefold degraded resolution (C1-MIDR's). Furthermore, although the F-MIDR's, which are approximately equidimensional, are much better suited for scientific interpretation than the F-BIDR's, they are still an unwieldy dataset: over 1500 quadrangles, each showing a region only about 600 km on a side, would be required to cover the entire planet. The USGS has therefore undertaken to produce and distribute a global, full resolution set of mosaics of the Magellan image data in a format that will be efficient for both hardcopy and digital use. The initial motivation was that it would provide an efficient means of verifying the integrity of the F-BIDR's to be archived on computer-compatible tape at the USGS Flagstaff facility. However, the resulting product, known as the FMAP, should also serve as an important resource for future scientific interpretation. It will offer several advantages beyond global coverage at full resolution. The first, alluded to above, is its division of the planet's surface to minimize the number of quadrangles and maximize their area, subject to the limits on the number of pixels imposed by state-of-the-art digital recording media and hardcopy output devices. The second, the use of improved 'cosmetic' processing techniques, will greatly reduce tonal discontinuities between component F-BIDR's in the FMAP compared to the standard Magellan mosaic products. Finally, wherever possible, the FMAP will incorporate data that were unavailable (e.g., because of processing delays) when the standard MIDR products were created, as well as data that were reprocessed to improve their radiometric or geometric quality.

Kirk, R. L.↗
NASA NTRS1995

DREAMS and IMAGE: A Model and Computer Implementation for Concurrent, Life-Cycle Design of Complex Systems

Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks. Built on the successes of initial architectures, requirements for a next-generation design computing infrastructure can be developed. These requirements concentrate on those needed by a designer in decision-making processes from product conception to recycling and can be categorized in two areas: design process and design information management. A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while expanding fewer resources. In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition. A foundation has been laid that captures these requirements in a design architecture called DREAMS (Developing Robust Engineering Analysis Models and Specifications). In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies.

Hale, Mark A.↗
NASA NTRS2012

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the NASA FUN3D code to be accelerated in parallel with up to four processor cores sharing a single GPU. For codes to scale and fully use resources on these and the next generation machines, codes will need to employ some type of GPU sharing model, as presented in this work. Findings include the effects of GPU sharing on overall performance. A discussion of the inherent challenges that parallel unstructured CFD codes face in accelerator-based computing environments is included, with considerations for future generation architectures. This work was completed by the author in August 2010, and reflects the analysis and results of the time.

Duffy, Austen C.↗
NASA NTRS2002

Contingency Planning for Planetary Rovers

There has been considerable work in AI on planning under uncertainty. But this work generally assumes an extremely simple model of action that does not consider continuous time and resources. These assumptions are not reasonable for a Mars rover, which must cope with uncertainty about the duration of tasks, the power required, the data storage necessary, along with its position and orientation. In this paper, we outline an approach to generating contingency plans when the sources of uncertainty involve continuous quantities such as time and resources. The approach involves first constructing a "seed" plan, and then incrementally adding contingent branches to this plan in order to improve utility. The challenge is to figure out the best places to insert contingency branches. This requires an estimate of how much utility could be gained by building a contingent branch at any given place in the seed plan. Computing this utility exactly is intractable, but we outline an approximation method that back propagates utility distributions through a graph structure similar to that of a plan graph.

Dearden, Richard↗
NASA NTRS2003

Incremental Contingency Planning

There has been considerable work in AI on planning under uncertainty. However, this work generally assumes an extremely simple model of action that does not consider continuous time and resources. These assumptions are not reasonable for a Mars rover, which must cope with uncertainty about the duration of tasks, the energy required, the data storage necessary, and its current position and orientation. In this paper, we outline an approach to generating contingency plans when the sources of uncertainty involve continuous quantities such as time and resources. The approach involves first constructing a "seed" plan, and then incrementally adding contingent branches to this plan in order to improve utility. The challenge is to figure out the best places to insert contingency branches. This requires an estimate of how much utility could be gained by building a contingent branch at any given place in the seed plan. Computing this utility exactly is intractable, but we outline an approximation method that back propagates utility distributions through a graph structure similar to that of a plan graph.

Dearden, Richard↗
NASA NTRS2009

Exploring Alternate Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
NASA NTRS2025

Assessment of Model Outcomes Between the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a computational model that provides human health and medical risk predictions for crewed spaceflight missions. MEDPRAT utilizes discrete event modeling and dynamic probabilistic simulation to predict critical mission outcomes (total medical events, crew health index, quality time lost, loss of crew life, removal to definitive care), condition occurrences, and resource consumption. Input parameters for MEDPRAT include crew attributes (e.g., sex), types of mission activities (e.g., whether and where crew members perform an extravehicular activity (EVA)), available resources, treatment information, and probability distributions for medical conditions. As an evolution of the Integrated Medical Model (IMM), MEDPRAT provides enhanced capabilities and higher fidelity, and incorporates more appropriate assumptions for long-duration spaceflight. IMM is the currently accepted standard for quantifying spaceflight mission medical risk in NASA operations that uses a probabilistic risk assessment (PRA) approach. MEDPRAT builds on the same logical foundation as IMM but implements the model architecture through highly optimized Monte Carlo sampling methods. An analysis is performed comparing the outputs from IMM with those from MEDPRAT V1.0 and V2.0 for the same reference missions in order to quantify similarities and differences in the model outcomes. The juxtaposition between IMM and MEDPRAT V1.0 and 2.0 shown in this report demonstrates that these two models generate very similar results; where differences in outcomes are shown, these are in accordance with the underlying assumptions and differences in the model architectures. This validation effort further establishes the credibility and reliability of the MEDPRAT software.

Matthew T Prelich↗
NASA NTRS1987

Mapping between parallel processor structures and programs

This paper reports some ongoing research efforts at Stanford in allocation of parallel processing resources. Both processor structures and program structures have their own characteristics. Resource allocation binds the two structures during program execution. The mapping problem determines what processor structure and program structure may be combined to obtain maximum speedup. Three approaches to this mapping problem are considered. Two important factors, granularity and interaction delay, are also considered. A new hierarchical approach to structure definition is outlined. Effective and efficient tools are necessary for the study of the mapping problem. A fast turn-around simulation environment developed for investigating partition strategies for distributed computations and a computationally efficient method to predict performance of parallel processor structures are described.

Ngai, Tin-Fook↗
NASA NTRS1990

Autoplan: A self-processing network model for an extended blocks world planning environment

Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc.

Dautrechy, C. Lynne↗