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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1992

RAID-2: Design and implementation of a large scale disk array controller

We describe the implementation of a large scale disk array controller and subsystem incorporating over 100 high performance 3.5 inch disk drives. It is designed to provide 40 MB/s sustained performance and 40 GB capacity in three 19 inch racks. The array controller forms an integral part of a file server that attaches to a Gb/s local area network. The controller implements a high bandwidth interconnect between an interleaved memory, an XOR calculation engine, the network interface (HIPPI), and the disk interfaces (SCSI). The system is now functionally operational, and we are tuning its performance. We review the design decisions, history, and lessons learned from this three year university implementation effort to construct a truly large scale system assembly.

Katz, R. H.↗

NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗

NASA NTRS1994

Precision DSN Radiometer Systems: Impact on Microwave Calibrations

The NASA Deep Space Network (DSN) has a long history of providing large parabolic dish antennas with precision surfaces, low-loss feeds and ultra-low noise amplifiers for deep space telecommunications. To realize the benefits of high sensitivity, it is important that receiving systems are accurately calibrated and monitored to maintain peak performance.

deep space network dsn microwave microwave technol↗

NASA NTRS1986

Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

Mak, Victor W. K.↗

NASA NTRSDate not supplied

NASA Space Communications and Navigation: One Network Evolution

The NASA Space Communications and Navigation (SCaN) Program is responsible for providing the essential connectivity to robotic and human space explorers. The missions relying on SCaN range from suborbital and balloon missions to those traveling beyond the edge of the solar system. The demands for communications and navigation services enabled by SCaN (and its affiliated partners) are projected to increase and outpace the current network capacity. At the same time, the Agency finds itself surrounded by a burgeoning commercial space marketplace, technological advancement, and other government agencies that share common interests in space resiliency, robustness, and performance. As a result, SCaN has begun pivoting toward commercial services and collaborating with partners to close capacity and capability gaps. Given these growing demands of the Agency there is increasing need for multi-network solutions. Future mission concepts will rely on both government and commercial capabilities, both Near Space Network capacity and Deep Space Network capacity. Integrating these diverse support services together from a technical, programmatic and implementation standpoint will be key to meet the growing needs of the future. To accomplish this, a more substantive shift is required, and SCaN is reshaping itself to be a customer-centric, service-oriented, high-performance leader in the space communications community. This paper outlines the SCaN One Team, One Mission, One Network approach, and provides a vision for future mission community experience that includes streamlined mission commitment interfaces and clear processes, dynamic network scheduling and load balancing, and higher efficiency data transport and delivery through the integration of cloud infrastructure and services.

Near Space Network↗

NASA NTRS2023

High-Rate Delay Tolerant Networking (HDTN) User Guide Version 1.0

Delay Tolerant Networking (DTN) has been identified as a key technology to enable and facilitate the development and growth of future space networks. Classically, space communications networks are collections of disparate links that are manually managed either point-to-point or use space relays. The accelerating accessibility of space enables a new scaling of space nodes, yet both the manual management of configurations and scheduling and the lack of structure connecting links precisely prohibit scaling. This challenge gives rise to newer and larger classes of communications needs that are met by DTN, which must overcome the disconnection, disruption, latency, and mobility featured in space communications systems. DTN joins the underlying links as an overlay, and can be made to communicate over any protocol stack. The core actions of DTN are store, carry, and forward, where data are stored instead of dropped if there is no immediately available outduct. It does this by taking the DTN unit of data, bundles, and providing necessary layers to adapt these bundles to the underlying transport protocols of choice; these are called convergence layers. DTN's Bundle Protocol (BP) can then be used on top of terrestrial protocol stacks, such as TCP/IP, as well as protocols for space, such as LTP/AOS, all in the same network. For emphasis it is noted that bundles can be of essentially any size, and hence this convergence to lower layers of choice is necessary. Existing DTN implementations have operated in constrained environments with limited resources, resulting in low data speeds. However, as various technologies have advanced, data transfer rates and efficiency have advanced, which has pushed the need for a DTN implementation for ground systems and for spacecraft that is performance-oriented in order to not impose an unnecessary bottleneck. High-rate Delay Tolerant Networking (HDTN) takes advantage of modern hardware platforms to substantially reduce latency and improve throughput compared to today’s DTN operations. The HDTN implementation maintains interoperability with existing deployments of DTN that conform to IETF RFCs 4838, 5050, and 9171. At the same time, HDTN defines a new data format better suited to higher-rate operation. It defines and adopts a massively parallel pipelined and message-oriented architecture, allowing the system to scale gracefully as its resources increase. HDTN’s architecture also supports hooks to replace various processing pipeline elements with specialized hardware accelerators. This offers improved Size, Weight, and Power (SWaP) characteristics while reducing development complexity and cost.

Delay Tolerant Networking↗

NASA NTRS2024

High-Rate Delay Tolerant Networking (HDTN) User Guide Version 1.3.0

Delay Tolerant Networking↗

NASA NTRS2009

Photonic Links for High-Performance Arraying of Antennas

An architecture for arraying microwave antennas in the next generation of NASA s Deep Space Network (DSN) involves the use of all photonic links between (1) the antennas in a given array and (2) a signal processing center. In this architecture, all affected parts at each antenna pedestal [except a front-end low-noise amplifier for the radio-frequency (RF) signal coming from the antenna and an optical transceiver to handle monitor and control (M/C) signals] would be passive optical parts

Huang, Shouhua↗

NASA NTRSDate not supplied

Signal and Power Integrity Design Methodology for High-Performance Flight Computing Systems

Computing capabilities of space systems have in-creased onboard performance by orders of magnitude with the use of radiation-tolerant field-programmable gate arrays (FPGA)and processors. The incorporation of signal and power integrity analysis with printed circuit board (PCB) design in reliable computing architectures for space systems has become critical to enable future mission capabilities. Developers launch high-performance processors into a breadth of orbits and missions, running varying applications that create challenges for designing reliable computing hardware. Specifically, for these designs, academic and industry research has focused on component radiation performance, fault mitigation, and reliable architectures. How-ever, other design parameters including electromagnetic interference (EMI), PCB stackup, signal integrity (SI), voltage regulator module (VRM) design, and power distribution network (PDN)are often deprioritized or disregarded as the design matures. Since these characteristics are becoming more significant in high-performance processor designs, this research presents a hardware design and analysis methodology for high-performance, space-computing systems that focuses on a holistic design approach and PDN reliability. While these challenges exist across all space hardware, the reduced PCB dimensions imposed by SmallSats and CubeSats introduce additional hurdles, specifically to VRM and decoupling design. By examining the relationship between the PDN and radiation performance, an analytical relationship is developed that incorporates Total Ionizing Dose and Single-Event Transients to ensure reliability throughout the mission duration. The presented design methodology is applied to the SpaceCube v3.0 Mini, an FPGA-based on-board science data processing system developed at NASA Goddard Space Flight Center.

Advanced avionics↗

NASA NTRS1998

COOP 3D ARPA Experiment 109 National Center for Atmospheric Research

Coupled atmospheric and hydrodynamic forecast models were executed on the supercomputing resources of the National Center for Atmospheric Research (NCAR) in Boulder, Colorado and the Ohio Supercomputing Center (OSC)in Columbus, Ohio. respectively. The interoperation of the forecast models on these geographically diverse, high performance Cray platforms required the transfer of large three dimensional data sets at very high information rates. High capacity, terrestrial fiber optic transmission system technologies were integrated with those of an experimental high speed communications satellite in Geosynchronous Earth Orbit (GEO) to test the integration of the two systems. Operation over a spacecraft in GEO orbit required modification of the standard configuration of legacy data communications protocols to facilitate their ability to perform efficiently in the changing environment characteristic of a hybrid network. The success of this performance tuning enabled the use of such an architecture to facilitate high data rate, fiber optic quality data communications between high performance systems not accessible to standard terrestrial fiber transmission systems. Thus obviating the performance degradation often found in contemporary earth/satellite hybrids.

Source record↗

NASA NTRS2021

Identifying Planetary Transit Candidates in TESS Full-frame Image Light Curves via Convolutional Neural Networks

The Transiting Exoplanet Survey Satellite(TESS)mission measured light from stars in∼75% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that both is computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in∼5 ms on a single GPU, enabling large-scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives.Our neural network model is additionally provided as open-source code for public use and extension

Gregory Olmschenk↗

NASA NTRS1994

Regular Topologies for Gigabit Wide-Area Networks

In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source code of all software developed under CATE.

Shacham, Nachum↗

NASA NTRS1986

Stanford Hardware Development Program

Architectures for high performance, digital signal processing, particularly for high resolution, wide band spectrum analysis were developed. These developments are intended to provide instrumentation for NASA's Search for Extraterrestrial Intelligence (SETI) program. The real time signal processing is both formal and experimental. The efficient organization and optimal scheduling of signal processing algorithms were investigated. The work is complemented by efforts in processor architecture design and implementation. A high resolution, multichannel spectrometer that incorporates special purpose microcoded signal processors is being tested. A general purpose signal processor for the data from the multichannel spectrometer was designed to function as the processing element in a highly concurrent machine. The processor performance required for the spectrometer is in the range of 1000 to 10,000 million instructions per second (MIPS). Multiple node processor configurations, where each node performs at 100 MIPS, are sought. The nodes are microprogrammable and are interconnected through a network with high bandwidth for neighboring nodes, and medium bandwidth for nodes at larger distance. The implementation of both the current mutlichannel spectrometer and the signal processor as Very Large Scale Integration CMOS chip sets was commenced.

Peterson, A.↗

NASA NTRS1994

VLSI neuroprocessors

Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques. The concurrent processing architectures developed are derived from a variety of advanced computing paradigms including neural network models, fuzzy logic, and cellular automata. Hardware implementation technologies range from state-of-the-art digital/analog custom-VLSI to advanced optoelectronic devices such as computer-generated holograms and e-beam fabricated Dammann gratings. JPL's concurrent processing devices group has developed a broad technology base in hardware implementable parallel algorithms, low-power and high-speed VLSI designs and building block VLSI chips, leading to application-specific high-performance embeddable processors. Application areas include high throughput map-data classification using feedforward neural networks, terrain based tactical movement planner using cellular automata, resource optimization (weapon-target assignment) using a multidimensional feedback network with lateral inhibition, and classification of rocks using an inner-product scheme on thematic mapper data. In addition to addressing specific functional needs of DOD and NASA, the JPL-developed concurrent processing device technology is also being customized for a variety of commercial applications (in collaboration with industrial partners), and is being transferred to U.S. industries. This viewgraph p resentation focuses on two application-specific processors which solve the computation intensive tasks of resource allocation (weapon-target assignment) and terrain based tactical movement planning using two extremely different topologies. Resource allocation is implemented as an asynchronous analog competitive assignment architecture inspired by the Hopfield network. Hardware realization leads to a two to four order of magnitude speed-up over conventional techniques and enables multiple assignments, (many to many), not achievable with standard statistical approaches. Tactical movement planning (finding the best path from A to B) is accomplished with a digital two-dimensional concurrent processor array. By exploiting the natural parallel decomposition of the problem in silicon, a four order of magnitude speed-up over optimized software approaches has been demonstrated.

Kemeny, Sabrina E.↗

NASA NTRS1988

Performance modeling and measurement of real-time multiprocessors with time-shared buses

A closed queueing network model is constructed to address workload effects on computer performance for a highly reliable unibus multiprocessor used in real-time control. The queueing model consists of multiserver nodes and a nonpreemptive priority queue. Use of this model requires partitioning the workload into task classes. The time average steady-state solution of the queueing model directly produces useful results that are necessary in performance evaluation. The model is experimentally justified with the Fault-Tolerant Multiprocessor (FTMP) located at the NASA AIRLAB. Extensive experiments are performed on FTMP with a synthetic workload generator (SWG) to directly measure performance parameters, such as processor idle time, system bus contention, and task processing times. These measurements determine values for parameters in the queueing model. Experimental and analytic results are then compared.

Woodbury, Michael H.↗

NASA NTRS2000

Electromagnetic Propulsion

The design and development of an Electromagnetic Propulsion is discussed. Specific Electromagnetic Propulsion Topics discussed include: (1) Technology for Pulse Inductive Thruster (PIT), to design, develop, and test of a multirepetition rate pulsed inductive thruster, Solid-State Switch Technology, and Pulse Driver Network and Architecture; (2) Flight Weight Magnet Survey, to determine/develop light weight high performance magnetic materials for potential application Advanced Space Flight Systems as these systems develop; and (3) Magnetic Flux Compression, to enable rapid/robust/reliable omni-planetary space transportation within realistic development and operational costs constraints.

Schafer, Charles↗

NASA NTRS2012

Evaluating the Efficacy of the Cloud for Cluster Computation

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

Cloud Computing↗

NASA NTRS2015

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗