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IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the spaceCube GR740 Host for Onboard Science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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Comparison of Multi-Phase Power Converters and Power Delivery Networks for Next-Generation space Architectures

Comparison of Multi-Phase Power Converters and Power Deliver...

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IEEE Conference on Aerospace

作者： Nicholas Franconi Thomas Cook Christopher Wilson Alan D. George NASA Goddard Space Flight Center Science Data Processing Branch Code 587 Software Engineering Division Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh

Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the current generation of radiation-hardened processors are inferior compared to commercial-off-the-shelf alternatives in terms of the computational performance required by modern AI applications. To address this disparity, space-system designers have started employing novel radiation-tolerant architectures combining both commercial and radiation-hardened components to mitigate radiation effects at a system level. Unfortunately, developing single-board computers with radiation-tolerant, high-performance processors is challenging because designers must balance the sparce selection of radiation-hardened power converters and high-reliability decoupling capacitors with SmallSat/CubeSat area constraints and limited thermal conduction. Consequently, the next-generation of space processors, including the AMD-Xilinx Versal Adaptive Compute Acceleration Platform require demanding power solutions capable of supplying core rails with 0.8V ± 18mV and currents up to 150 A. In this paper, we present a multifaceted analysis of the power system and decoupling network for a future Versal-based design. We developed preliminary power estimates based on expected processor and FPGA resource utilization for common AI processing applications. These estimates drive the power system requirements and a comparative analysis of single-phase integrated converters and multi-phase discrete converters for high-current FPGA supplies. We develop a tradespace between the number of phases, input voltage, load current, switching frequency, power efficiency, and printed circuit board area. Finally, we design and simulate four power delivery networks based on commercial, high-reliability, and flight-qualified capacitors to compare the efficacy of 0201 decoupling capacitors in flight missions.

关键词： Rails Program processors Capacitors Voltage Power systems Resource management Artificial intelligence

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Adaptively Lossy Image Compression for Onboard Processing

Adaptively Lossy Image Compression for Onboard Processing

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IEEE Aerospace Conference

作者： Justin Goodwill David Wilson Sebastian Sabogal Alan D. George Christopher Wilson NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh 4420 Bayard St Suite 560 Pittsburgh PA Software Engineering Division NASA Goddard Space Flight Center Science Data Processing Branch Code 587 8800 Greenbelt Rd Greenbelt MD

ISBN: (数字)9781728127347

ISBN: (纸本)9781728127354

More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers. While these codecs reduce transmitted data volume, many are compute-intensive and require rapid processing to sustain sensor data rates. Emerging next-generation small satellite (SmallSat) computers provide compelling computational capability to enable more onboard processing and compression than previously considered. For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG);and (2) adaptive image compression through feature-point detection (FPD-JPEG). These algorithms rely on intelligent data-processing pipelines that adapt to sensor data to compress it more effectively, ensuring efficient use of limited downlink bandwidths. The first algorithm, CNN-JPEG, employs a hybrid approach adapted from literature combining convolutional neural networks (CNNs) and JPEG;however, we modify and tune the training scheme for satellite imagery to account for observed training instabilities. This hybrid CNN-JPEG approach shows 23.5% better average peak signal-to-noise ratio (PSNR) and 33.5% better average structural similarity index (SSIM) versus standard JPEG on a dataset collected on the space Test Program - Houston 5 (STP-H5-CSP) mission onboard the International space Station (ISS). For our second algorithm, we developed a novel adaptive image-compression pipeline based upon JPEG that leverages the Oriented FAST and Rotated BRIEF (ORB) feature-point detection algorithm to adaptively tune the compression ratio to allow for a tradeoff between PSNR/SSIM and combined file size over a batch of STP-H5-CSP images. We achieve a less than 1% drop in average PSNR and SSIM while reducing the combined file size by 29.6% compared to JPEG using a static quality factor (QF) of 90.

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NANOGrav 15-year gravitational-wave background methods

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Physical Review D 2024年第10期109卷 103012-103012页

作者： Aaron D. Johnson Patrick M. Meyers Paul T. Baker Neil J. Cornish Jeffrey S. Hazboun Tyson B. Littenberg Joseph D. Romano Stephen R. Taylor Michele Vallisneri Sarah J. Vigeland Ken D. Olum Xavier Siemens Justin A. Ellis Rutger van Haasteren Sophie Hourihane Gabriella Agazie Akash Anumarlapudi Anne M. Archibald Zaven Arzoumanian Laura Blecha Adam Brazier Paul R. Brook Sarah Burke-Spolaor Bence Bécsy J. Andrew Casey-Clyde Maria Charisi Shami Chatterjee Katerina Chatziioannou Tyler Cohen James M. Cordes Fronefield Crawford H. Thankful Cromartie Kathryn Crowter Megan E. DeCesar Paul B. Demorest Timothy Dolch Brendan Drachler Elizabeth C. Ferrara William Fiore Emmanuel Fonseca Gabriel E. Freedman Nate Garver-Daniels Peter A. Gentile Joseph Glaser Deborah C. Good Kayhan Gültekin Ross J. Jennings Megan L. Jones Andrew R. Kaiser David L. Kaplan Luke Zoltan Kelley Matthew Kerr Joey S. Key Nima Laal Michael T. Lam William G. Lamb T. Joseph W. Lazio Natalia Lewandowska Tingting Liu Duncan R. Lorimer Jing Luo Ryan S. Lynch Chung-Pei Ma Dustin R. Madison Alexander McEwen James W. McKee Maura A. McLaughlin Natasha McMann Bradley W. Meyers Chiara M. F. Mingarelli Andrea Mitridate Cherry Ng David J. Nice Stella Koch Ocker Timothy T. Pennucci Benetge B. P. Perera Nihan S. Pol Henri A. Radovan Scott M. Ransom Paul S. Ray Shashwat C. Sardesai Carl Schmiedekamp Ann Schmiedekamp Kai Schmitz Brent J. Shapiro-Albert Joseph Simon Magdalena S. Siwek Ingrid H. Stairs Daniel R. Stinebring Kevin Stovall Abhimanyu Susobhanan Joseph K. Swiggum Jacob E. Turner Caner Unal Haley M. Wahl Caitlin A. Witt Olivia Young Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 Milwaukee Wisconsin 53201 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Department of Physics and Astronomy Widener University One University Place Chester Pennsylvania 19013 USA Department of Physics Montana State University Bozeman Montana 59717 USA Department of Physics Oregon State University Corvallis Oregon 97331 USA NASA Marshall Space Flight Center Huntsville Alabama 35812 USA Department of Physics Texas Tech University Box 41051 Lubbock Texas 79409 USA Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville Tennessee 37235 USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California 91109 USA Institute of Cosmology Department of Physics and Astronomy Tufts University Medford Massachusetts 02155 USA Department of Astronomy University of Maryland College Park Maryland 20742 Center for Research and Exploration in Space Science and Technology NASA/GSFC Greenbelt Maryland 20771 NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut) Callinstrasse 38 D-30167 Hannover Germany Newcastle University NE1 7RU Newcastle United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt Maryland 20771 USA Physics Department University of Florida Gainesville Florida 32611 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca New York 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca New York 14853 USA Institute for Gravitational Wave Astronomy and School of Physics and Astronomy University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom Department of Physics and Astronomy West

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms enterprise and ptmcmcsampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulation recovery, and Bayes factors. For the frequentist analysis, we test that the optimal statistic, when modified to account for a non-negligible gravitational-wave background, accurately recovers the amplitude of the background. We also summarize recent advances and tests performed on the optimal statistic in the literature from both gravitational wave background detection and parameter estimation perspectives. The tests presented here validate current analyses of PTA data.

关键词： Gravitational waves Gravitational wave detection

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Single-dish and VLBI observations of Cygnus X-3 during the 2016 giant flare episode

arXiv

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arXiv 2017年

作者： Egron, E. Pellizzoni, A. Giroletti, M. Righini, S. Stagni, M. Orlati, A. Migoni, C. Melis, A. Concu, R. Barbas, L. Buttaccio, S. Cassaro, P. de Vicente, P. Nski, M.P. Gawro Lindqvist, M. Maccaferri, G. Stanghellini, C. Wolak, P. Yang, J. Navarrini, A. Loru, S. Pilia, M. Bachetti, M. Iacolina, M.N. Buttu, M. Corbel, S. Rodriguez, J. Markoff, S. Wilms, J. Pottschmidt, K. Bel, M. Cadolle Kalemci, E. Belloni, T. Grinberg, V. Marongiu, M. Vargiu, G.P. Trois, A. INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 Selargius09047 Italy INAF Istituto di Radio Astronomia di Bologna Via P. Gobetti 101 Bologna40129 Italy Spain INAF Istituto di Radioastronomia Sezione di Noto Contrada Renna Bassa Noto96017 Italy Torú n Centre for Astronomy N. Copernicus University Gagarina 11 Torú n87-100 Poland Department of Earth and Space Sciences Chalmers University of Technology Onsala Space Observatory Onsala439 92 Sweden Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Laboratoire AIM UMR 7158 CEA/CNRS Université Paris Diderot CEA DRF/IRFU/DAp Gif-sur-Yvette91191 France Station de Radioastronomie de Nançay Observatoire de Paris PSL Research University CNRS Univ. Orléans Nançay18330 France Anton Pannekoek Institute for Astronomy University of Amsterdam PO Box 94249 Amsterdam1090 GE Netherlands Friedrich Alexander Universität Erlangen-Nürnberg Sternwartstr. 7 Bamberg96049 Germany CRESST and NASA Goddard Space Flight Center Astrophysics Science Division Code 661 GreenbeltMD20771 United States Center for Space Science and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Max Planck Computing and Data Facility Garching85748 Germany Faculty of Engineering and Natural Sciences Sabancı University Orhanlı -Tuzla Istanbul34956 Turkey INAF Osservatorio Astronomico di Brera via E. Bianchi 46 Merate23807 Italy ESA/ESTEC Keplerlaan 1 Noordwijk2201 AZ Netherlands Department of Physics and Earth Sciences University of Ferrara via Saragat 1 Ferrara44122 Italy

In September 2016, the microquasar Cygnus X-3 underwent a giant radio flare, which was monitored for 6 days with the Medicina Radio Astronomical Station and the Sardinia Radio Telescope. Long observations were performed in order to follow the evolution of the flare on a hourly scale, covering six frequency ranges from 1.5 GHz to 25.6 GHz. The radio emission reached a maximum of 13.2 ± 0.7 Jy at 7.2 GHz and 10±1 Jy at 18.6 GHz. Rapid flux variations were observed at high radio frequencies at the peak of the flare, together with rapid evolution of the spectral index: α steepened from 0.3 to 0.6 (with Sν ∝ ν−α) within 5 hours. This is the first time that such fast variations are observed, giving support to the evolution from optically thick to optically thin plasmons in expansion moving outward from the core. Based on the Italian network (Noto, Medicina and SRT) and extended to the European antennas (Torun, Yebes, Onsala), VLBI observations were triggered at 22 GHz on five different occasions, four times prior to the giant flare, and once during its decay phase. Flux variations of 2-hour duration were recorded during the first session. They correspond to a mini-flare that occurred close to the core ten days before the onset of the giant flare. From the latest VLBI observation we infer that four days after the flare peak the jet emission was extended over 30 mas. Copyright © 2017, The Authors. All rights reserved.

关键词： Stars

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Asymptotically optimal probabilistic embedding algorithms for supporting tree structured computations in hypercubes

Asymptotically optimal probabilistic embedding algorithms fo...

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Frontiers of Massively Parallel Computation

作者： Keqin Li J.E. Dorband Department of Mathematics and Computer Science State University of New York New Paltz NY USA Space Data and Computing Division-Code 934 NASA Goddard Space Flight Center Greenbelt MA USA

We show two asymptotically optimal probabilistic tree embedding algorithms in hypercubes with constant dilation. These algorithms are slight extension of the random walk algorithm. The first algorithm allows a tree node to have a stay option during each step of a random walk. The second algorithm permits varying length of random walks. Numerical data are given to demonstrate performance improvement.

关键词： Embedded computing Hypercubes Concurrent computing Mathematics Computer science NASA Uniform resource locators World Wide Web HTML Heuristic algorithms

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Pythia and Pythia/WK: tools for the performance analysis of mass storage systems

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Software: Practice and Experience 1999年第9期27卷

作者： Odysseas I. Pentakalos Daniel A. Menascé Yelena Yesha Code 930 Space Data and Computing Division NASA Goddard Space Flight Center Greenbelt MD 20771 U.S.A. Department of Computer Science George Mason University Fairfax VA 22030 U.S.A. Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21228 U.S.A.

The constant growth on the demands imposed on hierarchical mass storage systems creates a need for frequent reconfiguration and upgrading to ensure that the response times and other performance metrics are within the desired service levels. This paper describes the design and operation of two tools, Pythia and Pythia/WK, that assist system managers and integrators in making cost-effective procurement decisions. Pythia automatically buids and solves an analytic model of a mass storage system based on a graphical description of the architecture of the system, and on a description of the workload imposed on the system. The use of a modeling wizard to perform this conversion from a graphical description of a mass storage system to an analytic model makes Pythia unique among analytic performance tools. Pythia/WK uses clustering algorithms to characterize the workload from the log files of the mass storage system. The resulting workload characterization is used as input to Pythia. © 1997 John Wiley & Sons, Ltd.

关键词： mass storage systems object oriented design workload characterization graphical user interfaces performance prediction tool Tcl/Tk

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A practical approximation algorithm for the LMS line estimator 97

A practical approximation algorithm for the LMS line estimat...

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Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms

作者： David M. Mount Nathan S. Netanyahu Kathleen Romanik Ruth Silverman Angela Y. Wu Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park MD Center for Automation Research University of Maryland College Park MD and Center of Excellence in Space Data and Information Sciences Code 930.5 Space Data and Computing Division NASA Goddard Space Flight Center Greenbelt MD Center for Automation Research University of Maryland College Park MD and National Institute of Standards and Technology Gaithersburg MD Department of Computer Science University of the District of Columbia Washington DC and Center for Automation Research University of Maryland College Park MD Department of Computer Science and Information Systems The American University Washington DC

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Parallel computation of discrete Legendre transforms

Parallel computation of discrete Legendre transforms

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M.F. Aburdene J.E. Dorband Electrical Engineering Department Bucknell University Lewisburg PA USA Space Data Computing Division-Code 934 NASA''s Goddard Space Flight Center Greenbelt MD USA

This paper presents a parallel implementation of the discrete Legendre transform using SIMD machines, MasPar MP-1 and MP-2. Laguerre, Hermite, and binomial transforms can be implemented in a similar manner using some of the computations required to compute the discrete Legendre transform. The time required to compute the discrete Legendre transform vs. number of points for an MP-2 machine is included.

关键词： Concurrent computing Discrete transforms Polynomials Signal processing Discrete wavelet transforms Process control Control systems Systems engineering and theory Physics Equations

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A new method for visualizing data on a sphere

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Computer in Physics 1991年第5期5卷 505-513页

作者： David Hon Space Data and Computing Division NASA Goddard Space Flight Center Code 936 Greenbelt Maryland 20771

A new method for visualizing data on a globe or unit sphere is described. Information that is distributed over a sphere—global oceanographic or geographic measurements, all‐sky astronomy observations, or any quantities that are best represented in spherical coordinates—can benefit from this technique. Retaining a better sense of the geometry and information content of the data, 3‐D graphics can provide an unobstructed view of the entire sphere, without undo deformation of its surface area. A ‘‘parametrized ray trace’’ produces look‐up tables (LUTs) that can be used for all visualizations. The ray trace result shows one or more spheres with the data as a texture map and three reflecting rectangles that ‘‘mirror’’ the far sides of the sphere(s) into view. The LUTs need only be created once, and a general purpose computer will do. No special purpose hardware is required beyond a PC or workstation that supports color. Examples from astronomical and geophysical datasets, which are commonly displayed with an area deforming (2‐D) projection, are presented.

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