The Deep Space Network: A Radio Communications Instrument for Deep Space Exploration

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Stelzried, C. T.; Noreen, G. K.; Slobin, S. D.; Petty, S. M.; Trowbridge, D. L.; Donnelly, H.; Kinman, P. W.; Armstrong, J. W.; Burow, N. A.

1983-01-01

The primary purpose of the Deep Space Network (DSN) is to serve as a communications instrument for deep space exploration, providing communications between the spacecraft and the ground facilities. The uplink communications channel provides instructions or commands to the spacecraft. The downlink communications channel provides command verification and spacecraft engineering and science instrument payload data.

The Future of the Deep Space Network: Technology Development for K2-Band Deep Space Communications

NASA Technical Reports Server (NTRS)

Bhanji, Alaudin M.

1999-01-01

Projections indicate that in the future the number of NASA's robotic deep space missions is likely to increase significantly. A launch rate of up to 4-6 launches per year is projected with up to 25 simultaneous missions active [I]. Future high resolution mapping missions to other planetary bodies as well as other experiments are likely to require increased downlink capacity. These future deep space communications requirements will, according to baseline loading analysis, exceed the capacity of NASA's Deep Space Network in its present form. There are essentially two approaches for increasing the channel capacity of the Deep Space Network. Given the near-optimum performance of the network at the two deep space communications bands, S-Band (uplink 2.025-2.120 GHz, downlink 2.2-2.3 GHz), and X-Band (uplink 7.145-7.19 GHz, downlink 8.48.5 GHz), additional improvements bring only marginal return for the investment. Thus the only way to increase channel capacity is simply to construct more antennas, receivers, transmitters and other hardware. This approach is relatively low-risk but involves increasing both the number of assets in the network and operational costs.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1975-01-01

Work accomplished on the Deep Space Network (DSN) was described, including the following topics: supporting research and technology, advanced development and engineering, system implementation, and DSN operations pertaining to mission-independent or multiple-mission development as well as to support of flight projects.

Future Plans for NASA's Deep Space Network

NASA Technical Reports Server (NTRS)

Deutsch, Leslie J.; Preston, Robert A.; Geldzahler, Barry J.

2008-01-01

This slide presentation reviews the importance of NASA's Deep Space Network (DSN) to space exploration, and future planned improvements to the communication capabilities that the network allows, in terms of precision, and communication power.

The Deep Space Network. [tracking and communication functions and facilities

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

Statistical porcess control in Deep Space Network operation

NASA Technical Reports Server (NTRS)

Hodder, J. A.

2002-01-01

This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).

The deep space network, Volume 11

NASA Technical Reports Server (NTRS)

1972-01-01

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

The deep space network, volume 15

NASA Technical Reports Server (NTRS)

1973-01-01

The DSN progress is reported in flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations. Topics discussed include: DSN functions and facilities, planetary flight projects, tracking and ground-based navigation, communications, data processing, network control system, and deep space stations.

The Deep Space Network, volume 39

NASA Technical Reports Server (NTRS)

1977-01-01

The functions, facilities, and capabilities of the Deep Space Network and its support of the Pioneer, Helios, and Viking missions are described. Progress in tracking and data acquisition research and technology, network engineering and modifications, as well as hardware and software implementation and operations are reported.

The deep space network, volume 12

NASA Technical Reports Server (NTRS)

1972-01-01

Progress in the development of the DSN is reported along with TDA research and technology, network engineering, hardware, and software implementation. Included are descriptions of the DSN function and facilities, Helios mission support, Mariner Venus/Mercury 1973 mission support, Viking mission support, tracking and ground-based navigation, communications, network control and data processing, and deep space stations.

Considerations on communications network protocols in deep space

NASA Technical Reports Server (NTRS)

Clare, L. P.; Agre, J. R.; Yan, T.

2001-01-01

Communications supporting deep space missions impose numerous unique constraints that impact the architectural choices made for cost-effectiveness. We are entering the era where networks that exist in deep space are needed to support planetary exploration. Cost-effective performance will require a balanced integration of applicable widely used standard protocols with new and innovative designs.

Deep space network Mark 4A description

NASA Technical Reports Server (NTRS)

Wallace, R. J.; Burt, R. W.

1986-01-01

The general system configuration for the Mark 4A Deep Space Network is described. The arrangement and complement of antennas at the communications complexes and subsystem equipment at the signal processing centers are described. A description of the Network Operations Control Center is also presented.

A history of the deep space network

NASA Technical Reports Server (NTRS)

Corliss, W. R.

1976-01-01

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

The deep space network, volume 10

NASA Technical Reports Server (NTRS)

1972-01-01

Progress on the Deep Space Network (DSN) supporting research and technology is reported. The objectives, functions and facilities of the DSN are described along with the mission support for the following: interplanetary flight projects, planetary flight projects, and manned space flight projects. Work in advanced engineering and communications systems is reported along with changes in hardware and software configurations in the DSN/MSFN tracking stations.

The deep space network, volume 19

NASA Technical Reports Server (NTRS)

1974-01-01

The progress is reported in the DSN for Nov. and Dec. 1973. Research is described for the following areas: functions and facilities, mission support for flight projects, tracking and ground-based navigation, spacecraft/ground communication, network control and operations technology, and deep space stations.

Operability engineering in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wilkinson, Belinda

1993-01-01

Many operability problems exist at the three Deep Space Communications Complexes (DSCC's) of the Deep Space Network (DSN). Four years ago, the position of DSN Operability Engineer was created to provide the opportunity for someone to take a system-level approach to solving these problems. Since that time, a process has been developed for personnel and development engineers and for enforcing user interface standards in software designed for the DSCC's. Plans are for the participation of operations personnel in the product life-cycle to expand in the future.

Deep Space Network equipment performance, reliability, and operations management information system

NASA Technical Reports Server (NTRS)

Cooper, T.; Lin, J.; Chatillon, M.

2002-01-01

The Deep Space Mission System (DSMS) Operations Program Office and the DeepSpace Network (DSN) facilities utilize the Discrepancy Reporting Management System (DRMS) to collect, process, communicate and manage data discrepancies, equipment resets, physical equipment status, and to maintain an internal Station Log. A collaborative effort development between JPL and the Canberra Deep Space Communication Complex delivered a system to support DSN Operations.

The deep space network. [tracking and communication support for space probes

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization of the deep space network are summarized. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. Interface support for the Mariner Venus Mercury 1973 flight and Pioneer 10 and 11 missions is included.

High-power transmitter automation. [deep space network

NASA Technical Reports Server (NTRS)

Gosline, R.

1980-01-01

The current status of the transmitter automation development applicable to all transmitters in the deep space network is described. Interface and software designs are described that improve reliability and reduce the time required for subsystem turn-on and klystron saturation to less than 10 minutes.

Operation's Concept for Array-Based Deep Space Network

NASA Technical Reports Server (NTRS)

Bagri, Durgadas S.; Statman, Joseph I.; Gatti, Mark S.

2005-01-01

The Array-based Deep Space Network (DSNArray) will be a part of more than 10(exp 3) times increase in the downlink/telemetry capability of the Deep space Network (DSN). The key function of the DSN-Array is to provide cost-effective, robust Telemetry, Tracking and Command (TT&C) services to the space missions of NASA and its international partners. It provides an expanded approach to the use of an array-based system. Instead of using the array as an element in the existing DSN, relying to a large extent on the DSN infrastructure, we explore a broader departure from the current DSN, using fewer elements of the existing DSN, and establishing a more modern Concept of Operations. This paper gives architecture of DSN-Array and its operation's philosophy. It also describes customer's view of operations, operations management and logistics - including maintenance philosophy, anomaly analysis and reporting.

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.

The Deep Space Network Array

NASA Technical Reports Server (NTRS)

Gatti, Mark S.

2006-01-01

This document is a viewgraph presentation that reviews the costs, and technological processing required to replace the current network of Deep Space Antennas. The concept of using an array for space communications is much less of a concern than the cost of implementing and operating such an array. Within the cost question, the cost uncertainty of the front-end components (repeated n-times) is of most importance. The activities at JPL have focused on both these aspects of the cost. A breadboard array of three antennas at JPL has been the vehicle to perform many investigations into the development of the new DSN. Several pictures of the antennas at JPL are shown.

Evolutionary Scheduler for the Deep Space Network

NASA Technical Reports Server (NTRS)

Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

2010-01-01

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

The Network Information Management System (NIMS) in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wales, K. J.

1983-01-01

In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

Preliminary Concept of Operations for the Deep Space Array-Based Network

NASA Astrophysics Data System (ADS)

Bagri, D. S.; Statman, J. I.

2004-05-01

The Deep Space Array-Based Network (DSAN) will be an array-based system, part of a greater than 1000 times increase in the downlink/telemetry capability of the Deep Space Network. The key function of the DSAN is provision of cost-effective, robust telemetry, tracking, and command services to the space missions of NASA and its international partners. This article presents an expanded approach to the use of an array-based system. Instead of using the array as an element in the existing Deep Space Network (DSN), relying to a large extent on the DSN infrastructure, we explore a broader departure from the current DSN, using fewer elements of the existing DSN, and establishing a more modern concept of operations. For example, the DSAN will have a single 24 x 7 monitor and control (M&C) facility, while the DSN has four 24 x 7 M&C facilities. The article gives the architecture of the DSAN and its operations philosophy. It also briefly describes the customer's view of operations, operations management, logistics, anomaly analysis, and reporting.

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

Deep Space Telecommunications

NASA Technical Reports Server (NTRS)

Kuiper, T. B. H.; Resch, G. M.

2000-01-01

The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

NASA deep space network operations planning and preparation

NASA Technical Reports Server (NTRS)

Jensen, W. N.

1982-01-01

The responsibilities and structural organization of the Operations Planning Group of NASA Deep Space Network (DSN) Operations are outlined. The Operations Planning group establishes an early interface with a user's planning organization to educate the user on DSN capabilities and limitations for deep space tracking support. A team of one or two individuals works through all phases of the spacecraft launch and also provides planning and preparation for specific events such as planetary encounters. Coordinating interface is also provided for nonflight projects such as radio astronomy and VLBI experiments. The group is divided into a Long Range Support Planning element and a Near Term Operations Coordination element.

Sub-microradian pointing for deep space optical telecommunications network

NASA Technical Reports Server (NTRS)

Ortiz, G.; Lee, S.; Alexander, J.

2001-01-01

This presentation will cover innovative hardware, algorithms, architectures, techniques and recent laboratory results that are applicable to all deep space optical communication links, such as the Mars Telecommunication Network to future interstellar missions.

The Telecommunications and Data Acquisition Report. [Deep Space Network

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported. Also included is TDA funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA.

The Telecommunications and Data Acquisition Report. [Deep Space Network

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

This publication, one of a series formerly titled The Deep Space Network Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

Automating Deep Space Network scheduling and conflict resolution

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Clement, Bradley

2005-01-01

The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.

Summary of DSN (Deep Space Network) reimbursable launch support

NASA Technical Reports Server (NTRS)

Fanelli, N. A.; Wyatt, M. E.

1988-01-01

The Deep Space Network is providing ground support to space agencies of foreign governments as well as to NASA and other agencies of the Federal government which are involved in space activities. DSN funding for support of missions other than NASA are on either a cooperative or a reimbursable basis. Cooperative funding and support are accomplished in the same manner as NASA sponsored missions. Reimbursable launch funding and support methods are described.

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.

Neural network based satellite tracking for deep space applications

NASA Technical Reports Server (NTRS)

Amoozegar, F.; Ruggier, C.

2003-01-01

The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

Major technological innovations introduced in the large antennas of the Deep Space Network

NASA Technical Reports Server (NTRS)

Imbriale, W. A.

2002-01-01

The NASA Deep Space Network (DSN) is the largest and most sensitive scientific, telecommunications and radio navigation network in the world. Its principal responsibilities are to provide communications, tracking, and science services to most of the world's spacecraft that travel beyond low Earth orbit. The network consists of three Deep Space Communications Complexes. Each of the three complexes consists of multiple large antennas equipped with ultra sensitive receiving systems. A centralized Signal Processing Center (SPC) remotely controls the antennas, generates and transmits spacecraft commands, and receives and processes the spacecraft telemetry.

Three-Dimensional Analysis of Deep Space Network Antenna Coverage

NASA Technical Reports Server (NTRS)

Kegege, Obadiah; Fuentes, Michael; Meyer, Nicholas; Sil, Amy

2012-01-01

There is a need to understand NASA s Deep Space Network (DSN) coverage gaps and any limitations to provide redundant communication coverage for future deep space missions, especially for manned missions to Moon and Mars. The DSN antennas are required to provide continuous communication coverage for deep space flights, interplanetary missions, and deep space scientific observations. The DSN consists of ground antennas located at three sites: Goldstone in USA, Canberra in Australia, and Madrid in Spain. These locations are not separated by the exactly 120 degrees and some DSN antennas are located in the bowl-shaped mountainous terrain to shield against radiofrequency interference resulting in a coverage gap in the southern hemisphere for the current DSN architecture. To analyze the extent of this gap and other coverage limitations, simulations of the DSN architecture were performed. In addition to the physical properties of the DSN assets, the simulation incorporated communication forward link calculations and azimuth/elevation masks that constrain the effects of terrain for each DSN antenna. Analysis of the simulation data was performed to create coverage profiles with the receiver settings at a deep space altitudes ranging from 2 million to 10 million km and a spherical grid resolution of 0.25 degrees with respect to longitude and latitude. With the results of these simulations, two- and three-dimensional representations of the area without communication coverage and area with coverage were developed, showing the size and shape of the communication coverage gap projected in space. Also, the significance of this communication coverage gap is analyzed from the simulation data.

Deep Space Network-Wide Portal Development: Planning Service Pilot Project

NASA Technical Reports Server (NTRS)

Doneva, Silviya

2011-01-01

The Deep Space Network (DSN) is an international network of antennas that supports interplanetary spacecraft missions and radio and radar astronomy observations for the exploration of the solar system and the universe. DSN provides the vital two-way communications link that guides and controls planetary explorers, and brings back the images and new scientific information they collect. In an attempt to streamline operations and improve overall services provided by the Deep Space Network a DSN-wide portal is under development. The project is one step in a larger effort to centralize the data collected from current missions including user input parameters for spacecraft to be tracked. This information will be placed into a principal repository where all operations related to the DSN are stored. Furthermore, providing statistical characterization of data volumes will help identify technically feasible tracking opportunities and more precise mission planning by providing upfront scheduling proposals. Business intelligence tools are to be incorporated in the output to deliver data visualization.

Office of Tracking and Data Acquisition. [deep space network and spacecraft tracking

NASA Technical Reports Server (NTRS)

1975-01-01

The Office of Tracking and Data Acquisition (OTDA) and its two worldwide tracking network facilities, the Spaceflight Tracking and Data Network and the Deep Space Network, are described. Other topics discussed include the NASA communications network, the tracking and data relay satellite system, other OTDA tracking activities, and OTDA milestones.

Range Measurement as Practiced in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.; Kinman, Peter W.

2007-01-01

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

The Deep Space Network: An instrument for radio astronomy research

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Levy, G. S.; Kuiper, T. B. H.; Walken, P. R.; Chandlee, R. C.

1988-01-01

The NASA Deep Space Network operates and maintains the Earth-based two-way communications link for unmanned spacecraft exploring the solar system. It is NASA's policy to also make the Network's facilities available for radio astronomy observations. The Network's microwave communication systems and facilities are being continually upgraded. This revised document, first published in 1982, describes the Network's current radio astronomy capabilities and future capabilities that will be made available by the ongoing Network upgrade. The Bibliography, which includes published papers and articles resulting from radio astronomy observations conducted with Network facilities, has been updated to include papers to May 1987.

Propagation Effects of Importance to the NASA/JPL Deep Space Network (DSN)

NASA Technical Reports Server (NTRS)

Slobin, Steve

1999-01-01

This paper presents Propagation Effects of Importance To The NASA/JPL Deep Space Network (DSN). The topics include: 1) DSN Antennas; 2) Deep Space Telecom Link Basics; 3) DSN Propagation Region of Interest; 4) Ka-Band Weather Effects Models and Examples; 5) Existing Goldstone Ka-Band Atmosphere Attenuation Model; 6) Existing Goldstone Atmosphere Noise Temperature Model; and 7) Ka-Band delta (G/T) Relative to Vacuum Condition. This paper summarizes the topics above.

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

Deep space network support of the manned space flight network for Apollo, volume 3. [support for Apollo 14, 15, 16, and 17 flights

NASA Technical Reports Server (NTRS)

Hartley, R. B.

1974-01-01

The Deep Space Network (DSN) activities in support of Project Apollo during the period of 1971 and 1972 are reported. Beginning with the Apollo 14 mission and concluding with the Apollo 17 mission, the narrative includes, (1) a mission description, (2) the NASA support requirements placed on the DSN, and, (3) a comprehensive account of the support activities provided by each committed DSN deep space communication station. Associated equipment and activities of the three elements of the DSN (the Deep Space Instrumentation Facility (DSIF), the Space Flight Operations Facility (SFOF), and the Ground Communications Facility (GCF)) used in meeting the radio-metric and telemetry demands of the missions are documented.

The Deep Space Network as an instrument for radio science research

NASA Technical Reports Server (NTRS)

Asmar, S. W.; Renzetti, N. A.

1993-01-01

Radio science experiments use radio links between spacecraft and sensor instrumentation that is implemented in the Deep Space Network. The deep space communication complexes along with the telecommunications subsystem on board the spacecraft constitute the major elements of the radio science instrumentation. Investigators examine small changes in the phase and/or amplitude of the radio signal propagating from a spacecraft to study the atmospheric and ionospheric structure of planets and satellites, planetary gravitational fields, shapes, masses, planetary rings, ephemerides of planets, solar corona, magnetic fields, cometary comae, and such aspects of the theory of general relativity as gravitational waves and gravitational redshift.

Frequency Domain Beamforming for a Deep Space Network Downlink Array

NASA Technical Reports Server (NTRS)

Navarro, Robert

2012-01-01

This paper describes a frequency domain beamformer to array up to 8 antennas of NASA's Deep Space Network currently in development. The objective of this array is to replace and enhance the capability of the DSN 70m antennas with multiple 34m antennas for telemetry, navigation and radio science use. The array will coherently combine the entire 500 MHz of usable bandwidth available to DSN receivers. A frequency domain beamforming architecture was chosen over a time domain based architecture to handle the large signal bandwidth and efficiently perform delay and phase calibration. The antennas of the DSN are spaced far enough apart that random atmospheric and phase variations between antennas need to be calibrated out on an ongoing basis in real-time. The calibration is done using measurements obtained from a correlator. This DSN Downlink Array expands upon a proof of concept breadboard array built previously to develop the technology and will become an operational asset of the Deep Space Network. Design parameters for frequency channelization, array calibration and delay corrections will be presented as well a method to efficiently calibrate the array for both wide and narrow bandwidth telemetry.

A Deep Space Network Portable Radio Science Receiver

NASA Technical Reports Server (NTRS)

Jongeling, Andre P.; Sigman, Elliott H.; Chandra, Kumar; Trinh, Joseph T.; Navarro, Robert; Rogstad, Stephen P.; Goodhart, Charles E.; Proctor, Robert C.; Finley, Susan G.; White, Leslie A.

2009-01-01

The Radio Science Receiver (RSR) is an open-loop receiver installed in NASA s Deep Space Network (DSN), which digitally filters and records intermediate-frequency (IF) analog signals. The RSR is an important tool for the Cassini Project, which uses it to measure perturbations of the radio-frequency wave as it travels between the spacecraft and the ground stations, allowing highly detailed study of the composition of the rings, atmosphere, and surface of Saturn and its satellites.

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Deep Space Network (DSN) Data Systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit DSN software life cycle statistics. The estimation model output scales a standard DSN Work Breakdown Structure skeleton, which is then input into a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

Optical subnet concepts for the deep space network

NASA Technical Reports Server (NTRS)

Shaik, K.; Wonica, D.; Wilhelm, M.

1993-01-01

This article describes potential enhancements to the Deep Space Network, based on a subnet of receiving stations that will utilize optical communications technology in the post-2010 era. Two optical subnet concepts are presented that provide full line-of-sight coverage of the ecliptic, 24 hours a day, with high weather availability. The technical characteristics of the optical station and the user terminal are presented, as well as the effects of cloud cover, transmittance through the atmosphere, and background noise during daytime or nighttime operation on the communications link. In addition, this article identifies candidate geographic sites for the two network concepts and includes a link design for a hypothetical Pluto mission in 2015.

The Deep Space Network: The challenges of the next 20 years - The 21st century

NASA Technical Reports Server (NTRS)

Dumas, L. N.; Edwards, C. D.; Hall, J. R.; Posner, E. C.

1990-01-01

The Deep Space Network (DSN) has been the radio navigation and communications link between NASA's lunar and deep space missions for 30 years. In this paper, new mission opportunities over the next 20 years are discussed. The system design drivers and the DSN architectural concepts for those challenges are briefly considered.

Deep space communication - A one billion mile noisy channel

NASA Technical Reports Server (NTRS)

Smith, J. G.

1982-01-01

Deep space exploration is concerned with the study of natural phenomena in the solar system with the aid of measurements made at spacecraft on deep space missions. Deep space communication refers to communication between earth and spacecraft in deep space. The Deep Space Network is an earth-based facility employed for deep space communication. It includes a network of large tracking antennas located at various positions around the earth. The goals and achievements of deep space exploration over the past 20 years are discussed along with the broad functional requirements of deep space missions. Attention is given to the differences in space loss between communication satellites and deep space vehicles, effects of the long round-trip light time on spacecraft autonomy, requirements for the use of massive nuclear power plants on spacecraft at large distances from the sun, and the kinds of scientific return provided by a deep space mission. Problems concerning a deep space link of one billion miles are also explored.

Relocation of the Deep Space Network Maintenance Center

NASA Technical Reports Server (NTRS)

Beutler, K. F.

1981-01-01

The Jet Propulsion Laboratory maintains a Deep Space Network (DSN) maintenance center (DMC), whose task is to engineer and manage the repair and calibration program for the electronic and mechanical equipment used in the tracking stations located at Madrid, Spain, and Canberra, Australia. The DMC also manages the Goldstone complex maintenance facility (GCMF), whose task is to repair and calibrate the Goldstone electronic and mechanical equipment. The rationale for moving the facility to Barstow, California, and the benefits derived from the move are discussed.

Ramp time synchronization. [for NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Hietzke, W.

1979-01-01

A new method of intercontinental clock synchronization has been developed and proposed for possible use by NASA's Deep Space Network (DSN), using a two-way/three-way radio link with a spacecraft. Analysis of preliminary data indicates that the real-time method has an uncertainty of 0.6 microsec, and it is very likely that further work will decrease the uncertainty. Also, the method is compatible with a variety of nonreal-time analysis techniques, which may reduce the uncertainty down to the tens of nanosecond range.

Deep space network resource scheduling approach and application

NASA Technical Reports Server (NTRS)

Eggemeyer, William C.; Bowling, Alan

1987-01-01

Deep Space Network (DSN) resource scheduling is the process of distributing ground-based facilities to track multiple spacecraft. The Jet Propulsion Laboratory has carried out extensive research to find ways of automating this process in an effort to reduce time and manpower costs. This paper presents a resource-scheduling system entitled PLAN-IT with a description of its design philosophy. The PLAN-IT's current on-line usage and limitations in scheduling the resources of the DSN are discussed, along with potential enhancements for DSN application.

NASA's next generation all-digital deep space network breadboard receiver

NASA Technical Reports Server (NTRS)

Hinedi, Sami

1993-01-01

This paper describes the breadboard advanced receiver (ARX) that is currently being built for future use in NASA's deep space network (DSN). This receiver has unique requirements in having to operate with very weak signals from deep space probes and provide high quality telemetry and tracking data. The hybrid analog/digital receiver performs multiple functions including carrier, subcarrier and symbol synchronization. Tracking can be achieved for either residual, suppressed or hybrid carriers and for both sinusoidal and square wave subcarriers. System requirements are specified and a functional description of the ARX is presented. The various digital signal processing algorithms used are also discussed and illustrated with block diagrams. Other functions such as time tagged Doppler extraction and monitor/control are also discussed including acquisition algorithms and lock detection schemes.

Deep Space Network Antenna Monitoring Using Adaptive Time Series Methods and Hidden Markov Models

NASA Technical Reports Server (NTRS)

Smyth, Padhraic; Mellstrom, Jeff

1993-01-01

The Deep Space Network (DSN)(designed and operated by the Jet Propulsion Laboratory for the National Aeronautics and Space Administration (NASA) provides end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system.

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

NASA Technical Reports Server (NTRS)

Navarro, Robert

2006-01-01

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

Deep Space Networking Experiments on the EPOXI Spacecraft

NASA Technical Reports Server (NTRS)

Jones, Ross M.

2011-01-01

NASA's Space Communications & Navigation Program within the Space Operations Directorate is operating a program to develop and deploy Disruption Tolerant Networking [DTN] technology for a wide variety of mission types by the end of 2011. DTN is an enabling element of the Interplanetary Internet where terrestrial networking protocols are generally unsuitable because they rely on timely and continuous end-to-end delivery of data and acknowledgments. In fall of 2008 and 2009 and 2011 the Jet Propulsion Laboratory installed and tested essential elements of DTN technology on the Deep Impact spacecraft. These experiments, called Deep Impact Network Experiment (DINET 1) were performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. The DINET 1 software was installed on the backup software partition on the backup flight computer for DINET 1. For DINET 1, the spacecraft was at a distance of about 15 million miles (24 million kilometers) from Earth. During DINET 1 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. The first DINET 1 experiment successfully validated many of the essential elements of the DTN protocols. DINET 2 demonstrated: 1) additional DTN functionality, 2) automated certain tasks which were manually implemented in DINET 1 and 3) installed the ION SW on nodes outside of JPL. DINET 3 plans to: 1) upgrade the LTP convergence-layer adapter to conform to the international LTP CL specification, 2) add convergence-layer "stewardship" procedures and 3) add the BSP security elements [PIB & PCB]. This paper describes the planning and execution of the flight experiment and the

Gravitational wave searches using the DSN (Deep Space Network)

NASA Technical Reports Server (NTRS)

Nelson, S. J.; Armstrong, J. W.

1988-01-01

The Deep Space Network Doppler spacecraft link is currently the only method available for broadband gravitational wave searches in the 0.01 to 0.001 Hz frequency range. The DSN's role in the worldwide search for gravitational waves is described by first summarizing from the literature current theoretical estimates of gravitational wave strengths and time scales from various astrophysical sources. Current and future detection schemes for ground based and space based detectors are then discussed. Past, present, and future planned or proposed gravitational wave experiments using DSN Doppler tracking are described. Lastly, some major technical challenges to improve gravitational wave sensitivities using the DSN are discussed.

A distributed data base management system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Bryan, A. I.

1975-01-01

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

The Deep Space Network information system in the year 2000

NASA Technical Reports Server (NTRS)

Markley, R. W.; Beswick, C. A.

1992-01-01

The Deep Space Network (DSN), the largest, most sensitive scientific communications and radio navigation network in the world, is considered. Focus is made on the telemetry processing, monitor and control, and ground data transport architectures of the DSN ground information system envisioned for the year 2000. The telemetry architecture will be unified from the front-end area to the end user. It will provide highly automated monitor and control of the DSN, automated configuration of support activities, and a vastly improved human interface. Automated decision support systems will be in place for DSN resource management, performance analysis, fault diagnosis, and contingency management.

Deep Space Network Revitalization: Operations for the 21st Century

NASA Technical Reports Server (NTRS)

Statman, Joseph I.

1999-01-01

The National Aeronautics and Space Administration (NASA) supports unmanned space missions through a Deep Space Network (DSN) that is developed and operated by the Jet Propulsion Laboratory (JPL and its subcontractors. The DSN capabilities have been incrementally upgraded since its establishment in the late '50s and are delivered from three Deep Space Communications Complexes (DSCC's) near Goldstone, California, Madrid, Spain, and Canberra, Australia. At present each DSCC includes large antennas with diameters from 11 meters to 70 meters, that operate largely in S-band and X-band frequencies. In addition each DSCC includes all the associated electronics to receive and process the low-level telemetry signals, and radiate the necessary command with high-power transmitters. To accommodate support of the rapidly increasing number of missions by NASA and other space agencies, and to facilitate maintaining and increasing the level of service in a shrinking budget environment, JPL has initiated a bold road map with three key components: 1. A Network Simplification Project (NSP) to upgrade aging electronics, replacing them with modem commercially based components. NSP and related replacement tasks are projected to reduce the cost of operating the DSN by 50% relative to the 1997 levels. 2. Upgrade of all 34-m and 70-m antennas to provision of Ka-Band telemetry downlink capability, complemented by an existing X-band uplink capability. This will increase the effective telemetry downlink capacity by a factor of 4, without building any new antennas. 3. Establishment of an optical communications network to support for high data rate unmanned missions that cannot be accommodated with radiofrequency (RF) communications, as well as establish a path toward support of manned missions at Mars. In this paper we present the mission loading projected for 1998-2008 and the elements of the JPL road map that will enable supporting it with a reduced budget. Particular emphasis will be on

Microwave analog fiber-optic link for use in the deep space network

NASA Technical Reports Server (NTRS)

Logan, R. T., Jr.; Lutes, G. F.; Maleki, L.

1990-01-01

A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.

New tracking implementation in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.

2001-01-01

As part of the Network Simplification Project, the tracking system of the Deep Space Network is being upgraded. This upgrade replaces the discrete logic sequential ranging system with a system that is based on commercial Digital Signal Processor boards. The new implementation allows both sequential and pseudo-noise types of ranging. The other major change is a modernization of the data formatting. Previously, there were several types of interfaces, delivering both intermediate data and processed data (called 'observables'). All of these interfaces were bit-packed blocks, which do not allow for easy expansion, and many of these interfaces required knowledge of the specific hardware implementations. The new interface supports four classes of data: raw (direct from the measuring equipment), derived (the observable data), interferometric (multiple antenna measurements), and filtered (data whose values depend on multiple measurements). All of the measurements are reported at the sky frequency or phase level, so that no knowledge of the actual hardware is required. The data is formatted into Standard Formatted Data Units, as defined by the Consultative Committee for Space Data Systems, so that expansion and cross-center usage is greatly enhanced.

Automated monitor and control for deep space network subsystems

NASA Technical Reports Server (NTRS)

Smyth, P.

1989-01-01

The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel

2012-01-01

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Remote observing with NASA's Deep Space Network

NASA Astrophysics Data System (ADS)

Kuiper, T. B. H.; Majid, W. A.; Martinez, S.; Garcia-Miro, C.; Rizzo, J. R.

2012-09-01

The Deep Space Network (DSN) communicates with spacecraft as far away as the boundary between the Solar System and the interstellar medium. To make this possible, large sensitive antennas at Canberra, Australia, Goldstone, California, and Madrid, Spain, provide for constant communication with interplanetary missions. We describe the procedures for radioastronomical observations using this network. Remote access to science monitor and control computers by authorized observers is provided by two-factor authentication through a gateway at the Jet Propulsion Laboratory (JPL) in Pasadena. To make such observations practical, we have devised schemes based on SSH tunnels and distributed computing. At the very minimum, one can use SSH tunnels and VNC (Virtual Network Computing, a remote desktop software suite) to control the science hosts within the DSN Flight Operations network. In this way we have controlled up to three telescopes simultaneously. However, X-window updates can be slow and there are issues involving incompatible screen sizes and multi-screen displays. Consequently, we are now developing SSH tunnel-based schemes in which instrument control and monitoring, and intense data processing, are done on-site by the remote DSN hosts while data manipulation and graphical display are done at the observer's host. We describe our approaches to various challenges, our experience with what worked well and lessons learned, and directions for future development.

Deep space communication - Past, present, and future

NASA Technical Reports Server (NTRS)

Posner, E. C.; Stevens, R.

1984-01-01

This paper reviews the progress made in deep space communication from its beginnings until now, describes the development and applications of NASA's Deep Space Network, and indicates directions for the future. Limiting factors in deep space communication are examined using the upcoming Voyager encounter with Uranus, centered on the downlink telemetry from spacecraft to earth, as an example. A link calculation for Voyager at Uranus over Australia is exhibited. Seven basic deep space communication functions are discussed, and technical aspects of spacecraft communication equipment, ground antennas, and ground electronics and processing are considered.

The Deep Space Network stability analyzer

NASA Technical Reports Server (NTRS)

Breidenthal, Julian C.; Greenhall, Charles A.; Hamell, Robert L.; Kuhnle, Paul F.

1995-01-01

A stability analyzer for testing NASA Deep Space Network installations during flight radio science experiments is described. The stability analyzer provides realtime measurements of signal properties of general experimental interest: power, phase, and amplitude spectra; Allan deviation; and time series of amplitude, phase shift, and differential phase shift. Input ports are provided for up to four 100 MHz frequency standards and eight baseband analog (greater than 100 kHz bandwidth) signals. Test results indicate the following upper bounds to noise floors when operating on 100 MHz signals: -145 dBc/Hz for phase noise spectrum further than 200 Hz from carrier, 2.5 x 10(exp -15) (tau =1 second) and 1.5 x 10(exp -17) (tau =1000 seconds) for Allan deviation, and 1 x 10(exp -4) degrees for 1-second averages of phase deviation. Four copies of the stability analyzer have been produced, plus one transportable unit for use at non-NASA observatories.

Deep Space Network and Lunar Network Communication Coverage of the Moon

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2006-01-01

In this article, we describe the communication coverage analysis for the lunar network and the Earth ground stations. The first part of this article focuses on the direct communication coverage of the Moon from the Earth's ground stations. In particular, we assess the coverage performance of the Moon based on the existing Deep Space Network (DSN) antennas and the complimentary coverage of other potential stations at Hartebeesthoek, South Africa and at Santiago, Chile. We also address the coverage sensitivity based on different DSN antenna scenarios and their capability to provide single and redundant coverage of the Moon. The second part of this article focuses on the framework of the constrained optimization scheme to seek a stable constellation six relay satellites in two planes that not only can provide continuous communication coverage to any users on the Moon surface, but can also deliver data throughput in a highly efficient manner.

Temperature control simulation for a microwave transmitter cooling system. [deep space network

NASA Technical Reports Server (NTRS)

Yung, C. S.

1980-01-01

The thermal performance of a temperature control system for the antenna microwave transmitter (klystron tube) of the Deep Space Network antenna tracking system is discussed. In particular the mathematical model is presented along with the details of a computer program which is written for the system simulation and the performance parameterization. Analytical expressions are presented.

Iris Transponder-Communications and Navigation for Deep Space

NASA Technical Reports Server (NTRS)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Near Earth Architectural Options for a Future Deep Space Optical Communications Network

NASA Technical Reports Server (NTRS)

Edwards, B. L.; Liebrecht, P. E.; Fitzgerald, R. J.

2004-01-01

In the near future the National Aeronautics and Space Administration anticipates a significant increase in demand for long-haul communications services from deep space to Earth. Distances will range from 0.1 to 40 AU, with data rate requirements in the 1's to 1000's of Mbits/second. The near term demand is driven by NASA's Space Science Enterprise which wishes to deploy more capable instruments onboard spacecraft and increase the number of deep space missions. The long term demand is driven by missions with extreme communications challenges such as very high data rates from the outer planets, supporting sub-surface exploration, or supporting NASA's Human Exploration and Development of Space Enterprise beyond Earth orbit. Laser communications is a revolutionary communications technology that will dramatically increase NASA's ability to transmit information across the solar system. Lasercom sends information using beams of light and optical elements, such as telescopes and optical amplifiers, rather than RF signals, amplifiers, and antennas. This paper provides an overview of different network options at Earth to meet NASA's deep space lasercom requirements. It is based mainly on work done for the Mars Laser Communications Demonstration Project, a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory, California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL). It reports preliminary conclusions from the Mars Lasercom Study conducted at MIT/LL and on additional work done for the Tracking and Data Relay Satellite System Continuation Study at GSFC. A lasercom flight terminal will be flown on the Mars Telesat Orbiter (MTO) to be launched by NASA in 2009, and will be the first high rate deep space demonstration of this revolutionary technology.

Single-mode fiber systems for deep space communication network

NASA Technical Reports Server (NTRS)

Lutes, G.

1982-01-01

The present investigation is concerned with the development of single-mode optical fiber distribution systems. It is pointed out that single-mode fibers represent potentially a superior medium for the distribution of frequency and timing reference signals and wideband (400 MHz) IF signals. In this connection, single-mode fibers have the potential to improve the capability and precision of NASA's Deep Space Network (DSN). Attention is given to problems related to precise time synchronization throughout the DSN, questions regarding the selection of a transmission medium, and the function of the distribution systems, taking into account specific improvements possible by an employment of single-mode fibers.

The Deep Space Atomic Clock Mission

NASA Technical Reports Server (NTRS)

Ely, Todd A.; Koch, Timothy; Kuang, Da; Lee, Karen; Murphy, David; Prestage, John; Tjoelker, Robert; Seubert, Jill

2012-01-01

The Deep Space Atomic Clock (DSAC) mission will demonstrate the space flight performance of a small, low-mass, high-stability mercury-ion atomic clock with long term stability and accuracy on par with that of the Deep Space Network. The timing stability introduced by DSAC allows for a 1-Way radiometric tracking paradigm for deep space navigation, with benefits including increased tracking via utilization of the DSN's Multiple Spacecraft Per Aperture (MSPA) capability and full ground station-spacecraft view periods, more accurate radio occultation signals, decreased single-frequency measurement noise, and the possibility for fully autonomous on-board navigation. Specific examples of navigation and radio science benefits to deep space missions are highlighted through simulations of Mars orbiter and Europa flyby missions. Additionally, this paper provides an overview of the mercury-ion trap technology behind DSAC, details of and options for the upcoming 2015/2016 space demonstration, and expected on-orbit clock performance.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Estimating the Deep Space Network modification costs to prepare for future space missions by using major cost drivers

NASA Technical Reports Server (NTRS)

Remer, Donald S.; Sherif, Josef; Buchanan, Harry R.

1993-01-01

This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The paper focuses on the costs required to modify and/or enhance the DSN to prepare for future space missions. The model is a function of eight major mission cost drivers and estimates both the total cost and the annual costs of a similar future space mission. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Estimates derived from the model are tested against actual cost data for two independent missions, Viking and Mariner Jupiter/Saturn (MJS).

Ka-band (32 GHz) allocations for deep space

NASA Technical Reports Server (NTRS)

Degroot, N. F.

1987-01-01

At the 1979 World Administrative Conference, two new bands were allocated for deep space telecommunications: 31.8 to 32.3 GHz, space-to-Earth, and 34.2 to 34.7 GHz, Earth-to-space. These bands provide opportunity for further development of the Deep Space Network and its support of deep space research. The history of the process by which JPL/NASA developed the rationale, technical background, and statement of requirement for the bands are discussed. Based on this work, United States proposals to the conference included the bands, and subsequent U.S. and NASA participation in the conference led to successful allocations for deep space telecommunications in the 30 GHz region of the spectrum. A detailed description of the allocations is included.

The 26-meter antenna s-x conversion project. [Deep Space Network

NASA Technical Reports Server (NTRS)

1982-01-01

Programmatic and management aspects of converting an existing 26-meter S-band subnet to a 34-meter S- and X-band subnet of the Deep Space Network are described. The stations involved were DSS 12 near Barstow, DSS 44 in Australia, and DSS 62 in Spain. The main subsystems affected by the conversion were the antenna mechanical, antenna microwave, and receiver-exciter. Antenna mechanial modifications and electronic additions and changes are described. The design and analysis of critical areas are considered and antenna performance is discussed.

Deep Space Network Radiometric Remote Sensing Program

NASA Technical Reports Server (NTRS)

Walter, Steven J.

1994-01-01

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid, and precipitation, emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band because communication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of water vapor-induced propagation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remote sensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity wave experiments, and radio science missions. During 1993, WVRs provided data for propagation model development, supported planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily

The Deep Impact Network Experiment Operations Center

NASA Technical Reports Server (NTRS)

Torgerson, J. Leigh; Clare, Loren; Wang, Shin-Ywan

2009-01-01

Delay/Disruption Tolerant Networking (DTN) promises solutions in solving space communications challenges arising from disconnections as orbiters lose line-of-sight with landers, long propagation delays over interplanetary links, and other phenomena. DTN has been identified as the basis for the future NASA space communications network backbone, and international standardization is progressing through both the Consultative Committee for Space Data Systems (CCSDS) and the Internet Engineering Task Force (IETF). JPL has developed an implementation of the DTN architecture, called the Interplanetary Overlay Network (ION). ION is specifically implemented for space use, including design for use in a real-time operating system environment and high processing efficiency. In order to raise the Technology Readiness Level of ION, the first deep space flight demonstration of DTN is underway, using the Deep Impact (DI) spacecraft. Called the Deep Impact Network (DINET), operations are planned for Fall 2008. An essential component of the DINET project is the Experiment Operations Center (EOC), which will generate and receive the test communications traffic as well as "out-of-DTN band" command and control of the DTN experiment, store DTN flight test information in a database, provide display systems for monitoring DTN operations status and statistics (e.g., bundle throughput), and support query and analyses of the data collected. This paper describes the DINET EOC and its value in the DTN flight experiment and potential for further DTN testing.

Preliminary design work on a DSN VLBI correlator. [Deep Space Network

NASA Technical Reports Server (NTRS)

Lushbaugh, W. A.; Layland, J. W.

1978-01-01

The Deep Space Network is in the process of fielding high-density digital instrumentation recorders for support of the Pioneer Venus 1978 entry experiment and other related tasks. It has long been obvious that these recorders would also serve well as the recording medium for very long base interferometry (VLBI) experiments with relatively weak radio sources, provided that a suitable correlation processor for these tape recordings could be established. The overall design and current status of a VLBI correlator designed to mate with these tape recorders are described.

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

An OSI architecture for the deep space network

NASA Technical Reports Server (NTRS)

Heuser, W. Randy; Cooper, Lynne P.

1993-01-01

The flexibility and robustness of a monitor and control system are a direct result of the underlying inter-processor communications architecture. A new architecture for monitor & Control at the Deep Space Network Communications Complexes has been developed based on the Open System Interconnection (OSI) standards. The suitability of OSI standards for DSN M&C has been proven in the laboratory. The laboratory success has resulted in choosing an OSI-based architecture for DSS-13 M&C. DSS-13 is the DSN experimental station and is not part of the 'operational' DSN; it's role is to provide an environment to test new communications concepts can be tested and conduct unique science experiments. Therefore, DSS-13 must be robust enough to support operational activities, while also being flexible enough to enable experimentation. This paper describes the M&C architecture developed for DSS-13 and the results from system and operational testing.

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

NASA Technical Reports Server (NTRS)

Lu, Thomas; Pham, Timothy; Liao, Jason

2011-01-01

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

Deep Space Network Capabilities for Receiving Weak Probe Signals

NASA Technical Reports Server (NTRS)

Asmar, Sami; Johnston, Doug; Preston, Robert

2005-01-01

Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

A federated information management system for the Deep Space Network. M.S. Thesis - Univ. of Southern California

NASA Technical Reports Server (NTRS)

Dobinson, E.

1982-01-01

General requirements for an information management system for the deep space network (DSN) are examined. A concise review of available database management system technology is presented. It is recommended that a federation of logically decentralized databases be implemented for the Network Information Management System of the DSN. Overall characteristics of the federation are specified, as well as reasons for adopting this approach.

NASA Near Earth Network (NEN), Deep Space Network (DSN) and Space Network (SN) Support of CubeSat Communications

NASA Technical Reports Server (NTRS)

Schaire, Scott H.; Altunc, Serhat; Bussey, George; Shaw, Harry; Horne, Bill; Schier, Jim

2015-01-01

There has been a historical trend to increase capability and drive down the Size, Weight and Power (SWAP) of satellites and that trend continues today. Small satellites, including systems conforming to the CubeSat specification, because of their low launch and development costs, are enabling new concepts and capabilities for science investigations across multiple fields of interest to NASA. NASA scientists and engineers across many of NASAs Mission Directorates and Centers are developing exciting CubeSat concepts and welcome potential partnerships for CubeSat endeavors. From a communications and tracking point of view, small satellites including CubeSats are a challenge to coordinate because of existing small spacecraft constraints, such as limited SWAP and attitude control, low power, and the potential for high numbers of operational spacecraft. The NASA Space Communications and Navigation (SCaN) Programs Near Earth Network (NEN), Deep Space Network (DSN) and the Space Network (SN) are customer driven organizations that provide comprehensive communications services for space assets including data transport between a missions orbiting satellite and its Mission Operations Center (MOC). The NASA NEN consists of multiple ground antennas. The SN consists of a constellation of geosynchronous (Earth orbiting) relay satellites, named the Tracking and Data Relay Satellite System (TDRSS). The DSN currently makes available 13 antennas at its three tracking stations located around the world for interplanetary communication. The presentation will analyze how well these space communication networks are positioned to support the emerging small satellite and CubeSat market. Recognizing the potential support, the presentation will review the basic capabilities of the NEN, DSN and SN in the context of small satellites and will present information about NEN, DSN and SN-compatible flight radios and antenna development activities at the Goddard Space Flight Center (GSFC) and across

Long-range planning cost model for support of future space missions by the deep space network

NASA Technical Reports Server (NTRS)

Sherif, J. S.; Remer, D. S.; Buchanan, H. R.

1990-01-01

A simple model is suggested to do long-range planning cost estimates for Deep Space Network (DSP) support of future space missions. The model estimates total DSN preparation costs and the annual distribution of these costs for long-range budgetary planning. The cost model is based on actual DSN preparation costs from four space missions: Galileo, Voyager (Uranus), Voyager (Neptune), and Magellan. The model was tested against the four projects and gave cost estimates that range from 18 percent above the actual total preparation costs of the projects to 25 percent below. The model was also compared to two other independent projects: Viking and Mariner Jupiter/Saturn (MJS later became Voyager). The model gave cost estimates that range from 2 percent (for Viking) to 10 percent (for MJS) below the actual total preparation costs of these missions.

The role of the deep space network's frequency and timing system in the detection of gravitational waves

NASA Technical Reports Server (NTRS)

Mankins, J. C.

1982-01-01

A review of the Deep Space Network's (DSN) use of precision Doppler-tracking of deep space vehicles is presented. The review emphasizes operational and configurational aspects and considers: the projected configuration of the DSN's frequency and timing system; the environment within the DSN provided by the precision atomic standards within the frequency and timing system--both current and projected; and the general requirements placed on the DSN and the frequency and timing system for both the baseline and the nominal gravitational wave experiments. A comment is made concerning the current probability that such an experiment will be carried out in the foreseeable future.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Evolving Deep Networks Using HPC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Steven R.; Rose, Derek C.; Johnston, Travis

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus oftenmore » a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.« less

The Future of NASA's Deep Space Network and Applications to Planetary Probe Missions

NASA Technical Reports Server (NTRS)

Deutsch, Leslie J.; Preston, Robert A.; Vrotsos, Peter

2010-01-01

NASA's Deep Space Network (DSN) has been an invaluable tool in the world's exploration of space. It has served the space-faring community for more than 45 years. The DSN has provided a primary communication pathway for planetary probes, either through direct- to-Earth links or through intermediate radio relays. In addition, its radiometric systems are critical to probe navigation and delivery to target. Finally, the radio link can also be used for direct scientific measurement of the target body ('radio science'). This paper will examine the special challenges in supporting planetary probe missions, the future evolution of the DSN and related spacecraft technology, the advantages and disadvantages of radio relay spacecraft, and the use of the DSN radio links for navigation and scientific measurements.

(abstract) Deep Space Network Radiometric Remote Sensing Program

NASA Technical Reports Server (NTRS)

Walter, Steven J.

1994-01-01

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid,and precipitation , emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band becausecommunication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of watervapor-induced prop agation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remote sensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity waveexperiments, and r adio science missions. During 1993, WVRs provided data for propagation mode development, supp orted planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily

Coherent Frequency Reference System for the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Tucker, Blake C.; Lauf, John E.; Hamell, Robert L.; Gonzaler, Jorge, Jr.; Diener, William A.; Tjoelker, Robert L.

2010-01-01

The NASA Deep Space Network (DSN) requires state-of-the-art frequency references that are derived and distributed from very stable atomic frequency standards. A new Frequency Reference System (FRS) and Frequency Reference Distribution System (FRD) have been developed, which together replace the previous Coherent Reference Generator System (CRG). The FRS and FRD each provide new capabilities that significantly improve operability and reliability. The FRS allows for selection and switching between frequency standards, a flywheel capability (to avoid interruptions when switching frequency standards), and a frequency synthesis system (to generate standardized 5-, 10-, and 100-MHz reference signals). The FRS is powered by redundant, specially filtered, and sustainable power systems and includes a monitor and control capability for station operations to interact and control the frequency-standard selection process. The FRD receives the standardized 5-, 10-, and 100-MHz reference signals and distributes signals to distribution amplifiers in a fan out fashion to dozens of DSN users that require the highly stable reference signals. The FRD is also powered by redundant, specially filtered, and sustainable power systems. The new DSN Frequency Distribution System, which consists of the FRS and FRD systems described here, is central to all operational activities of the NASA DSN. The frequency generation and distribution system provides ultra-stable, coherent, and very low phase-noise references at 5, l0, and 100 MHz to between 60 and 100 separate users at each Deep Space Communications Complex.

Application of the Deep Space Network (DSN) to the testing of general relativity

NASA Technical Reports Server (NTRS)

Anderson, J. D.; Levy, G. S.; Renzetti, N. A.

1986-01-01

The NASA Deep Space Network, a precision telecommunications and radio navigation facility, is described in detail. The first spacecraft relativity test with Mariner 6 and Mariner 7 at solar conjunction is discussed as well as more accurate tests using the Mariner 9 anchored to Mars. Consideration is also given to solar system tests of relativistic celestial mechanics and future prospects. It is noted that the NASA Mars Observer orbital mission is under development and is expected to reach Mars in 1991.

Publications of the Jet Propulsion Laboratory, January through December 1974. [deep space network, Apollo project, information theory, and space exploration

NASA Technical Reports Server (NTRS)

1975-01-01

Formalized technical reporting is described and indexed, which resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. The five classes of publications included are technical reports, technical memorandums, articles from the bimonthly Deep Space Network Progress Report, special publications, and articles published in the open literature. The publications are indexed by author, subject, and publication type and number.

Stochastic availability analysis of operational data systems in the Deep Space Network

NASA Technical Reports Server (NTRS)

Issa, T. N.

1991-01-01

Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.

Enhancing the Radio Astronomy Capabilities at NASA's Deep Space Network

NASA Astrophysics Data System (ADS)

Lazio, Joseph; Teitelbaum, Lawrence; Franco, Manuel M.; Garcia-Miro, Cristina; Horiuchi, Shinji; Jacobs, Christopher; Kuiper, Thomas; Majid, Walid

2015-08-01

NASA's Deep Space Network (DSN) is well known for its role in commanding and communicating with spacecraft across the solar system that produce a steady stream of new discoveries in Astrophysics, Heliophysics, and Planetary Science. Equipped with a number of large antennas distributed across the world, the DSN also has a history of contributing to a number of leading radio astronomical projects. This paper summarizes a number of enhancements that are being implemented currently and that are aimed at increasing its capabilities to engage in a wide range of science observations. These enhancements include* A dual-beam system operating between 18 and 27 GHz (~ 1 cm) capable of conducting a variety of molecular line observations, searches for pulsars in the Galactic center, and continuum flux density (photometry) of objects such as nearby protoplanetary disks* Enhanced spectroscopy and pulsar processing backends for use at 1.4--1.9 GHz (20 cm), 18--27 GHz (1 cm), and 38--50 GHz (0.7 cm)* The DSN Transient Observatory (DTN), an automated, non-invasive backend for transient searching* Larger bandwidths (>= 0.5 GHz) for pulsar searching and timing; and* Improved data rates (2048 Mbps) and better instrumental response for very long baseline interferometric (VLBI) observations with the new DSN VLBI processor (DVP), which is providing unprecedented sensitivity for maintenance of the International Celestial Reference Frame (ICRF) and development of future versions.One of the results of these improvements is that the 70~m Deep Space Station 43 (DSS-43, Tidbinbilla antenna) is now the most sensitive radio antenna in the southern hemisphere. Proposals to use these systems are accepted from the international community.Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics & Space Administration.

The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

NASA Technical Reports Server (NTRS)

Webb, W. A.

1978-01-01

The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

Recycling used lubricating oil at the deep space stations

NASA Technical Reports Server (NTRS)

Koh, J. L.

1981-01-01

A comparison is made of the lubricating oil recycling methods used in the Deep Space Station 43 test and the basic requirements which could favor recycling of oil for continuous reuse. The basic conditions for successful recycling are compared to the conditions that exist in the Deep Space Network (DSN). This comparison shows that to recycle used oil in the DSN would not only be expensive but also nonproductive.

Uplink-Downlink: A History of the Deep Space Network, 1957-1997

NASA Technical Reports Server (NTRS)

Mudgway, Douglas J.; Launius, Roger (Technical Monitor)

2001-01-01

In these pages, the informed reader will discover a simple description of what the Deep Space Network (DSN) is about, and how it works an aspect of NASA's spectacular planetary program that seldom found its way into the popular media coverage of those major events. Future historical researchers will find a complete record of the origin and birth of the DSN, its subsequent development and expansion over the ensuing four decades, and a description of the way in which the DSN was used to fulfill the purpose for which it was created. At the same time, the specialist reader is provided with an abundant source of technical references that address every aspect of the advanced telecommunications technology on which the success of the DSN depended. And finally, archivists, educators, outreach managers, and article writers will have ready recourse to the inner workings of the DSN and how they related to the more publicly visible events of the planetary space program.

Proposed Array-based Deep Space Network for NASA

NASA Technical Reports Server (NTRS)

Bagri, Durgadas S.; Statman, Joseph I.; Gatti, Mark S.

2007-01-01

The current assets of the Deep Space Network (DSN) of the National Aeronautics and Space Administration (NASA), especially the 70-m antennas, are aging and becoming less reliable. Furthermore, they are expensive to operate and difficult to upgrade for operation at Ka-band (321 GHz). Replacing them with comparable monolithic large antennas would be expensive. On the other hand, implementation of similar high-sensitivity assets can be achieved economically using an array-based architecture, where sensitivity is measured by G/T, the ratio of antenna gain to system temperature. An array-based architecture would also provide flexibility in operations and allow for easy addition of more G/T whenever required. Therefore, an array-based plan of the next-generation DSN for NASA has been proposed. The DSN array would provide more flexible downlink capability compared to the current DSN for robust telemetry, tracking and command services to the space missions of NASA and its international partners in a cost effective way. Instead of using the array as an element of the DSN and relying on the existing concept of operation, we explore a broader departure in establishing a more modern concept of operations to reduce the operations costs. This paper presents the array-based architecture for the next generation DSN. It includes system block diagram, operations philosophy, user's view of operations, operations management, and logistics like maintenance philosophy and anomaly analysis and reporting. To develop the various required technologies and understand the logistics of building the array-based lowcost system, a breadboard array of three antennas has been built. This paper briefly describes the breadboard array system and its performance.

Software for Allocating Resources in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wang, Yeou-Fang; Borden, Chester; Zendejas, Silvino; Baldwin, John

2003-01-01

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Experimental Evaluation of Optically Polished Aluminum Panels on the Deep Space Network's 34 Meter Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, V.

2011-01-01

The potential development of large aperture ground?based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation is to polish the aluminum reflector panels of 34?meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by state of?the?art polished aluminum panels. Theoretical analyses of receiving antenna pointing, temporal synchronization and data detection have been addressed in previous papers. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. Two polished aluminum panels (a standard DSN panel polished to high reflectance, and a custom designed aluminum panel with much better surface quality) have been mounted on the 34 meter research antenna at Deep?Space Station 13 (DSS?13), and a remotely controlled CCD camera with a large CCD sensor in a weather?proof container has been installed next to the subreflector, pointed directly at the custom polished panel. The point?spread function (PSF) generated by the Vertex polished panel has been determined to be smaller than the sensor of the CCD camera, hence a detailed picture of the PSF can be obtained every few seconds, and the sensor array data processed to determine the center of the intensity distribution. In addition to estimating the center coordinates, expected communications performance can also been evaluated with the recorded data. The results of preliminary pointing experiments with the Vertex polished panel receiver using the planet Jupiter to simulate the PSF generated by a deep?space optical transmitter are presented and discussed in this paper.

Architectural Options for a Future Deep Space Optical Communications Network

NASA Technical Reports Server (NTRS)

Edwards, B. L.; Benjamin, T.; Scozzafava, J.; Khatri, F.; Sharma, J.; Parvin, B.; Liebrecht, P. E.; Fitzgerald, R. J.

2004-01-01

This paper provides an overview of different options at Earth to provide Deep Space optical communication services. It is based mainly on work done for the Mars Laser Communications Demonstration (MLCD) Project, a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory, California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL). It also reports preliminary conclusions from the Tracking and Data Relay Satellite System Continuation Study at GSFC. A lasercom flight terminal will be flown on the Mars Telecommunications Orbiter (MTO) to be launched by NASA in 2009, and will be the first high rate deep space demonstration of this revolutionary technology.

Software cost/resource modeling: Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. J.

1980-01-01

A parametric software cost estimation model prepared for JPL deep space network (DSN) data systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models, such as those of the General Research Corporation, Doty Associates, IBM (Walston-Felix), Rome Air Force Development Center, University of Maryland, and Rayleigh-Norden-Putnam. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software lifecycle statistics. The estimation model output scales a standard DSN work breakdown structure skeleton, which is then input to a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

DCMDN: Deep Convolutional Mixture Density Network

NASA Astrophysics Data System (ADS)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

Challenges for deep space communications in the 1990s

NASA Technical Reports Server (NTRS)

Dumas, Larry N.; Hornstein, Robert M.

1991-01-01

The discussion of NASA's Deep Space Network (DSN) examines the evolving character of aerospace missions and the corresponding changes in the DSN architecture. Deep space missions are reviewed, and it is noted that the two 34-m and the 70-m antenna subnets of the DSN are heavily loaded and more use is expected. High operational workload and the challenge of network cross-support are the design drivers for a flexible DSN architecture configuration. Incorporated in the design are antenna arraying for aperture augmentation, beam-waveguide antennas for frequency agility, and connectivity with non-DSN sites for cross-support. Compatibility between spacecraft and ground-facility designs is important for establishing common international standards of communication and data-system specification.

Report on the survey for electrostatic discharges on Mars using NASA's Deep Space Network (DSN)

NASA Astrophysics Data System (ADS)

Arabshahi, S.; Majid, W.; Geldzahler, B.; Kocz, J.; Schulter, T.; White, L.

2017-12-01

Mars atmosphere has strong dust activity. It is suggested that the larger regional storms are capable of producing electric fields large enough to initiate electrostatic discharges. The storms have charging process similar to terrestrial dust devils and have hot cores and complicated vortex winds similar to terrestrial thunderstorms. However, due to uncertainties in our understanding of the electrical environment of the storms and absence of related in-situ measurements, the existence (or non-existence) of such electrostatic discharges on the planet is yet to be confirmed. Knowing about the electrical activity on Mars is essential for future human explorations of the planet. We have recently launched a long-term monitoring campaign at NASA's Madrid Deep Space Communication Complex (MDSCC) to search for powerful discharges on Mars. The search occurs during routine tracking of Mars orbiting spacecraft by Deep Space Network (DSN) radio telescope. In this presentation, we will report on the result of processing and analysis of the data from the first six months of our campaign.

Cryogenic, low-noise high electron mobility transistor amplifiers for the Deep Space Network

NASA Technical Reports Server (NTRS)

Bautista, J. J.

1993-01-01

The rapid advances recently achieved by cryogenically cooled high electron mobility transistor (HEMT) low-noise amplifiers (LNA's) in the 1- to 10-GHz range are making them extremely competitive with maser amplifiers. In order to address future spacecraft navigation, telemetry, radar, and radio science needs, the Deep Space Network is investing both maser and HEMT amplifiers for its Ka-band (32-GHz) downlink capability. This article describes the current state cryogenic HEMT LNA development at Ka-band for the DSN. Noise performance results at S-band (2.3 GHz) and X-band (8.5 GHz) for HEMT's and masers are included for completeness.

Representational Distance Learning for Deep Neural Networks

PubMed Central

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

Representational Distance Learning for Deep Neural Networks.

PubMed

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor A.

2012-01-01

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

Deep Space Wide Area Search Strategies

NASA Astrophysics Data System (ADS)

Capps, M.; McCafferty, J.

There is an urgent need to expand the space situational awareness (SSA) mission beyond catalog maintenance to providing near real-time indications and warnings of emerging events. While building and maintaining a catalog of space objects is essential to SSA, this does not address the threat of uncatalogued and uncorrelated deep space objects. The Air Force therefore has an interest in transformative technologies to scan the geostationary (GEO) belt for uncorrelated space objects. Traditional ground based electro-optical sensors are challenged in simultaneously detecting dim objects while covering large areas of the sky using current CCD technology. Time delayed integration (TDI) scanning has the potential to enable significantly larger coverage rates while maintaining sensitivity for detecting near-GEO objects. This paper investigates strategies of employing TDI sensing technology from a ground based electro-optical telescope, toward providing tactical indications and warnings of deep space threats. We present results of a notional wide area search TDI sensor that scans the GEO belt from three locations: Maui, New Mexico, and Diego Garcia. Deep space objects in the NASA 2030 debris catalog are propagated over multiple nights as an indicative data set to emulate notional uncatalogued near-GEO orbits which may be encountered by the TDI sensor. Multiple scan patterns are designed and simulated, to compare and contrast performance based on 1) efficiency in coverage, 2) number of objects detected, and 3) rate at which detections occur, to enable follow-up observations by other space surveillance network (SSN) sensors. A step-stare approach is also modeled using a dedicated, co-located sensor notionally similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) tower. Equivalent sensitivities are assumed. This analysis quantifies the relative benefit of TDI scanning for the wide area search mission.

NASA Integrated Space Communications Network

NASA Technical Reports Server (NTRS)

Tai, Wallace; Wright, Nate; Prior, Mike; Bhasin, Kul

2012-01-01

The NASA Integrated Network for Space Communications and Navigation (SCaN) has been in the definition phase since 2010. It is intended to integrate NASA s three existing network elements, i.e., the Space Network, Near Earth Network, and Deep Space Network, into a single network. In addition to the technical merits, the primary purpose of the Integrated Network is to achieve a level of operating cost efficiency significantly higher than it is today. Salient features of the Integrated Network include (a) a central system element that performs service management functions and user mission interfaces for service requests; (b) a set of common service execution equipment deployed at the all stations that provides return, forward, and radiometric data processing and delivery capabilities; (c) the network monitor and control operations for the entire integrated network are conducted remotely and centrally at a prime-shift site and rotating among three sites globally (a follow-the-sun approach); (d) the common network monitor and control software deployed at all three network elements that supports the follow-the-sun operations.

The Deep Space Network in the Common Platform Era: A Prototype Implementation at DSS-13

NASA Technical Reports Server (NTRS)

Davarian, F.

2013-01-01

To enhance NASA's Deep Space Network (DSN), an effort is underway to improve network performance and simplify its operation and maintenance. This endeavor, known as the "Common Platform," has both short- and long-term objectives. The long-term work has not begun yet; however, the activity to realize the short-term goals has started. There are three goals for the long-term objective: 1. Convert the DSN into a digital network where signals are digitized at the output of the down converters at the antennas and are distributed via a digital IF switch to the processing platforms. 2. Employ a set of common hardware for signal processing applications, e.g., telemetry, tracking, radio science and Very Long Baseline Interferometry (VLBI). 3. Minimize in-house developments in favor of purchasing commercial off-the-shelf (COTS) equipment. The short-term goal is to develop a prototype of the above at NASA's experimental station known as DSS-13. This station consists of a 34m beam waveguide antenna with cryogenically cooled amplifiers capable of handling deep space research frequencies at S-, X-, and Ka-bands. Without the effort at DSS-13, the implementation of the long-term goal can potentially be risky because embarking on the modification of an operational network without prior preparations can, among other things, result in unwanted service interruptions. Not only are there technical challenges to address, full network implementation of the Common Platform concept includes significant cost uncertainties. Therefore, a limited implementation at DSS-13 will contribute to risk reduction. The benefits of employing common platforms for the DSN are lower cost and improved operations resulting from ease of maintenance and reduced number of spare parts. Increased flexibility for the user is another potential benefit. This paper will present the plans for DSS-13 implementation. It will discuss key issues such as the Common Platform architecture, choice of COTS equipment, and the

The Deep Space Network: Noise temperature concepts, measurements, and performance

NASA Technical Reports Server (NTRS)

Stelzried, C. T.

1982-01-01

The use of higher operational frequencies is being investigated for improved performance of the Deep Space Network. Noise temperature and noise figure concepts are used to describe the noise performance of these receiving systems. The ultimate sensitivity of a linear receiving system is limited by the thermal noise of the source and the quantum noise of the receiver amplifier. The atmosphere, antenna and receiver amplifier of an Earth station receiving system are analyzed separately and as a system. Performance evaluation and error analysis techniques are investigated. System noise temperature and antenna gain parameters are combined to give an overall system figure of merit G/T. Radiometers are used to perform radio ""star'' antenna and system sensitivity calibrations. These are analyzed and the performance of several types compared to an idealized total power radiometer. The theory of radiative transfer is applicable to the analysis of transmission medium loss. A power series solution in terms of the transmission medium loss is given for the solution of the noise temperature contribution.

Remote Operations of the Deep Space Network Radio Science Subsystem

NASA Astrophysics Data System (ADS)

Caetta, J.; Asmar, S.; Abbate, S.; Connally, M.; Goltz, G.

1998-04-01

The capability for scientists to remotely control systems located at the Deep Space Network facilities only recently has been incorporated in the design and implementation of new equipment. However, time lines for the implementation, distribution, and operational readiness of such systems can extend much farther into the future than the users can wait. The Radio Science Systems Group was faced with just that circumstance; new hardware was not scheduled to become operational for several years, but the increasing number of experiments and configurations for Cassini, Galileo, Mars missions, and other flight projects made that time frame impractical because of the associated increasing risk of not acquiring critical data. Therefore, a method of interfacing with the current radio science subsystem has been developed and used with a high degree of success, although with occasional problems due to this capability not having been originally designed into the system. This article discusses both the method and the problems involved in integrating this new (remote) method of control with a legacy system.

Delta-DOR: The One-Nanoradian Navigation Measurement System of the Deep Space Network --- History, Architecture, and Componentry

NASA Astrophysics Data System (ADS)

Curkendall, D. W.; Border, J. S.

2013-05-01

Doppler and range data alone supported navigation for the earliest missions into deep space. Though extremely precise in line-of-sight coordinates, the navigation system built on these data had a weakness for determining the spacecraft declination component. To address this, the Deep Space Network (DSN) developed the capability for very long baseline interferometry measurements beginning in the late 1970s. Both the implementation of the interferometric system and the importance of such measurements to flight projects have evolved significantly over the past three decades. Innovations introduced through research and development programs have led to continuous improvements in performance. Today's system provides data approaching one-nanoradian accuracy with reliability of 98 percent. This article provides an overview of the development and use of interferometric tracking techniques in the DSN starting with the Viking era and continuing with a description of the current system and its planned use to support interplanetary cruise navigation of the Mars Science Laboratory spacecraft.

Deep Space Quantum Link

NASA Astrophysics Data System (ADS)

Mohageg, M.; Strekalov, D.; Dolinar, S.; Shaw, M.; Yu, N.

2018-02-01

The Deep Space Quantum Link will test the effects of gravity on quantum systems, test the non-locality of quantum states at deep space distances, and perform long distance quantum teleportation to an Earth-based receiver.

Study of Jovian synchrotron emission with the NASA's Deep Space Network for Juno mission

NASA Astrophysics Data System (ADS)

Garcia-Miro, Cristina; Horiuchi, Shinji; Levin, Steve; Orton, Glenn S.; Bolton, Scott; Jauncey, David; Kuiper, T. B. H.; Teitelbaum, Lawrence

2016-10-01

We are monitoring Jupiter's synchrotron emission with the purpose of connecting the measurements of the Juno mission's MicroWave Radiometer (MWR) experiment to the historical baseline of non-thermal emission, using NASA's Deep Space Network (DSN). The DSN has the most sensitive network of antennas dedicated to tracking spacecraft that are exploring deep space, whose state-of-the-art receivers are considered among the best radio telescopes in the world. Availability for radio astronomy studies is subject to demand from space projects using the DSN. These antennas have previously contributed to the study of the Jovian non-thermal synchroton emission [1].NASA's New Frontiers Juno mission was placed into a nominal orbit on the 4th of July, 2016, allowing it to begin a detailed exploration of Jupiter. Among its scientific objectives is the characterization and exploration of the 3D structure of Jupiter's polar magnetosphere and auroras. It is important to provide a means to connect these detailed MWR measurements with the historical record of synchrotron emission. Ideally, these measurements should be performed on a regular basis during the whole extent of the mission. The DSN has the advantage of being able to perform uninterrupted 24-hour observations using antennas from the different complexes located in USA, Australia and Spain.Additionally, this monitoring program links with and validates the Jupiter observations currently performed by the triplet of educational programs GAVRT, STARS and PARTNeR in USA, Australia and Spain, respectively. These educational programs are partially supported by the DSN and use some of its antennas for teaching purposes, involving students in professional research and exploration.We will describe the DSN single-dish continuum observations of Jupiter in detail: the antennas, receivers and the equipment used to collect the data, the observing procedure, and the data-reduction process. Preliminary results of the Jupiter beaming curve will

Deep Space Network Antenna Logic Controller

NASA Technical Reports Server (NTRS)

Ahlstrom, Harlow; Morgan, Scott; Hames, Peter; Strain, Martha; Owen, Christopher; Shimizu, Kenneth; Wilson, Karen; Shaller, David; Doktomomtaz, Said; Leung, Patrick

2007-01-01

The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.

A growth path for deep space communications

NASA Technical Reports Server (NTRS)

Layland, J. W.; Smith, J. G.

1987-01-01

Increased Deep Space Network (DPN) receiving capability far beyond that now available for Voyager is achievable through a mix of increased antenna aperture and increased frequency of operation. In this note a sequence of options are considered: adding midsized antennas for arraying with the existing network at X-band; converting to Ka-band and adding array elements; augmenting the DSN with an orbiting Ka-band station; and augmenting the DSN with an optical receiving capability, either on the ground or in space. Costs of these options are compared as means of achieving significantly increased receiving capability. The envelope of lowest costs projects a possible path for moving from X-band to Ka-band and thence to optical frequencies, and potentially for moving from ground-based to space-based apertures. The move to Ka-band is clearly of value now, with development of optical communications technology a good investment for the future.

Amateur Radio Communications with a Deep Space Probe (Yes, It's Possible)

NASA Astrophysics Data System (ADS)

Cudnik, Brian; Rahman, Mahmudur; Saganti, Seth; Erickson, Gary M.; Saganti, Premkumar

2015-05-01

Prairie View A&M University through the collaboration with NASA-Johnson Space Center has partnered with the Kyushu Institute of Technology (KIT), Japan and developed a payload for the Shinen-2 spacecraft that was launched from Japan on December 3, 2014 as part of the Hayabusa2 mission. The main purpose of the Shinen-2 spacecraft is deep space communication experiment to test the feasibility of deep-space radio communications from the spacecraft to Earth without the need of the Deep Space Network (DSN) of NASA. This presents an opportunity to the wider community of amateur astronomers, ham radio operators, and other research personnel in that they will have the opportunity to work with deep space communication such as Shinen-2 spacecraft. It should be possible to detect a signal as an increased strength from Shinen-2 spacecraft at a rest frequency of 437.385 MHz, using commercially available equipment procured at low-cost, when the spacecraft approaches to within 3,000,000 km of the Earth during December 2015.

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel; Arroyo, Belinda; Sorensen, Sugi; Tay, Peter; Carruth, Butch; Coffman, Adam; Wallace, Mike

2012-01-01

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. S(sup 3) has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S(sup 3) supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S(sup 3) system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Maintenance of time and frequency in the Jet Propulsion Laboratory's Deep Space Network using the Global Positioning System

NASA Technical Reports Server (NTRS)

Clements, P. A.; Borutzki, S. E.; Kirk, A.

1984-01-01

The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, must maintain time and frequency within specified limits in order to accurately track the spacecraft engaged in deep space exploration. Various methods are used to coordinate the clocks among the three tracking complexes. These methods include Loran-C, TV Line 10, Very Long Baseline Interferometry (VLBI), and the Global Positioning System (GPS). Calculations are made to obtain frequency offsets and Allan variances. These data are analyzed and used to monitor the performance of the hydrogen masers that provide the reference frequencies for the DSN Frequency and Timing System (DFT). Areas of discussion are: (1) a brief history of the GPS timing receivers in the DSN, (2) a description of the data and information flow, (3) data on the performance of the DSN master clocks and GPS measurement system, and (4) a description of hydrogen maser frequency steering using these data.

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

NASA Technical Reports Server (NTRS)

Connell, Andrea M.

2011-01-01

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

Optical ground station site diversity for Deep Space Optical Communications the Mars Telecom Orbiter optical link

NASA Technical Reports Server (NTRS)

Wilson, K.; Parvin, B.; Fugate, R.; Kervin, P.; Zingales, S.

2003-01-01

Future NASA deep space missions will fly advanced high resolution imaging instruments that will require high bandwidth links to return the huge data volumes generated by these instruments. Optical communications is a key technology for returning these large data volumes from deep space probes. Yet to cost effectively realize the high bandwidth potential of the optical link will require deployment of ground receivers in diverse locations to provide high link availability. A recent analysis of GOES weather satellite data showed that a network of ground stations located in Hawaii and the Southwest continental US can provide an average of 90% availability for the deep space optical link. JPL and AFRL are exploring the use of large telescopes in Hawaii, California, and Albuquerque to support the Mars Telesat laser communications demonstration. Designed to demonstrate multi-Mbps communications from Mars, the mission will investigate key operational strategies of future deep space optical communications network.

On Applications of Disruption Tolerant Networking to Optical Networking in Space

NASA Technical Reports Server (NTRS)

Hylton, Alan Guy; Raible, Daniel E.; Juergens, Jeffrey; Iannicca, Dennis

2012-01-01

The integration of optical communication links into space networks via Disruption Tolerant Networking (DTN) is a largely unexplored area of research. Building on successful foundational work accomplished at JPL, we discuss a multi-hop multi-path network featuring optical links. The experimental test bed is constructed at the NASA Glenn Research Center featuring multiple Ethernet-to-fiber converters coupled with free space optical (FSO) communication channels. The test bed architecture models communication paths from deployed Mars assets to the deep space network (DSN) and finally to the mission operations center (MOC). Reliable versus unreliable communication methods are investigated and discussed; including reliable transport protocols, custody transfer, and fragmentation. Potential commercial applications may include an optical communications infrastructure deployment to support developing nations and remote areas, which are unburdened with supporting an existing heritage means of telecommunications. Narrow laser beam widths and control of polarization states offer inherent physical layer security benefits with optical communications over RF solutions. This paper explores whether or not DTN is appropriate for space-based optical networks, optimal payload sizes, reliability, and a discussion on security.

The NASA Deep Space Network (DSN) Array

NASA Technical Reports Server (NTRS)

Gatti, Mark

2006-01-01

The DSN Array Project is currently working with Senior Management at both JPL and NASA to develop strategies towards starting a major implementation project. Several studies within NASA are concluding, all of which recommend that any future DSN capability include arraying of antennas to increase performance. Support of Deep Space, Lunar, and CEV (crewed exploration vehicle) missions is possible. High data rate and TDRSS formatting is being investigated. Any future DSN capacity must include Uplink. Current studies ongoing to investigate and develop technologies for uplink arraying; provides advantages in three ways: 1) N2 effect. EIRP grows as N2(-vs-N for a downlink array); 2) Improved architectural options (can separate uplink and downlink); and 3) Potential for more cost effective transmitters for fixed EIRP.

Simulator of Space Communication Networks

NASA Technical Reports Server (NTRS)

Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

2005-01-01

Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

Development of a prototype real-time automated filter for operational deep space navigation

NASA Technical Reports Server (NTRS)

Masters, W. C.; Pollmeier, V. M.

1994-01-01

Operational deep space navigation has been in the past, and is currently, performed using systems whose architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational data flow has been developed. This system can allow for more rapid response as well as much reduced staffing to support mission navigation operations.

Space Station-based deep-space optical communication experiments

NASA Technical Reports Server (NTRS)

Chen, Chien-Chung; Schwartz, Jon A.

1988-01-01

A series of three experiments proposed for advanced optical deep-space communications is described. These proposed experiments would be carried out aboard the Space Station to test and evaluate the capability of optical instruments to conduct data communication and spacecraft navigation for deep-space missions. Techniques for effective data communication, precision spacecraft ranging, and accurate angular measurements will be developed and evaluated in a spaceborne environment.

Enabling Planetary Geodesy With the Deep Space Network

NASA Astrophysics Data System (ADS)

Park, R. S.; Asmar, S. W.; Armstrong, J. W.; Buccino, D.; Folkner, W. M.; Iess, L.; Konopliv, A. S.; Lazio, J.

2015-12-01

For five decades of planetary exploration, missions have carried out Radio Science experiments that led to numerous discoveries in planetary geodesy. The interior structures of many planets, large moons, asteroids and comet nuclei have been modeled based on their gravitational fields and dynamical parameters derived from precision Doppler and range measurements, often called radio metrics. Advanced instrumentation has resulted in the high level of data quality that enabled scientific breakthroughs. This instrumentation scheme, however, is distributed between elements on the spacecraft and others at the stations of the Deep Space Network (DSN), making the DSN a world-class science instrument. The design and performance of the DSN stations directly determines the quality of the science observables and radio link-based planetary geodesy observations are established by methodologies and capabilities of the DSN. In this paper, we summarize major recent discoveries in planetary geodesy at the rocky planets and the Moon, Saturnian and Jovian satellites, Phobos, and Vesta; experiments and analysis in progress at Ceres and Pluto; upcoming experiments at Jupiter, Saturn and Mars (InSight), and the long-term outlook for approved future missions with geodesy objectives. The DSN's role will be described along the technical advancements in DSN transmitters, receivers, atomic clocks, and other specialized instrumentation, such as the Advanced Water Vapor Radiometer, Advanced Ranging Instrument, as well as relevant mechanical and electrical components. Advanced techniques for calibrations of known noise sources and Earth's troposphere, ionosphere, and interplanetary plasma are also presented. A typical error budget will be presented to aid future investigations in carrying out trade-off studies in the end-to-end system performance.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Deep Neural Network Emulation of a High-Order, WENO-Limited, Space-Time Reconstruction

NASA Astrophysics Data System (ADS)

Norman, M. R.; Hall, D. M.

2017-12-01

Deep Neural Networks (DNNs) have been used to emulate a number of processes in atmospheric models, including radiation and even so-called super-parameterization of moist convection. In each scenario, the DNN provides a good representation of the process even for inputs that have not been encountered before. More notably, they provide an emulation at a fraction of the cost of the original routine, giving speed-ups of 30× and even up to 200× compared to the runtime costs of the original routines. However, to our knowledge there has not been an investigation into using DNNs to emulate the dynamics. The most likely reason for this is that dynamics operators are typically both linear and low cost, meaning they cannot be sped up by a non-linear DNN emulation. However, there exist high-cost non-linear space-time dynamics operators that significantly reduce the number of parallel data transfers necessary to complete an atmospheric simulation. The WENO-limited Finite-Volume method with ADER-DT time integration is a prime example of this - needing only two parallel communications per large, fully limited time step. However, it comes at a high cost in terms of computation, which is why many would hesitate to use it. This talk investigates DNN emulation of the WENO-limited space-time finite-volume reconstruction procedure - the most expensive portion of this method, which densely clusters a large amount of non-linear computation. Different training techniques and network architectures are tested, and the accuracy and speed-up of each is given.

The Deep Impact Network Experiment Operations Center Monitor and Control System

NASA Technical Reports Server (NTRS)

Wang, Shin-Ywan (Cindy); Torgerson, J. Leigh; Schoolcraft, Joshua; Brenman, Yan

2009-01-01

The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.

Antennas for the array-based Deep Space Network: current status and future designs

NASA Technical Reports Server (NTRS)

Imbriale, William A.; Gama, Eric

2005-01-01

Development of very large arrays1,2 of small antennas has been proposed as a way to increase the downlink capability of the NASA Deep Space Network DSN) by two or three orders of magnitude thereby enabling greatly increased science data from currently configured missions or enabling new mission concepts. The current concept is for an array of 400 x 12-m antennas at each of three longitudes. The DSN array will utilize radio astronomy sources for phase calibration and will have wide bandwidth correlation processing for this purpose. NASA has undertaken a technology program to prove the performance and cost of a very large DSN array. Central to that program is a 3-element interferometer to be completed in 2005. This paper describes current status of the low cost 6-meter breadboard antenna to be used as part of the interferometer and the RF design of the 12-meter antenna.

Deep Space 1 Ion Engine

NASA Image and Video Library

2002-12-21

Kennedy Space Center, Florida. - Deep Space 1 is lifted from its work platform, giving a closeup view of the experimental solar-powered ion propulsion engine. The ion propulsion engine is the first non-chemical propulsion to be used as the primary means of propelling a spacecraft. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Another onboard experiment includes software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but may also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, Cape Canaveral Air Station, in October. Delta II rockets are medium capacity expendable launch vehicles derived from the Delta family of rockets built and launched since 1960. Since then there have been more than 245 Delta launches. http://photojournal.jpl.nasa.gov/catalog/PIA04232

Emergency Communications for NASA's Deep Space Missions

NASA Technical Reports Server (NTRS)

Shambayati, Shervin; Lee, Charles H.; Morabito, David D.; Cesarone, Robert J.; Abraham, Douglas S.

2011-01-01

The ability to communicate with spacecraft during emergencies is a vital service that NASA's Deep Space Network (DSN) provides to all deep space missions. Emergency communications is characterized by low data rates(typically is approximately10 bps) with the spacecraft using either a low-gain antenna (LGA, including omnidirectional antennas) or,in some cases, a medium-gain antenna (MGA). Because of the use of LGAs/MGAs for emergency communications, the transmitted power requirements both on the spacecraft andon the ground are substantially greater than those required for normal operations on the high-gain antenna (HGA) despite the lower data rates. In this paper, we look at currentand future emergency communications capabilities available to NASA's deep-space missions and discuss their limitations in the context of emergency mode operations requirements.These discussions include the use of the DSN 70-m diameter antennas, the use of the 34-m diameter antennas either alone or arrayed both for the uplink (Earth-to-spacecraft) and the downlink (spacecraft-to-Earth), upgrades to the ground transmitters, and spacecraft power requirements both with unitygain (0 dB) LGAs and with antennas with directivity (>0 dB gain, either LGA or MGA, depending on the gain). Also discussed are the requirements for forward-error-correctingcodes for both the uplink and the downlink. In additional, we introduce a methodology for proper selection of a directionalLGA/MGA for emergency communications.

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

NASA Technical Reports Server (NTRS)

Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale

2017-01-01

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

NASA Technical Reports Server (NTRS)

Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

2017-01-01

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

Simulating Autonomous Telecommunication Networks for Space Exploration

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.

2008-01-01

Currently, most interplanetary telecommunication systems require human intervention for command and control. However, considering the range from near Earth to deep space missions, combined with the increase in the number of nodes and advancements in processing capabilities, the benefits from communication autonomy will be immense. Likewise, greater mission science autonomy brings the need for unscheduled, unpredictable communication and network routing. While the terrestrial Internet protocols are highly developed their suitability for space exploration has been questioned. JPL has developed the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to help characterize network designs and protocols. The results will allow future mission planners to better understand the trade offs of communication protocols. This paper discusses various issues with interplanetary network and simulation results of interplanetary networking protocols.

Deep Space Network, Cryogenic HEMT LNAs

NASA Technical Reports Server (NTRS)

Bautista, J. Javier

2006-01-01

Exploration of the Solar System with automated spacecraft that are more than ten astronomical units (1 AU = 149,597,870.691 km) from earth requires very large antennae employing extremely sensitive receivers. A key figure of merit in the specification of the spacecraft-to-earth telecommunications link is the ratio of the antenna gain to operatio nal noise temperature (G/Top) of the system. The Deep Space Network (DSN) receivers are cryogenic, low-noise amplifiers (LNAs) which addres s the need to maintain Top as low as technology permits. Historicall y, the extra-ordinarily sensitive receive systems operated by the DSN have required ctyogenically cooled, ruby masers, operating at a physi cal temperature near the boiling point of helium, as the LNA. Althoug h masers continue to be used today, they are hand crafted at JPL and expensive to manufacture and maintain. Recent advances in the developm ent of indium phosphide (InP) based high electron mobility transistor s (HEMTs) combined with cryogenic cooling near the boiling point of h ydrogen have made this alternate technology comparable with and a fraction of the cost of maser technology. InP HEMT LNA modules are demons trating noise temperatures less than ten times the quantum noise limi t (10hf/k) from 1 to 100 GHz. To date, the lowest noise LNA modules developed for the DSN have demonstrated noise temperatures of 3.5 K and 8.5 K at 8.5 K at 32 GHz, respectively. Front-end receiver packages employing these modules have demonstrated operating system noise temperatures of 17 K at 8.4 GHz (on a 70m antenna at zenith) and 39 K at 3 2 GHz (on a 34m antenna at zenith). The development and demonstration of cryogenic, InP HEMT based front-end amplifiers for the DSN requir es accurate component and module characterization, and modeling from 1 to 100 GHz at physical temperatures down to 12 K. The characterizati on and modeling begins with the HEMT chip, proceeds to the multi-stag e HEMT LNA module, and culminates with the

The deep space 1 extended mission

NASA Astrophysics Data System (ADS)

Rayman, Marc D.; Varghese, Philip

2001-03-01

The primary mission of Deep Space 1 (DS1), the first flight of the New Millennium program, completed successfully in September 1999, having exceeded its objectives of testing new, high-risk technologies important for future space and Earth science missions. DS1 is now in its extended mission, with plans to take advantage of the advanced technologies, including solar electric propulsion, to conduct an encounter with comet 19P/Borrelly in September 2001. During the extended mission, the spacecraft's commercial star tracker failed; this critical loss prevented the spacecraft from achieving three-axis attitude control or knowledge. A two-phase approach to recovering the mission was undertaken. The first involved devising a new method of pointing the high-gain antenna to Earth using the radio signal received at the Deep Space Network as an indicator of spacecraft attitude. The second was the development of new flight software that allowed the spacecraft to return to three-axis operation without substantial ground assistance. The principal new feature of this software is the use of the science camera as an attitude sensor. The differences between the science camera and the star tracker have important implications not only for the design of the new software but also for the methods of operating the spacecraft and conducting the mission. The ambitious rescue was fully successful, and the extended mission is back on track.

Using The Global Positioning System For Earth Orbiter and Deep Space Network

NASA Technical Reports Server (NTRS)

Lichten, Stephen M.; Haines, Bruce J.; Young, Lawrence E.; Dunn, Charles; Srinivasan, Jeff; Sweeney, Dennis; Nandi, Sumita; Spitzmesser, Don

1994-01-01

The Global Positioning System (GPS) can play a major role in supporting orbit and trajectory determination for spacecraft in a wide range of applications, including low-Earth, high-earth, and even deep space (interplanetary) tracking.

Toward an embedded training tool for Deep Space Network operations

NASA Technical Reports Server (NTRS)

Hill, Randall W., Jr.; Sturdevant, Kathryn F.; Johnson, W. L.

1993-01-01

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor. The cognitive model was developed in Soar for the DSN's Link Monitor and Control (LMC) system; it led to several insights about learning in the task domain that were used to design an intelligent tutor called REACT that implements a method called 'impasse-driven tutoring'. REACT is one component of the LMC training system, which also includes a communications link simulator and a graphical user interface. A pilot study of the LMC training system indicates that REACT shows promise as an effective way for helping operators to quickly acquire expert skills.

Table-driven configuration and formatting of telemetry data in the Deep Space Network

NASA Technical Reports Server (NTRS)

Manning, Evan

1994-01-01

With a restructured software architecture for telemetry system control and data processing, the NASA/Deep Space Network (DSN) has substantially improved its ability to accommodate a wide variety of spacecraft in an era of 'better, faster, cheaper'. In the new architecture, the permanent software implements all capabilities needed by any system user, and text tables specify how these capabilities are to be used for each spacecraft. Most changes can now be made rapidly, outside of the traditional software development cycle. The system can be updated to support a new spacecraft through table changes rather than software changes, reducing the implementation, test, and delivery cycle for such a change from three months to three weeks. The mechanical separation of the text table files from the program software, with tables only loaded into memory when that mission is being supported, dramatically reduces the level of regression testing required. The format of each table is a different compromise between ease of human interpretation, efficiency of computer interpretation, and flexibility.

Research Possibilities Beyond Deep Space Gateway

NASA Astrophysics Data System (ADS)

Smitherman, D. V.; Needham, D. H.; Lewis, R.

2018-02-01

This abstract explores the possibilities for a large research facilities module attached to the Deep Space Gateway, using the same large module design and basic layout planned for the Deep Space Transport.

Delay/Disruption Tolerant Networking for the International Space Station (ISS)

NASA Technical Reports Server (NTRS)

Schlesinger, Adam; Willman, Brett M.; Pitts, Lee; Davidson, Suzanne R.; Pohlchuck, William A.

2017-01-01

Disruption Tolerant Networking (DTN) is an emerging data networking technology designed to abstract the hardware communication layer from the spacecraft/payload computing resources. DTN is specifically designed to operate in environments where link delays and disruptions are common (e.g., space-based networks). The National Aeronautics and Space Administration (NASA) has demonstrated DTN on several missions, such as the Deep Impact Networking (DINET) experiment, the Earth Observing Mission 1 (EO-1) and the Lunar Laser Communication Demonstration (LLCD). To further the maturation of DTN, NASA is implementing DTN protocols on the International Space Station (ISS). This paper explains the architecture of the ISS DTN network, the operational support for the system, the results from integrated ground testing, and the future work for DTN expansion.

Deep Pyriform Space: Anatomical Clarifications and Clinical Implications.

PubMed

Surek, Christopher K; Vargo, James; Lamb, Jerome

2016-07-01

The purpose of this study was to define the anatomical boundaries, transformation in the aging face, and clinical implications of the Ristow space. The authors propose a title of deep pyriform space for anatomical continuity. The deep pyriform space was dissected in 12 hemifacial fresh cadaver dissections. Specimens were divided into three separate groups. For group 1, dimensions were measured and plaster molds were fashioned to evaluate shape and contour. For group 2, the space was injected percutaneously with dyed hyaluronic acid to examine proximity relationships to adjacent structures. For group 3, the space was pneumatized to evaluate its cephalic extension. The average dimensions of the deep pyriform space are 1.1 × 0.9 cm. It is bounded medially by the depressor septi nasi and cradled laterally and superficially in a "half-moon" shape by the deep medial cheek fat and lip elevators. The angular artery courses on the roof of the space within a septum between the space and deep medial cheek fat. Pneumatization of the space traverses cephalic to the level of the tear trough ligament in a plane deep to the premaxillary space. The deep pyriform space is a midface cavity cradled by the pyriform aperture and deep medial cheek compartment. Bony recession of the maxilla with age predisposes this space for use as a potential area of deep volumization to support overlying cheek fat and draping lip elevators. The position of the angular artery in the roof of the space allows safe injection on the bone without concern for vascular injury.

Deep Space Optical Link ARQ Performance Analysis

NASA Technical Reports Server (NTRS)

Clare, Loren; Miles, Gregory

2016-01-01

Substantial advancements have been made toward the use of optical communications for deep space exploration missions, promising a much higher volume of data to be communicated in comparison with present -day Radio Frequency (RF) based systems. One or more ground-based optical terminals are assumed to communicate with the spacecraft. Both short-term and long-term link outages will arise due to weather at the ground station(s), space platform pointing stability, and other effects. To mitigate these outages, an Automatic Repeat Query (ARQ) retransmission method is assumed, together with a reliable back channel for acknowledgement traffic. Specifically, the Licklider Transmission Protocol (LTP) is used, which is a component of the Disruption-Tolerant Networking (DTN) protocol suite that is well suited for high bandwidth-delay product links subject to disruptions. We provide an analysis of envisioned deep space mission scenarios and quantify buffering, latency and throughput performance, using a simulation in which long-term weather effects are modeled with a Gilbert -Elliot Markov chain, short-term outages occur as a Bernoulli process, and scheduled outages arising from geometric visibility or operational constraints are represented. We find that both short- and long-term effects impact throughput, but long-term weather effects dominate buffer sizing and overflow losses as well as latency performance.

Detection Performance of Upgraded "Polished Panel" Optical Receiver Concept on the Deep-Space Network's 34 Meter Research Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor A.

2012-01-01

The development and demonstration of a "polished panel" optical receiver concept on the 34 meter research antenna of the Deep Space Network (DSN) has been the subject of recent papers. This concept would enable simultaneous reception of optical and microwave signals by retaining the original shape of the main reflector for microwave reception, but with the aluminum panels polished to high reflectivity to enable focusing of optical signal energy as well. A test setup has been installed on the DSN's 34 meter research antenna at Deep Space Station 13 (DSS-13) of NASA's Goldstone Communications Complex in California, and preliminary experimental results have been obtained. This paper describes the results of our latest efforts to improve the point-spread function (PSF) generated by a custom polished panel, in an attempt to reduce the dimensions of the PSF, thus enabling more precise tracking and improved detection performance. The design of the new mechanical support structure and its operation are described, and the results quantified in terms of improvements in collected signal energy and optical communications performance, based on data obtained while tracking the planet Jupiter with the 34 meter research antenna at DSS-13.

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

PubMed

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Calibration and performance measurements for the nasa deep space network aperture enhancement project (daep)

NASA Astrophysics Data System (ADS)

LaBelle, Remi C.; Rochblatt, David J.

2018-06-01

The NASA Deep Space Network (DSN) has recently constructed two new 34-m antennas at the Canberra Deep Space Communications Complex (CDSCC). These new antennas are part of the larger DAEP project to add six new 34-m antennas to the DSN, including two in Madrid, three in Canberra and one in Goldstone (California). The DAEP project included development and implementation of several new technologies for the X, and Ka (32 GHz) -band uplink and downlink electronics. The electronics upgrades were driven by several different considerations, including parts obsolescence, cost reduction, improved reliability and maintainability, and capability to meet future performance requirements. The new antennas are required to support TT&C links for all of the NASA deep-space spacecraft, as well as for several international partners. Some of these missions, such as Voyager 1 and 2, have very limited link budgets, which results in demanding requirements for system G/T performance. These antennas are also required to support radio science missions with several spacecraft, which dictate some demanding requirements for spectral purity, amplitude stability and phase stability for both the uplink and downlink electronics. After completion of these upgrades, a comprehensive campaign of tests and measurements took place to characterize the electronics and calibrate the antennas. Radiometric measurement techniques were applied to characterize, calibrate, and optimize the performance of the antenna parameters. These included optical and RF high-resolution holographic and total power radiometry techniques. The methodology and techniques utilized for the measurement and calibration of the antennas is described in this paper. Lessons learned (not all discussed in this paper) from the commissioning of the first antenna (DSS-35) were applied to the commissioning of the second antenna (DSS-36). These resulted in achieving antenna aperture efficiency of 66% (for DSS-36), at Ka-Band (32-Ghz), which is

Life Support for Deep Space and Mars

NASA Technical Reports Server (NTRS)

Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

2014-01-01

How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

Space Station technology testbed: 2010 deep space transport

NASA Technical Reports Server (NTRS)

Holt, Alan C.

1993-01-01

A space station in a crew-tended or permanently crewed configuration will provide major R&D opportunities for innovative, technology and materials development and advanced space systems testing. A space station should be designed with the basic infrastructure elements required to grow into a major systems technology testbed. This space-based technology testbed can and should be used to support the development of technologies required to expand our utilization of near-Earth space, the Moon and the Earth-to-Jupiter region of the Solar System. Space station support of advanced technology and materials development will result in new techniques for high priority scientific research and the knowledge and R&D base needed for the development of major, new commercial product thrusts. To illustrate the technology testbed potential of a space station and to point the way to a bold, innovative approach to advanced space systems' development, a hypothetical deep space transport development and test plan is described. Key deep space transport R&D activities are described would lead to the readiness certification of an advanced, reusable interplanetary transport capable of supporting eight crewmembers or more. With the support of a focused and highly motivated, multi-agency ground R&D program, a deep space transport of this type could be assembled and tested by 2010. Key R&D activities on a space station would include: (1) experimental research investigating the microgravity assisted, restructuring of micro-engineered, materials (to develop and verify the in-space and in-situ 'tuning' of materials for use in debris and radiation shielding and other protective systems), (2) exposure of microengineered materials to the space environment for passive and operational performance tests (to develop in-situ maintenance and repair techniques and to support the development, enhancement, and implementation of protective systems, data and bio-processing systems, and virtual reality and

Spaceport operations for deep space missions

NASA Technical Reports Server (NTRS)

Holt, Alan C.

1990-01-01

Space Station Freedom is designed with the capability to cost-effectively evolve into a transportation node which can support manned lunar and Mars missions. To extend a permanent human presence to the outer planets (moon outposts) and to nearby star systems, additional orbiting space infrastructure and great advances in propulsion system and other technologies will be required. To identify primary operations and management requirements for these deep space missions, an interstellar design concept was developed and analyzed. The assembly, test, servicing, logistics resupply, and increment management techniques anticipated for lunar and Mars missions appear to provide a pattern which can be extended in an analogous manner to deep space missions. A long range, space infrastructure development plan (encompassing deep space missions) coupled with energetic, breakthrough level propulsion research should be initiated now to assist in making the best budget and schedule decisions.

Deep Space Chronicle: A Chronology of Deep Space and Planetary Probes 1958-2000

NASA Technical Reports Server (NTRS)

Siddiqi, Asif A.; Launius, Roger (Technical Monitor)

2002-01-01

This monograph contains brief descriptions of all robotic deep space missions attempted since the opening of the space age in 1957. The missions are listed strictly chronologically in order of launch date (not by planetary encounter).

Processing of chromatic information in a deep convolutional neural network.

PubMed

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

Project Report: Design and Analysis for the Deep Space Network BWG Type 2 Antenna Feed Platform

NASA Technical Reports Server (NTRS)

Crawford, Andrew

2011-01-01

The following report explains in detail the solid modeling design process and structural analysis of the LNA (Low Noise Amplifier) feed platform to be constructed and installed on the new BWG (Beam Wave Guide) Type-2 tracking antenna in Canberra, Australia, as well as all future similar BWG Type-2 antennas builds. The Deep Space Networks new BWG Type-2 antennas use beam waveguides to funnel and 'extract' the desired signals received from spacecraft, and the feed platform supports and houses the LNA(Low Noise Amplifier) feed-cone and cryogenic cooling equipment used in the signal transmission and receiving process. The mandated design and construction of this platform to be installed on the new tracking antenna will be used and incorporated on all future similar antenna builds.

Request-Driven Schedule Automation for the Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel; Arroyo, Belinda; Call, Jared; Mercado, Marisol

2010-01-01

The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

The NASA Space Communications Data Networking Architecture

NASA Technical Reports Server (NTRS)

Israel, David J.; Hooke, Adrian J.; Freeman, Kenneth; Rush, John J.

2006-01-01

The NASA Space Communications Architecture Working Group (SCAWG) has recently been developing an integrated agency-wide space communications architecture in order to provide the necessary communication and navigation capabilities to support NASA's new Exploration and Science Programs. A critical element of the space communications architecture is the end-to-end Data Networking Architecture, which must provide a wide range of services required for missions ranging from planetary rovers to human spaceflight, and from sub-orbital space to deep space. Requirements for a higher degree of user autonomy and interoperability between a variety of elements must be accommodated within an architecture that necessarily features minimum operational complexity. The architecture must also be scalable and evolvable to meet mission needs for the next 25 years. This paper will describe the recommended NASA Data Networking Architecture, present some of the rationale for the recommendations, and will illustrate an application of the architecture to example NASA missions.

Service Quality Assessment for NASA's Deep Space Network: No Longer a Luxury

NASA Technical Reports Server (NTRS)

Barkley, Erik; Wolgast, Paul; Zendejas, Silvino

2010-01-01

When NASA's Deep Space Network (DSN) was established almost a half century ago, the concept of computer-based service delivery was impractical or infeasible due to the state of information technology As a result, the interface the DSN exposes to its customers tends to be equipment-centric, lacking a clear demarcation between the DSN and the mission operation systems (MOS) of its customers. As the number of customers has continued to increase, the need to improve efficiency and minimize costs has grown. This growth has been the impetus for a DSN transformation from an equipment-forrent provider to a provider of standard services. Service orientation naturally leads to requirements for service management, including proactive measurement of service quality and service levels as well as the efficiency of internal processes and the performance of service provisioning systems. DSN System Engineering has surveyed industry offerings to determine if commercial successes in decision support and Business Intelligence (BI) solutions can be applied to the DSN. A pilot project was initiated, and subsequently executed to determine the feasibility of repurposing a commercial Business Intelligence platform for engineering analysis in conjunction with the platform's intended business reporting and analysis functions.

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

NASA Technical Reports Server (NTRS)

Vilnrotter, V.; Tsao, P.; Lee, D.; Cornish, T.; Jao, J.; Slade, M.

2011-01-01

A coherent uplink array consisting of up to three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R-4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor; Tsao, P.; Lee, D.; Cornish, T.; Jao, J.; Slade, M.

2011-01-01

A coherent Uplink Array consisting of two or three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

Implementation of the 64-meter-diameter Antennas at the Deep Space Stations in Australia and Spain

NASA Technical Reports Server (NTRS)

Bartos, K. P.; Bell, H. B.; Phillips, H. P.; Sweetser, B. M.; Rotach, O. A.

1975-01-01

The management and construction aspects of the Overseas 64-m Antenna Project in which two 64-m antennas were constructed at the Tidbinbilla Deep Space Communications Complex in Australia, and at the Madrid Deep Space Communications Complex in Spain are described. With the completion of these antennas the Deep Space Network is equipped with three 64-m antennas spaced around the world to maintain continuous coverage of spacecraft operations. These antennas provide approximately a 7-db gain over the capabilities of the existing 26-m antenna nets. The report outlines the project organization and management, resource utilization, fabrication, quality assurance, and construction methods by which the project was successfully completed. Major problems and their solutions are described as well as recommendations for future projects.

A Ten-Meter Ground-Station Telescope for Deep-Space Optical Communications: A Preliminary Design

NASA Technical Reports Server (NTRS)

Britcliffe, M.; Hoppe, D.; Roberts, W.; Page, N.

2001-01-01

This article describes a telescope design for a 10-m optical ground station for deep-space communications. The design for a direct-detection optical communications telescope differs dramatically from a telescope for imaging applications. In general, the requirements for optical manufacturing and tracking performance are much less stringent for direct detection of optical signals. The technical challenge is providing a design that will operate in the daytime/nighttime conditions required for a Deep Space Network tracking application. The design presented addresses these requirements. The design will provide higher performance at lower cost than existing designs.

Deep Space Earth Observations from DSCOVR

NASA Astrophysics Data System (ADS)

Marshak, A.; Herman, J.

2018-02-01

The Deep Space Climate Observatory (DSCOVR) at Sun-Earth L1 orbit observes the full sunlit disk of Earth. There are two Earth science instruments on board DSCOVR — EPIC and NISTAR. We discuss if EPIC and NISAR-like instruments can be used in Deep Space Gateway.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Space Network Time Distribution and Synchronization Protocol Development for Mars Proximity Link

NASA Technical Reports Server (NTRS)

Woo, Simon S.; Gao, Jay L.; Mills, David

2010-01-01

Time distribution and synchronization in deep space network are challenging due to long propagation delays, spacecraft movements, and relativistic effects. Further, the Network Time Protocol (NTP) designed for terrestrial networks may not work properly in space. In this work, we consider the time distribution protocol based on time message exchanges similar to Network Time Protocol (NTP). We present the Proximity-1 Space Link Interleaved Time Synchronization (PITS) algorithm that can work with the CCSDS Proximity-1 Space Data Link Protocol. The PITS algorithm provides faster time synchronization via two-way time transfer over proximity links, improves scalability as the number of spacecraft increase, lowers storage space requirement for collecting time samples, and is robust against packet loss and duplication which underlying protocol mechanisms provide.

Single- and dual-carrier microwave noise abatement in the deep space network. [microwave antennas

NASA Technical Reports Server (NTRS)

Bathker, D. A.; Brown, D. W.; Petty, S. M.

1975-01-01

The NASA/JPL Deep Space Network (DSN) microwave ground antenna systems are presented which simultaneously uplink very high power S-band signals while receiving very low level S- and X-band downlinks. Tertiary mechanisms associated with elements give rise to self-interference in the forms of broadband noise burst and coherent intermodulation products. A long-term program to reduce or eliminate both forms of interference is described in detail. Two DSN antennas were subjected to extensive interference testing and practical cleanup program; the initial performance, modification details, and final performance achieved at several planned stages are discussed. Test equipment and field procedures found useful in locating interference sources are discussed. Practices deemed necessary for interference-free operations in the DSN are described. Much of the specific information given is expected to be easily generalized for application in a variety of similar installations. Recommendations for future investigations and individual element design are given.

Flight Software Implementation of the Beacon Monitor Expreiment On the NASA New Millennium Deep Space 1 (DS-1) Mission

NASA Technical Reports Server (NTRS)

Foster, R.; Schlutsmeyer, A.

1997-01-01

A new technology that can lower the cost of mission operations on future spacecraft will be tested on the NASA New Millennium Deep Space 1 (DS-1) Mission. This technology, the Beacon Monitor Experiment (BMOX), can be used to reduce the Deep Space Network (DSN) tracking time and its associated costs on future missions.

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

PubMed

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Diabetic retinopathy screening using deep neural network.

PubMed

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

(abstract) Spacecraft Doppler Tracking with the Deep Space Network in the Search for Gravitational Waves

NASA Technical Reports Server (NTRS)

Asmar, Sami; Renzetti, Nicholas

1994-01-01

The Deep Space Network generates accurate radio science data observables for investigators who use radio links between spacecraft and the Earth to examine small changes in the phase and/or amplitude of the signal to study a wide variety of structures and phenomena in space. Several such studies are directed at aspects of the theory of general relativity such as gravitational redshift and gravitational waves. A gravitational wave is a propagating, polarized gravitational field, a ripple in the curvature of space-time. In Einstein's theory of general relativity, the waves are propagating solutions of the Einstein field equations. Their amplitudes are dimensionless strain amplitudes that change the fractional difference in distance between test masses and the rates at which separated clocks keep time. Predicted by all relativistic theories of gravity, they are extremely weak (the ratio of gravitational forces to electrical forces is about 10(sup -40)) and are generated at detectable levels only by astrophysical sources - very massive sources under violent dynamical conditions. The waves have never been detected but searches in the low-frequency band using Doppler tracking of many spacecraft have been conducted and others are being planned. Upper limits have been placed on the gravitational wave strength with the best sensitivities to date are for periodic waves being 7 x 10(sup -15).

The Evolution of Technology in the Deep Space Network: A History of the Advanced Systems Program

NASA Technical Reports Server (NTRS)

Layland, J. W.; Rauch, L. L.

1994-01-01

The Deep Space Network (DSN) of 1995 might be described as the evolutionary result of 45 years of deep space communication and navigation, together with the synergistic activities of radio science and radar and radio astronomy. But the evolution of the DSN did not just happen - it was carefully planned and created. The evolution of the DSN has been an ongoing engineering activity, and engineering is a process of problem solving under constraints, one of which is technology. In turn, technology is the knowledge base providing the capability and experience for practical application of various areas of science, when needed. The best engineering solutions result from optimization under the fewest constraints, and if technology needs are well anticipated (ready when needed), then the most effective engineering solution is possible. Throughout the history of the DSN it has been the goal and function of DSN advanced technology development (designated the DSN Advanced Systems Program from 1963 through 1994) to supply the technology needs of the DSN when needed, and thus to minimize this constraint on DSN engineering. Technology often takes considerable time to develop, and when that happens, it is important to have anticipated engineering needs; at times, this anticipation has been by as much as 15 years. Also, on a number of occasions, mission malfunctions or emergencies have resulted in unplanned needs for technology that has, in fact, been available from the reservoir of advanced technology provided by the DSN Advanced Systems Program. Sometimes, even DSN engineering personnel fail to realize that the organization of JPL permits an overlap of DSN advanced technology activities with subsequent engineering activities. This can result in the flow of advanced technology into DSN engineering in a natural and sometimes almost unnoticed way. In the following pages, we will explore some of the many contributions of the DSN Advanced Systems Program that were provided to DSN

Time Analyzer for Time Synchronization and Monitor of the Deep Space Network

NASA Technical Reports Server (NTRS)

Cole, Steven; Gonzalez, Jorge, Jr.; Calhoun, Malcolm; Tjoelker, Robert

2003-01-01

A software package has been developed to measure, monitor, and archive the performance of timing signals distributed in the NASA Deep Space Network. Timing signals are generated from a central master clock and distributed to over 100 users at distances up to 30 kilometers. The time offset due to internal distribution delays and time jitter with respect to the central master clock are critical for successful spacecraft navigation, radio science, and very long baseline interferometry (VLBI) applications. The instrument controller and operator interface software is written in LabView and runs on the Linux operating system. The software controls a commercial multiplexer to switch 120 separate timing signals to measure offset and jitter with a time-interval counter referenced to the master clock. The offset of each channel is displayed in histogram form, and "out of specification" alarms are sent to a central complex monitor and control system. At any time, the measurement cycle of 120 signals can be interrupted for diagnostic tests on an individual channel. The instrument also routinely monitors and archives the long-term stability of all frequency standards or any other 1-pps source compared against the master clock. All data is stored and made available for

The JPL roadmap for Deep Space navigation

NASA Technical Reports Server (NTRS)

Martin-Mur, Tomas J.; Abraham, Douglas S.; Berry, David; Bhaskaran, Shyam; Cesarone, Robert J.; Wood, Lincoln

2006-01-01

This paper reviews the tentative set of deep space missions that will be supported by NASA's Deep Space Mission System in the next twenty-five years, and extracts the driving set of navigation capabilities that these missions will require. There will be many challenges including the support of new mission navigation approaches such as formation flying and rendezvous in deep space, low-energy and low-thrust orbit transfers, precise landing and ascent vehicles, and autonomous navigation. Innovative strategies and approaches will be needed to develop and field advanced navigation capabilities.

Unified Simulation and Analysis Framework for Deep Space Navigation Design

NASA Technical Reports Server (NTRS)

Anzalone, Evan; Chuang, Jason; Olsen, Carrie

2013-01-01

As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.

Parallel Distributed Processing Theory in the Age of Deep Networks.

PubMed

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

Strategic Technologies for Deep Space Transport

NASA Technical Reports Server (NTRS)

Litchford, Ronald J.

2016-01-01

Deep space transportation capability for science and exploration is fundamentally limited by available propulsion technologies. Traditional chemical systems are performance plateaued and require enormous Initial Mass in Low Earth Orbit (IMLEO) whereas solar electric propulsion systems are power limited and unable to execute rapid transits. Nuclear based propulsion and alternative energetic methods, on the other hand, represent potential avenues, perhaps the only viable avenues, to high specific power space transport evincing reduced trip time, reduced IMLEO, and expanded deep space reach. Here, key deep space transport mission capability objectives are reviewed in relation to STMD technology portfolio needs, and the advanced propulsion technology solution landscape is examined including open questions, technical challenges, and developmental prospects. Options for potential future investment across the full compliment of STMD programs are presented based on an informed awareness of complimentary activities in industry, academia, OGAs, and NASA mission directorates.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

A common-aperture X- and S-band four-function feedcone. [hornfeed design for antennas of Deep Space Network

NASA Technical Reports Server (NTRS)

Withington, J. R.; Williams, W. F.

1982-01-01

Williams and Withington (1979) have considered a prototype X-S-band feedhorn which enabled simultaneous X- and S-band reception from a Cassegrain antenna. This feedhorn has quite successfully demonstrated an alternate method to the standard Deep Space Network (DSN) system of multiple subreflectors and dichroic plate for dual-band reception. In connection with a Network Consolidation Program, involving centralized control of existing antennas and construction of new reflector antennas, a second-generation feedhorn/combiner was conceived to show that this common-aperture feedhorn system was capable of performing all necessary functions the DSN would be called upon to perform with existing and future X-S-band spacecraft. Attention is given to the feedhorn concept, the combiner concept, the first and the second generation of the horn, Sand X-band tuning, and planned capabilities. The feedhorn greatly extends the state of the art in DSN performance and will enhance DSN capabilities in the future.

Deep space optical communications experiment

NASA Technical Reports Server (NTRS)

Kinman, P.; Katz, J.; Gagliardi, R.

1983-01-01

An optical communications experiment between a deep space vehicle and an earth terminal is under consideration for later in this decade. The experimental link would be incoherent (direct detection) and would employ two-way cooperative pointing. The deep space optical transceiver would ride piggyback on a spacecraft with an independent scientific objective. Thus, this optical transceiver is being designed for minimum spacecraft impact - specifically, low mass and low power. The choices of laser transmitter, coding/modulation scheme, and pointing mechanization are discussed. A representative telemetry link budget is presented.

An Improved X-Band Maser System for Deep Space Network Applications

NASA Astrophysics Data System (ADS)

Britcliffe, M.; Hanson, T.; Fernandez, J.

2000-01-01

An 8450-MHz (X-band) maser system utilizing a commercial Gifford--McMahon (GM) closed-cycle cryocooler (CCR) was designed, fabricated, and demonstrated. The CCR system was used to cool a maser operating at 8450 MHz. The prototype GM CCR system meets or exceeds all Deep Space Network requirements for maser performance. The two-stage GM CCR operates at 4.2 K; for comparison, the DSN's current three-stage cryocooler, which uses a Joule--Thompson cooling stage in addition to GM cooling, operates at 4.5 K. The new CCR withstands heat loads of 1.5 W at 4.2 K as compared to 1 W at 4.5 K for the existing DSN cryocooler used for cooling masers. The measured noise temperature, T_e, of the maser used for these tests is defined at the ambient connection to the antenna feed system. The T_e measured 5.0 K at a CCR temperature of 4.5 K, about 1.5 K higher than the noise temperature of a typical DSN Block II-A X-band traveling-wave maser (TWM). Reducing the temperature of the CCR significantly lowers the maser noise temperature and increases maser gain and bandwidth. The new GM CCR gives future maser systems significant operational advantages, including reduced maintenance time and logistics requirements. The results of a demonstration of this new system are presented. Advantages of using a GM-cooled maser and the effects of the reduced CCR temperature on maser performance are discussed.

Compact Deep-Space Optical Communications Transceiver

NASA Technical Reports Server (NTRS)

Roberts, W. Thomas; Charles, Jeffrey R.

2009-01-01

Deep space optical communication transceivers must be very efficient receivers and transmitters of optical communication signals. For deep space missions, communication systems require high performance well beyond the scope of mere power efficiency, demanding maximum performance in relation to the precious and limited mass, volume, and power allocated. This paper describes the opto-mechanical design of a compact, efficient, functional brassboard deep space transceiver that is capable of achieving megabyte-per-second rates at Mars ranges. The special features embodied to enhance the system operability and functionality, and to reduce the mass and volume of the system are detailed. System tests and performance characteristics are described in detail. Finally, lessons learned in the implementation of the brassboard design and suggestions for improvements appropriate for a flight prototype are covered.

Deep Space Gateway Science Opportunities

NASA Technical Reports Server (NTRS)

Quincy, C. D.; Charles, J. B.; Hamill, Doris; Sidney, S. C.

2018-01-01

The NASA Life Sciences Research Capabilities Team (LSRCT) has been discussing deep space research needs for the last two years. NASA's programs conducting life sciences studies - the Human Research Program, Space Biology, Astrobiology, and Planetary Protection - see the Deep Space Gateway (DSG) as affording enormous opportunities to investigate biological organisms in a unique environment that cannot be replicated in Earth-based laboratories or on Low Earth Orbit science platforms. These investigations may provide in many cases the definitive answers to risks associated with exploration and living outside Earth's protective magnetic field. Unlike Low Earth Orbit or terrestrial locations, the Gateway location will be subjected to the true deep space spectrum and influence of both galactic cosmic and solar particle radiation and thus presents an opportunity to investigate their long-term exposure effects. The question of how a community of biological organisms change over time within the harsh environment of space flight outside of the magnetic field protection can be investigated. The biological response to the absence of Earth's geomagnetic field can be studied for the first time. Will organisms change in new and unique ways under these new conditions? This may be specifically true on investigations of microbial communities. The Gateway provides a platform for microbiology experiments both inside, to improve understanding of interactions between microbes and human habitats, and outside, to improve understanding of microbe-hardware interactions exposed to the space environment.

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Knowledge engineering for temporal dependency networks as operations procedures. [in space communication

NASA Technical Reports Server (NTRS)

Fayyad, Kristina E.; Hill, Randall W., Jr.; Wyatt, E. J.

1993-01-01

This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

Deep Space Gateway - Enabling Missions to Mars

NASA Technical Reports Server (NTRS)

Rucker, Michelle; Connolly, John

2017-01-01

There are many opportunities for commonality between Lunar vicinity and Mars mission hardware and operations. Best approach: Identify Mars mission risks that can be bought down with testing in the Lunar vicinity, then explore hardware and operational concepts that work for both missions with minimal compromise. Deep Space Transport will validate the systems and capabilities required to send humans to Mars orbit and return to Earth. Deep Space Gateway provides a convenient assembly, checkout, and refurbishment location to enable Mars missions Current deep space transport concept is to fly missions of increasing complexity: Shakedown cruise, Mars orbital mission, Mars surface mission; Mars surface mission would require additional elements.

Microbial survival in deep space environment.

NASA Technical Reports Server (NTRS)

Silverman, G. J.

1971-01-01

Review of the knowledge available on the extent to which microorganisms (mainly microbial spores, vegetative cells, and fungi) are capable of surviving the environment of deep space, based on recent simulation experiments of deep space. A description of the experimental procedures used is followed by a discussion of deep space ecology, the behavior of microorganisms in ultrahigh vacuum, and factors influencing microbial survival. It is concluded that, so far, simulation experiments have proved far less lethal to microorganisms than to other forms of life. There are, however, wide gaps in the knowledge available, and no accurate predictions can as yet be made on the degree of lethality that might be incurred by a microbial population on a given mission. Therefore, sterilization of spacecraft surfaces is deemed necessary if induced panspermia (i.e., interplanetary life propagation) is to be avoided.

Forecasting Space Weather Hazards for Astronauts in Deep Space

NASA Astrophysics Data System (ADS)

Martens, P. C.

2018-02-01

Deep Space Gateway provides a unique platform to develop, calibrate, and test a space weather forecasting system for interplanetary travel in a real life setting. We will discuss requirements and design of such a system.

Optimizing interplanetary trajectories with deep space maneuvers

NASA Astrophysics Data System (ADS)

Navagh, John

1993-09-01

Analysis of interplanetary trajectories is a crucial area for both manned and unmanned missions of the Space Exploration Initiative. A deep space maneuver (DSM) can improve a trajectory in much the same way as a planetary swingby. However, instead of using a gravitational field to alter the trajectory, the on-board propulsion system of the spacecraft is used when the vehicle is not near a planet. The purpose is to develop an algorithm to determine where and when to use deep space maneuvers to reduce the cost of a trajectory. The approach taken to solve this problem uses primer vector theory in combination with a non-linear optimizing program to minimize Delta(V). A set of necessary conditions on the primer vector is shown to indicate whether a deep space maneuver will be beneficial. Deep space maneuvers are applied to a round trip mission to Mars to determine their effect on the launch opportunities. Other studies which were performed include cycler trajectories and Mars mission abort scenarios. It was found that the software developed was able to locate quickly DSM's which lower the total Delta(V) on these trajectories.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

A small satellite design for deep space network testing and training

NASA Technical Reports Server (NTRS)

Mcwilliams, Dennis; Slatton, Clint; Norman, Cassidy; Araiza, Joe; Jones, Jason; Tedesco, Mark; Wortman, Michael; Opiela, John; Lett, Pat; Clavenna, Michael

1993-01-01

With the continuing exploration of the Solar System and the reemphasis on Earth focused missions, the need for faster data transmission rates has grown. Ka-band could allow a higher data delivery rate over the current X-band, however the adverse effects of the Earth's atmosphere on Ka are as yet unknown. The Deep Space Network and Jet Propulsion Lab have proposed to launch a small satellite that would simultaneously transmit X and Ka signals to test the viability of switching to Ka-band. The Mockingbird Design Team at the University of Texas at Austin applied small satellite design principles to achieve this objective. The Mockingbird design, named BATSAT, incorporates simple, low-cost systems designed for university production and testing. The BATSAT satellite is a 0.64 m diameter, spherical panel led satellite, mounted with solar cells and omni-directional antennae. The antennae configuration negates the need for active attitude control or spin stabilization. The space-frame truss structure was designed for 11 g launch loads while allowing for easy construction and solar-panel mounting. The communication system transmits at 1 mW by carrying the required Ka and X-band transmitters, as well as an S band transmitter used for DSN training. The power system provides the 8.6 W maximum power requirements via silicon solar arrays and nickel-cadmium batteries. The BATSAT satellite will be lofted into an 1163 km, 70 deg orbit by the Pegasus launch system. This orbit fulfills DSN dish slew rate requirements while keeping the satellite out of the heaviest regions of the Van Allen radiation belts. Each of the three DSN stations capable of receiving Ka-band (Goldstone, Canberra, and Madrid) will have an average of 85 minutes of view-time per day over the satellites ten year design life. Mockingbird Designs hopes that its small satellite design will not only be applicable to this specific mission scenario, but that it could easily be modified for instrument capability for

Development of realtime connected element interferometry at the Goldstone Deep Space Communications Complex

NASA Technical Reports Server (NTRS)

Edwards, C. D.

1990-01-01

Connected-element interferometry (CEI) has the potential to provide high-accuracy angular spacecraft tracking on short baselines by making use of the very precise phase delay observable. Within the Goldstone Deep Space Communications Complex (DSCC), one of three tracking complexes in the NASA Deep Space Network, baselines of up to 21 km in length are available. Analysis of data from a series of short-baseline phase-delay interferometry experiments are presented to demonstrate the potential tracking accuracy on these baselines. Repeated differential observations of pairs of angularly close extragalactic radio sources were made to simulate differential spacecraft-quasar measurements. Fiber-optic data links and a correlation processor are currently being developed and installed at Goldstone for a demonstration of real-time CEI in 1990.

Ending Year in Space: NASA Goddard Network Maintains Communications from Space to Ground

NASA Image and Video Library

2016-03-01

NASA's Goddard Space Flight Center in Greenbelt, Maryland, will monitor the landing of NASA Astronaut Scott Kelly and Russian Cosmonaut Mikhail Kornienko from their #YearInSpace Mission. Goddard's Networks Integration Center, pictured above, leads all coordination for space-to-ground communications support for the International Space Station and provides contingency support for the Soyuz TMA-18M 44S spacecraft, ensuring complete communications coverage through NASA's Space Network. The Soyuz 44S spacecraft will undock at 8:02 p.m. EST this evening from the International Space Station. It will land approximately three and a half hours later, at 11:25 p.m. EST in Kazakhstan. Both Kelly and Kornienko have spent 340 days aboard the International Space Station, preparing humanity for long duration missions and exploration into deep space. Read more: www.nasa.gov/feature/goddard/2016/ending-year-in-space-na... Credit: NASA/Goddard/Rebecca Roth NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Ending Year in Space: NASA Goddard Network Maintains Communications from Space to Ground

NASA Image and Video Library

2017-12-08

NASA's Goddard Space Flight Center in Greenbelt, Maryland, will monitor the landing of NASA Astronaut Scott Kelly and Russian Cosmonaut Mikhail Kornienko from their #YearInSpace Mission. Goddard's Networks Integration Center, pictured above, leads all coordination for space-to-ground communications support for the International Space Station and provides contingency support for the Soyuz TMA-18M 44S spacecraft, ensuring complete communications coverage through NASA's Space Network. The Soyuz 44S spacecraft will undock at 8:02 p.m. EST this evening from the International Space Station. It will land approximately three and a half hours later, at 11:25 p.m. EST in Kazakhstan. Both Kelly and Kornienko have spent 340 days aboard the International Space Station, preparing humanity for long duration missions and exploration into deep space. Read more: www.nasa.gov/feature/goddard/2016/ending-year-in-space-na... Credit: NASA/Goddard/Rebecca Roth NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Deep Space Habitat Concept Demonstrator

NASA Technical Reports Server (NTRS)

Bookout, Paul S.; Smitherman, David

2015-01-01

This project will develop, integrate, test, and evaluate Habitation Systems that will be utilized as technology testbeds and will advance NASA's understanding of alternative deep space mission architectures, requirements, and operations concepts. Rapid prototyping and existing hardware will be utilized to develop full-scale habitat demonstrators. FY 2014 focused on the development of a large volume Space Launch System (SLS) class habitat (Skylab Gen 2) based on the SLS hydrogen tank components. Similar to the original Skylab, a tank section of the SLS rocket can be outfitted with a deep space habitat configuration and launched as a payload on an SLS rocket. This concept can be used to support extended stay at the Lunar Distant Retrograde Orbit to support the Asteroid Retrieval Mission and provide a habitat suitable for human missions to Mars.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

PubMed

Kang, Min-Joo; Kang, Je-Won

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

PubMed Central

Kang, Min-Joo

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Ka-Band Transponder for Deep-Space Radio Science

NASA Technical Reports Server (NTRS)

Dennis, Matthew S.; Mysoor, Narayan R.; Folkner, William M.; Mendoza, Ricardo; Venkatesan, Jaikrishna

2008-01-01

A one-page document describes a Ka-band transponder being developed for use in deep-space radio science. The transponder receives in the Deep Space Network (DSN) uplink frequency band of 34.2 to 34.7 GHz, transmits in the 31.8- to 32.3 GHz DSN downlink band, and performs regenerative ranging on a DSN standard 4-MHz ranging tone subcarrier phase-modulated onto the uplink carrier signal. A primary consideration in this development is reduction in size, relative to other such transponders. The transponder design is all-analog, chosen to minimize not only the size but also the number of parts and the design time and, thus, the cost. The receiver features two stages of frequency down-conversion. The receiver locks onto the uplink carrier signal. The exciter signal for the transmitter is derived from the same source as that used to generate the first-stage local-oscillator signal. The ranging-tone subcarrier is down-converted along with the carrier to the second intermediate frequency, where the 4-MHz tone is demodulated from the composite signal and fed into a ranging-tone-tracking loop, which regenerates the tone. The regenerated tone is linearly phase-modulated onto the downlink carrier.

Challenges of Integrating NASA's Space Communications Networks

NASA Technical Reports Server (NTRS)

Reinert, Jessica; Barnes, Patrick

2013-01-01

The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a

Challenges of Integrating NASAs Space Communication Networks

NASA Technical Reports Server (NTRS)

Reinert, Jessica M.; Barnes, Patrick

2013-01-01

The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a

DeepQA: improving the estimation of single protein model quality with deep belief networks.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Wishart Deep Stacking Network for Fast POLSAR Image Classification.

PubMed

Jiao, Licheng; Liu, Fang

2016-05-11

Inspired by the popular deep learning architecture - Deep Stacking Network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named as Wishart Deep Stacking Network (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following Neural Network (NN). Then a single-hidden-layer neural network based on the fast Wishart distance is defined for POLSAR image classification, which is named as Wishart Network (WN) and improves the classification accuracy. Finally, a multi-layer neural network is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768000 pixels can be classified in 0.53s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.

Heliophysics Radio Observations Enabled by the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Kasper, J. C.

2018-02-01

This presentation reviews the scientific potential of low frequency radio imaging from space, the SunRISE radio interferometer, and the scientific value of larger future arrays in deep space and how they would benefit from the Deep Space Gateway.

NASA's Space Launch System: SmallSat Deployment to Deep Space

NASA Technical Reports Server (NTRS)

Robinson, Kimberly F.; Creech, Stephen D.

2017-01-01

Leveraging the significant capability it offers for human exploration and flagship science missions, NASA's Space Launch System (SLS) also provides a unique opportunity for lower-cost deep-space science in the form of small-satellite secondary payloads. Current plans call for such opportunities to begin with the rocket's first flight; a launch of the vehicle's Block 1 configuration, capable of delivering 70 metric tons (t) to Low Earth Orbit (LEO), which will send the Orion crew vehicle around the moon and return it to Earth. On that flight, SLS will also deploy 13 CubeSat-class payloads to deep-space destinations. These secondary payloads will include not only NASA research, but also spacecraft from industry and international partners and academia. The payloads also represent a variety of disciplines including, but not limited to, studies of the moon, Earth, sun, and asteroids. While the SLS Program is making significant progress toward that first launch, preparations are already under way for the second, which will see the booster evolve to its more-capable Block 1B configuration, able to deliver 105t to LEO. That configuration will have the capability to carry large payloads co-manifested with the Orion spacecraft, or to utilize an 8.4-meter (m) fairing to carry payloads several times larger than are currently possible. The Block 1B vehicle will be the workhorse of the Proving Ground phase of NASA's deep-space exploration plans, developing and testing the systems and capabilities necessary for human missions into deep space and ultimately to Mars. Ultimately, the vehicle will evolve to its full Block 2 configuration, with a LEO capability of 130 metric tons. Both the Block 1B and Block 2 versions of the vehicle will be able to carry larger secondary payloads than the Block 1 configuration, creating even more opportunities for affordable scientific exploration of deep space. This paper will outline the progress being made toward flying smallsats on the first

Advances in Planetary Protection at the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Spry, J. A.; Siegel, B.; Race, M.; Rummel, J. D.; Pugel, D. E.; Groen, F. J.; Kminek, G.; Conley, C. A.; Carosso, N. J.

2018-02-01

Planetary protection knowledge gaps that can be addressed by science performed at the Deep Space Gateway in the areas of human health and performance, space biology, and planetary sciences that enable future exploration in deep space, at Mars, and other targets.

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generating Seismograms with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

Self-learning Monte Carlo with deep neural networks

NASA Astrophysics Data System (ADS)

Shen, Huitao; Liu, Junwei; Fu, Liang

2018-05-01

The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

The deep space network, volume 16

NASA Technical Reports Server (NTRS)

1973-01-01

The objectives, functions, and organization of the DSN are summarized, and the instrumentation facility, ground communication facility, and the network control system are described. The requirements for supporting planetary flight projects are discussed along with the research and technology for tracking, navigation, network control, and data processing.

Facial expression recognition based on improved deep belief networks

NASA Astrophysics Data System (ADS)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Adaptation of a software development methodology to the implementation of a large-scale data acquisition and control system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Madrid, G. A.; Westmoreland, P. T.

1983-01-01

A progress report is presented on a program to upgrade the existing NASA Deep Space Network in terms of a redesigned computer-controlled data acquisition system for channelling tracking, telemetry, and command data between a California-based control center and three signal processing centers in Australia, California, and Spain. The methodology for the improvements is oriented towards single subsystem development with consideration for a multi-system and multi-subsystem network of operational software. Details of the existing hardware configurations and data transmission links are provided. The program methodology includes data flow design, interface design and coordination, incremental capability availability, increased inter-subsystem developmental synthesis and testing, system and network level synthesis and testing, and system verification and validation. The software has been implemented thus far to a 65 percent completion level, and the methodology being used to effect the changes, which will permit enhanced tracking and communication with spacecraft, has been concluded to feature effective techniques.

Deep Space 1 moves to CCAS for testing

NASA Technical Reports Server (NTRS)

1998-01-01

After covering the bulk of Deep Space 1 in thermal insulating blankets, workers in the Payload Hazardous Servicing Facility lift it from its work platform before moving it onto its transporter (behind workers at left). Deep Space 1 is being moved to the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station, for testing. At either side of the spacecraft are its solar wings, folded for launch. When fully extended, the winds measure 38.6 feet from tip to tip. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include a solar-powered ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. The ion propulsion engine is the first non-chemical propulsion to be used as the primary means of propelling a spacecraft. Deep Space 1 will complete most of its mission objectives within the first two months, but may also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, Cape Canaveral Air Station, in October. Delta II rockets are medium capacity expendable launch vehicles derived from the Delta family of rockets built and launched since 1960. Since then there have been more than 245 Delta launches.

Architecture for Cognitive Networking within NASAs Future Space Communications Infrastructure

NASA Technical Reports Server (NTRS)

Clark, Gilbert J., III; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

2016-01-01

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

Operator assistant to support deep space network link monitor and control

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Desai, Rajiv; Martinez, Elmain

1992-01-01

Preparing the Deep Space Network (DSN) stations to support spacecraft missions (referred to as pre-cal, for pre-calibration) is currently an operator and time intensive activity. Operators are responsible for sending and monitoring several hundred operator directivities, messages, and warnings. Operator directives are used to configure and calibrate the various subsystems (antenna, receiver, etc.) necessary to establish a spacecraft link. Messages and warnings are issued by the subsystems upon completion of an operation, changes of status, or an anomalous condition. Some points of pre-cal are logically parallel. Significant time savings could be realized if the existing Link Monitor and Control system (LMC) could support the operator in exploiting the parallelism inherent in pre-cal activities. Currently, operators may work on the individual subsystems in parallel, however, the burden of monitoring these parallel operations resides solely with the operator. Messages, warnings, and directives are all presented as they are received; without being correlated to the event that triggered them. Pre-cal is essentially an overhead activity. During pre-cal, no mission is supported, and no other activity can be performed using the equipment in the link. Therefore, it is highly desirable to reduce pre-cal time as much as possible. One approach to do this, as well as to increase efficiency and reduce errors, is the LMC Operator Assistant (OA). The LMC OA prototype demonstrates an architecture which can be used in concert with the existing LMC to exploit parallelism in pre-cal operations while providing the operators with a true monitoring capability, situational awareness and positive control. This paper presents an overview of the LMC OA architecture and the results from initial prototyping and test activities.

Trajectory design for the Deep Space Program Science Experiment (DSPSE) mission

NASA Astrophysics Data System (ADS)

Carrington, D.; Carrico, J.; Jen, J.; Roberts, C.; Seacord, A.; Sharer, P.; Newman, L.; Richon, K.; Kaufman, B.; Middour, J.

In 1994, the Deep Space Program Science Experiment (DSPSE) spacecraft will become the first spacecraft to perform, in succession, both a lunar orbiting mission and a deep-space asteroid encounter mission. The primary mission objective is to perform a long-duration flight-test of various new-technology lightweight components, such as sensors, in a deep-space environment. The mission has two secondary science objectives: to provide high-resolution imaging of the entire lunar surface for mapping purposes and flyby imaging of the asteroid 1620 Geographos. The DSPSE mission is sponsored by the Strategic Defense Initiative Organization (SDIO). As prime contractor, the Naval Research Laboratory (NRL) is building the spacecraft and will conduct mission operations. The Goddard Space Flight Center's (GSFC) Flight Dynamics Division is supporting NRL in the areas of The Deep Space Network (DSN) will provide tracking support. The DSPSE mission will begin with a launch from the Western Test Range in late January 1994. Following a minimum 1.5-day stay in a low-Earth parking orbit, a solid kick motor burn will boost DSPSE into an 18-day, 2.5-revolution phasing orbit transfer trajectory to the Moon. Two burns to insert DSPSE into a lunar polar orbit suitable for the mapping mission will be followed by mapping orbit maintenance and adjustment operations over a period of 2 sidereal months. In May 1994, a lunar orbit departure maneuver, in conjunction with a lunar swingby 26 days later, will propel DSPSE onto a heliocentric transfer that will intercept Geographos on September 1, 1994. This paper presents the characteristics, deterministic delta-Vs, and design details of each trajectory phase of this unique mission, together with the requirements, constraints, and design considerations to which each phase is subject. Numerous trajectory plots and tables of significant trajectory events are included. Following a discussion of the results of a preliminary launch window analysis, a

Developing a Fault Management Guidebook for Nasa's Deep Space Robotic Missions

NASA Technical Reports Server (NTRS)

Fesq, Lorraine M.; Jacome, Raquel Weitl

2015-01-01

NASA designs and builds systems that achieve incredibly ambitious goals, as evidenced by the Curiosity rover traversing on Mars, the highly complex International Space Station orbiting our Earth, and the compelling plans for capturing, retrieving and redirecting an asteroid into a lunar orbit to create a nearby a target to be investigated by astronauts. In order to accomplish these feats, the missions must be imbued with sufficient knowledge and capability not only to realize the goals, but also to identify and respond to off-nominal conditions. Fault Management (FM) is the discipline of establishing how a system will respond to preserve its ability to function even in the presence of faults. In 2012, NASA released a draft FM Handbook in an attempt to coalesce the field by establishing a unified terminology and a common process for designing FM mechanisms. However, FM approaches are very diverse across NASA, especially between the different mission types such as Earth orbiters, launch vehicles, deep space robotic vehicles and human spaceflight missions, and the authors were challenged to capture and represent all of these views. The authors recognized that a necessary precursor step is for each sub-community to codify its FM policies, practices and approaches in individual, focused guidebooks. Then, the sub-communities can look across NASA to better understand the different ways off-nominal conditions are addressed, and to seek commonality or at least an understanding of the multitude of FM approaches. This paper describes the development of the "Deep Space Robotic Fault Management Guidebook," which is intended to be the first of NASA's FM guidebooks. Its purpose is to be a field-guide for FM practitioners working on deep space robotic missions, as well as a planning tool for project managers. Publication of this Deep Space Robotic FM Guidebook is expected in early 2015. The guidebook will be posted on NASA's Engineering Network on the FM Community of Practice

Environmental projects. Volume 13: Underground storage tanks, removal and replacement. Goldstone Deep Space Communications Complex

NASA Technical Reports Server (NTRS)

Bengelsdorf, Irv

1991-01-01

The Goldstone Deep Space Communications Complex (GDSCC), located in the Mojave Desert about 40 miles north of Barstow, California, and about 160 miles northeast of Pasadena, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. As a large-scale facility located in a remote, isolated desert region, the GDSCC operations require numerous on-site storage facilities for gasoline, diesel oil, hydraulic oil, and waste oil. These fluids are stored in underground storage tanks (USTs). This present volume describes what happened to the 26 USTs that remained at the GDSCC. Twenty-four of these USTs were constructed of carbon steel without any coating for corrosion protection, and without secondary containment or leak detection. Two remaining USTs were constructed of fiberglass-coated carbon steel but without secondary containment or leak protection. Of the 26 USTs that remained at the GDSCC, 23 were cleaned, removed from the ground, cut up, and hauled away from the GDSCC for environmentally acceptable disposal. Three USTs were permanently closed (abandoned in place).

Clementine, Deep Space Program Science Experiment

NASA Technical Reports Server (NTRS)

1993-01-01

Clementine, also called the Deep Space Program Science Experiment, is a joint Department of Defense (DoD)/National Aeronautics and Space Administration (NASA) mission with the dual goal of testing small spacecraft, subsystems, and sensors in the deep space environment and also providing a nominal science return. The Clementine mission will provide technical demonstrations of innovative lightweight spacecraft components and sensors, will be launced on a spacecraft developed within 2 years of program start, and will point a way for new planetary mission options under consideration by NASA. This booklet gives the background of the Clementine mission (including the agencies involved), the mission objectives, the mission scenario, the instruments that the mission will carry, and how the data will be analyzed and made accessible.

Deep Space 1 is encapsulated on launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

On Launch Pad 17A at Cape Canaveral Air Station, released from its protective payload transportation container, Deep Space 1 waits to have the fairing attached before launch. Targeted for launch aboard a Boeing Delta 7326 rocket on Oct. 25, Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Low Gravity Issues of Deep Space Refueling

NASA Technical Reports Server (NTRS)

Chato, David J.

2005-01-01

This paper discusses the technologies required to develop deep space refueling of cryogenic propellants and low cost flight experiments to develop them. Key technologies include long term storage, pressure control, mass gauging, liquid acquisition, and fluid transfer. Prior flight experiments used to mature technologies are discussed. A plan is presented to systematically study the deep space refueling problem and devise low-cost experiments to further mature technologies and prepare for full scale flight demonstrations.

The JPL optical communications telescope laboratory (OCTL) test bed for the future optical Deep Space Network

NASA Technical Reports Server (NTRS)

Wilson, K. E.; Page, N.; Wu, J.; Srinivasan, M.

2003-01-01

Relative to RF, the lower power-consumption and lower mass of high bandwidth optical telecommunications make this technology extremely attractive for returning data from future NASA/JPL deep space probes.

Deep Space 1 is prepared for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility prepare Deep Space 1 for launch aboard a Boeing Delta 7326 rocket in October. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Most of its mission objectives will be completed within the first two months. A near- Earth asteroid, 1992 KD, has also been selected for a possible flyby.

Quantum-chemical insights from deep tensor neural networks

PubMed Central

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. PMID:28067221

Quantum-chemical insights from deep tensor neural networks.

PubMed

Schütt, Kristof T; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R; Tkatchenko, Alexandre

2017-01-09

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol -1 ) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

Quantum-chemical insights from deep tensor neural networks

NASA Astrophysics Data System (ADS)

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol-1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

DEEP ATTRACTOR NETWORK FOR SINGLE-MICROPHONE SPEAKER SEPARATION.

PubMed

Chen, Zhuo; Luo, Yi; Mesgarani, Nima

2017-03-01

Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.

Two-Stage Approach to Image Classification by Deep Neural Networks

NASA Astrophysics Data System (ADS)

Ososkov, Gennady; Goncharov, Pavel

2018-02-01

The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

Deep Space Ka-band Link Management and the MRO Demonstration: Long-term Weather Statistics Versus Forecasting

NASA Technical Reports Server (NTRS)

Davarian, Faramaz; Shambayati, Shervin; Slobin, Stephen

2004-01-01

During the last 40 years, deep space radio communication systems have experienced a move toward shorter wavelengths. In the 1960s a transition from L- to S-band occurred which was followed by a transition from S- to X-band in the 1970s. Both these transitions provided deep space links with wider bandwidths and improved radio metrics capability. Now, in the 2000s, a new change is taking place, namely a move to the Ka-band region of the radio frequency spectrum. Ka-band will soon replace X-band as the frequency of choice for deep space communications providing ample spectrum for the high data rate requirements of future missions. The low-noise receivers of deep space networks have a great need for link management techniques that can mitigate weather effects. In this paper, three approaches for managing Ka-band Earth-space links are investigated. The first approach uses aggregate annual statistics, the second one uses monthly statistics, and the third is based on the short-term forecasting of the local weather. An example of weather forecasting for Ka-band link performance prediction is presented. Furthermore, spacecraft commanding schemes suitable for Ka-band link management are investigated. Theses schemes will be demonstrated using NASA's Mars Reconnaissance Orbiter (MRO) spacecraft in the 2007 to 2008 time period, and the demonstration findings will be reported in a future publication.

Space Biology Model Organism Research on the Deep Space Gateway to Pioneer Discovery and Advance Human Space Exploration

NASA Astrophysics Data System (ADS)

Sato, K. Y.; Tomko, D. L.; Levine, H. G.; Quincy, C. D.; Rayl, N. A.; Sowa, M. B.; Taylor, E. M.; Sun, S. C.; Kundrot, C. E.

2018-02-01

Model organisms are foundational for conducting physiological and systems biology research to define how life responds to the deep space environment. The organisms, areas of research, and Deep Space Gateway capabilities needed will be presented.

Architecture and System Engineering Development Study of Space-Based Satellite Networks for NASA Missions

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2003-01-01

Traditional NASA missions, both near Earth and deep space, have been stovepipe in nature and point-to-point in architecture. Recently, NASA and others have conceptualized missions that required space-based networking. The notion of networks in space is a drastic shift in thinking and requires entirely new architectures, radio systems (antennas, modems, and media access), and possibly even new protocols. A full system engineering approach for some key mission architectures will occur that considers issues such as the science being performed, stationkeeping, antenna size, contact time, data rates, radio-link power requirements, media access techniques, and appropriate networking and transport protocols. This report highlights preliminary architecture concepts and key technologies that will be investigated.

Amplitude Scintillation due to Atmospheric Turbulence for the Deep Space Network Ka-Band Downlink

NASA Technical Reports Server (NTRS)

Ho, C.; Wheelon, A.

2004-01-01

Fast amplitude variations due to atmospheric scintillation are the main concerns for the Deep Space Network (DSN) Ka-band downlink under clear weather conditions. A theoretical study of the amplitude scintillation variances for a finite aperture antenna is presented. Amplitude variances for weak scattering scenarios are examined using turbulence theory to describe atmospheric irregularities. We first apply the Kolmogorov turbulent spectrum to a point receiver for three different turbulent profile models, especially for an exponential model varying with altitude. These analytic solutions then are extended to a receiver with a finite aperture antenna for the three profile models. Smoothing effects of antenna aperture are expressed by gain factors. A group of scaling factor relations is derived to show the dependences of amplitude variances on signal wavelength, antenna size, and elevation angle. Finally, we use these analytic solutions to estimate the scintillation intensity for a DSN Goldstone 34-m receiving station. We find that the (rms) amplitude fluctuation is 0.13 dB at 20-deg elevation angle for an exponential model, while the fluctuation is 0.05 dB at 90 deg. These results will aid us in telecommunication system design and signal-fading prediction. They also provide a theoretical basis for further comparison with other measurements at Ka-band.

Robust hepatic vessel segmentation using multi deep convolution network

NASA Astrophysics Data System (ADS)

Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

2017-03-01

Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

PubMed

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Architecture for Cognitive Networking within NASA's Future Space Communications Infrastructure

NASA Technical Reports Server (NTRS)

Clark, Gilbert; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

2016-01-01

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, GEO, MEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes an architecture enabling the development and deployment of cognitive networking capabilities into the envisioned future NASA space communications infrastructure. We begin by discussing the need for increased automation, including inter-system discovery and collaboration. This discussion frames the requirements for an architecture supporting cognitive networking for future missions and relays, including both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture, and results of implementation and initial testing of a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

Deep Space Habitat Wireless Smart Plug

NASA Technical Reports Server (NTRS)

Morgan, Joseph A.; Porter, Jay; Rojdev, Kristina; Carrejo, Daniel B.; Colozza, Anthony J.

2014-01-01

NASA has been interested in technology development for deep space exploration, and one avenue of developing these technologies is via the eXploration Habitat (X-Hab) Academic Innovation Challenge. In 2013, NASA's Deep Space Habitat (DSH) project was in need of sensors that could monitor the power consumption of various devices in the habitat with added capability to control the power to these devices for load shedding in emergency situations. Texas A&M University's Electronic Systems Engineering Technology Program (ESET) in conjunction with their Mobile Integrated Solutions Laboratory (MISL) accepted this challenge, and over the course of 2013, several undergraduate students in a Capstone design course developed five wireless DC Smart Plugs for NASA. The wireless DC Smart Plugs developed by Texas A&M in conjunction with NASA's Deep Space Habitat team is a first step in developing wireless instrumentation for future flight hardware. This paper will further discuss the X-Hab challenge and requirements set out by NASA, the detailed design and testing performed by Texas A&M, challenges faced by the team and lessons learned, and potential future work on this design.

Deep Space 1 is prepared for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility test equipment on Deep Space 1 to prepare it for launch aboard a Boeing Delta 7326 rocket in October. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Most of its mission objectives will be completed within the first two months. A near-Earth asteroid, 1992 KD, has also been selected for a possible flyby.

Deep Space 1 is prepared for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility check equipment on Deep Space 1 to prepare it for launch aboard a Boeing Delta 7326 rocket in October. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Most of its mission objectives will be completed within the first two months. A near-Earth asteroid, 1992 KD, has also been selected for a possible flyby.

Deep Space 1 is prepared for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility remove a solar panel from Deep Space 1 as part of the preparations for launch aboard a Boeing Delta 7326 rocket in October. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Most of its mission objectives will be completed within the first two months. A near- Earth asteroid, 1992 KD, has also been selected for a possible flyby.

Deep Space 1 is prepared for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility check out Deep Space 1 to prepare it for launch aboard a Boeing Delta 7326 rocket in October. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Most of its mission objectives will be completed within the first two months. A near-Earth asteroid, 1992 KD, has also been selected for a possible flyby.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

NASA Astrophysics Data System (ADS)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Deep Space Telecommunications Systems Engineering

NASA Technical Reports Server (NTRS)

Yuen, J. H. (Editor)

1982-01-01

Descriptive and analytical information useful for the optimal design, specification, and performance evaluation of deep space telecommunications systems is presented. Telemetry, tracking, and command systems, receiver design, spacecraft antennas, frequency selection, interference, and modulation techniques are addressed.

Plant Species Identification by Bi-channel Deep Convolutional Networks

NASA Astrophysics Data System (ADS)

He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

2018-04-01

Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

Quantitative phase microscopy using deep neural networks

NASA Astrophysics Data System (ADS)

Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

2018-02-01

Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

PubMed

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Fiber Orientation Estimation Guided by a Deep Network.

PubMed

Ye, Chuyang; Prince, Jerry L

2017-09-01

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

Workstation Designs for a Cis-Lunar Deep Space Habitat

NASA Technical Reports Server (NTRS)

Howe, A. Scott

2014-01-01

Using the International Standard Payload Rack (ISPR) system, a suite of workstations required for deep space missions have been proposed to fill out habitation functions in an International Space Station (ISS) derived Cis-lunar Deep Space Habitat. This paper introduces the functional layout of the Cis-lunar habitat design, and describes conceptual designs for modular deployable work surfaces, General Maintenance Workstation (GMWS), In-Space Manufacturing Workstation (ISMW), Intra-Vehicular Activity Telerobotics Work Station (IVA-TRWS), and Galley / Wardroom.

Geocoronal Imaging from the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Waldrop, L.; Immel, T.; Clarke, J.; Fillingim, M.; Rider, K.; Qin, J.; Bhattacharyya, D.; Doe, R.

2018-02-01

UV imaging of geocoronal emission at high spatial and temporal resolution from deep space would provide crucial new constraints on global exospheric structure and dynamics, significantly advancing models of space weather and atmospheric escape.

Analysis of large optical ground stations for deep-space optical communications

NASA Astrophysics Data System (ADS)

Garcia-Talavera, M. Reyes; Rivera, C.; Murga, G.; Montilla, I.; Alonso, A.

2017-11-01

Inter-satellite and ground to satellite optical communications have been successfully demonstrated over more than a decade with several experiments, the most recent being NASA's lunar mission Lunar Atmospheric Dust Environment Explorer (LADEE). The technology is in a mature stage that allows to consider optical communications as a high-capacity solution for future deep-space communications [1][2], where there is an increasing demand on downlink data rate to improve science return. To serve these deep-space missions, suitable optical ground stations (OGS) have to be developed providing large collecting areas. The design of such OGSs must face both technical and cost constraints in order to achieve an optimum implementation. To that end, different approaches have already been proposed and analyzed, namely, a large telescope based on a segmented primary mirror, telescope arrays, and even the combination of RF and optical receivers in modified versions of existing Deep-Space Network (DSN) antennas [3][4][5]. Array architectures have been proposed to relax some requirements, acting as one of the key drivers of the present study. The advantages offered by the array approach are attained at the expense of adding subsystems. Critical issues identified for each implementation include their inherent efficiency and losses, as well as its performance under high-background conditions, and the acquisition, pointing, tracking, and synchronization capabilities. It is worth noticing that, due to the photon-counting nature of detection, the system performance is not solely given by the signal-to-noise ratio parameter. To start with the analysis, first the main implications of the deep space scenarios are summarized, since they are the driving requirements to establish the technical specifications for the large OGS. Next, both the main characteristics of the OGS and the potential configuration approaches are presented, getting deeper in key subsystems with strong impact in the

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) finish installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), install an ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Deep Space Gateway Science Opportunities

NASA Astrophysics Data System (ADS)

Quincy, C. D.; Charles, J. B.; Hamill, D. L.; Sun, S. C.

2018-02-01

Life sciences see the Deep Space Gateway as an opportunity to investigate biological organisms in a unique environment that cannot be replicated in Earth-based labs or on LEO platforms. The needed capabilities must be built into the Gateway facility.

TRI-Worthy Projects for the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Wotring, V. E.; Strangman, G. E.; Donoviel, D.

2018-02-01

Preparations for exploration will require exposure to the actual deep space environment. The new TRI for Space Health proposes innovative projects using real space radiation to make medically-relevant measurements affecting human physiology.

Deep Space 1 moves to CCAS for testing

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Payload Hazardous Servicing Facility lower Deep Space 1 onto its transporter, for movement to the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station, where it will undergo testing. At either side of the spacecraft are its solar wings, folded for launch. When fully extended, the wings measure 38.6 feet from tip to tip. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include a solar-powered ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. The ion propulsion engine is the first non-chemical propulsion to be used as the primary means of propelling a spacecraft. Deep Space 1 will complete most of its mission objectives within the first two months, but may also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, Cape Canaveral Air Station, in October. Delta II rockets are medium capacity expendable launch vehicles derived from the Delta family of rockets built and launched since 1960. Since then there have been more than 245 Delta launches.

Using the Deep Space Atomic Clock for Navigation and Science.

PubMed

Ely, Todd A; Burt, Eric A; Prestage, John D; Seubert, Jill M; Tjoelker, Robert L

2018-06-01

Routine use of one-way radiometric tracking for deep space navigation and radio science is not possible today because spacecraft frequency and time references that use state-of-the-art ultrastable oscillators introduce errors from their intrinsic drift and instability on timescales past 100 s. The Deep Space Atomic Clock (DSAC), currently under development as a NASA Technology Demonstration Mission, is an advanced prototype of a space-flight suitable, mercury-ion atomic clock that can provide an unprecedented frequency and time stability in a space-qualified clock. Indeed, the ground-based results of the DSAC space demonstration unit have already achieved an Allan deviation of at one day; space performance on this order will enable the use of one-way radiometric signals for deep space navigation and radio science.

MarCOs Cruise in Deep Space

NASA Image and Video Library

2018-03-29

An artist's rendering of the twin Mars Cube One (MarCO) spacecraft as they fly through deep space. The MarCOs will be the first CubeSats -- a kind of modular, mini-satellite -- attempting to fly to another planet. They're designed to fly along behind NASA's InSight lander on its cruise to Mars. If they make the journey, they will test a relay of data about InSight's entry, descent and landing back to Earth. Though InSight's mission will not depend on the success of the MarCOs, they will be a test of how CubeSats can be used in deep space. https://photojournal.jpl.nasa.gov/catalog/PIA22314

Electronics for Deep Space Cryogenic Applications

NASA Technical Reports Server (NTRS)

Patterson, R. L.; Hammond, A.; Dickman, J. E.; Gerber, S. S.; Elbuluk, M. E.; Overton, E.

2002-01-01

Deep space probes and planetary exploration missions require electrical power management and control systems that are capable of efficient and reliable operation in very cold temperature environments. Typically, in deep space probes, heating elements are used to keep the spacecraft electronics near room temperature. The utilization of power electronics designed for and operated at low temperature will contribute to increasing efficiency and improving reliability of space power systems. At NASA Glenn Research Center, commercial-off-the-shelf devices as well as developed components are being investigated for potential use at low temperatures. These devices include semiconductor switching devices, magnetics, and capacitors. Integrated circuits such as digital-to-analog and analog-to-digital converters, DC/DC converters, operational amplifiers, and oscillators are also being evaluated. In this paper, results will be presented for selected analog-to-digital converters, oscillators, DC/DC converters, and pulse width modulation (PWM) controllers.

Deep Space Gateway "Recycler" Mission

NASA Astrophysics Data System (ADS)

Graham, L.; Fries, M.; Hamilton, J.; Landis, R.; John, K.; O'Hara, W.

2018-02-01

Use of the Deep Space Gateway provides a hub for a reusable planetary sample return vehicle for missions to gather star dust as well as samples from various parts of the solar system including main belt asteroids, near-Earth asteroids, and Mars moon.

Why Deep Space Habitats Should Be Different from the International Space Station

NASA Technical Reports Server (NTRS)

Griffin, Brand; Brown, MacAulay

2016-01-01

It is tempting to view the International Space Station (ISS) as a model for deep space habitats. This is not a good idea for many reasons. The ISS does not have a habitation module; instead the individual crew quarters are dispersed across several modules, the galley is in the US Laboratory and the waste hygiene compartment is in a Node. This distributed arrangement may be inconvenient but more important differences distinguish a deep space habitat from the ISS. First, the Space Shuttle launch system that shaped, sized, and delivered most ISS elements has been retired. Its replacement, the Space Launch System (SLS), is specifically designed for human exploration beyond low-Earth orbit and is capable of transporting more efficient, large diameter, heavy-lift payloads. Next, because of the Earth's protective geomagnetic field, ISS crews are naturally shielded from lethal radiation. Deep space habitat designs must include either a storm shelter or strategically positioned equipment and stowage for radiation protection. Another important difference is the increased transit time with no opportunity for an ISS-type emergency return. It takes 7 to 10 days to go between Earth and cis-lunar locations and 1000 days for the Mars habitat transit. This long commute calls for greater crew autonomy with habitats designed for the crew to fix their own problems. The ISS rack-enclosed, densely packaged subsystems are a product of the Shuttle era and not maintenance friendly. A solution better suited for deep space habitats spreads systems out allowing direct access to single-layer packaging and providing crew access to each component without having to remove another. Operational readiness is another important discriminator. The ISS required over 100 flights to build, resupply, and transport the crew, whereas SLS offers the capability to launch a fully provisioned habitat that is operational without additional outfitting or resupply flights.

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

PubMed Central

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

PubMed

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), workers place an anti-static blanket over the lower portion of Deep Space 1, to protect the spacecraft during transport to the launch pad. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), attach a strap during installation of the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), maneuver the ion propulsion engine into place before installation on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Ion propulsion engine installed on Deep Space 1 at CCAS

NASA Technical Reports Server (NTRS)

1998-01-01

Workers at the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station (CCAS), make adjustments while installing the ion propulsion engine on Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS, in October.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communication Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), move to the workstand the second conical section leaf of the payload transportation container for Deep Space 1. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Instruments for Deep Space Weather Prediction and Science

NASA Astrophysics Data System (ADS)

DeForest, C. E.; Laurent, G.

2018-02-01

We discuss remote space weather monitoring system concepts that could mount on the Deep Space Gateway and provide predictive capability for space weather events including SEP events and CME crossings, and advance heliophysics of the solar wind.

Science and Exploration Deep Space Gateway Workshop

NASA Technical Reports Server (NTRS)

Spann, James F.

2017-01-01

We propose a workshop whose outcome is a publically disseminated product that articulates SMD investigations and HEOMD Life Science research, including international collaborations, that are made possible by the new opportunities in space that result from the Deep Space Gateway.

The design and implementation of the Technical Facilities Controller (TFC) for the Goldstone deep space communications complex

NASA Technical Reports Server (NTRS)

Killian, D. A.; Menninger, F. J.; Gorman, T.; Glenn, P.

1988-01-01

The Technical Facilities Controller is a microprocessor-based energy management system that is to be implemented in the Deep Space Network facilities. This system is used in conjunction with facilities equipment at each of the complexes in the operation and maintenance of air-conditioning equipment, power generation equipment, power distribution equipment, and other primary facilities equipment. The implementation of the Technical Facilities Controller was completed at the Goldstone Deep Space Communications Complex and is now operational. The installation completed at the Goldstone Complex is described and the utilization of the Technical Facilities Controller is evaluated. The findings will be used in the decision to implement a similar system at the overseas complexes at Canberra, Australia, and Madrid, Spain.

Deep Space 1 Using its Ion Engine Artist Concept

NASA Image and Video Library

2003-07-02

NASA's New Millennium Deep Space 1 spacecraft approaching the comet 19P/Borrelly. With its primary mission to serve as a technology demonstrator--testing ion propulsion and 11 other advanced technologies--successfully completed in September 1999, Deep Space 1 is now headed for a risky, exciting rendezvous with Comet Borrelly. NASA extended the mission, taking advantage of the ion propulsion and other systems to target the daring encounter with the comet in September 2001. Once a sci-fi dream, the ion propulsion engine has powered the spacecraft for over 12,000 hours. Another onboard experiment includes software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. The first flight in NASA's New Millennium Program, Deep Space 1 was launched October 24, 1998 aboard a Boeing Delta 7326 rocket from Cape Canaveral Air Station, FL. Deep Space 1 successfully completed and exceeded its mission objectives in July 1999 and flew by a near-Earth asteroid, Braille (1992 KD), in September 1999. http://photojournal.jpl.nasa.gov/catalog/PIA04604

Deep Space 1 Using its Ion Engine (Artist's Concept)

NASA Technical Reports Server (NTRS)

2003-01-01

NASA's New Millennium Deep Space 1 spacecraft approaching the comet 19P/Borrelly. With its primary mission to serve as a technology demonstrator--testing ion propulsion and 11 other advanced technologies--successfully completed in September 1999, Deep Space 1 is now headed for a risky, exciting rendezvous with Comet Borrelly. NASA extended the mission, taking advantage of the ion propulsion and other systems to target the daring encounter with the comet in September 2001. Once a sci-fi dream, the ion propulsion engine has powered the spacecraft for over 12,000 hours. Another onboard experiment includes software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. The first flight in NASA's New Millennium Program, Deep Space 1 was launched October 24, 1998 aboard a Boeing Delta 7326 rocket from Cape Canaveral Air Station, FL. Deep Space 1 successfully completed and exceeded its mission objectives in July 1999 and flew by a near-Earth asteroid, Braille (1992 KD), in September 1999.

Concepts for a Shroud or Propellant Tank Derived Deep Space Habitat

NASA Technical Reports Server (NTRS)

Howard, Robert L.

2012-01-01

Long duration human spaceflight missions beyond Low Earth Orbit will require much larger spacecraft than capsules such as the Russian Soyuz or American Orion Multi-Purpose Crew Vehicle. A concept spacecraft under development is the Deep Space Habitat, with volumes approaching that of space stations such as Skylab, Mir, and the International Space Station. This paper explores several concepts for Deep Space Habitats constructed from a launch vehicle shroud or propellant tank. It also recommends future research using mockups and prototypes to validate the size and crew station capabilities of such a habitat. Keywords: Exploration, space station, lunar outpost, NEA, habitat, long duration, deep space habitat, shroud, propellant tank.

Creating food for deep space

NASA Astrophysics Data System (ADS)

Wendel, JoAnna

2014-07-01

Explorers and scientists have to eat, whether they're on top of a mountain, deep in the sea, or in space. NASA scientists are working to develop a viable food program by 2030 that could feed six crew members for a 3-year mission to Mars.

Availability analysis of the traveling-wave maser amplifiers in the deep space network. Part 1: The 70-meter antennas

NASA Technical Reports Server (NTRS)

Issa, T. N.

1992-01-01

The results of the reliability and availability analyses of the individual S- and X-band traveling-wave maser (TWM) assemblies and their operational configurations in the 70-meter antennas of NASA's Deep Space Network (DSN) are described. For the period 1990 through 1991, the TWM availability parameters for the Telemetry Data System are: mean time between failures (MTBF), 930 hr; mean time to restore services (MTTRS), 1.4 hr; and the average availability, 99.85 percent. In previously published articles, the performance analysis of the TWM assemblies was confined to the determination of the parameters specified above. However, as the mean down time (MDT) for the repair of TWM's increases, the levels of the TWM operational availabilities and MTTRS are adversely affected. A more comprehensive TWM availability analysis is presented to permit evaluation of both MTBF and MDT effects. Performance analysis of the TWM assemblies, based on their station monthly failure reports, indicates that the TWM's required MTBF and MDT levels of 3000 hr and 36 to 48 hr, respectively, have been achieved by the TWM's only at the Canberra Deep Space Station (DSS 43). The Markov Process technique is employed to develop suitable availability measures for the S- and X-band TWM configurations when each is operated in a two-assembly standby mode. The derived stochastic expressions allow for the evaluation of those configurations' simultaneous availability for the Antenna Microwave Subsystem. The application of these expressions to demonstrate the impact of various levels of TWM maintainability (or MDT) on their configurations' operational availabilities is presented for each of the 70-m antenna stations.

Investigation of Secondary Neutron Production in Large Space Vehicles for Deep Space

NASA Technical Reports Server (NTRS)

Rojdev, Kristina; Koontz, Steve; Reddell, Brandon; Atwell, William; Boeder, Paul

2016-01-01

Future NASA missions will focus on deep space and Mars surface operations with large structures necessary for transportation of crew and cargo. In addition to the challenges of manufacturing these large structures, there are added challenges from the space radiation environment and its impacts on the crew, electronics, and vehicle materials. Primary radiation from the sun (solar particle events) and from outside the solar system (galactic cosmic rays) interact with materials of the vehicle and the elements inside the vehicle. These interactions lead to the primary radiation being absorbed or producing secondary radiation (primarily neutrons). With all vehicles, the high-energy primary radiation is of most concern. However, with larger vehicles, there is more opportunity for secondary radiation production, which can be significant enough to cause concern. In a previous paper, we embarked upon our first steps toward studying neutron production from large vehicles by validating our radiation transport codes for neutron environments against flight data. The following paper will extend the previous work to focus on the deep space environment and the resulting neutron flux from large vehicles in this deep space environment.

A Deep Space Orbit Determination Software: Overview and Event Prediction Capability

NASA Astrophysics Data System (ADS)

Kim, Youngkwang; Park, Sang-Young; Lee, Eunji; Kim, Minsik

2017-06-01

This paper presents an overview of deep space orbit determination software (DSODS), as well as validation and verification results on its event prediction capabilities. DSODS was developed in the MATLAB object-oriented programming environment to support the Korea Pathfinder Lunar Orbiter (KPLO) mission. DSODS has three major capabilities: celestial event prediction for spacecraft, orbit determination with deep space network (DSN) tracking data, and DSN tracking data simulation. To achieve its functionality requirements, DSODS consists of four modules: orbit propagation (OP), event prediction (EP), data simulation (DS), and orbit determination (OD) modules. This paper explains the highest-level data flows between modules in event prediction, orbit determination, and tracking data simulation processes. Furthermore, to address the event prediction capability of DSODS, this paper introduces OP and EP modules. The role of the OP module is to handle time and coordinate system conversions, to propagate spacecraft trajectories, and to handle the ephemerides of spacecraft and celestial bodies. Currently, the OP module utilizes the General Mission Analysis Tool (GMAT) as a third-party software component for highfidelity deep space propagation, as well as time and coordinate system conversions. The role of the EP module is to predict celestial events, including eclipses, and ground station visibilities, and this paper presents the functionality requirements of the EP module. The validation and verification results show that, for most cases, event prediction errors were less than 10 millisec when compared with flight proven mission analysis tools such as GMAT and Systems Tool Kit (STK). Thus, we conclude that DSODS is capable of predicting events for the KPLO in real mission applications.

The deep space network, volume 14

NASA Technical Reports Server (NTRS)

1973-01-01

DSN progress during Jan. and Feb. 1973 is reported. Areas of accomplishment include: flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), after covering the lower portion of Deep Space 1, workers adjust the anti-static blanket covering the upper portion. The blanket will protect the spacecraft during transport to the launch pad. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. Deep Space 1 will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, CCAS.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

Deep Space Test Bed for Radiation Studies

NASA Technical Reports Server (NTRS)

Adams, James H.; Adcock, Leonard; Apple, Jeffery; Christl, Mark; Cleveand, William; Cox, Mark; Dietz, Kurt; Ferguson, Cynthia; Fountain, Walt; Ghita, Bogdan

2006-01-01

The Deep Space Test-Bed (DSTB) Facility is designed to investigate the effects of galactic cosmic rays on crews and systems during missions to the Moon or Mars. To gain access to the interplanetary ionizing radiation environment the DSTB uses high-altitude polar balloon flights. The DSTB provides a platform for measurements to validate the radiation transport codes that are used by NASA to calculate the radiation environment within crewed space systems. It is also designed to support other Exploration related investigations such as measuring the shielding effectiveness of candidate spacecraft and habitat materials, testing new radiation monitoring instrumentation and flight avionics and investigating the biological effects of deep space radiation. We describe the work completed thus far in the development of the DSTB and its current status.

Nuclear Electric Propulsion for Deep Space Exploration

NASA Astrophysics Data System (ADS)

Schmidt, G.

Nuclear electric propulsion (NEP) holds considerable promise for deep space exploration in the future. Research and development of this technology is a key element of NASA's Nuclear Systems Initiative (NSI), which is a top priority in the President's FY03 NASA budget. The goal is to develop the subsystem technologies that will enable application of NEP for missions to the outer planets and beyond by the beginning of next decade. The high-performance offered by nuclear-powered electric thrusters will benefit future missions by (1) reducing or eliminating the launch window constraints associated with complex planetary swingbys, (2) providing the capability to perform large spacecraft velocity changes in deep space, (3) increasing the fraction of vehicle mass allocated to payload and other spacecraft systems, and, (3) in some cases, reducing trip times over other propulsion alternatives. Furthermore, the nuclear energy source will provide a power-rich environment that can support more sophisticated science experiments and higher- speed broadband data transmission than current deep space missions. This paper addresses NASA's plans for NEP, and discusses the subsystem technologies (i.e., nuclear reactors, power conversion and electric thrusters) and system concepts being considered for the first generation of NEP vehicles.

Development and application of deep convolutional neural network in target detection

NASA Astrophysics Data System (ADS)

Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

2018-04-01

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

Deep Space Detection of Oriented Ice Crystals

NASA Astrophysics Data System (ADS)

Marshak, A.; Varnai, T.; Kostinski, A. B.

2017-12-01

The deep space climate observatory (DSCOVR) spacecraft resides at the first Lagrangian point about one million miles from Earth. A polychromatic imaging camera onboard delivers nearly hourly observations of the entire sun-lit face of the Earth. Many images contain unexpected bright flashes of light over both ocean and land. We constructed a yearlong time series of flash latitudes, scattering angles and oxygen absorption to demonstrate conclusively that the flashes over land are specular reflections off tiny ice crystals floating in the air nearly horizontally. Such deep space detection of tropospheric ice can be used to constrain the likelihood of oriented crystals and their contribution to Earth albedo.

Space Programs Summary 37-33. Volume 3. The Deep Space Network for the period 1 March-30 April 1965

DTIC Science & Technology

1965-05-31

designed to communicate To improve the data rate and distance capability, a 210-ft with, and permit control of, spacecraft designed for deep antenna is...51 experienced doppler problems. It was neces- tracking momentarily to make this change. It was de - sary to determine the bias oscillator frequencies...is being designed and constructed for the Mars site of the Gold- stone space communications station. The operating fre- quency of the AAS will be at

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Deep Space 1 Ion Engine Completed a 3-Year Journey

NASA Technical Reports Server (NTRS)

Sovey, James S.; Patterson, Michael J.; Rawlin, Vincent K.; Hamley, John A.

2001-01-01

A xenon ion engine and power processor system, which was developed by the NASA Glenn Research Center in partnership with the Jet Propulsion Laboratory and Boeing Electron Dynamic Devices, completed nearly 3 years of operation aboard the Deep Space 1 spacecraft. The 2.3-kW ion engine, which provided primary propulsion and two-axis attitude control, thrusted for more than 16,000 hr and consumed more than 70 kg of xenon propellant. The Deep Space 1 spacecraft was launched on October 24, 1998, to validate 12 futuristic technologies, including the ion-propulsion system. After the technology validation process was successfully completed, the Deep Space 1 spacecraft flew by the small asteroid Braille on July 29, 1999. The final objective of this mission was to encounter the active comet Borrelly, which is about 6 miles long. The ion engine was on a thrusting schedule to navigate the Deep Space 1 spacecraft to within 1400 miles of the comet. Since the hydrazine used for spacecraft attitude control was in short supply, the ion engine also provided two-axis attitude control to conserve the hydrazine supply for the Borrelly encounter. The comet encounter took place on September 22, 2001. Dr. Marc Rayman, project manager of Deep Space 1 at the Jet Propulsion Laboratory said, "Deep Space 1 plunged into the heart of the comet Borrelly and has lived to tell every detail of its spinetingling adventure! The images are even better than the impressive images of comet Halley taken by Europe's Giotto spacecraft in 1986." The Deep Space 1 mission, which successfully tested the 12 high-risk, advanced technologies and captured the best images ever taken of a comet, was voluntarily terminated on December 18, 2001. The successful demonstration of the 2-kW-class ion propulsion system technology is now providing mission planners with off-the-shelf flight hardware. Higher power, next generation ion propulsion systems are being developed for large flagship missions, such as outer planet

Towards testing quantum physics in deep space

NASA Astrophysics Data System (ADS)

Kaltenbaek, Rainer

2016-07-01

MAQRO is a proposal for a medium-sized space mission to use the unique environment of deep space in combination with novel developments in space technology and quantum technology to test the foundations of physics. The goal is to perform matter-wave interferometry with dielectric particles of up to 10^{11} atomic mass units and testing for deviations from the predictions of quantum theory. Novel techniques from quantum optomechanics with optically trapped particles are to be used for preparing the test particles for these experiments. The core elements of the instrument are placed outside the spacecraft and insulated from the hot spacecraft via multiple thermal shields allowing to achieve cryogenic temperatures via passive cooling and ultra-high vacuum levels by venting to deep space. In combination with low force-noise microthrusters and inertial sensors, this allows realizing an environment well suited for long coherence times of macroscopic quantum superpositions and long integration times. Since the original proposal in 2010, significant progress has been made in terms of technology development and in refining the instrument design. Based on these new developments, we submitted/will submit updated versions of the MAQRO proposal in 2015 and 2016 in response to Cosmic-Vision calls of ESA for a medium-sized mission. A central goal has been to address and overcome potentially critical issues regarding the readiness of core technologies and to provide realistic concepts for further technology development. We present the progress on the road towards realizing this ground-breaking mission harnessing deep space in novel ways for testing the foundations of physics, a technology pathfinder for macroscopic quantum technology and quantum optomechanics in space.

Key Challenges for Life Science Payloads on the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Anthony, J. H.; Niederwieser, T.; Zea, L.; Stodieck, L.

2018-02-01

Compared to ISS, Deep Space Gateway life science payloads will be challenged by deep space radiation and non-continuous habitation. The impacts of these two differences on payload requirements, design, and operations are discussed.

Deep Space 1 moves to CCAS for testing

NASA Technical Reports Server (NTRS)

1998-01-01

KSC workers lower the 'can' over Deep Space 1. The can will protect the spacecraft during transport to the Defense Satellite Communications System Processing Facility (DPF), Cape Canaveral Air Station, for testing. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include a solar-powered ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. The ion propulsion engine is the first non- chemical propulsion to be used as the primary means of propelling a spacecraft. Deep Space 1 will complete most of its mission objectives within the first two months, but may also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999. The spacecraft will be launched aboard a Boeing Delta 7326 rocket from Launch Pad 17A, Cape Canaveral Air Station, in October. Delta II rockets are medium capacity expendable launch vehicles derived from the Delta family of rockets built and launched since 1960. Since then there have been more than 245 Delta launches.

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Magnetoencephalographic imaging of deep corticostriatal network activity during a rewards paradigm.

PubMed

Kanal, Eliezer Y; Sun, Mingui; Ozkurt, Tolga E; Jia, Wenyan; Sclabassi, Robert

2009-01-01

The human rewards network is a complex system spanning both cortical and subcortical regions. While much is known about the functions of the various components of the network, research on the behavior of the network as a whole has been stymied due to an inability to detect signals at a high enough temporal resolution from both superficial and deep network components simultaneously. In this paper, we describe the application of magnetoencephalographic imaging (MEG) combined with advanced signal processing techniques to this problem. Using data collected while subjects performed a rewards-related gambling paradigm demonstrated to activate the rewards network, we were able to identify neural signals which correspond to deep network activity. We also show that this signal was not observable prior to filtration. These results suggest that MEG imaging may be a viable tool for the detection of deep neural activity.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Deep multi-scale convolutional neural network for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Constrained coding for the deep-space optical channel

NASA Technical Reports Server (NTRS)

Moision, B. E.; Hamkins, J.

2002-01-01

We investigate methods of coding for a channel subject to a large dead-time constraint, i.e. a constraint on the minimum spacing between transmitted pulses, with the deep-space optical channel as the motivating example.

Cough event classification by pretrained deep neural network.

PubMed

Liu, Jia-Ming; You, Mingyu; Wang, Zheng; Li, Guo-Zheng; Xu, Xianghuai; Qiu, Zhongmin

2015-01-01

Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. In this paper, we tried pretrained deep neural network in

Optimizing interplanetary trajectories with deep space maneuvers. M.S. Thesis

NASA Technical Reports Server (NTRS)

Navagh, John

1993-01-01

Analysis of interplanetary trajectories is a crucial area for both manned and unmanned missions of the Space Exploration Initiative. A deep space maneuver (DSM) can improve a trajectory in much the same way as a planetary swingby. However, instead of using a gravitational field to alter the trajectory, the on-board propulsion system of the spacecraft is used when the vehicle is not near a planet. The purpose is to develop an algorithm to determine where and when to use deep space maneuvers to reduce the cost of a trajectory. The approach taken to solve this problem uses primer vector theory in combination with a non-linear optimizing program to minimize Delta(V). A set of necessary conditions on the primer vector is shown to indicate whether a deep space maneuver will be beneficial. Deep space maneuvers are applied to a round trip mission to Mars to determine their effect on the launch opportunities. Other studies which were performed include cycler trajectories and Mars mission abort scenarios. It was found that the software developed was able to locate quickly DSM's which lower the total Delta(V) on these trajectories.

NASA's Space Launch System: Deep-Space Delivery for SmallSats

NASA Technical Reports Server (NTRS)

Robinson, Kimberly F.; Norris, George

2017-01-01

Designed for human exploration missions into deep space, NASA's Space Launch System (SLS) represents a new spaceflight infrastructure asset, enabling a wide variety of unique utilization opportunities. While primarily focused on launching the large systems needed for crewed spaceflight beyond Earth orbit, SLS also offers a game-changing capability for the deployment of small satellites to deep-space destinations, beginning with its first flight. Currently, SLS is making rapid progress toward readiness for its first launch in two years, using the initial configuration of the vehicle, which is capable of delivering more than 70 metric tons (t) to Low Earth Orbit (LEO). Planning is underway for smallsat accomodations on future configurations of the vehicle, which will present additional opportunities. This paper will include an overview of the SLS vehicle and its capabilities, including the current status of progress toward first launch. It will also explain the current and future opportunities the vehicle offers for small satellites, including an overview of the CubeSat manifest for Exploration Mission-1 in 2018 and a discussion of future capabilities.

Deep Space Station (DSS-13) automation demonstration

NASA Technical Reports Server (NTRS)

Remer, D. S.; Lorden, G.

1980-01-01

The data base collected during a six month demonstration of an automated Deep Space Station (DSS 13) run unattended and remotely controlled is summarized. During this period, DSS 13 received spacecraft telemetry data from Voyager, Pioneers 10 and 11, and Helios projects. Corrective and preventive maintenance are reported by subsystem including the traditional subsystems and those subsystems added for the automation demonstration. Operations and maintenance data for a comparable manned Deep Space Station (DSS 11) are also presented for comparison. The data suggests that unattended operations may reduce maintenance manhours in addition to reducing operator manhours. Corrective maintenance for the unmanned station was about one third of the manned station, and preventive maintenance was about one half.

Background rejection in NEXT using deep neural networks

DOE PAGES

Renner, J.; Farbin, A.; Vidal, J. Muñoz; ...

2017-01-16

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the usemore » of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.« less

Advancing Navigation, Timing, and Science with the Deep Space Atomic Clock

NASA Technical Reports Server (NTRS)

Ely, Todd A.; Seubert, Jill; Bell, Julia

2014-01-01

NASA's Deep Space Atomic Clock mission is developing a small, highly stable mercury ion atomic clock with an Allan deviation of at most 1e-14 at one day, and with current estimates near 3e-15. This stability enables one-way radiometric tracking data with accuracy equivalent to and, in certain conditions, better than current two-way deep space tracking data; allowing a shift to a more efficient and flexible one-way deep space navigation architecture. DSAC-enabled one-way tracking will benefit navigation and radio science by increasing the quantity and quality of tracking data. Additionally, DSAC would be a key component to fully-autonomous onboard radio navigation useful for time-sensitive situations. Potential deep space applications of DSAC are presented, including orbit determination of a Mars orbiter and gravity science on a Europa flyby mission.

Utilization of the Deep Space Atomic Clock for Europa Gravitational Tide Recovery

NASA Technical Reports Server (NTRS)

Seubert, Jill; Ely, Todd

2015-01-01

Estimation of Europa's gravitational tide can provide strong evidence of the existence of a subsurface liquid ocean. Due to limited close approach tracking data, a Europa flyby mission suffers strong coupling between the gravity solution quality and tracking data quantity and quality. This work explores utilizing Low Gain Antennas with the Deep Space Atomic Clock (DSAC) to provide abundant high accuracy uplink-only radiometric tracking data. DSAC's performance, expected to exhibit an Allan Deviation of less than 3e-15 at one day, provides long-term stability and accuracy on par with the Deep Space Network ground clocks, enabling one-way radiometric tracking data with accuracy equivalent to that of its two-way counterpart. The feasibility of uplink-only Doppler tracking via the coupling of LGAs and DSAC and the expected Doppler data quality are presented. Violations of the Kalman filter's linearization assumptions when state perturbations are included in the flyby analysis results in poor determination of the Europa gravitational tide parameters. B-plane targeting constraints are statistically determined, and a solution to the linearization issues via pre-flyby approach orbit determination is proposed and demonstrated.

Analysis of Near-field of Circular Aperture Antennas with Application to Study of High Intensity Radio Frequency (HIRF) Hazards to Aviation from JPL/NASA Deep Space Network Antennas

NASA Technical Reports Server (NTRS)

Jamnejad, Vahraz; Statman, Joseph

2013-01-01

This work includes a simplified analysis of the radiated near to mid-field from JPL/NASA Deep Space Network (DSN) reflector antennas and uses an averaging technique over the main beam region and beyond for complying with FAA regulations in specific aviation environments. The work identifies areas that require special attention, including the implications of the very narrow beam of the DSN transmitters. The paper derives the maximum averaged power densities allowed and identifies zones where mitigation measures are required.

Space-Data Routers: Enhancing Deep Space communications for scientific data transmission and exploitation from Mars through Space Internetworking

NASA Astrophysics Data System (ADS)

Sykioti, Olga; Daglis, Ioannis; Rontogiannis, Athanasios; Tsaoussidis, Vassilis; Diamantopoulos, Sotirios

2014-05-01

Dissemination and exploitation of data from Deep Space missions, such as planetary missions, face two major impediments: limited access capabilities due to narrow connectivity window via satellites (thus, resulting to confined scientific capacity) and lack of sufficient communication and dissemination mechanisms between deep space missions such the current missions to Mars, space data receiving centers, space-data collection centers and the end-user community. Although large quantities of data have to be transferred from deep space to the operation centers and then to the academic foundations and research centers, due to the aforementioned impediments more and more stored space data volumes remain unexploited, until they become obsolete or useless and are consequently removed. In the near future, these constraints on space and ground segment resources will rapidly increase due to the launch of new missions. The Space-Data Routers (SDR) project aims into boosting collaboration and competitiveness between the European Space Agency, the European Space Industry and the European Academic Institutions towards meeting these new challenges through Space Internetworking. Space internetworking gradually replaces or assists traditional telecommunication protocols. Future deep space operations, such as those to Mars, are scheduled to be more dynamic and flexible; many of the procedures, which are now human-operated, will become automated, interoperable and collaborative. As a consequence, space internetworking will bring a revolution in space communications. For this purpose, one of the main scientific objectives of the project is, through the examination of a specific scenario, the enhanced transmission and dissemination of Deep Space data from Mars, through unified communication channels. Specifically, the scenario involves enhanced data transmission acquired by the OMEGA sensor on-board ESA's Mars Express satellite. We consider two separate issues considering the

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

Using Autonomous Bio Nanosatellites for Deep Space Exploration

NASA Astrophysics Data System (ADS)

Santa Maria, S. R.; Liddell, L. C.; Tieze, S. M.; Ricco, A. J.; Hanel, R.; Bhattacharya, S.

2018-02-01

NASA's BioSentinel mission will conduct the first study of biological response to deep-space radiation in 45 years. It is an automated nanosatellite that will measure the DNA damage response to ambient space radiation in a model biological organism.

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

PubMed

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2017-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

PubMed Central

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2018-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them. PMID:27896980

The space physics analysis network

NASA Astrophysics Data System (ADS)

Green, James L.

1988-04-01

The Space Physics Analysis Network, or SPAN, is emerging as a viable method for solving an immediate communication problem for space and Earth scientists and has been operational for nearly 7 years. SPAN and its extension into Europe, utilizes computer-to-computer communications allowing mail, binary and text file transfer, and remote logon capability to over 1000 space science computer systems. The network has been used to successfully transfer real-time data to remote researchers for rapid data analysis but its primary function is for non-real-time applications. One of the major advantages for using SPAN is its spacecraft mission independence. Space science researchers using SPAN are located in universities, industries and government institutions all across the United States and Europe. These researchers are in such fields as magnetospheric physics, astrophysics, ionosperic physics, atmospheric physics, climatology, meteorology, oceanography, planetary physics and solar physics. SPAN users have access to space and Earth science data bases, mission planning and information systems, and computational facilities for the purposes of facilitating correlative space data exchange, data analysis and space research. For example, the National Space Science Data Center (NSSDC), which manages the network, is providing facilities on SPAN such as the Network Information Center (SPAN NIC). SPAN has interconnections with several national and international networks such as HEPNET and TEXNET forming a transparent DECnet network. The combined total number of computers now reachable over these combined networks is about 2000. In addition, SPAN supports full function capabilities over the international public packet switched networks (e.g. TELENET) and has mail gateways to ARPANET, BITNET and JANET.

Matroshka AstroRad Radiation Experiment (MARE) on the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Gaza, R.; Hussein, H.; Murrow, D.; Hopkins, J.; Waterman, G.; Milstein, O.; Berger, T.; Przybyla, B.; Aeckerlein, J.; Marsalek, K.; Matthiae, D.; Rutczynska, A.

2018-02-01

The Matroshka AstroRad Radiation Experiment is a science payload on Orion EM-1 flight. A research platform derived from MARE is proposed for the Deep Space Gateway. Feedback is invited on desired Deep Space Gateway design features to maximize its science potential.

Deep Space 1 fairing arrives at pad 17A for launch

NASA Technical Reports Server (NTRS)

1998-01-01

The fairing for Deep Space 1 is raised upright before being lifted on the Mobile Service Tower to its place on the Boeing Delta 7326 rocket that will launch on Oct. 15, 1998. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 fairing arrives at pad 17A for launch

NASA Technical Reports Server (NTRS)

1998-01-01

The fairing for Deep Space 1 nears the top of the Mobile Service Tower before being attached to the Boeing Delta 7326 rocket that will launch on Oct. 15, 1998. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 fairing arrives at pad 17A for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers watch as the fairing for Deep Space 1 is lifted on the Mobile Service Tower to its place on the Boeing Delta 7326 rocket that will launch on Oct. 15, 1998. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

DEEP SPACE: High Resolution VR Platform for Multi-user Interactive Narratives

NASA Astrophysics Data System (ADS)

Kuka, Daniela; Elias, Oliver; Martins, Ronald; Lindinger, Christopher; Pramböck, Andreas; Jalsovec, Andreas; Maresch, Pascal; Hörtner, Horst; Brandl, Peter

DEEP SPACE is a large-scale platform for interactive, stereoscopic and high resolution content. The spatial and the system design of DEEP SPACE are facing constraints of CAVETM-like systems in respect to multi-user interactive storytelling. To be used as research platform and as public exhibition space for many people, DEEP SPACE is capable to process interactive, stereoscopic applications on two projection walls with a size of 16 by 9 meters and a resolution of four times 1080p (4K) each. The processed applications are ranging from Virtual Reality (VR)-environments to 3D-movies to computationally intensive 2D-productions. In this paper, we are describing DEEP SPACE as an experimental VR platform for multi-user interactive storytelling. We are focusing on the system design relevant for the platform, including the integration of the Apple iPod Touch technology as VR control, and a special case study that is demonstrating the research efforts in the field of multi-user interactive storytelling. The described case study, entitled "Papyrate's Island", provides a prototypical scenario of how physical drawings may impact on digital narratives. In this special case, DEEP SPACE helps us to explore the hypothesis that drawing, a primordial human creative skill, gives us access to entirely new creative possibilities in the domain of interactive storytelling.

Gas Classification Using Deep Convolutional Neural Networks.

PubMed

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Gas Classification Using Deep Convolutional Neural Networks

PubMed Central

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Wrapped in an anti-static blanket for protection, Deep Space 1 is moved out of the Defense Satellite Communications Systems Processing Facility (DPF) at Cape Canaveral Air Station (CCAS) for its trip to Launch Pad 17A. The spacecraft will be launched aboard a Boeing Delta 7326 rocket on Oct. 25. Deep Space 1 is the first flight in NASA's New Millennium Program, and is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

In the Defense Satellite Communications Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), the lower part of Deep Space 1 is enclosed with the conical section leaves of the payload transportation container prior to its move to Launch Pad 17A. The spacecraft is targeted for launch Oct. 25 aboard a Boeing Delta 7326 rocket. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight-tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Deep Space 1 is prepared for transport to launch pad

NASA Technical Reports Server (NTRS)

1998-01-01

Workers in the Defense Satellite Communication Systems Processing Facility (DPF), Cape Canaveral Air Station (CCAS), begin attaching the conical section leaves of the payload transportation container on Deep Space 1 before launch, targeted for Oct. 25 aboard a Boeing Delta 7326 rocket from Launch Pad 17A. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century, including the engine. Propelled by the gas xenon, the engine is being flight- tested for future deep space and Earth-orbiting missions. Deceptively powerful, the ion drive emits only an eerie blue glow as ionized atoms of xenon are pushed out of the engine. While slow to pick up speed, over the long haul it can deliver 10 times as much thrust per pound of fuel as liquid or solid fuel rockets. Other onboard experiments include software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

The Gateway Garden — A Prototype Food Production Facility for Deep Space Exploration

NASA Astrophysics Data System (ADS)

Fritsche, R. F.; Romeyn, M. W.; Massa, G.

2018-02-01

CIS-lunar space provides a unique opportunity to perform deep space microgravity crop science research while also addressing and advancing food production technologies that will be deployed on the Deep Space Transport.

Delay/Disruption Tolerance Networking (DTN) Implementation and Utilization Options on the International Space Station

NASA Technical Reports Server (NTRS)

Holbrook, Mark; Pitts, Robert Lee; Gifford, Kevin K.; Jenkins, Andrew; Kuzminsky, Sebastian

2010-01-01

The International Space Station (ISS) is in an operational configuration and nearing final assembly. With its maturity and diverse payloads onboard, the opportunity exists to extend the orbital lab into a facility to exercise and demonstrate Delay/Disruption Tolerant Networking (DTN). DTN is an end-to-end network service providing communications through environments characterized by intermittent connectivity, variable delays, high bit error rates, asymmetric links and simplex links. The DTN protocols, also known as bundle protocols, provide a store-and-forward capability to accommodate end-to-end network services. Key capabilities of the bundling protocols include: the Ability to cope with intermittent connectivity, the Ability to take advantage of scheduled and opportunistic connectivity (in addition to always up connectivity), Custody Transfer, and end-to-end security. Colorado University at Boulder and the Huntsville Operational Support Center (HOSC) have been developing a DTN capability utilizing the Commercial Generic Bioprocessing Apparatus (CGBA) payload resources onboard the ISS, at the Boulder Payload Operations Center (POC) and at the HOSC. The DTN capability is in parallel with and is designed to augment current capabilities. The architecture consists of DTN endpoint nodes on the ISS and at the Boulder POC, and a DTN node at the HOSC. The DTN network is composed of two implementations; the Interplanetary Overlay Network (ION) and the open source DTN2 implementation. This paper presents the architecture, implementation, and lessons learned. By being able to handle the types of environments described above, the DTN technology will be instrumental in extending networks into deep space to support future missions to other planets and other solar system points of interest. Thus, this paper also discusses how this technology will be applicable to these types of deep space exploration missions.

NASA's Space Launch System: A Transformative Capability for Deep Space Missions

NASA Technical Reports Server (NTRS)

Creech, Stephen D.

2017-01-01

Already making substantial progress toward its first launches, NASA’s Space Launch System (SLS) exploration-class launch vehicle presents game-changing new opportunities in spaceflight, enabling human exploration of deep space, as well as a variety of missions and mission profiles that are currently impossible. Today, the initial configuration of SLS, able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), is well into final production and testing ahead of its planned first flight, which will send NASA’s new Orion crew vehicle around the moon and will deploy 13 CubeSats, representing multiple disciplines, into deep space. At the same time, production work is already underway toward the more-capable Block 1B configuration, planned to debut on the second flight of SLS, and capable of lofting 105 tons to LEO or of co-manifesting large exploration systems with Orion on launches to the lunar vicinity. Progress being made on the vehicle for that second flight includes initial welding of its core stage and testing of one of its engines, as well as development of new elements such as the powerful Exploration Upper Stage and the Universal Stage Adapter “payload bay.” Ultimately, SLS will evolve to a configuration capable of delivering more than 130 tons to LEO to support humans missions to Mars. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles or substantially increased spacecraft mass. In the field of astrophysics, SLS’ high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe. This presentation will give an overview of SLS�

Using DSG to Build the Capability of Space Weather Forecasting in Deep Space

NASA Astrophysics Data System (ADS)

DeLuca, E. E.; Golub, L.; Korreck, K.; Savage, S.; McKenzie, D. D.; Rachmeler, L.; Winebarger, A.; Martens, P.

2018-02-01

The prospect of astronaut missions to deep space and off the Sun-Earth line raises new challenges for space weather awareness and forecasting. We need to identify the requirements and pathways that will allow us to protect human life and equipment.

Deep Space Test Bed for Radiation Studies

NASA Technical Reports Server (NTRS)

Adams, James H.; Christl, Mark; Watts, John; Kuznetsov, Eugene; Lin, Zi-Wei

2006-01-01

A key factor affecting the technical feasibility and cost of missions to Mars or the Moon is the need to protect the crew from ionizing radiation in space. Some analyses indicate that large amounts of spacecraft shielding may be necessary for crew safety. The shielding requirements are driven by the need to protect the crew from Galactic cosmic rays (GCR). Recent research activities aimed at enabling manned exploration have included shielding materials studies. A major goal of this research is to develop accurate radiation transport codes to calculate the shielding effectiveness of materials and to develop effective shielding strategies for spacecraft design. Validation of these models and calculations must be addressed in a relevant radiation environment to assure their technical readiness and accuracy. Test data obtained in the deep space radiation environment can provide definitive benchmarks and yield uncertainty estimates of the radiation transport codes. The two approaches presently used for code validation are ground based testing at particle accelerators and flight tests in high-inclination low-earth orbits provided by the shuttle, free-flyer platforms, or polar-orbiting satellites. These approaches have limitations in addressing all the radiation-shielding issues of deep space missions in both technical and practical areas. An approach based on long duration high altitude polar balloon flights provides exposure to the galactic cosmic ray composition and spectra encountered in deep space at a lower cost and with easier and more frequent access than afforded with spaceflight opportunities. This approach also results in shorter development times than spaceflight experiments, which is important for addressing changing program goals and requirements.

Deep Space Systems Technology Program Future Deliveries

NASA Technical Reports Server (NTRS)

Salvo, Christopher G.; Keuneke, Matthew S.

2000-01-01

NASA is in a period of frequent launches of low cost deep space missions with challenging performance needs. The modest budgets of these missions make it impossible for each to develop its own technology, therefore, efficient and effective development and insertion of technology for these missions must be approached at a higher level than has been done in the past. The Deep Space Systems Technology Program (DSST), often referred to as X2000, has been formed to address this need. The program is divided into a series of "Deliveries" that develop and demonstrate a set of spacecraft system capabilities with broad applicability for use by multiple missions. The First Delivery Project, to be completed in 2001, will provide a one MRAD-tolerant flight computer, power switching electronics, efficient radioisotope power source, and a transponder with services at 8.4 GHz and 32 GHz bands. Plans call for a Second Delivery in late 2003 to enable complete deep space systems in the 10 to 50 kg class, and a Third Delivery built around Systems on a Chip (extreme levels of electronic and microsystems integration) around 2006. Formulation of Future Deliveries (past the First Delivery) is ongoing and includes plans for such developments as highly miniaturized digital/analog/power electronics, optical communications, multifunctional structures, miniature lightweight propulsion, advanced thermal control techniques, highly efficient radioisotope power sources, and a unified flight ground software architecture to support the needs of future highly intelligent space systems. All developments are targeted at broad applicability and reuse, and will be commercialized within the US.

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, Lawrence A.; Hodas, Nathan O.

Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning. One use for deep learning is in language acquisition where it is useful to know if a linguistic phenomenon can be learned through domain-general means. To assess whether unsupervised deep learning is appropriate, we first pose a smaller question: Can unsupervised neural networks apply linguistic rules productively, using them in novel situations. We draw from the literature on determiner/noun productivity by training an unsupervised, autoencoder network measuring its ability to combine nouns with determiners. Our simple autoencoder creates combinations it has not previously encountered, displaying a degree ofmore » overlap similar to actual children. While this preliminary work does not provide conclusive evidence for productivity, it warrants further investigation with more complex models. Further, this work helps lay the foundations for future collaboration between the deep learning and cognitive science communities.« less

Evaluation of current tropospheric mapping functions by Deep Space Network very long baseline interferometry

NASA Technical Reports Server (NTRS)

Sovers, O. J.; Lanyi, G. E.

1994-01-01

To compare the validity of current algorithms that map zenith tropospheric delay to arbitrary elevation angles, 10 different tropospheric mapping functions are used to analyze the current data base of Deep Space Network Mark 3 intercontinental very long baseline interferometric (VLBI) data. This analysis serves as a stringent test because of the high proportion of low-elevation observations necessitated by the extremely long baselines. Postfit delay and delay-rate residuals are examined, as well as the scatter of baseline lengths about the time-linear model that characterizes tectonic motion. Among the functions that utilize surface meteorological data as input parameters, the Lanyi 1984 mapping shows the best performance both for residuals and baselines, through the 1985 Davis function is statistically nearly identical. The next best performance is shown by the recent function of Niell, which is based on an examination of global atmospheric characteristics as a function of season and uses no weather data at the time of the measurements. The Niell function shows a slight improvement in residuals relative to Lanyi, but also an increase in baseline scatter that is significant for the California-Spain baseline. Two variants of the Chao mapping function, as well as the Chao tables used with the interpolation algorithm employed in the Orbit Determination Program software, show substandard behavior for both VLBI residuals and baseline scatter. The length of the California-Australia baseline (10,600 km) in the VLBI solution can vary by as much as 5 to 10 cm for the 10 mapping functions.

Deep Space 1 fairing arrives at pad 17A for launch

NASA Technical Reports Server (NTRS)

1998-01-01

Workers check the position of the fairing for Deep Space 1 as it reaches the top of the Mobile Service Tower where it will be attached to the Boeing Delta 7326 rocket that will launch on Oct. 15, 1998. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. Onboard experiments include an ion propulsion engine and software that tracks celestial bodies so the spacecraft can make its own navigation decisions without the intervention of ground controllers. Deep Space 1 will complete most of its mission objectives within the first two months, but will also do a flyby of a near-Earth asteroid, 1992 KD, in July 1999.

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

Characterization of Outer Space Radiation Induced Changes in Extremophiles Utilizing Deep Space Gateway Opportunities

NASA Astrophysics Data System (ADS)

Venkateswaran, K.; Wang, C.; Smith, D.; Mason, C.; Landry, K.; Rettberg, P.

2018-02-01

Extremophilic microbial survival, adaptation, biological functions, and molecular mechanisms associated with outer space radiation can be tested by exposing them onto Deep Space Gateway hardware (inside/outside) using microbiology and molecular biology techniques.

Issues and Design Drivers for Deep Space Habitats

NASA Technical Reports Server (NTRS)

Rucker, Michelle A.; Anderson, Molly

2012-01-01

A cross-disciplinary team of scientists and engineers applied expertise gained in Lunar Lander development to the conceptual design of a long-duration, deep space habitat for Near Earth Asteroid (NEA) missions. The design reference mission involved two launches to assemble 5-modules for a 380-day round trip mission carrying 4 crew members. The conceptual design process yielded a number of interesting debates, some of which could be significant design drivers in a detailed Deep Space Habitat (DSH) design. These issues included: Design to minimize crew radiation exposure, launch loads, communications challenges, docking system and hatch commonality, pointing and visibility, consumables, and design for contingency operations.

Evaluating Space Weather Architecture Options to Support Human Deep Space Exploration of the Moon and Mars

NASA Astrophysics Data System (ADS)

Parker, L.; Minow, J.; Pulkkinen, A.; Fry, D.; Semones, E.; Allen, J.; St Cyr, C.; Mertens, C.; Jun, I.; Onsager, T.; Hock, R.

2018-02-01

NASA's Engineering and Space Center (NESC) is conducting an independent technical assessment of space environment monitoring and forecasting architecture options to support human and robotic deep space exploration.

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.

PubMed

Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima

2016-03-01

A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.

Madrid space station

NASA Technical Reports Server (NTRS)

Fahnestock, R. J.; Renzetti, N. A.

1975-01-01

The Madrid space station, operated under bilateral agreements between the governments of the United States and Spain, is described in both Spanish and English. The space station utilizes two tracking and data acquisition networks: the Deep Space Network (DSN) of the National Aeronautics and Space Administration and the Spaceflight Tracking and Data Network (STDN) operated under the direction of the Goddard Space Flight Center. The station, which is staffed by Spanish employees, comprises four facilities: Robledo 1, Cebreros, and Fresnedillas-Navalagamella, all with 26-meter-diameter antennas, and Robledo 2, with a 64-meter antenna.

Deep Space Positioning System

NASA Technical Reports Server (NTRS)

Vaughan, Andrew T. (Inventor); Riedel, Joseph E. (Inventor)

2016-01-01

A single, compact, lower power deep space positioning system (DPS) configured to determine a location of a spacecraft anywhere in the solar system, and provide state information relative to Earth, Sun, or any remote object. For example, the DPS includes a first camera and, possibly, a second camera configured to capture a plurality of navigation images to determine a state of a spacecraft in a solar system. The second camera is located behind, or adjacent to, a secondary reflector of a first camera in a body of a telescope.

A Ka-Band Celestial Reference Frame with Applications to Deep Space Navigation

NASA Technical Reports Server (NTRS)

Jacobs, Christopher S.; Clark, J. Eric; Garcia-Miro, Cristina; Horiuchi, Shinji; Sotuela, Ioana

2011-01-01

The Ka-band radio spectrum is now being used for a wide variety of applications. This paper highlights the use of Ka-band as a frequency for precise deep space navigation based on a set of reference beacons provided by extragalactic quasars which emit broadband noise at Ka-band. This quasar-based celestial reference frame is constructed using X/Ka-band (8.4/32 GHz) from fifty-five 24-hour sessions with the Deep Space Network antennas in California, Australia, and Spain. We report on observations which have detected 464 sources covering the full 24 hours of Right Ascension and declinations down to -45 deg. Comparison of this X/Ka-band frame to the international standard S/X-band (2.3/8.4 GHz) ICRF2 shows wRMS agreement of approximately 200 micro-arcsec in alpha cos(delta) and approximately 300 micro-arcsec in delta. There is evidence for systematic errors at the 100 micro-arcsec level. Known errors include limited SNR, lack of instrumental phase calibration, tropospheric refraction mis-modeling, and limited southern geometry. The motivation for extending the celestial reference frame to frequencies above 8 GHz is to access more compact source morphology for improved frame stability and to support spacecraft navigation for Ka-band based NASA missions.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

Time Synchronization and Distribution Mechanisms for Space Networks

NASA Technical Reports Server (NTRS)

Woo, Simon S.; Gao, Jay L.; Clare, Loren P.; Mills, David L.

2011-01-01

This work discusses research on the problems of synchronizing and distributing time information between spacecraft based on the Network Time Protocol (NTP), where NTP is a standard time synchronization protocol widely used in the terrestrial network. The Proximity-1 Space Link Interleaved Time Synchronization (PITS) Protocol was designed and developed for synchronizing spacecraft that are in proximity where proximity is less than 100,000 km distant. A particular application is synchronization between a Mars orbiter and rover. Lunar scenarios as well as outer-planet deep space mother-ship-probe missions may also apply. Spacecraft with more accurate time information functions as a time-server, and the other spacecraft functions as a time-client. PITS can be easily integrated and adaptable to the CCSDS Proximity-1 Space Link Protocol with minor modifications. In particular, PITS can take advantage of the timestamping strategy that underlying link layer functionality provides for accurate time offset calculation. The PITS algorithm achieves time synchronization with eight consecutive space network time packet exchanges between two spacecraft. PITS can detect and avoid possible errors from receiving duplicate and out-of-order packets by comparing with the current state variables and timestamps. Further, PITS is able to detect error events and autonomously recover from unexpected events that can possibly occur during the time synchronization and distribution process. This capability achieves an additional level of protocol protection on top of CRC or Error Correction Codes. PITS is a lightweight and efficient protocol, eliminating the needs for explicit frame sequence number and long buffer storage. The PITS protocol is capable of providing time synchronization and distribution services for a more general domain where multiple entities need to achieve time synchronization using a single point-to-point link.

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

NASA's Space Launch System: Deep-Space Deployment for SmallSats

NASA Technical Reports Server (NTRS)

Schorr, Andy

2017-01-01

From its upcoming first flight, NASA's new Space Launch System (SLS) will represent a game-changing opportunity for smallsats. On that launch, which will propel the Orion crew vehicle around the moon, the new exploration-class launch vehicle will deploy 13 6U CubeSats into deep-space, where they will continue to a variety of destinations to perform diverse research and demonstrations. Following that first flight, SLS will undergo the first of a series of performance upgrades, increasing its payload capability to low Earth orbit from 70 to 105 metric tons via the addition of a powerful upper stage. With that change to the vehicle's architecture, so too will its secondary payload accommodation for smallsats evolve, with current plans calling for a change from the first-flight limit of 6U to accommodating a range of sizes up to 27U and potentially ESPA-class payloads. This presentation will provide an overview and update on the first launch of SLS and the secondary payloads it will deploy. Currently, flight hardware has been produced for every element of the vehicle, testing of the vehicle's propulsion elements has been ongoing for years, and structural testing of its stages has begun. Major assembly and testing of the Orion Stage Adapter, including the secondary payload accommodations, will be completed this year, and the structure will then be shipped to Kennedy Space Center for integration of the payloads. Progress is being made on those CubeSats, which will include studies of asteroids, Earth, the sun, the moon, and the impacts of radiation on organisms in deep space. They will feature revolutionary innovations for smallsats, including demonstrations of use of a solar sail as propulsion for a rendezvous with an asteroid, and the landing of a CubeSat on the lunar surface. The presentation will also provide an update on progress of the SLS Block 1B configuration that will be used on the rocket's second flight, a discussion of planned secondary payload accommodations

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

Optical deep space communication via relay satellite

NASA Technical Reports Server (NTRS)

Gagliardi, R. M.; Vilnrotter, V. A.; Dolinar, S. J., Jr.

1981-01-01

The possible use of an optical for high rate data transmission from a deep space vehicle to an Earth-orbiting relay satellite while RF links are envisioned for the relay to Earth link was studied. A preliminary link analysis is presented for initial sizing of optical components and power levels, in terms of achievable data rates and feasible range distances. Modulation formats are restricted to pulsed laser operation, involving bot coded and uncoded schemes. The advantage of an optical link over present RF deep space link capabilities is shown. The problems of acquisition, pointing and tracking with narrow optical beams are presented and discussed. Mathematical models of beam trackers are derived, aiding in the design of such systems for minimizing beam pointing errors. The expected orbital geometry between spacecraft and relay satellite, and its impact on beam pointing dynamics are discussed.

Phase-space networks of geometrically frustrated systems.

PubMed

Han, Yilong

2009-11-01

We illustrate a network approach to the phase-space study by using two geometrical frustration models: antiferromagnet on triangular lattice and square ice. Their highly degenerated ground states are mapped as discrete networks such that the quantitative network analysis can be applied to phase-space studies. The resulting phase spaces share some comon features and establish a class of complex networks with unique Gaussian spectral densities. Although phase-space networks are heterogeneously connected, the systems are still ergodic due to the random Poisson processes. This network approach can be generalized to phase spaces of some other complex systems.

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

PubMed

Ouyang, Wanli; Zeng, Xingyu; Wang, Xiaogang; Qiu, Shi; Luo, Ping; Tian, Yonglong; Li, Hongsheng; Yang, Shuo; Wang, Zhe; Li, Hongyang; Loy, Chen Change; Wang, Kun; Yan, Junjie; Tang, Xiaoou

2016-07-07

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

Mastering the game of Go with deep neural networks and tree search.

PubMed

Silver, David; Huang, Aja; Maddison, Chris J; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis

2016-01-28

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

The Space Mobile Network

NASA Technical Reports Server (NTRS)

Israel, David

2017-01-01

The definition and development of the next generation space communications and navigation architecture is underway. The primary goals are to remove communications and navigations constraints from missions and to enable increased autonomy. The Space Mobile Network (SMN) is an architectural concept that includes new technology and operations that will provide flight systems with an similar user experience to terrestrial wireless mobile networks. This talk will describe the SMN and its proposed new features, such as Disruption Tolerant Networking (DTN), optical communications, and User Initiated Services (UIS).

Impact Flash Monitoring Facility on the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Needham, D. H.; Moser, D. E.; Suggs, R. M.; Cooke, W. J.; Kring, D. A.; Neal, C. R.; Fassett, C. I.

2018-02-01

Cameras mounted to the Deep Space Gateway exterior will detect flashes caused by impacts on the lunar surface. Observed flashes will help constrain the current lunar impact flux and assess hazards faced by crews living and working in cislunar space.

PEPE is installed on Deep Space 1 in the PHSF

NASA Technical Reports Server (NTRS)

1998-01-01

The Plasma Experiment for Planetary Exploration (PEPE), one of two advanced science experiments flying on the Deep Space l mission, is prepared for installation on the spacecraft in the Payload Hazardous Servicing Facility. PEPE combines several instruments that study space plasma in one compact 13-pound (6- kilogram) package. Space plasma is composed of charged particles, most of which flow outward from the Sun. The first flight in NASA's New Millennium Program, Deep Space 1 is designed to validate 12 new technologies for scientific space missions of the next century. The spacecraft is scheduled to launch during a period opening Oct. 15 and closing Nov. 10, 1998. Most of its mission objectives will be completed within the first two months. A near-earth asteroid, 1992 KD, has also been selected for a possible flyby.

Launching a Projectile into Deep Space

ERIC Educational Resources Information Center

Maruszewski, Richard F., Jr.

2004-01-01

As part of the discussion about Newton's work in a history of mathematics course, one of the presentations calculated the amount of energy necessary to send a projectile into deep space. Afterwards, the students asked for a recalculation with two changes: First the launch under study consisted of a single stage, but the students desired to…

NOAA's new deep space solar monitoring satellite launches

Science.gov Websites

Related link: NASA Kennedy Space Center DSCOVR Launch Photos on flickr Media Contact: John Leslie 202-527 forecasts February 11, 2015 Watch the DSCOVR launch on NASA's YouTube channel. (Photo: NASA). NOAA's Deep space mission. (Photo: NASA). NOAA's DSCOVR satellite launch. (Photo: NASA). Visit www.nesdis.noaa.gov

Space-Time Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Shelton, Robert O.

1992-01-01

Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.

Precision time distribution within a deep space communications complex

NASA Technical Reports Server (NTRS)

Curtright, J. B.

1972-01-01

The Precision Time Distribution System (PTDS) at the Golstone Deep Space Communications Complex is a practical application of existing technology to the solution of a local problem. The problem was to synchronize four station timing systems to a master source with a relative accuracy consistently and significantly better than 10 microseconds. The solution involved combining a precision timing source, an automatic error detection assembly and a microwave distribution network into an operational system. Upon activation of the completed PTDS two years ago, synchronization accuracy at Goldstone (two station relative) was improved by an order of magnitude. It is felt that the validation of the PTDS mechanization is now completed. Other facilities which have site dispersion and synchronization accuracy requirements similar to Goldstone may find the PTDS mechanization useful in solving their problem. At present, the two station relative synchronization accuracy at Goldstone is better than one microsecond.

A note on deep space optical communication link parameters

NASA Technical Reports Server (NTRS)

Dolinar, S. J.; Yuen, J. H.

1982-01-01

Topical communication in the context of a deep space communication link. Communication link analysis at the optical frequencies differs significantly from that at microwave frequencies such as the traditional S and X-bands used in deep space applications, due to the different technology of transmitter, antenna, modulators, and receivers. In addition, the important role of quantum noise in limiting system performance is quite different than that of thermal noise. The optical link design is put in a design control table format similar to a microwave telecom link design. Key considerations unique to the optical link are discussed.

Dust Measurements Onboard the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Horanyi, M.; Kempf, S.; Malaspina, D.; Poppe, A.; Srama, R.; Sternovsky, Z.; Szalay, J.

2018-02-01

A dust instrument onboard the Deep Space Gateway will revolutionize our understanding of the dust environment at 1 AU, help our understanding of the evolution of the solar system, and improve dust hazard models for the safety of crewed and robotic missions.

The Importance of Conducting Life Sciences Experiments on the Deep Space Gateway Platform

NASA Astrophysics Data System (ADS)

Bhattacharya, S.

2018-02-01

Life science research on the Deep Space Gateway platform is an important precursor for long term human exploration of deep space. Ideas for utilizing flight hardware and well characterized model organisms will be discussed.

Deep hierarchical attention network for video description

NASA Astrophysics Data System (ADS)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

Autonomous Science Operations Technologies for Deep Space Gateway

NASA Astrophysics Data System (ADS)

Barnes, P. K.; Haddock, A. T.; Cruzen, C. A.

2018-02-01

Autonomous Science Operations Technologies for Deep Space Gateway (DSG) is an overview of how the DSG would benefit from autonomous systems utilizing proven technologies performing telemetry monitoring and science operations.

Advantages of Science Cubesat and Microsat Deployment Using DSG Deep Space Exploration Robotics

NASA Astrophysics Data System (ADS)

Shaw, A.; Rembala, R.; Fulford, P.

2018-02-01

Important scientific missions can be accomplished with cubesats/microsats. These missions would benefit from advantages offered by having an independent cubesat/microsat deployment capability as part of Deep Space Gateway's Deep Space Exploration Robotics system.

Periodically Launched, Dedicated CubeSats/SmallSats for Space Situational Awareness Through NASA Communications Networks

NASA Astrophysics Data System (ADS)

Stromberg, E. M.; Shaw, H.; Estabrook, P.; Neilsen, T. L.; Gunther, J.; Swenson, C.; Fish, C. S.; Schaire, S. H.

2014-12-01

Space Situational Awareness (SSA) is an area where spaceflight activities and missions can directly influence the quality of life on earth. The combination of space weather, near earth orbiting objects, atmospheric conditions at the space boundary, and other phenomena can have significant short-term and long-term implications for the inhabitants of this planet. The importance of SSA has led to increased activity in this area from both space and ground based platforms. The emerging capability of CubeSats and SmallSats provides an opportunity for these low-cost, versatile platforms to augment the SSA infrastructure. The CubeSats and SmallSats can be launched opportunistically with shorter lead times than larger missions. They can be organized both as constellations or individual sensor elements. Combining CubeSats and SmallSats with the existing NASA communications networks (TDRS Space Network, Deep Space Network and the Near Earth Network) provide a backbone structure for SSA which can be tied to a SSA portal for data distribution and management. In this poster we will describe the instruments and sensors needed for CubeSat and SmallSat SSA missions. We will describe the architecture and concept of operations for a set of opportunistic, periodically launched, SSA CubeSats and SmallSats. We will also describe the integrated communications infrastructure to support end-to-end data delivery and management to a SSA portal.

The Development of a Simulator System and Hardware Test Bed for Deep Space X-Ray Navigation

NASA Astrophysics Data System (ADS)

Doyle, Patrick T.

2013-03-01

Currently, there is a considerable interest in developing technologies that will allow using photon measurements from celestial x-ray sources for deep space navigation. The impetus for this is that many envisioned future space missions will require spacecraft to have autonomous navigation capabilities. For missions close to Earth, Global Navigation Satellite Systems (GNSS) such as GPS are readily available for use, but for missions far from Earth, other alternatives must be provided. While existing systems such as the Deep Space Network (DSN) can be used, latencies associated with servicing a fleet of vehicles may not be compatible with some autonomous operations requiring timely updates of their navigation solution. Because of their somewhat predictable emissions, pulsars are the ideal candidates for x-ray sources that can be used to provide key parameters for navigation. Algorithms and simulation tools that will enable designing and analyzing x-ray navigation concepts are presented. The development of a compact x-ray detector system is pivotal to the eventual deployment of such navigation systems. Therefore, results of a high altitude balloon test to evaluate the design of a compact x-ray detector system are described as well.

Networks Technology Conference

NASA Technical Reports Server (NTRS)

Tasaki, Keiji K. (Editor)

1993-01-01

The papers included in these proceedings represent the most interesting and current topics being pursued by personnel at GSFC's Networks Division and supporting contractors involved in Space, Ground, and Deep Space Network (DSN) technical work. Although 29 papers are represented in the proceedings, only 12 were presented at the conference because of space and time limitations. The proceedings are organized according to five principal technical areas of interest to the Networks Division: Project Management; Network Operations; Network Control, Scheduling, and Monitoring; Modeling and Simulation; and Telecommunications Engineering.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro