Human Performance with Telerobotic Systems Ames Research Center
Project Title
Human Performance with Telerobotic Systems
Participating
NASA Center
Ames Research Center
Research
Area/Field
Research Area/Field
Project
Description
The focus of our work in the Advanced Controls and Displays Lab is on human-system
interaction for space telerobotic applications. We conduct laboratory experiments
and build mathematical models to better understand how factors typical of remote
teleoperation such as signal communication delays and camera-to-control
misalignment between the remote worksite and local workstation can degrade task
performance. The ultimate goal of our research is the development of effective
countermeasures to mitigate the impact these problems.
The intern will participate in experimental research using virtual environment
technology, including a high performance head-mounted display, to help test and
evaluate alternative telerobotic viewing conditions. The research involves human
performance testing with what is essentially a telerobotic simulation system. The
intern's work will include assistance in data collection, data analysis and 3D graphic
display, and possibly some web-based research in support of an existing project
studying the impact of response latency on teleoperator performance.
Requirements
Good analytic skills using MatLab are helpful. A Biomedical or Aeronautical
Engineering background with a focus on human performance would be ideal
preparation for this internship.
Session
Fall or Spring
Dates
TBD
Hours
40 hours per week (standard)
CIF-NASA Biocapsule Technology for Delivery of Protein Therapeutics in Space Ames Research Center
Project Title
CIF-NASA Biocapsule Technology for Delivery of Protein Therapeutics in Space
Participating
NASA Center
Ames Research Center
Research
Area/Field
Biomedical Engineering
Project
Description
The goal of the CIF-NASA Biocapsule Technology for Delivery of Protein Therapeutics
in Space project is to further develop the NASA Biocapsule, a novel drug delivery
technology that involves encapsulation of living cells to facilitate administration of
protein therapeutics needed for deep space missions. This includes laboratory work
to further fabricate the biocapsule under varying physical parameters, conduct
durability studies, and character the biocapsules by scanning electron microscopy.
The effort will primarily focus on the preparation of a draft manuscript that describes
the laboratory methodologies and results for submission to a peer-reviewed journal.
They will help in the lab where needed on the above effort.
Requirements
Current undergraduate/graduate in biomedical engineering. Experience working in a
biolab. Experience working with biocapsules. Understanding of biotransport and
biomechanics. Previous work with Matlab and Labview a plus.
Session
Fall 2013
Dates
TBD
Hours
40 hours per week (standard)
Advanced Life Support/Water Recycling Internship Opportunity Ames Research Center
Project Title
Advanced Life Support/Water Recycling Internship Opportunity
Participating
NASA Center
Ames Research Center
Research
Area/Field
Chemistry, Biology, Life Sciences, Mechanical Engineering, Electrical Engineering,
Environmental Sciences, Aerospace Engineering
Project
Description
Advanced life support systems include all systems and technologies required to keep
astronauts alive in space. They include water recycling, air recycling, and waste
treatment. This internship is primarily focused on water but is cognizant that an
optimized system will include integration with air and waste systems, so those with
interest in these areas are solicited. Also of interest are systems that recover energy
from wastes. In situ resource utilization in spacecraft and on planetary surfaces is also
of interest. Our primary interest is in innovative, out of the box, concepts.
Requirements
Innovation is the only skill required. This internship is designed to train the next
generation of scientists on how to innovate and to develop the next generation of
space flight systems that will enable the human exploration and colonization of the
Solar System.
Session
Fall or Spring
Dates
TBD
Hours
40 hours per week (standard)
Air Revitalization Systems Ames Research Center
Project Title
Air Revitalization Systems
Participating
NASA Center
Ames Research Center
Research
Area/Field
Engineering or other scientific discipline, i.e. chemical, mechanical, or biochemical
engineering.
Project
Description
The air revitalization lab at NASA ARC develops air-recycling technologies for use in
spacecraft. The objective is to reduce water and carbon dioxide in the cabin air of the
spacecraft (high level of carbon dioxide in the air is toxic). One common method is to
first remove the water and then remove the carbon dioxide. Water is removed first
because the adsorbent that is used in removing carbon dioxide can also adsorb
water.
The air lab designs, fabricates, and tests air revitalization system components. The
design process involves brainstorming, literature searches, defining specifications,
and generating drawings using either AutoCAD or Solidworks. The fabricating process
involves working with the vendor, in-house engineer, and machinist to fabricating the
correct parts. The test process involves conducting tests using National Instrument
LabView hardware and software to run days, weeks, and month long tests.
We collaborate very closely with other NASA centers and universities to validate our
test data and at the same time ensure that we are constantly working on cuttingedge technologies.
Requirements
We provide a very open environment for interns to explore, learn, and contribute to
any of our projects. Those who would like to explore new ideas, learn new skills, and
have a thirst to achieve are highly desirable. You will conduct literature searches on
the web, study peer-reviewed journals, as well as talk to vendors to obtain needed
information. You will be able to have hands-on experience to work with shop and
machine tools to build small and large-scale units, depending on the current project
available at the time. You will have an opportunity to conduct tests, analyze data,
draft presentations, and write papers. Engineering students with basic engineering
courses are highly desirable.
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
Electronics Prognostics: Application to Capacitors Ames Research Center
Project Title
Electronics Prognostics: Application to Capacitors
Participating
NASA Center
Ames Research Center
Research
Area/Field
Prognostics and Health Management
Project
Description
The development of prognostic methodologies (i.e., the in-situ ability to estimate
remaining life of components) for the electronics field has become more important as
more electrical systems are being used to replace traditional systems in applications
areas like aeronautics, maritime, and automotive. However, the development of
prognostics methods for electronics faces several challenges due to the great variety
of components used in a given system, continuous development of new electronics
technologies, and a general lack of understanding of how electronics fail.
Power supplies are a critical component of modern avionics systems. Within those,
capacitors are one of the components of concern. Capacitors are used as filtering
elements on power electronics systems. Electrical power drivers for motors require
capacitors to filter the rail voltage for the H-bridges that provide bidirectional current
flow to the windings of electrical motors. These capacitors help to ensure that the
heavy dynamic loads generated by the motors do not perturb the upstream power
distribution system.
OBJECTIVE:
Electrolytic capacitors have become critical components in electronics systems in
aeronautics and other domains. This type of capacitor is known for its low reliability
and frequent breakdown on critical systems like power supplies of avionics
equipment and electrical drivers of electro-mechanical actuators of control surfaces.
The objective of this work will be the development of validated prognostics
methodologies for electrolytic capacitors. In particular, the development of conditionbased health assessment techniques based on physics. Leveraging knowledge of the
device physics and degradation physics to predict remaining useful life as a function
of current state of health and anticipated operational and environmental conditions.
OUTCOME:
The expected outcome of this fellowship is the following:

Accelerated life test experiment systems and stress level settings for key
failure mechanisms of electrolytic capacitors.

Accelerated aging experiment datasets for key failure mechanisms under
different loading and environmental conditions.

Physics-based degradation models for key failure mechanisms validating with
experimental data based on accelerated life test experiments.

Model validation methodology for degradation model under relevant usage
environments like switched-mode power supply and permanent magnet
motor power electronics driver.

Remaining useful life prediction algorithms under relevant environment timescale considering varying future loading and environmental conditions.

Validation methodology for remaining useful life prognostics algorithms under
a relevant usage environment.
Organization Description:
The Intelligent Systems Division (Ames Code TI) provides leadership in information
technology for NASA by conducting mission-driven, user-centered computational
sciences research, developing and demonstrating innovative technologies, and
transferring these new capabilities for utilization in support of NASA missions.
Located at Ames Research Center, in the heart of Silicon Valley, the division
comprises four concentrated areas of computer science and information technology
research and development: Autonomous Systems and Robotics, Discovery and
Systems Health, Collaborative and Assistant Systems and Robust Software
Engineering. The Intelligent Systems Division is within Ames Research Center's
Exploration Technologies Directorate, which provides advanced technologies for
NASA's Aeronautics, Science, and Exploration Systems Mission Directorates.
The Discovery and Systems Health (DaSH) technical area focuses on challenges in
understanding engineering and science data. The engineering data understanding
work is centered around the emerging systems engineering discipline of Integrated
Systems Health Management (ISHM). Ames is NASA's premier ISHM research and
development facility, with strengths in design of health management systems, ISHM
systems engineering, sensor selection and optimization, monitoring, data analysis,
prognostics, diagnostics, failure recovery, diagnostic decision aids, data and
knowledge management, and ISHM human factors.
The Prognostics Center of Excellence (PCoE) at Ames Research Center provides an
umbrella for prognostic technology development, specifically addressing prognostic
technology gaps within the application areas of aeronautics and space exploration.
The PCoE is currently investigating damage propagation mechanisms on select safetycritical actuators for transport-class aircraft, damage mechanisms on aircraft wiring
insulation, and damage propagation mechanisms for critical electrical and electronic
components in avionic equipment. We are also in the process of extending a testbed
that will allow the comparative analysis of different prognostic algorithms. In
addition, data collected from aging processes will be made available to the research
community (see link to data repository below).
The work to be performed forms falls under the Prognostics Center of Excellence
umbrella and it will be performed in coordination with the Diagnostics and
Prognostics group within DaSH in the Intelligent Systems Division.
Requirements
Engineering Coursework in Electrical Engineering or Mechanical engineering
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
Power Electronics Prognostics Ames Research Center
Project Title
Power Electronics Prognostics
Participating
NASA Center
Ames Research Center
Research
Area/Field
Prognostics and Health Management
Project
Description
In the aerospace domain, flight and ground crews require health state awareness and
prediction technologies to ensure proper operation of all systems including vehicle
systems like structures, propulsion and avionics. The capabilities encompass detecting
abnormal behavior, diagnosing faults, predicting failure, and mitigating the effects of
faults and failures. Increasingly, electronic components take on more critical
functionality in on-board autonomous operations for vehicle controls,
communications, navigation, radar systems, etc. Future aircraft systems, such as the
more electric aircraft or the Next Generation Air Transportation Systems will rely on
more electric and electronic components. The assumption of new functionality will
also increase the number of faults due to electronic components with perhaps
unanticipated fault modes. In order to improve aircraft reliability, assure in-flight
performance, and reduce maintenance costs, it is therefore imperative to provide
system health awareness for electronics. To that end, an understanding of the
behavior of deteriorated components is needed as well as the capability to anticipate
failures and predict the remaining life (prognostics) of embedded electronics.
The Diagnostics and Prognostics group (Intelligent Systems Division) works in
fundamental research in Prognostics and Health Management of power electronic
components and systems. Current research activities focus on the investigation of
accelerated aging methodologies and identification of precursors of failures for power
electronics devices. In addition, state of the art prognostics algorithms are being
developed. In particular, power electronics devices like MOSFETs and IGBTs are being
studied in order to develop physics of failure and degradation models for prognostics.
These devices are subject to thermal and electrical overstresses in order to accelerate
its aging while recording important external variables for further study. The test
system under use focuses on the application of electrical and thermal overstress to
these devices while controlling the applied power.
This work applies to different application domains like spacecraft and other vehicles.
Research in electronics prognostics could be applied to small satellite technologies
where electronics components related to instrumentation, power management and
control are critical for the success of the mission.
Responsibilities:
The student will be responsible of performing the following tasks to support research
in electronics prognostics:

Build circuit level simulation models for different MOSFET/IGBT circuit
configurations. Perform simulations and comparative studies between
simulation and experimental results from accelerated aging.

Perform accelerated aging experiments to explore effects of thermal,
electrical and mechanical stresses. The student will be trained in the operation
of our accelerated aging system for power electronics devices. This will include
hardware, software, experiment setup and experiment documentation.

Aid on data analysis activities related to accelerated aging experiments. This
consists on development of data analysis and processing and visualization
routines using Matlab.

Aid in the electrical characterization of test devices before and after
accelerated aging. The student will be trained in the use of equipment like a
source measurement unit and curve tracer.

Design and implementation of active/passive filters and buffers. This includes
simulation, printed circuit board design and construction.

Development of temperature controller for isothermal aging system. This will
include the modeling of the thermal process, the controller designs and the
controller implementation.
Organization Description:
The Intelligent Systems Division (Ames Code TI) provides leadership in information
technology for NASA by conducting mission-driven, user-centered computational
sciences research, developing and demonstrating innovative technologies, and
transferring these new capabilities for utilization in support of NASA missions.
Located at Ames Research Center, in the heart of Silicon Valley, the division comprises
four concentrated areas of computer science and information technology research
and development: Autonomous Systems and Robotics, Discovery and Systems Health,
Collaborative and Assistant Systems and Robust Software Engineering. The Intelligent
Systems Division is within Ames Research Center's Exploration Technologies
Directorate, which provides advanced technologies for NASA's Aeronautics, Science,
and Exploration Systems Mission Directorates.
The Discovery and Systems Health (DaSH) technical area focuses on challenges in
understanding engineering and science data. The engineering data understanding
work is centered around the emerging systems engineering discipline of Integrated
Systems Health Management (ISHM). Ames is NASA's premier ISHM research and
development facility, with strengths in design of health management systems, ISHM
systems engineering, sensor selection and optimization, monitoring, data analysis,
prognostics, diagnostics, failure recovery, diagnostic decision aids, data and
knowledge management, and ISHM human factors.
The Prognostics Center of Excellence (PCoE) at Ames Research Center provides an
umbrella for prognostic technology development, specifically addressing prognostic
technology gaps within the application areas of aeronautics and space exploration.
The PCoE is currently investigating damage propagation mechanisms on select safetycritical actuators for transport-class aircraft, damage mechanisms on aircraft wiring
insulation, and damage propagation mechanisms for critical electrical and electronic
components in avionic equipment. We are also in the process of extending a testbed
that will allow the comparative analysis of different prognostic algorithms. In
addition, data collected from aging processes will be made available to the research
community (see link to data repository below).
The work to be performed forms falls under the Prognostics Center of Excellence
umbrella and it will be performed in coordination with the Diagnostics and
Prognostics group within DaSH in the Intelligent Systems Division.
Requirements
Engineering Coursework in Electrical Engineering or Mechanical engineering
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
Developing Biologically Inspired Machine Intelligence for Sustainability Base–ARC07 Ames Research Center
Project Title
Developing Biologically Inspired Machine Intelligence for Sustainability Base–ARC07
Participating
NASA Center
Ames Research Center
Research
Area/Field
Machine Learning
Project
Description
The Intelligent Systems Division at NASA Ames Research Center will be integrating
advanced technologies into a new "Green" building known as "Sustainability Base" at
the Ames campus. Sustainability Base is high-performance, LEED Platinum certified
building that will incorporate NASA innovations and technologies to improve energy
efficiency, reduce carbon footprint, and lower operating and maintenance expenses
compared to traditional buildings. It will function as a living experimental platform,
integrating the latest technologies as they evolve.
This internship opportunity will assist in defining and implementing demonstrations of
NASA technology in Sustainability Base. In particular, the intern will employ advanced
data mining algorithms on data acquired from Sustainability Base to learn how the
building operates and then monitor how it is performing over time. This could include
measurements of energy use, mechanical system performance, environmental
parameters, and other key performance indicators. For example, correlations between
environmental control system settings and temperature ranges in workspaces can be
established and then monitored to give early indication of performance degradation
or unexpected changes to the building configuration. However, basic data analysis and
gaining an intuitive understanding of data from various building systems (BACnet data,
lighting, shade, photovoltaic sensor data, etc.) will also be an important precursor to
any application of the advanced data mining algorithms. In addition to global building
performance, the algorithms can also be used to detect changes in individual energy
use as well. In either case, the algorithms will provide early indications of off-nominal
performance to building operators or occupants, enabling corrective actions to
maximize building performance and efficiency.
Additional information on Sustainability Base can be found
athttp://www.nasa.gov/sustainability-base/.
Additional information on data mining algorithms can be found
athttp://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/.
Requirements
The focus of this effort will relate to adverse event prediction and control using
biologically inspired machine intelligence in Sustainability Base. Open source tools
made available by Numenta/Grok (NuPIC) will need to be used. As such, the ideal
candidate should have a working knowledge of Python, MySQL, and distributed
processing. Also, experience with MATLAB; Familiarity with Linux OS is preferred;
Strong analytical and organizational skills; Interest in sustainability; Interest in data
mining algorithms for health management. Senior undergraduate at junior/senior level
or higher preferred.
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
Developing an Intelligent Integrated Control and Alarm System for Sustainability Base–ARC08 Ames Research Center
Project Title
Developing an Intelligent Integrated Control and Alarm System for Sustainability Base–
ARC08
Participating
NASA Center
Ames Research Center
Research
Area/Field
Control Theory and Detection/Estimation Theory
Project
Description
The Intelligent Systems Division at NASA Ames Research Center will be integrating
advanced technologies into a new "Green" building known as "Sustainability Base" at
the Ames campus. Sustainability Base is high-performance, LEED Platinum building
that will incorporate NASA innovations and technologies to improve energy efficiency,
reduce carbon footprint, and lower operating and maintenance expenses compared
to traditional buildings. It thus provides a unique R&D testbed within the Agency, and
functions as a living experimental platform, integrating the latest technologies as they
evolve.
This fellowship opportunity will assist in defining and implementing demonstrations of
NASA technology in Sustainability Base. In particular, the fellow will assist with the
development of an intelligent integrated control and alarm system for Sustainability
Base. Building systems are increasingly capable of distributed sensing and automatic
building control down to individual actuators, however overall objectives such as
system efficiency and energy usage are not controlled directly. This project
hypothesizes that integration of a control and an alarm system through advanced
control and level-crossing prediction techniques can achieve significant improvements
in building control efficiency and performance.
The fellow will be responsible for developing a detailed model of Sustainability Base
using EnergyPlus or equivalent modeling framework. The fellow will also become
familiarized with the building operations sequence and existing constraints to be
imposed as part of an optimization of the overall objectives stated previously. The
optimization will be conducted using Stochastic Model Predictive Control (SMPC)
techniques, that yield control actuation and guidance strategies. These control
sequences can be derived for automated windows, shades, dynamic glass, fans, even
plug loads. The integrated control and alarm system will minimize overall energy
expenditure and performance objectives - such as response rate and actuator
effectiveness in an uncertain environment while providing a robust constraint
violation risk-management strategy via an optimal alarm system. Constraints to be
imposed may include equipment and/or environmental limits, as well as thermal
comfort limits. Near-optimal plans to roughly approximate the guidance strategies
recommended by the MPC optimization will be developed by the fellow in the context
and constraints established by the existing building operations sequence, through a
combined utilization of available actuators.
Additional information on Sustainability Base can be found
athttp://www.nasa.gov/sustainability-base/.
Requirements
Since this work will involve working with a variant of MPC, the qualified fellow will be
familiar with chance-constrained MPC, as well as various convex and nonlinear
optimization solvers. Control theory, optimization, and machine learning/statistics
background; Specifically, Model Predictive Control (MPC) and time-series based event
prediction. Experience with MATLAB, C/C++, JAVA, EnergyPlus, Latex; Experience in a
laboratory environment through class work or otherwise; Interest sustainable
technologies; Strong analytical and organizational skills. Advanced graduate or Ph.D.
student preferred.
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
Data Mining and Analysis for Sustainability Base–ARC09 Ames Research Center
Project Title
Data Mining and Analysis for Sustainability Base–ARC09
Participating
NASA Center
Ames Research Center
Research
Area/Field
Data Mining
Project
Description
The Intelligent Systems Division at NASA Ames Research Center will be integrating
advanced technologies into a new "Green" building known as "Sustainability Base" at
the Ames campus. Sustainability Base is high-performance, LEED Platinum certified
building that will incorporate NASA innovations and technologies to improve energy
efficiency, reduce carbon footprint, and lower operating and maintenance expenses
compared to traditional buildings. It will function as a living experimental platform,
integrating the latest technologies as they evolve.
This internship opportunity will assist in defining and implementing demonstrations of
NASA technology in Sustainability Base. In particular, the intern will employ advanced
data mining algorithms on data acquired from Sustainability Base to learn how the
building operates and then monitor how it is performing over time. This could include
measurements of energy use, mechanical system performance, environmental
parameters, and other key performance indicators. For example, correlations between
environmental control system settings and temperature ranges in workspaces can be
established and then monitored to give early indication of performance degradation
or unexpected changes to the building configuration. However, basic data analysis and
gaining an intuitive understanding of data from various building systems (BACnet data,
lighting, shade, photovoltaic sensor data, etc.) will also be an important precursor to
any application of the advanced data mining algorithms. In addition to global building
performance, the algorithms can also be used to detect changes in individual energy
use as well. In either case, the algorithms will provide early indications of off-nominal
performance to building operators or occupants, enabling corrective actions to
maximize building performance and efficiency.
Additional information on Sustainability Base can be found
athttp://www.nasa.gov/sustainability-base/.
Additional information on data mining algorithms can be found
athttp://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/.
Requirements
The focus of this effort may relate more to automated tracking and consolidation of
energy data and plug load management and analysis, so the ideal candidate will have
experience in scripting or application development to extract real-time data from APIs
and websites for logging into a PostgreSQL database. Experience with MATLAB;
Familiarity with Linux OS is preferred; Strong analytical and organizational skills;
Interest in sustainability; Interest in data mining algorithms for health management.
Senior undergraduate at junior/senior level or higher preferred.
Session
All sessions
Dates
TBD
Hours
40 hours per week (standard)
AztechSat–1 Ames Research Center
Project Title
AztechSat–1
Participating
NASA Center
Ames Research Center
Research
Area/Field
Small Satellites Development
Project
Description
Students are encouraged to sign up and participate in the AztechSat-1 spaceflight
project, with opportunities ranging from Concept Studies through Decommissioning
of small satellites using design and implementation techniques and products available
to the general public. This work will be done with the NASA Ames Research Center
Engineering Directorate under the mentorship of Ames' International Space Station
CubeSat development project lead.
Mission Objectives are as follows:

Develop a flight ready CubeSat for deployment from the ISS.

Demonstrate alternative methods to improve communication availability in
CubeSat missions.

Investigate advanced materials for CubeSat components.
Requirements
Students currently in senior class status in Mechanical, Electrical, Computer, or
Aerospace Engineering, or Computer Science.
Session
Spring / Summer / Fall 2014
Dates
TBD
Hours
40 hours per week (standard)
Support development of multi-node communications test-bed (two-member team)
Project Title
Support development of multi-node communications test-bed (two-member team)
Mentor Name
Belgacem Jaroux
Research
Area/Field
Small Satellites
Project
Description
Team members will use available off-the-shelf or spare laboratory hardware to
develop laboratory test bed of at least two remote units and one central station. The
remote units will consist of a smart-phone connected to a simple radio or RF modem
for network communications between themselves and with the central station. They
will also be equipped with a "sensor" (e.g. thermocouples) to create data to move
around the network. This system is intended for use in on-going experiments in
distributed, time-synchronized measurements through networks. The team will
develop ground software as necessary to demonstrate operation of the units including
simulated inter-nodal communications and simulated communications with the central
station.
Mission Objectives are as follows:

Lab set-up of two remote units and a central station

Support hardware and software necessary for the operation of the system

Demonstration of movement of data around the system, passing from one
remote unit to a second and then to the ground

Procedures for running the experiment

Report on the experiment, including lessons learned and suggestions for future
expansion.
Requirements
Student should have an Aerospace Engineering, Mechanical Engineering or
Mechatronics, Electrical Engineering, Systems Engineering or other related engineering
major.
Session
Spring / Summer / Fall 2014
Dates
TBD
Hours
40 hours per week (standard)
Explore impact of network delays on distributed spacecraft testing (two-member team)
Project Title
Explore impact of network delays on distributed spacecraft testing (two-member
team)
Mentor Name
Belgacem Jaroux
Research
Area/Field
Small Satellites
Project
Description
Team members will use available off-the-shelf or spare laboratory hardware to explore
the possibility of using standard network systems and protocols to run mission
simulation and closed-loop hardware-in-the-loop tests remotely where significant
parts of the system are connected over the internet. For example, a spacecraft bus
could be at one location, a payload at a second location and a dynamic simulation
environment could be at a third location, all connected over the internet. The team
would identify the problems associated with such an arrangement (e.g. latency) and
suggest approaches to mitigate them.
Key milestones and deliverables:

Characterization of typical network delays and variations in delay between
points on a network

Set-up of two computers. Computer A runs a simple control law based on the
simulated sensor signals created by Computer B. Computer B provides a truth
measure and integrates the system dynamics based on the control outputs of
Computer A.

Characterize the system with the two computers connected directly together
(no internet)

Characterize the system with the two computers connected over the internet

If possible, implement some mitigations for network delay and variations in the
delay

Write a report comparing the performance of the system in the various
configurations, including lessons learned and suggestions for future expansion.
Requirements
Student should have an Aerospace Engineering, Mechanical Engineering or
Mechatronics, Electrical Engineering, Systems Engineering or other related engineering
major.
Session
Spring / Summer / Fall 2014
Dates
TBD
Hours
40 hours per week (standard)
Advanced Life Support Internship Opportunity
Project Title
Advanced Life Support Internship Opportunity
Mentor Name
Michael Flynn
Organization
Code
Code SC, Bioengineering
Research
Area/Field
Water Recycling
Project
Description
Advanced life support systems include all systems and technologies required to keep
astronauts alive in space: water recycling, air recycling and waste treatment. This
Internship is primarily focused on water recycling but is cognizant that an optimized
system will include integration with air and waste systems. Our research areas include:

Systems that can recover energy from waste.

In situ resource utilization in spacecraft and on planetary surfaces

Application of space flight systems technologies to sustainable terrestrial
development.
Requirements
Innovation a required skill. Our group focuses on training the next generation of NASA
scientists on how to innovate and to develop the next generation of water recycling
space flight systems that will enable the human exploration and colonization of the
Solar System.
The ideal candidate is an undergraduate or graduate student in the fields of:
Engineering (Chemical, Environmental, Electrical, Industrial, Civil, Computer),
Mathematics, Chemistry, Biology, Physics, and Environmental Science and must have
at least completed their freshman year of college and a GPA of 3.00 (out of 4).
Professional Working Proficiency (ILR level 3) of the English language is the minimum
level required. The participant must be a team player and comfortable working with
professionals of different cultural and scientific background. At the end of the
internship the participant will be required to submit a white paper.
Session
Spring/Summer/ Fall 2015
Dates
TBD
Hours
40 hours per week (standard)
Upgrading a Space Debris Simulation Software for planetary defense assessments
Project Title
Upgrading a Space Debris Simulation Software for planetary defense assessments
Mentor Name
Chad Frost / Jan Stupl (support)
Organization
Code
RD, Mission Design Division
Research
Area/Field
Space Debris Mitigation / Planetary Defense
Project
Description
NASA Ames Research Center has developed a simulation software that models the
space debris environment in Low Earth Orbit (LEO). The goal of the current software is
to assess the efficiency of a concept for collision avoidance between debris and active
satellites. The investigated system would use photon pressure from ground based
lasers to slightly change orbits to avoid collisions on warning.
For the internship, the main task will be to upgrade the simulation software to include
the near earth object (NEO) environment (asteroids) and enable the assessment of
cubesat based asteroid detection systems. You will change the main body of the
previous simulation from the sun to the earth, introduce a population of asteroids into
the model and investigate the utility of cubesats to detect those asteroids as they
come close to Earth. In addition, you also will help to maintain the original software for
space debris modeling.
Requirements
The intern should have a background in the sciences or engineering, and ideally
Aerospace Engineering or Physics. The project requires programming skills in C and
Matlab and an understanding of orbital dynamics.
Session
Spring / Summer / Fall 2015
Dates
TBD
Hours
40 hours per week (standard)
Metabolic control for adaptation to spaceflight environment
Project Title
Metabolic control for adaptation to spaceflight environment
Mentor Name
Yuri Griko
Organization
Code
Code SC, Division of Space Biosciences
Research
Area/Field
Space Biology/Metabolism
Project
Description
With the growing interest in long haul flights and the colonization of the solar system,
it is becoming important to develop organism self-regulatory control systems which
would be able to meet the requirements of extraterrestrial environments rather than
requiring an Earthly environment in space. A better mechanistic understanding of
metabolism offers a means for sustaining astronauts in long-duration missions beyond
the low Earth orbit. Recent data obtained from several research reports have shown
that metabolic suppression could protect biological organisms from damaging effects
of space radiation and microgravity. The ability to drastically reduce and suspend
metabolism appears to be closely tied to the unique survival of bacteria and some
invertebrates (e.g., tardigrades) after a prolonged exposure to cosmic vacuum and
radiation. It is possible that there is a monophyletic origin for this adaptation at the
molecular level among a variety of different organisms. Our ultimate goals are to
demonstrate proof-of-principle for metabolic suppression as means to reduce the
negative effects of spaceflight environmental issues such as radiation and microgravity.
In order to demonstrate the potential application of the metabolic control technology
the PI�s laboratory at NASA Ames Research Center has engineered a hypo-metabolic
chamber with a range of life-monitoring equipment for high-throughput testing of
hypo-metabolic parameters and conditions that enable reversible induction of a state
of suspended animation in non-hibernating animals.
This internship opportunity will assist in defining and implementing demonstrations of
the metabolic control technology using different animal models.
Objectives of this research are:
1. To characterize the hypometabolic state
2. To develop methodology for real time monitoring of respiratory and other
physiological parameters and conditions associated with the hypometabolic
stasis.
In the proposed experiments, the intern will work in collaboration with molecular
biologists and engineers to (1) reproduce induction of the reversible suspended
animation-like state in selected animal models, and to (2) establish a comprehensive
life support system for monitoring physiological parameters of the hypometabolic
state.
Requirements
Student should be willing to work with animals. He/she should have basic knowledge
of life support systems (respiratory parameters, ventilation, and core body
temperature control), have basic laboratory skills and technical knowledge for
monitoring physical parameter from telemetric devises, and have software
management skills.
Strong analytical and organizational skills; interest in biology; interest in data analysis.
Senior undergraduate at junior/senior level or higher preferred.
Session
Any
Dates
TBD
Hours
40 hours per week (standard)
Developing Underwater System using CubeSat-related Technologies
Project Title
Developing Underwater System using CubeSat-related Technologies
Mentor Name
Jonas Jonsson (Co-mentor Chad Frost)
Organization
Code
Code RD, Mission Design Division
Research
Area/Field
Small Satellite and Submersible technologies
Project
Description
The Mission Design Center at NASA Ames Research Center is exploring the ability to
apply CubeSat technologies to submersible applications, with the long-term view of
exploring the subsurface liquid environments of scientifically rich and potentially
habitable icy extraterrestrial worlds in our solar system such as Ceres, Titan, Enceladus,
and Europa. These worlds are very compelling targets in our effort to find life beyond
Earth. An exploration mission to an ice-covered ocean world may be a long-term
vision, but this work also offers immediate scientific and engineering benefits by
lowering the barriers to access previously limited and hard-to-reach underwater
environments. One of these is the icy-world analog of Antarctica, where NASA is
performing extensive work, but which require further technological developments for
the proper exploration of the ice-covered and subglacial lakes.
Ames Research Center has been working to develop low-cost small spacecraft mission
concepts capable of reaching Europa. To send an exploration vehicle that will be able
to land and start exploring, both above and below the ice, within an affordable budget,
will require innovative engineering that takes advantage of new technologies to keep
size and mass, and thus cost, low.
The NASA Ames' Mission Design Center (MDC) provides concurrent engineering
facilities and software for rapid mission development and analysis. The MDC is staffed
by subject matter experts covering the domains required to fully develop successful
spacecraft mission concepts. The MDC staff use and develop design tools supporting
the full mission, including for example, orbit / trajectory design, thermal and electrical
analyses, avionics and guidance, navigation and control design, development of
operations concepts, and costing. The MDC facilities and staff conduct scheduled and
rapid-response design cycles to develop initial mission concepts into proposal-ready,
fully supportable technical packages.
The intern will be involved in early-stage concept development and technology
maturation supporting such a future mission using CubeSat technologies and
commercial off-the-shelf (COTS) components. This project will make use of NASA Ames
expertise in smartphone based satellites and free-flyers, facilities for design and rapid
prototyping, and world-renown scientific leadership in Space Science and Astrobiology.
In particular, the intern will employ his/her knowledge in areas, such as mechanics,
electronics, and software development, to develop and test technologies. The intern
will use resources at Ames, such as rapid-prototyping facilities and the local pool for
fast turnaround design and testing. For example, recent studies include subsurface
multi-node networking and distributed sensing using Arduino-based components.
Requirements
The student(s) should have, or be willing to learn, a mixture of skills in order to carry a
prototyping project forward, including mechanics, electronics, and software
development. Familiarity with mechatronics and microcontrollers are preferred, as
well as CubeSat technologies and submersibles.
Session
Any of the listed periods.
Dates
TBD
Hours
40 hours per week (standard)
Intern for Nano-biosensor development
Project Title
Intern for Nano-biosensor development
Mentor Name
Jessica Koehne
Organization
Code
Entry Systems and Vehicle Development Branch (TSS)
Research
Area/Field
Nanotechnology
Project
Description
Development of biosensors is an active field due to a wide range of applications in labon-a-chip, diagnostics of infectious diseases, cancer diagnostics, environment
monitoring, biodetection and others. One of the strategies used for selective
identification of a target is to /preselect/ a probe that has a unique affinity for the
target or can uniquely interact or hybridize with the target: sort of a "lock and key"
approach. In this approach, one then needs a platform to support the probe and a
recognizing element that can recognize the said interaction between the probe and the
target. The interaction result can manifest optically (by using dyes, quantum dots for
example) or electrically. The platform design and configuration may vary depending on
whether optical or electrical readout is used and what environment the sensor will be
utilized. Electrical readout biosensors have gained much attention because, in
principle, they can be made more compact than optical technologies. Advances in
microfabrication and related technologies have been aiding the electrical readout
based biosensor development to the forefront. A previous NASA Ames innovation
involves a nanoelectrode array consisting of an array of carbon nanofibers as individual
nanoelectrodes. Each nanofiber, which is a solid nanocylinder, has a probe attached to
it. The array size, chip size and wafer size can be controlled. In order to maintain that
this device is stable over a wide range of testing conditions, the sensor will placed in
various chemical and electrical environments. The project involves pursuing the above
or closely related avenues to demonstrate the sensor functionality in a variety of
testing conditions. Intended NASA applications include water quality monitoring for ISS
and lab-on-a-chip for point of care diagnostics for astronaut health monitoring.
Requirements

Proficiency in Microsoft Office

Majors: Electrical Engineering, Chemical Engineering, Biomedical Engineering,
Materials Science, Chemistry, Nanotechnology

Education Level: Undergraduate junior, undergraduate senior, masters level,
doctoral
Session
Spring 2015
Dates
TBD
Hours
40 hours per week (standard)
Data Mining and Analysis for Sustainability Base
Project Title
Data Mining and Analysis for Sustainability Base
Mentor Name
Rodney A. Martin
Organization
Code
Code TI, Intelligent Systems Division
Research
Area/Field
Machine Learning and Data Mining
Project
Description
The Intelligent Systems Division at NASA Ames Research Center will be integrating
advanced technologies into a new "Green" building known as "Sustainability Base" at
the Ames campus. Sustainability Base is high-performance, LEED Platinum certified
building that will incorporate NASA innovations and technologies to improve energy
efficiency, reduce carbon footprint, and lower operating and maintenance expenses
compared to traditional buildings. It will function as a living experimental platform,
integrating the latest technologies as they evolve. This internship opportunity will assist
in defining and implementing demonstrations of NASA technology in Sustainability
Base. In particular, the intern will employ advanced data mining algorithms on data
acquired from Sustainability Base to learn how the building operates and then monitor
how it is performing over time. This could include measurements of energy use,
mechanical system performance, environmental parameters, and other key
performance indicators. For example, correlations between environmental control
system settings and temperature ranges in workspaces can be established and then
monitored to give early indication of performance degradation or unexpected changes
to the building configuration. However, basic data analysis and gaining an intuitive
understanding of data from various building systems (BACnet data, plug load data,
photovoltaic sensor data, etc.) will also be an important precursor to any application of
the advanced data mining algorithms. In addition to global building performance, the
algorithms can also be used to detect changes in individual energy use as well. In either
case, the algorithms will provide early indications of off-nominal performance to
building operators or occupants, enabling corrective actions to maximize building
performance and efficiency.
Additional information on Sustainability Base can be found
athttp://www.nasa.gov/sustainability-base/
Additional information on data mining algorithms can be found
athttp://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/
Requirements
The ideal candidate will have strong experience with MATLAB; Familiarity with Linux OS
is preferred; Strong analytical and organizational skills; Interest in sustainability;
Interest in data mining algorithms for health management. Senior undergraduate at
junior/senior level or higher preferred.
Session
Spring 2015
Dates
TBD
Hours
40 hours per week (standard)
Nanotechnology in electronics and sensor development
Project Title
Nanotechnology in electronics and sensor development
Mentor Name
Dr. Meyya Meyyappan
Co-Mentors: Dr. Jin-woo Han, Dr. Beomseok Kim, Dr. Michael Oye
Organization
Code
Code T; co-mentors: Code-TSN
Research
Area/Field
Nanotechnology
Project
Description
Nanomaterials such as carbon nanotubes (CNTs), graphene and a variety of inorganic
nanowires offer tremendous potential for future nanoelectronics, nanosensors and
related devices. We have active ongoing programs in these areas. Several examples are
given below. Chemical sensors to detect trace amounts of gases and vapors are
needed in planetary exploration, crew cabin air quality monitoring and leak detection;
there are numerous societal applications as well. We have been working on CNT based
sensors amenable for various platforms including smartphones.
Flexible electronics on substrates such as textile and paper is of great deal of interest
to us. We have fabricated gas/vapor sensors on cotton textile as well as cellulose
paper. Other interests in paper electronics and flexible substrates include memory
devices, energy storage devices, displays and detectors. Finally, we have also been
revisiting vacuum tubes although in the nanoscale, using entirely silicon based
technology. These radiation resistant devices offer exceptionally high frequency
performance. Our interest here extends to exploring the nano vacuum tubes for THz
electronics applications.
In all the areas, the projects include material growth, characterization, device
fabrication, device testing and evaluation, reliability and lifetime assessment.
Requirements
For device related aspects, majoring in electrical engineering or physics is preferred.
For the remaining aspects of the project, majors in material science, chemistry and
other engineering disciplines are welcome. PhD candidates and talented
undergraduates will get preference.
Session
All available sessions
Dates
TBD
Hours
40 hours per week (standard)
Detecting Human Automation Interaction Errors in Safety-critical Systems
Project Title
Detecting Human Automation Interaction Errors in Safety-critical Systems
Mentor Name
Neha Rungta
Organization
Code
Intelligent Systems Division, Code TI
Research
Area/Field
Verification and design of interfaces for safety-critical systems
Project
Description
Human automation interaction (HAI) is an interdisciplinary domain that has been
studied for several years by researchers and spans cognitive science, systems
engineering, computer science, and human factors. The complexity of designing and
verifying such systems has also driven researchers to investigate the use of formal
specification and verification methods in their development. The design of automated
systems in the presence of user interactions is a complex task that is susceptible to a
number of known Human-Automation Interaction (HAI) vulnerabilities. Such
vulnerabilities can be generic, for example non-determinism or incompleteness in
requirements; or the vulnerabilities can be specific to HAI, for example, mode
confusion. Analysis of such vulnerabilities is better performed during the early design
stage, where both the analysis cost and the cost of making modifications are lower. It
is a well-known fact that the cost of finding and fixing errors in a system increases by
an order of magnitude as we move from one phase of the software development
process to the next. This is an important problem in the context of NextGen systems
where new automated systems will be added onboard vehicles. Several aspects of
these problems are currently being studied by researchers in the Intelligent System
Division and Human System Integration Division at NASA Ames Research Center.
In this work we will develop an analysis and verification framework for interfaces and
models that describe interactions between humans and automated systems. Various
modeling frameworks are used for defining interactions between humans and
automated system within the safety-critical systems.
With students with a background in program analysis, software engineering, or formal
methods the goal of this internship is to build a precise verification framework for
interface models used in safety critical systems. These that can analyze and reason
about various safety and other complex properties (such as avoid mode
inconsistencies and mode confusion). With students with an HCI background the goal
is the use information provided by the analysis and present it to the user in a more
usable meaningful manner.
Requirements
Graduate student in computer science. Has a strong background in HCI design or
design of safety critical systems. Alternatively can also have a strong background in
formal methods, verification, or automated analysis. Good technical writing skills,
strong Java programming skills, familiarity with version control tools such as
subversion or mercurial, ability to communicate effectively, successfully work with
members of a team.
Session
Any
Dates
TBD
Hours
40 hours per week (standard)