u. s. department of energy neena imam complex systems computer science and mathematics division oak...
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U. S. DEPARTMENT OF ENERGY
Neena ImamComplex Systems
Computer Science and Mathematics DivisionOAK RIDGE NATIONAL LABORATORY
Highlights of Selected Complex Systems Research Activities
Algorithm to Ultra-fast Signal Processing
Presented at
RAMS Faculty WorkshopOak Ridge, TN
December 10, 2007
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Outline Introduction
acknowledgments & collaborators overview of Complex Systems
Research activities missile tracking and interception hyperspectral sensors sonar signal processing quantum devices
Future directions and contacts for collaboration collaboration topics Complex Systems contact points
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Acknowledgements … for activities presented hereafter
Collaborators Jacob Barhen ORNL / Complex Systems (Group Leader) Travis Humble ORNL / Complex Systems Jeffery Vetter ORNL / Future Technologies Aeromet Corporation Tulsa, OK Thomas Gaylord Georgia Tech Eustace Dereniak U. Arizona Albert Wynn, Deirdre Johnson students, Research Alliance for Mathematics
and Science
Technology Sponsors Missile Defense Agency Naval Sea Systems Command Office of Naval Research
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Complex Systems Overview
Mission: Innovative Technology in Support of DOE & DOD Theory – Computation – Experiments
Mission: Innovative Technology in Support of DOE & DOD Theory – Computation – Experiments
Research topics: Missile defense: C2BMC (tracking and discrimination), NATO(ALTBD), flash
hyperspectral imaging.
Modeling and Simulation: Sensitivity and uncertainty analysis of complex nonlinear models, global optimization.
Laser arrays: directed energy, ultraweak signal detection, terahertz sources, underwater communications, SNS laser stripping.
Terascale embedded computing: emerging multicore processors for real-time signal processing applications (CELL, Optical Processor, …).
Anti-submarine warfare: ultra-sensitive detection, sensor networks, advanced computational architectures, Doppler-sensitive waveforms.
Quantum optics: cryptography, quantum teleportation (remote sensing).
Computer Science: UltraScience network.
Intelligent Systems: neural networks, multisensor fusion, robotics.
Materials Science: control of friction at micro and nanoscale.UltraScience Net
Sponsors: DOD(DARPA, MDA, ONR, NAVSEA ), DOE(SC), IC (CIA, IARPA, NSA), NASA, NSF
U. S. DEPARTMENT OF ENERGY
TARGET TRACKING AND DISCRIMINATION
Complex Systems Imam_RAMS Faculty Workshop_2007’12
MDA's HALO-II/AIRS Project
Independent Verification and Validation (IV&V) of software. Improved tracking algorithm development. Sensitivity analysis of system modules using Automatic Differentiation (AD).
ORNL TASKS
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Orbital Signatures
Meet MDA T&E Requirements Sensor / Technology Testbed
Kill Assessment or
Miss Distance
VehicleSeparation
ChemicalReleases
Booster Tracks
InterceptorPerformance
Flash Radiometry
Plume Signatures
Counter-measure
Signatures
TargetSignatures
Photo documentation
TrajectoryReconstruction
FailureDiagnostics
Exo-AtmosphericTarget Characterization
FOR
Motivation For HALO-II/AIRS
Complex Systems Imam_RAMS Faculty Workshop_2007’12
HALO-II System Overview
Closed Loop Tracking
Image Processing
Airborne Pointing System
Object Track Generation)
RTPS pointing Pointing hardware
highest level viewhighest level view
Five Subsystems. Sensors installed in aerodynamic pod. In-Pod
PointingAcquisitionTracking
In-CabinReal time processorSurveillance processor
Five Subsystems. Sensors installed in aerodynamic pod. In-Pod
PointingAcquisitionTracking
In-CabinReal time processorSurveillance processor
In-Pod
In-Cabin
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Sensitivity and Uncertainty AnalysisMotivation
For example, modeling of battlespace threat signatures encompasses a large set of varied phenomenologies
importance of accurate threat signature discrimination precludes confidence analysis based solely on parameters and model features selected by “engineering judgment”.
How much confidence should be placed in decisions obtained on the basis of predictions from complex mathematical and / or physical models embedded in complex code systems?
Uncertainties- input data
- outputs
- model parameters
- sensor measurements
Code BCode A
Code C
Code D
Code E
Code F
Complex Systems Imam_RAMS Faculty Workshop_2007’12
The methodology has two primary goals: determine confidence limits of predictions by large code systems consistently combine sensor measurements with computational
results ► obtain best estimates of model parameters► reduce uncertainties in estimates
Recognized need for computational tools that explicitly account for model sensitivities and data uncertainties. The design of complex multisensor-based target–detection / tracking architectures illustrates typical application.
For each model
inputs parameters
system responses i.e., outputs
Sensitivity and Uncertainty AnalysisObjective
N. Imam and J. Barhen, “Reduction of uncertainties in the USNO astronomical refraction code using sensitivities generated by Automatic Differentiation”, 2004 International Conference on Automatic Differentiation (7/04), Chicago, IL.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
ORNL Developed Improved NOGA Tracker
NOGA is an ORNL developed method that produces best estimates for quantities of interest by explicitly incorporating uncertainties in the estimation process. It involves a fast, nonlinear Lagrange optimization. The tracking implemented in conjunction with NOGA is based on a second order auto regression.
HALO Weighted Backvalues Least Squares Algorithm
Elevation Tracking Benchmark
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
Ele
vati
on
sensor data HALO prediction (NB = 10)
Simulation ResultsElevation and Elevation Uncertainty
Sensor Data vs HALO prediction
HALO Weighted Backvalues Least Squares Algorithm Tracking Benchmark Elevation Uncertainties
0
0.05
0.1
0.15
0.2
0.25
0.3
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
Ele
va
tio
n s
tdv
sensor data HALO original (NB=10) HALO standard fit error HALO ORNL corrected (NB=10)
N. Imam, J. Barhen, and C. W. Glover, “Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications”, Proc. of SPIE, Signal and Data Processing of Small Targets, vol. 5913, pp. 59130Z1- 59130Z1, 2005.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Sensitivity Analysis of the Airborne Pointing System Module
Astronomical Refraction: Observer in earth’s atmosphere, object outside. USNO code uses numerical integration.
The real part of the atmospheric index of refraction is a nonlinear function of pressure, temperature, elevation, humidity, and wavelength. Therefore, light propagating in the vertical direction is bent towards lower altitude.
The real part of the atmospheric index of refraction is a nonlinear function of pressure, temperature, elevation, humidity, and wavelength. Therefore, light propagating in the vertical direction is bent towards lower altitude.
calculated responsesensitivities
input parametersUSNO code
reduced uncertainties
experimental response
NOGAAutomatic Differentiation
APS drives the sensors. Calibrates using USNO astronomical refraction code. APS drives the sensors. Calibrates using USNO astronomical refraction code.
ER
Troposphere
0
0rr
Stratosphere
ORNL devised experiments to improve APS performance after sensitivity analysis was completed. The sensitivity and uncertainty analysis highlighted the approximations/limitations inherent in this model and aid in the design of more accurate refraction algorithms.
U. S. DEPARTMENT OF ENERGY
SONAR SIGNAL PROCESSING
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Wideband Sonar Signal Processing
For wideband signals, the effect of target velocity is no longer approximated as a simple "shift" in frequency.
Doppler effect: a compression/stretching of the transmitted pulse.
Wideband Ambiguity Function (WAF): a function of time delay and Doppler compression factor
Doppler Cross Power Spectrum (DCPS): forms a Fourier pair with the ambiguity function and can be used to calculate the ambiguity function and the Q function [1, 2]
1 /
1 /
c
c
uu
( , ) ( ) [ ( )]s s t s t dt
1
( , ) ( ) ( )s
ff S f S
2 2( ) ( , ) ( , )s s sQ f df d
1. R. A. Altes, "Some invariance properties of the wideband ambiguity function," J. Acoust. Soc. Am. 53, pp. 1154-1160, 1973.2. E. J. Kelly and R. P. Wishner, "Matched filter theory for high velocity accelerating targets," IEEE Trans. Mil. Electron. MIL 9, pp. 59-69, 1965.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Wideband Ambiguity Function
For a low Q function, and hence a high reverberation processing, it is necessary to minimize the area under the square of the modulus of the DCPS along a line of constant Doppler scaling [1].
spread the energy of the transmitted pulse over a broad bandwidth
CW signal can use a very narrow bandwidth to achieve low Q but compromises parameter estimation
use of Comb spectrum, SFM or LFM signals
1.T. Collins and P. Atkins, "Doppler-sensitive active sonar pulse designs for reverberation processing," IEE Proc. Radar Sonar Navig. 145, 347-353 , 1998.
here w(t) is the window function
B = bandwidth
SFM signal
2 (1 )mB f
[2 sin(2 )]( ) ( ) c mj f t f ts t w t e
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Ambiguity Functions of DSW
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Matched Filtering for Active Sonar Processing
A synthetic echo is generated for a particular target range and velocity. The echo signal is correlated with a bank of replicas. Spectral techniques are used. The correlation with the highest magnitude provides an estimate of the Doppler velocity bin. The location of the maximum within that correlation yields the time delay of the echo, and thus provides an estimate of the range.
MatchedFilter 2
Envelopedetector
MatchedFilter 1
Envelopedetector
MatchedFilter 4
Envelopedetector
MatchedFilter 3
Envelopedetector
Output vs. time
Out
put v
s. v
eloc
ity
Optimum ReceiverTypical output
r(t)
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Matched Filtering for Active Sonar Processing
SFM pulse of fc=1200 Hz Bandwidth B= 400 Hz Pulse duration = 1 s Modulation frequency = 5 Hz Sonar sampling rate fs = 5000Hz FFT length = 80K
Target• assumed range: 3Km• assumed velocity: - 5m/s (bin#1)• 32 matched filter bank.
Result:• output of the first filter has the
closest match to the received signal.• Time delay = 4 seconds; thus,
estimated target range = 3 Km.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
EnLight 64 demonstrator
Power dissipation (at 8000 GOPS throughput):• EnLight: 40 W (single board)
• DSP solution: 2.79 kW [ 62 boards, 16 DSPs (TMS320C64x) per board ]
The EnLight TM Prototype Optical Core Processor
Full matrix ( 256 x 256 ) - vector multiplication per single clock cycle
Fixed point architecture, 8-bit native accuracy per clock cycle
Enhanced by on node FPGA-based processing and control
Demonstrated accuracy and performance in complex signal processing tasks
Developed by Israeli startup
Application Programs FORTRAN C MATLAB
SIMULINK
VHDL
Libraries FPGAs
Optical Core
Information provided by Lenslet, Inc
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Matched filter calculation on EnLight-64 hardware
Speed-up factor per processor E_64 : 6,826 2 > 13,000
actual hardware E_256 : 56,624 2 > 113,000
emulator
Performance Comparison
Hardware Implementation ResultsTime Performance
Intel Dual
Xeon
Enlight
64α
Enlight
256
Specs2 GHz
1 GB RAM
60 MHz 125 MHz
FFT radix 2 32 128
Timing 9,626 ms 1.41 ms 0.17 ms
Computation parameters FFTs: 80K complex samples
number of filter banks
33 filter banks: 32 Doppler cells, 1 target echo
-30
-35
-40
-45
-50
-55
2000 2600 40003200 3400 3600 38002800 300024002200
Range (meters)
Ou
tpu
t o
f fi
lte
r #
1, d
B
MATLABAlphaMATLABAlpha
U. S. DEPARTMENT OF ENERGY
HYPERSPECTRAL IMAGE PROCESSING
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Hyperspectral SensorComputer Tomography Imaging Spectrometer (CTIS)
CTIS: Simultaneously acquires spectral information from every position element within a 2-D FOV with high spatial and spectral resolution.
CTIS is being developed at Optical Detection Lab of U. Arizona by Eustace Dereniak et. al.
Objective is to collect a set of registered, spectrally contiguous images of a scene’s
spatial-radiation distribution within the shortest possible data collection time
Complex Systems Imam_RAMS Faculty Workshop_2007’12
CTIS Instrumentation at U. Arizona
Complex Systems Imam_RAMS Faculty Workshop_2007’12
CTIS Principle
Linear relationship between object and image data:
g: 2-D (x, y) raw image f: 3-D (x, y, ) object cube H: System matrix n: Additive noise
g Hf +nx
y
420 nm
740 nm
Voxel
450 nm
xy
420 nm
740 nm
710 nm
Mapping of signal from the Mapping of signal from the object cube to the focal plane object cube to the focal plane arrayarray
1ˆ Hf ggH
Optical systemOptical system Acquired Raw Image g(x,y)Acquired Raw Image g(x,y)ObjectObject Reconstructed Data Cube fReconstructed Data Cube f
ImagingImaging ReconstructionReconstruction
f
Object Cube = fo(x,y,)Dispersive Element –
Computer Generated Hologram
Acquired Raw Image g(x,y)
1ˆ ˆˆ
Tk k
T k
H g
f fH Hf
Multiplicative Algebraic Reconstruction Technique - MART
Expectation Maximization
1ˆ( / )ˆ ˆ
T kk k
mnm
H g Hff f
H
Complex Systems Imam_RAMS Faculty Workshop_2007’12
CTIS Code Acceleration
Improved algorithm employing conjugate gradient method Parallel programming for CELL Broadband Engine (CBA) multicore
processor Reconfigurable computing via FPGAs
Computationally demanding Convergence issues An example reconstruction:
5 sec for each iteration for a 0.1 micrometer spectral sampling interval (3-5 m region) and 46X46 spatial sampling. Total of 46X26X21 sampling. 10
iterations needed for convergence. 1/3 hour computation time for each frame.
Algorithms must be developed for less computational time and better convergence
Complex Systems Imam_RAMS Faculty Workshop_2007’12
IBM Cell Multicore Device
Courtesy IBM 2006
CELL Broadband Engine Architecture (CBEA) jointly developed by Sony, Toshiba and IBM
Took 5 years, over 400 Million dollars, and hundreds of engineers
New design relies on heterogeneous multicore architecture abandons mechanisms such as cache hierarchies, speculative execution, etc based on fast local memories and powerful DMA engines
CELL Broadband Engine Architecture (CBEA) jointly developed by Sony, Toshiba and IBM
Took 5 years, over 400 Million dollars, and hundreds of engineers
New design relies on heterogeneous multicore architecture abandons mechanisms such as cache hierarchies, speculative execution, etc based on fast local memories and powerful DMA engines
Research Centers contributing
IBM USA• Austin, TX (lead, STIDC)
• Almaden, CA
• Raleigh, NC
• Rochester, MN
• Yorktown Heights, NY
IBM Germany• Boeblingen
IBM Israel• Haifa
IBM Japan• Yasu
IBM India• Bangalore
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Mapping Communications to SPEs
Original single-threaded program performs many computation stages on data. How to map to SPEs?
Each SPE contains all computation stages. Split up data and send to SPEs.
Map computation stages to different SPEs.Use DMA to transfer intermediate results from SPE to SPE in pipeline fashion.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Overlapping DMA and Computation
We are currently doing this:
We can use pipelining to achieve communication-computation concurrency.
► Start DMA for next piece of data while processing current piece.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Reconfigurable Computing via FPGAs
The emergence of high capacity reconfigurable devices has ignited a revolution in general-purpose processing.
It is now possible to tailor and dedicate functional units and interconnects to take advantage of application dependent dataflow.
Early research in this area of reconfigurable computing has shown encouraging results in a number of areas including signal processing, achieving 10-100x computational density and reduced latency over more conventional processor solutions.
FPGA, short for Field-Programmable Gate Array, is a type of logic chip that can be programmed.
An FPGA is similar to a PLD, but whereas PLDs are generally limited to hundreds of gates, FPGAs support thousands of gates.
SPECT Laboratory is involved in the development and demonstration of latest generation FPGA computing
applications.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Xilinx XtremeDSPTM FPGA Hardware
500 MHz Clocking. Multi-Gigabit Serial I/O. 256 GMACS Digital Signal Processing. 450 MHz PowerPC™ Processors with H/W
Acceleration . Highest Logic Integration. 200,000 Logic Cells. Reduced Power Consumption. Achieve performance goals while staying
within your power budget.
The Xilinx XtremeDSP™ initiative helps develop tailored high performance DSP solutions for aerospace and naval defense, digital
communications, and imaging applications.
VIRTEX-4 XtremeDSPTM Development Board
Complex Systems Imam_RAMS Faculty Workshop_2007’12
FPGA Signal Processing Station at SPECT Laboratory
1. Pegasus Demo Board with SPARTAN-2
2. Digilent VIRTEX-2 Development board
3. VIRTEX-4 XtremeDSPTM Development Board
U. S. DEPARTMENT OF ENERGY
QUANTUM HETEROSTRUCTURES
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Quantum Heterostructures
Heterostructures consist of alternating layers of semiconductor materials of similar lattice constants. Quantum confinement alters the electronic band structure. Electron potential can be tailored by appropriate choice of materials.
Heterostructures consist of alternating layers of semiconductor materials of similar lattice constants. Quantum confinement alters the electronic band structure. Electron potential can be tailored by appropriate choice of materials.
E
n = 2
n = 1
E2
E1
hE
k
Electronic energy levels are discretized resulting from one-dimensional confinement potential of semiconductor heterostructures.The levels are broadened into “subbands” due to the in-plane momentum of carriers.
Electronic energy levels are discretized resulting from one-dimensional confinement potential of semiconductor heterostructures.The levels are broadened into “subbands” due to the in-plane momentum of carriers.
Conduction band minimum
Valence band minimum
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Intersubband Lasers and Photodetectors
Intersubband Laser Quantum Well Infrared Photodetector (QWIP)
Bound to continuum transition
3m m
300 K pulsed, CW up to 110 K.
Dual wavelength (8 m, 10 m) lasers.
3m m
300 K pulsed, CW up to 110 K.
Dual wavelength (8 m, 10 m) lasers.
Voltage tunablem m
= 10-3.
Multicolor detectors.
Voltage tunablem m
= 10-3.
Multicolor detectors.
E2
E1
h
E1
E3
Growth Axis
h
E2
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Applications of Intersubband Devices
Medical treatment
Wireless infrared networks
Automotive sensing, pollution monitoring
Laser printers
Computer networking
Pause Play
FF RW
1 2 3
4 5 6
7 8 9
PIP 0 TVVCR
Volume
Power
Channel
Remote sensing
Earth science monitoring
FOR
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Quantum Well Infrared Photodetector (QWIP)
Voltage tunable. = 10-3. Multicolor detectors.
Argument Principle Method (APM)
E2
E1
h
E r
E i
C 1
Bound states Type 2QB states
VN +1
C2
XV0+VBIAS
X
Type 1QB states
C 3
Apply transfer matrix method to structure to find equivalent matrix M. Use APM to find the zeros of the complex function Det(M)=0 to determine the eigen-states
E
V0V1 V2
Vi VN VN+1 Bound States
Type 1Quasibound States
d1d2
diVbias
Type 2Quasibound States
ZNZi-1Z2Z1Z0 ZiZ
Bound eigen-states have real energies. Types 1 and 2 quasibound states have
complex energies.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
QWIPs for Multicolor Infrared Detection
Using bandgap engineering it is possible to extend the functionality of aQWIP for multicolor detection.
Multispectral applications may be very useful in spectral analysis of Infrared sources and target discrimination.
In one possible configuration, several conventional QWIP structures with different selectivity are stacked together.
Use different transitions within the same structure. Symmetric and asymmetric wells have been used.
Martinet et al., Appl. Phys. Lett. 61, 246 (1992).
Grave et al., Appl. Phys. Lett. 60, 2362 (1992).
Kheng et al., Appl. Phys. Lett. 61, 666 (1992).
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Design Methodology of An Optimized QWIP
Eigen-state determination using APM. Dipole matrix (absorption strength) calculation.
Self Consistent Solution: Two factors contribute to carrier potential energy.
Poisson’s equation and Schroedinger’s equation must be solved iteratively until convergence is achieved.
Cost Function Formulation and Iterative Optimization: simulated annealing, genetic algorithm etc.
Eigen-state determination using APM. Dipole matrix (absorption strength) calculation.
Self Consistent Solution: Two factors contribute to carrier potential energy.
Poisson’s equation and Schroedinger’s equation must be solved iteratively until convergence is achieved.
Cost Function Formulation and Iterative Optimization: simulated annealing, genetic algorithm etc.
0 2
L
ij j i ij
LZ e dz
( ) ( ) ( )c cE z z E z
20 ( ) ( ) ( ) ( ) ( )r A D
d dz z e n z N z N z
dz dz
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Absorption Spectrum of Bicolor Equal-Absorption-Peak QWIP Structure at Room Temperature
Wavenumber, (cm-
Transition Energy, E (meV)
2600 2420 22402060 1880 1700 1520 1340 1160 980 800
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1560 cm-1
phonon
1082 cm-1
767 cm-1
310 289 268 247 226 205 184 163 142 121 100
E12 = 134 meV, 12 = 9.25 m. E13 = 193.4 meV, 13 = 6.4 m. R = 0.71.
Imam et al., IEEE J. Quantum Electron. 39, pp. 468-477, 2003
MCT detector 90, 000 scans MCT detector 90, 000 scans
Sharp, well resolved peaks, Lorentzian in Lineshape, no other peaks present.
The absorption spectrum is very high quality and has little noise due to large number of scans taken .
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Current and Future Directions in Quantum Heterostructure Devices
Multi-wavelength detectors
Hyperspectral sensors
Room-temperature devices
Less costly devices
Improved device modeling and simulation
Imam et. al. Superlatt. Microstruct., vol. 28, pp. 11-28, July 2000.Imam et. al. Superlatt. Microstruct., vol. 29, pp. 41-425, June 2001 .Imam et. al. Superlatt. Microstruct., vol. 30, pp. 28-43, Aug. 2001.Imam et. al. Superlatt. Microstruct., vol. 32, pp. 1-9, 2002.Imam et al., IEEE J. Quantum Electron. Vol. 39, pp. 468-477, 2003.
Bandgap Engineering is the key!!
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Examples of Possible Collaboration Topics
Algorithms for Vectorized Fourier Transforms and Implementation on Multicore Processors.
Digital Signal Processing Design and FPGA Implementation.
Quantum Well/Dot Device Modeling, Simulation, and Fabrication.
Tracking Algorithm Development.
Complex Systems Imam_RAMS Faculty Workshop_2007’12
Contacts
Neena ImamResearch and Development Staff
Phone: 865-574-8701
Fax: 865-574-0405
E-mail: [email protected]
Jacob BarhenGroup Leader
Phone: 865-574-7131
Fax: 865-574-0405
E-mail: [email protected]
1 Bethel RoadBldg 5600, MS 6016Oak Ridge, TN 37831-6016USA
Center for Engineering Science Advanced Research (CESAR)Computer Science and Mathematics Division
Oak Ridge National Laboratory
Patty BoydAdministration
Phone: 865-574-6162
Fax: 865-574-0405
E-mail: [email protected]