Neena Imam Complex Systems Computer Science and Mathematics Division OAK RIDGE NATIONAL LABORATORY

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


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 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


TARGET TRACKING AND DISCRIMINATION


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


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


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


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


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.


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.


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.


SONAR SIGNAL PROCESSING


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.


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


Ambiguity Functions of DSW


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)


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.


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


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


HYPERSPECTRAL IMAGE PROCESSING


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


CTIS Instrumentation at U. Arizona


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


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


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


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.


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.


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.


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


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


QUANTUM HETEROSTRUCTURES


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


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


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


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.


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).


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


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 .


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!!


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.


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


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


mailto:[email protected]



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Neena Imam Complex Systems Computer Science and Mathematics Division OAK RIDGE NATIONAL LABORATORY