1. U. S. DEPARTMENT OF ENERGY Neena Imam Complex Systems Computer Science and Mathematics Division OAK RIDGE NATIONAL LABORATORY Highlights of Selected Complex Systems Research Activities Algorithm to Ultra-fast Signal Processing Presented at RAMS Faculty Workshop Oak Ridge, TN December 10, 2007

2. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Outline  Introduction u acknowledgments & collaborators u 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

3. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Acknowledgements … for activities presented hereafter Collaborators  Jacob Barhen ORNL / Complex Systems (Group Leader) u Travis Humble ORNL / Complex Systems u Jeffery Vetter ORNL / Future Technologies u Aeromet Corporation Tulsa, OK u Thomas Gaylord Georgia Tech u Eustace Dereniak U. Arizona u Albert Wynn, Deirdre Johnsonstudents, Research Alliance for Mathematics and Science Technology Sponsors u Missile Defense Agency u Naval Sea Systems Command u Office of Naval Research

4. Complex SystemsImam_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

5. U. S. DEPARTMENT OF ENERGY TARGET TRACKING AND DISCRIMINATION

6. Complex SystemsImam_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

7. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Orbital Signatures  Meet MDA T&E Requirements  Sensor / Technology Testbed Kill Assessment or Miss Distance Vehicle Separation Chemical Releases Booster Tracks Interceptor Performance Flash Radiometry Plume Signatures Counter- measure Signatures Target Signatures Photo documentation Trajectory Reconstruction Failure Diagnostics Exo-Atmospheric Target Characterization FOR Motivation For HALO-II/AIRS

8. Complex SystemsImam_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 view  Five Subsystems. Sensors installed in aerodynamic pod. In-Pod Pointing Acquisition Tracking In-Cabin Real time processor Surveillance processor  Five Subsystems. Sensors installed in aerodynamic pod. In-Pod Pointing Acquisition Tracking In-Cabin Real time processor Surveillance processor In-Pod In-Cabin

9. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Sensitivity and Uncertainty Analysis Motivation For example, modeling of battlespace threat signatures encompasses a large set of varied phenomenologies è 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 B Code A Code C Code D Code E Code F

10. Complex SystemsImam_RAMS Faculty Workshop_2007’12 The methodology has two primary goals : u determine confidence limits of predictions by large code systems u 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 u inputs u parameters u system responses i.e., outputs Sensitivity and Uncertainty Analysis Objective 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.

11. Complex SystemsImam_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. Simulation Results Elevation and Elevation Uncertainty Sensor Data vs HALO prediction 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.

12. Complex SystemsImam_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 response sensitivities input parameters USNO code reduced uncertainties experimental response NOGA Automatic Differentiation  APS drives the sensors. Calibrates using USNO astronomical refraction code. Troposphere 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.

13. U. S. DEPARTMENT OF ENERGY SONAR SIGNAL PROCESSING

14. Complex SystemsImam_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. 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.

15. Complex SystemsImam_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

16. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Ambiguity Functions of DSW

17. Complex SystemsImam_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. Matched Filter 2 Envelope detector Matched Filter 1 Envelope detector Matched Filter 4 Envelope detector Matched Filter 3 Envelope detector Output vs. time Output vs. velocity Optimum Receiver Typical output r(t)

18. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Matched Filtering for Active Sonar Processing  SFM pulse of f c =1200 Hz  Bandwidth B= 400 Hz  Pulse duration = 1 s  Modulation frequency = 5 Hz  Sonar sampling rate f s = 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.

19. Complex SystemsImam_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 LibrariesFPGAs Optical Core Information provided by Lenslet, Inc

20. Complex SystemsImam_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 Results Time Performance Intel Dual Xeon Enlight 64α Enlight 256 Specs 2 GHz 1 GB RAM 60 MHz125 MHz FFT radix 232128 Timing9,626 ms1.41 ms0.17 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 20002600400032003400 3600 38002800300024002200 Range (meters) Output of filter #1, dB MATLAB Alpha

21. U. S. DEPARTMENT OF ENERGY HYPERSPECTRAL IMAGE PROCESSING

22. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Hyperspectral Sensor Computer 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

23. Complex SystemsImam_RAMS Faculty Workshop_2007’12 CTIS Instrumentation at U. Arizona

24. Complex SystemsImam_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 Mapping of signal from the object cube to the focal plane array g H Optical system Acquired Raw Image g(x,y) Object Reconstructed Data Cube f ImagingReconstruction f Object Cube = f o (x,y, ) Dispersive Element – Computer Generated Hologram Acquired Raw Image g(x,y) Multiplicative Algebraic Reconstruction Technique - MART Expectation Maximization

25. Complex SystemsImam_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

26. Complex SystemsImam_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 u abandons mechanisms such as cache hierarchies, speculative execution, etc u 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 u abandons mechanisms such as cache hierarchies, speculative execution, etc u 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

27. Complex SystemsImam_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.

28. Complex SystemsImam_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.

29. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Reconfigurable Computing via FPGAs u The emergence of high capacity reconfigurable devices has ignited a revolution in general-purpose processing. u It is now possible to tailor and dedicate functional units and interconnects to take advantage of application dependent dataflow. u 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. u FPGA, short for Field-Programmable Gate Array, is a type of logic chip that can be programmed. u 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.

30. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Xilinx XtremeDSP TM 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 XtremeDSP TM Development Board

31. Complex SystemsImam_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 XtremeDSP TM Development Board

32. U. S. DEPARTMENT OF ENERGY QUANTUM HETEROSTRUCTURES

33. Complex SystemsImam_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.  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.

34. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Intersubband Lasers and Photodetectors Intersubband Laser Quantum Well Infrared Photodetector (QWIP) Bound to continuum transition   3  m  m    300 K pulsed, CW up to 110 K.  Dual wavelength (8  m, 10  m) lasers.   3  m  m    300 K pulsed, CW up to 110 K.  Dual wavelength (8  m, 10  m) lasers.   Voltage tunable  m  m    = 10 -3.  Multicolor detectors.   Voltage tunable  m  m    = 10 -3.  Multicolor detectors.

35. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Applications of Intersubband Devices  Medical treatment  Wireless infrared networks  Automotive sensing, pollution monitoring  Laser printers  Computer networking  Remote sensing  Earth science monitoring FOR

36. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Quantum Well Infrared Photodetector (QWIP)   Voltage tunable.   = 10 -3.  Multicolor detectors. Argument Principle Method (APM)  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  Bound eigen-states have real energies.  Types 1 and 2 quasibound states have complex energies.

37. Complex SystemsImam_RAMS Faculty Workshop_2007’12 QWIPs for Multicolor Infrared Detection  Using bandgap engineering it is possible to extend the functionality of a QWIP 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).

38. Complex SystemsImam_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.

39. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Absorption Spectrum of Bicolor Equal-Absorption-Peak QWIP Structure at Room Temperature   E 12 = 134 meV, 12 = 9.25  m.   E 13 = 1 93.4 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.

40. Complex SystemsImam_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!!

41. Complex SystemsImam_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.

42. Complex SystemsImam_RAMS Faculty Workshop_2007’12 Contacts Neena Imam Research and Development Staff Phone:865-574-8701 Fax:865-574-0405 E-mail:imamn@ornl.gov@ornl.gov Jacob Barhen Group Leader Phone:865-574-7131 Fax:865-574-0405 E-mail:barhenj@ornl.gov@ornl.gov 1 Bethel Road Bldg 5600, MS 6016 Oak Ridge, TN 37831-6016 USA Center for Engineering Science Advanced Research (CESAR) Computer Science and Mathematics Division Oak Ridge National Laboratory Patty Boyd Administration Phone:865-574-6162 Fax:865-574-0405 E-mail:boydpa@ornl.gov@ornl.gov