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

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

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

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

  2. 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

  3. 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 Alliancefor Mathematics and Science Technology Sponsors • Missile Defense Agency • Naval Sea Systems Command • Office of Naval Research

  4. UltraScience Net Complex Systems Overview 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. Sponsors:DOD(DARPA, MDA, ONR, NAVSEA ), DOE(SC), IC (CIA, IARPA, NSA), NASA, NSF

  5. TARGET TRACKING AND DISCRIMINATION

  6. ORNL TASKS 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).

  7. Motivation For HALO-II/AIRS • Meet MDA T&E Requirements • Sensor / Technology Testbed Orbital Signatures Exo-AtmosphericTarget Characterization Chemical Releases Counter- measureSignatures Vehicle Separation Plume Signatures Target Signatures Kill Assessment orMiss Distance TrajectoryReconstruction Booster Tracks Failure Diagnostics Photo documentation FOR Interceptor Performance Flash Radiometry

  8. HALO-II System Overview Image Processing Airborne Pointing System Closed Loop Tracking Object Track Generation) RTPS pointing Pointing hardware • Five Subsystems. Sensors installed in aerodynamic pod. In-Pod Pointing Acquisition Tracking In-Cabin Real time processor Surveillance processor In-Pod In-Cabin highest level view

  9. Code A Code E Code C Code F Code D Sensitivity and Uncertainty AnalysisMotivation 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? 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”. • Uncertainties • input data • outputs • model parameters • sensor measurements Code B

  10. For each model • inputs • parameters • system responses • i.e., outputs Sensitivity and Uncertainty AnalysisObjective 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. The methodology has two primary goals: • determine confidence limits of predictions by large code systems • consistently combine sensor measurements with computational results • obtainbest estimates of model parameters • reduceuncertaintiesin estimates 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. ORNL Developed Improved NOGA Tracker Simulation ResultsElevation and Elevation Uncertainty Sensor Data vs HALO prediction 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. 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. Sensitivity Analysis of the Airborne Pointing System Module Troposphere Stratosphere USNO code NOGA Automatic Differentiation reduced uncertainties experimental response • APS drives the sensors. Calibrates using USNO astronomical refraction code. • 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. • 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. calculated response sensitivities input parameters

  13. SONAR SIGNAL PROCESSING

  14. 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 t and Doppler compression factor h. • 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. Wideband Ambiguity Function SFM signal • 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 here w(t) is the window function B = bandwidth 1.T. Collins and P. Atkins, "Doppler-sensitive active sonar pulse designs for reverberation processing," IEE Proc. Radar Sonar Navig. 145, 347-353 , 1998.

  16. Ambiguity Functions of DSW

  17. Matched Filtering for Active Sonar Processing r(t) Matched Filter 2 Matched Filter 3 Output vs. velocity Matched Filter 4 Matched Filter 1 Envelope detector Envelope detector Envelope detector Envelope detector Optimum Receiver Output vs. time Typical output 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.

  18. 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.

  19. Application Programs • FORTRAN • C • MATLAB SIMULINK VHDL Libraries FPGAs Optical Core The EnLightTM 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 nodeFPGA-based processing and control • Demonstrated accuracy and performance in complex signal processing tasks • Developed by Israeli startup 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 ] Information provided by Lenslet, Inc

  20. Matched filter calculation on EnLight-64 hardware Performance Comparison -30 • Speed-up factor per processor • E_64 : 6,826  2 > 13,000 actual hardware • E_256 : 56,624  2 > 113,000 emulator MATLABAlpha MATLABAlpha -35 -40 Output of filter #1, dB -45 -50 -55 2000 2200 2400 2600 2800 3000 3200 3400 3800 4000 3600 Range (meters) • Computation parameters • FFTs: 80K complex samples number of filter banks • 33 filter banks: 32 Doppler cells, 1 target echo

  21. HYPERSPECTRAL IMAGE PROCESSING

  22. 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

  23. CTIS Instrumentation at U. Arizona

  24. Object Cube = fo(x,y,) Dispersive Element – Computer Generated Hologram Imaging Reconstruction Multiplicative Algebraic Reconstruction Technique - MART Reconstructed Data Cube f Object Acquired Raw Image g(x,y) Optical system Acquired Raw Image g(x,y) g H f Expectation Maximization Linear relationship between object and image data: g: 2-D (x, y) raw image f: 3-D (x, y, l) object cube H: System matrix n: Additive noise CTIS Principle Mapping of signal from the object cube to the focal plane array

  25. CTIS Code Acceleration • 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. • Improved algorithm employing conjugate gradient method • Parallel programming for CELL Broadband Engine (CBA) multicore processor • Reconfigurable computing via FPGAs Algorithms must be developed for less computational time and better convergence

  26. Courtesy IBM 2006 IBM Cell Multicore Device • 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 • 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

  27. 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. 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. 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.

  30. 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. VIRTEX-4 XtremeDSPTM Development Board The Xilinx XtremeDSP™ initiative helps develop tailored high performance DSP solutions for aerospace and naval defense, digital communications, and imaging applications.

  31. FPGA Signal Processing Station at SPECT Laboratory • Pegasus Demo Board with SPARTAN-2 • Digilent VIRTEX-2 Development board • VIRTEX-4 XtremeDSPTM Development Board

  32. QUANTUM HETEROSTRUCTURES

  33. 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. • 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. Intersubband Lasers and Photodetectors Quantum Well Infrared Photodetector(QWIP) IntersubbandLaser Bound to continuum transition • Voltage tunable (7 mm - 9 mm). • Dl/l = 10-3. • Multicolor detectors. • l = 3 mm - 11 mm . • 300 K pulsed, CW up to 110 K. • Dual wavelength (8 mm, 10 mm) lasers.

  35. Applications of Intersubband Devices • Automotive sensing, pollution monitoring • Laser printers FOR • Medical treatment • Wireless infrared networks • Computer networking • Remote sensing • Earth science monitoring

  36. Quantum Well Infrared Photodetector (QWIP) • Voltage tunable. • Dl/l = 10-3. • Multicolor detectors. • Bound eigen-states have real energies. • Types 1 and 2 quasibound states have complex energies. • 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 Argument Principle Method (APM)

  37. 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. Grave et al., Appl. Phys. Lett. 60, 2362 (1992). Kheng et al., Appl. Phys. Lett. 61, 666 (1992). Martinet et al., Appl. Phys. Lett. 61, 246 (1992).

  38. 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.

  39. Absorption Spectrum of Bicolor Equal-Absorption-Peak QWIP Structure at Room Temperature • MCT detector • 90, 000 scans • DE12= 134 meV, l12= 9.25 mm. • DE13= 193.4 meV, l13= 6.4mm. • R = 0.71. Imam et al., IEEE J. Quantum Electron. 39, pp. 468-477, 2003 • 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. Current and Future Directions in Quantum Heterostructure Devices • Multi-wavelength detectors • Hyperspectral sensors • Room-temperature devices • Less costly devices • Improved device modeling and simulation Bandgap Engineering is the key!! 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.

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

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