# Presentation: 582279

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

**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**

## 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** **

**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**

## 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).**

## Motivation For HALO-II/AIRS

**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

**Notes:**
This chart provides a brief schematic view of the major missions that HALO II was designed to address:
MDA has a full range of test and evaluation needs that require an extremely capable and well characterized sensor system. The span of data types and evaluation needs is cited with thumb-nail examples of impact, reentry, boost and trajectory estimation. The capabilities and flexibility of the HALO II will permit a large number of issues to be effectively examined using high quality EO/IR data.
Boost phase interests include resolved plume and hardbody phenomenology, tracking studies and the ability to assist failure resolution. Boost phase transitions to the Midcourse after the cutoff of main propulsion and the deployment or ejection of items, such as reentry vehicles (RVs). The Mid-course interests include the the signatures and tracking aspects of various objects including debris, fuel, hardbodies and decoys. The Reentry or terminal phase begins when object trajectories and dynamics are perturbed by the tenuous atmosphere.  The final aspect of BMD testing to be supported by the HALO II will be for intercept characterization and photo documentation of the entire event sequence.

Non-BMD goals can span the range of Satellite tracking / Signatures to characterization of ground targets.

## HALO-II System Overview

**HALO-II System Overview**

**highest level view**

-  **Five Subsystems.**** Sensors installed in **
**aerodynamic pod.**
**    **
**    ****In-Pod**
**	****Pointing**
**	****Acquisition**
**	****Tracking**
**   ****In-Cabin**
**	****Real time processor**
**	****Surveillance processor**

_**I**__n-Pod_

_**In-Cabin**_

**Notes:**
The HALO-II system consists of five major subsystems.  Three of the major subsystem including the pointing, acquisition, and tracking subsystem are located in an aircraft pod.  The other two subsystems, included the real time and surveillance processors and are located within the HALO-II cabin.

## Sensitivity and Uncertainty AnalysisMotivation

**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 B

## 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

- 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.

- 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.

##  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.

## 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.

- calculated response
- sensitivities
- input parameters

-  APS  drives the sensors. Calibrates using  USNO astronomical refraction code.

-  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. 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

## Ambiguity Functions of DSW 

## Matched Filtering for Active 

**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 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**.

## The EnLight TM Prototype Optical Core Processor

***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**

**Information provided by *****Lenslet, Inc****** ***

## Matched filter calculation on EnLight-64 hardware

- 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 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**

**4000**

**3200**

**3400**

**3600**

**3800**

**2800**

**3000**

**2400**

**2200**

**Range (meters)**

**Output of filter #1, dB**

**MATLAB****Alpha**

**MATLAB****Alpha**

## HYPERSPECTRAL IMAGE PROCESSING

## 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**

**Notes:**
Several cellular parameters may change either simultaneously or in sequence during the course of many physical responses (e.g. hormone secretion, muscle contraction). 
many tissues are heterogeneous with respect to the time course of cellular response, 
it is critical to examine multiple parameters in individual cells with adequate spatial (1 μm or less) and temporal resolution (msec – sec). 
such experiments require the use of spectral imaging systems that collect spectra simultaneously for all spatial locations across the cellular field
Observations of multiple physiological parameters may also require the loading of several distinct probes into the cellular environment
These probes may exhibit overlapping spectral signatures, dye-dye interactions or cross-sensitivities to multiple analyses. 
Under these circumstances, spectral unmixing algorithms can be utilized to precisely evaluate the signal from individual probes and to evaluate possible spectral variations due to probe interactions 
due to the need for spectral-band scanning in filtered imaging systems, an improvement in spectral resolution occurs with a concomitant reduction in temporal resolution. 
Examples of filtered imaging systems currently used in fluorescence microscopy are cameras equipped with either an acousto-optic tunable filter (AOTF) or liquid crystal tunable filter (LCTF). Although these filters exhibit rapid switching rates between spectral bands, the signal within each band ultimately limits the temporal resolution. 
Point-scan and slit-scan spectrometers overcome this drawback by collecting high-resolution spectral data over a limited region of the cellular field during a single integration time. Again the scan rates of these systems are not the limiting factor in determining their temporal resolution. Instead one must consider the dwell time required at each point or line within the sample. In a recent publication, total acquisition times of up to 5 minutes have been reported for slit scan imaging spectrometers in fluorescence microscopy. This corresponds to object cubes with 200  200 spatial positions and a spectral resolution of 0.4 nm across the visible spectrum. 
The computed tomography imaging spectrometer (CTIS) overcomes previous sampling limitations by acquiring full spectral information for all spatial samples within a 2-D field of view (FOV) during a single integration time. For this reason, the CTIS can be considered a snapshot spectral imaging device.  Spatial and spectral multiplexing is accomplished through the use of a computer-generated hologram (CGH) disperser which maps the fluorescence signal from a three-dimensional (3-D)  specimen onto a 2D  image plane (CCD array). The mapping may be experimentally determined through an  calibration of the CTIS and then mathematically inverted to reconstruct object cube estimates from captured raw images. 

## CTIS Instrumentation at U. Arizona

## CTIS Principle

***Mapping of signal from the object cube to the focal plane array***

**Notes:**
The CTIS can be modeled as a linear imaging system and thus the mapping between object and image space may be described in terms of linear algebra. 
In its most general form, each column of H corresponds to the response of the CTIS to a single volume element, or voxel, within the object cube. Each column therefore represents a specific voxel spread function (VSF) and the corresponding system matrix is referred to specifically as a voxel-based system matrix.
The CTIS maps the signal from each voxel to a distinct diffraction pattern on a CCD array by means of a CGH disperser 

Multiplicative Algebraic Reconstruction Technique (MART).[i]  In MART, the iterative progression from the kth estimated object cube, , to the (k + 1)st occurs according to the equation:
	T indicates the matrix transpose and  and  form the backprojection of the collected raw image and the current image estimate, respectively. The algebraic operations are taken to be element-by-element operations.[ii]  MART is a double-blurring algorithm and therefore converges slowly for sharp spatial and spectral features.  This is not a significant limitation given the spatial resolution of the CTIS and the smoothness of fluorescence emission spectra. Instead, the double blurring provides the benefit that noise artifacts are diminished within object cube estimates. 

EM converges more rapidly than MART; however, reconstructions based on this technique are highly susceptible to noise artifacts in the form of sharp spectral features.  Thus far, MART has proven a more suitable algorithm within the explored fluorescence applications; however, the applicability of EM may increase with the ongoing development of fluorescent semiconductor nanocrystals.[i]  These nanoprobes can be designed to exhibit narrow and symmetric emission spectra (10 to 20 nm full width half maximum) over a broad spectral range (400 nm to 2 mm).[ii] Additionally, the broad excitation ranges and large Stoke’s shifts associated with these nanoprobes allows for simultaneous excitation and detection of multiple nanoprobes. The combined emission spectra may be better served by EM due to the sharper spectral features to be expected.  

## 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

**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 PowerPCTM 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 XtremeDSPTM initiative helps develop tailored high performance DSP solutions for aerospace  and naval defense, digital communications, and imaging applications.**

**VIRTEX-4 XtremeDSP****TM**** Development Board**

## FPGA Signal Processing Station at SPECT Laboratory

- Pegasus Demo Board with SPARTAN-2
- Digilent VIRTEX-2 Development board
- VIRTEX-4 XtremeDSPTM Development Board

## QUANTUM HETEROSTRUCTURES

## 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.

-   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.

## 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.

- Voltage tunablem m
-  = 10-3.
-  Multicolor detectors.

## Applications of Intersubband Devices

** ****Medical treatment**

** ****Wireless infrared networks**

** ****Computer networking**

**  ****Remote sensing **
**  ****Earth science monitoring**

## 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.

## 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).**

## 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.

## Absorption Spectrum of Bicolor Equal-Absorption-Peak QWIP Structure at Room Temperature

**********E**12* *= 134  meV, ****12* *= 9.25 m.
-  ******E**13* *= 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

-  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

**Contacts**

**Neena Imam**
- Research and Development Staff
- Phone:	865-574-8701
- Fax:	865-574-0405
- E-mail:	imamn_[@ornl.gov](mailto:i8@ornl.gov)_
**Jacob Barhen**
- Group Leader
- Phone:	865-574-7131
- Fax:	865-574-0405
- E-mail:	barhenj_[@ornl.gov](mailto:p7v@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](mailto:p7v@ornl.gov)_