1. Education and Research in the Center for Signal and Image Processing

2. CSIP Summary
Our Ph.D. graduates have impact worldwide in DSP education and research
Distinguished faculty
17 faculty (7 IEEE Fellows, 2 National Academy members)
Co-authors of over 25 books on DSP & its applications
Over 80 current Ph.D. students
Located in GCATT building
with excellent, modern
facility
Support from Georgia Research Alliance has provided outstanding well equipped labs.

3. Beowulf Cluster
26 dual processors
1 Gbyte memories

4. CSIP Faculty Yucel Altunbasak David V. Anderson Thomas P. Barnwell
Mark A. Clements
Faramarz Fekri
Monson H. Hayes
Joel R. Jackson
Fred Juang
Aaron Lanterman
Chin Lee
Vijay K. Madisetti
Francois Malassenet
James H. McClellan
Russell M. Mersereau
Ronald W. Schafer
Douglas B. Williams
G. Tong Zhou

5. Past and Present Funding
Industry: Texas Instruments, Intel, BAE Systems, Hewlett-Packard, Mathworks, National Semiconductor, Analog Devices, Lucent, Harris, Hughes, Prentice-Hall
Federal: NSF, U.S. Army, DARPA, ONR, NASA,MPO
State: Georgia Research Alliance
Private Foundation: John and Mary Franklin Foundation
Total Funding: Current funding from government and industry totals about $6.5M

6. Current Research Areas - I
Speech Processing
Robust automatic speech recognition
New architectures for speech recognition
High-quality low-bit-rate speech coding for voice over IP
Blind separation of speech signals
Audio Signal Processing
Music analysis and synthesis
Compressed-domain processing of audio
Acoustic Signal Processing
Noise and reverberation removal
Microphone array processing
Spatialization

7. Current Research Areas - II
Video Signal Processing
Target tracking in video
Video streaming with error concealment and MDC
Graphics streaming for the Internet
Automated analysis of video
Video indexing for smart VCR
Super-resolution of video
Face Recognition
Video compression
Image Processing
Image-based graphical rendering
Image interpolation for digital color cameras

8. Current Research Areas - III
Multimedia & Multi-modal Signal Processing
“Intelligent Environments”
Automatic storage/retrieval of speech and audio
Audio-visual speech recognition
Speech-driven facial animation
Application of multimedia processing in education
Communications Signal Processing
Chaos in wireless communication systems
Space-time coding and OFDM
Compensation for selective fading effects
Finite field wavelet transforms and applications to error control coding and cryptography
Compensation of nonlinear power amps

9. Current Research Areas - IV
Signal Modeling
Multi-scale sinusoidal modeling
Biological Signal Processing
Automated measurement and modeling of behavior in biological systems
Military Signal Processing
Buried mine detection using GPR, seismic & EMI
Target Tracking in sensor networks
Hyperspectral imaging and target classification
SAR imaging
Medical Signal Processing
Segmentation of cardiac MRI images
DSP for hand-held communication devices

10. Industrial Partnership Examples
Texas Instruments Leadership Univ. Program
Members with MIT and Rice U.
Seven projects - 7 faculty and 7 Ph.D. students
Wireless video, CFA interpolation, speech coding, speech recognition, chaotic systems, face recognition, MIMO communication systems
Hewlett Packard Laboratories
Four faculty and six students
Focus on PDAs: low-power analog front-ends, structured audio, applications in education. Also, 3D video for video conferencing,
HP Labs researcher in residence

11. Linearization of RF Power Amplifiers G. Tong Zhou, J. Stevenson Kenney
Power amplifiers (PAs) are inherently nonlinear.
Desire: high efficiency PAs, leading to low cost.
Downside of high efficiency: high nonlinearity.
Nonlinearity causes: (1) high bit error rate; (2) adjacent channel interference: must satisfy FCC.
DSP-based predistortion linearization.
Challenging issue: memory nonlinear effects in high power amplifiers (e.g., base station PAs).
Indirect Learning Architecture adapts to changing characteristics
RF TESTBED

12. Indirect Learning Architecture
A/D
Advantage: No need to model or identify the PA.

13. 8-Tone Test Result 8-tone, 1.2MHz signal, Siemens CGY0819 dual-band PA
Purple: w/o PD;
green: w/ memoryless PD (K=7);
cyan: w/ memory polynomial PD (K=7, Q=10)
35 dB of spectral regrowth suppression w/ memory polynomial PD

14. Video Resolution Enhancement Y. Altunbasak and R. Mersereau
Future broadcasting will be all digital.
High definition displays will dominate the market.
However, most programming is expected to be in SDTV format.
HDTV
NTSC
Set
SDTV
Multi-frame Spatial
PC Video
Resolution Enhancement
PC Monitor
HDTV
There is a clear need and technical opportunity to design systems to enhance the quality of the SDTV signal so that it matches the quality and capabilities of high definition displays.

15. Applications - Digital Cameras
Subsequent
multiple pictures
(JPEG format)
Reconstructed
high-resolution picture
Also applicable to high-quality printing from video sources such as DVD players, set-top boxes, TV sets, software MPEG players and camcorders. Requires a resolution enhancing print driver.

16. Face Recognition Monson H. Hayes
Major problem
is lighting and
pose variations.

17. Results and Next Step
We have developed a new face recognition system based on a segmented linear subspace model
Robust to varying illuminations and tolerant to different poses,
Has recognition accuracy equaling or exceeding (>99%) other state-of-the-art systems, and
Has a fraction of the complexity.
Next Step: Face Recognition from Video
Face detection (patent awarded).
Pose detection (find best frontal view).
Face recognition (robust to varying illuminations, poses, facial expressions).
The Intriguing Question
How can we incorporate the multitude of images that are extracted from video to enhance the recognition system?

18. Finite Field Wavelet Transforms
F. Fekri and D. Williams
Goal: Establishment of a new research field that brings together researchers from signal processing, error control coding, data security and multicarrier signaling systems.
Error Control
Coding
Finite Field Wavelets
And I would like to conclude my talk by this slides that summarize my research plan:
in which the finite field wavelet plays a central role to do coding, data security and muliuser access and combine them under unifying theory. Thus I intend to propose a program that will systematically explore these application areas
and I will encourage and welcome collaboration with other faculty members.
OFDM
Modulation
Security
coding

19. New Research Directions in Data SecurityLL HL LH
row-wise
column-wise LL LH HL HH HH
New Research Directions in Error Control Coding

20. Passive Radar Systems Aaron Lanterman Target Tracking Positions
Exploit “illuminators of opportunity” such as commercial TV and FM radio broadcasts for covert operation
Target Tracking
Positions
Velocities
Radar Imaging
Radar
Cross
Section
Passive Radar System
Target
Classification
Signature
Prediction via
Computational EM
Target Library

21. Imaging With 100.0 on Your FM Dial
Falcon-100
Target
Shape
Formatted
Raw Data
Image
Formed
Via
Processing
VFY-218

22. Detection of Obscured Targets Jim McClellan & Waymond Scott
Landmines
No single sensor has proven capable of reliable detection across many types of “targets”
Can multiple sensors be used cooperatively to produce a system with robust performance?
A three sensor experiment
Electromagnetic Induction (EMI) Sensor
Ground Penetrating Radar (GPR) Sensor
Seismic Sensor
Multimodal processing
Imaging & Inversion
Cooperative Fusion of multiple sensors

23. EMI Sensor and GPR Tx Rx EMI Sensor: 0.6 - 60 kHz GPR: 500 MHz – 8 GHz
Physical Properties of Target
Permittivity Contrast
Low Conductivity
(Dielectric)
High Conductivity
(Metal)
Mechanical Contrast
EMI No
Weak
Yes
GPR
Yes*
Seismic
EMI Sensor: kHz
GPR: 500 MHz – 8 GHz Tx Rx
4.5”

24. Seismic Sensor: Surface Waves
Man-made items
often resonate

25. Comparison of EMI, GPR and Seismic Responses: VS-1.6, 6.5 cm deepx
depth y t

26. Comparison of EMI, GPR & Seismic Responses Uncrushed Aluminum Can, 2 cm deepx
depth y t

27. Cooperative Analog/Digital Signal Processing
D. Anderson and P. Hasler
Target: Complex signal processing functionality with extremely low power
Approach: Perform substantial amounts of the processing in programmable analog VLSI
Real
world
(analog)
DSP
Processor
A/D
Convertor
Computer
(digital)
Specialized A/D
Real
world
(analog)
ASP IC
Computer
(digital)
DSP
Processor
A/D

28. Cooperative Analog/Digital Signal Processing
Advantages of CADSP:
Better problem “fit”
Orders of magnitude improvement in power consumption / efficiency
Simpler A/D converter requirements,
Smaller size.
Current Applications Include:
Audio noise suppression
Audio source localization / beam-steering
Focal plane image / video processing
Speech Recognition
Field Programmable Analog Processor Arrays

29. Digital Media Asset Management Mark Clements
Sam Nunn Archives: Cooperative Effort between CSIP, IMTC, GT and Emory Libraries.
Fast searching of audio based on phonetic content. Typical speed of search: 72,000x real time (20 hours of content searched in 1 elapsed second).
Basis for startup company Fast-Talk which has received over $10M venture funding.
New results demonstrate rapid searching of music by lyrics and melodies using same approach.
The speech part can be the basis for voic management, data-mining, call center monitoring and alerting, market research, distance learning tool.
The music part can be for indexing content by melody, detection copyright infringement, accessing music by “humming a tune.”

30. An Integrated Auditory-Cognitive Model
speech
Auditory Model
Neural
Transduction Model
3-D Cortical Representation s f
Cortical Scene
Analysis
(Phonemic Detection)
Language
Model
Sound
Units
Semantics
& Schema
Multi-target tracking
Reinforcement
Syntactic & Semantic Analysis
(error correcting)
Cortical Scene
Analysis
(Phonological Tracking)
Segment
Units
Understanding results
Enhancement
results
Re-generation
Recognition results

31. Immersive Telecollaboration
Presentation
Capturing, transmission and reconstruction of audio and visual information (conventional view)
Projection and rendering of the interaction in a 3-dimensional space (virtual view)
Participation
Coexistence of all participants in a shared virtual space (“shared reality”)
Control and manipulation of shared virtual objects (“virtual collaboration” for hands-on experience)

32. Perceptual Spatialization
Sound spatialization makes talker-tracking easier in multi-party conferencing environments, resulting in improved effectiveness in communication
Spatial separation plays a role.
Compare mono with stereo
Binaural Hearing & Cocktail Party Effect
Stream segregation also plays a role.
Compare one talker (m1+m2) with two (m1+f2)
(m m f )
Stereophonic Conferencing Demonstration

33. Multi-channel Source Separationx1
W11
s’1
W21
W12 s2 x2
W22
s’2
mixing
un-mixing
(room impulse responses)
One possible approach (Ikram of Gatech and Morgan of Bell Labs):
x = H s R’ = x xH s’ = W x
Find un-mixing filter matrix W such that
s’ = W R’ WH is diagonalized by minimizing the
squared Frobenius norm of the off-diagonal matrix of s’

34. Sound Source Localization
1. Time Delay Estimation
2. Source Location Estimation
Various methods:
triangulation - solve a set of hyperbolic
equations
spherical intersection - solve a set of
linearized spherical equations
spherical interpolation - similar to SI,
but with reduced constraint
one-step-least-squares – transforms the
problem into an estimation/minimization
problem; works the best 
talker 
Further challenge
Applications:
Conferencing with participant tracking
Improved sound and sight pickup
Developed at Bell Labs
& Georgia Tech

35. Summary
The premier academic program in the country in the signal processing field is in the Georgia Tech School of Electrical and Computer Engineering.
We have many outstanding graduate students.
Internships
Long-term contributors
We have lots of outstanding technology waiting to be developed.
We have a demonstrated capability to work with industry.
Contact if you want to come for a visit.