Research Directions: Douglas L. Jones's Group
Current Research
Acoustic signal processing with small arrays
Biologically Inspired Sensory Systems
Ultra-Low-Power Computer Systems
Prior Research
Time-frequency analysis
Wavelets and denoising
Adaptive Signal Processing
Biomedical and hearing aid applications
Communications
Low-Power Computer Systems
Algorithms and implementation
Teaching Innovations
Acoustic signal processing with small arrays
Array processing has been a subject of intense research for several decades,
but most successful methods depend on relatively large arrays of sensors,
narrowband processing of stationary sources, fewer sources than sensors,
and simple acoustic environments.
The capabilities of human and other animal hearing systems demonstrate that
these constraints can be overcome.
Our prior work has developed new bio-inspired binaural algorithms
for the localization and separation of desired sources from a
cluttered acoustic environment, such as in a
restaurant or cocktail party,
that show remarkable performance
improvements over conventional techniques while using only two to four
microphones.
Current research is developing meta-adaptive algorithms that
automatically and blindly learn the characteristics of complex
acoustic environments (such as reverberation or many nonstationary
interferers) and adapt or auto-calibrate to them to maximize performance.
Four-dimensional sound recording, transmission, and playback that preserves
the directional characterics of the sound is being developed.
These new methods are being applied to a number of applications
such as advanced hearing aids, hands-free telephony, advanced multimedia
systems, automotive and military applications,
noise suppression for speech recognition systems, and highly accurate
direction-finding of RF sources with small antennas.
Some real-world audio examples can be found here.
Biologically Inspired Sensory Systems
Animals have many types of sensory systems that provide vital information
about their environment.
In collaboration with biologists and MEMS engineers who develop new forms of
sensors, we are developing novel bio-inspired sensing systems with funadamentally new capabilities.
Recent work has developed an artificial lateral line that detects and locates
moving objects in the water surrounding the array, an artificial weak electrosense
that performs a similar function using a self-generated electric field,
and bio-inspired algorithms for high-resolution acoustic source localization
and separation using very small arrays.
Current work is extending these methods to localization of neurons,
new tactile and vibrissal (whisker) sensing systems, and an "electronic bat"
4-D imaging system.
Ultra-Low-Power Electronic Systems
Reducing energy dissipation and size of wireless sensing systems
could enable many high-impact applications such as intelligent materials,
smart objects that are aware of and adjust to their surrounding conditions,
and enriched human sensory and monitoring interfaces as well as
military systems.
We believe that only dynamically self-optimizing systems that
adaptively select the right information, data resolution and rate, signal
representation, communication bandwidth, and data latency over a broad dynamic
range can provide the needed functionality at an absolute minimal energy cost.
As part of the Multiscale Systems Center,
we are developing "attentional" signal processing systems that optimally
adapt all system components to the current demands of the environment and
the application to maximize the expected long-term system utility within
a very limited overall energy budget.
This includes highly scalable detection and estimation algorithms that
trade off performance and energy over orders of magnitude to dynamically
match current system needs, as well as system management algorithms that
maximize the total system utility.
Adaptive time-frequency representations
Developed the concept of adaptive time-frequency representations (TFRs),
and several of the leading signal-dependent and adaptive transforms.
These include adaptive window short-time Fourier transforms, adaptive
wavelet transforms, adaptive optimal kernels, adaptive cone kernels,
and fast algorithms for their computation.
Our recent "consistent time-frequency representation" achieves
unprecedented resolution and cross-term suppression.
Statistical time-frequency analysis
Developed a fundamental and comprehensive theory of statistical time-frequency
analysis.
We derived optimal kernels for nonstationary spectrum estimation and
time-frequency estimation of TFRs of noisy or random signals.
A theory of time-frequency detection has determined the class of
detection problems for which time-frequency-based detection is globally
optimal, the optimal kernels for these classes, and efficient methods
of implementing these detectors.
These techniques have been applied in a number
of applications, including machine-fault detection and diagnosis,
microembolus detection, and ECG classification.
Time-frequency-space processing
Developed efficient, near-optimal time-frequency-space detectors and estimators
for partially coherent arrays.
Highly efficient, nearly optimal quadratic
narrowband array detection algorithms have
been an important by-product of this research.
Generalized joint signal representations
Contributed several advances in generalized
joint signal representations, including a time-frequency-based
derivation of the chirplet transform, new orthogonal chirped bases,
and unitarily transformed joint signal representations.
We showed the equivalence of Cohen's and Baraniuk's methods for
constructing general joint signal representations and contributed
new insights to this theory.
We extended the theory of adaptive and statistically optimal TFRs
to generalized joint signal representations.
We have developed four-parameter joint
quadratic time-frequency-delay-doppler
representations for applications such as
improved adaptive time-frequency analysis
and detection and classification.
Nonstationary blind source separation and
interference cancellation
Introduced new adaptive methods for blind
source separation of nonstationary signals.
These methods are simpler than existing methods
and continuously track environmental changes.
New methods for extraction of speech signals
in cluttered environments (the "cocktail party"
environment) have yielded great
improvements over existing methods
(see expanded discussion below).
Generalized wavelet decompositions and transforms
Developed new orthogonal chirped wavelet bases,
unitarily transformed basis decompositions,
and efficient algorithms and implementations.
Developed the chirplet transform from
a time-frequency context.
Denoising
Innovative new methods for denoising multichannel data provide much better performance than
single-channel methods and are very efficient;
applications to hyperspectral imagery have
shown more than 10 dB SNR gain.
Derived worst-case bounds for the performance
of denoising methods for both orthonormal
bases and overdetermined frames.
New "Bayesian pursuit" methods offer improve denoising
performance for using overdetermined frames and a
hierarchical statistical signal model.
MEMS sensors
In a joint project with Profs. Chang Liu
and Naresh Shanbhag, we are developing
distributed, multi-element touch and flow
sensors and embedded architectures and
algorithms for extracting sophisticated
touch and flow information, such as texture,
turbulence, softness, and slippage to create
an "artificial skin."
In collaboration with Prof. Ron Miles at SUNY Binghamton,
we are developing algorithms for high-accuracy
direction-finding and signal recovery using
colocated arrays of directional MEMS sensors.
Adaptive signal processing
Blind equalization
Developed a vector constant modulus algorithm
for blind equalization of shaped channels,
the first practical solution to this problem.
Nonlinear adaptive filters
Developed a low-complexity, LMS-like
algorithm for general systems with a
memoryless nonlinearity.
Application to nonlinear echo cancellation
demonstrated substantial improvement over
conventional linear echo cancellers.
Nonstationary blind source separation
Introduced new adaptive methods for blind
source separation of nonstationary signals,
including both instantaneous and convolutive
(dynamic) mixtures.
These methods are simpler than existing methods
and continuously track environmental changes.
Research continues on faster algorithms.
Nonstationary adaptive beamforming
Developed a frequency-domain minimum-variance
distortionless-response beamformer for small
arrays with unprecedented performance in
the recovery of speech in nonstationary
interference.
Algorithms
Developed reduced-complexity and reduced-delay
implementations for adaptive filters.
Analyzed the transpose-form implementation
for FIR adaptive filters and demonstrated
its advantages for high-speed pipelined
implementation.
Binaural hearing aids
A multidisciplinary group based in the Beckman Institute has developed new binaural algorithms
for the extraction of a desired source from a
cluttered acoustic environment, such as in a
restaurant or cocktail party.
The new methods show remarkable performance
improvements over conventional techniques in
such environments, and have been implemented
in a real-time DSP-based system.
Major research efforts toward commercialization
for advanced hearing aids and other acoustic
extraction applications such as hands-free
telephony, automotive and military applications,
and noise suppression for speech recognition
systems continue.
Electrocardiogram analysis
Developed improved methods for denoising
electrocardiograms using adaptive time-frequency processing.
Microembolus detection
Developed wavelet-based and chirped wavelet detectors
from ultrasound reflections from microemboli.
Theoretical and experimental studies demonstrated that these methods approach optimal performance.
Ultrasound image formation and analysis
Developed fast frequency-domain three-dimensional reconstruction
algorithms for image reconstruction from
measurements on a circular aperture.
Applications include high-resolution imaging
from ultrasound microprobes on the end of a
needle and from small ultrasound catheters.
Developed methods for detecting edges and tissue boundaries
in ultrasound images.
In collaboration with Prof. W.D. O'Brien, developed
new methods for aberration correction in ultrasound imaging
that perform much better than existing approaches in severe
aberration.
fMRI Image Denoising
Are developing
(In collaboration with Prof. Farzad Kamalabadi and Dr. Keith Thulborn at UIC)
efficient methods for blind removal of noise from functional
MRI image sequences.
These techniques will allow precise imaging at much faster
rates by greatly reducing the necessary averaging time to
construct low-noise functional images.
Telecommunications and other applications
Peak Power Reduction for OFDM systems
Developing new methods providing unprecedented peak-to-average power ratio (PAR)
reduction for large-constellation OFDM systems.
These methods are based on novel constellation-shaping
approaches, and obtain these reductions with
no loss of data rate or increase in symbol error rate.
Waveform-modification methods that offer more
modest reductions but are compatible with
current standards have also been developed.
Our Active Constellation Extension method has been included in the DVB-T2
next-generation digital terrestrial television broadcast standard.
Extension of these ideas to peak power reduction in
optical, CDMA, and MIMO communication systems continues.
Optimal discrete multi-tone (DMT) power allocation
Developed the first fast, exactly optimal algorithm for power allocation
in discrete multi-tone modulation.
Joint source-channel matching
In collaboration with Profs. Shanbhag and Ramchandran,
developed joint source-channel coding methods
for wireless image and video transmission.
These general methods allow near-optimal matching
of most source and channel codes, as well as
on-line adaptation to time-varying channels.
Techniques that minimize the total system power
have also been developed.
Continuing research is developing optimal
methods for multi-level broadcasting,
joint source-network coding for video
transmission over wireless networks and
the internet, and total system optimization
for distributed wireless sensor networks.
Wireless communication for binaural hearing aids
Developing new methods for low-power wireless
communication for binaural hearing aids and
other near-the-body applications.
Nonstationary interference cancellation
Developed new frame-based methods for blind
removal of nonstationary interference
from direct-sequence spread-spectrum
communications signals.
Instantaneous frequency estimation/FM demodulation
Developed an adaptive TFR-based IF estimator for FM demodulation that
lowers the SNR threshhold by 3-4 dB over existing methods.
Stochastic sensor networks
Developing a very simple, robust, low-power sensor network
approach and protocol for large networks of sensors.
Each sensor controls power by operating, independently of
the others, on a low duty cycle. Recent results prove that
such a network can operate reliably with very high probability
and can perform all network functions
without any coordination of wake/sleep cycle
between the nodes.
Low-Power Computer Systems
Global Resource Allocation through Cooperation (GRACE)
In collaboration with Profs. S. Adve, R. Kravets, and K. Nahrstedt
in the Computer Science Department, we are developing a new
computing framework allowing joint, cooperative adaptation
of the hardware, networking, operating system, and media
application software to jointly minimize the total
energy consumption in power-limited, mobile, general-purpose
computers.
Signal processing algorithms and implementations
FFTs and Hartley transforms
Performed the first accurate analysis of the computational complexity
of the Hartley transform, showing conclusively that it is virtually
equivalent to the real-valued FFT.
Co-authored two heavily-cited papers on the Hartley transform and
real-valued FFTs.
Joint hardware/algorithm design
Developed several new algorithms/architectures for FFTs, FIR filters, and
adaptive filters offering higher performance or reduced hardware complexity.
Teaching Innovations
Introductory Computer Engineering
With Prof. Steven Lumetta, developing a new first Computer Engineering course for all ECE majors.
A new, "21st century" integrated hardware/software/systems approach to teaching computer engineering develops an across-the-layers understanding of computing, with an emphasis on digital logic and hardware but with an introduction to programming so that students can understand the context and primary application of that hardware.
Nonmajors course on Information Technology
With Prof. Michael Loui, developed a course,
ECE 101, on information technology and engineering
for non-engineering students.
Digital information technology is introduced at several levels,
including audio and video media, digital logic, and the Internet.
The course is half lab-based, with students doing real
engineering designs so that they also learn the process
of technology development and the tradeoffs faced by engineers.
The course will satisfy the General Education requirements
for non-science-and-engineering students.
Digital Signal Processing Laboratory Textbooks
Developed the first textbook for a DSP-microprocessor-based laboratory
course.
This text spurred the development of hands-on DSP laboratory courses
at many universities, and similar courses are now a mainstay of
many electrical and computer engineering curricula
in the U.S. and around the world.
Completed the first open-source, on-line DSP laboratory
textbook as part of the Connexions project.
ECE 420: Digital Signal Processing Laboratory
Introduced a digital signal processing laboratory at the University
of Illinois in 1989.
Innovations in both the content and the teaching methods
keep this laboratory at the forefront of
hands-on DSP education.
During the next few years, this laboratory
and the students will participate
in a NSF-supported research project for the development of
next-generation DSP compiler technology.
Over the years, equipment donations from Texas Instruments, Motorola,
and Analog Devices have equipped the instructional laboratory
with state-of-the-art real-time DSP platforms.
This course has become one of the most popular laboratory courses
in the ECE department (capped at 60 students per semester due to
space limitations) and is now offered every semester.
ECE 320 has been designated a Texas Instruments
Elite Laboratory.
ECE 210: Analog Signal Processing
Assisted in the development of the course outline and the laboratories
for this innovative sophomore course, which replaces the traditional
sophomore circuits course and the junior signals and systems course.
Taught the first full-scale offering of ECE 210 (110+ students).
This course is required for all EE and CompE majors in the current
undergraduate curricula at Illinois.
Email:dl-jones@uiuc.edu
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Created by Douglas L. Jones.
Last updated January 7, 2010