EE491D
Special Topics in Communications
Adaptive Signal Processing
Spring 2005
Prof. Anthony Kuh
POST 205E
Dept. of Elec. Eng.
University of Hawaii
Phone: (808)-956-7527, Fax: (808)-956-3427
Email: kuh@spectra.eng.hawaii.edu
Preliminaries
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Class Meeting Time: MWF 11:30-12:20
Office Hours: MWF 10-11 (or by appointment)
Prerequisites:
– Probability and Random Variables: EE342 or
equivalent
– Digital Signal Processing: EE 415 can be taken
concurrently
– Programming: Matlab or C experience
Objectives and Grading
Topics: Adaptive signal processing.
Objectives: Understand basic concepts,
applications. Design project chosen from
text or literature synthesizing basic ideas.
Grading:
 Homework: 25%
 Exam:25%
 Final project: 50% (oral presentation and
written report)
Overview of Course Material
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Background Material
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Linear Algebra
 Vector and Matrix operations
 Eigenvalues and Eigenvectors
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Probability and Random Variables
 Gaussian
Random vectors, Stationary
processes, 2nd order processes
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Discrete time filters
Matlab
Overview Continued
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Optimum Filtering
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Estimation and Detection
Mean Squared Error Criterion, Energy surface
Wiener Filter
Steepest Descent
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Algorithm
Convergence and Step Size
Overview Continued
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Least Mean Square (LMS) Algorithm
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Algorithm
Convergence and step size
Applications
Variations
Least Square Algorithms
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Algorithm
Properties
Applications
Overview Continued
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Recursive Least Square (RLS) Algorithms
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Algorithm
Convergence and behavior
Applications
Variants
Overview Continued
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Kernel Methods
– Kernel transformation
– Optimization
– Least squares support vector machine
– Support vector regression
Overview Continued
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Pattern recognition
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Linear threshold unit: Perceptron Learning
Algorithm
Optimum Margin Classifiers
Support Vector Machine
Overview Continued
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Other Topics
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Component analysis: Principal Component
Analysis (PCA), Kernel PCA, Independent
Component Analysis, Blind Source Separation
Multilayer feedforward networks: Error
backpropagation algorithm
Linear prediction and Kalman Filtering
References
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S. Haykin. Adaptive Filter Theory 4th Ed. Prentice
Hall, Englewood Cliffs, NJ, 2001.
B. Widrow and S. Stearns. Adaptive Signal
Processing. Prentice Hall, Englewood Cliffs, NJ,
1985.
S. Haykin. Neural Networks, A comprehensive
foundation, 2nd Ed. Prentice Hall, Englewood
Cliffs,NJ, 1998.
What is Signal Processing?
``The theory and application of
filtering, coding, transmitting, estimating,
detecting, analyzing, recognizing, synthesizing,
recording, and reproducing signals
by digital or analog devices or techniques.
“Signal" includes audio, video, speech, image,
communication, geophysical, sonar, radar,
medical, musical, and other signals’’
IEEE Signal Processing Society
Why ``Adaptive’’ Signal Processing?
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System or channel characteristics are
unknown.
System or channel characteristics are time
varying.